Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis
1. Introduction
Over the last six decades, substantial economic advancement and a significant increase in the world’s population have been accompanied by negative impacts on the environment [1]. The Fifth Assessment Report [2] provides an important framework for understanding the impacts of climate change on natural and human systems across the globe [3,4]. This urgent call for action stems from the growing scientific evidence that shows the detrimental effects of global warming and the escalating environmental damage caused by carbon dioxide (CO2) emissions [5]. This has prompted international organizations and governments to seek solutions for reducing emissions around the globe [6]. Consequently, a sequence of accords has been established among various nations to regulate worldwide CO2 emissions, encompassing agreements such as the United Nations Framework Convention on Climate Change (UNFCCC), the Kyoto Protocol, and the Paris Agreement [7]. CO2 is commonly accepted as a greenhouse gas and is a key contributor to global warming [8]. CO2 emissions primarily originate from anthropogenic sources such as deforestation, transportation, and the combustion of carbon-emitting fuels by the industrial sector and power stations [9]. Along with other greenhouse gases such as methane and nitrous oxide, CO2 traps heat within the Earth’s atmosphere, leading to an increase in average global temperature, commonly referred to as global warming [10]. The rising international level of CO2 emissions has become a major concern due to its role in temperature elevation and subsequent climate change [11]. According to the UN report conducted by the IPCC (Intergovernmental Panel on Climate Change), CO2 emissions from fossil fuel consumption are the main source of CO2 emissions.
Emerging Asia is susceptible to environmental risks due to its unique geographical characteristics and socioeconomic conditions. The changing climate is anticipated to result in heightened occurrence and intensity of cyclones, inundation, heat waves, and droughts in the area. Furthermore, the Asian continent is home to almost 70% of the world’s population that might be impacted by rapidly rising sea levels [12]. Approximately one-third of the region’s workforce is engaged in agriculture and fishing, two industries heavily reliant on natural resources and thus subject to climate change. If global warming continues at its current rate of acceleration, Asia’s emerging economies as an entire region would see a 24% drop in the GDP by the year 2100 [13].
Despite the initial low levels of historical emissions from emerging Asia, they have shown a more rapid growth rate compared with the world average. The region’s proportion of global greenhouse gas (GHG) emissions has increased twofold, from 22% in 1990 to nearly 50% in 2021 [14]. It is projected to maintain this proportion until the middle of the century, assuming existing policies stay unchanged. Given the present amount of GHG emissions, this area alone could exhaust the amount of remaining global carbon budget that is in line with the goal of reducing global warming to 1.5 degrees Celsius (°C) by 2040 [15].
In recent years, the issue of CO2 emissions has become a pressing concern, especially in Asian countries [7]. Asian countries have emerged as major emitters of carbon dioxide, contributing significantly to the global climate change crisis [16]. According to United Nations data, China currently holds the top position as the lead emitter of carbon dioxide [17]. This is due not only to its large population but also to its rapid economic development and industrialization [18]. Furthermore, other Asian countries, such as India, Japan, South Korea, and Southeast Asian nations, have also experienced substantial growth in CO2 emissions due to their rapid economic growth and industrial expansion [16]. These countries have seen a significant increase in their industrial sectors, including manufacturing industries, transportation, and energy production [19]. In terms of specific contributions, different industrial sectors have varying impacts on CO2 emissions in Asian countries [20]. Additionally, the area had a carbon footprint that was about 45% greater than the global average and more than twice as high as that of North America and the European Union in 2022 [21]. When the high level of intensity is coupled with the quick economic expansion of emerging Asia, there is a possibility of a swift increase in emissions. The energy industry is responsible for 75% of the region’s greenhouse gas emissions. Electricity and heat generation dominate the energy sector as the primary and rapidly expanding contributors to emissions, constituting over 40% of total emissions. Manufacturing follows closely behind, accounting for 18% of emissions. Agriculture, land use, and forestry contribute significantly to emissions, accounting for 13% of total emissions [1,14]. Manufacturing, transportation, and energy generation are some of the region’s most environmentally conscious sectors, which impact both employment and productivity. Between 2015 and 2021, these industries were responsible for 42% of all jobs and 43% of the GDP. When juxtaposed with various regions of the globe, the contributions to the GDP in the area are much larger. Approximately 18% of the GDP in the US is derived from these activities, but in Europe it is roughly 23%, and in Latin America and sub-Saharan Africa it is 24%.
