Enviroment Archives - Inergency https://inergency.com/media/news/climate-change/enviroment/ An online hub for emergency and natural disaster solutions Fri, 29 Mar 2024 01:14:40 +0000 en-US hourly 1 https://wordpress.org/?v=6.4.3 https://i0.wp.com/inergency.com/wp-content/uploads/2023/11/cropped-Copia-de-inergency-logo-1.jpeg?fit=32%2C32&ssl=1 Enviroment Archives - Inergency https://inergency.com/media/news/climate-change/enviroment/ 32 32 227046876 The Power of Responsibility: Unlocking the Potential of CSR in Hospitality’s Fight against Food Waste https://inergency.com/the-power-of-responsibility-unlocking-the-potential-of-csr-in-hospitalitys-fight-against-food-waste/ Fri, 29 Mar 2024 01:14:40 +0000 https://inergency.com/the-power-of-responsibility-unlocking-the-potential-of-csr-in-hospitalitys-fight-against-food-waste/ The Power of Responsibility: Unlocking the Potential of CSR in Hospitality’s Fight against Food Waste1. Introduction This research draws attention to the issue of food wastage, which is a significant environmental concern especially in this era of environmental sustainability and climate change. The worldwide dimension of food wastage is alarming, with about one third of all foods that are produced for human consumption globally, or nearly 1.3 billion tons […]]]> The Power of Responsibility: Unlocking the Potential of CSR in Hospitality’s Fight against Food Waste


1. Introduction

This research draws attention to the issue of food wastage, which is a significant environmental concern especially in this era of environmental sustainability and climate change. The worldwide dimension of food wastage is alarming, with about one third of all foods that are produced for human consumption globally, or nearly 1.3 billion tons per annum, being lost or wasted [1]. It represents an enormous economic loss as well as a major contributor to environmental deterioration. Environmental implications include increased emission of greenhouse gases, reduction of resource base, and biodiversity depletion [2]. For instance, in EU countries alone, food waste produces 186 million tons of CO2-equivalent emissions; 1.7 million tons of SO2; and 0.7 million tons of PO4; representing 15–16% of the total impact across all stages of the food supply chain [3]. Additionally, the management and treatment of food waste, such as through composting techniques, are vital in mitigating its environmental impact [4].
The connection between the food-waste problem and the hospitality industry, being the sector that generates the most food waste, underscores the need for a solution that involves the development of effective waste-reduction strategies. Although consumer behavior regarding food waste in the hospitality sector is widely studied [5,6], this research focuses on the less explored, but equally important, area; that is, the employee behavior towards food waste. Indeed, employees are the ones who must deal with the implementation and maintenance of the approaches that have a high potential to reduce food waste. Knowledge and control of their behavior is a strategic position that can help to push forward environmental sustainability within the industry [7]. Such a shift in focus highlights the employees’ contribution to the practical implementation of the waste-reduction strategies, from the preparation stage to the service point, which is a gap that is lacking in the literature. Employee behavior influenced by organizational and personal/psychological factors [8,9] is vital in ensuring the success of these strategies. The imperativeness of employees’ ethical attitudes and the effect of management practices on their behavior regarding the reduction of food waste are emphasized in the recent literature [10]. The conversation is also extended to corporate social responsibility (CSR) within the hospitality industry, which further illustrates its role in the creation of sustainable employee behaviors that contribute to the reduction of food waste [11,12].
Although it has been acknowledged that the CSR practices do affect employee behavior with regard to sustainability issues [13,14], the literature on the CSR–employee behavior relation in the hospitality context and, more specifically, on food-waste reduction is still scarce. This stringency still rings true despite the fact that preliminary studies show that corporations’ CSR can greatly affect societies’ response concerning the environment and waste management [15]. This study intends to fill the knowledge gap by bringing out empirical proof of how CSR impacts employee food-waste reduction and, thus, tends to generate applicable information to help enrich our understanding of the subject. The study also focuses on the psychological factors of moral attitude and employee admiration, as well as their role as mediators in the CSR framework, proposing that they are the most important factors in influencing employees’ behavior in terms of food-waste reduction [16,17]. In addition to that, we also investigate the moderating effect of climate-change awareness on the connection between CSR, moral attitude, employee admiration, and food-waste-reduction behavior. A higher awareness of climate change among employees helps to amplify the effect of CSR on their sustainable behavior, reinforcing the need to integrate CSR activities with climate-change-awareness activities [18].
Given China’s unique position in the global debate on sustainability and food-waste management, it is imperative that we test our hypothesized relationships within China’s hospitality sector. With rapid economic development, a huge ecological footprint, and social changes occurring within its population, China provides a unique context for exploring relationships between CSR, climate-change awareness, and reduction in food wastage by hotels. China is home to one of the world’s largest populations, with a growing hospitality industry that contributes significantly to global food waste, resulting in formidable implications related to environmental sustainability, resource utilization, and climate change. Researches have indicated that there exist significant amounts of food waste generated by the food-service industry, including hotels, which are contributed to by various factors [19]. Hence, it is an ideal place for discovering how CSR, combined with employee’s attitudes towards climate change, can impact behaviors leading to lessening food wastage. To date, there has been limited knowledge regarding how CSR initiatives can go together with employees’ consciousness about climate change so as to enhance effective strategies for reducing this type of waste among Chinese hotels. Such research will contribute majorly towards the existing knowledge pool by providing guidance on the role of CSR towards promoting sustainable practices in an influential market such as China.
Several research lacunae are intended to be filled through this study. For instance, most studies on CSR have either adopted rational models or emotional theories, which creates a gap in our understanding of employees’ behavior towards food-waste reduction. Recent criticisms postulate that emotions are not independent from cognitions [20]. This study will address this anomaly by offering an integrated model comprising both rational (e.g., moral attitudes) and emotional (e.g., admiration) perspectives of human psychology within a CSR framework. This makes it unique since it captures multiple major aspects of employee behavior about food waste in the hospitality industry at once. Similarly, there has been an increasing number of works on food-waste management in the hospitality industry, but there is limited research focusing on China as a case study. This study will, therefore, help to fill this void through its focus on how CSR initiatives made by Chinese hospitality businesses can influence employee attitudes toward food-waste reduction with due consideration for the cultural, economic, and environmental circumstances associated with China’s uniqueness. Additionally, our research aims at bridging the gap left by previous studies that did not consider climate-change awareness as a moderating factor between CSR and employees’ behavior towards food-waste reduction. Even though it has been noted that individual climate-change awareness is important for promoting sustainable practices, there is a scarcity of knowledge about how such awareness particularly interacts with CSR initiatives so as to affect food-waste behavior within hotels. Therefore, our research focuses on this interaction, thereby divulging information that can be useful to managers who want to make their activities more environmentally friendly through reducing the amount of waste produced.
On a further note, several existing studies touching on CSR and food wastage are theoretical or conceptual in nature rather than empirical works [21]. A study is therefore necessary to test the usability of these theories under different cultural and operational environments such as China. Empirically, this study will attempt to fill this gap by testing an integrated model of factors influencing food-waste-reduction behavior from emotional and rational perspectives within CSR. Finally, our research seeks to provide an enriched understanding of the dynamics of food-waste generation and reduction in the hospitality industry. This includes investigations into how factors such as organizational policies, employee attitudes, customer behavior, and different socioeconomic backgrounds interrelate with one another. In-depth studies like this are important if we must develop good strategies for managing food waste in the hospitality industry.

Theoretical Background and Literature

The theory that underlies our research is the theory of planned behavior (TPB) that was first proposed by Ajzen [22]. TPB has been vital in understanding how beliefs, attitudes, and behavior are related. It argues that an individual’s behavior is a direct function of their intention to perform the behavior, which in turn is determined by their attitude toward the behavior, subjective norms, and perceived behavioral control. This theory is particularly pertinent to our study as it offers a strong framework for investigating the psychological mechanisms behind employees’ practices of food-waste reduction within the hospitality sector. Drawing on TPB, we can analyze how employees’ attitudes toward CSR initiatives are influenced by their feelings on morality and their emotions; which can, together with perceived social norms and the control over waste-reduction practices, lead to food waste reduction practice. This theoretical perspective, therefore, enables us to comprehensively understand rationalities and emotions within a CSR framework, thus deepening our knowledge about sustainable changes in behavior towards environmental-conservation issues and food-wastage management.
Several researchers have shown that CSR positively influences various employee behaviors at work [23,24]. CSR as an umbrella term encompasses a range of activities done by companies to evaluate their impact on society generally or specifically (employees) [25]. Through these programs, they affect employee’s attitudes and behaviors too [26]. It has been found that effective CSR programs lead to increased levels of job satisfaction among employees as well as raising engagement levels while creating positive work environments [27]. All these outcomes are important as they contribute towards having a more motivated workforce that is committed to implementing sustainable practices within an organization. In relation to food wastage specifically, CSR has a particularly significant impact [28]. Employees who perceive their organizations to be environmentally responsible and dedicated to achieving sustainability goals are more likely to engage in food-wastage-reduction methods [7,29]. This is because CSR activities often create awareness about climate change and the depletion of resources due to food wastage. Therefore, employees become more cautious about their actions and the ability of the environment to be affected by them, which, in turn, increases their willingness to take part in food-waste-reduction projects.
From the standpoint of our theoretical backdrop, CSR can be seen as influencing its three main components—attitudes, subjective norms, and perceived behavioral control. In terms of reducing food losses, a positive attitude towards waste minimization can be developed through corporate social responsibility advocating its environmental advantages. Additionally, CRS affects subjective norms by fostering a culture where sustainable practices are embraced and expected [30]. Finally, CSR helps to enhance employees’ perceived behavioral control by providing necessary resources, training, and supportive measures that might enable them engage in effective food-loss reductions. Based on this theoretical and empirical background, we state our first hypothesis:
H1. 

CSR initiatives within the hospitality industry positively influence employees’ food-waste-reduction behavior.

Employees’ attitudes towards food wasting can be shaped by their moral attitude, which is critical to behavior change [31]. If employees possess moral attitudes that are against food waste, they are expected to behave in ways that reduce wastage. This has been confirmed through studies showing the direct impact of moral attitudes on sustainability and environmental conservation [32]. Moral attitudes among employees in hospitality industry play a central role in determining their food-waste-behaviors, characterizing their intentions and actual practices [7]. While the existing research acknowledges the direct role of moral attitude in behavior formation, its mediating role has also been realized by the extant behavioral scholars [33]. We, in this regard, argue that moral attitude mediates the relationship between CSR and employee’s food waste reduction behavior. This means that corporate initiatives such as ethical and environmental-related issues may influence moral attitudes against food waste among employees, hence leading to more sustainable actions. The mediation mechanism suggests that CSR not only directly determines what employees do but also changes their internal principles, which have a bearing on their conduct toward reducing food waste [34]. TPB explains the links between moral attitude and the three determinants of behavior: attitude towards behavior, subjective norms, and perceived behavioral control. Therefore, a strong sense of morality regarding food wasting plays an important role in enhancing one’s view towards food wastage, thereby increasing the ability of organizations to manipulate such effects at workplaces. We thus propose,
H2. 

Employees’ moral attitudes positively affect their engagement in reducing food waste.

H3. 

Moral attitudes mediate the path between CSR activities and employees’ food-waste-reduction behavior.

Employee admiration acts like a bridge, translating the organization’s CSR efforts into concrete employee actions aimed at sustainability [35]. When employees admire their organization’s commitment to sustainability and CSR efforts, they are likely to mirror these values in their own behaviors [36]. This admiration can be cultivated through effective communication and demonstration of the organization’s commitment to sustainability [37]. By doing so, they will be able to better help the organization’s broader environmental objectives. Employee admiration bridges the gap between CSR and employees’ food-waste-reduction behavior. It is postulated by this hypothesis that the discretion of an organization’s employees, in terms of their perception towards CSR, dictates their admiration for it [38]. This increased admiration, in turn, motivates employees to align their behavior with the CSR goals of the organization, including reducing food waste. Employee admiration acts as a bridge between what an organization does to promote sustainability and what its employees do about it [39].
Accordingly, from a TPB standpoint, these assumptions are supported by emotional factors like employees’ admiration as well as by behavioral determinants such as attitude, subjective norms, and perceived behavioral control. Employee admiration fosters more optimistic attitudes by means of establishing positive emotions toward organizational values [40], reinforces subjective norms by promoting sustainability culture inside organizations, and increases perceived behavioral control through giving incentives for taking steps to reduce food waste.
H4. 

Employee admiration has a positive effect on food-waste-reduction behavior of employees.

H5. 

Employee admiration mediates the path between CSR activities and employee’s food-waste-reduction behavior.

Employee climate-change awareness can be thought of as an important element in moderating relationships between moral attitudes, CSR initiatives, admiration for employees, and food-waste-reduction behavior. Concerning the workplace, climate change awareness can be explained as a comprehension that workers have about climate change and its devastating impacts on the environment [41]. This is especially important in the hospitality industry, where the environmental impact of operation, including the amount of food wasted, is substantial [42]. Employees with a greater understanding of the consequences of climate change are more likely to be more accepting towards CSR initiatives that promote sustainable practices such as food-waste reduction [43]. Moreover, this awareness can amplify the effects of moral attitude and admiration toward an organization, thereby motivating employees to adopt and continue to act in line with environmental objectives of their organizations. In our study’s context, we expect that our hypothesized relationships will be strengthened by employee climate-change awareness that moderates these variables. Specifically, greater levels of climate-change awareness are expected among employees who engage more actively in food-waste reduction due to their participation in CSR initiatives and those who develop a stronger sense of respect towards their organization’s societal reputation. This enhanced state may result into a deeper sustained behavioral transformation since employees not only appreciate significance of these initiations but also feel more responsible for their outcome [44].

According to TPB, climate-change awareness can affect subjective norms and perceived behavioral control. Workers who have knowledge of how climate change affects food waste are expected to experience increased appreciation of organizational norms favoring sustainability practices. They may also perceive themselves as empowered enough to contribute towards these acts thereby increasing their perceived behavioral control over behaviors concerning reducing food waste. Hence:

H6. 

Employee climate-change awareness moderates the relationship between CSR and moral attitude/employee admiration such that these relationships are stronger when awareness is higher, leading to greater food-waste-reduction behavior.

2. Materials and Methods

The Chinese hospitality industry plays a vital part in China’s service sector, which is characterized by rapid growth rates and a significant contribution towards the economy. This sector is made up of a wide range of hospitality organizations from luxury hotels to budget accommodation, and it is central to tourism, employment, and economic development. The significance of the Chinese hospitality sector is demonstrated by its ability to adapt to changing market conditions and influence global tourism trends [45]. In our study, we focus on data collection from key cities in China that are representative of diverse hospitality environments. These cities include Beijing, Shanghai, and Guangzhou, which are not only economic powerhouses but also cultural and tourist hubs with a significant influx of both domestic and international travelers. Taking into consideration the fact that the geographical context can influence CSR practices as well as the behaviors of employees towards food-waste reduction, the study has looked into how the city in which employees are working has an effect on the latter. This is especially relevant as the specific economic, cultural, and regulatory settings of different cities can affect both the design and the reception of CSR plans by employees. Beijing, Shanghai, and Guangzhou, the cities at the focus of our research, offer distinct environments: For instance, Beijing’s immense historical and political legacy, Shanghai’s modernity and international business focus, and Guangzhou’s role as a key economic and trading hub in Southern China. Differences in the settings either serve to represent the types and vary of environments within which hospitality companies function and apply CSR strategies, or to create an overall view of the contexts within which hospitality organizations operate and implement CSR strategies. Through analyzing employee responses and CSR practices in these cities, our research seeks to uncover how place-specific conditions can affect the success of CSR programs in promoting sustainable food-waste-reduction behavior. This approach yields the opportunity for us to consider not only the entire effect of CSR on food-waste-reduction behaviors across the Chinese hospitality industry but also how the food-waste-reduction behaviors might vary in different urban settings.
Beijing is rich in historical and cultural significance because it is the capital city, making it an ideal location for studying the impact of CSR initiatives in the hospitality sector. In contrast, Shanghai’s modernity and global business appeal provide insights into how cosmopolitan influences affect hospitality practices. On the other hand, Guangzhou, as a major economic and trading center in South China, provides a view on how economic growth correlates with hospitality trends. The selection of these cities is justified by their diversity as well as the potential insights they can offer into different aspects of food-waste-reduction behaviors in the hospitality sector within the broader context of China’s economic and cultural landscape [46].

This study specifically targeted hotels with visible and active CSR programs among other factors. We combined a variety of methods, such as research and direct inquires, to verify and select hotels that have visible CSR plans. At the beginning, we carried out an initial review of the information that is in the public domain, such as sustainability reports, company websites, and press releases, so we can learn about the visibility and activeness of the CSR initiatives of the selected hotels. Furthermore, we encompassed a series of direct inquiries to the hotels, where the hotels were requested to provide detailed descriptions of their CSR activities. The hotel that offered tangible proof of the operations of a CSR program were selected to be part of the study. This methodological step ensures that our focus remains on organizations with a genuine commitment to CSR, aligning with our research objectives. We formally reached out to these hotels requesting them to cooperate in our data-collection process for the mutual benefit of academia and industry. Those who positively replied to our request were later approached to schedule appointments so that we can collect data from their employees. We decided to use a three-wave method in collecting information from participants, minimizing the response bias which might have occurred if we used a one-wave method where all variables were collected at once, thereby increasing chances for common-method bias. In wave one, we focused on gathering data about CSR initiatives and employee climate-change awareness. In wave two, we collected data regarding employees’ moral attitudes towards their company, including whether or not they admired it. Finally, wave three concentrated on measuring employees’ food-waste-reduction behavior. This staggered approach to data collection ensured a more reliable and nuanced understanding of the various factors influencing food-waste-reduction behaviors in the hospitality sector.

During our data-collection process, we strictly followed the guidelines stipulated in the Helsinki Declaration in order to maintain ethical standards. In this regard, we obtained informed consent from all participants, confirming that they will remain anonymous and their responses confidential throughout our research. They were briefed about the objectives of our study and their involvement, and assured about the use of such responses only for scholarly purposes. Importantly, it must be noted that participation was voluntary; therefore, any respondent can withdraw from the study at any stage without facing consequences. Throughout the data-collection process, the dignity and autonomy of our respondents were respected while ensuring no harm or discomfort was caused to them. These guidelines were established with an aim of ensuring that our research is conducted ethically so as to enhance the integrity and validity of the information provided by our participants.

For determining the sample size of our study, we utilized an a priori sample-size calculator, an essential tool for ensuring statistical validity and power in research. It helps to estimate an appropriate sample size to detect the effects of interest while minimizing the risk of type I and type II errors, hence it is important. The calculator’s recommended sample size was 388 based on input parameters such as: alpha level, power, and effect size. Aware that survey research may have low response rates, we chose to distribute 600 questionnaires among the employees of selected hotels. This step was taken to anticipate non-response cases or incomplete data.

The data-collection process went through three waves, and we examined data cleaning checks to ensure maximum quality and consistency regarding respondents’ feedback. We finally received 422 valid questionnaires after thoroughly scrubbing our database. This number exceeded our desired minimum sample size, cementing a strong basis for statistical analysis while providing more data for a holistic understanding of employees’ behaviors and attitudes towards food-waste reduction in relation to CSR initiatives within the hospitality sector.

We used several strategies in this study to decrease social-desirability bias and common-method variance (CMV), which are potential problems in behavioral research. In the first place, the questionnaire was constructed to be as neutral as possible, avoiding leading or emotionally charged questions that might prompt socially desirable responses. We also used indirect questioning techniques to ask about sensitive topics such as personal attitudes and behaviors so that participants might respond more candidly rather than providing answers they deem socially acceptable.

Moreover, we carried out a three-wave collection of data to reduce CMV. Also, we performed Harman’s single-factor test. By collecting data on different variables at different times, we minimized the likelihood of earlier responses influencing their subsequent responses. Additionally, respondents were assured of the confidentiality and anonymity of their answers to encourage honest feedback and minimize attempts to conform with social expectations. These measures collectively enhanced the reliability and validity of our data, providing a more accurate reflection of employees’ real attitudes and behaviors towards CSR and food-waste reduction in hospitality industry.

To prepare our dataset for analysis with Smart PLS, we undertook a rigorous coding process. At first, responses from the questionnaires were coded numerically to enable quantitative analysis. This referred to the allocation of numerical values (1 to 5) to categorical data, for example, responses to Likert-scale questions on CSR initiatives, employee behavior towards food waste, and other variables of interest. The coding scheme was developed to warrant consistent use across the whole dataset, thus paving the way for accurate and structured analysis. After the coding, we used Smart PLS software 3.28 version for the data analysis. Smart PLS has a comparative advantage in dealing with complicated models, which makes it more suitable for research aimed at developing theories [47,48]. The software’s capabilities enabled us to accurately model the interactions between the coded variables. Additionally, our selection of purposive sampling helped us in gathering data from hotels that have CSR programs and, thus, provided a focused lens to explore our research questions. This sampling method, along with our coding and analysis using Smart PLS, highlights the research rigor of our study and strengthens the validity and usefulness of our findings to the arena of CSR in the hospitality industry.
We operationalized the variables using already established scales that guarantee the reliability and validity of our measurements. The use of established scales ensures comparability with other studies, thereby enhancing consistence and accuracy in our research. For instance, CSR was measured using a six-item scale by Alvarado-Herrera, et al. [49] including a sample item “this hotel is really trying to recycle its waste materials properly”. This is a well-reputed scale to measure CSR perceptions of individuals, especially in the context of tourism and hospitality. This is why several other researchers have used this scale to operationalize the construct of CSR [50,51] On the other hand, food-waste-reduction behavior (FWRB) was measured through five items taken from the study of Bell and Ulhas [52] aimed at capturing how employees are engaging in practices that help reduce food wastage. One sample item from this scale was “I try to throw away no food at all”. The same scale has also been utilized by Lavén [53] in the context of restaurants.
As mediators, we used a three-item scale by Stancu, et al. [54] for moral attitude (MOA) focusing on cognitive aspects concerning what employees think about throwing away food, including a sample item “Wasting food might make me feel guilty about people who do not have enough food” The other authors have also used this variable to predict the sustainable behavior of individuals [55]. Similarly, employee admiration (ADM) was measured employing the five-item scale of Sweetman, et al. [56]. One sample item from this scale was “I feel admiration when I think about this hotel”. Prior researchers like Ahmad, et al. [57] have also used the same scale to evaluate employee admiration as a mediator that influences the sustainable behavior of individuals. Finally, climate-change awareness (CLA) served as a moderator for which we utilized the four-item scale presented in the study of Bell and Ulhas [52] measuring how much employees know and care about implications of climate change. One sample item from this scale was “I am concerned about climate change”. Clayton and Karazsia [58] have also asserted the significance of this variable in influencing individuals’ green psychology. Table 1 includes the demographic information of our sample.

3. Results

Our study established the psychometric properties of ADM, CLA, CSR, FWRB, and MOA. Notably, factor loadings for constructs under investigation provided a sophisticated picture of validity and reliability. For example, ADM items showed factor loadings ranging between 0.925 and 0.745, representing a strong yet diverse connection to ADM construct. Similarly, the range of CLA items from 0.740 to 0.780, and that of CSR items from 0.713 to 0.880, shows wide but rational involvement in those critical dimensions addressed in our study. In addition, this was also demonstrated through various items that exhibited similar patterns across FWRB as well as MOA, among other factors. The statistical analysis further solidified these findings, with T statistics for all items surpassing the critical threshold of 1.96, alongside p values falling below the 0.05 threshold, affirming the statistical significance of our results. Thus, it is evident that such details have contributed towards ensuring that there are no methodological weaknesses in our research. In addition, a glance at the high Cronbach’s alpha, composite reliability, and average variance extracted (AVE) scores across various constructs, such as ADM’s impressive Cronbach’s alpha of 0.930 and FWRB’s composite reliability 0.926, not only support internal consistency/reliability checks on measures used but also indicate their abilities to capture latent constructs. Among other things, these metrics include AVE scores above the 0.5 cut-off value, which implies sufficient convergent validity, ensuring that our constructs aptly capture the essence of the variables of interest. In sum, our findings are more than just generic descriptions of psychometric analyses, but rather form a pathway that provides insights into how ADM, CLA, CSR, FWRB, and MOA interact within the context of sustainability and responsible consumption in hospitality. The above analysis not only confirms the methodological strength of our study but also demonstrates the level of sophistication involved in promoting environmental stewardship among hospitality organizations. Table 2 includes more detail, whereas Figure 1 includes the measurement model of this study.
Focusing on the R-square and f-square values from the Table 3, we unveil some important insights underlying the influence and interrelation among our variables. The R-square value of 0.331, in combination with an f-square of 0.405 for ADM, establishes a significant variance in FWRB, which can be attributed to the level of ADM. It implies that the ADM has a significant impact on the adoption of the sustainable practices. Moreover, MOA is proven to have been greatly affected by CSR through f-square, which is 0.311. This means that CSR is the one factor on which the employee’s ethical attitudes towards food waste depend; therefore, the vitality of CSR programs is a key element in creating a sustainable culture. The r-square values for FWRB and MOA, at 0.456 and 0.371, respectively, show a large portion of the variance in the dependent constructs that is explained by our model, supporting the depth of influence created by ADM and CSR. By taking a closer look at these results, we go beyond the generic presentation to stress the specific mechanisms that guide the food-waste-reduction endeavors of the hospitality industry. Statistical significance and the relationships between our constructs give a rich and detailed visualization of how these factors interact to drive the adoption of sustainable behaviors, thereby demonstrating the complexity and the multifaceted nature of the implementation of CSR.
A quick look at Table 4 shows that the square root of AVEs exceeds inter-construct correlations, thus supporting discriminant validity. ADM’s correlation with CLA is 0.560, and their square roots of AVEs are 0.886 and 0.759, respectively. Moreover, for most pairs of relationships, HTMT ratios (lower triangle) are less than the threshold. For instance, CSR-FWRB HTMT ratio of 0.503 suggesting that these constructs are clearly different from each other. This table, as such, captures all relationships and the distinctiveness existing in our study’s constructs, thereby affirming the soundness of our measurement model.
For hypotheses testing, we examined the effects of CSR, MOA, and ADM on FWRB, while also considering the interaction effect of CLA (Table 5). In H1 there was a positive relationship between CSR and FWRB with a beta value equal to 0.191. This significant result has p-value of 0.000 and, therefore, suggests that FWRB is discernibly influenced by CSR initiatives, as is evidenced in the confidence interval ranging from 0.121 to 0.306, affirming the consistency of our results. Similarly, H2 assessed MOA’s influence on FWRB, generating a beta value of 0.134 and a p-value of 0.014, implying that MOA has a statistically significant effect, albeit one that is smaller than CSR’s direct effect on FWRB. The confidence interval for this hypothesis is 0.031–0.243, supporting MOA’s significant effect on FWRB. Our H4 showed that ADM had the greatest impact, with the beta coefficient being equal to 0.509. The low p-value of this finding, at 0.000, and its confidence interval range from 0.412 to 0.612, highlights its significance as an influential factor in shaping FWRB behavior. H3 (CSR→MOA→FWRB) and H5 (CSR→ADM→FWRB) involve mediating effects whose results are statistically significant. While H3 has a beta value of 0.066, its p-value stands at 0.02, and H5 reveals a beta value of 0.063 alongside a p-value at 0.013; hence, both MOA and ADM act as mediators in the relationship between CSR & FWRB.
Additionally, we explored how the influence of MOA and ADM on FWRB were affected through the interaction between CSR and CLA in H6 and H7. The beta values for the interaction terms (CSRxCLA→MOA→FWRB: H6 and CSRxCLA→ADM→FWRB: H7) are 0.093 and 0.107, respectively, both being significant at a p-value of 0.000. Thus, these findings reveal the critical role that CLA plays in influencing MOA and ADM while acting as a moderating factor for FWRB initiated by CSR measures. The confidence intervals for H6 (0.008 to 0.113) and H7 (0.061 to 0.167) strengthen these results. Our findings extend beyond generic interpretations, offering targeted insights into the mechanisms driving FWRB and outlining potential pathways for future research and policy formulation. Figure 2 includes the structural model.

4. Discussion

Our research seeks to understand the relationship between CSR and the food-waste-reduction behavior of employees in the hospitality industry, providing a much deeper level of insight than what is already known. Specifically, this study explores the CSR→FWRB link (H1), which reveals how critical CSR is in promoting sustainable practices to employees, just as Luu [7] and Islam, et al. [29] have shown that the sustainability initiatives of organizations greatly influences employee behavior regarding food waste. However, our research indicates that the effect of CSR on FWRB is not just an enactment of policies but is rather rooted in the company’s environmental culture as reflected by its employees’ psychological engagement. The narrative of CSR sustainability becomes even more complex when both moral attitude and employee admiration are brought into focus as mediators between CSR and the food-waste-reduction behavior of employees. This cognitive-emotional connection to environmental issues was also supported by H2 (MOA→FWRB) and H4 (ADM→FWRB). These findings support the argument of Turner [39] that CSR initiatives not only directly influence employees’ behavior but also indirectly shape their moral attitudes and admiration towards the company. Our study enriches this perspective by quantitatively proving these mediational pathways, underscoring the complex interplay between organizational initiatives and personal values.
Additionally, we elucidate how climate-change awareness alters these relationships (H6), which contributes to a growing body of literature emphasizing the significance of awareness for effective CSR initiatives. Higher levels of environmental consciousness among employees strengthen the direct impact of CRS on food-waste-reduction behavior and increase the intermediary roles played by moral attitude as well admiration in this regard. This enhanced state may result in a deeper, sustained behavioral transformation since employees not only appreciate the significance of these initiatives but also feel more responsible for their outcome, which is in line with the findings of Kallmuenzer, et al. [44]. This suggests that for CSR initiatives to fully realize their potential in promoting sustainable practices, they must be accompanied by efforts to raise awareness about environmental issues among employees. We further analyze our findings within the framework of the TPB and offer a comprehensive explanation of how and why CSR initiatives affect employee behavior in the hospitality industry. The TPB claims that behavior is a function of behavioral intentions, which, in turn, are shaped by attitudes, subjective norms, and perceived behavioral control as indicated by Ajzen [22]. In our case, socially responsible corporate initiatives become a substitute for an objective norm, shaping employees’ attitudes (reflected by moral attitude) and perceived behavioral control (heightened through admiration and climate-change awareness) towards food-waste reduction. Additionally, this theoretical alignment not only strengthens our empirical findings but also emphasizes the need of a holistic approach that incorporates cognitive, emotional, and normative dimensions in encouraging sustainable behaviors.

The conversation transcends academia, providing not only theory but also practical insights for hospitality managers. Through CSR involvement and focusing on moral attitudes, admiration of the organization, and climate-change awareness, managers can produce a workforce that is not only conscious of but also actively engaged in sustainability practices. This holistic approach emphasizes the use of a combination of policy implementation, emotional attachment, and education to fight food waste successfully. In summary, our study provides a more detailed insight into the mechanisms through which CSR initiatives affect food-waste-reduction behavior in the hospitality sector. Through dissection of the mediating and moderating variables within the TPB framework, we explored the complex psychological terrain that drives ecologically sound behaviors of employees. Additionally, it strengthens the theoretical fundamentals of CSR and sustainability in hospitality, and offers industry practitioners a guide to how to improve their sustainability practices.

4.1. Theoretical Implications

The theoretical significance of our research is colossal, as it adds to the existing knowledge base regarding CSR, employee behavior, and environmental sustainability. To begin with, this study has been able to indicate how different combined effects like those of CSR initiatives, moral attitudes, employee admiration and climate change awareness affect food-waste-reduction behavior in hotels. This multidimensional approach provides a deeper understanding of how various elements interact in order to influence employee behavior, which has been scantily addressed by previous researchers.

Further, one of the key theoretical contributions of our research is the integration of emotional and cognitive factors within a CSR framework, which is not often discussed in traditional CSR studies. Furthermore, investigating moral attitudes (cognitive component) and employee admiration (emotional component) as mediators, our study offers insight into the two paths through which CSR initiatives influence employee behavior. Therefore, future CSR research should consider the complex interplay of rational and emotional dimensions of employees’ responses. Moreover, our research has highlighted how climate-change awareness modifies the relationship between CSR and behavior. This finding is significant in light of the current global concerns over environmental issues. It implies that, when employees are aware of climate change, it intensifies the effectiveness of their company’s CSR programs, providing another way to increase the impact of corporate sustainability programs.

What is more, this study positions its findings within TPB, thereby expanding its application in the organizational behavior and CSR literature. This theoretical extension thus offers a fine insight into sustainable employee-behavior mechanisms with an emphasis on subjective norms (CSR), attitudes (moral attitudes), and perceived behavioral control (employee admiration and climate change awareness). In a nutshell, this study not only fills existing gaps in the literature but also provides a new theoretical framework for understanding the dynamics between CSR and employee behavior in the hospitality industry. This contributes to better comprehension of how organizations can effectively engage employees in sustainability initiatives, thus assisting broader efforts towards mitigating environmental challenges.

4.2. Practical Implications

In terms of practical implications in the hospitality industry, especially in fostering sustainability practices, this research is of major significance. Firstly, since it is evident that CSR initiatives have a major impact on food-waste-reduction actions by employees in hotels, there will be a need for managers to create all-inclusive strategies that focus on environmental matters besides enhancing greenness. For them to construct such strategies that might make an impact on their employees’ daily activities, they must be designed to align with them and allow employees to be active in environmental sustainability. The other point is that the cognitive and emotional aspects of CSR communication are equally important according to our study, where we found that moral attitudes and employee admiration have dual mediating effects. Ethical training and awareness programs for hospitality managers should emphasize the moral aspects of food waste and environmental sustainability. Further, open and consistent communication about the company’s achievements in sustainability as well as its goals can build pride among employees, leading to admiration for its CSR engagement.

In addition, an increase in employees’ awareness of climate change which acts as a moderator is a way of enhancing the effectiveness of CSR initiatives. This can be done through training seminars, workshops, and engaging employees in the company’s efforts towards sustainability. The goal of such programs is not just to educate employees but also to enable them to contribute actively towards the achievement of the organization’s sustainability objectives. Moreover, the possibilities for practical application of TPB provided by our study gives hospitality managers a tool for understanding employee behavior towards sustainability. By realizing their influence on subjective norms (CSR initiatives), personal attitudes (moral attitudes), and perceived behavioral control (employee admiration and climate-change awareness), managers can come up with strategies directed at motivating employees to engage in food-waste-reduction activities on an ongoing basis. Our analysis provides practical knowledge for the hotel industry to develop their CSR programs as well as encourage and motivate staff to engage in sustainable activities. This is largely applicable in a sector that experiences significant ecological ramifications and where employees’ actions impact sustainability measures.

4.3. Limitation and Future Research Directions

There are some limitations in our study, which have been used as grounds for future research. Initially, the study is confined to the hospitality industry in China, thereby restricting the generalizability of findings to other areas or sectors. Future research may replicate this study across various cultural and geographical contexts to confirm and expand on our findings. Moreover, the reliance on self-reported measures is another limitation; although we tried to minimize it, it can still be affected by social-desirability bias. For example, future studies can incorporate more objective criteria or observation data to support self-reported behavior, especially regarding reducing food waste. Another limitation of this study is its cross-sectional design that prevents causal inferences from being made. In view of theoretical expansion, potential research can investigate additional psychological constructs that may affect the relationship between CSR and employee behavior like job satisfaction or organizational identification. More so, an integration of other theoretical frameworks such as social identity theory, as well as the concept of organizational justice, might lead to a better understanding of how CSR initiatives influence employee behavior. Finally, given the growing significance of digital technology and social media platforms, there is a need for future investigations into the role played by these platforms in enhancing employees’ awareness and involvement in CSR activities. Exploring the impact of digital CSR communication on employee behavior can provide valuable insights to contemporary organizations navigating the digital environment.

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Can Environmental, Social, and Governance Ratings Promote Green Innovation in Chinese Heavy Polluters? Perspectives from “Greening” Behaviors https://inergency.com/can-environmental-social-and-governance-ratings-promote-green-innovation-in-chinese-heavy-polluters-perspectives-from-greening-behaviors/ Thu, 28 Mar 2024 23:48:00 +0000 https://inergency.com/can-environmental-social-and-governance-ratings-promote-green-innovation-in-chinese-heavy-polluters-perspectives-from-greening-behaviors/ Can Environmental, Social, and Governance Ratings Promote Green Innovation in Chinese Heavy Polluters? Perspectives from “Greening” Behaviors1. Introduction Green innovation represents the future of China’s economy. Combining the principles of greenness and innovation, it is committed to maximizing the comprehensive benefits of technology, resources, economy, and environment, serving as a core driving force and a realistic requirement for regional high-quality development [1,2]. Heavy-polluting enterprises are central to green innovation, as their […]]]> Can Environmental, Social, and Governance Ratings Promote Green Innovation in Chinese Heavy Polluters? Perspectives from “Greening” Behaviors


1. Introduction

Green innovation represents the future of China’s economy. Combining the principles of greenness and innovation, it is committed to maximizing the comprehensive benefits of technology, resources, economy, and environment, serving as a core driving force and a realistic requirement for regional high-quality development [1,2]. Heavy-polluting enterprises are central to green innovation, as their ability to achieve energy saving, emission reduction, and green development determines the success of sustainable development strategy in China. China’s existing environmental policies mostly adhere to Porter’s hypothesis. It argues that well-designed environmental regulations can furnish enterprises with information and incentives for technological innovation and lead to an “innovation compensation effect” and “first-mover advantage” for enterprises in the long run [3]. However, the effect of environmental policies that have been in place for several years has not been satisfactory [4], with ineffective law enforcement by local governments and even collusion between the government and enterprises. These challenges have resulted in a “softening” of environmental regulation. Consequently, China’s ecological and environmental situation is still serious [5]. The relationship between coercive policies and green innovation strategies demonstrates an inverted U-shape, where moderate coercive policies can effectively promote the implementation of green innovation strategies by enterprises. However, excessively high or low coercive policies may dissuade enterprises from adopting green innovation strategies [6].
As a result, aiming at decreasing resource depletion and environmental pollution, China has introduced the environmental, social, and governance (ESG) rating system as a market mechanism, which is not legally enforceable, to incentivize relevant enterprises to engage in green innovation activities and enhance their sustainable development capabilities. ESG ratings, serving as a comprehensive assessment methodology that considers environmental, social, and governance factors, have attracted widespread attention from investors to evaluate the sustainable performance of companies from a more holistic perspective [7]. Compared with the mandatory pressure of environmental regulations, ESG ratings, as a third-party regulatory tool, exert normative pressure from the external market in a “bottom-up” fashion, compelling companies to proactively initiate changes and actively engage in green innovation activities [8]. Under the leadership of the “dual carbon” goal, China’s ESG development has accelerated. According to the China Association of Listed Companies, the number of ESG-related reports issued by A-share listed companies increased from 371 in 2009 to 1738 by the end of June 2023, with the disclosure rate of 34%. Notably, the ESG disclosure rate of Sino-Securities 300 exceeds 90%. ESG, as an important value driver for sustainable development, is increasingly acknowledged by both real enterprises and investment institutions.
With the promotion of ESG, the positive impact of ESG ratings on corporate investment efficiency, financial performance, corporate value, and stock market performance has been demonstrated in research [9,10,11,12,13]. However, some scholars have raised concerns about potential “greenwashing” behavior resulting from ESG ratings. They argue that conducting environmental testing and establishing an ESG system may require substantial capital investment and not yield short-term benefits. Therefore, companies have an incentive to divert public opinion through “greenwashing” behavior to mislead the public and seize undue advantages [14,15,16]. Lax regulatory penalties have also been the main reason for the prevalence of “greenwashing” in recent years. China has not clarified the penalties for “greenwashing” in the ESG field, resulting in a very low cost of non-compliance and further exacerbating the “greenwashing” behavior among domestic companies [17]. Considering the possible “greenwashing” behavior of heavy polluters, it remains to be explored whether the ESG ratings really promote the green activities among them and effectively enhance their green innovation capabilities.

In this research, the ESG rating is regarded as an exogenous market shock event. Meanwhile, we adopt a multi-time-point difference in difference (DID) approach to explore the impact of ESG ratings on the green innovation of listed heavily polluting enterprises in China. The results reveal the following:

(1)

The implementation of ESG ratings exerts a significant positive impact on listed companies in heavily polluting industries, reflected in the increased number and citation frequency of green patent applications. This quantitatively and qualitatively confirms that the ESG ratings can effectively enhance the green innovation capabilities of high-polluting enterprises. Moreover, this conclusion is further verified and consolidated through a series of robustness tests, including parallel trend tests, placebo experiments, variable substitution, and adjustments during the regression.

(2)

ESG ratings play a key role in reducing financing constraints and increasing risk-taking, thereby promoting green innovation in high-polluting enterprises. Due to the inherent uncertainty of innovation activities, enterprises should pay attention to stable and continuous financial support. In addition, the acceptance of risk by management and external stakeholders also determines the enterprise’s ability to engage in green innovation activities. Therefore, ESG ratings, by alleviating financing constraints and enhancing the risk-bearing capacity of internal and external stakeholders, facilitate the progress of enterprises in green innovation.

(3)

More obviously, ESG ratings significantly enhance the green innovation capabilities of private and large enterprises. This difference may stem from the government credit endorsements and broader financing channels of state-owned enterprises (SOEs) as well as the direct participation of party organizations in major decisions. This, in turn, limits the impact of ESG ratings on easing financing constraints and enhancing risk-taking capabilities. In contrast, the ESG ratings demonstrate a limited effect on small and medium-sized enterprises because they often face resource scarcity and tend to pursue certain development and survival rather than investing in risky innovation projects that may yield returns. Furthermore, our research reveals that ESG ratings not only promote the end-of-pipe governance in heavily polluting enterprises but also strengthen source governance, thereby advancing environmental protection and sustainable development practices throughout the governance chain.

The marginal contributions of this research are primarily reflected in the following aspects:

Firstly, previous research has focused more on the role of government regulation and support as well as the impact of intrinsic corporate characteristics, with relatively less discussion on market factors [18,19,20,21,22]. This research reveals, from the perspective of market incentives, how ESG ratings function as effective market information transmission mechanisms and significantly promote green innovation in listed companies in the heavily polluting industries of China. For this purpose, a theoretical analysis framework is constructed, and strong empirical evidence is provided, offering new insights and references for academics and policymakers.
Secondly, we expanded the scope of traditional credit market research by shifting the focus from the information asymmetry among financing entities to the influence of market incentive mechanisms [23,24]. With special attention to the market ESG rating, this research analyzes how it acts as an information intermediary in the credit market to transmit signals and mitigate information asymmetry. Furthermore, based on the role of market incentive mechanisms, it alleviates corporate financing constraints, increases stakeholders’ risk-bearing willingness, and stimulates corporate investment and outcomes in green innovation, thus promoting sustainable development.

Lastly, our research is subjected to the following relevance and significance for stakeholders. For government departments, “voluntary regulation” in the Chinese market, such as ESG ratings, can provide effective, flexible, and dynamic incentives. This conclusion yields theoretical and empirical support for the government to exert its role in market coordination, facilitating the formulation of more precise and matching policies to market development, thus promoting the long-term interests of enterprises. Moreover, our research reveals the signaling effect of ESG ratings for enterprises. Specifically, the ESG ratings help enterprises to convey their commitment to environmental and social responsibilities. Furthermore, they provide investors and regulatory agencies with new means to assess corporate value and risk, aiding them in making wiser investment and regulatory decisions. Overall, this research highlights the importance of market incentive mechanisms in promoting the green development of enterprises and enhancing the overall welfare of society, thereby providing new insights and strategies for sustainable corporate development.

The subsequent content of this article is arranged as follows. The second part elaborates on the institutional background of this research, develops theoretical derivations, and proposes research hypotheses. The third part describes the data sources, empirical models, and descriptive statistics adopted. The fourth part presents the main empirical results of ESG ratings and green innovation in heavily polluting enterprises and then analyzes the intrinsic mechanisms. The fifth section explores whether ESG ratings improve both source control and end-of-pipe governance for green innovation and analyzes the heterogeneity in the relationship between ESG ratings and green innovation in heavily polluting enterprises. Subsequently, the sixth part summarizes and discusses the main findings of this research and gives relevant policy recommendations.