The environmental impact of foreign direct investment may also be felt in many ways [22]. It is possible to classify these channels into three groups: Some people think that foreign direct investment (FDI) is the root cause of “pollution havens.” As a result of different countries’ approaches to environmental regulation, the pollution refuge theory was put up. When compared with industrialized nations, developing economies tend to be lax or nonexistent when it comes to environmental legislation. Furthermore, trade liberalization has both positive and negative consequences. It additionally contributes to a country’s economic growth but also gives rise to both ecological and climatic issues [23]. Given this, the influence of trade openness on carbon emissions has progressively emerged as a significant concern for scholars and policymakers.
According to the Urban Development Overview by the World Bank Group, the rapid growth of urbanization has been accompanied by an increase in carbon dioxide emissions. This is primarily due to the increasing energy demand of societies, which is driven by the development of industries and the expansion of urban areas [24]. Urbanization processes have a significant impact on carbon dioxide emissions in urban areas. Numerous studies have focused on the relationship between economic processes and carbon dioxide emissions in urban areas [25]; however, these studies have shown that as cities become more urbanized, there is a corresponding increase in carbon dioxide emissions [26]. However, in recent years, there has been growing concern about the impact of communication technology infrastructure on CO2 emissions [27]. On one hand, information and communication technologies (ICTs) play a pivotal role in fostering economic expansion, serving as one of the fundamental drivers of growth [28]. The Internet, mobile phones, telephone calls, computer systems, and associated applications, collectively known as ICTs, have become the primary drivers of societal transformation, growth, and invention [29]. However, information and communication technology infrastructures are predicted to be responsible for 3% of global annual electricity usage and 2% of CO2 emissions [30]. This level of energy consumption and carbon emissions is significant and cannot be ignored [31].
Agricultural activities continue to play a significant role in driving climate change and are responsible for approximately one-fourth of the overall human-caused greenhouse gas emissions [32]. The agricultural industry is recognized as a key contributor to greenhouse gas emissions. The sector has experienced a 13.5% increase in emissions due to heightened deforestation and the excessive utilization of synthetic inputs such as pesticides and fertilizers [33,34]. These activities account for approximately 20% of the total carbon dioxide emissions resulting from all human activities globally [35]. Urgent actions are required to address the negative environmental impacts of agriculture and reduce CO2 emissions from the sector [36].
This study contributes to the literature in different ways. First, to the best of our knowledge, this is the first research investigating the dynamic relationships between urbanization, Internet, FDI, agriculture, and GDP on CO2 emissions for 14 top CO2 emitters in Asia. Second, our work also contributes methodologically to the literature on the relationship between the environment and economic development by using the innovative econometric estimate approach known as the method of moments quantile regression (MMQR) under the EKC hypothesis [37]. Consequently, we surmount the limitations of previous research stemming from mean-based linear estimation methods by employing MMQR, which reveals the influence of regressors on the conditional distribution of the dependent variable, as opposed to solely on the mean specification. Consequently, due to the varying degrees of economic development among the sample countries, this approach is especially suitable for examining the heterogeneous effects of regressors on environmental quality indicators. The applied approach is also resilient against skewness, heteroskedasticity, and other outliers. Our research also offers reliable results by utilizing alternative estimation techniques, such as FMOLS, DOLS, and Driscoll–Kraay standard error estimators. Third, the current research employs an extensive set of data spanning from 1996 to 2020. Finally, the research outcomes will provide valuable insights for the fourteen countries in Asia with the highest CO2 emissions, facilitating them in the development and execution of environmentally friendly measures.
The rest of this research is organized as follows. The next section is the literature review and hypothesis development. The next section describes the data and methodology used, which is followed by the results. The discussion section follows, and a summary and conclusion are provided.