2. Institutional Background, Theoretical Analysis, and Research Hypothesis

2.1. Institutional Background

In the historical context of China’s economic development, environmental issues have consistently occupied a pivotal position. During the era of planned economy, industrialization often came at the expense of the environment, leading to resource depletion and environmental pollution, with insufficient awareness regarding environmental protection among enterprises and citizens. Since the inception of the reform and opening-up policy in 1978, marking China’s transition from a planned to a market-oriented economy, environmental policies have undergone a multi-stage evolution. Initially, environmental protection primarily served the planned economy, relying heavily on mandatory regulations. Various government laws and regulations were utilized to restrict market participants, aiming to maintain market order and social stability. However, with the establishment of the socialist market economy system, environmental regulatory policies have shifted from a singular focus on “pollution prevention” to embracing the broader concept of “ecological civilization”. This shift signifies a deeper understanding of environmental issues and a commitment to pursuing sustainable development. In this evolutionary process, the limitations of mandatory regulations gradually became apparent, especially in promoting innovation and adapting to dynamic market conditions. Analyzing the evolution of China’s environmental regulatory policies against the backdrop of market economy development reveals a transition from reliance on mandatory regulation toward market-driven transformation. This highlights the evolving role of the government in environmental protection, gradually shifting from direct intervention to leveraging market mechanisms and social forces. Especially in the process of the dual carbon goals, carbon peaking, and carbon neutrality, the importance of market participation has become increasingly prominent.

Market participants, including enterprises, third-party institutions, and citizens, have been endowed with increased responsibilities and autonomy to innovate and adapt to rapidly evolving market demands. This shift promotes innovation in environmental protection technologies and management approaches, enhances resource utilization efficiency, and promotes the progression of a green economy. Concurrently, the introduction of market mechanisms such as carbon trading and ESG ratings provides the possibility of achieving a win-win outcome for environmental protection and economic development.

Overall, the evolution of China’s environmental initiatives represents a transition from government-led to market-driven transformation, facilitating environmental protection and establishing the groundwork for sustainable economic development. With the proposal and implementation of the dual carbon goals, market participants will exert increasingly pivotal effects, while the government will continue to play its role in policy formulation and regulation to achieve the environmental protection goals.

China’s economic and institutional context provides this research with a unique perspective and advantage. Firstly, with the active involvement and guidance of the government, the Chinese market exhibits distinct characteristics from those of other countries, presenting a rich case study for examining the effectiveness of market tools under diverse institutional frameworks. Secondly, the continuous development and improvement of the Chinese market serve as a testing ground for exploring market incentive mechanisms, helping to reveal the potential effects and trajectories of such mechanisms. These characteristics render this research academically pioneering and forward-looking, while also ensuring the rigor and practical applicability of the research results. Lastly, as the world’s second-largest economy, China boasts a diversified industrial landscape and regional distribution, and the breadth and diversity of company samples provide sufficient credibility and extensibility for the research.

2.2. Theoretical Analysis and Research Hypothesis

2.2.1. ESG Ratings and Corporate Green Innovation

In China, with the increasing emphasis on the concept of green sustainable development, heavy-polluting enterprises characterized by high energy consumption and pollution are facing unprecedented challenges. Meanwhile, they have drawn significant attention from the government and the public in various dimensions, such as environmental protection, social responsibility fulfillment, and governance. The emergence of ESG ratings has rendered the environmental costs of these enterprises more transparent. Faced with external pressure from public opinion and regulation, these enterprises are motivated to seek green innovation to adapt to the constantly evolving external environment and mitigate ESG risks. ESG ratings not only afford enterprises an opportunity to demonstrate their potential for green development but also exert a positive impact on green innovation in terms of resource allocation optimization, governance structure enhancement, and innovation in incentive mechanisms [25].
Firstly, ESG ratings rely on the information disclosed by companies concerning their environmental protection and social responsibilities. They can reflect whether heavily polluting enterprises are actively assuming responsibility and adhering to the principles of sustainable development. Green innovation, as a risky and uncertain activity, needs substantial sustained financial support in its initial stages. ESG ratings, through signaling, guide the influx of investor funds by garnering policy support, such as tax incentives and government grants, to generate a resource effect on green innovation and reduce the financial burden on corporations [26]. Secondly, ESG ratings improve corporate information transparency and effectively mitigate information asymmetry among corporate stakeholders [27]. Based on the stakeholder theory, studies have scrutinized the factors driving corporate “greenwashing” behavior and concluded that such behavior is complex and usually involves interactions among multiple stakeholders. The lack of effective supervision of management’s speculative actions, motivated by economic benefits and the avoidance of regulatory penalties, is a primary driver behind the “greenwashing” of enterprises [28]. Therefore, promoting the effectiveness of supervision can partially inhibit the “greenwashing” behavior of enterprises. As an important external governance mechanism in the capital market, ESG ratings are issued by practitioners with professional knowledge backgrounds, facilitating the identification of “greenwashing” behaviors of corporate managers. The ESG ratings issued by them are closely scrutinized by the public and the media, which reduces the cost of monitoring the management by external investors [29]. As a result, strengthening external supervision can effectively curb managerial opportunism, promoting management to allocate corporate resources, improve governance practices, actively fulfill social responsibilities, and mitigate the risk of corporate “greenwashing” by addressing agency problems and optimizing internal controls, thus exerting a governance effect on green innovation [30]. Furthermore, in the context of promoting the development of a green economy, innovation becomes an indispensable means for heavily polluting enterprises to break through the limitations caused by high emissions and high pollution and eliminate backward production capacity. ESG ratings, as a soft market regulatory tool, exert an incentive effect on green innovation, motivating enterprises to proactively enhance their production processes, ramp up research and development efforts in areas such as green technology and green products, elevate the levels of green innovation, improve the resource utilization efficiency, and achieve the long-term sustainable development of enterprises. Finally, while the issue of greenwashing cannot be overlooked in ESG ratings, it is particularly challenging within China’s heavily polluting industrial sectors due to stringent legal and environmental regulations [31]. Such industries face significant barriers and costs associated with greenwashing, making them susceptible to market backlash, reputational harm, and reduced stakeholder support, ultimately impacting their performance [32]. Thus, compared to greenwashing, ESG ratings are more likely to truly promote substantial progress in green innovation for these enterprises. Based on the above analysis, this research proposes the following research hypothesis:
Hypothesis 1. 

ESG ratings promote green innovation of heavily polluting companies.

2.2.2. ESG Ratings, Financing Constraints, and Green Innovation

The high-input and high-risk characteristics of green innovation activities require enterprises to secure financial support from external sources [33]. With the growing societal awareness of sustainable development and environmental protection, ESG ratings have emerged as a crucial tool for enterprises to gain investors’ trust and financial support in the financing market. Firstly, ESG ratings can improve the management level and transparency of enterprises. Driven by ESG ratings, enterprises are compelled to comprehensively review their environmental, social, and governance performance as well as continuously improve and enhance their management level. Additionally, ESG ratings require companies to disclose relevant information and data to enhance their transparency and credibility. These initiatives bolster the operational efficiency and management level of enterprises, reduce their operational risks, and increase the trust of investors and financial institutions [34]. Consequently, they expand the financing possibilities for enterprises and increase the availability of funds, thus protecting green innovation activities necessitating large capital investment by enterprises. Secondly, good ESG ratings can help companies reduce financing costs. Companies with good ESG ratings signal long-term value creation and can negotiate more advantageous terms in the financing market, such as lower interest rates and longer repayment terms. This reduces financing costs and releases more funds for corporate green innovation activities. Therefore, this research proposes the following research hypothesis:
Hypothesis 2. 

ESG ratings facilitate green innovation in heavily polluting companies by alleviating corporate financing constraints.

2.2.3. ESG Ratings, Risk-Taking, and Green Innovation

The development of corporate green innovation activities requires enterprises to have a high risk-taking capability. This is primarily attributed to the fact that green innovation involves high risks and uncertainties that are difficult to assess and requires significant time and human costs while encountering a variety of risks, such as technological difficulties that cannot be broken through and difficulties in obtaining innovation results, or lagging in innovation results [35]. The principal–agent dilemma in enterprises is also a critical cause for insufficient risk-taking behavior. Owners prefer to achieve the long-term goal of the enterprise through innovation and other activities. In contrast, managers are more inclined to choose conventional projects with stable cash flows and lower risks to safeguard their interests and reputation. External investors typically advocate for reduced risk-taking behavior and invest in lower-risk and more prudent short-term projects for capital safety and a quick return [36]. Various principal–agent conflicts arising from differing objectives of the parties will further reduce corporate willingness to take risks and engage in high-risk green innovation and other activities. ESG ratings can effectively alleviate information asymmetry and principal–agent conflicts [37]. Firstly, ESG ratings represent the demand to embed the concept of green development into all decisions of corporate strategic planning. The pressure exerted by ESG ratings can constrain management behaviors and mitigate possible short-sightedness. In addition, ESG ratings convey socially responsible information that reflects the long-term orientation of enterprises toward sustainable development, thus enhancing the willingness of external investors to take risks on green innovation activities and to pay increased attention to corporate long-term development goals [38]. Based on the above analysis, this research posits the following research hypothesis:
Hypothesis 3. 

ESG ratings facilitate green innovation in heavily polluting companies by enhancing corporate risk-taking capability.

6. Conclusions and Policy Recommendations

ESG adheres to multiple value orientations, emphasizes the coordinated development of the economy, environment, and society, and promotes enterprises from the single pursuit of maximizing self-interest to maximizing social value. Therefore, it plays a pivotal role for enterprises in achieving sustainable development. Based on the sample data of heavy-polluting enterprises listed on the Shanghai and Shenzhen A-shares from 2010 to 2020, we investigate the impact of ESG rating on the green innovation of these enterprises. The results are summarized as follows: (1) Hypothesis 1 was confirmed: ESG ratings significantly foster green innovation within enterprises in heavily polluting sectors. Subsequent robustness tests reinforced this finding. While there are parallels with prior research [40,41], this study uniquely addresses the oversight of heavy-polluting industries, thereby contributing novel insights to the field. (2) Hypotheses 2 and 3 were substantiated, demonstrating that the alleviation of financing constraints and the enhancement of risk-taking capabilities are pivotal mechanisms through which ESG ratings encourage green innovation in enterprises within heavily polluting industries. These conclusions draw primarily upon the concept of information asymmetry and align with the perspectives and analytical framework presented by Wu et al. [42]. (3) Moreover, our research suggests that ESG ratings do not markedly enhance green innovations in SMEs and SOEs operating within heavily polluting sectors. We propose that this may be due to small and medium-sized enterprises placing a higher emphasis on survival and growth rather than on social and ecological responsibilities. Furthermore, the intricate governance structures and resource allocations of state-owned enterprises may dampen the efficacy of ESG ratings on their green innovation initiatives. This stance is corroborated by a substantial body of literature and numerous studies [49,50,51]. Drawing from the empirical findings, this research delineates the following policy implications.

Firstly, there is a need to strengthen the role of ESG ratings in guiding and incentivizing green innovation. This can be achieved by promoting the integration of ESG concepts into the strategic development plans of enterprises. Through the transmission mechanism of ESG ratings, enterprises can be incentivized to increase their investment in low-carbon technological progress and scientific and technological innovation. Simultaneously, they are encouraged to strengthen the focus of supervision and crackdowns on possible “greenwashing” behaviors at the law and regulation levels, thereby curbing such deceptive behaviors. Consideration should also be given to incorporating ESG principles into the assessment criteria for SOEs. Additionally, the private economy can also leverage ESG to enhance credit enhancement, thereby solving the financing difficulties and high financing costs, ultimately comprehensively promoting the favor of social capital to the green industry.

Secondly, it is imperative to establish a mandatory ESG disclosure system. At present, domestic ESG disclosure largely remains at a stage of encouragement and voluntary participation. Consequently, consideration can be given to expanding the scope of enterprises with mandatory disclosure, such as from large listed companies to SMEs or from key industries (like finance, minerals, and electric power) to other industries. At the specific institutional level, the introduction of a “non-disclosure is interpretation” rule can be instrumental in actively promoting China’s ESG practice gradually toward standardization, systematization, and localization.

Thirdly, it is recommended to actively create a favorable ESG external environment. Government regulators should clear the obstacles to ESG disclosure such as a lack of practical rules, insufficient motivation for disclosure, and difficulties in data integration. Meanwhile, government regulators can give certain incentives to companies participating in ESG. In particular, financial regulators can provide policy incentives to enterprises with higher ESG ratings in IPO, refinancing, bond issuance, etc. Simultaneously, stringent penalties should be imposed on enterprises that fail to disclose or provide false information in accordance with the requirements. In this way, it may create a favorable environment for the development of ESG investment.

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Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing Parameters https://inergency.com/changes-in-temperature-and-vapor-pressure-behavior-of-bamboo-scrimber-in-response-to-hot-pressing-parameters/ Thu, 28 Mar 2024 22:15:19 +0000 https://inergency.com/changes-in-temperature-and-vapor-pressure-behavior-of-bamboo-scrimber-in-response-to-hot-pressing-parameters/ Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing ParametersForests, Vol. 15, Pages 620: Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing Parameters Forests doi: 10.3390/f15040620 Authors: Ge Lu Li Hao Yang Lu Xu Li This study investigated the heat-transfer behavior of heat-treated and phenolic resin-impregnated bamboo bundle slabs during the hot-pressing process. The significance of these findings lies […]]]> Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing Parameters


Forests, Vol. 15, Pages 620: Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing Parameters

Forests doi: 10.3390/f15040620

Authors:
Ge
Lu
Li
Hao
Yang
Lu
Xu
Li

This study investigated the heat-transfer behavior of heat-treated and phenolic resin-impregnated bamboo bundle slabs during the hot-pressing process. The significance of these findings lies in their potential to drive advancements in hot-pressing technology, contribute to energy-conservation efforts, and facilitate emission reduction within the bamboo scrimber industry. In this study, the variations in temperature and vapor pressure were investigated during the hot-pressing of bamboo slabs under various conditions, including hot-pressing temperatures (140 °C, 150 °C, 160 °C, and 170 °C), hot-pressing holding times (15 min, 20 min, 25 min, and 30 min), and hot-pressing pressures (4 MPa, 5 MPa, 6 MPa, and 7 MPa). This was achieved using thermocouple sensors and a self-made vapor pressure-monitoring system. The results indicated that higher hot-pressing temperatures significantly increased the heating rate, peak temperature, and core-layer vapor peak pressure of the bamboo bundle slab, with the vapor peak pressure at 170 °C being twice that at 140 °C. Furthermore, extending the holding time had a lesser effect on increasing the peak temperature of the slab but significantly increased the peak vapor pressure in the core layer. Thus, increasing the hot-pressing pressure proved beneficial for slab heating but had a lesser effect on the surface and core-layer peak temperatures. The core-layer vapor pressure of the slab subjected to a hot-press pressure of 7 MPa was 1.8 times higher than that at 4 MPa.

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Natural Environments in University Campuses and Students’ Well-Being https://inergency.com/natural-environments-in-university-campuses-and-students-well-being/ Thu, 28 Mar 2024 21:33:29 +0000 https://inergency.com/natural-environments-in-university-campuses-and-students-well-being/ Natural Environments in University Campuses and Students’ Well-BeingIJERPH, Vol. 21, Pages 413: Natural Environments in University Campuses and Students’ Well-Being International Journal of Environmental Research and Public Health doi: 10.3390/ijerph21040413 Authors: Helena Ribeiro Keila Valente de Souza Santana Sofia Lizarralde Oliver Most recent university campuses follow the North American model, built on city limits or countryside, with large separate buildings in open […]]]> Natural Environments in University Campuses and Students’ Well-Being


IJERPH, Vol. 21, Pages 413: Natural Environments in University Campuses and Students’ Well-Being

International Journal of Environmental Research and Public Health doi: 10.3390/ijerph21040413

Authors:
Helena Ribeiro
Keila Valente de Souza Santana
Sofia Lizarralde Oliver

Most recent university campuses follow the North American model, built on city limits or countryside, with large separate buildings in open green spaces. Studies suggest that the prevalence and severity of mental health issues among university students has been increasing over the past decade in most countries. University services were created to face this growing problem, however individual-based interventions have limited effects on mental health and well-being of a large population. Our aim was to verify if and how the natural environment in campuses is focused on programs to cope with the issue of mental health and well-being among students. A systematic review of literature was undertaken with search in Scopus and LILACS with the keywords “green areas” AND “well-being” AND “Campus”, following PRISMA guidelines. As a result, 32 articles were selected. Research on the topic is recent, mostly in the USA, Bulgaria, and China. Most studies used objective information on campuses’ greenness and/or university students’ perception. Mental health was usually measured by validated scores. Findings of all the studies indicated positive association between campus greenery and well-being of students. We conclude that there is a large potential for use of university campuses in programs and as sites for students’ restoration and stress relief.

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A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids https://inergency.com/a-novel-machine-learning-based-approach-for-fault-detection-and-location-in-low-voltage-dc-microgrids/ Thu, 28 Mar 2024 20:54:10 +0000 https://inergency.com/a-novel-machine-learning-based-approach-for-fault-detection-and-location-in-low-voltage-dc-microgrids/ A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids1. Introduction The increasing growth of renewable energy sources (RESs), including solar photovoltaics (PV), fuel cells, energy storage systems, and also electric vehicles (EV) [1], has attracted more attention to DC microgrids as a practical solution for powering the DC loads in modern power systems [2]. The advancement of DC microgrids is progressing very fast […]]]> A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids


1. Introduction

The increasing growth of renewable energy sources (RESs), including solar photovoltaics (PV), fuel cells, energy storage systems, and also electric vehicles (EV) [1], has attracted more attention to DC microgrids as a practical solution for powering the DC loads in modern power systems [2]. The advancement of DC microgrids is progressing very fast due to their several advantages compared to the AC system. DC microgrids introduce an inherent decrease in AC systems’ common problems, including synchronization, voltage quality, and frequency. Furthermore, the DC microgrid technology improves the power quality and reliability of the power system [3,4]. In a DC system, the reactive power drop and skin effects are eliminated, which results in more power flow compared to the AC system and a considerable reduction in power loss [5]. Due to the presence of renewable energy sources and DC loads in the system and the capability of DC microgrids to directly power them, the conversion stages are reduced, and the efficiency of DC microgrids is substantially increased [6]. Having all the referred advantages, the utilization of DC microgrids in power grids is still not as prevalent. One of the most important obstacles to widespread DC microgrids is the lack of comprehensive protection guidelines [7,8]. The challenging issues for DC microgrid protection include fault characteristics, fault type, fault location, and transient occurrence. In a DC microgrid, during a fault, fast discharging of the capacitors in the DC link results in increasing a significant multiplication of the fault current amplitude [9,10]. The elevated current magnitude may seriously damage the equipment in the DC microgrid [11]. Accordingly, the utilization of very fast fault detection and precise fault location techniques is felt in these grids.
To safeguard DC microgrids from such faults, several approaches have been proposed. Due to its simplicity and cost-effectiveness, overcurrent protection is still one of the favorable protection techniques in DC microgrids. However, defining an accurate threshold value has always been a crucial issue for this method since the fault characteristics may vary by changing the DC microgrid topology [12,13]. The current derivative method is also proposed for fault detection in DC microgrids [14,15]. This technique uses the local measurements of the current signal derivatives to detect the fault. Based on this method, the fault is detected when the calculated current derivative is bigger than the pre-determined threshold value [16]. Using the local measurements, this method performs very fast and is a noise-resistant technique. Nevertheless, this method also suffers from determining a precise threshold value in different topologies of DC microgrids [17]. In some studies, the researchers have proposed differential protection as a fast and reliable protection for DC microgrids compared to overcurrent [18,19]. The advantage of using this method is its freedom from DG effects, fault current magnitude, and grid loading. However, this method requires a proper determination of threshold values to distinguish faults from transients occurring in microgrids [20]. For fault detection and location in DC systems, a method using traveling waves is proposed in [21], which this method shows a small error in this aim. Intelligent methods provide a swift and precise performance to detect faults in DC microgrids. However, in different grid types, they may illustrate different operations [22,23,24]. Regarding fault location, challenges arise due to the absence of frequency and phase data and the short line lengths within DC microgrids [20]. To address this, various methods have been developed for fault location determination. Utilizing online methodologies grounded in voltage and current fluctuations, the authors present a novel approach for fault location within a DC Ring Bus microgrid. The proposed method involves identifying fault occurrences by analyzing current oscillations following an incident and determining the faulted section through the examination of transient power variations during the initial cycle of the fault, as detailed in [25]. Furthermore, in [26], fault location within a Low-Voltage DC distribution network is achieved by leveraging DC magnitudes and directions, along with DC voltage levels. Proposed as an offline solution employing the Probe Power Unit (PPU) [5], a protective system has been introduced to both detect and locate faults. Another method for fault location in an LVDC microgrid is outlined in [27]. In contrast to the approach in [26], this method utilizes the attenuation constant of the damped probe current response. In [28], a fault location module is deployed at each end of a DC line, determining fault distance by sampling the discharge current through the line.
To overcome conventional fault detection and location problems, intelligent approaches like neural networks, fuzzy logic, and machine learning (ML) algorithms are employed [29,30]. Among the prediction techniques implemented to cope with power system challenges, ML approaches offer more accurate results. ML methods have been introduced as the primary tool for fault detection in distribution networks in [29]. This paper highlights methods including expert systems, Bayesian neural networks, LSTM networks, and support vector machines (SVM) as the most commonly utilized approaches for fault detection. However, they have more system requirements. In this regard, a technique based on the LSTM network is introduced in [31] for fault detection. This method uses the LSTM network to capture temporal features and SVM for classification. However, to implement this approach current, voltage, and active power have to be measured and used as input. Using the LSTM neural network, the authors have proposed a method for probabilistic sequence classification for fault detection in distribution grids in [32]. To detect faulty equipment in a microgrid, a convolutional neural network is proposed by [33]. However, this technique has a problem with protecting lines and loads. In [34], a combination of recurrent neural networks and a decision tree-based classifier is used to locate the fault and detect disturbances in the system. Although this study shows an appropriate accuracy, it does not consider all potential components in the DC microgrid, such as the EV charging station. A fault detection and classification approach based on a decision tree is presented in [35] for microgrid protection. To extract features from raw data, discrete Fourier transform is utilized in this research. To detect faults in a microgrid, a method based on the combination of decision tree and wavelet transform is proposed in [36]. In [37], the authors have used a naive classifier, SVM, and an Extreme learning machine (ELM) to detect and classify the fault types in a microgrid. The data’s feature is extracted using the Hilbert–Huang transform to implement the presented method. The authors in [38] have introduced an anomaly detection approach utilizing a deep neural network autoencoder, which effectively detects faults within the PV system. Considering the wind speed uncertainty, an ELM-based method is used in [39] to protect the microgrid. This study combines ELM and discrete wavelet transform to detect and classify the faults. A fault detection method based on a semi-supervised machine learning (SSML) model is introduced in [40]. In [41], an intelligent method for fault detection in a microgrid is proposed by a combination of wavelet transform and deep neural networks. The Markov model is another approach that is used by researchers in [42] to differentiate the fault condition from transients in a DC microgrid. The authors in [43,44] have used SVMs to identify fault conditions in DC systems. A variety of faults are taken into account and utilized to train the model parameters in [45], employing deep reinforcement learning as the foundation. A fault identification method for microgrids is developed using ML in [46]. In this research, initially, the voltage data are clustered using a modified K-means, and then association rules are extracted using the FP-growth algorithm. Finally, the fault identification model is trained using the mini-batch gradient descent (MBGD) algorithm based on ML principles.
According to the reviewed studies, it can be concluded that each method has its advantages and disadvantages. Based on the effectiveness of ML techniques in predictive tasks [47], this paper introduces a ML-based approach for fault detection and location in DC microgrids. The comparison of the proposed method with other existing studies in fault detection demonstrates the superiority of the presented technique given speed and accuracy in the detection of faults occurring in different locations in the microgrid. Furthermore, the proposed fault location method presents a high accuracy in locating the fault in the DC microgrid. In conclusion, the innovations presented in this article can be summarized as follows: (1) The combination of compressed sensing (CS) and regression tree (RT) for detecting various types of faults in DC microgrids. (2) Utilizing the feature matrix to train the LSTM model for fault location in DC microgrids. (3) Reduction in the required sampling rate compared to other signal processing methods. (4) Elimination of the threshold value due to preprocessing of training data. (5) Removal of telecommunication links and fault detection using sampled data from the main bus of the microgrid. (6) Considering the uncertainties of PVs and EVs and transient phenomena created by control systems.

This paper is structured into the following sections: the second part details the studied DC microgrid and provides the definitions and equations of the proposed fault detection and fault location techniques. In the third part, the proposed methods are evaluated, and the results are provided by figures and tables. Moving on to the fourth section, a comparison is conducted between the proposed fault detection method and recently published studies in this field. Finally, the fifth part concludes this paper.

2. Problem Formulation

The proposed method for fault detection in this paper takes advantage of the two techniques combination, CS and RT. The application of CS involves designing acquisition devices that utilize signal structure to lower the sampling rate, consequently reducing storage and digital signal processing (DSP) demands [48], while RT is a robust ML tool for building predictive models from data [49] On the other hand, to locate the fault in the DC microgrid, we have used the Long Short-Term Memory (LSTM ) model. To obtain higher accuracy in locating faults, a feature matrix from the current signal data has been used as input for this model. In the subsequent sections of this paper, the sample DC microgrid used to evaluate the proposed method has been reviewed. Then, proposed methods for fault detection and fault location in the DC microgrid are individually explained. To implement the proposed fault detection method, initially, a data set related to the normal and transient states of the DC microgrid is gathered by simulating these conditions. Afterward, CS is used to process this data set, and using the imaginary part of the signal the RT is trained. In this case, RT can predict the signal as long as it is in a normal or transient state; however, when a fault occurs in the DC microgrid, the difference between the predicted and the actual value of the signal will have a significant difference, and this feature can be used to detect the fault within the DC microgrid. The proposed fault detection approach is very fast and shows appropriate performance when transients occur in the system. In addition, this paper provides an accurate fault location method based on the feature matrix and LSTM model. In this step, the same DC microgrid is used and to generate data numerous pole-to-ground (PG) and pole-to-pole (PP) faults are applied to the main DC line. Therefore, current and voltage signals are measured at the IED location, and a feature matrix is extracted from these data. Afterward, the LSTM model is trained by the feature matrix and consequently, the proposed fault location method is evaluated by applying faults with random distances in the main DC line. The results present more than 93% accuracy for the fault locating with different fault resistances.

2.1. DC Microgrid Used for New Method Implementation

Figure 1 illustrates the structure of the DC microgrid being studied. The microgrid is equipped with different components including photovoltaic solar panels, an EV charging station, hybrid energy storage units (battery and flywheel), AC/DC and DC/DC converters, and various loads operating on both AC and DC power. Additionally, a modified five-layer control system, introduced by reference [50], has been used to stabilize the DC bus voltage. The DC bus voltage within this network is set to 600 V, while the AC voltage is defined at 380 V. Given the inherent unpredictability of generated power from PV and power consumption and delivery from EV, a hybrid energy storage system combining batteries and flywheel has been integrated to optimize energy management and distribution within the DC microgrid.
The PV model introduced in this paper is designed based on the model described in reference [50]. Key parameters of the PV cell, such as short-circuit current ( I s c ) , open-circuit voltage ( U o c ) , maximum power voltage ( U m ) , and current ( I m ) , are typically provided by the manufacturer. These parameters are determined under standard conditions with a temperature of T r e f = 25 °C, solar irradiance of S r e f = 1000 W/m2, and spectrum AM1.5G.

In the EV model, the nominal voltage for the battery is 360 V, the rated capacity is 100 Ah, the initial state-of-charge is 70%, and the battery has a response time of 10 ms. The DC/DC EV converter also includes two IGBT/Diodes and an inductor as the filter.

Within this DC microgrid, we have incorporated two AC loads and one DC load, in which individual loads have their own distinct electrical parameters. The three-phase AC loads are resistive and work at the determined voltage level. The microgrid being examined is a two-wire DC system, comprising four lines. Each line has a cross-sectional area of 240 mm2 and employs aluminum cables with PVC type A insulation and ST-1 PVC sheathing. The specific details about the microgrid are presented in Table 1.

2.2. Fault Detection Method

In this section, we introduce our novel approach for fault detection, harnessing the combined power of CS and RT. CS offers an efficient data representation method, while RT is a powerful tool for modeling complex data relationships. Sampling the signal using CS and utilizing the RT for prediction, the proposed method provides a robust and accurate fault detection system.

2.2.1. Compressed Sensing Theory

Following the conventional signal processing technique, the introduction of CS created significant progress within signal processing methods [52,53]. CS typically finds application in the acquisition of signals that have either sparsity or compressibility. We can determine a signal sparse/compressible in both original form or in transform domains such as Fourier transform, wavelet transform, and cosine transform.
CS operates based on recording fewer non-adaptive random measurements. Mathematically, CS can be formulated as follows: Let x be the original n-dimensional signal vector that we want to recover, and let y be the m-dimensional measurement vector obtained by multiplying the signal vector with the measurement matrix ϕ [54]:

In this equation, the input signal x R n (or we can say x C n ) has a length of n. Sensing matrix ϕ R m × n (can also be shown as ϕ C m × n ) is typically a random or structured m × n matrix representing the measurements taken from the signal. Each row of ϕ corresponds to a measurement or sample of the signal. Finally, the measurement vector is shown by y (y R m or y C m ) having a length of m that here y represents the measurement vector obtained by sampling the signal x using a sensing matrix ϕ . To generate the compressive measurements, the random measurement matrix and the input signal should be multiplied together. Accordingly, the number of taken measurements is significantly lower than the input signal length ( m n ).

Here, the measurement random matrix ϕ is decomposed into two matrices H and P (2):
where H is a matrix that defines the relationship between the original signal x and the measurements y. It essentially maps the original signal to the measurement space. P is a permutation matrix that rearranges or selects specific elements from the original signal vector x. The number of required samples for measurement is defined by the total number of samples in each of the rows in H; the ratio here is R = n / m . Here n represents the dimensionality of the signal being measured, and m shows the number of measurements or samples needed to accurately recover the signal. In the following, (3) and (4) represent the P and H matrices.

P H = 111 111 111

In addition, we can calculate x ¯ as:
where x ¯ represents the permuted or selected version of the original signal x. Therefore, we can rewrite (5) as the following:

Finally, in this equation, y represents the measurement vector obtained by linearly mapping the original signal x using the matrix H.

2.2.2. Regression Tree

Recent advancements in ML techniques have garnered attention to cope with the challenges confronting modern power systems [55]. The RT is known as a powerful ML tool used for decision-making and predictive modeling. It belongs to the family of decision trees and is specifically designed to solve regression problems, where the goal is to predict a continuous numeric value according to input characteristics. In this case, prediction errors are usually quantified by measuring the squared difference between the observed values and their related predictions [49]. Given the similarity between the RT and the classification tree, this section will commence with an expression of the classification tree, followed by a comprehensive examination of the RT.
In the context of a classification problem, we are presented with a training sample comprising n observations of a class variable denoted as Y, which assumes values 1 , 2 , , k , alongside p predictor variables, X 1 , X 2 , , X p . The primary objective is to establish a predictive model for estimating the values of Y based on new sets of X values. In principle, the solution involves partitioning the X space into K distinct sets—namely, A 1 , A 2 , , A k —such that the predicted value of Y corresponds to j if X falls within A j , where j ranges from 1 to k. When the X variables assume ordered values, there are two well-established approaches, namely, linear discriminant analysis and nearest-neighbor classification [56]. These methodologies generate sets A j characterized by piecewise linear and nonlinear boundaries, respectively.

The classification tree approaches employ a recursive partitioning approach to create rectangular sets A j by sequentially dividing the dataset, focusing on one X variable at a time. This strategy results in sets that are inherently easier to interpret. A noteworthy advantage of the decision tree structure is its versatility in handling any number of variables, whereas the visualization on the left panel is inherently limited to a maximum of two variables.

A RT shares similarities with a classification tree, but it diverges in that the dependent variable Y takes ordered values, and each node employs a regression model to predict Y s values [57].
As explained before, when the output variable is in nature qualitative, the classification tree is used. Conversely, an RT is considered when the data are quantitative. Accordingly, RT is of interest to this paper since our detection method deals with a quantitative issue. RT is a powerful technique for prediction and is constructed through a recursive process of dividing a dataset and fitting a simple model for each segment of the data [49]. The RT method is defined as a non-parameter algorithm and is known as an automatic classifier. Considering a learning sample having n cases that have the variables x and y ( ( x 1 , y 1 ) , ( x 2 , y 2 ) , ( x n , y n ) )

, an RT creates a classifier structured as a binary tree. In this definition, x t represents the tth independent variable taking an m-dimensional vector form, while y t corresponds to the response variable having a numerical value. In such RTs, the tree is built through iterative splits of subsets into a pair of descendant subsets based on sample input variables. Each split involves a question about the input variables, with “yes” and “no” responses guiding the creation of left and right descendant subsets, respectively [58].

2.2.3. Proposed Fault Detection Algorithm

The initial step to implement the proposed method in this paper is the precise sampling of the current signal within various grid conditions. These measurements are gathered at the Intelligent Electronic Device (IED) location. By simulating a wide range of transient and steady-state scenarios using the MATLAB/Simulink (version R2023a-academic use) environment, a diverse dataset is generated through sampling of the current signal. This comprehensive dataset mirrors the DC microgrid’s behavior under different operational conditions, making it a valuable resource for fault detection. The current signal waveform in normal operation, transient, and the faulty condition of the system is demonstrated in Figure 2 and Figure 3. According to the depicted waveform, for 2.2 s the DC microgrid is in normal operation. At exactly 2.2 s, a transient occurs due to the PV output change, and as shown in Figure 3, at the time of 2.5 s a PG fault occurs in the main DC line.
Subsequently, we employ CS to process this dataset. CS extracts the data of real and imaginary measurements. In the next stage, we undertake an offline procedure to train the RT using the output data derived from the CS. The trained RT is depicted in Figure A1 (Appendix A). This training phase equips our fault detection algorithm with the capability to effectively differentiate between normal operating conditions and anomalies. A visual representation of this detection algorithm is presented in Figure 4, enabling continuous monitoring and assessment of the DC microgrid’s condition.

Let us consider the algorithm in two parts; the offline part is related to RT training. On the other hand, the online part is related to the performance of the algorithm and checking if there is any fault occurrence in the grid.

In fact, we have trained the RT utilizing the data resulting from the imaginary part of the current signal, in which the signal is processed by CS. Since the RT is trained using DC microgrid normal conditions data, the difference between the actual and the predicted imaginary value of the current signal by RT will be zero for normal conditions of the grid. This statement can be explained by (7).

E = actual I m predicted I m

where E is the representative of the absolute value of the error (difference between actual and predicted value), and Im refers to the imaginary part of the current signal. Accordingly, by defining the condition demonstrated in (8), the algorithm can decide how to perform for the various disturbances of the grid.

If E > 0 , then ( fault condition )

According to Figure 4 and considering (8), if the difference between the actual and predicted value is zero, the occurred disturbance in the grid is a steady state or transient state and the algorithm should block the protection system operation. On the other hand, when the error value is greater than zero, the fault has occurred in the DC microgrid, and the trip command must be issued.

2.3. Fault Location

The previous section provided a fault detection algorithm in DC microgrids based on the CS signal processing technique and RT algorithm. However, in a DC microgrid, fault detection by itself is not enough to advance reliability, and the location of the fault should also be accurately determined. Moving forward, a robust technique for fault location in DC microgrids is developed by the implementation of the feature matrix and LSTM method. This approach harnesses the power of feature matrix and LSTM networks to enhance pinpointing the precise location of faults within the DC microgrid.

2.3.1. Feature Matrix

Since signals typically consist of complex data with high dimensions, they are not suitable for training intelligent techniques. For this reason, it is necessary to convert the signals into a straightforward and practical representation for intelligent models. Feature extraction from the signal enables intelligent algorithms to learn patterns, predict, and perform various data analysis tasks. Accordingly, in this article, the feature matrix extraction method presented in [59] is used to extract features from the current signal. This feature matrix includes features such as M e a n , R M S , D i f f e r e n c e , and M a x i m u m value of the signal data. M e a n , R M S , D i f f e r e n c e , and M a x i m u m value are major metrics used in feature extraction. Among these metrics, M e a n represents the average value of a set of numbers. In error calculation, the M e a n error indicates the average disparity between the predicted and the actual values over all data points in the dataset and provides an overall examination of the model’s bias. The R M S is a statistical estimate computed by taking the square root of the average of the data set. The D i f f e r e n c e measures the differences between individual values. The M a x i m u m value within a feature matrix denotes the highest value existing in the dataset. To calculate these metrics, Equations (9)–(12) are used.

M e a n = a b s ( I F ) l e n g t h ( I F )

R M S = i = n N I F 2 N

D i f f e r e n c e = i = n N ( I F ( i ) I F ( i 1 ) )

M a x = m a x ( I F )

To create the feature matrix, numerous simulations are conducted at various intervals along the line, with each simulation representing a one percent increment of the line’s length. In fact, these distances represent the fault impedance, and accordingly, the fault characteristics will be different for each distance. In these simulations, the fault time characteristics are taken into account to create the feature matrix. Consequently, the final form of the feature matrix is obtained by Equation (13). This matrix will be used to train the proposed LSTM model.

F e a t u r e M a t r i x = M e a n R M S D i f f M a x

2.3.2. LSTM Model

The LSTM model is a type of Recurrent Neural Network (RNN) that resolves the challenges in traditional RNN models [60]. In this paper, the LSTM model has been used to implement the proposed method for fault location having input from the feature matrix. Figure 5 depicts the overview of the LSTM model.
The fundamental principle of the LSTM network is the introduction of several key components known as gates (forget gate, input gate, update gate, and output gate). The relationships governing these gates and memory units are determined based on the following equations [61,62].
The forget gate decides which information from the previous cell state to discard. Mathematically, it can be represented as:

f t = σ ( W f · [ h t 1 , x t ] + b f )

where σ represents the sigmoid activation function, W f and b f are the weight matrix and bias vector related to the forget gate, and h t 1 , x t describes the sequence of the previous hidden state and the current input.

The input gate operation can be expressed mathematically as below:

i t = σ ( W i · [ h t 1 , x t ] + b i )

In addition, a candidate update vector is computed using the same input and is subsequently modified as follows:

C ˜ t = tanh ( W c · [ h t 1 , x t ] + b c )

where W i , W c , b i , and b c are the weight matrices and bias vectors specific to the input gate and candidate update, respectively, and tanh shows the activation function.

The update gate determines the proportion of new candidate values to replace old cell state values. Mathematically, it can be expressed as:

C t = f t C t 1 + i t C ˜ t

In this equation, the previous cell state is shown by C t 1 and i t and C ˜ t determines the new values.

Finally, the output gate controls which section of the current cell state has to be exposed or moved on to the output. The operation of the output gate can be expressed by the following equations.

o t = σ ( W o · [ h t 1 , x t ] + b o )

h t = o t · tanh ( C t )

In these equations, o t represents the output gate activation vector, and h t is the next hidden state. The tanh function is also applied to the updated cell state for the generation of a new hidden state.

2.3.3. Proposed Fault Location Algorithm

To implement the fault location algorithm, at first, the PG and PP faults are applied to the main DC line at different distances. The intervals for applying the fault are divided by one percentage of the line. Afterward, the voltage and currents at the line are measured at the IED location, and the data are used to create the feature matrix and then train the LSTM model. To have a better overview of the proposed algorithm, let us consider Figure 6.
As can be seen from this figure, in an offline procedure, the current and voltage signals are continuously sampled. When a fault occurs, the process is started by creating the feature matrix. When the matrices M e a n , R M S , D i f f e r e n c e , and M a x i m u m value are calculated, the feature matrix is obtained using these key matrices. Consequently, the feature matrix extracted from every window signal of the voltage and current signals is saved in a dataset. In the next step, the LSTM model is trained utilizing this dataset. Now, the trained LSTM model needs to be evaluated to ensure its accuracy in fault location. M A E (Mean Absolute Error), M S E (Mean Squared Error), and R M S E (Root Mean Squared Error) are commonly used metrics for evaluating the accuracy of LSTM models in prediction tasks. Based on the equations, M A E measures the average absolute difference between predicted and actual values, M S E measures the average squared difference between predicted and actual values, and R M S E is the square root of M S E and provides a measure of the typical error magnitude. To do this, the Equations (20)–(22) are considered, and the results show the model accuracy [60].

M S E = 1 N × t = 1 N ( L t M e a s u r e d L t F o r c a s t ) 2

R M S E = 1 N t = 1 N ( L t M e a s u r e d L t F o r c a s t ) 2

M A E = 1 N t = 1 N | L t M e a s u r e d L t F o r c a s t |

Finally, achieving the accuracy of the LSTM model, the precise location of the fault can be ensured. Therefore, in the online procedure, the algorithm starts gathering data by the occurrence of a fault, and utilizing the trained LSTM model faults in different distances of the line can be located. According to this figure, when the fault occurs on the main DC line, the algorithm is activated, and using the data obtained from the current and voltage signals at the IED location locates the fault with high accuracy and in a short time.

4. Comparison of the Proposed Fault Detection Method with Other Existing Methods

In this section, we evaluate the effectiveness of the proposed fault detection method in comparison to several established methodologies in this area. The comparison encompasses key criteria, including speed, computational efficiency, the need for defining thresholds, detection time, etc. Compared to the existing fault detection methods, the proposed method in this paper demonstrates critical advantages. First, it is a swift method of detecting the fault and isolating the DC microgrid components. In addition, this method is threshold-free, which makes it stronger when facing transient states in the DC microgrid. Moreover, compared to the recent studies, the studied DC microgrid encompasses different components, including EV, PV, combined energy storage system, and DC and AC loads.

Table 5 shows the comparison between the protection system proposed in this article and the prior DC microgrid protection methods. Some advantages of the proposed method over prior methods include implementing a combined battery and flywheel hybrid storage systems, and not using telecommunication links. Due to the existence of the flywheel, the battery and flywheel storage system have a fast response to the system changes. These quick changes can mistake the protection systems and cause undesired tripping. In addition, the method presented in this article does not require the use of communication systems. Using protection systems, in addition to increasing the performance speed, also tackles the challenges of telecommunication link uncertainty. Most importantly, the proposed method does not need the threshold consideration that is always a serious challenge for protection systems in DC microgrids.
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Historic Court Decision Puts Big Polluters on Notice in Latin America https://inergency.com/historic-court-decision-puts-big-polluters-on-notice-in-latin-america/ Thu, 28 Mar 2024 20:03:49 +0000 https://inergency.com/historic-court-decision-puts-big-polluters-on-notice-in-latin-america/ Historic Court Decision Puts Big Polluters on Notice in Latin AmericaA small mountain community in the Peruvian Andes has won a victory that can echo across Latin America. In a historic ruling, the Inter-American Court of Human Rights has charged the Peruvian government with violating its people’s right to a healthy environment by allowing a century-old metal smelter to contaminate the community of La Oroya. […]]]> Historic Court Decision Puts Big Polluters on Notice in Latin America


A small mountain community in the Peruvian Andes has won a victory that can echo across Latin America.

In a historic ruling, the Inter-American Court of Human Rights has charged the Peruvian government with violating its people’s right to a healthy environment by allowing a century-old metal smelter to contaminate the community of La Oroya. In its scathing decision, the Court found that the government “was aware of these high levels of contamination” yet chose not to take appropriate actions to prevent it, “nor to provide care for persons who had acquired diseases” caused by the pollution.

La Oroya is perched over 12,000 feet high in the Andes along the banks of the River Mantaro – a river so polluted with lead and arsenic that one local called it “a dead river.” Huge swaths of the surrounding mountains have been cleaved for mining, and pollution from the smelter has killed much of the area’s vegetation. Yet generations of locals still live here, fighting to maintain their homes and health.

Residents of La Oroya, Peru, photographed in 2008 near the the smelter complex that has made the city one of the most polluted places on earth. (Ernesto Benavides / AFP via Getty)

Since the smelter began operating in 1922, the people of La Oroya have been exposed to extreme levels of lead and other harmful contaminants, including arsenic, cadmium, and sulfur dioxide. At one point, 99% of children under 6 years old in La Oroya tested had lead levels known to cause severe health harms. Many residents suffer from developmental and behavioral disorders, cardiovascular diseases, chronic respiratory illness, and cancer, among several other health issues.

Peruvian researchers and La Oroya locals who tried to disseminate information about the smelter’s toxic impacts were harassed and intimidated. In the absence of government action, the Latin America-based environmental legal group the Inter-American Association for Environmental Defense (AIDA), with the support of Earthjustice, began advocating for the people of La Oroya. In 2006, AIDA and Earthjustice, together with other groups, filed a petition against the Peruvian government at the Inter-American Commission on Human Rights. AIDA and APRODEH, a Peruvian human rights organization, represented families from La Oroya in the proceedings.

In the 15 years it took the complaint to get before the Court, the Peruvian government repeatedly gave the smelter’s owner, Doe Run Peru (a subsidiary of the United States.-based Renco Group, Inc), free leave to emit massive levels of air pollution that frequently went well beyond Peruvian regulations and World Health Organization standards for air quality. Since the government sold the plant in 1997, Doe Run Peru was given special exemptions from complying with environmental laws until it can install much needed technology to control pollution. Later, despite the company repeatedly failing to install these controls, the government granted extension after extension allowing the company to continue operating and polluting.

Doe Run Peru eventually declared bankruptcy and creditors later sold the smelter to its local workers in La Oroya – potentially transferring its environmental liability onto the people it had poisoned.

A large industrial facility and smoke stack in the foreground with a collection of smaller homes and buildings near it.

Aerial view of the smelting complex in the city of La Oroya, Peru in 2022. La Oroya is one of the most polluted localities on the planet. (Ernesto Benavides / AFP via Getty Images)

Now, two decades after the people of La Oroya first sought justice, the Inter-American Court of Human Rights has ruled that the Peruvian government violated their right to a healthy environment. The Court found that, over decades of changing ownership, the government knowingly “allowed the existence of pollution levels that put people’s health at significant risk.”

Critically, the court is requiring the Peruvian government to make reparations to the people who have been impacted by its inaction. Those measures include prosecuting those responsible for harassing locals as they sought justice; preparing an environmental remediation plan for La Oroya’s contaminated air, water, and soil; updating air quality standards to guarantee the protection of the environment and public health; providing monetary compensation and free medical care to residents with symptoms and illnesses related to the mining; and ensuring that future operations of the smelter and all mining activity in Peru comply with international environmental standards.

This ruling sets a historic precedent for other polluters across Latin America. Millions of people in Latin America breathe polluted air in countries where air quality regulations – if they exist at all – are not adequate or enforced. Advocating for human rights against big polluters in the region can be deadly. Corporations are now on notice that exposing people to unhealthy levels of industrial pollution is a violation of international law, and that governments must hold polluters accountable.

Three women share an emotional hug.

Yolanda Zurita, left, and Rosa Amaro, right, victims of toxic pollution in La Oroya, Peru, are embraced after the Community of La Oroya case against the Peruvian State hearing during the 153rd session sessions of the Inter-American Court of Human Rights in Montevideo, Uruguay in 2022. (Pablo Porciuncula / AFP via Getty Images)

“Twenty years ago, when this fight started, I was carrying my banner saying that the health of the children is worth more than gold,” recalls Don Pablo, a resident of La Oroya. “We never gave up, and now I am very happy with the Court’s decision.”

Other Earthjustice efforts to expand the right to a healthy environment globally beyond this case include decades of advocacy culminating in the 2022 UN General Assembly recognition of the right to a healthy environment as a universal human right. Earthjustice is also participating in a public consultation process the Inter-American Court is currently holding regarding the responsibility of governments to protect human rights in the face of the ongoing climate crisis.  That proceeding can potentially result in an additional historic precedent that will help support climate justice litigation across the Americas.

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Complex Interplay of Metabolic Pathways in Grafting of Ziziphus Species: Transcriptomic Insights into Regulatory Networks of Carbohydrates and Secondary Metabolite Biosynthesis https://inergency.com/complex-interplay-of-metabolic-pathways-in-grafting-of-ziziphus-species-transcriptomic-insights-into-regulatory-networks-of-carbohydrates-and-secondary-metabolite-biosynthesis/ Thu, 28 Mar 2024 18:38:59 +0000 https://inergency.com/complex-interplay-of-metabolic-pathways-in-grafting-of-ziziphus-species-transcriptomic-insights-into-regulatory-networks-of-carbohydrates-and-secondary-metabolite-biosynthesis/ Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing ParametersForests, Vol. 15, Pages 618: Complex Interplay of Metabolic Pathways in Grafting of Ziziphus Species: Transcriptomic Insights into Regulatory Networks of Carbohydrates and Secondary Metabolite Biosynthesis Forests doi: 10.3390/f15040618 Authors: Saiyang Zhang Song Sheng Jiqing Peng Zhiming Liu Fengxia Shao Sen Wang Grafting serves as a pervasive methodology in the propagation of jujube plants, yet […]]]> Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing Parameters


Forests, Vol. 15, Pages 618: Complex Interplay of Metabolic Pathways in Grafting of Ziziphus Species: Transcriptomic Insights into Regulatory Networks of Carbohydrates and Secondary Metabolite Biosynthesis

Forests doi: 10.3390/f15040618

Authors:
Saiyang Zhang
Song Sheng
Jiqing Peng
Zhiming Liu
Fengxia Shao
Sen Wang

Grafting serves as a pervasive methodology in the propagation of jujube plants, yet the nuanced molecular mechanisms that dictate rootstock‒scion interactions remain inadequately understood. We examined the transcriptomic landscapes of jujube heterograft combinations. Contrary to self-grafting conditions, early-stage heterografting yielded no discernible advantageous effects on scion biomass accretion. Interestingly, the rootstock’s biomass was significantly impacted by the scion, varying by species. The differentially expressed genes (DEGs) across graft combinations were mainly enriched for the vegetative growth of rootstocks, secondary metabolism, and resistance improvement of scions. Weighted gene co-expression network analysis (WGCNA) identified 27 hub genes which were negatively correlated with plant growth and biomass enlargement, serving as negative regulators, while the genes, L484_001734, ATHB-15, and BPC1, were involved in positive regulation. With biomass measurements, the transcriptomic data supported that an incomplete vascular recovery during early grafting led to nutrient accumulation at the graft junction, temporarily limiting plant growth while providing development resources for callus. In summary, our work has demonstrated that the intricate biological connections between the rootstock and scion guarantee the effective jujube grafting process by elucidating the molecular processes involved in the process.

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Bullying at School, Cyberbullying, and Loneliness: National Representative Study of Adolescents in Denmark https://inergency.com/bullying-at-school-cyberbullying-and-loneliness-national-representative-study-of-adolescents-in-denmark/ Thu, 28 Mar 2024 18:00:10 +0000 https://inergency.com/bullying-at-school-cyberbullying-and-loneliness-national-representative-study-of-adolescents-in-denmark/ Natural Environments in University Campuses and Students’ Well-Being1. Introduction Loneliness is a subjective feeling of isolation. It is often defined as a cognitive discrepancy between the social relations an individual wishes to have and those that one perceives to have, and the affective reactions of sadness and emptiness that follow [1]. The feeling of loneliness is common in adolescence [2,3,4,5], and several […]]]> Natural Environments in University Campuses and Students’ Well-Being


1. Introduction

Loneliness is a subjective feeling of isolation. It is often defined as a cognitive discrepancy between the social relations an individual wishes to have and those that one perceives to have, and the affective reactions of sadness and emptiness that follow [1]. The feeling of loneliness is common in adolescence [2,3,4,5], and several adolescents will experience loneliness for short periods. The reasons may be feeling left out among peers, a change of school, parental divorce, or other adverse life events [6,7]. However, some adolescents experience prolonged feelings of loneliness that result from repeated failure to reconnect with others, which is a serious threat to their quality of life [6,7,8] and academic performance [9]. A recent meta-analysis of longitudinal studies suggested that loneliness tended to remain stable from adolescence to adulthood [10]. Loneliness is also an important public health problem because it is associated with a range of health problems [1,6,11,12,13,14] and risk behaviors [15,16,17]. It is important to understand the precursors of loneliness to strengthen preventive efforts. The current study focuses on two potential precursors: bullying victimization at school and exposure to cyberbullying.
Bullying victimization at school is common among adolescents [2,18,19], although the prevalence has been diminishing over the past decades in Europe and North America [20,21]. There is abundant documentation for an association between exposure to bullying and adverse psychological consequences such as poor life satisfaction [18], mental health problems, and suicidal behavior [5,22,23,24,25,26,27,28,29]. A small number of cross-sectional [30,31,32,33,34,35] and prospective [35,36,37] studies confirm that there is an association between loneliness and bullying victimization at school. For instance, Due et al. [30] found a strong and graded association between loneliness and exposure to bullying at school, a finding which was consistent across twenty-eight countries. The effect sizes vary across studies, from small to large. The variation in effect sizes suggests a need for further studies.
Exposure to cyberbullying, sometimes labeled internet bullying, online victimization, or internet harassment, is the use of digital technologies to harass, threaten, embarrass, or target another person. This phenomenon is also common among adolescents [2,18,21,24,38,39], although the prevalence of exposure to cyberbullying is lower than exposure to bullying at school [21]. The studies that find an association between exposure to cyberbullying and loneliness [40,41,42] show considerable variations in effect sizes, from weak to strong. There is some doubt about the causal pathway since a few prospective studies show that loneliness is a precursor of cyberbullying rather than the reverse [43]. As with face-to-face, in-person bullying victimization, the variation in effect sizes across studies for the association between loneliness and cyberbullying highlights the need for further studies.
There are reasons why there might be differences in the associations between loneliness and face-to-face, in-person bullying and loneliness and cyberbullying. For example, Van den Eijnden et al., 2014 [44], emphasize that findings from research on bullying at school cannot automatically be transferred to cyberbullying because these two phenomena differ in important ways: Cyberbullying via the internet has much higher accessibility to the target than bullying during school hours. Cyberbullying can reach a much larger audience than bullying at school and may remain visible for a long time to the victim and the audience, potentially resulting in longer-lasting negative effects, e.g., loneliness. A study covering six North European countries showed little overlap between bullying at school and cyberbullying, suggesting that the two may be different phenomena [18]. It is, therefore, important to analyze which kind of exposure is more closely associated with loneliness and to analyze the association between loneliness and double exposure (to bullying at school and cyberbullying). Only a few studies focus on such combined effects, and they found higher rates of loneliness among adolescents who were exposed to bullying in both contexts [31]. Van den Eijnden et al. [44] suggest that the two kinds of bullying are mutually reinforcing, i.e., exposure to bullying in one context increases the risk of exposure in the other. Studies about the association between exposure to bullying and loneliness use different reference periods, which makes comparisons difficult [40]. A few studies suggest that the association between loneliness and exposure to bullying varies by sex and age group [33,43,45]. According to Cava et al. [45], older teenage girls might be more vulnerable to cyberbullying than boys, for instance when exposed to cyber-control from a romantic relationship. These girls reported more feelings of loneliness and assessed their social network as worse than those never victimized. Landstedt and Persson [24] suggest more focus on the gender issue.

There is a need for further exploration of the association between loneliness and exposure to bullying, which includes both kinds of bullying and uses identical reference periods for the measurement of exposure. The aim of this study was to examine how loneliness was associated with exposure to bullying at school, to cyberbullying, and combinations of bullying at school and cyberbullying.

3. Results

The overall prevalence of loneliness was 9.0%. Table 1 shows that loneliness was significantly more prevalent among girls vs. boys, 15-year-olds vs. 11-year-olds, immigrants vs. Danish origin, and students from lower vs. higher OSC. The proportion exposed to bullying at school at least a couple of times per month was 6.3%, significantly more prevalent among girls vs. boys, 11-year-olds vs. 15-year-olds, immigrants vs. Danish origin, and students from lower vs. higher OSC. The proportion exposed to cyberbullying at least a couple of times per month was 4.8% and not significantly related to any of the socio-demographic variables. The variable that combined exposure to habitual bullying at school and cyberbullying classified the students into four categories: 4867 (90.4%) were not exposed to any bullying, 175 (3.3%) were exposed to cyberbullying but not bullying at school, 259 (4.8%) were exposed to bullying at school but not cyberbullying, and 81 (1.5%) were exposed to both kinds of bullying. These figures suggest that there is little overlap between exposure to the two types of bullying since most of the students who were exposed to bullying in one context were unexposed in the other context.
Table 2 shows a strong and graded association between loneliness and exposure to bullying. Even among students with low exposure to bullying (once or twice in the past couple of months), the odds ratio (OR) for loneliness was significantly elevated compared to non-exposed students. The OR (95% CI) for loneliness was 11.58 (7.21–18.61) among students exposed to bullying at school several times a week. The corresponding figure for exposure to cyberbullying was 5.79 (3.37–9.86). Finally, the OR for loneliness among the few students exposed to both kinds of bullying was 10.80 (6.87–16.97). Table 2 also shows that the OR estimates changed little when adjusted for socio-demographic control variables. Separate analyses for boys and girls and the three age groups showed a significant association between loneliness and the three measures of bullying in every sub-group (not shown in the table). The association between exposure to bullying at school and loneliness was steeper for boys than girls, manifested via a statistically significant interaction term between sex and exposure to bullying at school, p = 0.0165.
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Fungicides Market Size is projected to reach USD 23,393.85 million by 2031, growing at a CAGR of 4.6%: Straits Research https://inergency.com/fungicides-market-size-is-projected-to-reach-usd-23393-85-million-by-2031-growing-at-a-cagr-of-4-6-straits-research/ Thu, 28 Mar 2024 16:41:22 +0000 https://inergency.com/fungicides-market-size-is-projected-to-reach-usd-23393-85-million-by-2031-growing-at-a-cagr-of-4-6-straits-research/ Fungicides Market Size is projected to reach USD 23,393.85 million by 2031, growing at a CAGR of 4.6%: Straits ResearchNew York, United States, March 28, 2024 (GLOBE NEWSWIRE) — Modern crop protection against fungal diseases must include fungicides. Due to changes in farming practices and technological advancements, farmers are now encouraged to practice effective pest management. The ability of fungicides to save crops has become more widely known due to the increased use of […]]]> Fungicides Market Size is projected to reach USD 23,393.85 million by 2031, growing at a CAGR of 4.6%: Straits Research


New York, United States, March 28, 2024 (GLOBE NEWSWIRE) — Modern crop protection against fungal diseases must include fungicides. Due to changes in farming practices and technological advancements, farmers are now encouraged to practice effective pest management. The ability of fungicides to save crops has become more widely known due to the increased use of pest management strategies. These are used on crops to improve quality and eliminate fungus spores. While decreasing crop losses before and after harvest, fungicide increases crop yield. The chemical and pharmaceutical industries are primarily responsible for producing fungicides. In order to increase crop yield and protect crops from various diseases, fungicide use has increased in agriculture, horticulture, and particularly in floriculture. In agriculture, cereal crops are the main target of fungicide use.

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Growing Number of Plant Diseases Drives the Global Market

The fungicide market has seen an upsurge in crop protection demand due to an increase in plant diseases. About 85% of all plant diseases, which influence crop yield and quality, are caused by fungal organisms. The main causes of fungus diseases are humid, warm, and wet weather conditions. In addition, brown rot, powdery mildew, downy mildew, sclerotium rots, fusarium wilts, botrytis rots, apple scab, rust, black root rot, wilt, white blisters, and blight are a few of the common fungi-caused diseases. Crops are harmed by fungi when they obstruct the water-conducting cells, which causes the crop to die. In order to treat and prevent disease, fungicides are pesticides that destroy the microorganisms that produce fungi.

Rising Bio-Fungicide Acceptance Creates Tremendous Opportunities

Bio-fungicides are made from beneficial fungi and bacteria that combat pathogens and thereby control the diseases they cause. Since these microorganisms are found naturally in soil, they are a secure substitute for synthetic fungicides. Bio-fungicides can only protect the roots from fungi when applied before the disease manifests. It also contributes significantly to enhancing the soil quality of arable lands by balancing the mineral contents of the soil. In addition, the use of bio-fungicides will not harm the environment or people because they are made of eco-friendly organisms. Bio-fungicides, an environmentally friendly substitute for chemical fungicides, are expected to create opportunities for market growth over the forecast period.

Regional Analysis

Europe is the most significant global fungicides market shareholder in the global fungicides market and is anticipated to exhibit a CAGR of 4.5% during the forecast period. The European market includes France, Gerseveral, Italy, Spain, and the rest of Europe. Disease control is a top priority for this region because wheat crops are widely grown there. Wheat is one of Europe’s most significant agro-economic crops, so managing wheat crop diseases here is essential. Foliar fungicides, the first fungicides to be used to control diseases of wheat crops, are responsible for the increase in wheat yields in Europe. In addition, the European Union (EU) has restricted the use of copper fungicides because prolonged use causes copper formations in the soil. Europe is a major fungicide market because of its extensive use in cultivating fruits, vegetables, grapes, and wheat.

North America is anticipated to exhibit a CAGR of 4.6% over the forecast period. North America’s fungicide market is expanding due to the increased use of fungicides in agricultural areas. Various fungicides are widely used for cultivation in the North American continent. These fungicides can protect a wide range of plants, including cereals, lentils, chickpeas, peas, soybeans, sugar beets, potatoes, oilseeds, and beans. In addition, the companies are undertaking extensive R&D projects to address the problem of fungicide resistance. The use of Kocide, a copper fungicide from DuPont, in the cultivation of beans, tomatoes, potatoes, and short crops has been authorized by the Canadian government as a means of preventing diseases like blight, halo blight, and bacterial brown spots.

Key Highlights

  • Based on active ingredients, the global fungicides market is divided into dithiocarbamates, benzimidazoles, chloronitriles, triazoles, phenylamides, and strobilurins. The triazoles segment is the largest contributor to the market and is expected to exhibit a CAGR of 4.4% over the forecast period.
  • Based on crop type, the global fungicides market is divided into cereals and grains, oilseeds and pulses, and fruits and vegetables. The fruits and vegetables segment is the highest contributor to the market and is anticipated to exhibit a CAGR of 4.7% during the forecast period.
  • Europe is the most significant global fungicides market shareholder in the global fungicides market and is anticipated to exhibit a CAGR of 4.5% during the forecast period.

Competitive Players

The major key players in the global fungicides market are Adama Ltd., Basf Se, Bayer Ag, Fmc Corporation, Corteva Inc., Novo Nordisk A/S, Nufarm Limited, Sumitomo Chemical Company, Syngenta Ag, And Tata Chemicals Ltd.

Market News

  • In February 2023, Lavie Bio Ltd., a subsidiary of Evogene Ltd. and a leading ag-biological company focusing on improving food quality, sustainability, and agriculture productivity through the introduction of microbiome-based products, announced progress in its bio-fungicides programs based on successful results from field trials conducted in 2022 for its bio-fungicides LAV311 and LAV321, after the conclusion of the analysis of its statistic results.
  • In March 2023, Corteva Agriscience announced the commercial launch of Adavelt™ active, with recent product registrations in three countries – Australia, Canada, and South Korea. Adavelt Active is a novel fungicide with a new mode of action that protects against a wide range of diseases that can impact crop yields.

Global Fungicides Market: Segmentation

By Active Ingredient

  • Dithiocarbamates
  • Benzimidazoles
  • Chloronitriles
  • Triazoles
  • Phenylamides
  • Strobilurins

By Crop Type

  • Cereals and Grains
  • Oilseeds and Pulses
  • Fruits and Vegetables

By Regions

  • North America
  • Europe
  • Asia-Pacific
  • LAMEA

Get Detailed Market Segmentation @ https://straitsresearch.com/report/fungicides-market/segmentation

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Forestry Ergonomics Publications in the Last Decade: A Review https://inergency.com/forestry-ergonomics-publications-in-the-last-decade-a-review/ Thu, 28 Mar 2024 15:56:29 +0000 https://inergency.com/forestry-ergonomics-publications-in-the-last-decade-a-review/ Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing ParametersThis is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessReview by Marin Bačić Marin Bačić Dr. Marin Bačić is a Senior Research Assistant at the Institute of Forest Engineering, Faculty of […] Dr. Marin Bačić is a Senior Research Assistant at the Institute of Forest Engineering, Faculty […]]]> Changes in Temperature and Vapor-Pressure Behavior of Bamboo Scrimber in Response to Hot-Pressing Parameters


This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Review

by

Marin Bačić

,
Matija Landekić

*,
Zdravko Pandur

,
Marijan Šušnjar

,
Mario Šporčić

,
Hrvoje Nevečerel

and
Kruno Lepoglavec

Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimunska Cesta 23, 10000 Zagreb, Croatia

*

Author to whom correspondence should be addressed.

Forests 2024, 15(4), 616; https://doi.org/10.3390/f15040616 (registering DOI)

Submission received: 28 February 2024
/
Revised: 26 March 2024
/
Accepted: 27 March 2024
/
Published: 28 March 2024

Abstract

Compiling the research on forestry ergonomics, which is still a marginal field in terms of the sheer volume of published forestry-related articles, gives a good foundation and guidance for future research and publishing. This review aims to compile, classify, and analyze forestry ergonomics publications in JIF (Journal Impact Factor) journals regarding their spatial and temporal distribution, observed operations, machines and tools, and risk factors. A reference period from 2014 to 2023 was observed in this study. The Web of Science Core Collection database was used to filter publications in the field of forestry and ergonomics. A total number of 102 articles were selected. After selection, data regarding publishing year, journal name, main field, country of origin, forest operation, machine/tool, and risk factor were noted. The number of articles is ever-increasing with the last four years having above average numbers of articles. Countries from Europe and South America (Brazil) have the most publications. Most of the journals are ranked in the top 50%. Harvesting, wood extraction, and pre-harvesting operations have the highest number of records. Chainsaw, skidder, and pre-harvesting tools are the most observed means of work. The risk factors with the highest percentage of records are workload (23%), noise (20%), vibration (20%), postural load (16%), and MSD (Musculoskeletal Disorder) occurrence (7%).

Share and Cite

MDPI and ACS Style

Bačić, M.; Landekić, M.; Pandur, Z.; Šušnjar, M.; Šporčić, M.; Nevečerel, H.; Lepoglavec, K.
Forestry Ergonomics Publications in the Last Decade: A Review. Forests 2024, 15, 616.
https://doi.org/10.3390/f15040616

AMA Style

Bačić M, Landekić M, Pandur Z, Šušnjar M, Šporčić M, Nevečerel H, Lepoglavec K.
Forestry Ergonomics Publications in the Last Decade: A Review. Forests. 2024; 15(4):616.
https://doi.org/10.3390/f15040616

Chicago/Turabian Style

Bačić, Marin, Matija Landekić, Zdravko Pandur, Marijan Šušnjar, Mario Šporčić, Hrvoje Nevečerel, and Kruno Lepoglavec.
2024. “Forestry Ergonomics Publications in the Last Decade: A Review” Forests 15, no. 4: 616.
https://doi.org/10.3390/f15040616

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.

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A Settings and Systems Approach to Promoting the Health and Wellbeing of People with an Intellectual Disability https://inergency.com/a-settings-and-systems-approach-to-promoting-the-health-and-wellbeing-of-people-with-an-intellectual-disability/ Thu, 28 Mar 2024 15:13:29 +0000 https://inergency.com/a-settings-and-systems-approach-to-promoting-the-health-and-wellbeing-of-people-with-an-intellectual-disability/ Natural Environments in University Campuses and Students’ Well-Being1. Introduction Due to different measurement criteria that have changed over time and different interpretations of classifications, precise estimates of the number of people with an intellectual disability are difficult to determine accurately [1]. Traditionally, intellectual disability was defined solely with regard to low intellectual functioning based on the standardised measurement of an IQ test; […]]]> Natural Environments in University Campuses and Students’ Well-Being


1. Introduction

Due to different measurement criteria that have changed over time and different interpretations of classifications, precise estimates of the number of people with an intellectual disability are difficult to determine accurately [1]. Traditionally, intellectual disability was defined solely with regard to low intellectual functioning based on the standardised measurement of an IQ test; but more recently, the categorisation has broadened to impairment in intellectual function alongside limitations in daily activities [1]. Australian figures from 2003 indicate that estimates can vary between 1.6% of the population and 2.7% of the population, depending on the activity limitation classification used [1]. This variation depends on the sampling characteristics with respect to age (whether children under school age are included, for example), what cut-off criteria are used for the IQ score, and what other criteria are used [1]. Prevalence is also higher in males compared to females [1]. Worldwide, the estimated prevalence of intellectual disability is 1% [2]. Research has shown that people with an intellectual disability have higher prevalence rates of certain preventable health conditions including diabetes [3], obesity [3,4], and a higher rate of avoidable mortality relative to the general population [5]. There have been mixed findings in terms of cardiovascular risk factors, with research finding that women with an intellectual disability have higher rates of hypertension [6], but with more recent research indicating a lower risk profile for cardiovascular disease relative to population norms [3]. People with an intellectual disability are also more likely to have a lower rating of overall health and an increased rate of mental health problems [7].
With respect to health behaviours, the research indicates lower levels of physical activity and poorer nutrition; however, more positively, they have reduced rates of smoking and alcohol consumption relative to population norms [6,8,9]. Despite the increased health needs, this population cohort is less likely to have the opportunity to participate in prevention and health promotion programs [10]. There has also been relatively little health promotion research and evaluation with this population cohort [11]. A systematic review of physical activity interventions for people with an intellectual disability only found five relevant articles [8]. The focus of the interventions that were reviewed was largely programmatic, although there was some commentary on broader policy and settings approaches. A systematic review of nutrition interventions for people with an intellectual disability did uncover more articles, with 44 papers meeting the inclusion criteria [12]. However, the majority of interventions were focused on the individual level, and only four out of forty-four intervention studies had a settings component of change to the physical environment, three in a school setting and only one in a community residential setting.
Health promotion interventions for people with intellectual disabilities have tended to be programmatic and individual in focus and have lacked a broader ecological and settings focus [13]. There have been recommendations for more settings-based research and approaches for people with an intellectual disability [8,13,14,15]. Health promotion programs designed for the general population, such as lifestyle campaigns, often do not reach people with an intellectual disability, and there are few specific campaigns for this population cohort [12,15]. This increases the importance of ensuring that the settings in which they live and engage with are as health promoting as possible [15]. The purpose of this paper is to review the best practice approaches to settings-based health promotion, particularly in workplace settings, and to explore how this approach can be of benefit to people with an intellectual disability.

2. Settings Approaches

Health promotion has been implemented in settings for several years. Settings often have some sort of structure, culture, policies, and institutional values that can influence health behaviour [16]. Some of the common settings for health promotion include the workplace, schools, neighbourhoods or communities, and primary health care and hospitals [17,18]. It is important that a health promotion settings approach is inclusive of people with an intellectual disability in these community settings [14,15]. Whitelaw et al. [18] discussed the different elements of a settings-based approach and showed that there were different ways in which a settings-based model has been used. The most conservative approach uses the settings as a means to access populations for the delivery of individual-based programs. The ecological or ‘comprehensive/structural’ approach addresses the culture and structure of the organisation to promote health. In between these two positions are various combinations of individual and ecological approaches.
When considering the ecological perspective, organisational settings provide a way to focus on the determinants of health beyond personal control. They provide a good middle ground between individual behaviour and higher levels of social organisation. When working within settings it is important to recognise that settings often have multiple roles and functions. For example, settings such as schools and hospitals are not only organisations that provide for students and patients, respectively, but they are also workplaces and, for some people, homes (for example, boarding schools and residents of longer-term care facilities and nursing homes). Therefore each ‘type’ of setting may also perform the function of other settings. Coordination of efforts across settings and the integration of interventions in multiple settings are widely advocated. This is because people move in and out of settings in the course of their daily lives. In addition, it is recognised that interventions can work within several settings to maximise effectiveness [19].
With respect to settings-based work with people with an intellectual disability, there has been some work in supported accommodation. A scoping review of health promotion interventions in supported accommodation found that health education and exercise programs were the most common intervention types [20]. While the results were mixed, it was concluded that there were some promising findings with interventions that delivered health education for supported accommodation staff. It was also concluded that there needs to be more codesign with people with disabilities, which seemed to be lacking in the studies reviewed. Of further interest is that while there were some interventions that focused on integrating intervention components within the normal routines and procedures of the accommodation settings [20], for the most part, the interventions were programmatic in focus and were delivered in settings rather than taking an explicit settings focus. Thus, they were not structural and ecological to the full extent that is recommended for a comprehensive settings-based approach.
There has been some helpful research on the principles required for a settings-based approach for people with an intellectual disability [15]. These principles included ensuring the home and community environment is accessible and enabling for health. Key among these principles is the important role of care providers in creating an empowering environment and ensuring these care providers have the capacity themselves to create a health promoting environment [13]. Part of this involves managing the tension between enabling autonomy of choice over diet and also ensuring that healthy choices can be made [13]. For people with an intellectual disability, their support network involves care workers, and they form a key part of the “Healthy Settings for People with Intellectual Disabilities (HeSPID)” framework developed, which centres on ‘People, Places, and Preconditions’ [13]. This research is largely based on home and community settings, and while the principles and framework can be applied to workplaces, this was a not a focus of the research. Many people with an intellectual disability are employed in various types of workplaces, and this is another important health promotion setting.

3. Workplace as a Health Promotion Setting

Work conditions have a significant potential to influence health in either a positive or negative way, due to the amount of time spent at work [21]. Many of the early workplace health promotion programs, from the 1970s onwards, focused on promoting fitness through the provision of corporate fitness programs and providing facilities. This was followed by a focus on individual health issues such as weight control, cardiovascular disease risk appraisals, stress management, and ‘quit’ smoking programs. The focus of these programs was on individual behavioural change strategies, commonly as a component of screening, educational, or counselling programs [22]. The workplace provides an infrastructure and organization for coordinating and developing programs. This environment allows for health promotion messages to be efficiently and effectively communicated at a minimal cost [23,24,25].
The main limitation of this style of workplace health promotion programs is the traditional focus on behavioural programs, particularly those targeting individuals. This type of program supports the theory that the workplace is a convenient place to implement health promotion programs as opposed to being a setting that is involved in developing a program. Behavioural workplace health promotion approaches do not effectively deal with the social and economic determinants of health that are emphasised in the Ottawa Charter and other relevant workplace health promotion guides [26,27]. Creating healthy environments in workplaces and other settings together with strong community action are key aspects of the Ottawa Charter, which are missed if only taking a behavioural approach [26]. Work itself should be a ‘source of health’ [26]. In addition, even after controlling for lifestyle differences, there remains a significant gradient in health outcomes across occupational hierarchy. Behavioural changes tend to be short term unless there are concurrent changes to the social and cultural context that shapes an individual [25].
The comprehensive approach to workplace health promotion suggests that instead of the workplace being used simply as a good location for health promotion practitioners to implement programs, workplace environmental change needs to occur, which is instigated in partnership between staff and managers. This involves adopting multiple strategies that aim to improve the health status of employees and the population as a whole [23]. Over the last few years there have been a number of reviews of workplace health promotion covering mental health, nutrition, and physical activity that all conclude that multi-component programs are more effective [28,29,30,31,32,33]. This includes incorporating a range of topics and a range of strategies inclusive of individually focused strategies and organisational change strategies [28,30,32,34]. Education-only interventions have shown to be ineffective [28,33]. There is good evidence that multi-component work health promotion interventions can improve a range of health and wellbeing indicators and that they can reduce absenteeism [29,30,32]. Another consistent recommendation from the different reviews is that interventions need to be adapted to suit different workplace contexts, which can make replication and scaling more challenging [30,34]. Finally, it was noted in one review that there was a lack of research conducted with different population groups [34]. One such population group that has been missed in workplace health promotion comprises people with an intellectual disability.

4. Employment and Wellbeing among People with an Intellectual Disability

Employment forms an important role in the lives of several people with an intellectual disability; however, there are scant health promotion interventions and research in this setting for this population group. The history of employment for people with intellectual disability in Australia has been characterised by various policy approaches. In the 1950s and 1960s, the predominant approach was segregation of people with disabilities and the funding of what was termed sheltered workshops. In subsequent decades, there was more emphasis on inclusion, and the Disability Services Act of 1986 established two broad types of employment services, open and supported employment services [35]. What the Act produced was a bifurcated model in which open employment was only an option if someone did not need any support [35].
In the 1990s, there started to be a preference for open employment, and supported employment services were labelled as ‘disability business services’ or ‘business enterprises’. This name changed to Australian Disability Enterprises (ADEs) in 2008. Further reform has taken place more recently, as Australian Disability Enterprise Services was discontinued as a government funded program in 2021 [36]. Data from 2022 revealed there were 477 ADEs in Australia that were being operated by 147 organisations, employing 16,000 people [37]. Many ADE organisations have attempted to reposition themselves as social enterprises in the employment landscape. Despite all this reform for inclusion, ADEs are considered a setting at risk for exploitation, violence, and abuse [38]. Current policy and service delivery has directed school leavers with an intellectual disability into ADEs as the first option, and as data have revealed, transition out of an ADE is very unlikely [38].
The Disability Royal Commission has recommended that open and inclusive employment settings should be the first option for school leavers, and there were varying opinions among commissioners as to whether ADEs should be phased out or significantly reformed [38]. While several ADEs are now self-referring as social enterprises, the Commission felt that they had not undergone sufficient reform to provide an inclusive and community facing workplace that had a diverse workforce and provided training and other opportunities to transition to open employment. They described workplaces with these attributes as ‘social firms’ [38]. Data from the NDIS revealed very little movement from ADEs to other employment opportunities. Data from 2020 revealed that only 4% of 15–24 year olds had changed from an ADE to open employment, while 3% moved from open employment to ADEs [39]. The data for those older than 25 showed even lower levels of movement to open employment. Only 1% of people aged over 25 years moved from ADEs to open employment, while 3% moved from open employment to ADEs [39]. These results mirror studies from other countries, which show very low employment transition rates for people with an intellectual disability [40].
An inclusive health promotion workplace approach can be one of the key areas for action to address the current employment barriers and challenges that people with an intellectual disability experience. Despite all the reform, there remain significant barriers to inclusive employment and, at least from a research perspective, a seeming lack of focus on wellbeing within the workplace. While there is very little health and wellbeing intervention research conducted in workplaces for people with an intellectual disability, there is an emerging area of research in understanding job satisfaction for this population cohort. There have been a few papers that have explored the application of job satisfaction models and evaluation tools for people with an intellectual disability [41,42,43]. Having the psychological needs of a sense of autonomy, connection, and a sense of competence met in the workplace is associated with higher levels of job satisfaction [41].
Some of this research has been limited by small sample sizes from single organisations and other sampling limitations preventing a thorough test of job satisfaction models for people with an intellectual disability [44]. A recent study conducted a larger study on job satisfaction using Job Demands–Resource theory [44]. The study took place with 554 workers from Spain from 19 different workplaces. Eleven of these were sheltered workshops, and eight were supported employment opportunities (more community focused). Job Demands–Resource theory is based on the similarly named Job Demands–Resources model and the interaction of the personal (physical and psychological), organisational, and social demands of the job together with the resources available at these various levels as well [45]. Research has shown how the interaction of these two elements (demands and resources) influences wellbeing and job satisfaction in the workplace [44]. Previous research has utilised this model to reveal that low job demands and high levels of social support from co-workers and supervisors are related to an increased quality of working life [42].
Flores et al. [44] conducted a range of job satisfaction and job demand and resource survey instruments including the well-known Job Content Questionnaire (JCQ) [46]. Survey items were modified in some instances, and the data were collected through interviews with modified response options. The results showed that overall job satisfaction was high among all participants; but interestingly, those in inclusive employment had higher levels of job satisfaction, work engagement, lower overall scores on job demands, and increased scores on job resources. Other research has not found differences in job satisfaction between sheltered and inclusive employment [47].
The results of Flores et al. [44] were similar to past research with analysis revealing that high psychological demands were related to increased exhaustion and lower job satisfaction. Previous research has also found that the relationship between job demands and job satisfaction is mediated by personality characteristics such as conscientiousness [47]. It was concluded in this study that considering personality factors is important when matching for tasks [47]. Conversely, higher levels of job resources are related to increased job satisfaction, which has also been found in previous research [47]. In the Flores et al. [44] study, support from supervisors was the single biggest predictor of job satisfaction, and co-worker support was also found to be important. Qualitative research has also revealed the importance of supervisor and co-worker support for a sense of connection and wellbeing in the workplace for people with an intellectual disability [48]. Flores et al. [44] concluded that enhancing social connections can be a focus of workplace interventions, but further qualitative research is required to understand in depth other factors that may be important in determining job satisfaction for people with an intellectual disability.
There has also been some work understanding the role of managers in the support of workplace wellbeing for employees with an intellectual disability [49]. Using in-depth interviews with managers, the goal of this research was to understand how workplace health promotion is delivered at various stages of needs assessment, planning, intervention delivery, and evaluation. While conceptually, the analytic focus of the research was at a programmatic level, it was interesting that a number of systems concepts emerged. The managers discussed the importance of a culture of continuous improvement with respect to evaluation and always checking in with employees on their perceptions of their roles and various interventions. It was also apparent that there was great flexibility in intervention delivery between the different case study workplaces, which highlights the needs to tailor intervention strategies to the unique employee and business context of the organisation. Finally, the importance of an empowerment and partnership approach between managers and employees was emphasised. Thus, while the research was focused on understanding intervention delivery and evaluation, it did reveal some aspects of the culture of the organisation that were important for workplace health promotion for this cohort. Further research is required to more explicitly detail the structural and cultural elements of the workplace that foster wellbeing. It is the structure and culture of a workplace itself rather than just the intervention delivery that is important to understand [50].
The other interesting area to emerge from this research was the topic focus of the interventions. While this was not made explicit, it might seem that job satisfaction and social connection were the dominant focuses given some of the intervention strategies [49]. There was no mention of physical activity or nutrition, even though they are areas of need and have been targets of workplace health promotion interventions for the general population. Thus, there are several gaps with respect to workplace health promotion for people with an intellectual disability. The emerging research indicates that the quality of the work experience determines the levels of job satisfaction and sense of connection. This mirrors research in the workplace setting with other vulnerable cohorts in that it is not just having employment that is important for wellbeing but the quality of the workplace experience [50]. What is lacking is intervention research that takes a comprehensive settings-based approach.

5. A Comprehensive Settings-and-Systems-Based Approach

There are a number of research gaps that currently exist with respect to a settings-based approach with people with an intellectual disability. While there is some evidence now on the characteristics of job satisfaction for people with an intellectual disability, there is little evidence, as far as we are aware, on intervention studies attempting to improve these factors. Further, there is no evidence, as far as we are aware, that has attempted to improve nutrition and physical activity within the workplace for people with an intellectual disability, despite this being an area of high need. Further, as reviewed earlier, several of the health and wellbeing interventions that have been delivered in other settings, particularly supported accommodation, are programmatic in focus and have not taken a settings-based approach. A comprehensive settings-based approach also needs to be coordinated across multiple settings such as the workplace, schools, supported accommodation, and community settings. Such an approach necessitates a systems orientation to health promotion delivery delivered with people with an intellectual disability.

Systems thinking is an approach that considers how different elements of a system (for instance different settings) connect with and influence each other [51]. A systems-based approach has several common elements with settings approaches, whereby the focus is on the changes to policies, routines, relationships, power structures, and values [52]. Addressing settings-based change has been recommended as a way to move away from the more limited approach of individual behavioural risk factors and to address higher levels of social organisation [18,19]. Key to this is understanding that settings are complex environments, and flexible approaches are required to address the culture and structure of a setting/system [53,54]. Whether there is positive change relies on how the intervention components interact with the system in which they are being delivered. That is, the same intervention component can have different results in different settings depending on how it is influenced by the people, culture, structure, and other elements within any one setting. Thus, is important to adjust interventions to suit the particular characteristics of the setting to ensure the best possible outcome [53,54].
Another important systems consideration is providing as several opportunities as possible for health behaviour change to occur. A health promotion practitioner needs to repeatedly provide a program with the aim of eventually producing a scenario where the information provided together with the psychological state of an individual produces a change effect, “hitting a lever point” [55]. This analogy can equally be applied at a socio-ecological level. The policy goal in this sense is to create as several healthy settings as possible to increase the likelihood of creating that requisite scenario for change. Addressing as several determinants and settings as possible, such as friends, family, neighbours, schools, workplaces, places of worship, community venues and groups, primary care, and media, increases the possibility of creating opportunities for healthy behaviour change.

The current research base for settings-based approaches for people with an intellectual disability resembles the criticisms made of settings-based approaches generally over twenty years ago. Settings level interventions tended to:

review single strategy interventions (health education in schools), single risk factor interventions (smoking cessation in workplaces) or single health impact measures, rather than exploring the total effect of a multi-focus health promotion approach across a range of outcomes.”