3. Methodology
The factors influencing emissions are intricate and diverse. However, within the scope of our analysis, we focused on urbanization, information and communication technology (ICT), renewable energy, agriculture, and economic development. Our empirical assessment was based on the following basic model:
where is the emissions per capita; represents the rate of urbanization, which measures the pace at which an area is becoming more urban; represents the GDP per capita, which indicates the economic output per person; is renewable energy consumption; is foreign direct investment, net inflows (% of GDP); is individuals using the internet (% of population) as a proxy for ICT; signifies agriculture, forestry, and fishing, value added (% of GDP); is squared for GDP; and is the error term. All the data are in logs.
The dataset consisted of yearly panel data from 1996 to 2020, encompassing 14 Asian countries that were among the top emitters. These countries included China, India, Indonesia, Iran, the Islamic Republic, Japan, Kazakhstan, Korea, Malaysia, the Russian Federation, Saudi Arabia, Thailand, Turkey, Uzbekistan, and Vietnam. The dataset for the variables analyzed in the study was obtained from World Development Indicators.
The approach involved six sequential steps. The first step was to assess the panel unit root and perform a cointegration analysis to determine the integration properties of the data. Second, the FMOLS and DOLS methods were employed [102]. These approaches were beneficial since they considered the presence of cross-sectional dependency and heteroscedasticity problems. Furthermore, our primary goal was to assess the impact of the independent variables under consideration on the whole distribution of the dependent variable. To do this, we implemented the MMQR approach to estimate Equation (1). Last, we implemented the Driscoll–Kraay estimator to further check the validity of the outcomes achieved by the MMQR, FMOLS, and DOLS estimation techniques.
5. Results
This section presents the initial data analyses, which include descriptive statistics and the Pearson correlation matrix of the variables under investigation. Furthermore, this section covers panel unit root and panel cointegration tests to ensure thorough screening of the variables, resulting in reliable outcomes from the model calculations and clarifications.
Table 2 presents the statistical features of the chosen variables, which include the maximum, minimum, mean, and standard deviation. The mean values of , URB, GDP, RNEW, FDI, ICT, AGR, and GDPsq were 1.58, 4.02, 8.66, 1.43, 0.42, 2.39, 2.03, and 76.07, respectively. Accordingly, a remarkable amount of standard deviation was shown for each of the variables investigated in this research, which were as follows: 0.82, 0.36, 1, 2.13, 1.18, 2.26, 0.85, and 17.36 for , URB, GDP, RNEW, FDI, ICT, and GDPsq, respectively. The descriptive properties of the factors enabled us to proceed to the unit root test.
To assess the correlation between variables, the Pearson correlation coefficient was calculated for matrix correlations, and the results are displayed in Table 3. The correlation matrix provides information on the strength and direction of the relationship between each pair of variables under investigation. A correlation coefficient that is closer to one indicates a higher degree of strength, while a negative correlation signifies a reverse correlation between two variables. The correlation matrix is symmetrical with respect to the diagonal, where the diagonal elements have a value of 1.000000, indicating that the variables are completely correlated. Table 2 shows that there was a strong positive relationship between the dependent variable (ln) and the independent variables lnurb (0.8552), lngdp (0.8155), lnict (0.4306), and lngdpsq (0.7998). On the other hand, there was a clear negative relationship between the dependent variable (ln) and the independent variables lnrnew (−0.7316), lnfdi (−0.1520), and lnagr (−0.7448). From these results, it was evident that there were strong and positive correlations among the variables ln, lnurb, lngdp, lngdpsq, lnrnew, and lnagr, as expected.
Table 4 contains the findings associated with the cross-sectional analysis (CD). The CD test demonstrated that the null hypothesis should not be accepted, therefore rejecting it. This indicated the existence of cross-sectional dependence within the data. These findings provided evidence that over the course of a longer time period, the variables could become cointegrated.