There has not been consideration of the broader relationships both within and between settings that are features of an ecological approach and considered single-issue outcomes [57]. In the last 20 years, there has been some improvement in the evidence base for health promotion settings aimed at the general population. Using a logic analysis approach, there is evidence that systems-based interventions can improve the health of workers, and this research can guide a routine approach to data collection [58]. A review of the workplace health literature commissioned by VicHealth concluded that the strongest evidence base was for workplace programs that addressed system level change such as communication and job redesign [59]. It was recommended that organisations take a systems-based approach to reducing stress in the workplace. Systems-based interventions targeting both nutrition and physical activity together, such as changes to canteen and food price, have also been effective in changing health behaviours [60]. Thus, workplaces can routinely track indicators for job stress, smoking, physical activity, and nutrition, where evidence based reviews have shown that these domains can be improved with well-planned interventions [59,60,61].
There have been a number of recommendations for systems approaches to health promotion, although there are not several practice examples to date [62,63]. However, it has been demonstrated that a coordinated approach to multiple settings (education, work, and community) can have a synergistic effect for the general population [63,64]. By having a common language and branding of settings work, practitioners can leverage existing partnerships to encourage personnel in other settings to engage in changes to promote wellbeing. Key to this approach is having a suite of resources that allows for flexibility in approach, whereby each setting can make the changes necessary to suit their particular context. We are not aware of any attempts to address multiple settings for people with an intellectual disability. There is a need for some kind of tool that can guide health promotion practice in workplaces and other settings that engage people with an intellectual disability. This same recommendation has been made for home and community settings [13]. What might be important is that these tools and guidance materials be complementary across the different settings. It cannot be assumed that settings guides for the general population will be relevant for people with an intellectual disability. They need to be purposely designed for these settings, taking into account the particular needs and environments, such as the important role of support staff [13].
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Gender and Socioeconomic Influences on Ten Pro-Environmental Behavior Intentions: A German Comparative Study https://inergency.com/gender-and-socioeconomic-influences-on-ten-pro-environmental-behavior-intentions-a-german-comparative-study/ Thu, 28 Mar 2024 14:42:01 +0000 https://inergency.com/gender-and-socioeconomic-influences-on-ten-pro-environmental-behavior-intentions-a-german-comparative-study/ Gender and Socioeconomic Influences on Ten Pro-Environmental Behavior Intentions: A German Comparative Study4. Discussion This study examined different PEB intentions in the German population and investigated their association with sociodemographic characteristics. Intentions are among the strongest predictors of behavior [33,34,35]. We assumed SES and female gender to be positively associated with PEB intentions, and these hypotheses were partly supported by the data. For waste reduction, durability, room […]]]> Gender and Socioeconomic Influences on Ten Pro-Environmental Behavior Intentions: A German Comparative Study


4. Discussion

This study examined different PEB intentions in the German population and investigated their association with sociodemographic characteristics. Intentions are among the strongest predictors of behavior [33,34,35]. We assumed SES and female gender to be positively associated with PEB intentions, and these hypotheses were partly supported by the data. For waste reduction, durability, room temperature, green electricity, energy saving, and using a bicycle, all hypotheses had to be rejected, as no group differences were found. However, as can be seen in Table 4, intentions to buy regional products were significantly higher in women with high SES than men with low SES. Intentions of not flying were significantly higher in women with high SES than in men with middle and high SES. Intentions to eat a vegetarian diet were significantly higher in women with low and middle SES than in men with middle and high SES.
As shown in Table 3, across all participants, the highest intentions were observed for waste reduction, regional products, durability, and energy saving. Interventions promoting these behaviors should focus on closing the intention–behavior gap [37] and might consider aspects of habits that can override intentions [61]. The lowest intentions were related to cycling and vegetarianism. To promote these PEBs, interventions should be intention building; for this, factors of the theory of planned behavior [33], as well as values and identities [62], should be implemented. Regardless of gender, a higher SES score went along with more flying and lower room temperature.
Regarding Hypothesis 1, that is, PEB intentions are higher in women, Figure 1 shows significant negative correlations for seven out of ten PEB intentions (waste reduction, regional products, energy saving, no flying, low laundry temperature, cycling, vegetarianism) with male gender. Therefore, we see the male gender as impeding PEB. This is in line with the current literature, e.g., [63,64] and can be explained with social role theory. Social role theory posits that people’s behavior conforms to their gender roles, as they are rewarded for conforming and penalized for deviating from these roles [65]. In a previous study, men who engaged in pro-environmental activities were described as being feminine and mocked for their behavior [66]; thus, it can be assumed that PEB is seen as female rather than male. Deviating from the perceived male role and behavior can lead to social consequences such as social distancing [65]. Hence, the association of PEB with the female gender might hinder men, as they fear social penalties when behaving pro-environmentally. Adding to this interpretation, it has been shown that identity has a high impact on pro-environmental intentions [62].
Another reason for a higher PEB engagement in women might be their higher levels of environmental concern [63], which might be connected to higher risk perception and thus motivate PEB change, in accordance with behavior change theory, which has been empirically proven numerous times, e.g., in [33,34,35]. An additional explanation, though less empirically proven, is social role theory. Following social role theory, it is possible that such concern might activate stereotypes of women being more caring [67] and thus lead to gendered perceptions of PEB. However, this hypothesis requires further research.
Moreover, previous research has investigated gender differences in PEB across the public and private spheres. Accordingly, findings that men engage more in PEB in the public space are rather heterogeneous and inconsistent, whereas the findings that women engage more in PEB in the private sphere are highly consistent [64]. As the PEB intentions of this article are completely in the private domain, our findings are in line with this general trend. For future research, the impact of gender and role perceptions and expectations on PEB in the population deserves more attention, for instance, across policy domains and social context, as women are also underrepresented in places of (political) power and thus have fewer opportunities to demonstrate PEB in these places [64].
Hypothesis 2, i.e., a higher SES being associated with higher PEB, can not be supported. On the contrary, a higher SES was associated with more flying. Flying can provide comfort to consumers, which may make it more appealing if they can afford it. Therefore, an ethical discourse and incentive must be created to compensate for this. Accordingly, environmentally friendly behavior must become “the easy behavior” and be made more appealing [68,69]. Conversely, a higher SES was associated with intentions to lower room temperature. The reason for this might be that participants with a higher SES have more opportunity to do so, because, in general, the GHG emissions of housing were found to be higher in households with a higher income (see Table 2; the only area in which higher income had lower GHG emissions was the food domain [70]). Moreover, higher income provides the opportunity to properly insulate houses to regulate room temperature. Since opportunity is a key component of behavior change theory, this also corresponds to previous research, e.g., [33,34,35].
Lastly, Hypothesis 3, that is, PEB intentions will be higher for women with high SES and lower for men with low SES, was supported for buying regional products and reducing the number of flights per year. Therefore, future PEB-related communication should address both groups differently: women with higher SES should receive support for behavioral implementation since they already report higher intentions, and men and low SES groups might benefit from tailored information to increase PEB intentions first. For vegetarianism, however, women of low and middle SES showed higher vegetarian intentions than men with middle and high SES. It seems that gender-based differences were not equalized by socioeconomic resources, highlighting the need for targeted interventions. This finding is complemented by another study showing that the proportion of vegetarians was highest among women of low SES, while it was lowest among men in this SES group [71]. This indicates that low SES may exacerbate gender-based differences in PEB as opposed to high SES. Nevertheless, looking at education as one aspect of SES showed that as the level of education increased, a higher proportion of both women and men usually ate a vegetarian diet [71]. Taken together, more research is needed to examine the mechanisms that connect different indicators of SES, gender roles, and PEBs in the population and to understand the higher proportion of PEBs, such as practicing a vegetarian diet, in women despite lower SES and how this can be applied to also reach men and support them in enacting dietary behavior change. The exploratory analyses showed that waste reduction, buying regional products, durability, and green electricity correlate with each other. These PEBs, therefore, might build a more general cluster of “conscious consumption”. In consumer psychology, conscious consumption decisions are differentiated from unconscious impulse purchases using either Wason and Evans’ dual process theory [72] or Bittmann’s contingency approach [73], according to which four main goals are relevant for purchasing decisions: (a) making an accurate decision, (b) effort avoidance, (c) good justifiability to oneself and others, and (d) avoiding negative emotions. However, we have not yet found empirical support for such clusters of pro-environmental consumption in the scientific literature.
Most of the PEB intentions in this study showed rather high levels (mean scores above 4 out of 5) except for cycling and vegetarianism. According to the low-cost hypothesis [74], perceived behavioral costs for these two PEBs might be higher than for other PEBs. Regarding cycling, these behavioral costs might mean less comfort in biking, loss of a car as a status symbol, and more dangerous travel due to unsafe or nonexistent bike lanes. Moreover, as bikes are an individual mode of traveling, this also excludes the social aspect of travel by car (e.g., for families). Therefore, future research should examine different aspects of cycling in everyday life, its association with GHG reductions, and how it can be integrated into different living situations, e.g., [75].
Behavioral costs in regard to giving up meat consumption might be taste preferences, culinary traditions (habits), and social norms [76]. However, these behavioral costs might have sunken over the last decades. Although reliable estimates of vegetarians in society before 2000 are rare, it is supposed that this number has been growing since the 1970s. Estimates of the number of vegetarians in Gerseveral between 1990 and 2016 range from 2% to 10%, with a tendency of more people becoming vegetarians within the last 30 years [71]. This might be due to the increased availability, quality, and lower price of vegetarian replacement products in supermarkets [77]. This observation corresponds to the aspect of opportunity in behavior change theory. Moreover, since several replacement products have a similar taste and texture compared to meat products, culinary traditions might be easily continued without meat. However, so far, the visibility and level of self-organization of vegetarians have increased more than the actual number of vegetarians [78]. According to a systematic review, people who are willing to change or have already changed their meat consumption are a minority, showing again the low intentions for vegetarianism [76].
Despite being unpopular, eating a vegetarian diet has a high impact, as livestock farming contributes 5% of yearly human CO2 production [29], and the phosphorus and nitrogen input caused by livestock causes further environmental problems [79]. Moreover, meat consumption fosters a loss of biodiversity [80]. Reducing meat consumption is accompanied by health improvements [29]. Therefore, more research should focus on meat-related behavior change and find ways to build stronger intentions to reduce meat consumption and encourage a vegetarian diet.

A first approach—considering our findings and the social role theory—might be to disentangle PEB from gender roles to provide men the opportunity to behave pro-environmentally without having to fear social punishment, thus lowering perceived behavioral costs. Secondly, parts of the population with high SES should be addressed with tailored interventions regarding flying and more environmentally friendly modes of travel for everyday life. This should be accompanied by environmental prevention, such as providing dedicated bike lanes or parking spots close to building entries at the workplace, reward systems for green travel options, and, overall, additional and safer bike lanes. Both aspects, gender and SES, should be considered when designing PEB-related messaging and advertisement campaigns. Moreover, reducing gender and income inequality in society might produce more female leaders and more diverse SES leaders who can act as pro-environmental role models and have the power to make political pro-environmental decisions.

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Progress Releases Corporate Social Responsibility Report for 2023 https://inergency.com/progress-releases-corporate-social-responsibility-report-for-2023/ Thu, 28 Mar 2024 13:49:58 +0000 https://inergency.com/progress-releases-corporate-social-responsibility-report-for-2023/ Progress Releases Corporate Social Responsibility Report for 2023Fifth annual report underscores company’s unwavering commitment to inclusion and diversity, charitable giving and sustainability BURLINGTON, Mass., March 28, 2024 (GLOBE NEWSWIRE) — Progress (Nasdaq: PRGS), the trusted provider of infrastructure software, announced the release of its fifth annual Corporate Social Responsibility (CSR) report today. This year’s report touches on initiatives to promote an inclusive […]]]> Progress Releases Corporate Social Responsibility Report for 2023


Fifth annual report underscores company’s unwavering commitment to inclusion and diversity, charitable giving and sustainability

BURLINGTON, Mass., March 28, 2024 (GLOBE NEWSWIRE) — Progress (Nasdaq: PRGS), the trusted provider of infrastructure software, announced the release of its fifth annual Corporate Social Responsibility (CSR) report today. This year’s report touches on initiatives to promote an inclusive work environment, professional growth, charitable giving and sustainable practices to reduce the company’s carbon footprint. Click here to view the full details of the report.

“I’m proud to see us continually attaining new heights as a socially responsible organization. Over the course of five years, we’ve formed seven employee resource groups, coordinated various charitable giving efforts, fostered inclusive programming geared toward mutual respect and allyship, and implemented sustainable practices across offices. What’s more, each member of our executive leadership team is actively engaged with our CSR program, either acting as executive sponsors or serving on our CSR and Inclusion and Diversity committees,” said Yogesh Gupta, CEO, Progress. “Recently we’re happy that our work touches the lives of thousands and the results captured in the 2023 CSR report show not only our progress, but also the good that can be done when people think and act with empathy.”

Highlights from the 2023 CSR report include:

Our People

  • Inclusion and Belonging: Progress’ seven employee resource groups (ERGs) and its Inclusion and Diversity (I&D) Committee hosted 40+ activities to support an inclusive and psychologically safe working environment.
  • Wellbeing: Knowing that physical, mental and financial health are key to performing well at work, Progress offered a range of activities and programs to support employees’ personal wellness, including sessions on mindfulness and stress management, personal finance and access to well-being resources.
  • Professional Development: Committed to the growth of its people, Progress expanded its professional development offerings with enhanced programs and resources, including a new educational webinar series for employees, an updated catalog in its internal Learning Hub, an inclusive leadership program and new resources added to its Team Enablement Portal.

Our Global Community

  • Charitable Donation: To enhance its philanthropic impact and aid employees in their pursuit to serve the global community through reputable organizations, Progress invested in an enterprise-wide charitable giving solution. As a result, the company more than doubled the number of organizations it supported from last year, donating to over 330 certified charitable organizations worldwide.
  • STEM Education: As a firm believer in the power of education, the company continued its global Women in STEM Scholarship series and granted college scholarships to five women in 2023. Since its establishment in 2019, Progress has awarded scholarships to 14 deserving women across the US, India and Bulgaria.
  • Aid and Allyship: A workforce of empathetic people, employees worldwide came together throughout the year to support humanitarian and social causes, including rescue and recovery endeavors in Turkey, Syria and Maui; LGBTQ+ rights; and racial justice and equity. ERGs raised close to $20k in giving efforts and hosted multiple item drives for shelters, veterans and children in need.

Our Planet

  • Office Emissions and Energy Usage: Continually working to minimize its impact on the environment, Progress reduced its Scope 1 and Scope 2 carbon footprint by 16%, energy use by 29% and its year-over-year office CO2 footprint by 3%.
  • Sustainability: By encouraging the use of water dispensers in its Burlington, MA and Sofia, Bulgaria offices, Progress saved approximately 115,070 plastic water bottles and avoided an estimated 9,527 kgCO2e. In addition, the Burlington, MA office composted 6,082 pounds of discarded food and organic material, an equivalent of 4,111 net pounds of CO2 saved.

Progress operates with a people-centric mindset and strives to conduct business in ways that positively impacts its people, customers, communities and the planet. Establishing a corporate culture of inclusion and belonging, encouraging philanthropic action and promoting sustainable thinking are just a few of the ways the company achieves that. Moreover, its employees are the catapult for goodness driving its CSR Program, Progress for Tomorrow. They voluntarily form employee resource groups, propose green programming and organize fundraisers to help those in need. All this work is why reputable organizations, including The Boston Globe, Boston Business Journal and The Green Organisation, have consistently recognized Progress as a top employer and a philanthropic leader.

For more information about Progress’ corporate social responsibility initiatives and to access the company’s 2023 CSR Report, visit https://www.progress.com/social-responsibility. To view career opportunities, go to https://www.progress.com/company/careers.

About Progress
Progress (Nasdaq: PRGS) provides software that enables organizations to develop and deploy their mission-critical applications and experiences, as well as effectively manage their data platforms, cloud and IT infrastructure. As an experienced, trusted provider, we make the lives of technology professionals easier. Over 4 million developers and technologists at hundreds of thousands of enterprises depend on Progress. Learn more at www.progress.com.

Progress is a trademark or registered trademark of Progress Software Corporation and/or its subsidiaries or affiliates in the United States. and other countries. Any other names contained herein may be trademarks of their respective owners.

Press Contact:
Kim Baker
Progress
+1-800-213-3407
pr@progress.com

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The Optimization of the Steam-Heat-Treated Process of Rattan (Calamus simplicifolius) Based on the Response Surface Analysis and Its Chemical Changes https://inergency.com/the-optimization-of-the-steam-heat-treated-process-of-rattan-calamus-simplicifolius-based-on-the-response-surface-analysis-and-its-chemical-changes/ Thu, 28 Mar 2024 13:06:56 +0000 https://inergency.com/the-optimization-of-the-steam-heat-treated-process-of-rattan-calamus-simplicifolius-based-on-the-response-surface-analysis-and-its-chemical-changes/ The Optimization of the Steam-Heat-Treated Process of Rattan (Calamus simplicifolius) Based on the Response Surface Analysis and Its Chemical ChangesRattan species, together with bamboo, are among the most significant non-timber forest products and components of tropical and subtropical forest ecosystems. There exist 631 species, subspecies, and varieties of rattan in 11 genera worldwide [1]. China alone possesses 41 species and varieties belonging to four genera: Daemonorops, Calamus, Plectocomia, and Myrialepis [2,3]. Thanks to their […]]]> The Optimization of the Steam-Heat-Treated Process of Rattan (Calamus simplicifolius) Based on the Response Surface Analysis and Its Chemical Changes


Rattan species, together with bamboo, are among the most significant non-timber forest products and components of tropical and subtropical forest ecosystems. There exist 631 species, subspecies, and varieties of rattan in 11 genera worldwide [1]. China alone possesses 41 species and varieties belonging to four genera: Daemonorops, Calamus, Plectocomia, and Myrialepis [2,3]. Thanks to their excellent strength, toughness, and elasticity and easy modeling characteristics, rattan species are an excellent material for interior decoration applications such as furniture making or craft equipment weaving [4,5]. Yet, like other lignocellulosic materials, they exhibit some undesirable characteristics including dimensional instability and low resistance against mold and rot fungi decay in a humid environment [6,7,8]. The heat treatment has been known for its advantages (e.g., environmental friendliness, non-toxicity, and simplicity in processing), which make it currently the most widely employed method for industrial wood improvement [9]. In recent years, it has also been used for rattan processing and its derived composite materials [10,11]. Extensive research has been conducted on various impacts of heat treatment processes [12], including color alteration [13], changes in mechanical properties [14,15], durability assessment of heat treatment [16], alterations in chemical composition [17,18], and mass loss. The impact of the fiber percentage and cell wall thickness on the shrinkage–swelling and Modulus of Elasticity of the cane is already defined [19]. Also, changes in durability properties of two rattan species of different diameters were researched [20]. These property changes are closely related to processing parameters such as the temperature, duration, medium, and pressure applied during the heat treatment.
The single-factor analysis method is commonly employed to analyze the impacts of processing parameters [21]. However, this approach is time-consuming and is unable to analyze the interactions among different factors, which can be addressed by a simultaneous single-factor method in experimental design [22]. The response surface analysis or methodology (RSM), initially introduced by Box and Wilson in 1951, serves as an optimization technique based on experimental results or simulations for determining the optimal factor levels that yield the desired response value [23,24].

The raw material selected for this study is Calamus simplicifolius, an important commercial rattan species in China known for its exceptional mechanical properties. The response surface analysis was employed to determine the optimal steam heat treatment process for C. Simplicifolius cane. In the optimization process, independent variables such as steam heat treatment temperature, treatment time, and pressure were considered while designing the impact toughness of C. Simplicifolius cane as the response variable. This study is to quantify the impacts of steam heat treatment parameters (e.g., temperature, time, and pressure) on the impact toughness of C. Simplicifolius. A better understanding of the steam-heat-treated C. Simplicifolius properties will lead to a more efficient utilization of the heat-treated rattan, particularly in outdoor settings.

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Association of Food Desert Residency and Preterm Birth in the United States https://inergency.com/association-of-food-desert-residency-and-preterm-birth-in-the-united-states/ Thu, 28 Mar 2024 12:26:04 +0000 https://inergency.com/association-of-food-desert-residency-and-preterm-birth-in-the-united-states/ Association of Food Desert Residency and Preterm Birth in the United States1. Introduction Despite recent national and local efforts to expand access to healthy food in the United States., several geographic areas continue to have limited access [1,2]. Areas with limited access to retailers that offer affordable nutritious foods (e.g., supermarkets and grocery stores) are considered food deserts. Residing in a food desert has been linked to […]]]> Association of Food Desert Residency and Preterm Birth in the United States


1. Introduction

Despite recent national and local efforts to expand access to healthy food in the United States., several geographic areas continue to have limited access [1,2]. Areas with limited access to retailers that offer affordable nutritious foods (e.g., supermarkets and grocery stores) are considered food deserts. Residing in a food desert has been linked to food insecurity, which occurs “when people lack secure access to sufficient amounts of safe and nutritious food for normal growth and development and an active and healthy life” [3]. Food deserts have been found to spatially co-exist with food swamps, areas inundated by retailers that primarily offer inexpensive, calorically dense, and less nutritious foods (e.g., corner stores, fast food restaurants) [4,5]. Similar to food deserts, food swamps are associated with adverse health risks such as obesity [6]. Limited healthy food access, coupled with excessive access to unhealthy foods, may promote poor dietary behaviors [7] and increase the likelihood of diet-related chronic diseases such as hypertension and diabetes [8,9].
National and local data have consistently shown that a lack of healthy food access disproportionately affects low-income and racialized communities. Census tract data indicate low-income Black communities often have few supermarkets but several corner stores and liquor stores [10]. Thus, they are more likely to be labeled a food desert or food swamp [10,11]. The healthy food access disparities impacting racialized communities may be more distally caused and exacerbated by systemic determinants such as racial segregation, chronic disinvestment, and poor community infrastructure, all of which have great potential to affect individuals and communities beyond health behaviors [12].
Poor, healthy food access can negatively affect maternal health outcomes within socially and economically disadvantaged populations, ultimately leading to health inequities. Inadequate food access during pregnancy increases the likelihood of developing at least one morbid condition [9]. One of the leading causes of maternal morbidity and mortality is hypertension disorders of pregnancy (HDP) [13], which occurs in 5% to 10% of all pregnancies [14]. Health organizations have categorized HDP into four primary types: gestational hypertension, chronic hypertension, chronic hypertension with superimposed preeclampsia, and preeclampsia/eclampsia [15]. Studies suggest that HDP, such as gestational hypertension (gHTN), may be significant factors in the occurrence of preterm birth [16]. Arfandi et al. (2023) reported that although preterm birth risk may differ by the type of HDP, each form of HDP carries a significant risk for preterm birth [17].
In the United States., the rates of preterm birth rose 12% between 2014 and 2022 [18]. Globally, preterm birth is the leading cause of death for children under the age of five [19,20]. It may also affect physical/neurological development and increase the risk of developing health conditions [21]. Preterm birth remains a public health concern, and racial inequities in preterm birth have not abated, with preterm birth rates 50% higher among non-Hispanic Black infants than non-Hispanic White infants [22].
The connection between geographic access to healthy food retailers and chronic conditions (i.e., obesity, hypertension, and diabetes) is well established [23,24]. Although proper diet and nutrition have been recognized in maternal and child health, proximity to healthy food has been an understudied determinant for maternal and infant health outcomes but may impact diet during the perinatal and postpartum periods as well as long before pregnancy. Racism has shaped United States. neighborhoods [25] and continues to shape both built and social neighborhood conditions, including food access and insecurity [26]. Given the pervasive racial inequities in not only maternal and infant health outcomes in the United States. but also neighborhood exposures, it is paramount that we better understand the role of these more distal neighborhood conditions in shaping these outcomes and the pathways through which that may occur.

Using a rigorously adjusted model as well as within-group and mediation analyses, this study aims to examine the association between residing in a United States. Department of Agriculture (USDA)-designated food desert and preterm birth and the mediating effect of gHTN in this association. We hypothesized that pregnant persons residing in food deserts have a higher risk of preterm birth and that this association might be explained, in part, by gHTN.

2. Materials and Methods

2.1. Study Population

An analysis was conducted using cross-sectional data including all live births in the US for the years 2018–2019, data obtained from the National Center for Health Statistics. Birth records with missing geographical identifiers for maternal county of residence or births that occurred in United States. territories (Guam, Virgin Islands, Puerto Rico, American Samoa, and Northern Marianas) were excluded from the analyses (n = 25,797), resulting in an analytic sample size of n = 7,533,319. As all data were deidentified, this study was deemed exempt by the Tulane University Institutional Review Board.

2.2. Exposure Variable

Food desert data were retrieved from the USDA Food Access Research Atlas (2019), which provides a spatial overview of low-income and low-access (LILA) census tract-level food access data. Included in these data is a tract-level indicator designating both LILA areas: low-income tracts where the populace of at least 500 people and/or at least one-third of the census tract live beyond a specified distance to a grocery store (>1 mile for urban communities; >10 miles for rural communities) [27]. A census tract is designated as low-income if it has a poverty rate of at least 20% or a median family income at or below 80 percent of the metropolitan area or state median income level [27]. Additional LILA tract-level indicators of food access were also examined: half mile (urban) and 10 miles (rural), 1 mile (urban) and 20 miles (rural). We counted the number and percentage for each LILA tract-indicator within each US county and operationalized county-level food desert variables to identify counties by the prevalence of LILA tracts within them (low: 0–33.4%; mid-range: 33.5–66.6%; and high: 66.7–100.0%). The food access indicator variables were linked to birth records by a geographic identifier for the maternal county of residence (Federal Information Processing System codes).

2.3. Outcome

Using the information provided on the birth record, preterm birth (occurring <37 weeks gestation) was measured as a binary outcome (yes or no), denoting whether an infant was delivered prior to 37 weeks gestation.

2.4. Individual-Level Covariates

Sociodemographic variables associated with pregnancy and birthing outcomes were retrieved from birth records and included in the fully adjusted model: maternal age (≤19, 20–34, and ≥35), education attainment (less than high school, diploma or equivalent, some college, or college degree), month prenatal care began (no prenatal care, 1st trimester, 2nd trimester, or 3rd trimester), and previous preterm birth (yes or no).

2.5. County-Level Covariates

We controlled for additional geographic and socioeconomic characteristics that differ by county, which may be associated with accessibility to health care services and maternal health outcomes. Data were obtained from the American Community Survey’s 2019 (5-year estimates) and included the percentage of families living below the poverty level, and the median household income. Additionally, we controlled for whether a county/parish was rural or urban based on the 2010 Census Urban Rural Classification Scheme.

2.6. Mediator

Gestational hypertension was defined as a blood pressure of ≥140/90 mmHg without or with proteinuria of no greater than trace levels after 20 weeks of gestation [28]. A diagnosis of gestational hypertension, or pregnancy-induced hypertension, was provided by birth record data. A binary response (yes or no) was used to indicate whether a pregnant person had the condition. We proposed a mediation pathway where gestational hypertension partially explains the relationship between food desert residency and preterm birth.

2.7. Statistical Analysis

Frequency analyses were used to describe the characteristics of the study population. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA). To estimate the association between living in a food desert and preterm birth, we fit crude and adjusted modified logistic regression models with generalized estimating equations, using the PROC Genmod function, and cluster-robust standard errors to account for multiple births occurring within the same county to obtain odds ratios (OR) and 95% confidence intervals (C.I.). In addition to the fully adjusted model, we conducted within-group analyses to provide insight on contributing social determinants and behaviors that may be lost in comparative analyses between racial groups [29]. This was completed by fitting the fully adjusted model for births to White and Black people separately.
A mediation analysis was examined using SAS macro statistical software that accounts for confounding between exposure, mediator, and outcome as well as interaction [30]. We were particularly interested in the natural indirect effect (NIE), or the effect of changing the mediator value given exposure is fixed, and the natural direct effect (NDE), or the effect of food desert exposure on PTB given the mediator, gHTN, takes the level it might have been under the absence of exposure. Spatial mapping and tests for global spatial clustering with Moran’s I were performed with GIS in ArcPro (v3.0.3, ESRI, Inc., Redlands, CA, USA). In general, the global Moran’s I ranges from −1.0 to 1.0, with positive values indicating the occurrence of similar values of the variable over space (either high or low values) and negative values indicating that there are areas with dissimilar values and no spatial clustering (often referred to as a checkerboard pattern).

3. Results

A large percentage of the study population (76.8%) were between the ages of 20 and 34, and 41.6% held college degrees at the time of birth. Approximately half (51.8%) identified as non-Hispanic White, with 14.7% racially identifying as non-Hispanic Black. Most (96.4%) had no previous preterm births, and 87.6% resided in urban counties. A total of 7.5% reported a diagnosis of gHTN; of those with gHTN, 55.4% identified as non-Hispanic White and 17.7% identified as non-Hispanic Black. The mean percentage of county tracts designated as low income/low access food desert indicators of 1 mile (urban) and 10 miles (rural) from a supermarket was 13.1% (Table 1).
The prevalence of high food deserts in the United States. across counties is substantial, as shown in Figure 1, with significant clustering (Moran’s I = 0.178; p
In the fully adjusted model, among persons residing in communities within 1 mile (urban) and 10 miles (rural) of the nearest grocery store, there was a slight increase in the likelihood of preterm birth for counties with a mid-range (OR = 1.04; 95% C.I. 1.03–1.05) and high (OR = 1.07; 95% C.I. 1.06–1.08) percentage of tract-level food deserts (Table 2). Similar results were seen in the fully adjusted stratified model. The food desert indicator, LILA 1 mile (urban) and 10 miles (rural), showed an increased likelihood of preterm delivery for non-Hispanic Black births as the percentage of census-tract level food deserts increased within a county; mid-range (OR = 1.06; 95% C.I. 1.04–1.08) and high percentage (OR 1.10; 95% C.I. 1.08–1.12). However, for non-Hispanic Whites, the mid-range percentage was not statistically significant. The high percentage was associated with a significant likelihood of preterm birth (OR = 1.06; 95% C.I. 1.05–1.07) (Table 3).
Prior to the mediation analysis, regression analyses were completed to determine the association between the variables (Figure 2). The level of county food desert residency for tract-levels with mid-range (OR = 1.13; 95% C.I. 1.02–1.24) and high (OR = 1.16; 95% C.I. 1.05–1.27) percentages had a significant association with gHTN. Similarly, women with gHTN were 3.0 times more likely to experience preterm birth (OR = 3.00; 95% C.I. 2.90–3.05). We also observed a significant indirect mediating effect of gHTN between food desert residency and preterm birth, both in crude (NIE = 1.02; 95% CI = 1.01, 1.02) and adjusted (NIE = 1.01; 95% CI = 1.00, 1.01) models, with approximately 5% of the total effect of food deserts on preterm birth eliminated by prevention of gHTN. The natural direct effect in the adjusted model was also significant (NDE = 1.08; 95% CI = 1.07, 1.09), indicating that there might be an 8% increase in PTB for those in higher food desert tracts in the absence of gHTN.

4. Discussion

Our findings add to the evidence of social and structural factors that may adversely impact reproductive health and birthing outcomes. Within our fully adjusted analysis, we determined that residing in a USDA-designated food desert increased the likelihood of preterm birth in a dose–response manner. Our results were consistent with existing studies that linked inadequate access to healthy foods to poor maternal health and birth outcomes [9,31,32]. Furthermore, residing in a food desert has been associated with health implications beyond conception health and birthing outcomes. Food desert residents were found to have decreased breastfeeding initiation rates compared to individuals who did not reside in USDA-designated food deserts [33].
In the racially stratified models, most of the food desert indicators were found to increase the likelihood of undergoing a preterm birth within both racial groups, which indicates that proximity to affordable food access is impactful on the health of all birthing persons regardless of their racial identity. Although food desert exposure was significantly associated with preterm birth within each racial group, in the adjusted model, the likelihood for preterm birth for non-Hispanic Black mothers was greater than that of their racial counterparts. This indicates that in addition to social and structural factors (medical bias, medical care accessibility, psychological stress, neighborhood infrastructure, and racial discrimination) found to contribute to the high incident rate of preterm labor among non-Hispanic Black birthing persons [34,35], the food environment should further be examined as a contributing factor for racial birthing inequities.
Moreover, there was an overrepresentation of non-Hispanic Black births in food desert areas (M =15.33; SD = 12.91); the mean for non-Hispanic White mothers residing in a food desert (M = 13.09; SD = 12.10) was the same as the national average. Researchers postulate that inequitable food access stems from systemic and structural racism [26,36]. Structural racism and biased ideologies impact infrastructure, policies, and access to resources for underserved populations. Specific to the food environment, the literature shows a clear racial disparity in food access, even when controlling for socioeconomic conditions [37]. Furthermore, the occurrence of the COVID-19 pandemic may have exacerbated the apparent racial inequities in food access [38]. Dubowitz and colleagues (2021) reported a spike in food insecurity among a cohort of low-income African American food desert residents during the early phases of the pandemic [39]. Crises, such as the pandemic, shed light on the vulnerabilities within the food system and underscore the importance of equitable food access. The high prevalence of preterm birth along with the overrepresentation of Black food desert residents underscores the role that structural racism plays in our social/living environment, which, in turn, affects our health and well-being, engendering inequities at the population level.
Similar to other studies, we found that food desert residency was associated with an increased risk of developing gestational hypertension. However, when additional covariates were added to the model, the association was no longer significant. When examining gHTN and preterm birth, the association remained significant in both the crude and adjusted models. This aligns with the existing literature, which has established gHTN as a potential risk factor for preterm birth [16,17,40]. It should also be noted that although there is a stronger statistical association between gHTN and preterm birth compared to food desert exposure and preterm birth, this does not minimize the primary findings of our study and the negative impact inadequate food access has on birthing outcomes. Furthermore, addressing distal factors (e.g., food deserts) may have a smaller association than proximal factors, but the impact on population health is greater. Distal factors often have effect sizes that are smaller in magnitude, but this study sheds light on the potential pathways between food deserts and preterm birth.
We also observed an indirect effect between food desert exposure and preterm birth, with a large proportion of the food desert and preterm birth association mediated by gHTN. The reported prevalence of hypertensive disorders in pregnancy in the United States is 16% [41]. Based on the current study data, the prevalence of gHTN is 7.5%, which suggests underreporting, which may be reflected in our findings. However, if the prevalence of gHTN is higher within our study population than actually reported, it might also mean that the mediation effect is an underestimation as well. Nevertheless, our analysis shows a mediated effect, and while food desert areas may also be a proxy for other meso-level conditions, we did control for the level of poverty in the area. Additional pathways between food deserts and preterm birth and other reproductive outcomes should be examined.

Inadequate food access poses a great concern for racially minoritized birthing people and those of lower income status. Health care professionals should include available survey instruments in prenatal screenings to identify food insecurity as a potential risk factor for morbid conditions and adverse birthing outcomes. Developing social support networks and discussing proper nutrition are steps health care practitioners can take to share the importance of prenatal health and weight management. Additionally, for those with pre-existing conditions, discussing ways to properly manage health conditions during pregnancy will minimize the risk of adverse health outcomes such as mortality.

However, solutions must go beyond prenatal appointments. Residents of food deserts require adequate care and resources to improve their health. Programs and initiatives have been implemented with the hope of improving access to healthy foods in underserved communities. A community food bank implemented a mobile market pilot program to sell fresh produce and other food items in low-resourced neighborhoods [42]. An evaluation of the pilot program found an increase in vegetable intake in a few of the selected neighborhoods and, on a larger scale, has the potential to reduce nutritional inequities [42]. Crowe et al. (2018) suggest improving neighborhood conditions and making smaller grocery stores and markets more affordable for food desert residents [43]. However, interventions to improve healthy food retail in underserved communities have resulted in mixed findings [44]. Dubowitz et al. (2015) found that the development of a supermarket in a designated food desert did not improve dietary intake but positively altered residents’ perception and satisfaction of healthy food accessibility within their neighborhood [45]. As food deserts point to greater structural and systemic inequities, strategies should focus on mitigating the effects of poverty, residential segregation, and their impact on disadvantaged communities. For instance, increased minimum wages, pay equity, safe, affordable housing, and investing in neighborhood infrastructure are a few examples of anti-poverty strategies to address structural and systemic issues.

Limitations

Despite the strengths of this national analysis, there are several limitations. First, it is cross-sectional, which limits the temporal interpretation of our findings. Second, although we controlled for a robust set of multi-level confounders, there are factors not included in the study that have been associated with preterm birth (i.e., structural racism, stress, physical occupation, physician–patient communications, and intimate partner violence). Moreover, chronic conditions appear to be underreported in birth records; therefore, we cannot determine the full extent to which morbid conditions contributed to birth outcomes. There is also no way to determine whether people moved during their pregnancy or if relocation impacted their proximity to food access.

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The Effects of Urbanization on Urban Land Green Use Efficiency of Yangtze River Delta Urban Agglomeration: Mechanism from the Technological Innovation https://inergency.com/the-effects-of-urbanization-on-urban-land-green-use-efficiency-of-yangtze-river-delta-urban-agglomeration-mechanism-from-the-technological-innovation/ Thu, 28 Mar 2024 11:41:56 +0000 https://inergency.com/the-effects-of-urbanization-on-urban-land-green-use-efficiency-of-yangtze-river-delta-urban-agglomeration-mechanism-from-the-technological-innovation/ The Effects of Urbanization on Urban Land Green Use Efficiency of Yangtze River Delta Urban Agglomeration: Mechanism from the Technological Innovation2.1. ULGUE Although the focus of studies on ULGUE has now shifted from theoretical interpretation to empirical analysis, it is still necessary to investigate its nuances. ULGUE originated from green development [12], which coincides with the concept of sustainable development and green use proposed by the Chinese government. Existing research suggests that the core of […]]]> The Effects of Urbanization on Urban Land Green Use Efficiency of Yangtze River Delta Urban Agglomeration: Mechanism from the Technological Innovation


2.1. ULGUE

Although the focus of studies on ULGUE has now shifted from theoretical interpretation to empirical analysis, it is still necessary to investigate its nuances. ULGUE originated from green development [12], which coincides with the concept of sustainable development and green use proposed by the Chinese government. Existing research suggests that the core of urban land green use consists of emphasizing the region’s access to higher economic and social development while promoting conservation and use intensification [19], reducing energy loss [20], conserving land and resources and reducing negative environmental impacts [21], and producing less pollutant emissions [22]. ULGUE has been widely used to analyze the relationship between government planning, industrial agglomeration, urbanization strategies, land-use patterns, and spatial effects. ULGUE includes resources consumed through green or non-eco-friendly pathways as well as desired and undesired urban land-use outputs [23]. While requiring beneficial economic and social outputs, ULGUE focuses on the green benefits of land-use outputs, i.e., environmental protection during land use.

Past studies have considered these green factors, and most of them have considered undesired outputs, but few have considered green desired outputs. On the basis of these outputs, this study incorporates ecological factors into the desired outputs, thus contributing to the understanding of ULGUE. In summary, this study considers ULGUE for its social, economic, and ecological output capacity and levels of urban land use under the double constraints of energy input factors and environmental pollution.

2.2. Impact of Urbanization on ULGUE

Human economic and social activities affect land use, e.g., production, recreation, and consumption, and are direct determinants of land-use patterns [22]. The impact of urbanization on land use has been discussed and consists of the following three main aspects.
First, from the perspective of socio-economic activities, urbanization drives economic and social development while inevitably having a negative impact on ULGUE. Spatial economic activities driven by urbanization are the key factor in the rapid changes in land use [24]. Urbanization puts great pressure on land use and the protection of environmental resources such as water [25] and air quality [26]. The challenge facing land use is to address the relationship between meeting human needs and maintaining the long-term capacity of the biosphere to provide goods and services [27]. Large areas of green land, including parks, green buffers, square spaces, and attached green space, are being transformed into urban and industrial areas to cater to economic growth, housing, and production [28], which decreases green outputs and creates significant undesired outputs. The spatial interaction between economies that is brought about by urbanization has benefited from the development of transport infrastructure. However, urbanization increases the number of vehicle kilometers travelled [29], which leads to increased negative externalities, including air pollution [30].
Second, from the perspective of population migration, urban population growth increases the inputs as well as the desired and undesired outputs of ULGUE. On the one hand, urbanization brings a large labor force, which together with industrialization, urbanization, and economic reform measures affects ULGUE as a factor of production [31]. The demand for various types of products and services generated by urban population agglomeration due to survival and development needs is transformed into a demand for different types of land within the urban area. This triggers the rapid accumulation of demands for land resource utilization. On the other hand, population migration from the urban fringes to cities leads to the abandonment of cultivated land in the area, which poses a greater threat to food security. A large influx of people into urban areas creates challenges for the urban living and productive land use, which negatively impacts the environment and increases undesired outputs. More seriously, in large cities, large public facilities for supplying energy and handling waste are often crowded out by residential areas, which include a wide variety of facilities, such as power plants, industrial parks, highways, and waste incinerators, which are identified as NIMBY (not in my back yard) facilities or LULU (locally unwanted land use) facilities; LULU facilities involve secondary air, water, soil, and noise pollution, which have a negative impact on the regional ULGUE.
Third, different modes of urbanization affect ULGUE in different ways. Urban sprawl leads to negative externalities such as high energy consumption [32], which reduces land-use efficiency [33,34]. Since the end of the 20th century, increasing land intensification activities have become an important factor affecting sustainable global growth. The process of urbanization in China is generally accompanied by a lack of development awareness of resource saving [35], which has resulted in the expansion of built-up areas and transport infrastructure areas and the reduction in the marginal efficiency of land use. Urban compactness is a response to control urban sprawl, but it also leads to urban problems such as traffic congestion, increased cost of living [5], and the undesired outputs of ULGUE. It has been shown that the ecological efficacy of urbanization in terms of pollution reduction and resource intensification can be better exploited through regulation and intervention by government departments [36]. As present, over-exploitation and the uncontrolled utilization of land resources in China have posed great challenges to regional sustainable development.

Taken together, there is growing evidence that urbanization has an amplifying or accelerating effect on ULGUE. Urbanization raises inputs and desired outputs while generating undesired outputs, with complex and intertwined positive and negative paths of influence on ULGUE.

2.3. Mechanisms for the Impact of Technological Innovation

Existing studies have found that technological innovations can amplify the positive effects of urbanization on land use, the mechanisms of which have been tentatively elucidated. Classical IPAT theory identifies population size, affluence, and technology as key forces shaping land use, especially technological innovation, which can trigger rapid land-use change [37]. There is a tendency for urban centers to gather and form urban clusters [38]. Influenced by the learning, matching, and sharing mechanism of the agglomeration economy [39], urbanization is conducive to exchange among technicians, which helps the spilling over, diffusion, and incubation of knowledge and technology and prompts the continuous improvement of regional technological innovation in the virtuous circle of “innovation–spillover, diffusion-re–innovation”. The agglomeration economy brings technological innovation and externalities, and in turn, technological innovation promotes economic transformation, further amplifying the role of the population as a factor of production to promote economic growth and thus enhancing ULGUE. The inflow of different types of talents and emerging technologies improve the existing industrial structure, and the technologies have a positive effect on urban land use [40] and help to reduce environmental degradation [41], while technological externalities affect land intensification [42].
However, not all types of technological innovation have a facilitating effect; some have the potential to reduce ULGUE. It has been pointed out that technological innovation has a “rebound effect” [16]; i.e., technological innovation reduces the cost of production, which lowers the price of and expands the demand for products, leading producers to generate large quantities of products, which ultimately increases both energy consumption and carbon emissions. It has been pointed out that technological innovation increases pollution when the economic level is low [43]. Therefore, technological innovation has a facilitating or inhibiting effect on the efficiency of the green economy, and the ultimate impact of technological innovation on ULGUE is uncertain; its performance depends on the allocation of the elements of science and technology innovation and the specific type of innovation [44]. Therefore, specific types of technological innovations need to be further discussed. Combining existing studies on the impact of urbanization on ULGUE and urban land-use practices in China, this study argues that green, digital, and transportation technological innovations strengthen the contribution of urbanization to ULGUE.
First, advances in green technologies increase the efficiency of land use. Green technological innovation includes both energy-saving technological innovation and technological progress in emission reduction [45]. Energy-saving technological innovation is able to reduce energy loss in production, improve urban economic efficiency [46], and reduce the cost of reducing pollution [47]. Emission reduction technology can improve clean energy and reduce pollution emissions in production, which is a key approach to alleviate the pressure on the living condition, production, and environment brought about by population urbanization and to solve the contradiction between production and pollution [48].
Secondly, digital technological innovation is thought to provide agglomeration power, sustainable food production, access to clean and safe drinking water, and green energy production and use [49]. Increasing the proportion of non-fossil energy use and optimizing industrial structures are the two mechanisms by which digital technological innovation contributes to green use [50]. More importantly, digital technological innovation has produced subversive changes in geography, boundaries, space, and time [51], which breaks down administrative boundaries, shortens spatial distances, and promotes the integration of peripheral areas into the inner-core region [5], thus enhancing the overall efficiency of urban land use.
Thirdly, the interaction between land use, transportation, and the environment has been well researched [52]. Longer travelling distances due to urban sprawl as well as urban congestion prolongs vehicle driving and idling times, leading to air pollution from inadequate fuel combustion [53]. Traffic noise and the occupation of vehicle infrastructures such as car parks and roads are detrimental to sustainable and green urban development. Technological innovations in transportation reduce urban carbon emissions through improved accessibility and energy efficiency [54]. The electrification of transportation will improve air quality in the streets [55] and alleviate the congestion pressure on the roads, achieving both economic and environmental benefits [56] and thus reducing the negative impacts of urbanization on the environment [57]. Further, transportation technological innovation has a positive impact on land-use efficiency in opening up new urban spaces for efficient use, changing the urban pattern, reallocating resources, and reducing the need for new urban land by maintaining the marginal benefits of land use while urbanizing [58].

In theory, green, digital and transportation technological innovations contribute to resource conservation and utilization, emission reduction, regional management, transportation, and agricultural production and thus increase ULGUE; however, in specific regions, these effects still require empirical evidence. Based on the above analysis, this paper puts forward the following research hypotheses for empirical testing:

Hypothesis 1.

Urbanization can promote significant improvements in ULGUE.

Hypothesis 2.

Green technology innovation, digital technology innovation, and transportation technological innovations can amplify the positive impact of urbanization on ULGUE.

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Design of a Modularization-Based Automation Performance Simulation Framework for Multi-Vehicle Interaction System https://inergency.com/design-of-a-modularization-based-automation-performance-simulation-framework-for-multi-vehicle-interaction-system/ Thu, 28 Mar 2024 11:07:44 +0000 https://inergency.com/design-of-a-modularization-based-automation-performance-simulation-framework-for-multi-vehicle-interaction-system/ Design of a Modularization-Based Automation Performance Simulation Framework for Multi-Vehicle Interaction System1. Introduction Under the background of growing market demands and stringent fuel consumption regulations, pure electric or hybrid electric vehicles (HEVs) have emerged as a vital solution to the dual challenges of energy scarcity and environmental pollution, by virtue of their easy access to electricity, clean, and low pollution benefits [1]. HEVs signify a developmental […]]]> Design of a Modularization-Based Automation Performance Simulation Framework for Multi-Vehicle Interaction System


1. Introduction

Under the background of growing market demands and stringent fuel consumption regulations, pure electric or hybrid electric vehicles (HEVs) have emerged as a vital solution to the dual challenges of energy scarcity and environmental pollution, by virtue of their easy access to electricity, clean, and low pollution benefits [1]. HEVs signify a developmental shift within the vehicle industry, with various configurations already in use on the roads. With the infrastructure construction of the Internet of Vehicles (IoV) and cloud computing [2], effective co-simulation of multiple vehicles is an important way to mine the big data of the IoV [3].
Large-scale vehicle simulation technology, leveraging traffic and vehicle simulation software, facilitates the acceleration of edge computing applications and reduces the costs associated with vehicle–road collaboration. The traffic environment plays a critical role in influencing energy consumption and drivability [4]. Under the background of the trend of electrification and networking in the automotive industry, the collaborative optimization design of traffic flow composed of various configurations of HEVs and traditional vehicles has put forward new requirements for the design of performance simulation environments. To achieve large-scale vehicle performance co-simulation, two primary objectives must be addressed as follows: firstly, the rapid and straightforward construction of vehicle models with various configurations, including the automatic initialization of model parameters, control of the running process, and postprocessing of results. HEVs involve several configurations and sub-component models, each constructed with its own unique specifications. Utilizing plug-and-play model construction technology and simulation file management methods [5], such as those proposed by Autonomie software 2019, AVL Cruise 2020, etc., enhances the reusability of simulation model files. By introducing modularity, a vehicle is divided into multiple modules, each containing different parameters and models. Those modules are combined with each other and their relationships analyzed to complete the modeling of a single vehicle, forming an automated framework for single vehicle performance simulation. Secondly, establishing an interaction channel between multiple vehicles to enable signal interaction while ensuring synchronization is crucial. This involves recording the input and output signals required by each vehicle to construct a communication matrix and establishing a signal scheduler for dynamic signal allocation between vehicles. Based on the above two aspects, the multiple vehicle performance simulation under the mixed traffic environment with various vehicle combinations is completed. The original contributions of this paper can be summarized as follows: (i) the simulated working principle of the drag-and-drop coupled mechanical system is described, (ii) the process of an automated performance simulation is designed, (iii) a method for the effective management of simulation system scripts and model files is proposed, and (iv) an efficient multiple vehicle control system model architecture and development framework is presented.
The structure of this paper is outlined as follows: Section 2 outlines the existing literature review and identifies the gaps in software tools for vehicle performance calculation. Section 3 delves into the architecture and design methodology of the comprehensive simulation environment. Section 4 elucidates the methodology for constructing vehicle models and detailing the simulation processes for individual vehicles, providing a foundation for the development of simulation models. Section 5 expands this discussion to the joint simulation methods for multiple vehicles, showcasing the application of these methods through practical use cases in Section 6. Finally, the conclusions and future perspectives are presented in Section 7.

2. Literature Review

Simulation of vehicle energy consumption and dynamic performance is widely embraced by vehicle research institutions for the development and analysis of new transmission systems [6]. Typically, a module-based design approach is employed to integrate components into a system via signal connections [7]. However, the interdependence of signals and parameters among components necessitates a unified standard for automated operation when dealing with large-scale interactions between vehicles of various configurations. This field of energy consumption and performance simulation for different vehicle configurations is well established, with numerous commercial and open-source software options available [8], such as MATLAB 2022, AVL Cruise 2020, AMESim 2020, ADVISOR 2002, Autonomie 2019, etc. AVL Cruise is a commercial vehicle simulation software that can analyze a variety of vehicle drivetrains. Graphical modeling, achieved by dragging and dropping vehicle components and connecting them, is the core feature, enabling the assembly of a complete vehicle model. A parameter matching study was carried out by J. W. Ma [9], who used AVL Cruise to design the motor, power battery, and final drive to meet the target performance. H. T. Arat [10] built multiple powertrain vehicles with AVL Cruise, to compare and analyze features of the vehicles in terms of performance and emission results. Autonomie is an open-source software developed at Argonne National Laboratory, and the Simulink-based simulation environment can capture the detailed mechanism of the model. J. Yao et al. [11] improved the simulation process efficiency of vehicle fuel economy with a novel large-scale learning and prediction process based on Autonomie. Micro-level traffic simulators are designed to simulate interactions between vehicles to evaluate mobility ecosystems, such as VISSIM 5.20 [12] and SUMO 4.24 [13]. Many projects evaluate the algorithm performance of autonomous driving, vehicle–road collaboration, and vehicle-to-everything communication in complex interactive scenarios by constructing virtual traffic environments [14]. T. Tettamanti et al. [15] used MATLAB and VISSIM to calculate mathematical problems and simulate the traffic environment online, respectively, and established an integrated simulation control environment based on the VISSIM COM interface to solve signal control problems. W. M. Griggs et al. [16] embedded a real vehicle into SUMO to realize the hardware-in-the-loop of the vehicle in a large-scale intelligent transportation system, and thus allowed drivers to obtain real-time traffic feedback. The design of multi-vehicle simulation architecture is generally oriented to the motion safety and information interaction between vehicles under various complex virtual environment conditions. M. A. Meyer et al. [17] proposed a closed-loop platoon simulation environment with intelligent transportation system, and implemented a large-scale vehicle simulation for cooperative adaptive cruise control. M. Guériau et al. [18] devised a virtual environment to validate and test the platoon control method that achieves a direct match between the perception and control of real and virtual vehicles. When multiple vehicles are simulated in a distributed manner, it is key to ensure the synchronization of data interaction and the simulation of real hardware characteristics. Z. Zhang et al. [19] presented a time-triggered automotive cyber-physical systems design framework with co-simulation between the software, network/platform, and physical components. E. Eyisi et al. [20] designed an integrated modeling and simulation tool for evaluating networked control systems. Based on the existing simulation tools, the test platform for vehicle energy consumption and multi-vehicle interaction is widely used. The multi-vehicle simulation environment considering vehicle dynamics is mainly used to study the intelligent sensing and advanced assisted driving functions of vehicles [21]. To study the functional performance of connected vehicles in a wide range of traffic scenarios and road conditions, the software PreScan 5.0 was developed to form a comprehensive environment for designing and evaluating a multi-vehicle simulation system [22]. C. S. Wang et al. [23] used PreScan software to construct a simulation environment by importing real road sections and tested the vehicle cooperative driving sense system. However, the current vehicle energy consumption calculation is only for single-vehicle simulation tests under a specific driving cycle, and there are few multi-vehicle performance collaborative optimization platforms for hybrid vehicle configurations.
While current vehicle performance analysis software is widely used, it primarily focuses on simulating specific vehicle configurations under fixed driving cycles [24]. However, there is a notable absence of multi-vehicle simulation environments that incorporate co-simulation for investigating intelligent sensing and advanced assisted driving functions, especially concerning hybrid electric vehicles (HEVs) with diverse configurations. In contrast, traffic simulation software serves as a multiple vehicle interaction environment, to facilitate the development of collaborative algorithms, such as energy consumption and traffic efficiency optimization. Nevertheless, existing micro-traffic simulation software treats vehicles as mass blocks with fixed lengths [25], neglecting the dynamic characteristics of real vehicles and failing to refine the transmission systems of individual vehicles.
Currently, technologies such as vehicle-to-vehicle wireless communication are in the process of practical implementation [26]. Consideration of the specific configuration of each vehicle and the communication and interaction between different vehicles can significantly impact the development of new hybrid vehicles and enhance performance by leveraging operating condition information, as shown in Figure 1. Addressing the limitations of the current research, this paper proposes a large-scale vehicle performance automation simulation framework based on a modular design.

4. Single-Vehicle Platform Design

For the comprehensive simulation analysis of various vehicle configurations, ranging from different models to the parameters of each component, the processing program needs to accommodate all potential simulation requirements, which can be summarized as a variety of vehicle models and simulation processes in general. Since signal connections and parameter calculations between components are related to their interdependencies, it is crucial to execute necessary tasks to ensure the proper operation of the simulation system and identify potential configuration errors, such as scripts running sequences and signal connection relationships. A single-vehicle performance simulation involves system construction to meet the diverse requirements of essential functions to maintain the system operation, as shown in Figure 3.

4.1. Simulation Construction

The diversification of vehicle models primarily encompasses the following aspects: the vehicle model library (including passenger cars, commercial vehicles, special vehicles, etc.), the types of components and their connection configurations following specific rules, and the configurable parameters of each component. A single-vehicle model design architecture comprises module files, information extraction tools, and module connection rules, as illustrated in Figure 4. In the general simulation process, the initialization file provides all the parameters required by the model. Subsequently, the model undergoes execution, and the results produced by the model are subjected to postprocessing. Each component serves as a module and consists of four types of files: initialization scripts, preprocessing scripts, component models, and postprocessing scripts. The initialization script is solely dedicated to initializing the parameters essential for the corresponding model. It directly assigns values to parameters that do not necessitate further processing. It does not involve referencing parameters from other initialization or preprocessing files, nor does it engage in mutual calculations between parameters. Parameters that users can directly configure via the UI are included in this script, such as the engine fuel consumption map and the car body mass in the car body model. When the model entails parameters obtained from the initialization or preprocessing script of other components, or when parameters require mutual calculation to derive new parameters, these assignment statements referring to other component parameters or mutual calculations must be housed in the preprocessing script. For instance, the engine fuel consumption map may be further calculated to derive the optimal working curve of the engine, while the total mass of the vehicle is obtained by aggregating the masses of all individual components. After the simulation of the model, the postprocessing script can leverage all the parameters and model output signals. Based on these data, the postprocessing script generates the calculation results.

With the aid of information extraction tools, key information from the parameter scripts can be recognized and extracted based on the characters’ positions around the equals sign. This includes identifying the input parameters (IPs), output parameters (OPs), input signals (ISs), and output signals (OSs). Input parameters (IPs) are those necessary for script or model execution, found on the right side of equations within assignment statements or within the configuration boxes of models. Output parameters (OPs) represent new parameters generated after script execution, located on the left side of assignment statements. Input signals (ISs) are provided by other component models, while output signals (OSs) are the outputs generated by the component during operation.

Furthermore, based on the interdependence of the input and output variables between parameter scripts, we can perform automated operation sorting and parameter integrity testing. In addition, based on the input and output properties of the module files, with the help of the module’s connection tool, we can achieve interconnection between modules. Through the operation of various module files, three major functions are ultimately achieved, including a simulation feasibility check, module scripts run order, and model automatic construction.

4.2. Component Connection Relationship

The entire simulation model is divided into four parts: the driver, the environment, vehicle control system, and vehicle powertrain system, as illustrated in Figure 5. In the Simulink modeling software, due to the presence of numerous input and output signals in each component, to facilitate the signal wiring between modules and automatic program splicing of modules, multiple signals in the module are bundled in the form of a bus. Multiple signals are collected in the bus and can be extracted directly from the bus where needed. Each part corresponds to its own signal bus, namely the driver bus, environment bus, controller bus, and powertrain bus. These four buses are combined into a global bus, which facilitates the transfer of signals between various controllers, plants, driver, and environment. The controller buses consist of different components or vehicle controller buses, such as MCU, TCU, HCU, etc. Similarly, the plant buses comprise various plant buses, such as gearbox bus, wheel bus, etc.
The input signal set of each component interface must match the physical characteristics of the output signal set of the interface of another component to be connected in sequence. The properties of the component interface are shown in Table 1, while the rules governing component connections are shown in Figure 6.
To streamline user connectivity and minimize unnecessary errors, a restriction is imposed wherein an interface can only be connected to one other interface at a time. Simultaneous connections of two or more interfaces, even if they adhere to connection rules, are not permitted. Additionally, interfaces within the same module cannot be interconnected, even if they satisfy the basic connection criteria. Given scenarios where one component needs to connect to multiple components simultaneously, such as the battery module requiring connections to both the motor and the power converter, auxiliary modeling components are introduced, as shown in Table 2.

The controller, driver, and environment components do not require physical connections. Consequently, their inputs originate from the global bus, while their outputs are directed to the corresponding signal output buses. Apart from these two buses, different plant inputs and outputs need to be interconnected to establish various configurations. Given that different powertrain configurations entail distinct plant and connection relationships, the connection relationship between plants is represented by effort and flow signals. It is important to note that the number of effort and flow signals for each plant vary according to the plant type, corresponding to the number of interfaces in the table and the type of input and output signals for each interface. Furthermore, with the aid of the auxiliary modeling modules outlined in the table, the effort signals (or flow signals) of the same attribute can be superimposed or distributed.

The connection relationship configuration of the components and the corresponding models’ connection relationship are shown in Figure 7. In Figure 7a, the configuration connection relationship between the gearbox and final drive is represented. Referring to Table 1, both the gearbox and final drive have two interfaces, and the input–output relationship is shown in Figure 7b. Following the connection relationship between components, interface 2 of the gearbox can be connected to interface 1 of the final drive.
The dashed connection on the component configuration (Figure 7a) represents two connections on the corresponding model (Figure 7b). For the gearbox, the connections are the gearbox output torque (gearbox effort out) and gearbox flow in. For the final drive, the connections are the final drive input torque (final drive effort in) and final drive output speed (final drive flow out). It is important to note that the gearbox flow in is equivalent to the final drive output speed, while the gearbox output torque is equivalent to the final drive input torque.
For each model, aside from the input effort and flow, there may be a requirement for input signals from other external models. Similarly, besides the output effort and flow, the model’s output signals need to be disseminated to other models. Taking the final drive model as an example in Figure 8, the necessary signals are selected from the global bus via the signal selection subsystem, while all the final drive output signals are gathered onto the final drive bus by the signal collection subsystem.

4.3. Simulation Feasibility Checking

Different models have their own running files, including initialization, preprocessing, result postprocessing, etc. These files are interdependent during execution, necessitating a specific sequence for processing. To ensure the correct execution of the file sequence and verify any missing parameters or signals, distinct names are defined for various files, as outlined in Table 3.

In accordance with the order of operations, the inspection of possible unreasonable situations encompasses script and model parameter integrity checking, signal integrity checking, and configuration checking.

After obtaining the script set that requires IPs again, compare the number of scripts in the current set with the number of scripts in step d. If the script set is empty or the length is not equal, return to step d and repeat the process. However, if the script set is not empty and its length remains the same, it is determined that the scripts in the current script set lack IPs and cannot run correctly. Each script in the script set is then individually analyzed to retrieve its required IPs, which are then communicated to the users.

2.

Model parameter integrity checking

  • Catalog OPs provided by all initialization and preprocessing scripts and save all OPs in the OPs collector.

  • For each model, catalog its required IPs and determine the difference between the IPs of each model and the OPs collector. If the difference set is empty, it indicates that the parameters required by the model can be fully provided by the scripts. Otherwise, it signifies that some parameters required by the model are missing.

  • Gather the parameters and corresponding models from the non-empty difference set and output them to the user for inspection.

3.

Model signal integrity checking

  • Catalog the OSs of all models and save them in a signal collector.

  • For each model, catalog the ISs and determine the difference between the ISs of each model and the OSs collector. If the difference set is empty, it indicates that the signals required by the model can be fully provided by other models. Otherwise, it signifies that some signals required by the model are missing.

  • Gather the signals and corresponding models from the non-empty difference set and output them to the user for inspection.

4.

Postprocessing script parameter and signal integrity checking

  • Catalog the OPs provided by all initialization and preprocessing scripts, as well as the OSs provided by all models, and save them in a collector.

  • Utilize a method similar to the preprocessing script parameter integrity check to examine the integrity of postprocessing script parameters and signals. Note that postprocessing scripts can both require and provide parameters and signals simultaneously.

5.

Unreasonable configuration checking

The vehicle simulation model must align with the test process. For instance, distance-based operating conditions should be used with specific drivers. Additionally, the State of Charge (SOC) balance of the driving cycle must match the usage of hybrid electric vehicles.

Beyond the verifications discussed, additional checks include scrutinizing whether different models erroneously utilize identical names for Oss and detecting any instances of OPs being redundantly defined across scripts. Given the infrequency and straightforward nature of these issues, an extensive discussion is deemed unnecessary. Moreover, it is crucial to delineate the signal connection relationships between each model and its respective components, with a particular emphasis on accurately identifying the originating component model of the input signal.

4.4. Simulation Process Construction

The vehicle simulation test process encompasses various aspects such as dynamic performance, economy, driving range, and drivability performance. Additionally, it involves joint simulation and parameter sensitivity analysis across different test processes. Table 1 lists different types of computing configurations and performance indexes. Table 4 provides users with the ability to compute various combined performances, such as calculating both the acceleration time of 100 km and the maximum climbing degree simultaneously. Table 5 lists the different calculation modes, and each calculation mode in Table 2 can be flexibly combined with the calculation types in Table 1. Given the multitude of combinations, it is impractical to delineate the corresponding execution process for each combination.

To meet the diverse testing needs across different processes and enhance function reusability, each performance index solution is treated as a running process, while different calculation modes are considered modifications of various processes. Each simulation process is segmented into multiple process steps, such as system initialization, model execution, result saving, and outputting calculated performance indices. These process steps are then flexibly combined to form a process, and each process can be modified or combined flexibly to achieve complex processes.

For instance, consider the parameter sensitivity analysis of environmental temperature changes on the driving range of a pure electric vehicle under specific driving cycles, as depicted in Figure 10. The four major parts separated by dotted lines represent distinct processes: the parameter sensitivity analysis process, time-based cycle process, driving range calculation process, and simulation termination condition-setting process. It is important to note that these four processes are independent of each other. The functionality of the parameter sensitivity analysis process relies entirely on the nested entry calls it makes. For example, the parameter change analysis process calls the time-based cycle process to conduct vehicle economy simulation at different temperatures. Similarly, the parameter change analysis process can also focus on assessing the impact of altering the vehicle’s total mass on factors like the maximum gradeability or acceleration time.
The primary function of the time-based cycle process is to calculate the economics of a specific vehicle configuration under a time-based cycle. When no process is nested within the time-based cycle process, there are no other nested programs to execute the model simulation steps. Consequently, the model simulation step in the time-based cycle process will run directly. However, the additional functionalities of the time-based cycle process or other economic simulation processes depend on whether there is process nesting. For instance, in Figure 11, the driving range simulation process is nested, enabling the calculation of the driving range for pure electric vehicles. This process can also be combined with others to meet various simulation requirements. For example, it can be combined with the State of Charge (SOC) correction process for SOC balance correction in hybrid vehicles or with the cold start process of traditional vehicles to account for economic fuel consumption penalties.

6. Case Study and Discussion

Based on the proposed multi-vehicle interactive simulation platform, an economical speed planning algorithm based on net-connected information is validated. The algorithm process is depicted in Figure 16. Initially, leveraging the vehicle’s GPS-derived position, driving path, and signalized phase data retrieved from the cloud, the desired time intervals at each intersection are computed under speed constraints and signal phase limitations in a forward direction. Subsequently, appropriate time intervals are selected as reference points to ensure optimal passage efficiency. Backward recursion, based on the signal phase and speed constraints, is employed to determine the reference time interval for all signalized intersections. This reference time interval is then translated into reference speeds. Integrating this information with the position and speed data of the lead vehicle retrieved from the cloud, alongside the vehicle’s own speed information, an objective function is formulated within the Model Predictive Control (MPC) framework. This function considers vehicle economy, comfort, and reference speed.

Subsequently, under constraints such as road speed limits, safety distances, and vehicle dynamics, the optimization problem is reformulated into a nonlinear planning problem using the multi-targeting method. Economic speed is then determined through the solution of this problem. Finally, the economic speed trajectory, travel path, and vehicle position obtained from the solution are transmitted to the lower-level energy management system for implementation.

The optimal problem in the prediction time domain is defined as follows:

arg min u J = k k + N p [ w 1 f t q ( u ( t ) ) + w 2 f t i m e ( x ( t ) ) + w 3 f c o m ( x ( t ) ) ]

d t s . t . { d ( t ) d p r e ( t ) g min _ s a f e v min _ i v ( t ) v max _ i T min _ t q T t q ( t ) T max _ t q T min _ b T t q ( t ) T max _ b

where J is the total cost function, and f t q , f t i m e , and f c o m are the cost functions of the driving force, comfort, and reference speed, respectively. w 1 , w 2 , and w 3 are the weight coefficients corresponding to the three cost functions, respectively. d t is the time step. k is the current moment. N p is the step size in the predicted time domain. d is the safe distance d p r e and g min _ s a f e are the pre-set safety distances and minimum safety distances, respectively. v is the vehicle speed. v max _ i and v min _ i are the maximum and minimum vehicle speeds, respectively. T t q is the driving torque. T max _ t q and T min _ t q are the maximum and minimum driving torques, respectively. T max _ b and T min _ b are the maximum and minimum braking torques, respectively.

As the software primarily serves automation modeling, vehicle interface configuration, interface transmission, and interaction interface description, the emphasis in documentation tends to lean towards architecture and modeling methodologies. The focal point of this paper lies in delineating the design of a multi-vehicle communication software architecture, with vehicle algorithms taking a subsidiary role. Consequently, detailed discussions on vehicle control algorithms and related content are deemed unsuitable. However, for a clearer exposition of the algorithms utilized, additional references can be consulted for further elucidation [27].

In a distributed simulation setup, a PC serves as the master running servers and orchestrates the execution of vehicle models in MATLAB/Simulink. Two types of vehicles are configured: a pure electric vehicle (EV) and a plug-in hybrid with continuously electronically variable transmission (ECVT).

Firstly, for each vehicle, in accordance with the single-vehicle platform design outlined in Section 4.1, it is imperative to ascertain the components utilized in the vehicle, including but not limited to the engine, motor, and battery. Subsequently, a modular approach is adopted to select each component model, initialization script, and postprocessing script, such as the engine fuel consumption map and battery capacity. These components are then interconnected mechanically or electrically based on the vehicle configuration, adhering to the connection rules delineated in Section 4.2. In Section 4.3, a comprehensive summary of all scripts and models involved in the entirety of the vehicle is presented, followed by a meticulous verification of their rationality and effectiveness. Leveraging automated program splicing techniques, a comprehensive vehicle simulation model encompassing the model parameters is meticulously assembled within the MATLAB environment. This marks the attainment of a standalone simulation project capable of computing the vehicle performance metrics.
Subsequently, to facilitate multi-vehicle interaction, the multi-vehicle data transfer methodology outlined in Section 5.1 is employed to define the input and output interfaces for each vehicle, enabling communication with the surrounding environment. This entails sharing the speed and driving position data to formulate a communication matrix. Following this, the data transfer implementation methodology detailed in Section 5.2 is applied, wherein the communication matrix is documented in the address allocation file. This file is then inputted into the scheduler, streamlining communication and information exchange among vehicles. Finally, the simulation architecture of the entire vehicle performance simulation platform with multi-vehicle interaction is completed. The simulation architecture is depicted in Figure 17.
The SUMO 5.16 model comprises a road network file (net.xml) and a demand file (rou.xml). The road network file encompasses road modeling and signal light settings, while the demand file establishes vehicle and traffic flow models. Road network file editing methods include XML language editing, SUMO’s Netedit software for drawing, and Netconvert for road network identification and conversion [28]. Each method has its advantages and disadvantages, so this study integrates all three methods for modeling. Initially, Netconvert is used to read the OpenStreetMap road network of the target scene and convert it into .net.XML format. Then, Netedit simplifies the process by removing irrelevant road network elements and setting parameters such as signal light phases and speed limits. Finally, the road network file is further edited and enhanced using XML language [29].
The M language interface program utilizes Traci library functions to facilitate information interaction between SUMO and MATLAB [30]. The program first executes the Traci library function import command and initializes the Traci port based on the SUMO port. This enables access to the vehicle ID, speed, location, and other Traci library functions. Additionally, the program utilizes Traci library functions to set the vehicle speed, enabling feedback from the MATLAB model to the SUMO simulation scene.
In exploring the energy-saving control strategy of plug-in hybrid logistics vehicles, the optimization effect varies under different signal light phase settings, signal light intersection distances, or vehicle speed conditions. The speed planning algorithm, based on the Krauss car-following model [31], is simulated and verified. The resulting trajectories and speeds of both the Krauss and Model Predictive Control (MPC) following vehicles [32] are illustrated in Figure 18 and Figure 19, respectively. Notably, the plug-in hybrid ECVT demonstrates the ability to navigate each signal intersection effectively while also achieving a superior tracking performance relative to the pure electric vehicle (EV).
The simulation results of the economic speed planning system based on the networked information and the speed planning system based on the Krauss following model are compared and analyzed. The results are shown in Table 7.
From the results presented in Table 7, it is evident that compared to the Krauss following vehicle, the MPC-optimized vehicle experiences an increase in travel time. However, this is offset by the optimized comfort and economy of the vehicle. By leveraging signal light phase information along the vehicle’s driving path, the reference time at each intersection is reasonably planned, thereby reducing the number of vehicle stops and minimizing the acceleration and deceleration conditions.

In summary, both the EV and CEVT vehicles successfully complete the initialization script running, model construction, and information sharing between vehicles. They achieve collaborative following control seamlessly. The entire process operates as an automated scheduling process, facilitating mutual collaboration and communication among each vehicle. Utilizing the simulation technology platform proposed in this paper, in conjunction with the modular design principle and a flexible calculation method, enables the rapid construction of new energy vehicles featuring various hybrid drivetrains. During the early stages of product development, high-volume automated simulations facilitate powertrain matching and performance analysis, obviating the need for a manual configuration setup, parameter adjustments, and scenario replacements. This approach effectively eliminates time wastage, thereby swiftly reducing the costs associated with new product development, shortening development cycles, and enhancing overall work efficiency. By addressing the challenges of low modeling efficiency and the intricate integration of multiple models encountered in current simulation practices, this platform offers efficient technical support for the design and development of automotive simulation systems.

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Soil Respiration after Bark Beetle Infestation along a Vertical Transect in Mountain Spruce Forest https://inergency.com/soil-respiration-after-bark-beetle-infestation-along-a-vertical-transect-in-mountain-spruce-forest/ Thu, 28 Mar 2024 09:32:37 +0000 https://inergency.com/soil-respiration-after-bark-beetle-infestation-along-a-vertical-transect-in-mountain-spruce-forest/ Soil Respiration after Bark Beetle Infestation along a Vertical Transect in Mountain Spruce Forest1. Introduction It is estimated that global soil carbon stock represents 1700 Gt C [1] and the contribution of forest soil ecosystems, as stated by [2], equals about half of it (861 Gt C). Top-meter soil, live biomass, deadwood, and litter each contain a percentage of the total forest C stock: 44%, 42%, 8%, and […]]]> Soil Respiration after Bark Beetle Infestation along a Vertical Transect in Mountain Spruce Forest


1. Introduction

It is estimated that global soil carbon stock represents 1700 Gt C [1] and the contribution of forest soil ecosystems, as stated by [2], equals about half of it (861 Gt C). Top-meter soil, live biomass, deadwood, and litter each contain a percentage of the total forest C stock: 44%, 42%, 8%, and 5%, respectively. It creates an enormous carbon pool with the potential to highly increase atmospheric CO2 concentration [3] after large-scale disturbances. Carbon emission from global soil is considered the second largest carbon flux after photosynthesis, releasing 78–98 Gt C yr−1 in total [4,5]. Global forest soil carbon flux is an important contributor to global soil respiration (SR) because forests cover 26% of the Earth‘s total land area [6]. Under non-disturbance conditions, C uptake in the forest exceeds C emissions from ecosystem respiration [7] unless forest disturbance changes the balance and the forest becomes a carbon source [8,9]. Then, SR as the main component of ecosystem respiration [10] changes its rate as well.
Under climate change primarily induced by rising CO2 atmospheric concentrations [11], forests in Europe have experienced extreme heat and drought [12]. As a result, bark beetles as poikilothermic organisms have altered their behavior. They increase population size, and have extra generations per year [13]. Also, shifts of bark beetles to higher latitudes [14] and altitudes [15] have been observed. As a result, large-scale forest diebacks throughout the northern hemisphere have been caused by the European spruce bark beetle (Ips typographus L.) [16,17,18] recently.
Initially, after host tree death, the biogeochemical and biogeophysical processes (leaf area index, evapotranspiration, productivity, land surface temperature) of forest stands are altered [19,20]. Gross primary productivity (GPP) decreases as a result of tree mortality [21,22] and a reduction in the leaf area index corresponds to a contraction of GPP [19]. Fast nitrogen and carbon reduction take place in upper mineral soils, but an accumulation of soil inorganic N occurs as a result of the diminished contribution of roots, mycorrhizae, and rhizodeposition [23] which can be used by successional plant uptake [24], in the form of ammonium or nitrate [25]. The decline of fine root density increases with the level of tree mortality [26] and root biomass decomposition is accompanied by mycorrhizal fungi decomposition [27]. All of these factors contribute to changes in SR rate [28]. The decrease in SR after a disturbance event is mainly due to the reduced input of autotrophic SR [29] and nutrient losses [30]. After some time, infested trees shed their needles [20] and according to [31] approximately after 100 days spruce trees start to defoliate. Subsequently, increased solar radiation input [32,33,34] accelerates the decomposition rate of the litter as a consequence of higher temperatures [35].
Temperature is considered the most important factor influencing global SR [5,36,37]. SR positively correlates with ambient temperature [38]. With changes in temperature within different altitudinal zones, SR declines with increasing elevation [39,40]. Similarly, higher solar radiation intake on infested sites leads to rising soil and air temperatures on plots with bark beetle-infested trees [33]. SR follows seasonal dynamics of soil temperature with water surplus throughout the year [41], as it depends on both factors, with soil temperature having a more dominant effect [42,43].
Plant physiological–phenological relationships also influence SR rates, particularly the autotrophic component [44] and the successional stage after forest disturbance [45], as SR is linked to changes in phenology and photosynthesis [46]. After severe disturbance events, areas may shift to non-tree-dominated environments, with changes in plant species composition from shade-tolerant to shade-intolerant species due to altered solar conditions [32]. These plant communities, primarily grasses and perennial weeds, show increased biomass production and dispersive ability [47,48]. Over time after bark beetle attacks, grasses and perennials increase their surface coverage, leading to a greater influence on SR [49]. In the study site, four years after a windstorm, almost 100% of the area was covered by grasses and perennials [50]. The biomass production of successional grasses can exceed that of the original forest stand by a significant margin [51]. SR is also affected by Net Primary Productivity (NPP) [52,53,54]. Given that our study is on a vertical gradient on a homogenous slope, differences in physiological–phenological factors should be observed between infested and uninfested sites. Therefore, changes in SR are influenced not only by the soil temperature relationship but also by other factors [44], such as plant physiological–phenological dynamics and NPP.
There are studies that focus on SR within a vertical gradient [43,55,56] and studies that examine SR after windthrow or bark beetle attacks [22,42,49,57]. However, these studies do not address how different elevation zones of mountain spruce forests respond to disturbance events in terms of SR. Thus, in this study, we measured soil CO2 efflux for two consecutive years and investigated if there is a significant difference in SR between infested sites and uninfested sites with living trees at different elevational zones during the vegetation period. Our first hypothesis is that SR under dead trees might be lower than under uninfested trees due to a decrease in autotrophic SR (tree roots). Secondly, we predict that SR will not significantly vary across different altitudes due to minimal microclimatic differences within the measured mountain slope. Additionally, we expect the highest SR rates during summer (June to August) due to increased heterotrophic and autotrophic SR stimulated by higher soil temperatures.

3. Results

SR varied throughout the vegetation period at different altitudes and months in 2016 and 2017 (Figure 1). The mean SR during the vegetation period was highest at the elevation of 1200 m.a.s.l. at both infested and uninfested sites (Figure 1 and Figure 2). We observed that SR reached its peak in the summer (July or August) at each elevation zone. Almost every month and at each elevation zone, infested sites emitted more CO2 than uninfested ones, but in several cases it was insignificantly higher. The mean SR at different elevations between infested and uninfested plots was found to be insignificant at the elevation zone of 1400 m and 1300 m in 2016 and 2017, respectively (Figure 1 and Figure 2). A faster increase in SR at infested sites towards the peak of the growing season (July–August) was observed. We also noticed a slower decrease in SR towards the end of the growing season at infested sites compared to undisturbed sites in both years. Rates of SR differed between forest statuses within a year (Figure 1). In both years, infested sites showed significantly higher yearly average values than uninfested ones. The mean annual SR (during the vegetation period) in 2016 reached 0.625 ± 0.335 g CO2 m2 h−1 and 0.428 ± 0.248 g CO2 m2 h−1 at infested and uninfested sites, respectively. In 2017, SR rates were 0.576 ± 0.275 CO2 m2 h−1 and 0.438 ± 0.249 g CO2 m2 h−1 at infested and undisturbed sites, respectively. We observed no statistical difference in SR between the two subsequent vegetation periods. The critical p-value was set at 0.05 (α = 0.05).
The monthly average fluxes from the soil surface under infested sites are higher in both years, although not always significantly (Figure 1). The average SR at undisturbed sites showed the same amount of carbon emitted throughout the vegetation period at each elevation zone, at a significant level (Figure 2). On the other hand, a higher variation of SR was observed under infested plots (Figure 2). Our results show a clear pattern of decrease or increase in soil efflux within the elevation gradient in 2017 at infested sites. However, no decline in SR with elevation was observed at undisturbed sites in both years, at a significance level of p Figure 2). In 2016 at infested sites, we observed a significant decrease in SR with elevation gain.
SR in both years and types of forests did not show a high dependence on soil temperature and there were significant variations in 2016 between infected and undisturbed spruce forests (Figure 3). The disturbed forest exhibited a higher correlation between SR and temperature compared to the undisturbed forest. Additionally, the year 2016 shows a more rapid change in SR with changes in soil temperature (Figure 3). In 2017, a minimal difference in the dependence between SR and temperature was observed between disturbed and undisturbed forests. The correlation slope showed almost identical values. We also noticed a decrease in soil temperature between the highest and the lowest elevation zone in both years and forest conditions. The highest soil temperatures were measured during the summer months (from June to August) in both years and no correlation between soil moisture and respiration was observed. As optimal values of soil moisture were reached, it did not significantly impact SR rates. Additionally, we found a higher variance in moisture content at infested sites. The difference in moisture content between infected and uninfected sites was statistically significant. Disturbed sites covered with grasses and perennials had higher soil moisture content in both years (Table 1).
We observed significantly higher mean annual soil moisture and temperature at infested sites than at uninfested sites. The increase in soil temperature at infested sites within two years of the experiment was also observed. Similarly, significantly higher values for soil moisture were observed at infested sites. Some differences in soil moisture between the years were measured, but they were at the limit of significance (Table 1).

4. Discussion

In our experiment, we found that SR was significantly higher in the infested forest throughout both years. Changes in SR rate after disturbance events are not consistent throughout the published research papers [22,30,42,57,65,66,67]. In the girdling experiment in boreal Scots pine (Pinus sylvestris L.) forest, SR decreased by approximately 50% relative to ungirdled sites within one to two months [65], where forest mycorrhizae alone contribute to one-third of dissolved organic matter in forest soils, together with associated roots, contributing to 50% of dissolved organic matter [27]. However, a recent meta-analysis by [68] concluded that microbial, root, and mycorrhizal respiration contribute 57%, 28%, and 15%, respectively, to total SR. Soil fluxes decline as a consequence of altering key factors and nutrients [30] controlling SR rate.
Ref. [69] mentioned that up to 3 years after lodgepole pine (Pinus contorta Dougl. ex Loud.) infestation by mountain pine beetle, most needles remained on the trees. Therefore, no additional needle litter is added from dead pines to increase SR rate during this period of infestation. After a pulse of dead needles, SR almost fully recovered, lasting for up to 2 years and then followed by a decline again [22]. We suggest that needlefall and debris input from dying and dead spruce trees increased heterotrophic respiration, which compensates for the loss of autotrophic respiration. Since our study was conducted 5 to 6 years after the initial infestation by bark beetles, the results are quite similar to those of [22,49] with the difference that SR not only equaled undisturbed plots but exceeded them. This contradicts our initial hypothesis that SR will be higher at uninfested sites. Nonetheless, in our study, infested sites were covered by grasses and perennials with significant biomass productivity [51,70], which can increase autotrophic SR rates originating from the rhizosphere [71]. This phenomenon can also increase SR rates, in addition to hypothesized increased heterotrophic respiration due to higher mean soil temperatures [72]. The observed faster growth of SR towards the peak of the growing season (July and August) followed by a slower decrease in SR rates towards the end of the growing season at disturbed sites can be attributed to the gradual growth of perennials and grasses. This can be explained by the physiological–phenological development of a variety of plant species occupying research plots. Firstly, spring species start to grow (April–May), followed by summer species (May–June) and autumn species (June–August), creating a gradual and steady supply of autotrophic SR. Therefore, after disturbance, subsequent root biomass production of understory vegetation (grasses and perennials) [51,57,70] can account for increased SR within disturbed stands. Seven years after the disturbance, in terms of net primary productivity, successional understory vegetation acquires values that are only three times lower than forest stands before the disturbance event [73]. Nevertheless, developed understory vegetation combined with increased heterotrophic respiration caused by higher nutrient content [74,75] in the soil can compensate for severe tree death following a bark beetle attack.
Ref. [57] did not observe any significant changes in SR at a stand level from July to September over a period of 5 years between live lodgepole pine sites and bark beetle-infested sites. This phenomenon is attributed to surviving trees, understory vegetation, and the nutrient pulse from needlefall as noted by [74]. The decline in autotrophic respiration is offset by higher heterotrophic respiration induced by increased soil temperature, as discussed by [35,76]. Nonetheless, it is suggested that if mortality reaches 100%, total SR decreases to one-third of that in uninfested sites as stated by [57]. A similar pattern to that observed by [57] has been seen in fir-spruce forests by [42], attributed to root-respiring carbohydrates after tree death or a decrease in autotrophic respiration being replaced by heterotrophic respiration from dead roots and foliage. Dying roots and mycorrhizae release stored carbon for 2–3 years after disturbance, as reported by [77]. Additionally, an increase in soil temperature and soil moisture has been observed in infested ponderosa pine (Pinus ponderosa Laws.) forests by [33]. Ref. [49] confirmed that up to six years after disturbance, SR does not decrease at windthrow-disturbed sites. This is due to the substitution of decreased autotrophic SR with increasing heterotrophic SR supported by disturbance-induced alteration of soil temperature. In contrast, Ref. [30] concluded that the rapid decline of dissolved organic carbon, organic nitrogen, and phosphorus is accompanied by a decrease in SR after trees dieback, but after 4 years nutrients begin to recover due to litter mineralization. The post-disturbance chronosequence is an important factor influencing SR rate after bark beetle infestation. These varying results can be attributed to different mortality rates, gap size formation, and pre-existing understory vegetation.
As the temperature is considered the most important factor influencing global SR rates [5,36,37], with studies showing a positive correlation between SR and ambient temperature [71], we hypothesize that peak SR rates occur during the peak growing season in July and August, when temperatures are highest. This is likely due to the close relationship between SR and both air and soil temperature [78], as well as the increased contribution of heterotrophic respiration [79]. As temperatures decrease across different altitudinal zones [39,40], consequently, SR declines with increasing elevation [43,80]. Our results demonstrate a clear pattern of either decrease or increase in soil efflux along the elevation gradient in 2017 at infested sites, consistent with [56]. We observed a significant decrease in SR with elevation gain at infested sites in 2016, but no significant decline of SR with elevation had been observed at undisturbed sites in both years. This partially rejects our hypothesis that there will be no significant change in SR rates with increasing elevation.
The study of [75] conducted in the same vertical gradient shows that soil nutrient content increases with elevation and is statistically higher at infested sites. However, this finding contradicts the decreasing pattern of SR observed at infested sites in our study, as nutrient content plays a crucial role in supporting soil microorganisms that drive heterotrophic SR [30,81,82,83]. On the other hand, Ref. [75] only analyzed nutrient content for the year 2016. Nevertheless, we can suppose that increased nutrient availability of macroelements, increases autotrophic SR more than the heterotrophic component [84], but it can increase SR significantly because SR positively correlates with macroelement addition and increased temperature [81]. Also, like our study, Ref. [57] found no correlation between SR and moisture. We suppose that it can be supported by optimal moisture conditions during the studied period.
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Investigation of Underlying Association between Anthropometric and Cardiorespiratory Fitness Markers among Overweight and Obese Adolescents in Canada https://inergency.com/investigation-of-underlying-association-between-anthropometric-and-cardiorespiratory-fitness-markers-among-overweight-and-obese-adolescents-in-canada/ Thu, 28 Mar 2024 08:41:47 +0000 https://inergency.com/investigation-of-underlying-association-between-anthropometric-and-cardiorespiratory-fitness-markers-among-overweight-and-obese-adolescents-in-canada/ Investigation of Underlying Association between Anthropometric and Cardiorespiratory Fitness Markers among Overweight and Obese Adolescents in CanadaThis study provided a valuable opportunity to revisit and document various understudied anthropometric markers in the Canadian adolescent population. One such marker is WC, which has only recently received attention, with the first publication of normative values in Canada occurring in 2004. Notably, the data used to establish these norms originated from a survey conducted […]]]> Investigation of Underlying Association between Anthropometric and Cardiorespiratory Fitness Markers among Overweight and Obese Adolescents in Canada


This study provided a valuable opportunity to revisit and document various understudied anthropometric markers in the Canadian adolescent population. One such marker is WC, which has only recently received attention, with the first publication of normative values in Canada occurring in 2004. Notably, the data used to establish these norms originated from a survey conducted much earlier, in 1981 [55]. Consequently, considering the observed changes in obesity rates over the past few decades, the 2004 normative values likely underestimate current WC in Canadian adolescents. This is further supported by a 2010 study, which compared data from 1981 with 2007–2009, documenting increases in WC of 4.2 cm and 6.7 cm for boys and girls, respectively [18]. Conversely, the present study reveals an even greater increase, with values reaching 5.8 cm in boys and 7.4 cm in girls, suggesting a sustained secular trend. Remarkably, the observed trend in WC appears to exhibit a distinct trajectory compared to that of BMI.

4.2. Inclusion of CRF Markers

The inclusion of physiological markers represents a significant advancement in cardiometabolic risk assessment. The addition of markers such as VO2peak or the number of 1 min stages completed (FMAP) in the 20 m shuttle run test provides independent insights into adolescents’ health, leading to a more comprehensive characterization of their risk profile. Moreover, integrating this physiological dimension aligns directly with the American Heart Association’s recommendation, as outlined in their scientific statement, which promotes cardiorespiratory fitness (CRF) as a “vital sign” that should be routinely monitored in clinical practice [36]. In this model, low CRF assumes a central role in the estimation of cardiometabolic risk. In our study, CRF can either be determined using VO2peak or the number of stages completed. The choice between the two markers is left to the user, and this was chosen for pragmatic reasons. While diverse methods exist to measure VO2peak, several are resource-intensive. Treadmills, bicycle ergometers, and expired gas analyzers can be costly, time-consuming, and require specialized expertise. Fortunately, simpler and more cost-effective field tests still yield valid results. Therefore, our model accepts VO2peak values obtained through diverse methodologies (not necessarily the 20 m shuttle run test), promoting both versatility and accessibility.

Unlike anthropometric markers readily calculated from BM, BH, and WC and easily included in the proposed model, the number of 1 min stages completed, serving as an indicator of an individual’s FMAP, can only be obtained through the 20 m shuttle run test. Consequently, the obligatory inclusion of both CRF markers might restrict the possibility of obtaining a composite score, leading to the option of including only one of these two markers. In fact, the current study demonstrates the feasibility of developing an accurate and reliable model using a limited number of markers to predict the cardiometabolic risk among adolescents. While we advocate for the use of both CRF markers (Equation (1)), employing solely the VO2peak value offers a viable alternative for overall risk assessment with a marginal impact of only ~−6%.

4.3. Individual and Composite Scores as Cardiometabolic Risk Markers

The current study provides a more complete assessment of cardiometabolic risk as opposed to studies using individual markers. However, establishing an overall risk classification that encompasses all markers simultaneously presents a challenge. The composite score offers the benefit of summarizing the risk when analyzing all markers concurrently. It is important to acknowledge that both the individual risk zones and the final composite score have inherent limitations due to their arbitrary nature. However, the delineation of the various risk zones largely mirrors the rationale used by the WHO to determine the BMI risk zones. Consequently, each risk zone was established using corresponding BMI values. For instance, to determine WC risk, we first identified the raw values corresponding to the 85th percentile for BMI. Subsequently, the raw score was compared with the corresponding Z-score. Thus, for anthropometric variables, it has been determined that the risk zone corresponding to the 85th percentile for BMI aligns with the 80th percentile for WC, WHtR, and BSA. Moreover, previous studies have already recognized the value aligned with the 80th percentile as the most appropriate for adolescents concerning WC [10], which is consistent with our findings. This approach, adopted in previous studies [4,29], acknowledges the unique characteristics of the target population instead of applying a universal cutoff point. In this context, we have forsaken the “conventional” cutoffs for WC, WHtR, and BSA in favor of thresholds more tailored to the Canadian population. Nonetheless, our model can be adapted for other populations based on the same rationale. This more stringent 80th percentile cutoff enhances the sensitivity of the screening tool by minimizing the likelihood of overlooking adolescents who may be at risk of health issues.

Our regression analysis revealed collinearity issues among the anthropometric markers, particularly between WC and WHtR. We prioritized retaining WC due to its wider use and more extensive research base. Following the same rationale, BSA was excluded in favor of BMI. However, excluding WHtR and BSA from the model does not diminish their clinical value. From an individual analysis, the BSA and WHtR indices can bolster the clinician’s evaluation by confirming the trends observed through BMI or WC measurements. Consequently, these two parameters can supplement the clinician’s assessment in addition to BMI or WC values. For instance, when both BMI and BSA categorize an adolescent into the same risk group, it indicates that the risk linked with general obesity is supported by two indicators rather than solely one. In essence, the incorporation of BSA and WHtR significantly enhances the clinical evaluation, facilitating a more precise diagnosis and personalized patient management, while providing a more complete vision of their cardiometabolic status and the associated risk of obesity-related complications.