The results of the cross-sectional unit root test can be found in Table 5. The outcomes showed that all the variables examined showed evidence of stationarity when evaluated through first-order differencing. After careful analysis, the null hypothesis of the presence of a unit root could be rejected. This implied that there was proof that order integration occurred within the variables in question.
Table 6 reveals that the probability values for the rho and ADF statistics in the “within-dimension” analysis were not significant. Nevertheless, the probability values for the v and PP statistics were deemed significant at the 5% level. Extensive research revealed that there was a significant correlation between the variables under examination over an extended period of time.
6. Discussion
While our main objective was to evaluate the influence of the factors that determined emissions on the entire range of the dependent variable implementing the MMQR technique, we initially present the findings of three conventional estimators—FMOLS, DOLS, and the Driscoll–Kraay estimates—for the purpose of comparison. Table 7 shows the results of these tests. The results of various statistical techniques clearly demonstrated that renewable energy, agriculture, and the square of the GDP had significant and adverse influences on emissions. According to the FMOLS calculations, a mere one percent increase in the utilization of renewable energy led to a precise decrease of 0.142% in emissions per individual. Similarly, both the DOLS and the Driscoll–Kraay, which was implemented for a robustness check, estimating procedures had significant negative correlations. Based on these estimation techniques, a 1% rise in the usage of renewable energy led to decreases in emissions per capita of 0.133% and 0.158%. Our outcomes were consistent with those of other studies performed in numerous nations, which also revealed that the utilization of renewable energy sources may significantly reduce carbon emissions [116,117,118,119,120,121].
Our analysis revealed a significant and negative association between emissions and agriculture, which aligned with the expected relationship between emissions and the utilization of renewable energy sources. These findings were consistent across all three estimation methodologies. Based on the FMOLS and DOLS estimation methods, a 1% increase in agricultural output led to a decrease of approximately 0.428% to 0.430% in emissions per capita. In contrast to the prior illustration, ref. [102] estimates showed that the agricultural output was negatively and statistically significantly correlated with emissions Specifically, the estimates showed that a 1% increase in agricultural production led to a 0.421% decrease in per capita emissions [102]. These results align with the findings of previous research [73,109,122]. Our findings showed that all the estimation techniques indicated favorable and statistically significant relationships between urbanization, the GDP, and emissions [27,123]. The DOLS estimate was the sole indicator that showed an important and beneficial relationship between FDI and emissions. Similarly, the Driscoll–Kraay estimate was one piece of evidence indicating a significant and adverse correlation between emissions and ICT.
Our main goal was not to provide a conditional average of these estimations but rather to offer estimates that encompassed the many effects of various variables driving emissions. The findings of the MMQR are presented in Table 8. First, the favorable effect of urbanization on could be verified. The evidence clearly demonstrated that urbanization had a substantial impact on an upsurge in levels, with values varying from 0.312 to 1.177 across all quantiles. With the increase in population, there was a corresponding increase in the need for energy. Due to their cost-effectiveness and easy availability, fossil fuels are heavily relied upon for energy generation, considering the high demand. Urbanization is a contributing factor to the increase in emissions. Moreover, it is intriguing to explore the relationship between the GDP and emissions. The table provides a clear and comprehensible explanation of the substantial increase in emissions attributed to the GDP. The frequency of the rise varied from 3.205 to 2.050 as the quantile increased. In the initial stages of economic expansion, the rate of primary production slowly increased, which eventually transitioned to a more rapid acceleration. Consequently, a rise in these economic activities led to a beneficial effect on carbon emissions. Conversely, the square of the GDP had a significant and harmful impact on emissions at all levels, as demonstrated by statistical research. Moreover, the EKC theory was valid for all quantiles. Our analysis suggested that the selected economies achieved a specific degree of economic advancement, as demonstrated by the validity of the inverted U-shaped EKC. Presently, there is a movement toward achieving economic growth that is both ecologically friendly and capable of being maintained over time [37]. Moreover, the increase in economic growth stimulates technical progress, promotes the emergence of alternative energy sources, amplifies the production of renewable energy, and accelerates the expansion of the tertiary and service sectors. These endeavors have successfully contributed to the decrease in emissions.