CRF markers represent a distinct facet of cardiometabolic risk that needs to be considered. While a consensus on risk values for adolescent health remains elusive, some researchers have proposed cutoff value below 42 mL·kg−1·min−1 for boys and 35 mL·kg−1·min−1 for girls, albeit with certain variations [36,38,43]. While these values account for sex differences, no studies have specifically addressed age-related cutoff values. Interestingly, during the 1980s, a single cutoff point was established for children and adolescents aged 6 to 17, particularly among boys, due to the remarkable consistency of relative VO2peak, around 50 mL·kg−1·min−1, throughout this period of physical growth [40]. However, current evidence clearly demonstrates that relative VO2peak values tend to decline with age in both boys and girls [41,42,43]. This growing trend, spanning several decades, is indicative of the shift observed in today’s adolescents who are forsaking physical activities in favor of adopting more sedentary behaviors, such as engaging in activities like playing video games, for instance. Thus, this age-related decline in VO2peak must be considered when estimating health risk. For instance, if the cutoff point is set at 42 mL·kg−1·min−1 for 12-year-old boys, considering their subsequent decline in VO2peak, these same boys are likely to fall below this threshold at the age of 13. Thus, in this particular case, the cutoff point of 42 mL·kg−1·min−1 may provide a false sense of security for an individual of this age.
Given the observed decline, a higher cutoff threshold might be more appropriate for 12-year-olds compared to 13-year-olds, considering their inherently higher median VO2peak. Based on this model, we note that more than 50% of adolescents in our sample are at high CRF risk. It is worth highlighting that this risk reaches its peak at the age of 17, with a value of 57.3% for boys and 65.8% for girls, corroborating recent published data [43]. These findings are undoubtedly concerning and warrant public health attention. It is crucial to take immediate measures to motivate adolescents to embrace a more active lifestyle, particularly older individuals who are already at a heightened risk of imminent cardiometabolic problems. One potential solution involves reevaluating the allocation of physical education (PE) within school curriculums. The significant reduction in PE minutes observed in Québec over the past three decades coincides with the decline in VO2peak and FMAP values, suggesting a potential link that merits further investigation [43].
Regarding the number of 1 min stages completed (FMAP), as far as we know, only one prior study has investigated its association with cardiometabolic risk [38]. In general, the reported values align with the results of the present study, showing similar magnitudes when considering values per year of chronological age. Even though the data originates from different populations, which can account for certain variations, it is worth noting that both studies were conducted during the same timeframe (2016 vs. 2017 for the present study), thereby minimizing the potential impact of the secular trend.

Therefore, the inclusion of a marker directly focusing on adolescents’ functional capacity, like FMAP, undoubtedly constitutes a significant contribution to their cardiometabolic risk assessment. While the data primarily originates from the province of Québec, which accounts for slightly less than 25% of the Canadian population, the proposed model can serve as a valuable reference for other regions within Canada and potentially worldwide. In the absence of regional data, the values from this study can serve as a provisional risk assessment tool until region-specific values becomes available. FMAP represents a cardiometabolic risk marker that is equally, if not more, significant compared to VO2peak. It holds the advantage of being less influenced by anthropometric characteristics in comparison to relative VO2peak. Consequently, FMAP’s interpretation is simpler, both for adolescents and for healthcare professionals. Particularly during growth spurts, when considering available data, FMAP should take precedence over VO2peak as the primary marker for assessing cardiometabolic risk. Nonetheless, for cardiometabolic composite risk assessment, regression Equation (2) demonstrates the feasibility of using VO₂peak alone when FMAP data is unavailable. Despite the absence of FMAP data, VO2peak alone can be a sufficient metric for assessing composite cardiometabolic risk (regression Equation (2)) without compromising on validation and accuracy, which remain excellent.

4.5. Limitations and Strengths

This study has several limitations. First, the cross-sectional design restricts the ability to draw causal inferences. Second, the estimation of VO2peak values instead of direct measurement introduces inherent uncertainties. Third, the analysis employs anthropometric markers derived from field measurements. These measurements may not consistently achieve the optimal level of precision required for drawing robust inferences, compared to those obtained from direct measures. Fourth, while the sample represents Canadian adolescents living in Québec, generalizing the findings to other regions requires caution. Furthermore, the associations between cardiometabolic risks and the measured markers are primarily based on existing literature, limiting the study’s ability to establish causality. However, this study also boasts notable strengths. The substantial participant size (N = 1864) provides a valid representation of Québec adolescents. Additionally, the stratification of several criteria, including age groups, sex, ethnicity, and socio-economic status, enhances the representativeness of the evaluated population. Beyond its user-friendly characteristics, the proposed model provides a level of validity and precision that ensures reliable data interpretation. Finally, despite the weaknesses mentioned above, all employed markers in this study are well established as valid and reliable measures, displaying acknowledged associations with cardiometabolic risks.

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Spatiotemporal Heterogeneities in the Impact of Chinese Digital Economy Development on Carbon Emissions https://inergency.com/spatiotemporal-heterogeneities-in-the-impact-of-chinese-digital-economy-development-on-carbon-emissions/ Thu, 28 Mar 2024 08:08:33 +0000 https://inergency.com/spatiotemporal-heterogeneities-in-the-impact-of-chinese-digital-economy-development-on-carbon-emissions/ Spatiotemporal Heterogeneities in the Impact of Chinese Digital Economy Development on Carbon Emissions1. Introduction The escalating emissions of greenhouse gases, exemplified by carbon dioxide (CO2), and consequent climate change phenomena, such as global warming, are presenting progressively severe challenges to the economic development, production, and livelihoods of human societies. Consequently, the imperative to reduce carbon emissions and achieve sustainable development has garnered consensus among nations worldwide [1]. […]]]> Spatiotemporal Heterogeneities in the Impact of Chinese Digital Economy Development on Carbon Emissions


1. Introduction

The escalating emissions of greenhouse gases, exemplified by carbon dioxide (CO2), and consequent climate change phenomena, such as global warming, are presenting progressively severe challenges to the economic development, production, and livelihoods of human societies. Consequently, the imperative to reduce carbon emissions and achieve sustainable development has garnered consensus among nations worldwide [1]. Countries around the world are striving to achieve carbon peak and carbon neutrality. According to the United Nations Framework Convention on Climate Change, more than 130 countries and regions have proposed “zero carbon” or “carbon neutral” climate targets [2]. As the largest rising economies in the world, the BRICS countries accounted for about 43.19% of global CO2 emissions in 2019 [3]. As one of the world’s major carbon-emitting countries, China is under imminent pressure from energy conservation and emission reduction targets [4]. As a responsible, developing country, China pledged at the United Nations General Assembly to achieve the carbon reduction targets of “peak carbon and carbon neutrality” before 2030 and 2060, respectively. This poses a huge challenge for China, which is in a period of economic transition, to balance environmental pollution with economic growth. But at the same time, it also provides new opportunities for the Chinese economy to move towards high-quality green development and realize sustainable economic development [5].
Simultaneously, the digital economy, characterized as a novel economic paradigm, leverages data resources as production factors and depends on digital technologies such as big data, blockchain, and cloud computing to furnish advanced technological support and contribute factors toward realizing the objective of “dual carbon” [6,7], for example, the use of remote sensing technology to estimate carbon emissions and the use of blockchain technology to track a company’s carbon footprint [8,9]. Currently, China’s digital economy is in a period of rapid development. According to the Research Report on the Development of China’s Digital Economy (2023) published by the China Academy of Information and Communications Technology (CAICT), the scale of the Chinese digital economy was projected to reach 50.2 trillion yuan in 2022, representing 41.5 percent of the country’s GDP, a proportion equivalent to that of the secondary industry in GDP. Developing the digital economy not only reduces the cost of traditional industries and improves management and production efficiency but also favors the promotion of investment in renewable energy and the development of green infrastructure services, accelerating green information dissemination, etc. [10,11,12,13,14]. Hence, the advancement of the digital economy presents fresh opportunities for carbon mitigation and stands as one of the pivotal initiatives toward achieving the objective of carbon neutrality.
As important means and goals in the process of China’s economic transformation and development, the development process and implementation effectiveness of the digital economy, energy conservation, and emission reductions are keys to determining whether China’s green transformation can be successfully realized in the future [15]. The intricate relationship between the development process and implementation effectiveness has complicated and diverse influencing mechanisms. The development of the digital economy can not only directly provide technical support for China to achieve its emission reduction targets but also enhance the degree of economic agglomeration by strengthening the efficiency of resource allocation and reducing the loss of marginal costs, which in turn can reduce the level of carbon emissions. Hence, this paper integrates the concepts of the “digital economy”, “economic agglomeration”, and “carbon emissions” within a unified analytical framework. It explores the relationship between the digital economy and carbon emissions from both spatial and temporal perspectives, offering significant reference value for China’s digital economy development in pursuit of the dual-carbon objective.

The three main contributions of this paper are as follows. Firstly, the paper engages in theoretical discussions concerning the pathways through which the rapid evolution of the digital economy influences carbon emissions. These pathways encompass the direct impact of the digital economy on carbon emission intensity, indirect influences through the augmentation of economic agglomeration, and the “snowball effect” of carbon emissions. Secondly, from a spatial perspective, empirical analyses are conducted to examine the spatial spillover effect of digital economy development on carbon emissions, employing the static spatial Durbin model while also verifying the mediating effect of economic agglomeration. Lastly, from a temporal standpoint, the paper investigates the path-dependent characteristics of carbon emissions by utilizing the dynamic spatial Durbin model based on Han–Phillips GMM.

The subsequent sections of the paper are organized as follows. Section 2 lays out the theoretical analyses and research hypotheses. Section 3 presents an empirical modeling study based on the research data and methodology. Section 4 presents results of robustness tests in terms of replacing the explanatory variables, the weight matrix, and the sample interval. Section 5 divides the sample into an eastern region and a central and western region for heterogeneity testing. Finally, we present conclusions and policy recommendations.

2. Theoretical Analysis and Hypotheses

2.1. The Impact of the Digital Economy on Carbon Emissions

In the global climate governance framework, reducing carbon emissions has become the core task of each country. The digital economy, as a novel paradigm propelling the evolution of the “technology–economy” interface, facilitates ecological and environmental governance, aligning with contemporary trends and imperatives for sustainable development. With the ongoing advancement of China’s digital economy, the influence of the digital economy on carbon emissions can be examined from two perspectives. On the one hand, from a direct perspective, the development of the digital economy has given rise to a large number of digital industries, whose green levels are significantly higher than those of traditional industries and which are inherently environmentally friendly [7,16]. Secondly, digital technologies are gradually penetrating traditional industries and accelerating the process of industrial digitization; Wang et al. showed that the development of digital economy models such as digital finance and online trade has contributed to the high-quality development of China’s energy resources and slowed down the growth of carbon emissions [15]. Industrial digitization not only enhances the energy efficiency of traditional industries, hastens the production transformation of conventional sectors, and fosters shifts towards industrial intelligence and sustainability but also facilitates improvements in energy conservation and emission reduction efficiency, diminishes electricity consumption intensity, and enables the sharing of energy and carbon emissions, thereby propelling the green sharing economy [17,18,19,20,21]. This is conducive to a more efficient and environmentally friendly production process, thereby reducing the level of carbon emissions and ultimately achieving green economic development. Thirdly, as the digital economy evolves, there is a concurrent rise in the extent of the tracking and management of carbon emissions within the production process. For example, Fasogbon and Igboabuchukwu showed that real-time carbon footprinting techniques based on energy consumption offer a broad potential applications for quantifying and managing carbon emissions [22]. Fourthly, the increased level of development of the digital economy can not only reduce industrial carbon emissions from business production but also reduce consumers’ living carbon emissions. For example, the rapid development of the digital economy has given rise to new forms of online trade, video conferencing, cloud-based information sharing, etc., which greatly reduce the frequency of offline travelling and trade, thus reducing the carbon emissions generated by offline economic activities [23]. Moreover, green consumption platforms, green consumption products, etc., which are built on digital technologies, have greatly enhanced people’s environmental awareness while facilitating their lives [24].
On the other hand, from an indirect perspective, the digital economy can affect the level of carbon emissions by changing the concentration of production and life within a region. The concentration of economic activities within a region is commonly measured by economic agglomeration indicators [25]. The digital economy can integrate and share fragmented data and knowledge to form a systematic and scaled data and information network, break the original information barriers between different regions and industries, and reduce the cost of searching for information, thus increasing economic agglomeration. Further economic agglomeration can affect the level of carbon emissions by exerting positive externalities such as factor allocation advantages and strengthening knowledge and technology spillovers [26]. Specifically, the “network agglomeration effect” of the digital economy is beneficial to all kinds of economic entities attempting to break the original form of agglomeration constrained by geographic space; these entities can use digital technology to transform this constrained agglomeration into a digital network agglomeration mode centered on data and information, thus increasing the degree of economic agglomeration, which in turn has an impact on the level of carbon emissions. It has been shown that an increase in economic agglomeration will have a two-way impact on the level of carbon emissions. The scale effect of economic agglomeration results in an augmentation in the spatial concentration of economic activities and a gradual expansion in output scale, consequently resulting in an increase in carbon emissions per unit of space and thereby engendering adverse environmental externalities [27,28]. The economies of scale generated by economic agglomeration can enhance production efficiency and mitigate energy consumption and transportation costs through technology spillovers and knowledge sharing, thereby creating positive environmental externalities [11,29,30]. Shao et al. noted that the ascent of Chinese urban agglomerations and the escalating level of economic agglomeration have favorably propelled China’s energy-saving and emission reduction endeavors, facilitating the adjustment process of energy-saving and emission reduction technological innovation [31]. Therefore, based on the above analysis, we propose H1.
H1. 

The digital economy not only directly reduces carbon emissions but also indirectly reduces carbon emissions by increasing economic agglomeration.

2.2. Spatial Spillover Effects of the Digital Economy on Carbon Emissions

The development of digital technology and carbon emission levels in China is uneven. From a regional perspective, the level of digital technology development is higher in the east, while the west lags behind. The level of carbon emissions as a whole is characterized by a high level in the east and a low level in the west. As a new driving force for economic development, the digital economy breaks the traditional constraints of geography, information sharing, and resource flow, and it promotes unconscious “exchanges” between regions by strengthening the free flow of production factors such as information, technology and digital resources in space, which is conducive to the integration of industries, carbon emission reductions, technology sharing, and so on between different regions. First of all, digital technology is creative and pervasive, and it will have a spillover effect on the development of neighboring regions. Xue et al. showed that the development of a digital economy will increase the complexity of the supply chain and promote the development of the information and communication technology (ICT) industry [32]. This not only helps to reduce local carbon emissions but also generates spatial spillovers to nearby areas. Second, the development of digital technology accelerates the speed of information dissemination, which amplifies spatial demonstration and competition effects [33,34]. On the one hand, the digital economy weakens the law of diminishing technological spillovers due to geographical distance and strengthens the demonstration effect of spatial spillovers. This helps to guide different regions to learn from each other about carbon reduction and emission reduction technologies. The rapid development of the digital economy has given rise to a large number of carbon-reducing technologies, which not only help to reduce their own carbon emissions but also influence the carbon emission reductions of neighboring provinces in the process of their wide dissemination. As a result of the demonstration effect, neighboring provinces will follow the example of the provinces with the faster development of carbon emission reductions and adjust their production and consumption patterns, and the advanced regions will have a subtle demonstration effect on the lagging regions [35]. On the other hand, propelled by the “dual-carbon” goal, carbon reduction and emission mitigation have emerged as shared objectives among all provinces, leading to inter-provincial competition. Due to the competition effect, in order to better achieve the goal of carbon emission reduction, provinces will actively compete with each other in terms of talent, technology, environmental regulations, etc. Zheng et al. pointed out that the competition effect is asymmetric among provinces with different degrees of economic agglomeration [36]. Provinces with a low degree of economic agglomeration are more likely to receive the competition effect, which will exacerbate the spatial spillover effect of the digital economy on carbon emissions through indirect channels. Therefore, based on the above analysis, we propose H2.
H2. 

The digital economy can affect carbon emissions in neighboring regions through spatial spillovers.

2.3. The Snowball Effect of Carbon Emissions

Building upon the examination of the spatial spillover effect of the digital economy on carbon emissions, this paper additionally explores the temporal trajectory dependence of carbon emissions. Temporal path dependence refers to the fact that carbon emission levels are affected by not only the development level of the digital economy in the current period but also the carbon emission levels in the previous period, i.e., there is a “snowball” effect on carbon emission levels [37]. Provinces with higher carbon emission levels tend to have a closer relationship with high energy-consuming industries and traditional energy sources in their production methods, industrial structure, and energy consumption. In the process of low-carbon transition, they face greater difficulties in carbon emission reduction. For example, as the energy rebound effect is significantly higher for energy-intensive industries than for other provinces, it will reduce the carbon emission performance of energy-intensive industries [38]. Coal-producing provinces such as Shanxi and Inner Mongolia have more energy-intensive industries. Compared with other provinces, provinces with a higher traditional energy dependence have worse carbon emission performance due to the energy rebound effect, and the time accumulation effect of carbon emission levels is more obvious. Scholarly research shows that cities that implement low-carbon construction earlier can have stronger carbon emission reduction capacities by reserving professionals and adjusting policy tools [39,40]. Therefore, based on the above analyses, this paper proposes H3.
H3. 

Carbon emission levels are affected by both the spatial spillover effects of the digital economy and the snowball effect of carbon emission levels.

3. Methods

3.1. Model Settings

To investigate how the digital economy directly influence carbon emissions, we first constructed a benchmark model for the direct impact mechanism, as shown below:

c e i , t = α 0 + α 1 d i g i , t + α 2 s d i g i , t + α c Z i , t + μ i + δ t + ε i , t

where c e i , t represents the carbon emission of the province i during the period t, d i g i , t represents the digital economy level of the province i during the period t, and Z i , t stands for the control variables. μ i and δ t represent individual and time fixed effects, respectively. ε i , t represents random disturbance.

Secondly, in addition to the direct impact effect embodied by Model (1), there may be an indirect mechanism of action between the digital economy and carbon emissions. Based on previous analyses, this paper tested whether economic agglomeration (ag) is a mediating variable between the two via the stepwise regression method [41]. The first step involves estimating Model (1) and examining the overall impact of the digital economy on carbon emissions. If α 1 is statistically significanicates support for the total effect of the digital economy. In the second step, we constructed the benchmark regression equations for dig versus ag and dig and ag versus ce. To assess the presence of a mediating effect, we examined the significance of β i , γ 1 , and γ 2 . The specific settings of the stepwise regression model are as follows:

a g i , t = β 0 + β 1 d i g i , t + β c Z i , t + μ i + δ t + ε i , t

c e i , t = γ 0 + γ 1 d i g i , t + γ 2 a g i , t + γ 3 s a g i , t + γ c Z i , t + μ i + δ t + ε i , t

where a g i , t represents the economic agglomeration of the province i during period t and s a g i , t is the square term of a g i , t . If β 1 and γ 2 are significant, the indirect effect exists. Then, the third step is to test whether the coefficient γ 1 is significant. If γ 1 is not significant, the direct effect does not exist, indicating that there is only a mediating effect known as the full mediating effect. If γ 1 is significant, the direct effect is also significant and is called the partial mediation effect.

Thirdly, to delve deeper into the influence of the digital economy on carbon emissions, we analyzed the spatial spillover effects within the mediation model framework. Additionally, we examined the spatial spillover effect of the digital economy on carbon emissions by incorporating their spatial interaction term into Model (1), which was further expanded into a spatial panel econometric model:

c e i , t = α 0 + ρ 1 W c e i , t + ϕ 1 W d i g i , t + ϕ 2 W a g i , t + ϕ c W Z i , t + α 1 d i g i , t + α 2 a g i , t + α 3 s a g i , t + α c Z i , t + μ i + δ t + ε i , t

where ρ represents the spatial lag coefficient and W represents the spatial weight matrix, which indicates the relationship of each province. Model (4) incorporates the combined impact of the digital economy and carbon emissions on carbon emissions with spatial lagged effects, termed the spatial Durbin model (SDM).

Finally, we comprehensively considered the dynamic spillover impacts of the digital economy and carbon emissions. These encompassed potential path-dependence characteristics of carbon emissions in the temporal dimension, as well as the endogeneity issue arising from the potential bidirectional causality between carbon emissions and economic and technological factors [42]. Therefore, we introduced a lag period of ce and set up the following dynamic spatial panel model:

c e i , t = η 0 + θ 1 W c e i , t 1 + θ 2 W d i g i , t + θ 3 W a g i , t + θ c W Z i , t + η 1 c e i , t 1 + η 2 d i g i , t + η c Z i , t + μ i +   δ t + ε i , t

where θ 1 , θ 2 , θ 3 and θ c represent the elasticity coefficients of the spatial interaction terms for the explanatory variables, the core explanatory variables, and the control variables, respectively. Model (5) can simultaneously test the impact of the digital economy on carbon emissions and examine the time lag effect of carbon emissions.

3.2. Spatial Weighting Matrix Settings

A spatial weighting matrix is used to reflect the effect that the neighborhood explanatory variables have on the explained variables. Traditional weight matrices include the 0–1 matrix and neighborhood matrix [17]. In recent years, as economic and trade exchanges have become closer, the spillover effect between regions has been affected by not only geographical factors but also economic conditions. Therefore, we employed the following spatial adjacency matrix by combining the economic development status of each province and geographical distance:

W = x i x j d i j 2 , i j 0 ,   i = j

where x i and x j represent the GDP per capita for provinces i and j, respectively. d i j represents the geographical distance between provinces i and j.

3.3. Variable Descriptions

3.3.1. Explained Variable

We focused on the variable of carbon emission (ce). We estimated carbon emissions of provinces based on the measuring method provided by the United Nations Intergovernmental Special Committee on Climate Change (IPCC) and the consumption of eight major fossil energy sources in China. The eight major fossil energy sources are raw coal (10,000 tons), coke (10,000 tons), crude oil (10,000 tons), gasoline (10,000 tons), paraffin (10,000 tons), diesel fuel (10,000 tons), fuel oil (10,000 tons), and natural gas (100 million m3). The specific calculation formula is as follows.

c e i , t = C E i , t L i , t = n = 1 8 c n E n , i , t

L i , t

where c n ( n = 1 , 2 , , 8 ) represents the carbon emission factors for the eight main fossil energy sources, E n , i , t represents the consumption of the energy n in province i in period t, and L i , t represents the total population of province i in period t.

3.3.2. Core Explanatory Variable

The central explanatory variable examined in this paper is the digital economy (dig). We constructed an index system to measure the digital economy. The index system includes the length of long-distance fiber-optic cables; the number of people employed in the information transmission, software and information technology services industry; the turnover of the technology market; the total volume of telecommunications business; and the number of mobile phone subscribers. Then, to objectively measure the level of the digital economy, we applied the entropy method. The specific process of the entropy method is as follows.

Firstly, the indicators are dimensionless. When the indicator x i j is positive,

b i j = x i j m j M i m j

where M i and m j are the maximum and minimum of x i j , respectively. When the indicator is negative,

b i j = M j x i j M i m j

Secondly, the indicators are normalized:

P i j = b i j j = 1 n b i j

Thirdly, the entropy value of the indicator is calculated.

I j = k i = 1 m P i j ln P i j

,   j = 1 , 2 , 3 , , m

where k is a constant and k > 0 , usually taken as k = 1 / l n ( m ) . I j is greater than zero.

Fourthly, the entropy weights of the indicators are determined.

w j = 1 I j j = 1 m ( 1 I j )

Finally, linear weighting was applied to obtain the final composite evaluation index.

y i = j = 1 m w j × P i j

3.3.3. Mediator

Based on the theoretical analysis presented above, this study chose economic agglomeration (ag) as the mediator to test the mediating mechanism. Drawing on Zhang et al., we used the number of employed people per unit area to measure the degree of economic agglomeration in each province [43]. In addition, existing studies show that there is a non-linear relationship between economic agglomeration and carbon emissions [44]. Therefore, we added ag and its quadratic term (sag) to the regression model.

3.3.4. Control Variables

In order to more comprehensively analyze the spillover effects of the development of the digital economy in carbon emissions, it is also necessary to study the control variables that may have an impact on carbon emissions. Therefore, the following control variables were selected in this paper: economic development (py), for which GDP per capita was used to control for possible non-linear effects of the level of economic development; industrial structure, which is expressed as the share of value added of the secondary industry in GDP (ig) and the share of value added of the tertiary industry in GDP (sg), respectively; technological progress (rd), characterized by patents granted per 100 R&D personnel; non-farm output (lp), represented by consumption expenditure per urban resident; urbanization (rm), characterized by urban population density; and talent development (pt), characterized by the human capital index in the China Human Capital Report (2022).

3.4. Data Sources and Descriptive Statistics

This study employed China’s provincial-level panel data from 2000 to 2021, selected to ensure the accuracy of research and availability of data. The data of Tibet, Hong Kong, Macao and Taiwan were excluded due to a severe lack of relevant data. Primary sources for this article included the China Statistical Yearbook, the China Energy Statistical Yearbook and provincial statistical yearbooks. Table 1 presents the results of the descriptive statistics.
Based on Table 1, the minimum value of ce is 0.2857, the maximum value is 25.6360, and the standard deviation is 2.8883, implying substantial variance in per capita carbon emissions among provinces throughout the sampling period. Similarly, the disparity between the minimum and maximum values of dig is 0.6174, indicating significant discrepancies in digital economy development across provinces as well.

4. Empirical Results

4.1. Benchmark Regression

Column (1) in Table 2 presents the findings from the estimation of the impact of the digital economy on carbon emissions within the benchmark regression model. Here, the estimated coefficient of the primary explanatory variable, dig, demonstrates a statistically significant negative effect. This indicates that the development of the digital economy reduces carbon emissions, which is in line with the findings of Zhang and Liu [45,46]. In addition, the coefficients of py and ce exhibit a significant positive correlation, whereas the coefficients of spy and ce demonstrate a significant negative correlation. This suggests an inverted U-shaped relationship between per capita income and carbon emissions. Furthermore, ig presents a positive and statistically significant coefficient, implying that the rising proportion of the secondary industry within the GDP correlates with increased carbon emissions. Conversely, sg displays a significantly negative coefficient, indicating that the increasing proportion of the tertiary industry within the GDP is associated with a reduction in carbon emissions. The coefficient of ig is positive and significant, suggesting that the growing share of the secondary industry in GDP will elevate carbon emissions. Conversely, the coefficient of sg is notably negative, implying that with a higher proportion of tertiary industry in GDP, carbon emissions decline. This phenomenon might be attributed to the dominance of manufacturing activities within the secondary industry, which results in substantial emissions of carbon-containing pollutants during the manufacturing process, consequently escalating carbon emissions [7]. The tertiary sector, predominantly composed of the financial and service industries, plays a dominant role. A rise in the tertiary sector’s contribution to GDP facilitates the mitigation of carbon emissions during economic development. The significant positive coefficient of lp implies that higher per capita non-agricultural output increases carbon emissions, which may mainly be due to the fact that China is mainly driven by the development of the manufacturing sector. Thus, the rise in non-agricultural output primarily stems from the advancement of the secondary industry, resulting in a notable positive correlation between lp and ce. The coefficient of Rd is negative but not significant. This may be due to the low number of carbon-related patents granted in China. The coefficient of rm is significantly positive, indicating that regions with high population densities have higher levels of carbon emissions.

4.2. Mediating Effect Regression

Based on the previous theoretical analyses, the digital economy not only has the ability to reduce carbon emissions but also affects economic agglomeration, thus changing carbon emissions. Therefore, in addition to directly affecting carbon emissions, the digital economy also indirectly affects carbon emissions through economic agglomeration [47]. In summary, we provide an in-depth study of the mediating effect of economic agglomeration. The results of this mediating mechanism are shown in columns (2) and (3) of Table 2.

Column (1) shows that the digital economy can significantly reduce the level of carbon emissions. Then, column (2) verifies that there is a significant negative correlation between the digital economy and economic agglomeration. In column (3), the mediating variable is added to the regression equation of the digital economy affecting carbon emissions. By observing the coefficient of dig, it can be seen that the regression coefficient of dig for ce in Model (3) is significant at the 1% level, and the value is increased compared with that of Model (1). This suggests that the increase in economic agglomeration is the mechanism of action of the digital economy to reduce the level of carbon emissions, and this result supports H1.

4.3. SDM

Firstly, before conducting spatial econometric analyses, it was necessary to test whether there are spatial effects of carbon emissions and the digital economy. In this paper, we used Moran’s I index to calculate the spatial autocorrelation of the two under the nested matrix of economic geography, and the results are shown in Table 3.
The results in Table 3 show that the Moran’s I index for both ce and dig are significant at the 1% level under the economic geography weight matrix. This indicates that there is a significant spatial autocorrelation between the digital economy and carbon emissions in the provinces within the sample interval. Therefore, dig and ce show a clustering phenomenon in their spatial distribution.
Moving on, referring to Elhorst [48], this paper sequentially conducted the Hausman test, the LM test and the LR test. The spatial Durbin model (SDM) with spatiotemporal double fixed effects was finally identified as the optimal choice. The results are shown in column (1) of Table 4.
The estimation results in Table 4 show that the elasticity coefficient of dig is −13.7636 and significant at the 1% level. This result indicates that the current development of the digital economy significantly reduces carbon emissions in the provinces. Meanwhile, both the regression coefficients of ag and sag are statistically significant at 11.9170 and −1.0768, respectively. This observation suggests that economic agglomeration exhibits an inverted U-shaped relationship with carbon emissions. Specifically, when economic agglomeration measures less than 5.5335, carbon emissions display a positive correlation with economic agglomeration, indicating that the scale effect of the economy outweighs the economy of the scale effect during this phase. Conversely, when economic agglomeration exceeds 5.5335, a negative correlation emerges between the two variables. At this time, the environmental dividend brought about by economic agglomeration gradually appears, and the scale economy effect is greater than the scale effect.
Further examining the spatial spillover effect of the digital economy among provinces shows that the regression coefficient of dig is negative and significant at the 1% level, which indicates that the development of the digital economy in neighboring provinces can significantly reduce their carbon emissions. However, the regression results of the interaction term between the digital economy and weight matrix do not directly reflect the marginal impact of the digital economy on carbon emissions. Therefore, we adopted the partial differentiation of variable changes for interpretation, i.e., using direct and indirect effects to study the heterogeneous impact of the digital economy on carbon emissions in local and other regions [49].
In column (1) of Table 4, the results demonstrate significant reductions in carbon emission levels within local and other areas due to the influence of the digital economy, as indicated by the direct, indirect, and total effect measures. This finding supports H2. Specifically, the developmental progression of the digital economy, concurrent with the advancing maturity of digital technologies, contributes to reductions in local carbon emission levels through both production and consumption channels, and it can also give full play to the economies of scale of economic agglomeration by enhancing the degree of economic agglomeration, thus reducing carbon emissions. At the same time, this progression can also inhibit carbon emissions in neighboring regions through spillover and demonstration effects.
Furthermore, based on the perspective of spatial correlation, we once again applied the stepwise regression method to verify the mediating role of economic agglomeration on the development of the digital economy affecting carbon emissions. In column (3) of Table 4, the regression coefficient of dig on ag is shown to be significant. Column (2) verifies that the regression coefficient of dig on ce is equally significant. Given that the regression coefficients of dig and ag on ce in column (1) are both significant at the 1% level, it can be said that the digital economy can still affect carbon emissions through economic agglomeration under the influence of spatial spillovers. Once again, H1 is proven.

4.4. Dynamic SDM

Most previous studies have focused on the static perspective to study the factors affecting carbon emissions, while this paper argues that carbon emissions have time-dependent characteristics. Therefore, the dynamic hypothesis that carbon emissions have a snowball effect is proposed. In this paper, we used the dynamic SDM to test H3, and the time lag term was introduced into Model (5). In order to overcome the biased estimation of the least squares method (OLS), the endogeneity problem of the great likelihood estimation (MLE), and the weak instrumental variable problem of the differential GMM, we referred to the Han–Phillips generalized moment estimation to estimate the dynamic SDM. The results are shown in column (4) of Table 4.
In Table 4, the coefficient of the lagged term of carbon emission is significantly positive at the level of 1%. This verifies that carbon emissions have the dynamic snowball effect. Therefore, when the carbon emissions of the previous period are at a high level, the carbon emissions of the next period will continue to be high. Thus, H3 is proven. Specifically, every increase of one unit of carbon emission in the current period will lead to an increase of 1.4998 units of carbon emission in the next period. This means that along with the snowball phenomenon of carbon emissions, China’s current carbon emission reduction work has a serious urgency and arduousness. Analyzing column (1) of Table 4 shows that the impact of the digital economy and economic agglomeration on carbon emissions changes after considering the path-dependent characteristics of carbon emissions. The impact of the digital economy and economic agglomeration on local carbon emission levels remains significant, but their spatial spillover impact on the neighboring regions decreases.

5. Robustness Test

The regressions discussed earlier provide evidence that the digital economy reduces carbon emissions and that economic agglomeration plays a mediating role. To further ensure the credibility of these results, this paper checked the robustness from several perspectives. The results are presented in Table 5.

5.1. Change the Explanatory Variable

This paper previously used the per capita carbon emissions of each province as the explanatory variable. In the current analysis, the total carbon emissions of individual provinces served as the explanatory variable, with the regression outcomes presented in columns (1) and (4) of Table 5. The findings indicate a persistently significant negative regression coefficient for dig. This indicates that the development of the digital economy has a significant reduction effect on both total and per capita carbon emissions, which is consistent with the benchmark regression results.

5.2. Change the Geographic Weighting Matrix

In addition to the economic–geographic nested matrix, this paper applied a geographic distance matrix (W2) for robustness testing. The results of the static and dynamic SDM regressions are shown in columns (2) and (5) of Table 5, respectively. The static SDM regression results in column (2) show that the elasticity coefficients and spatial elasticity coefficients of dig to ce are significantly negative, which is consistent with the previous results. The study suggests that the advancement of the digital economy contributes to a reduction in carbon emissions. The results from the dynamic spatial Durbin model (SDM) regression in column (5) indicate that the digital economy and carbon emissions of dig and L.ce exhibit significant negative spatial spillover effects and positive time cumulative effects, respectively. These findings align with prior research outcomes.

5.3. Change the Sample Interval

The impact of the digital economy on carbon emissions was analyzed using data from 2000 to 2021. However, the outbreak of COVID−19 at the end of 2019 had great impacts on domestic and international living and production activities. Hence, this study modified the sampling interval to encompass the years 2000–2019 to facilitate a more comprehensive evaluation of the influence exerted by the digital economy and economic agglomeration on carbon emissions. The findings are depicted in columns (3) and (6) of Table 5. Notably, no substantial alterations were observed in the parameter estimations and their associated significance, thus affirming the robustness of the results.

5.4. Endogeneity Treatment

The regression results show that the development of the digital economy can significantly reduce carbon emissions. However, due to the need for green development, regions with higher levels of carbon emissions will accelerate the promotion of local digital economy construction levels, so there may be a bidirectional causal relationship between them. This may lead to endogeneity problems. Thus, we chose the instrumental variable method to verify it. The key to solving an endogeneity problem is to choose appropriate instrumental variables, which need to satisfy the two assumptions of correlation and exogeneity. In other words, the instrumental variables should exhibit correlation with the endogenous explanatory variables while remaining uncorrelated with the random disturbance terms. Therefore, the lagged one period of dig was chosen as the instrumental variable for the endogeneity test in this study. The regression results in column (7) in Table 5 show that the impact of the digital economy on reducing carbon emissions is still significant at the 1% level after accounting for endogeneity. In addition, the results of the test for the under-identification of instrumental variables show that the Kleibergen–Paap rk LM statistic is 35.017, which corresponds to a p-value of 0.0000, significantly rejecting H0. The results of the test for weak instrumental variables show that the Kleibergen–Paap rk Wald F statistic of 197.034 is greater than the Stock–Yogo critical value at the 10 percent level. This justifies the selection of the instrumental variables used in this paper.

6. Heterogeneity Analysis

Due to variations in resource allocation and developmental stages, both the advancement of the digital economy and carbon emissions exhibit pronounced heterogeneity in regional distribution. Consequently, the influence of the digital economy on carbon emissions is likely to manifest heterogeneously across regions, warranting an in-depth examination. We conducted a heterogeneity regression analysis for the eastern, central, and western regions. The eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Liaoning, Jilin and Heilongjiang. The central and western region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.

Before conducting empirical analyses of regional heterogeneity, this paper first provides descriptive statistics on the differences between different regions. The results in Table 6 show that for carbon emissions, digital economy development, and economic agglomeration, the eastern region has higher values than the central and western region. The differences in the mean values of the variables between the two types of regions were found to be 0.1838, 0.0451, and 1.2761, respectively.
Table 7 shows the static and dynamic SDM regression results for the eastern region and the central and western region. The findings presented in columns (1) and (2) indicate that the impact of the digital economy on carbon emissions is notably greater in the central and western region compared with the eastern region. This may be due to the fact that the development of the digital economy in the central and western region started later and is at a lower level than that of the eastern region, and it is now in the “novice dividend period”. On the other hand, the level of digital economy development in the eastern region is already high. While the continued advancement of the digital economy in the eastern region is anticipated to lead to a reduction in carbon emissions, the associated marginal utility is notably lower compared with that observed in the central and western region. Moreover, the coefficient of economic agglomeration in the eastern region exhibits a significant negative value, in contrast to the significantly positive coefficients observed in the central and western region. These findings suggest a negative correlation between economic agglomeration and carbon emissions in the eastern region, while a positive correlation was observed in the central and western region. A possible reason for this result is that the eastern provinces of China have a higher level of economic agglomeration and a more reasonable economic structure than the central and western provinces, which makes the economies of scale brought about by economic agglomeration greater than the scale effect and more fully releases the green emission reduction dividend. On the other hand, the development of economic agglomeration in the central and western region started late and to a low degree, which makes it difficult to use the effect of economies of scale. Thus, it is difficult for economic agglomeration to effectively reduce carbon emissions.
The outcomes presented in columns (3) and (4) of Table 7 demonstrate that the coefficients associated with the lagged terms of carbon emissions are all notably positive. This indicates that both the eastern region and the central and western region have significant time-dependent carbon emissions. This means that both the eastern region and the central and western region of China are facing urgent pressure to reduce emissions in the future, highlighting the importance of developing the digital economy and accelerating the task of reducing carbon emissions.

7. Discussion

In examining previous studies concerning the relationship between the digital economy and carbon emissions, this paper corroborates their findings that the digital economy significantly reduces carbon emissions and demonstrates a spatial spillover effect on neighboring provinces’ carbon emission levels [50]. What sets this paper apart from prior research is its expansion beyond the spatial impact analysis of the digital economy on carbon emissions [51]. This paper extends the research perspective to encompass the temporal dimension, analyzing factors influencing carbon emission levels across both spatial and temporal dimensions. Consequently, this paper confirms the existence of a snowball effect on carbon emissions and addresses a gap in this research field. Naturally, this paper exhibits certain limitations. We outline the limitations as follows.

1. This paper solely examines the impact of economic agglomeration as a mediating variable on the relationship between the digital economy and carbon emissions. Given that the digital economy can influence carbon emissions through several mediating variables, future research can delve deeper into analyzing different mediating transmission pathways of the digital economy on carbon emissions.

2. This paper employs the entropy weight method to gauge the level of the development of the digital economy. Consequently, the weight coefficients assigned to variables such as the length of long-distance fiber-optic cables, turnover of the technology market, and total volume of telecommunications business remain constant, indicating fixed weights for each variable. However, given the evolving nature of the context, dynamic weighting methods like the dynamic factor method offer a more nuanced reflection of variable weights across different time periods, rendering them more pertinent. Therefore, future research can explore the utilization of dynamic weighting methods to develop an approach that is both more objective and better aligned with changes in the digital economy’s development.

3. This paper examines the influence of China’s digital economy development on the level of carbon emissions from a provincial perspective, potentially obscuring variations in economic development within each province. Subsequently, further in-depth investigation into the interaction between these factors, taking into account the specific development levels of various prefecture-level cities within each province, can be conducted.

8. Conclusions and Policy Implications

8.1. Conclusions

The digital economy presents significant potential for reducing carbon emissions in China. This paper aimed to investigate the mechanisms through which the digital economy influences carbon emissions. Empirical analyses were conducted using fixed-effect models, intermediary effect models, an SDM, and a dynamic SDM by utilizing provincial panel data spanning from 2000 to 2021. The study sought to substantiate the impact of the digital economy on carbon emissions, elucidate the intermediary role of economic agglomeration, and assess the heterogeneity among different provinces. Ultimately, the following conclusions were derived: (1) The digital economy can effectively reduce carbon emissions. It can either directly reduce carbon emissions or indirectly affect carbon emissions through economic agglomeration, and the emission reduction effect is significant in the eastern, central and western regions. (2) The digital economy has significant spatial spillover effects. While developing the digital economy to reduce carbon emissions, different provinces can also influence the carbon emissions of neighboring provinces through the channels of spillover, demonstration, and competition effects. (3) Carbon emissions have a significant snowball effect, i.e., provinces with higher leveld of carbon emissions in the previous period will have significantly higher leveld of carbon emissions in the next period.

8.2. Policy Implications

In light of the foregoing discoveries, we propose the subsequent policy recommendations:

(1) There are spatial gradient differences in China’s digital economy development and carbon emissions. When setting carbon reduction targets, the government should set “dual-carbon” targets suitable for the level of development of different provinces according to the level of development of their digital economy.

(2) In the process of developing the digital economy, the government should pay close attention to the levels of economic agglomeration of different provinces. In regions characterized by limited economic agglomeration, such as the central and western areas, governmental intervention can be strategically employed to enhance the structure and density of economic agglomeration. This can be achieved by expediting the spillover impact of digital technology, leveraging the demonstrative influence of economic agglomeration observed in the eastern region, and fostering competitive dynamics.

(3) The government should consider the huge pressure of carbon reduction and emission reduction brought by the snowball effect on large carbon-emitting provinces. The aim of controlling the overall national carbon emissions can be realized through the development of the carbon market, the facilitation of carbon emission trading among different provinces, and the formulation of a carbon emission compensation policy. Ultimately, the challenge of climate risk arising from greenhouse gas emissions can be reasonably addressed.

Author Contributions

Conceptualization, Q.A. and L.Z.; methodology, L.Z.; software, L.Z.; validation, Q.A. and L.Z.; formal analysis, Q.A. and L.Z.; resources, Q.A.; writing—original draft preparation, L.Z.; writing—review and editing, Q.A., L.Z. and M.Y.; visualization, L.Z.; supervision, Q.A. and M.Y.; project administration, Q.A.; funding acquisition, Q.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Social Science Planning Project of Shandong Province, grant number 23CJJJ20.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used in this study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ibrahim, R.L.; Mohammed, A. On energy transition-led sustainable environment in COP26 era: Policy implications from tourism, transportation services, and technological innovations for Gulf countries. Environ. Sci. Pollut. Res. 2023, 30, 14663–14679. [Google Scholar] [CrossRef] [PubMed]
  2. Dong, F.; Hu, M.; Gao, Y.; Liu, Y.; Zhu, J.; Pan, Y. How does digital economy affect carbon emissions? Evidence from global 60 countries. Sci. Total Environ. 2022, 852, 158401. [Google Scholar] [CrossRef] [PubMed]
  3. Mngumi, F.; Huang, L.; Xiuli, G.; Ayub, B. Financial efficiency and CO2 emission in BRICS. Dose digital economy development matter? Heliyon 2024, 10, e24321. [Google Scholar] [CrossRef] [PubMed]
  4. Bai, L.; Guo, T.; Xu, W.; Liu, Y.; Kuang, M.; Jiang, L. Effects of digital economy on carbon emission intensity in Chinese cities: A life-cycle theory and the application of non-linear spatial panel smooth transition threshold model. Energy Policy 2023, 183, 113792. [Google Scholar] [CrossRef]
  5. Zhang, J.; Lyu, Y.; Li, Y.; Geng, Y. Digital economy: An innovation driving factor for low-carbon development. Environ. Impact Assess. Rev. 2022, 96, 106821. [Google Scholar] [CrossRef]
  6. Ding, C.; Liu, C.; Zheng, C.; Li, F. Digital economy, technological innovation and high-quality economic development: Based on spatial effect and mediation effect. Sustainability 2021, 14, 216. [Google Scholar] [CrossRef]
  7. Chang, H.; Ding, Q.; Zhao, W.; Hou, N.; Liu, W. The digital economy, industrial structure upgrading, and carbon emission intensity—Empirical evidence from China’s provinces. Energy Strategy Rev. 2023, 50, 101218. [Google Scholar] [CrossRef]
  8. Li, Z.; Xu, X.; Bai, Q.; Chen, C.; Wang, H.; Xia, P. Implications of information sharing on blockchain adoption in reducing carbon emissions: A mean–variance analysis. Transp. Res. Part E Logist. Transp. Rev. 2023, 178, 103254. [Google Scholar] [CrossRef]
  9. Wang, M.; Wang, Y.; Teng, F.; Ji, Y. The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data. J. Environ. Manag. 2023, 346, 119054. [Google Scholar] [CrossRef]
  10. Dedrick, J.; Green, I.S. Concepts and issues for information systems research. Commun. Assoc. Inf. Syst. 2010, 27, 11. [Google Scholar] [CrossRef]
  11. Kamal-Chaoui, L.; Robert, A. Competitive Cities and Climate Change; OECD Regional Development Working Papers 2009, No. 2009/02; OECD Publishing: Paris, France, 2009. [Google Scholar] [CrossRef]
  12. Najarzadeh, R.; Rahimzadeh, F.; Reed, M. Does the Internet increase labor productivity? Evidence from a cross-country dynamic panel. J. Policy Model. 2014, 36, 986–993. [Google Scholar] [CrossRef]
  13. Romm, J. The internet and the new energy economy. Resour. Conserv. Recycl. 2002, 36, 197–210. [Google Scholar] [CrossRef]
  14. Salahuddin, M.; Alam, K. Internet usage, electricity consumption and economic growth in Australia: A time series evidence. Telemat. Inform. 2015, 32, 862–878. [Google Scholar] [CrossRef]
  15. Wang, J.; Dong, K.; Dong, X.; Taghizadeh-Hesary, F. Assessing the digital economy and its carbon-mitigation effects: The case of China. Energy Econ. 2022, 113, 106198. [Google Scholar] [CrossRef]
  16. Liu, C.; Wang, W.; Ding, C.; Teng, X.; Ye, Y.; Zhang, Z. The bilateral effects of digital economy on regional carbon emissions in China. Front. Environ. Sci. 2023, 11, 1287811. [Google Scholar] [CrossRef]
  17. Hao, Y.; Li, Y.; Guo, Y.; Chai, J.; Yang, C.; Wu, H. Digitalization and electricity consumption: Does internet development contribute to the reduction in electricity intensity in China? Energy Policy 2022, 164, 112912. [Google Scholar] [CrossRef]
  18. Lange, S.; Pohl, J.; Santarius, T. Digitalization and energy consumption. Does ICT reduce energy demand? Ecol. Econ. 2020, 176, 106760. [Google Scholar] [CrossRef]
  19. Wang, J.; Dong, K.; Sha, Y.; Yan, C. Envisaging the carbon emissions efficiency of digitalization: The case of the internet economy for China. Technol. Forecast. Soc. Change 2022, 184, 121965. [Google Scholar] [CrossRef]
  20. Wu, H.; Xue, Y.; Hao, Y.; Ren, S. How does internet development affect energy-saving and emission reduction? Evidence from China. Energy Econ. 2021, 103, 105577. [Google Scholar] [CrossRef]
  21. Zhu, J.; Lin, N.; Zhu, H.; Liu, X. Role of sharing economy in energy transition and sustainable economic development in China. J. Innov. Knowl. 2023, 8, 100314. [Google Scholar] [CrossRef]
  22. Fasogbon, S.; Igboabuchukwu, C. Real-time carbon footprint assessment based on energy consumption: A comprehensive review for future research prospects. Renew. Sustain. Energy Rev. 2024, 192, 114225. [Google Scholar] [CrossRef]
  23. Wang, H.; Kang, C. Digital economy and the green transformation of manufacturing industry: Evidence from Chinese cities. Front. Environ. Sci. 2024, 12, 1324117. [Google Scholar] [CrossRef]
  24. Vidas-Bubanja, M. Implementation of green ICT for sustainable economic development. In Proceedings of the 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 26–30 May 2014; pp. 1592–1597. [Google Scholar] [CrossRef]
  25. Yu, Q.; Li, M.; Li, Q.; Wang, Y.; Chen, W. Economic agglomeration and emissions reduction: Does high agglomeration in China’s urban clusters lead to higher carbon intensity? Urban Clim. 2022, 43, 101174. [Google Scholar] [CrossRef]
  26. Hong, J.; Gu, J.; Liang, X.; Liu, G.; Shen, G.Q.; Tang, M. Spatiotemporal investigation of energy network patterns of agglomeration economies in China: Province-level evidence. Energy 2019, 187, 115998. [Google Scholar] [CrossRef]
  27. de Leeuw, F.A.; Moussiopoulos, N.; Sahm, P.; Bartonova, A. Urban air quality in larger conurbations in the European Union. Environ. Model. Softw. 2001, 16, 399–414. [Google Scholar] [CrossRef]
  28. Verhoef, E.T.; Nijkamp, P. Externalities in urban sustainability: Environmental versus localization-type agglomeration externalities in a general spatial equilibrium model of a single-sector monocentric industrial city. Ecol. Econ. 2002, 40, 157–179. [Google Scholar] [CrossRef]
  29. Ciccone, A.; Hall, R.E. Productivity and the Density of Economic Activity; Working Paper Series; National Bureau of Economic Research: Cambridge, MA, USA, 1993. [Google Scholar] [CrossRef]
  30. Glaeser, E.L.; Kahn, M.E. The greenness of cities: Carbon dioxide emissions and urban development. J. Urban Econ. 2010, 67, 404–418. [Google Scholar] [CrossRef]
  31. Shao, S.; Zhang, K.; Dou, J. Effects of Economic Agglomeration on Energy Saving and Emission Reduction: Theory and Empirical Evidence from China. J. Manag. World 2019, 35, 36–60+226. (In Chinese) [Google Scholar] [CrossRef]
  32. Xue, B.; Liu, R.; Dwivedi, R. Digital economy, structural deviation, and regional carbon emissions. J. Clean. Prod. 2024, 434, 139890. [Google Scholar] [CrossRef]
  33. Kichko, S. Competition, land prices and city size. J. Econ. Geogr. 2020, 20, 1313–1329. [Google Scholar] [CrossRef]
  34. Xue, L.; Li, H.; Xu, C.; Zhao, X.; Zheng, Z.; Li, Y.; Liu, W. Impacts of industrial structure adjustment, upgrade and coordination on energy efficiency: Empirical research based on the extended STIRPAT model. Energy Strategy Rev. 2022, 43, 100911. [Google Scholar] [CrossRef]
  35. Lan, F.; Jiao, C.; Deng, G.; Da, H. Urban agglomeration, housing price, and space–time spillover effect—Empirical evidences based on data from hundreds of cities in China. Manag. Decis. Econ. 2021, 42, 898–919. [Google Scholar] [CrossRef]
  36. Zheng, D.; Shi, M.; Pang, R. Agglomeration economies and environmental regulatory competition: Evidence from China. J. Clean. Prod. 2021, 280, 124506. [Google Scholar] [CrossRef]
  37. Liu, X.; Xu, H. Does low-carbon pilot city policy induce low-carbon choices in residents’ living: Holistic and single dual perspective. J. Environ. Manag. 2022, 324, 116353. [Google Scholar] [CrossRef] [PubMed]
  38. Xu, L.; Fan, M.; Yang, L.; Shao, S. Heterogeneous green innovations and carbon emission performance: Evidence at China’s city level. Energy Econ. 2021, 99, 105269. [Google Scholar] [CrossRef]
  39. Sima, V.; Gheorghe, I.G.; Subić, J.; Nancu, D. Influences of the industry 4.0 revolution on the human capital development and consumer behavior: A systematic review. Sustainability 2020, 12, 4035. [Google Scholar] [CrossRef]
  40. Zhuang, G. Policy design logic of low-carbon city pilots in China. China Popul. Resour. Environ. 2020, 30, 19–28. (In Chinese) [Google Scholar]
  41. Wen, Z.L.; Ye, B.J. Analysis of mediation effect: Method and model development. Adv. Psychol. Sci. 2014, 22, 731–745. [Google Scholar] [CrossRef]
  42. Shao, S.; Yang, L.; Yu, M.; Yu, M. Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994–2009. Energy Policy 2011, 39, 6476–6494. [Google Scholar] [CrossRef]
  43. Zhang, H.; Yuan, P.; Zhu, Z. City population size, industrial agglomeration and CO2 emission in Chinese prefectures. China Environ. Sci. 2021, 4, 2459–2470. (In Chinese) [Google Scholar] [CrossRef]
  44. Yu, H.; Wang, J.; Xu, J. Assessing the role of digital economy agglomeration in energy conservation and emission reduction: Evidence from China. Energy 2023, 284, 128667. [Google Scholar] [CrossRef]
  45. Zhang, C.; Liu, C. The impact of ICT industry on CO2 emissions: A regional analysis in China. Renew. Sustain. Energy Rev. 2015, 44, 12–19. [Google Scholar] [CrossRef]
  46. Wang, J.; Dong, X.; Dong, K. How digital industries affect China’s carbon emissions? Analysis of the direct and indirect structural effects. Technol. Soc. 2022, 68, 101911. [Google Scholar] [CrossRef]
  47. Wang, Y.; Wang, J. Does industrial agglomeration facilitate environmental performance: New evidence from urban China? J. Environ. Manag. 2019, 248, 109244. [Google Scholar] [CrossRef] [PubMed]
  48. Elhorst, J.P. Spatial Econometrics: From Cross-Sectional Data to Spatial Panels; Springer: Berlin/Heidelberg, Gerseveral, 2014; Volume 479, p. 480. [Google Scholar]
  49. Zhao, T.; Zhang, Z.; Liang, S.K. Digital Economy, Entrepreneurship, and High-Quality Economic Development: Empirical Evidence from Urban China. J. Manag. World 2020, 36, 65–76. (In Chinese) [Google Scholar] [CrossRef]
  50. Feng, G.; Ziwen, H. Digital economy, land resource misallocation and urban carbon emissions in Chinese resource-based cities. Resour. Policy 2024, 91, 104914. [Google Scholar] [CrossRef]
  51. Jiang, H.; Elahi, E.; Gao, M.; Huang, Y.; Liu, X. Digital economy to encourage sustainable consumption and reduce carbon emissions. J. Clean. Prod. 2024, 443, 140867. [Google Scholar] [CrossRef]

Table 1.
Statistical description of variables.

Table 1.
Statistical description of variables.

Variable N Mean Std Min Max
Dependent Variable ce 660 2.9794 2.8883 0.2857 25.6360
Independent Variable dig 660 0.0753 0.0809 0.0012 0.6186
Mediator Variable ag 660 4.8006 1.3008 1.3474 7.6828
Control Variable py 660 3.8482 3.0473 0.2662 18.398
ig 660 0.4684 0.2994 0.0658 4.9961
sg 660 0.4531 0.2776 0.1008 4.1660
lp 660 9.4494 0.6145 8.1952 10.8453
rd 660 2.9684 0.8564 0.2109 6.1585
rm 660 7.5874 0.7407 3.2581 8.7495
hc 660 6.8544 0.8100 4.9562 8.8030

Table 2.
Benchmark regression and mediating effect test results.

Table 2.
Benchmark regression and mediating effect test results.

Variable ce ag ce
(1) (2) (3)
dig −11.1086 *** −0.2105 ** −10.4128 ***
(−4.7854) (−2.3031) (−4.6981)
ag \ \ 13.9921 ***
(7.8503)
sag \ \ −1.3154 ***
(−6.6533)
py 0.6586 *** 0.0499 *** 0.4567 ***
(3.5633) (6.9218) (2.6485)
spy −0.0364 *** −0.0023 *** −0.0241 ***
(−3.9602) (−6.8703) (−2.9316)
ig 2.4163 ** 0.0915 * 2.7003 **
(2.2936) (1.8306) (2.5162)
sg −2.7732 ** −0.1578 *** −2.1980 **
(−2.5041) (−2.7849) (−1.9860)
lp 3.2146 ** 0.0098 1.2671
(2.4673) (0.1933) (1.0448)
rd 0.0295 −0.0375 *** 0.3245 *
(0.1554) (−4.7546) (1.7120)
rm 0.4546 *** 0.0083 0.2385 *
(3.2785) (0.9955) (1.8079)
hc −4.8212 * −1.7014 *** −7.6008 **
(−1.7176) (−15.3865) (−2.1707)
shc 0.2366 0.0820 *** 0.4572 **
(1.3558) (12.6329) (2.2070)
Constant −9.9639 14.8443 *** −14.7793
(−0.5432) (24.2531) (−0.6736)
IFE YES YES YES
TFE YES YES YES
R2 0.7815 0.9980 0.8037
F 55.34 8187.65 65.76

Table 3.
Moran’s I test results.

Table 3.
Moran’s I test results.

ce dig
Moran’s I 0.090 *** 0.343 ***
(7.477) (27.997)

Table 4.
Static and dynamic SDM results.

Table 4.
Static and dynamic SDM results.

Variable SDM Dynamic SDM
ce ce ag ce
(1) (2) (3) (4)
L.ce \ \ \ 1.4998 ***
(24.97)
dig −13.7636 *** −16.6513 *** −0.3427 *** −3.7661 ***
(−7.7547) (−9.2088) (−4.1404) (−3.30)
ag 11.9170 *** \ \ 7.4042 ***
(7.2632) (5.45)
sag −1.0768 *** \ \ −0.7460 ***
(−5.5107) (−5.18)
W × dig −106.8135 *** −142.4802 *** −2.7125 *** 0.0049 ***
(−5.8491) (−7.7829) (−3.2674) (3.46)
W × ag 41.8668 *** \ \ −0.0107
(3.0521) (−1.51)
W × sag −3.1848 ** \ \ 0.0015
(−2.0496) (1.50)
Control variable YES YES YES YES
Direct Effect −10.8741 *** −13.4744 *** −0.3101 *** \
(−6.2307) (−7.5124) (−3.7839)
Indirect Effect −55.5802 *** −85.0672 *** −2.1323 *** \
(−4.6435) (−5.6736) (−2.9417)
Total Effect −66.4543 *** −98.5416 *** −2.4424 *** \
(−5.2880) (−6.2727) (−3.2136)
LogL 50.9759 50.9759 50.9759 \
R2 0.2313 0.2588 0.1476 0.2799

Table 5.
Robustness test results.

Table 5.
Robustness test results.

Variable SDM Dynamic SDM IV Method
Changing the Dependent Variable Changing the Weight Matrix Changing the Sample Period Changing the Dependent Variable Changing the Weight Matrix Changing the Sample Period (7)
(1) (2) (3) (4) (5) (6)
L.ce \ \ \ 1.5034 *** 1.5068 *** 1.5183 *** \
(16.49) (25.38) (9.12)
dig −1.2691 *** −14.0533 *** −10.8760 *** −0.4520 ** −3.8163 *** −2.575 *** −12.9828 ***
(−5.5391) (−7.7277) (−7.4797) (−2.19) (−3.35) (−2.69) (−4.6132)
ag 2.2049 *** 12.2122 *** 13.6653 *** 1.2595 *** 7.5027 *** 5.9623 *** 13.3144 ***
(10.4523) (7.4250) (11.6922) (5.12) (5.53) (6.26) (8.1316)
sag −0.1818 *** −1.0755 *** −1.4939 *** −0.1109 *** −0.7544 *** −0.6329 *** −1.2763 ***
(−7.1967) (−5.5049) (−10.6148) (−4.25) (−5.24) (−6.16) (−6.8022)
W × dig −5.5826 ** −103.0876 *** −78.6314 *** 0.0006 ** 0.0050 *** 0.0028 ** \
(−2.3485) (−5.5331) (−5.3996) (2.16) (3.50) (2.31)
W × ag −2.7602 39.3231 *** 20.1965 ** 0.00003 −0.0108 −0.0119 ** \
(−1.5107) (2.7741) (2.0648) (0.02) (−1.47) (−2.35)
W × sag 0.1043 −2.9065 * −5.6515 *** 0.0001 0.0015 0.0016 ** \
(0.5023) (−1.8424) (−4.5997) (0.78) (1.50) (2.31)
ρ −1.0550 *** −0.8873 *** −1.2116 *** \ \ \ \
(−5.2206) (−4.1582) (−5.3394)
Direct Effect −1.1062 *** −11.2082 *** −8.1922 *** \ \ \ \
(−5.0863) (−6.3790) (−5.8521)
Indirect Effect −2.2449 * −51.4347 *** −32.4968 *** \ \ \ \
(−1.8639) (−4.4195) (−4.2135)
Total Effect −3.3511 *** −62.6430 *** −40.6889 *** \ \ \ \
(−2.6960) (−5.1158) (−5.0372)
Control variable YES YES YES YES YES YES YES
Fixed Effects YES YES YES YES YES YES YES
R2 0.1234 0.2252 0.2189 0.7372 0.3033 0.3191 0.8128

Table 6.
Statistical description of variables in different regions.

Table 6.
Statistical description of variables in different regions.

Variable Region N Mean Std Min Max
ce Eastern Region 286 5.3763 1.0023 0.0094 7.1497
Central and Western Region 374 5.1925 0.9694 −0.2053 7.6496
dig Eastern Region 286 0.1008 0.1028 0.0015 0.6186
Central and Western Region 374 0.0557 0.0512 0.0012 0.3019
ag Eastern Region 286 5.5237 0.9597 3.4298 7.6828
Central and Western Region 374 4.2476 1.2564 1.3474 5.8578

Table 7.
Heterogeneity test results.

Table 7.
Heterogeneity test results.

Variable SDM Dynamic SDM
Eastern Region Central and Western Region Eastern Region Central and Western Region
(1) (2) (3) (4)
L.ce \ \ 1.3512 *** 1.7597 ***
(15.48) (20.33)
dig −0.6698 ** −19.7209 *** 0.4356 −15.1738 ***
(−2.0675) (−4.1556) (1.19) (−5.50)
ag −4.5967 *** −6.5964 *** 0.7113 5.2898 ***
(−7.3620) (−2.5883) (0.48) (2.66)
sag 0.3735 *** 0.5610 * −0.1476 −0.2993
(5.9945) (1.8350) (−1.18) (−1.12)
W × dig −2.6689 −14.1425 0.0005 0.0564 ***
(−1.5383) (−0.3391) (0.29) (5.21)
W × ag −1.8186 −94.5845 *** 0.0826 *** −0.0166
(−0.5570) (−3.9273) (3.61) (−0.45)
W × sag 0.3704 3.3912 −0.0072 ** 0.0080
(1.3101) (1.1788) (−3.22) (1.49)
Control variable YES YES YES YES
Fixed Effects YES YES YES YES
R2 0.5862 0.1655 0.7629 0.7015

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JDE Peet’s commits to newly validated near-term and net-zero SBTi targets https://inergency.com/jde-peets-commits-to-newly-validated-near-term-and-net-zero-sbti-targets/ Thu, 28 Mar 2024 07:10:28 +0000 https://inergency.com/jde-peets-commits-to-newly-validated-near-term-and-net-zero-sbti-targets/ JDE Peet’s commits to newly validated near-term and net-zero SBTi targetsPRESS RELEASE Amsterdam, 28 March 2024 Commitment to target further reductions in Greenhouse Gas (GHG) emissions and specific Forest, Land, and Agriculture (FLAG) emissions JDE Peet’s (EURONEXT: JDEP) today has committed to newly validated Science Based Targets initiative (SBTi) targets as part of its Common Grounds sustainability programme. In line with the SBTi-validated targets and […]]]> JDE Peet’s commits to newly validated near-term and net-zero SBTi targets


PRESS RELEASE

Amsterdam, 28 March 2024

Commitment to target further reductions in Greenhouse Gas (GHG) emissions and specific Forest, Land, and Agriculture (FLAG) emissions

JDE Peet’s (EURONEXT: JDEP) today has committed to newly validated Science Based Targets initiative (SBTi) targets as part of its Common Grounds sustainability programme. In line with the SBTi-validated targets and new Forest Land and Agriculture Science-Based Target-Setting Guidance (FLAG), JDE Peet’s is committed to the following targets:

  • A 43.3% reduction in absolute scope 1 & 2 GHG emissions by 2030, from a 2020 base year;
  • A 25% reduction in absolute scope 3 GHG emissions by 2030, from a 2020 base year (industrial non-FLAG);
  • A 30.3% reduction in absolute Forest, Land, and Agriculture (FLAG) GHG emissions by 2030, from a 2020 base year; and
  • No deforestation across JDE Peet’s primary deforestation-linked commodities, coffee, pulp & paper, palm oil and cocoa with a target date of 31 December 2025, as described in JDE Peet’s’ Forest Policy.

All these targets are consistent with SBTi’s 1.5°C mitigation pathway to achieve net-zero by 2050. The Forest, Land and Agriculture Science-Based Target-Setting Guidance (FLAG) is the world’s first framework for companies in land-intensive sectors to set science-based targets that include land-based emissions reductions and removals.

These new targets increase JDE Peet’s’ previous SBTi-approved commitments that were in line with a well below 2°C pathway. The new net-zero compliant targets are currently the most ambitious designation available through the SBTi process. Since 2023, JDE Peet’s delivered a 21% reduction in its scope 1 & 2 GHG emissions and a 9% reduction in its scope 3 GHG emissions. As the result of a periodical review of its progress under its Common Grounds sustainability programme, JDE Peet’s is now able to commit to more extensive targets.

A key element of JDE Peet’s’ Common Grounds sustainability programme are the impactful coffee farmer programmes which help build resilience and reduce the environmental footprint of coffee farming. For example, the regenerative agricultural practices programme contributes to an increase in yields and optimised agrochemical usage, thus increasing income, improving livelihoods, and preventing deforestation.

Laurent Sagarra, Vice-President Sustainability at JDE Peet’s commented: “We believe that coffee, globally, can be grown in a net-zero way by 2050 if we work together – across the industry and with coffee farmers. We are pleased to have made considerable progress in our reductions in our scope 1, 2 and 3 GHG emissions in 2023, and are proud to announce new targets in line with SBTi FLAG guidance to continue to drive reductions. Through our inclusive farmer-first approach, we can address environmental challenges while helping to maintain coffee farmers’ livelihoods.”

To learn more about JDE Peet’s’ sustainability journey, including our climate transition plan, please visit the company’s website. Further information can also be found in the Annual Report.

# # #

Enquiries 

Media 
Will Hummel 
+31 6 3917 7280 
Media@JDEPeets.com

Investors & Analysts 
Robin Jansen 
+31 6 1594 4569 
IR@JDEPeets.com

About JDE Peet’s 
JDE Peet’s is the world’s leading pure-play coffee and tea company, serving approximately 4,100 cups of coffee or tea per second. JDE Peet’s unleashes the possibilities of coffee and tea in more than 100 markets, with a portfolio of over 50 brands including L’OR, Peet’s, Jacobs, Senseo, Tassimo, Douwe Egberts, OldTown, Super, Pickwick and Moccona. In 2023, JDE Peet’s generated total sales of EUR 8.2 billion and employed a global workforce of more than 21,000 employees. Read more about our journey towards a coffee and tea for every cup at www.jdepeets.com.

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A Systematic Umbrella Review of the Effects of Teledentistry on Costs and Oral-Health Outcomes https://inergency.com/a-systematic-umbrella-review-of-the-effects-of-teledentistry-on-costs-and-oral-health-outcomes/ Thu, 28 Mar 2024 06:31:05 +0000 https://inergency.com/a-systematic-umbrella-review-of-the-effects-of-teledentistry-on-costs-and-oral-health-outcomes/ A Systematic Umbrella Review of the Effects of Teledentistry on Costs and Oral-Health OutcomesAquilanti et al. (2020) [31] The aim of the systematic review is to assess the feasibility of teledentistry in the provision of oral healthcare to older adults living in residential aged-care facilities. In particular, the review focused on the evaluation of the accuracy and the effectiveness of teledentistry compared to traditional face-to-face dental visits, the […]]]> A Systematic Umbrella Review of the Effects of Teledentistry on Costs and Oral-Health Outcomes


Aquilanti et al. (2020) [31] The aim of the systematic review is to assess the feasibility of teledentistry in the provision of oral healthcare to older adults living in residential aged-care facilities. In particular, the review focused on the evaluation of the accuracy and the effectiveness of teledentistry compared to traditional face-to-face dental visits, the patient acceptability, and the costs related to the implementation of oral-health information technology provision. Both synchronous and real-time teledentistry. Results for different types of teledentistry applications are not mentioned. (4,5) Studies involving elderly people in nursing homes, in communities, or within in-home assistance were included. Young persons were excluded. (6) n = 5;
PubMed, Cochrane Library, Web of Science, Scopus and CINAHL databases.
(7) Until 30 June 2020
(8) 2024–2018 (9) Six studies were included in the review, but only three studies measured the outcome of interest (two measured the effects of teledentistry on indirect costs and one measured cost analyses). Types of studies measuring the effects on costs and indirect costs included a pilot study with a cost analysis; at 6 months, a quality-improvement study and cost analysis and a cost-analysis comparison study; a multicentre, cross-sectional study; and a mixed-methods comparative study.
The included studies were performed in Australia, France and Gerseveral. (10) The quality of the studies included in the review was evaluated by the two independent reviewers using the protocol described by Hailey et al. The overall quality score/the strength of evidence was defined by both the performance and study design.
The review included mostly articles with poor or poor to fair quality, characterized by substantial limitations in the study and only one with fair to good quality. The quality assessment of the studies that included a cost analysis was performed in accordance with the Drummond et al. 10-point checklist. The review included three studies reporting on the economic evaluations of teledentistry. Teledentistry was found to be as cost-effective as traditional face-to-face dental examinations. Ben-Omran et al. (2021) [32] The aim of the scoping review was to systematically explore and describe the literature on various uses of teledentistry in older adults, including its reported effectiveness and limitations. Both synchronous and real-time teledentistry. Types of teledentistry applications researched are tele-consultation, tele-diagnoses and tele-intervention. (4) Older adult population (≥60 years)
(5) Medical and dental settings: academia, private practice, community clinics or hospital (nursing home, dentist practice, pharmacy, community dental clinic, hospital, academic institution, long-term facilities, primary care clinics and private clinics). (6) n = 9
PubMed/MEDLINE (National Library of Medicine), Cochrane Library: Database of Systematic Reviews, Cochrane Library CENTRAL, Embase, Scopus, Web of Science Core Collection, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Health Technology Assessment database, and National Health Service Economic Evaluations Database.
(7) Searches were conducted in January 2020
(8) and limited to articles published from 1991 through to 2020. (9) n = 19, (of which n = 4 measured the effects of teledentistry on costs—only one study included a cost analysis).
Types of studies: non-rct; cross-sectional; rct; and observational with mixed retrospective and prospective designs.
Countries: Japan, United States, Northern Ireland, China, Australia, United Kingdom, Brazil, France, India, Gerseveral, Finland, and Portugal. (10) The instrument used to appraise the primary studies was not mentioned in the article. The only mentioned in the discussion the overall rating of their quality (unclear how this was measured): “A limitation was the quality of the studies included, as several were cross-sectional studies with no clear methodology stated, non-RCTs with small sample sizes, or clinical trials that were dependent on self-reports or subjective opinions of participants or their caregivers.” The authors identified cost reductions as a result of reducing avoidable dental visits to nurses with the guidance of a remote-dentist model. No significant difference was found between intervention and control groups in terms of Geriatric Oral Health Assessment Index scores, measuring the oral-health-related quality of life. Despite positive findings, Ben-Omran and his colleagues concluded that there was insufficient evidence to firmly advocate for the long-term clinical effectiveness of teledentistry. Da Costa et al. (2019) [33] The purpose of this integrative review was to collect information regarding the inclusion of the application of teledentistry tools in the public dental-health services. Types of teledentistry applications researched are tele-diagnosis and tele-screening. (4) a wide range of dental-patient groups, including paediatric, orthodontic and elderly patients, as well as prisoners. (5) Dental public-health services, including dental-health programs or dental-health-related actions taken at a community, state or federal level. (6) Searches were conducted on five electronic databases (PubMed/Medline, Virtual Health Library, CINAHL, Scopus and Web of Science); (7) studies that were published from 2007 to June 2019 were included. (8) Publication date range: 2007–2018 (9) Twenty-four studies were included, of which four measured the outcome of interest: economic evaluation (two in paediatric dentistry, one in older adults and one in oral medicine).
Types of studies included economic evaluations, exploratory descriptive studies, mixed-method comparative studies; cost-minimization analyses; cross-sectional studies.
Country-of-origin of studies: Australia and Brazil. (10) Due to the variety of research methods employed in the included studies, the mixed-methods appraisal tool (MMAT) was used to assess their quality.
Among the 24 studies that met the eligibility criteria, 7 studies can not be assessed using MMAT because they did not have enough information regarding the methods and criteria that were employed; however, the remaining 17 studies were assessed using MMAT. Most of them (14 studies) had good-quality scores, meeting three or more of the four criteria. Furthermore, three studies were considered to have moderate-quality scores, meeting only two of the four criteria. The authors concluded that teledentistry is cost-effective; however, no in-depth economic design is presented. (Da Costa) Flores et al. (2020) [34] The purpose of this systematic review is to summarize information on the use of teledentistry in the telediagnosis of oral lesions. The type of teledentistry application and modulation were not mentioned. (4, 5) Dental-clinic community patients (n = 41) (6) Four databases: PubMed, Embase, LILACS (Latin American and Caribbean Literature in Health Sciences and SUMSearch. The CAPES (bancodetes.capes.gov.br/) and Google Scholar databases were used to identify additional grey literature. (7) articles published until December 2018. (8) Range: 1999–2018; the included study was performed in 2010 (9) Eleven studies were included, of which only one feasibility study performed in New Zealand reported on the outcome of interest; (10) The bias risk and quality analyses of the study were performed independently by two authors using the Quality Assessment of Diagnostic Accuracy Studies questionnaire.
The original study presented good quality, as 12 out of 14 questions were answered with yes. The authors concluded that teledentistry is likely to be a cost-effective alternative compared with the standard practice of face-to-face consultation. However, this contention is not supported for any economic evaluation. Daniel et al. (2013) [35] The purpose of this systematic review is to identify clinical outcomes, healthcare utilization and costs associated with teledentistry. Types of teledentistry applications researched are tele-triage and tele-screening. (4) In the review of Daniel et al., there is no data-extraction table present nor did the text describe the participants’ details, setting and context for each original study, so we are unable to give a precise overview of the participants details of the original studies. Mentioned in the text: preschool urban children and orthodontics. (6) Literature searches were conducted in 15 databases: PubMed/Medline, EMBASE, CINAHL with Full Text, PsychINFO, EBM Reviews (e.g., Cochrane Database of Systematic Reviews, ACP Journal Club, Database of Abstracts of Reviews of Effects, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, Health Technology Assessment and NHS Economic Evaluation Database), Scopus, Education Resource Information Center (ERIC), Google Scholar and Turning Research into Practice (TRIP).
(7) Publication date from the earliest available date for each database to March 2012.
(8) Publication dates ranged from 2009–2019 (9) Nineteen studies were included, of which four of the included original studies measured the outcome of interest. Cost-analysis and comparative effectiveness study. The country of origin of the cost-analysis study is United Kingdom (10) The instrument that was used to appraise the primary studies and rate their quality was not described in the review.
The discussion stated the following:
Common methodological weaknesses in these studies included the lack of blinding of dentists, patients or assessors. While in teledentistry it is not always feasible to design studies with patients and dentists who are not aware of the group assignment, the use of outside assessors reduces the potential for evaluation bias. Many of the studies used convenience samples based on the geographical location of patients or patient preference, clearly introducing the possibility of selection bias. A total of 12 studies (60%) had sample sizes of fewer than 20 subjects, and only 1 of the studies provided power calculations. Small sample sizes can lead authors to conclude that no significant difference exists between groups, i.e., a type-II error, whereas the study may have insufficient power to identify a significant difference. Nevertheless, larger studies often remain challenging to carry out, as several of the teledentistry programs are still in their pilot phases and there is often a limited availability of the patient population concerned. In terms of economic evaluation, one study concluded on the cost-effectiveness of the teledentistry approach. Emami et al. (2022) [36] This systematic review evaluated the literature on patient satisfaction with e-oral healthcare in rural and remote communities. The teledentistry application researched is tele-consultation.
Most studies used teledentistry consultations, either live or store-and-forward. (4) Participant details not reported; (5) in rural and remote settings. (6) Searches were carried out in four databases: Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE and Global Health. (7) date range of database searching: published between 1946 and 2021; (8) publication date range: studies carried out in 1998 and 2019 (9) In total, 16 studies were included in the review, of which 7 studies focused on the outcome of interest.The types of studies comprised non-randomized clinical trials, observational studies, pilot intervention studies and cost analyses.
In total, five studies were from Australia, three from India, two studies were conducted in the USA, two in Spain, one in Canada, one in the UK, one in Italy, and one in Finland. (10) The risk of bias using the ROBINS-I risk-of-bias assessment tool for non-randomized studies.
Thirteen of the selected studies were found to have a moderate risk of bias, and two other studies had critical risk in the overall assessment. One article was found to be ineligible for performing risk for bias assessment using the ROBINS-I tool. The majority of studies (11 out of 16) were considered level 4 and 3b. Only a few studies reported the cost per unit of outcomes gained; rather, the level of satisfaction was related to reduced waiting time, the number of visits, travel, and the cost of care for patients. The review also commented on the heterogeneity and inconsistency of methodologies of the studies reviewed in terms of study design, perspective, sampling, setting, etc. Estai et al. (2018) [37] This systematic review of the benefits of teledentistry aims to inform decisionmakers who are doubtful about the capability and merit of integrating teledentistry into routine health services by presenting an objective overview of good-quality evidence for the effectiveness and economic impact of teledentistry. Studies were clustered into two major applications, telediagnosis and teleconsultation. (4) The majority of the reviewed studies were solely focused on the specialty of oral medicine, paediatric dentistry and orthodontics.
(5) The majority of the reviewed articles did not explicitly report the setting of the study (rural or urban); however, it appears that studies were carried out in either urban or rural settings such as hospitals, clinics, childcare centres or workplaces. (6) n = 3; PubMed, EMBASE and CINAHL databases
(7) Through November 2016
(8) 2001–2016 (9) n = 6; This review included three studies that performed economic evaluations. Of these, two studies were deemed to be of fair to good quality. The review included nine articles considering various clinical outcomes, of which three studies specifically addressing on the clinical outcome of interest, i.e., DFS scores, periodontal indices and oral hygiene scores. The studies included in the review were conducted in seven different countries, with the majority of studies from Europe (n = 5) and the USA (n = 3), with one each from Japan, India and Australia (10) The quality of each study, other than those aspects related to economic analysis, was evaluated independently by two authors using the protocol established by Hailey et al., taking into account the study performance and study design. Despite the diverse objectives, methodologies and outcome measures employed across the included studies, teledentistry interventions were comparable to, or had advantages over, non-telemedicine approaches. However, Estai and his colleagues’ overall conclusion was that there is not yet enough conclusive evidence for the effectiveness and long-term use of teledentistry. Irving et al. (2017) [38] This qualitative systematic review aims to explore the quantitative and qualitative framework associated with the effectiveness of teledentistry in an effort to uncover the interaction of multiple influences on its delivery and sustainability. The teledentistry application researched is tele-consultation. (4) General dental patients/orthodontics, oral-surgery patients, hospital-referral patients and adults with tetraplegia. (5) Dental practice in both general and specialist dental settings (6) Literature searches were conducted in nine databases: MEDLINE, Embase, CINAHL, PsychINFO, AMED, EBM Reviews, ERIC, Global Health and PREMEDLINE databases. We also searched the grey literature. (7) Database searches were conducted on 5 January 2015. (8) Publication date range: 2001–2013 (9) In total, 19 studies were included, but only 4 studies measured the outcome of interest. Study type: practitioner cohort, patient cohort and controlled trial. The country of origin of studies included in each review: UK (n = 2), Spain (n = 2) and USA. (10) A modified Downs and Black criterion scale, which examines validity, bias, power and other study attributes, was used to assess the methodological quality of the included papers. They modified the original Downs and Black scale, as described and recommended in prior methodological systematic reviews, to exclude items that were not applicable to the designs of eligible studies. For example, items specific to randomized trials were removed for observational studies. A percentage quality score was calculated by dividing the total score received by the maximum score possible for each study.
The majority of included studies were only rated as being of fair quality.
The majority of the studies were reported on by the developers of the programs, creating a possible opportunity for a bias in the reporting of the results included from the studies. The review concluded that teledentistry is a cost-saving alternative to conventional practice. However, the reduction of costs and cost-effectiveness is assumed, as no actual reviews of economic evaluation in teledentistry were provided. Joshi et al. (2021) [39] The aim of the scoping review was to identify the challenges, scope and assessment approaches of teledentistry from an Indian perspective. Both synchronous and real-time teledentistry. Types of teledentistry applications researched are tele-consultation, tele-diagnoses and tele-screening. (4, 5) Not described (6) n = 3; Google Scholar, PubMed/Medline and Scopus; (7) searched from April to August 2020; (8) publication dates ranged (9) Twenty studies were included in the scoping review. Only five studies reported on the outcome of interest. Types of studies were not reported; however, the review did report on the type of analyses. Analyses that have been carried out included cost-minimization analyses, cost-effectiveness analyses, model-based and cost-effectiveness analyses, and teledental asynchronous patient assessments and remote real-time oral examination. The review did not report on the countries of origin of studies. (10) It did not assess the rigor or quality of studies.
Note: outcomes were not described in detail, E.g., it was stated “Teledentistry is a cost-saving”, but no details on the design or outcome were reported. The authors concluded that the use of teledentistry is potentially cost-effective and cost-saving compared to traditional dentistry. However, none of the studies conducted in India provide any support for that assumption. Uhrin et al. (2023) [40] The aim of the systematic review was to collect available data on how oral medicine can benefit from teledentistry solutions, and to investigate whether teledentistry can provide a reliable diagnostic method compared with clinical oral examination (COE) in the diagnosis of oral potentially malignant disorders. Virtual examination. (4, 5) The review included adults with suspected oral lesions. One of the included articles that measured the outcome of interest included patients of a special care clinic, with intellectual disability, cerebral palsy, Down’s syndrome, autism, seizures, HIV, liver disease, neurologic disorders, stroke or schizophrenia; the other article included <18-year-old patients referred to the clinic with oral lesions. The mean age of the population was 47 (n = 29) and 50 (n = 33). (6) Three databases (Medline, EMBASE and CENTRAL); (7) date of searching: until November 2021. (8) Publication dates ranged (9) Thirteen studies were included; however, only two studies investigated the outcome of interest: time effectiveness. These studies included an observational study and a cross-sectional study. The review included a meta-analysis for the primary outcome, but not for the secondary outcome. These were only described narratively. One study was conducted in the US and the other one in Brazil. (10) Risk of bias was assessed using the QUADAS-2 tool. Certainty of evidence was evaluated based on the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) workgroup’s recommendations.
Four articles were excluded due to a lack of data. The QUADAS-2 tool showed that most of the domains had a low risk of bias. The authors performed a meta-analysis on the primary outcomes; however, no statistical analysis can be performed on the secondary outcome’s time-effectiveness.
One of the original studies measured the difference in time during in-person examinations (mean: 4.2 min, SD: 1.6) and virtual examinations (2.83 min, SD: 1.0).
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Drivers of Student Social Entrepreneurial Intention Amid the Economic Crisis in Lebanon: A Mediation Model https://inergency.com/drivers-of-student-social-entrepreneurial-intention-amid-the-economic-crisis-in-lebanon-a-mediation-model/ Thu, 28 Mar 2024 06:05:34 +0000 https://inergency.com/drivers-of-student-social-entrepreneurial-intention-amid-the-economic-crisis-in-lebanon-a-mediation-model/ Drivers of Student Social Entrepreneurial Intention Amid the Economic Crisis in Lebanon: A Mediation Model2.1. Entrepreneurial Education and Entrepreneurial Self-Efficacy Entrepreneurial education (EE) is a type of education that focuses on equipping individuals with the essential skills, understanding, and ethical principles required to become successful entrepreneurs [15]. There are three types of EE: education about entrepreneurship, which emphasizes the theoretical aspects of establishing and managing a business [16]; education […]]]> Drivers of Student Social Entrepreneurial Intention Amid the Economic Crisis in Lebanon: A Mediation Model


2.1. Entrepreneurial Education and Entrepreneurial Self-Efficacy

Entrepreneurial education (EE) is a type of education that focuses on equipping individuals with the essential skills, understanding, and ethical principles required to become successful entrepreneurs [15]. There are three types of EE: education about entrepreneurship, which emphasizes the theoretical aspects of establishing and managing a business [16]; education for entrepreneurship, aligning with the experiential teaching approach and prioritizing the pragmatic facets of initiating and overseeing a business [17]; and education in entrepreneurship, which pertains to specialized training for entrepreneurs with expertise in various areas aimed at fostering business expansion [16]. After completing entrepreneurship courses, students can gain insight into fundamental teaching methods by engaging in practical experiences such as participating in business simulations, visiting companies, or conducting interviews with successful entrepreneurs [18]. Therefore, the use of contextual and experiential learning approaches, as opposed to theoretical approaches, is the most effective method in improving students’ entrepreneurial skills [19,20].
Due to the rising social issues across the globe, there has been a growing need for programs that promote social entrepreneurship, as noted by various sources [21,22,23]. Jemari et al. [24] advocate that universities need to implement additional measures to enhance the innate abilities, knowledge, and comprehension of social entrepreneurship among students, as existing efforts are insufficient.
To distinguish the education provided to conventional entrepreneurs from that provided to social entrepreneurs, Tracey and Phillips [25] assert that social entrepreneurs must obtain the same competencies and knowledge as conventional entrepreneurs regarding opportunity identification, resource allocation, and organizational development. Nevertheless, they contend that social entrepreneurship education should go beyond this and also familiarize students with the unique obstacles related to ensuring accountability, managing the dual goals of social and financial impact, and maintaining a distinct social entrepreneurial identity encountered by entrepreneurs who seek to achieve a social impact.
Lucas and Cooper [26] suggest that in order to enhance individuals’ motivation for entrepreneurship, it is crucial for entrepreneurial education to impact their sense of self-efficacy, thereby encouraging them to acquire knowledge and persevere in their pursuit of entrepreneurial goals. Moreover, numerous research studies suggest that teaching entrepreneurship has a favorable impact on an individual’s belief in their own ability to succeed as an entrepreneur [27,28,29] which consequently leads to an improvement in their personal intentions to pursue entrepreneurship [27]. Simultaneously, several research studies demonstrate that an individual’s belief in their own ability to succeed as an entrepreneur, also known as entrepreneurial self-efficacy (ESE), serves as a beneficial mediator between EE and their personal intentions to pursue entrepreneurship [29,30,31].
Bandura and Walters’ social cognitive theory [32] states that self-efficacy arises from four types of factors: (1) individual accomplishments and successes; (2) learning from the experiences of others; (3) encouragement and feedback from others; and (4) physical and emotional states. Entrepreneurship education can contribute to the development of these sources of self-efficacy, as suggested by Zhao, Seibert, and Hills [33] and Nowinski et al. [16]. In other words, entrepreneurship education can help individuals acquire the knowledge, skills, and experiences needed to achieve success, learn from others, receive support and encouragement, and regulate their emotions and physiology in the face of challenges. More precisely, by taking part in entrepreneurship programs, prospective entrepreneurs can engage in practical projects that enhance their self-efficacy by providing them with concrete results and achievements, as well as learning from their own mistakes [34,35]. With regard to the experience of others, entrepreneurship learners can enhance their belief in their ability to start a business by discussing the successful stories of entrepreneurs who have a strong presence and reputation in the market, as it provides them with a positive example to follow [36]. Likewise, Laviolette, Lefebvre, and Brunel [37] emphasize that the educational programs and collaborative engagements in universities can instill in students the belief that pursuing entrepreneurial activities is feasible and achievable within their capabilities. Lastly, educators who teach entrepreneurship can also offer guidance to their learners, helping them to manage their feelings and cultivate positive emotional states [38]. In addition, the same authors argue that entrepreneurship education enhances learners’ understanding, increases their confidence, and reinforces their self-efficacy. Consequently, this leads to a greater belief in their ability to pursue entrepreneurial activities and solidifies their intention to do so. In this regard, Zhao et al. [33] suggest that the relationship between EE and entrepreneurial intentions (EI) is underpinned by the construct of self-efficacy. Along the same lines, Gist and Mitchell [39] state that self-efficacy can be cultivated through the training and observation of role models, and this is what differentiates it from innate personality characteristics. Consequently, the subsequent hypothesis is formulated:
H1: 

There is a positive correlation between EE and ESE among students in Lebanon.

2.2. Entrepreneurial Passion and Entrepreneurial Self-Efficacy

Entrepreneurial passion (EP) is considered a driving force for entrepreneurs, which fuels their motivation to face challenges, overcome obstacles, launch, and grow new business ventures [40,41]. Cardon et al. [42] define it as a crucial personal trait that inspires individuals to start a business venture. Consistently, the same authors view EP as a complex concept with three distinct facets linked to different aspects related to the entrepreneurial journey, including the inventor, founder, and developer. The first type is enthusiastic about identifying novel opportunities and exploring innovative ideas. The second type is highly motivated to establish a venture and capitalize on these opportunities, and the third type is enthusiastic about engaging in activities that focus on growing the business once established; these passions and identities impact an individual’s goal-oriented thoughts and actions, leading to specific results in entrepreneurship. Furthermore, there are two types of passion, the harmonious and the obsessive passion, which are differentiated by Vallerand et al. [43], depending on whether an individual can actively regulate their urge to participate in an activity or not.
Research indicates that passion can enhance both confidence and proficiency in individual pursuits and aspirations [44]. Individuals who have a strong passion for entrepreneurship may also possess a higher belief in their own ability to succeed in that field [45]. This belief in one’s own abilities is known as self-efficacy and is defined by Stroe et al. [46] as an individual’s capacity to initiate a new business based on their personal competencies and abilities. It is considered an essential principle in social cognitive theory (SCT), which motivates individuals to fulfill their obligations and attain their aspirations. Additionally, Drnovšek et al. [47] suggest that self-efficacy is a highly context-specific attribute and that tailoring it to a particular activity context can enhance an individual’s ability to forecast outcomes with greater accuracy.
According to Dalborg and Wincent [48], despite the benefits of entrepreneurship, establishing a business comes with a multitude of challenges. While some individuals may view these challenges as obstacles, others may be inspired to overcome them by generating creative and unconventional ideas. Therefore, entrepreneurs must have faith in their abilities and rely on their expertise to attain their full potential [49]. Similarly, Bagheri and Yazdanpanah [50] emphasize that new ideas often compel individuals to reassess their capacity for innovative thinking when it comes to launching a new business fueled by entrepreneurial passion. As a result, possessing a strong passion is a crucial factor in initiating a new business venture. This is because individuals who have entrepreneurial passion tend to view themselves as capable of successfully becoming entrepreneurs. Moreover, Baum and Locke [51] and Cardon et al. [42] find that entrepreneurial passion stimulates an individual’s inclination to participate in entrepreneurial activities. Consequently, individuals who possess a strong passion towards initiating a new venture are motivated to learn and increase their knowledge associated with entrepreneurship, thereby enhancing their capacity to perform entrepreneurial tasks and boosting their self-efficacy beliefs (ESE). As a result, the subsequent hypothesis is established:
H2: 

There is a positive correlation between EP and ESE among students in Lebanon.