The findings demonstrated a robust and negative correlation between the utilization of renewable energy and environmental deterioration across all levels of quantiles (Table 8). The negative repercussions of REC may result in a direct outcome; specifically, technological developments, especially in the realm of renewable energy generation, are essential for improving production quality and lowering production costs. Furthermore, it effectively counteracts environmental contaminants. Similarly, agriculture had a detrimental effect on emissions at all levels of assessment. By incorporating technical advancements in machinery and improving energy efficiency in farm buildings, farmers may greatly reduce fuel usage and emissions while also enjoying financial advantages.
The results indicated a strong and positive relationship between FDI and environmental deterioration across the 25th to 95th quantiles. FDI had multiple impacts on the carbon footprint of the host nation. First, it increased the overall size of economic activity. Second, it altered the structure of economic activity. Last, it introduced new manufacturing processes. When considered independently, the scale effect was anticipated to amplify carbon emissions since a larger economy signified greater output and, hence, higher emissions. Conversely, ICT had a detrimental impact on emissions in higher quantiles, whereas this connection was favorable and was statistically significant at the fifth quantile. Digital technology directly or indirectly contributed to the reduction in carbon emissions by fostering the development of environmentally friendly technical advancements and decreasing energy consumption. It additionally served a crucial role in the implementation of carbon emission trading regulations and extensive national large-scale data pilot regions aimed at lowering carbon emissions.
Ultimately, Figure 2 displays graphical plots of MMQR. It demonstrates the interconnectedness of the variables at various quantiles.
7. Conclusions and Policy Implications
Concerns regarding the environment continue to be popular and widely discussed in academic circles due to the ongoing shifts in climate change and the rising amount of carbon emissions. Numerous research efforts have explored the factors that contribute to pollution, but most of them rely on aggregate use of energy or traditional panel estimation techniques for their analysis. With regard to the top 14 -emitting economies in Asia, the primary purpose of this research was to investigate the impacts that factors such as consumption of renewable energy, urbanization, gross domestic product, agricultural production, information and communication technology development, and foreign direct investment had on emissions. Applying the innovative method of moments quantile regression (MMQR) from 1996 to 2020, the current research intended to investigate, for the first time, the impact of renewable energy consumption, urbanization, the GDP, agriculture, ICT development, and FDI on emissions in all countries under consideration. To gain an understanding of the characteristics of the dataset, this study used several preliminary analyses and panel sensitivity tests. Additionally, it utilized several panel estimation approaches in conjunction with quantile regression to assess the robustness of the dataset.
Research findings indicated that certain factors, such as REC and agriculture, were shown to reduce pollution. Furthermore, we revealed evidence for the EKC hypothesis and found that the GDP had an inverted U-shaped effect on CO2 emissions based on the relationship between the GDP squared and CO2 emissions. On the other hand, urbanization and the GDP were found to contribute to carbon emissions. These findings supported the validity of the EKC hypothesis. According to the findings, a 1% increase in REC resulted in decreases in carbon emissions by 0.142%, 0.133%, and 0.158% for FMOLS, DOLS, and the Driscoll–Kraay methods, respectively. On the other hand, a 1% growth in agriculture led to an increase in emissions by 0.428% for FMOLS, 0.430% for DOLS, and 0.421% for the Driscoll–Kraay method. In addition, a 1% increase in the GDP square led to corresponding rises in emissions of 0.152%, 0.131%, and 0.060% for FM-OLS, DOLS, and the Driscoll–Kraay method, respectively. The presence of EKC in Asian countries was confirmed by the negative and significant signs of coefficients of the GDP square in all three methods. On the other hand, the results of the study showed that a 1% increase in urbanization was associated with rises in carbon emissions of 0.793%, 0.879%, and 1.296% using the FMOLS, DOLS, and Driscoll–Kraay methods, respectively. However, a 1% growth in the GDP led to an increase in emissions of 2.629% using FMOLS, 2.254% using DOLS, and 1.054% using the Driscoll–Kraay method. Regarding the relationship between foreign direct investment and carbon dioxide emissions, the DOLS estimate was the only one that showed a significant and positive correlation. Comparably, the lone estimation that showed a significant but unfavorable correlation between emissions and ICT was the Driscoll–Kraay estimate.
The outcome of the MMQR revealed that urbanization, the GDP, and FDI all had a beneficial impact on carbon emissions across all quantiles, from the 5th to the 95th. However, it is worth noting that REC, ICT, agriculture, and the square of the GDP all had a detrimental effect on pollution levels across all quantiles. Therefore, the results confirmed the presence of the EKC hypothesis across all quantiles.
Additionally, we developed the graphical representation of the findings of our empirical analysis. Figure 3 compares the estimated coefficients for all methods employed, including MMQR, FE-OLS, DOLS, and FMOLS. As opposed to the DOLS, FMOLS, and FE-OLS coefficients, which were all fixed, the MMQR coefficients were variable and provided a lively picture throughout all quantiles.
Based on these results, the present study proposes several policy recommendations for the selected sample.
First, economic development is a crucial instrument in addressing climate change. According to the inverted U-shaped EKC theory, it is postulated that as development in the economy continues, there will be a point at which a specific income level is attained, leading to a drop in CO2 emissions. From this standpoint, it is essential to promote economic development.
Second, policymakers in the Asian countries that produce the most carbon dioxide should prioritize expanding the use of renewable energy sources to power agricultural expansion while simultaneously decreasing reliance on fossil fuels. To attain a consistent and enduring expansion in the utilization of energy from renewable sources, authorities must formulate and execute favorable legislation that incentivizes investments for enhancing the newly developed renewable energy facilities.
Third, to address the heat and electricity issues and lessen reliance on non-renewable energy sources, it would be beneficial to promote the construction by agricultural businesses of small biogas plants and power stations that are powered by wind and sun. Furthermore, it is essential for the legislative bodies of the aforementioned countries to enhance their laws and regulations, such as by implementing tax incentives, feed-in tariffs, tax refunds, and investment subsidies, in order to promote the adoption of renewable energy in the agriculture industry.
Fourth, it is crucial to boost FDI in industries characterized by low levels of CO2 emissions, while reducing FDI in sectors associated with substantial carbon emissions. Hence, it is essential to enact relevant regulations to bolster FDI, expedite the dissemination of state-of-the-art global technology, and optimize the advantages of environmental enhancement resulting from technological spillovers. Successful implementation of these measures would ultimately enable Asian nations with the highest CO2 emissions to achieve both a low-carbon economy and economic growth. In such a situation, it is imperative to gradually modify the worldwide trade and FDI structure while conducting “supply-side reform” in sectors that have a limited emphasis on carbon emissions. In addition, concurrent research and development efforts are underway to create new technologies aimed at safeguarding the environment and establishing an eco-friendly industrial setting.
Fifth, governments should prioritize the development and use of environmentally friendly types of ICT so that these advancements may support their endeavors to create a sustainable environment.
Last, it is advisable to implement initiatives aimed at slowing down the rate of urbanization in these nations. This might be achieved if governments focus on improving rural income initiatives. Furthermore, the association between the urban areas in Asian countries that have the highest levels of CO2 emissions and the increased demand for energy and environmental degradation highlights the crucial significance of strategic planning in the design, development, and management processes. This planning is essential in addressing urban expansion while simultaneously promoting higher urban density. Urban density has many benefits, including less environmental harm and a well-developed transportation network and infrastructure, especially public transportation, which enhances accessibility. Additionally, urban density promotes efficient energy supply and good water management systems.
The shortcomings of the current investigation highlight the need to explore prospective areas of investigation that ought to be explored in the future. Nevertheless, although factors such as institutional quality, research and development, and technological innovation are anticipated to exert an influence on the pollution haven and halo hypothesis, the theoretical framework fails to include these specific attributes. These issues, as well as similar ones, might potentially be the focus of future study. In the near future, researchers who seek to highlight the practical consequences of their findings could gain from a specialized terminology that elucidates the interplay between institutional quality and natural resources.