2.3. Entrepreneurial Self-Efficacy and Social Entrepreneurial Intentions

Krueger et al. [34] state that self-efficacy plays a crucial role in determining one’s intention to become an entrepreneur. People who possess high levels of self-efficacy demonstrate greater natural interest in entrepreneurial activities and are more inclined to exert themselves and preserve themselves in the face of challenges and obstacles [52]. When an individual is confident in their ability to accomplish a challenging task, they are more likely to take action towards it, as they perceive the attainment of success as an achievable outcome, based on their belief in their own capabilities. Indeed, self-efficacy is closely linked to perceptions of feasibility, as individuals’ beliefs in their ability to successfully execute a task influence their perception of its attainability. Moreover, self-efficacy serves as a mediator for entrepreneurial intentions [33], which are shaped by individuals’ perceptions of both the feasibility and desirability of their goals.
In essence, according to Chen et al. [53], individuals who possess high levels of entrepreneurial self-efficacy are likely to perceive the environment as offering more opportunities than risks, have faith in their capacity to influence the attainment of their goals, and perceive the chances of failure as being minimal. This means that they are more confident in their ability to succeed and have a positive outlook towards entrepreneurship.
Various research has demonstrated that self-efficacy is a crucial cognitive factor in the field of social entrepreneurship and an essential element in the development of intentions towards social entrepreneurship [54,55,56,57]. According to Wang et al. [58], the concept of entrepreneurial self-efficacy plays a crucial role in explaining an individual’s active involvement in the decision-making process to establish and manage their own business, rather than adopting a passive role. On top of that, the degree of belief or confidence an individual has in their ability to create a successful enterprise has been recognized as a crucial factor in predicting an individual’s intention to become an entrepreneur [59,60,61].
Social ESE, which pertains to an individual’s assurance and conviction in their ability to make a meaningful contribution towards addressing social issues [12], significantly impacts people’s decision-making and plays a crucial role in shaping their intention to engage in entrepreneurial endeavors, especially in situations involving complex social problems, as emphasized by Wu et al. [62]. This sense of self-assurance is regarded as a predictor of their social entrepreneurship intention (SEI), as highlighted by Hockerts [12]. Additionally, when individuals who aspire to be social entrepreneurs are not assured about their potential in establishing new social ventures, their belief in the social value of their actions may not be enough to motivate them to pursue social entrepreneurship. However, if aspiring entrepreneurs have a high degree of self-efficacy, they are more likely to feel confident in their capacity to undertake social entrepreneurship activities and contribute to the welfare of others. In line with this, the subsequent hypothesis is developed:
H3: 

There is a positive correlation between ESE and SEI among students in Lebanon.

2.4. Moral Obligation and Social Entrepreneurial Intentions

Social entrepreneurship is based on the idea that creating societal benefits is the primary objective of entrepreneurial activities, while financial gains are viewed as a necessary but secondary goal [63]. Moreover, the concept of social entrepreneurial intentions was first introduced by Mair and Noboa [64], which draws from Ajzen’s theory of planned behavior [65]. It can be viewed as a mindset that motivates individuals to gain expertise, generate innovative concepts, and execute social entrepreneurial approaches and tactics for managing a social enterprise [66,67]. It consists primarily of two components: the perceived desirability, which includes affective empathy and cognitive moral obligation; and perceived feasibility, which includes social backing and self-efficacy [34,64] Despite its significance, the moral aspect of social entrepreneurial intention has not been fully investigated in the study of intentions that lead to social entrepreneurial behavior [54].
In this regard, Hockerts [12] argues that the moral dimension is a critical component that enhances participation in social entrepreneurial activities. Moreover, in the study conducted by Ko and Kim [68], they demonstrate that individuals who feel a sense of moral obligation towards their community are more inclined to initiate social entrepreneurship. Rest [69] argues that having a sense of moral awareness is crucial for individuals when engaging in moral reasoning and ethical thinking, while Mair and Noboa [64] assert that moral judgment is what sets social entrepreneurs apart from commercial entrepreneurs. When entrepreneurs attempt to secure resources and navigate high pressure and an uncertain environment, there are often competing values and conflicting priorities that play a significant role in their decision-making [70,71]. Therefore, they frequently face ethical dilemmas where they have to make decisions about whether to prioritize their own interests over the well-being of others and whether to follow or violate established behavioral standards, especially in the dynamic environment in which they operate [72,73] Moral obligation, defined as the belief individuals have in their moral duty to address the issues faced by socially marginalized communities according to societal moral standards and values [74], is a precursor to forming intentions for entrepreneurship aimed at social impact, as proposed by Dion [75]. Moreover, ethical considerations and a sense of responsibility towards society are crucial for the success of entrepreneurial ventures with a positive impact [76]. However, Mair and Marti [77] suggest that personal fulfillment and motives beyond ethics may also motivate entrepreneurship. This broader perspective might explain why Hockerts’ study [12] did not find a significant relationship between moral obligation and intentions for social entrepreneurship, as moral obligation may not be the sole driving force. According to Koe Hwee Nga and Shamuganathan [78], social entrepreneurs are motivated by a deep sense of responsibility to address fundamental human needs and typically exhibit keen moral awareness. In accordance with this, the subsequent hypothesis is devised:
H4: 

There is a positive correlation between MO and SEI among students in Lebanon.

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Fostering Transversal Skills through Open Schooling with the CARE-KNOW-DO Framework for Sustainable Education https://inergency.com/fostering-transversal-skills-through-open-schooling-with-the-care-know-do-framework-for-sustainable-education/ Thu, 28 Mar 2024 05:21:40 +0000 https://inergency.com/fostering-transversal-skills-through-open-schooling-with-the-care-know-do-framework-for-sustainable-education/ Fostering Transversal Skills through Open Schooling with the CARE-KNOW-DO Framework for Sustainable Education4.1. RQ1—Students’ Views about Their Learning Experience with Open Schooling This study’ 12,074 contains complete responses from a total of 16,787 students, spanning diverse countries, ages, and genders, lends solid credibility to our analysis (Table 4). However, we acknowledge that excluding the 4713 participants who did not complete all questions is a limitation potentially affecting […]]]> Fostering Transversal Skills through Open Schooling with the CARE-KNOW-DO Framework for Sustainable Education


4.1. RQ1—Students’ Views about Their Learning Experience with Open Schooling

This study’ 12,074 contains complete responses from a total of 16,787 students, spanning diverse countries, ages, and genders, lends solid credibility to our analysis (Table 4).

However, we acknowledge that excluding the 4713 participants who did not complete all questions is a limitation potentially affecting the breadth of our exploratory factor analysis (EFA).

Data from the questionnaire, rated on a 1–5 Likert scale, were analyzed with SPSS version 24. The instrument’s reliability was confirmed by a Cronbach’s alpha of 0.929, indicating strong internal consistency [36]. The KMO measure was 0.957, and the significant Bartlett’s test of sphericity (chi-square = 136,957.314, df. = 435, and Sig. = 0.000) strongly indicates that a factor analysis is appropriate for the dataset, as there is enough evidence of underlying patterns or factors within the variables that can be extracted and analyzed [37].
The results of the EFA with Varimax rotation identified six comprehensive skill components, each comprising a group of specific skill items that collectively form the six transversal skills in open-schooling education (Table 4). These components are (c1) problem-solving, (c2) self-initiative, (c3) affective engagement, (c4) scientific citizenship, (c5) authentic learning, and (c6) future prospects.
These components comprehensively cover the spectrum of skills and attitudes critical for engaging with and understanding open-schooling learning experiences related to science for a sustainable life and sustainable future, spanning from personal efficacy and emotional investment to societal implications and future orientations. Each component (C1 to C6) had strong loadings on its respective factors, typically above 0.5, which indicates a good association with the factor; communalities for each item were reasonably high, above 0.3; and scree plots were used to validate the number of factors extracted. The 56% total variance explained by the EFA indicates a moderate and meaningful representation of the transversal skills model through six components, serving as a solid foundation for deeper analysis and practical implementation in this study [38].
  • C1. Problem-solving: This component highlights individuals’ confidence and ability to utilize scientific knowledge and mathematical skills to solve problems, support arguments with evidence, and participate actively in scientific discussions.

  • C2. Self-initiative: This component focuses on students’ proactive behavior in seeking science knowledge and engaging in science-related activities beyond formal education and showcases their autonomy and initiative in their learning journey.

  • C3. Affective engagement: This component addresses the emotional connection students have with science, including their intrinsic motivation, their enjoyment, fun, interest, and the value they place on science for personal and societal benefit, driving their sustained interest and participation in science.

  • C4. Scientific citizenship: This component emphasizes the importance of understanding science’s role in society and everyday life, promoting informed citizenship and recognizing scientific literacy as essential for making responsible decisions.

  • C5. Authentic learning: This component concentrates on the social aspects of science learning, including the significance of teacher-student interactions and collaborative learning environments, highlighting communication and cooperation in science education.

  • C6. Future prospects: This component combines a forward-looking view of science’s relevance to future careers with an engaged learning approach that values family influence and interaction with science professionals, fostering a comprehensive and open attitude towards science education and its opportunities.

To compute an EFA using the SPSS component’s composite score from Likert data for each respondent, each item score was multiplied by its loading, summed for these products, and divided by the total of the loadings. The weighted average, Score C1 = (item1 × loading1 + … + itemN × loadingN)/(loading1 + … + loadingN), reflects each item’s relative importance based on its loading. A threshold was used where scores over 3 indicated a positive connection (3.5 to 5), and then the percentage of each component per country, gender, and age was calculated.

To determine students’ learning gains in terms of transversal skills, a global weighted composite score was calculated. This was performed by taking the sum of each component’s average score multiplied by its proportion variance (for components C1 through C5) and then dividing by the sum of the proportion variances. A score above the threshold of 3 indicates that students have an overall positive perception of their transversal skills.

  • Results about Students’ Perceptions Related to Transversal Skills across Countries, Gender, and Age

Among the five countries, we calculated the number of students whose scores were more than 3 to represent transversal-skills confidence.

The highest levels of perceived transversal skills were observed in 83% of the Greek students, followed by 80% of the Brazilian students. In Romania and the UK, the percentage was 64%, with that in Spain being slightly lower, at 62%. These findings suggest that the CARE-KNOW-DO framework is effective in supporting underserved students’ positive perceptions of transversal skills based on self-assessment questionnaires of their learning through open-schooling activities.

Age-related differences revealed variation in perceptions of transversal skills, with the highest percentages observed in the 10–12 age group at 80% and in the 13–14 age group at 79%. The figure decreased to 55% among 15-to-16-year-olds before experiencing a slight increase to 57% in the 17-to-19-year-old group.

Gender differences showed that 82% of female students had slightly greater perceptions of transversal skills than did 79% of male students. This percentage decreased to 67% among students who identified with a gender other than male or female. As illustrated in Figure 5, the disparity is evident across all skills, with a difference of approximately 45%, except for authentic learning, which stands at 66%.
Figure 5 presents a detailed description of transversal skill component variations for comparative analysis in terms of geographical, gender, and age differences.

Each bar chart uses a variety of colors to represent six transversal skills: problem-solving, self-initiative, engagement, scientific citizenship, authentic learning, and future prospects.

Countries: Among the countries listed (Spain, Greece, the UK, Brazil, and Romania), Figure 5 shows notable variations. Brazil shows the highest percentages for scientific citizenship and authentic learning, while Greece leads in self-initiative. Brazil and Greece lead in scientific citizenship and authentic learning; Greece also leads in self-initiative. Meanwhile, the UK shows the least self-initiative. Although Spain has lower rates for problem-solving and self-initiative, it has higher rates for scientific citizenship. Each country exhibits distinct profiles in these skills.
Age: Figure 6 breaks down the perceptions by age group (10–12, 13–14, 15–16, and 17–18). The variance is very small across ages for almost all skills. All age groups show the lowest percentage for self-initiative learning but have relatively high percentages for authentic learning and scientific citizenship. Younger students (aged 10–12) had the highest percentage of confidence in problem-solving. There is a trend toward a small increase in the perception of skills related to scientific citizenship and self-initiative from low secondary school to upper secondary school across ages. However, perceptions related to problem-solving and affective engagement seem to decrease slightly from primary to middle secondary school years (ages 10–12 vs. 15–16).
Gender: Figure 7 divides perceptions by gender (female, male, and other). Female students show higher percentages of scientific citizenship and authentic learning, while male students show higher percentages of problem-solving. The percentage of students who identified as “other” was lower in all categories than in females and males.

Overall, there are distinct differences in the perceptions of transversal skills when dissected by country, age, and gender, indicating that these factors may influence how students relate to and develop these skills within the context of open schooling.

In all examined countries, the percentage of students demonstrating self-initiative in science was relatively low, with 8% in the UK, 15% in Spain, 21% in Romania and Brazil, and 29% in Greece. This suggests that a limited number of students are proactive in their science learning and skill development outside of school. Conversely, a high percentage of students across these countries are developing scientific citizenship skills, with Brazil leading at 85%, followed by Greece at 83%, Spain at 72%, the UK at 69%, and Romania at 62%. These figures indicate that open schooling may have a significant positive impact on fostering scientific citizenship. This trend is closely mirrored in the areas of authentic learning and future prospects. Apart from self-initiative skills, problem-solving emerges as another challenging skill, with engagement levels ranging from Spain’s lowest, at approximately 40%, to Greece’s highest, at 60%. Problem-solving is one of the key transversal skills, with half of the students feeling confident—60% in Greece, 56% in the UK, and 54% in Romania, followed by 44% in Brazil and 39% in Spain.

4.2. RQ2. Teachers’ Views on Challenges and Drivers of Open-Schooling Practices

To address this question, we employed a thematic analysis [39] to examine 20 teachers’ self-reported practices, including students’ learning achievements, difficulties, and pedagogical outcomes, in terms of the benefits and challenges associated with the open-schooling approach. (Table 5).

This sample included four teachers from different educational levels and disciplines in each of the five countries. To present the analysis, we selected representative distinctive snapshots according to each of the six specific transversal skills, also considering findings provided by students in terms of achievements and difficulties. To code the database, a qualitative codebook was developed that enables the identification of key teaching competencies with open schooling to foster transversal skills.

C1.Problem-solving: To address challenges in problem-solving within STEM, educators have identified critical obstacles, such as students’ difficulties with complex tasks and insufficient skills for decision-making. Research underscores that these deficits can undermine confidence and hinder performance. Recognizing the skills gap is crucial. This study revealed that heightened awareness for both educators and students is important for implementing targeted support and interventions to bolster students’ problem-solving abilities, such as teamwork, with suitable roles and meaningful discussions using personalized resources, thereby enhancing their STEM educational outcomes. This approach emphasizes the importance of tailored support to bridge this gap and improve problem-solving proficiency in STEM fields (Table 6).
C2.Self-Initiative: Teachers reported that challenges such as unfamiliar topics can reduce student initiative, but targeted support and experiential learning can enhance their motivation and curiosity to increase their participation. Additionally, a lack of understanding about open-schooling activities is a concern that can be mitigated by employing peer learning and mentorship to inspire students, boost confidence, and cultivate a supportive community that encourages proactive engagement. Encouraging students to take initiative in their STEM education with practical applications of STEM concepts can empower them to become future leaders in STEM fields and bridge the gap between classroom learning and real-world challenges (Table 7).
C3. Affective Engagement: The challenges and strategies highlighted by teachers indicated the importance of fostering affective engagement in STEM education through meaningful, collaborative projects that empower students to make a difference in their communities. By providing opportunities for students to explore, create, and contribute positively to society, educators can inspire a lasting passion for STEM subjects and cultivate a sense of purpose and agency among learners. Collaboration, discussion, and ongoing engagement are important for preventing disengagement or burnout (Table 8).
C4.Scientific Citizenship: Teachers expressed a need for access to science experts. Collaborating with science experts can enrich classroom experiences and provide students with real-world perspectives for appreciating science with and for society. Another issue is that students vary in their ability to present claims supported by evidence. By addressing challenges related to differentiation, support, access to expertise, and family involvement, educators can promote scientific citizenship among students by nurturing their critical thinking skills, scientific literacy, and ability to apply scientific knowledge to real-world issues. These strategies help cultivate a sense of responsibility and active participation in scientific inquiry for sustainable development among learners (Table 9).
C5. Authentic Learning: Teachers explained that authentic learning with open schooling is challenging. Due to scheduling constraints, students had limited time to work with extra activities. The pressures of the curriculum, difficulty managing time, and weak relationships between the curriculum and real-life issues also posed challenges for teachers. Continuous efforts by teachers are vital for innovating their practices despite curriculum limitations. By addressing challenges related to limited time and curriculum limitations, educators can create engaging and effective learning experiences that support students’ academic development and prepare them to shape a better future (Table 10).
C6.Future Prospects: A barrier highlighted by teachers was that open-schooling activities can be perceived as irrelevant by others not directly involved in the curriculum. Another issue is extra activities beyond students’ already demanding school schedules. Establishing partnerships with university students and professors in vulnerable communities poses another challenge. However, connecting extracurricular activities to the formal curriculum and developing strategic partnerships can help ensure the sustainability and effectiveness of these initiatives (Table 11).
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Educational Practice in Education for Environmental Justice: A Systematic Review of the Literature https://inergency.com/educational-practice-in-education-for-environmental-justice-a-systematic-review-of-the-literature/ Thu, 28 Mar 2024 04:37:46 +0000 https://inergency.com/educational-practice-in-education-for-environmental-justice-a-systematic-review-of-the-literature/ Educational Practice in Education for Environmental Justice: A Systematic Review of the Literature1. Introduction Environmental problems are one of the major concerns of our time, but this should come as no surprise, given that we were warned about this more than sixty years ago [1]. In this respect, our lifestyle and the values legitimised have led us to this crisis situation [2], determined by, among other factors, […]]]> Educational Practice in Education for Environmental Justice: A Systematic Review of the Literature


1. Introduction

Environmental problems are one of the major concerns of our time, but this should come as no surprise, given that we were warned about this more than sixty years ago [1]. In this respect, our lifestyle and the values legitimised have led us to this crisis situation [2], determined by, among other factors, accelerated climate change, the loss of biodiversity, or the unequal distribution of water and food [3].
Aware of this situation, several strategies have been developed to try to minimise the impact of our actions on the environment, which also affect other areas such as the economic, social, and health fields [4]. Among the international initiatives developed, the various conferences and agreements promoted by the UN from the 1970s to the present day stand out: Stockholm Conference (1972), International Seminar on Environmental Education (Belgrade, 1975), Tbilisi Intergovernmental Conference on Environmental Education (1977), Earth Summit (Rio de Janeiro, 1992), Millennium Summit (2000), World Summit on Sustainable Development (Johannesburg, 2002), United Nations Conference on Sustainable Development (Rio de Janeiro, 2012), and United Nations Summit on Sustainable Development (2015). All of them have resulted in reports focused on reducing inequalities and seeking environmental balance, which governments have subsequently considered to develop initiatives (e.g., low emission zones in cities) and suggest solutions to their citizens (e.g., use of public transport and purchase of eco-vehicles).
On the other hand, since the Stockholm Conference, the importance of education in building new sustainable habits in students during their compulsory education has been recognised. This was suggested by Stapp et al. in 1969 [5], when they first defined the concept of environmental education as the means to create a citizenship informed about the environment and its problems, and that was aware of how to help solve these problems and motivated to work towards their solution. This same idea has been contemplated in international summits and further work until the present day, emphasising the need to promote knowledge, skills, values, and attitudes from an early age to empower students to contribute to sustainable development [6]. Despite these intentions at the theoretical level, little work has been developed about how to include these ideas in the education system, and even less in classroom practice [2]. For this reason, one of the main criticisms that environmental education teaching has faced for some time has been its strong conceptual focus, which is far removed from behavioural change in favour of sustainability [7]. This is because it has traditionally been believed that reporting situations and problems can lead to changes in habits. Furthermore, interventions have mainly focused on secondary school students, even though educational research has confirmed that teenage behaviours are often less modifiable than those of children [7,8]. To this end, there has been a tendency to present environmental problems using fear-based narratives, which discourage and even generate feelings of rejection among students [9]. At the same time, the ecological perspective has been the primary focus of research [10,11], resulting in an incomplete understanding of environmental issues by neglecting other relevant perspectives such as social, economic, ethical, and health considerations [12].
In addition to the objectives established in the definition of the concept of environmental education, some authors have highlighted the inequalities in the field of environmental problems. They argue that these issues are inherently matters of justice [13]. In this sense, the environmental justice approach holds that the contribution to the environmental problems and the distribution of consequences is unequal across communities [14,15]. For example, in relation to climate change, we can talk about how melting and rising sea levels can affect small island states, causing their disappearance and forcing their inhabitants to move to new areas (climate displacement), leading to the dismantling of their business fabric. Emerging diseases may also arise, which will affect the population differently depending on the quality of their health system and the hygiene of their areas. Furthermore, decisions to address these issues are likely to be made and implemented at the political and legislative levels, without actively involving affected citizens [15].
The environmental justice approach has gained significant importance [10], and from an educational perspective it enables students to achieve social transformation for sustainability [16,17], which is essential for today’s current challenges. In this sense, although it is not explicitly mentioned in the Sustainable Development Goals (SDGs) of the 2030 Agenda [18], the environmental justice approach places the teaching of environmental education within a framework of a scientific literacy that is oriented towards dialogical emancipation, socio-ecojustice, and critical and participatory global citizenship [19]. It is understood as a tool for social change [20].
To effectively develop environmental education from an environmental justice perspective, didactic approaches oriented towards transformative environmental education are necessary. These approaches enable the critical evaluation and questioning of prevailing beliefs (cognitive, social, or moral) that have led to crisis contexts, in order to promote a change towards a sustainable future [21]. In this sense, encouraging students’ reflection, critical analysis, and complex problem solving is essential [22]. However, even today, we still have no theoretical frameworks for approaching environmental justice education from a classroom practice perspective, as the literature generally has a strong pedagogical and philosophical focus [23]. This results in the adoption of a more conventional, literacy-based teaching approach to tackle environmental concerns.
Despite the above, several elements have been identified that may be suitable for bringing this approach into the classroom [4,23]. These elements are interconnected and are systems thinking, critical literacy, and action competence. Systems thinking enables students to understand, explain, and interpret complex and dynamic problems in science classrooms in a holistic way [24]. To achieve this, it is crucial to identify the components of a system and their interrelationships [25]. Critical literacy involves reflecting on and questioning norms, information, practices, opinions, and actions to take a stand in the sustainability discourse, and potentially consider lifestyle changes [6,26]. Therefore, both elements are connected to action competence, which is defined as the capability to responsibly address and resolve environmental issues [11,27].
In general, educational interventions that include these elements are considered helpful in preparing students to become agents of change in favour of sustainability [28]. Research studies have analysed students’ performance in the above-mentioned elements. To this end, teaching sequences based on scientific practices such as argumentation or modelling have been designed and developed at different educational levels. Uskola and Puig [29], for instance, have developed an intervention with trainee teachers on the origin of epidemics to identify what level of systems thinking they achieved and what dimensions of future thinking they considered when developing the proposed activities. Additionally, Esquivel-Martín et al. [30] implemented an activity in the One Health education framework to promote evidence-based argumentation in secondary school students (critical literacy). The aim was to identify how participants used the evidence and what solutions they proposed (competence for action) when working on a case related to the use of pesticides in agriculture in different populations connected by the same river and its relationship with reproductive problems in different organisms in the ecosystem. Similarly, Brocos and Jiménez-Aleixandre [31] analysed the results of a didactic sequence with trainee teachers in which they had to construct arguments about healthy and sustainable diets (vegetarian and omnivorous) and decide about them (competence for action). Finally, Evagorou et al. [32] developed an educational intervention to work on the scientific practices of argumentation and modelling with primary school students. To do so, they designed a solution-focused learning situation (competence for action) in response to a local environmental problem: the excessive presence of mosquitoes due to the proximity of a saltwater lake.
In contrast, to the best of our knowledge, there are currently no other systematic reviews available that cover classroom interventions specifically targeting systems thinking, critical literacy, and action competence together. The systematic reviews found in the framework of environmental education focus on analysing other elements. For example, Varela-Losada et al. [27] examined the educational proposals in a formal context to analyse how they contribute to the development of action competence, paying attention to the role of students, the educational practices and conditions posed, and the factors related to environmental education learning. Ardoin and Bowers [33] developed a systematic review of the literature to investigate empirical findings related to early childhood environmental education programmes and practices. Güler Yildiz et al. [34] reviewed articles on early childhood education for sustainability for a descriptive assessment: country, year of publication, research method, participants, and pillars of sustainability addressed. They also examined interventional research and present qualitative data. Finally, O’Flaherty and Liddy [35] developed a systematic review of the literature to explore the impact of interventions in education for sustainable development and global citizenship education. They focused on forms of assessment, education content, and intervention outcomes.
Therefore, none of the above works consider the environmental justice approach. Thus, there is no comprehensive understanding of how environmental justice education has been implemented, despite the importance attached to education to achieve the SDGs and its implicit relationship with environmental justice. Taking into account this gap in the literature and the suggestions derived from the study by Varela-Losada et al. [27], this review aims to determine how environmental education has been implemented in educational practice to identify the existing limitations when working on environmental education in early childhood education (ECE), primary education (PE), and pre-service teacher training (PTT) classrooms from an environmental justice perspective. To this end, a systematic review of the literature was carried out as detailed in the following section.

2. Method

This work presents a quantitative research study with an exploratory-descriptive approach. The methodology used was a systematic review of the literature, as it provides information on the state of knowledge in a given area [36]. This methodology enabled us to determine how environmental education has been implemented in educational practice to identify the existing limitations when working with it from an environmental justice perspective in ECE, PE, and PTT classrooms. This systematic review was developed following the PRISMA 2020 guidelines [36] and the main phases of the study are shown in Figure 1.
As illustrated in Figure 1, this study builds on previous research [37] in which 715 articles were analysed to identify what and how environmental education has been addressed in high-impact educational research since the publication of the SDGs.

To locate published articles on environmental education (phase 1), 22 representative journals (focused on environmental education) and impact journals (JCR-SJR) in the fields of Science Education and Social Justice Education were selected. It was decided to focus the study on journals with a JCR-SJR impact factor because they are the ones that set the trend from an academic point of view, and this might also allow us to know their usefulness for the educational practice of environmental education.

Phase 2 identified articles published between 2015 and 2021 containing any of the following terms in the title, abstract, or keywords in English or Spanish: “Environmental Education” or “Environmental Justice” or “Education for Sustainability” or “Sustainable Development Goals” or “Science, Technology, Society and Environment”. The period (from 2015 to 2021) was selected to consider the impact of the 2030 Agenda on educational research on environmental education, because the SDGs are implicitly related to environmental justice. Furthermore, the terms were chosen because the framework of environmental education was redefined after the publication of the SDGs to incorporate a social and moral component (environmental justice). From this new perspective, to achieve sustainable development (education for Sustainability), environmental problems must be addressed by considering different points of view, which links environmental education with the educational approach of science, technology, society, and the environment.

Next, in phase 3 an analysis tool was constructed to categorise selected works on environmental education by type of study: literature reviews, reflections, document analyses, proposals, interventions, organisations, perceptions, and attitudes, and others. It was found that works with results about the implementation of proposals, programmes, or activities (categorised as interventions) were the most frequent type of study. Therefore, it was considered relevant to analyse these studies in depth in future research to identify the characteristics of these interventions and their limitations [37].
For this purpose (phase 4), the 88 interventions developed in the ECE (8), PE (45), and PTT (35) stages were categorised (phase 5) into “analysable”, “not analysable”, or “not applicable” based on their contents and level of detail. Works were considered “analysable” when their interventions were related to environmental education contents and provided the necessary information to identify the existing limitations when working from an environmental justice perspective in the classroom. Articles were categorised as “not analysable” if they dealt with environmental education content but did not explain in detail the intervention carried out to identify existing limitations. Finally, the “not applicable” category included those studies that, despite including some of the terms used to identify the sample, did not deal with environmental education content. Based on this categorisation, out of the 88 articles, 49 were considered “analysable”, 23 “not analysable”, and 16 “not applicable”. All the inclusion and exclusion criteria discussed above are listed in Table 1.
This paper shows the analysis of the “analysable” articles (ECE: 7; PE: 26 and PTT: 16), which were categorised according to different variables (phase 6): “educational stage”, “contents”, “depth level”, “actions required of students”, and “resources”. Figure 2 presents the categories and subcategories of analysis associated with each variable.
The categories established for the variables “educational stage”, “contents”, and “resources” were created in interaction with the data found in the sample analysed [38]. However, the categories associated with the variables “depth level” and “actions required of students” are based on previous work. On the one hand, in the variable “depth level”, the categories were defined based on the objectives of environmental education since its origin (see Belgrade Charter, 1975), up to the present time [6]. Thus, considering the need for students to acquire knowledge, skills, values, and attitudes to contribute to sustainable development, the categories “content knowledge” (knowledge), “awareness-raising” (values), and “action-taking” (skills and attitudes) were established:

“Content knowledge” refers to interventions that promote the learning of conceptual knowledge about the environment or environmental problems.

“Awareness-raising” refers to interventions that focus on students recognising the importance of environmental issues.

“Action-taking” refers to interventions that allow students to recognise their role in the issue at hand to propose and carry out actions on their own.

Different subcategories were identified within each of these categories.

Furthermore, the categorisation developed by Medir et al. [39] and applied with modifications by Pérez-Martín and Bravo-Torija [40] was considered in the variable “actions required of students”.
After defining the analysis tool, it was validated by two external experts and corresponding analyses were conducted. To do this, firstly, the absolute and relative values of each of the categories associated with the different variables were recorded for a descriptive statistical analysis. Secondly, an inferential analysis of hypothesis testing (χ2, p ≤ 0.05) was performed with IBM® SPSS® Statistics 19 2010 (IBM Company, Armonk, NY, USA) and Microsoft Excel TM. The procedure for developing inferential analysis through hypothesis testing is as follows: after recording the observed absolute values, they were analysed using SPSS to determine significant differences between two variables. As an example of the analysis carried out, the results of the resources used in the different educational stages are shown in Table 2.
The expected values were estimated using the observed values (Table 3). This was achieved by multiplying the total of each row by the total of each column and dividing by the overall total.
Finally, in those cases where the SPSS analysis indicated significant differences (χ2, p ≤ 0.05), the observed results were compared with the expected results (Table 1 and Table 2).

3. Results

The analysis results are presented below, considering the “contents” and the “depth level” of the interventions, the “actions required of students” and the “resources” used. In all cases, the results are presented in a general way and then compared according to “educational stage”. In addition, “actions required of students” and “resources” were also compared according to “contents”. This allows us to observe the distinctions among the categories linked to the variables based on other variables. A complete list of all publications analysed in this review can be found in Supplementary Material Table S1.

3.1. Contents

First, the analysis of the variable “content” (Figure 3) shows that works on issues related to “knowledge of the natural environment” (33.1%) or on “environmental problems from an ecological perspective” (20.0%) represent 53.1% of the sample. Others, such as work on “environmental problems or solutions from a socioeconomic perspective”, appear in around 13% and 12%, respectively. In contrast, addressing problems or solutions to problems from an ethical or health perspective in the classroom is less frequent (less than 5%).
Considering the educational stages in which these topics can be addressed (Table 4), it is evident that environmental education interventions cover the same topics regardless of the educational level (χ2, p ≤ 0.05). Throughout all stages, the focus is on issues related to “knowledge of the natural environment” (ECE: 37.5%; PE: 34.4%; PTT: 30.0%). The second most frequent content differs between stages. In ECE, it corresponds to work on “socioeconomic solutions to environmental problems” (18.8%), whereas in PE and PTT, the focus is on “environmental problems from an ecological perspective” (21.9% and 20.0% respectively).

3.2. Depth Level of Interventions

The analysis of the depth level of interventions (Figure 4) indicates that environmental education activities primarily aim to raise the awareness of students (44.1%), as well as to teach content knowledge (40.1%). However, promoting their participation and action is less frequent (15.8%).
Examining the subcategories, it is evident from Figure 4 that content knowledge is primarily approached from an ecological perspective (CK-SC.1: 17.8%), with less frequent consideration given to other perspectives such as ethics (CK-SC.3: 1.5%) or health (CK-SC.4: 2.5%). There is a similarity between all these problems in terms of their scale (CK-SC.5: 4.5%; CK.SC.6: 3.5%; and CK.SC.7: 3.5%). Regarding awareness-raising, the emphasis is again on the ecological (AR-SC.1: 13.4%) and socioeconomic perspectives (AR-SC.2: 10.8%), in contrast to other minority perspectives such as ethics (AR-SC.3: 3.0%) or health (AR-SC.4: 3.5%). To promote action-taking, it is common to allow students to propose ideas (AT-SC.1: 7.3%), rarely leading to action on a personal (AT-SC.2: 3.5%), local (AT-SC.3: 4.5%), or global (AT-SC.4: 0.5%) level.
The analysis revealed significant differences in the depth of the activities according to the level of education (χ2, p ≤ 0.05). Figure 5 shows that content knowledge is more frequent in the early stages (ECE: 40.0% and PE: 45.6%) than in the higher stages (PTT: 30.2%). Interventions promoting awareness-raising increase as the age of the students increases, reaching their peak at PTT (ECE: 24.0%; PE: 43.9%; and PTT: 52.3%). Action-taking is the least frequent category at all stages of education (PE: 10.5% and PTT: 17.5%), except in ECE (36.0%), whose frequency is higher than that of the “awareness-raising” level.
In terms of the subcategories (Figure 4) in ECE and PE, priority is given to “presenting environmental issues from an ecological perspective”, which is not the case in PTT (χ2, p ≤ 0.01). In addition, activities that require action at the global level are very rare, with only one case appearing at the ECE stage (χ2, p ≤ 0.05).

3.3. Actions Required of Students

Regarding the type of actions required of students (Table 5), the most important categories in total are: “learning conceptual knowledge” (13.8%), “learning to work collaboratively” (9.8%), “learning to search for information” (8.0%), and “discovering fundamental aspects of the environment” (8.0%). By contrast, the categories “discovering little-known aspects of the environment”, “acquiring a global vision of reality”, “reinforcing links between culture and nature” (3.1%), “learning attitudinal knowledge”, and “reflecting on the desired future” (2.8%) are less frequent.

If we analyse the type of specific actions that are demanded of students depending on the educational stage, we find that they are the same in all of them (χ2, p ≤ 0.05), except for the category “questioning assumed knowledge”, which is lower than expected in ECE and PE, and higher in PTT (χ2, p ≤ 0.05).

Furthermore, it was found that the actions required of students differ depending on the subject matter. Thus, the analysis carried out shows that works on “environmental problems from an ecological perspective”, the “learning of conceptual knowledge”, the “questioning of assumed knowledge”, and the “development of critical thinking” are more frequently demanded (χ2, p ≤ 0.05). However, it is not very common to work on this content in proposals that demand action in favour of the environment or health (χ2, p ≤ 0.05), something that does happen if problems are addressed from a socioeconomic perspective (χ2, p ≤ 0.05). To address the ethical perspective, the main actions are related to “acquiring a global vision of reality” and “reflecting on the desired future” (χ2, p ≤ 0.05).

It was also detected that, in order to work on socioeconomic solutions to environmental problems, priority is given to critical thinking (χ2, p ≤ 0.05), and when it is done from an ethical perspective, there is a predominance of actions related to environmental- or health-related action (χ2, p ≤ 0.01) and reflection on the desired future (χ2, p ≤ 0.05).

3.4. Resources

In the analysis of the resources used when implementing environmental education activities (Figure 6), there is a predominance of “self-made materials” (18.4%), followed by “games or group dynamics” (14.9%) and “field outings” (14.2%). On the other hand, using “literary resources” (6.4%) or “audiovisual material” (5.7%), or receiving visits from experts (5.7%), are less frequent. It is worth mentioning that, in environmental education, interventions using resources other than those considered in the categories is also very frequent (15.6%).
Reviewing the types of resources that are used according to the educational stage (Table 2), statistically significant differences are observed in the categories “(video)games or group dynamics”, “literary resources”, and “images”. In this sense, ECE prioritises the use of literary resources and images (χ2, p ≤ 0.05). In PE, (video)games or group dynamics and images are used less than statistically expected (χ2, p ≤ 0.05). In PTT, unlike in ECE, literary resources are not used, and little use is made of images, prioritising (video)games or group dynamics (χ2, p ≤ 0.05).

Finally, the analysis indicates that there are significant differences between the resources and the content being worked on in some cases. In particular, self-made materials are preferably used to work on socioeconomic solutions to environmental problems (χ2, p ≤ 0.05). However, this is less common when working on ecological solutions (χ2, p ≤ 0.05). On the other hand, field outings are used to work mainly on knowledge of the environment (χ2, p ≤ 0.05).

4. Discussion and Conclusions

The aim of this research was to identify the existing limitations when working on environmental education in ECE, PE, and PTT classrooms from an environmental justice perspective. It has long been emphasised that environmental problems must be understood as a complex system [24] involving social, economic, environmental, ethical, and health factors [12,41]. Therefore, the latest 2030 Agenda strategy and the SDGs [42] call for an integrated approach to understanding environmental degradation and lifestyles. However, our study suggests that environmental education teaching is still limited to developing environmental knowledge interventions or working on environmental problems from an ecological point of view. These results lead us to infer that this systemic approach has not yet been integrated into educational interventions published in relevant journals in recent years. This fact confirms, with high statistical and sampled evidence, the results found in previous works, which state that teachers frequently consider the ecological dimension in their classes but do not use the systemic approach in their teaching [43]. Since teachers are the ones who ultimately determine what is worked on in their classrooms and how it is done, the presented limitation also carries over to the educational reality, where a lack of holistic and pluralistic perception among students is detected [44]. This demonstrates that global policy efforts to understand environmental issues in an interconnected manner have not yet influenced educational practices.
This resistance to incorporating different dimensions and to limit oneself to working on problems from an ecological perspective may be due to the traditional approach used in educational practices [7]. As the results of this study show, these practices are still very much based on content knowledge and awareness-raising instead of action-taking, even though previous works have confirmed that environmental content knowledge is not sufficient to promote behavioural change [45]. Prioritising conceptual learning and awareness-raising leads to demands on students that are far removed from transformative environmental education. As this perspective, at a theoretical level, has been considered to be very useful for teaching environmental education [46], it was expected that studies of classroom interventions might find more proposals favouring actions such as “questioning assumed knowledge”, “developing critical thinking”, “acting in favour of the environment or health”, “reflecting on aspects of everyday life”, “raising questions about the problem addressed”, or “reflecting on the desired future”. Furthermore, in order to increase their impact, these actions should appear in the initial stages (ECE and PE), with the aim of training an informed, critical citizenship capable of participating in decision making to face current challenges [28]. However, they should also be promoted in PTT, but from a reflective approach in their professional practice, within the framework of the specific environmental competence for teachers [47,48]. Despite this, the results reflect a predominance of actions associated with conceptual and procedural learning at all educational stages (“learning conceptual knowledge”, “learning to search for information”, or “discovering fundamental aspects of the environment”).
In terms of resources, the results indicate that, when addressing environmental issues, teachers predominantly opt to create their own materials. This can suggest a scarcity of teaching resources or the inadequacy of existing ones for their students. This interpretation aligns with previous studies, in which the analysis of activities on sustainability in Spanish primary school textbooks revealed a significant proportion of activities with minimal cognitive demands related to environmental issues and that were mainly focused on recycling [49]. Group games or dynamics also predominate, as has been observed in previous studies [27], associated with the teaching interest in demanding collaborative work actions to build knowledge through social interaction. Additionally, as found in Ardoin and Bowers [33], who focus on the ECE stage, field trips are frequent. According to Herman et al. [50], experiences with nature can be useful for interpreting science in real situations, but they must be didactically structured and move away from simply acquiring knowledge about the environment. In this sense, they should show environmental issues to students in order for them to reflect on them and make informed decisions [51], which, based on our results, is not the case.
Focusing now on discussing the comparison between educational stages in the different variables, the findings of our study indicate that there is no sequencing of the contents by educational stage. In this sense, despite international intentions to improve the teaching of environmental education [6], no specific curricula have been designed to define the learning that a student should acquire according to his or her educational level. These plans are essential given that, as indicated by Otto et al. [52], environmental attitudes and behaviours differ throughout life, so it does not seem reasonable to work on the same content at all educational stages.
When considering the depth of interventions, variations are observed based on the educational level. The levels used as categories of analysis (“content knowledge”, “awareness-raising”, and “action-taking”) are frequent when talking about the objectives of environmental education in general [6]. However, no previous studies have been found that define the appropriate depth level for content based on the age of the students. Despite this lack of a framework, several studies have pointed out that the ability to have an impact on changing students’ behaviours decreases as age increases [7,8]. Therefore, considering that the higher the awareness the greater the possibility of changing habits, it does not seem reasonable that awareness is higher during PTT than in early stages (ECE and PE). Furthermore, we can also criticise the low frequency of the third level (action-taking) in PE and PTT. This suggests that students are not given enough opportunities to apply their knowledge in activities related to their field of action, whether as citizens in ECE and PE or as teachers in PTT, which restricts their development as agents of change.
Regarding the actions demanded, it can be observed that they are similar at all educational stages. In order to define the type of actions that should predominate at each level, it is necessary to have pedagogical models adapted to the age of the students. However, the proposals found [53] (e.g., Hadjichambis and Paraskeva-Hadjichambi, 2020) make suggestions on a more general educational level, making comparison challenging. In our opinion, it seems reasonable that frequencies of all actions of the category system used have been recorded in all three educational stages. Nevertheless, it is important to balance these frequencies to increase the presence of those actions related to transformative environmental education discussed above.
Considering the above, and that systems thinking, critical literacy, and action competence are key elements in environmental justice education [23], it can be concluded that the main limitation identified when working on environmental education in ECE, PE, and PTT classrooms from an environmental justice perspective is its lack of integration. One possible explanation for this is the strong theoretical focus of publications on both environmental education and environmental justice [37], which has generated a gap between research and educational practice, due to the lack of a definition of the didactic component [47]. This is also reflected in the selection of the study sample, since, out of more than 700 articles, only 88 proposals with classroom intervention were found, and only 49 of them contain detailed information on their implementation. Furthermore, by educational stage, there is a predominance of PE and PTT interventions, as opposed to ECE, which possibly limits the impact of the interventions that are developed [7,8].
For this reason, if the aim of educational research is to improve the teaching and learning of content (conceptual, procedural, and attitudinal), such as environmental problems, the development of a current of research focused on the didactics of environmental education that includes a social justice perspective should be promoted. This tendency should encourage the promotion of studies that define which methodologies (e.g., case study or learning in context), strategies (e.g., thought-provoking questions, mediating questions, socio-scientific issues, or storytelling), or didactic resources (e.g., tables, graphs, news, or videos) are most suitable for achieving educational success in terms of social change for environmental protection. To this end, research should be carried out in which their implementation in the classroom and the results obtained are analysed, such as [30]. In this way, research work closer to the reality of the classroom and to teachers’ interests will be promoted, potentially reducing the gap between research and practice. We should point out that this gap may be a consequence of the inclusion and exclusion criteria defined. Specifically, focusing our study on impact journals (SJR-JCR) provides us with a partial knowledge of the topic addressed. Therefore, as a future line of research, it might be interesting to carry out a similar study using lower impact journals as sources. This might allow us to compare the results and obtain a deeper understanding of the role of educational research in environmental justice education.
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