A Study on the Efficiency of Green Technology Innovation in Listed Chinese Water Environment Treatment Companies
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1. Introduction
In the 20th century, rapid economic growth and population expansion led to a substantial increase in human consumption of natural resources and consequent environmental damage. Among these issues, water scarcity and pollution have become particularly prominent. The discharge of wastewater from industrial, agricultural, and domestic activities has put significant pressure on water ecosystems, resulting in a growing scarcity of water resources and severe disruptions to water ecology. The importance of water environment governance at a global scale has increased in response to this significant challenge. Taking my country as an example, the urgent need for water environment management is further exacerbated by its large population and uneven distribution of water resources [1].
Green technology is widely recognized as a solution to this problem. It refers to the utilization of advanced scientific and technological innovations that are based on ecological principles and sustainable development concepts. Its primary objectives are to optimize resource utilization, protect the environment, and restore ecological balance [2]. Green technology is a type of technology that takes into account resource and environmental limitations, fulfills corporate social responsibility obligations, and demonstrates effectiveness through innovation. For companies engaged in green technology research and development, the adoption of green technology can bring both environmental and economic benefits [3]. By employing environmentally friendly technology, businesses can enhance their productivity and the quality of their output while reducing their expenses. Furthermore, they can integrate their supply chain at a broader level, leading to the most efficient allocation of resources. This integration can also facilitate the transformation, modernization, and advancement of traditional industries, while strengthening the technological innovation capabilities of industrial companies through digital economic empowerment, thereby enhancing the international competitiveness of these industries. This is also known as green technology innovation efficiency (GTIE). In recent years, there has been increasing interest in both academic and commercial circles in evaluating the effectiveness of green technology innovation in industrial organizations [4].
Green technological innovation possesses both social characteristics, such as environmental protection, energy conservation, and emission reduction, and economic characteristics, including enhancing enterprise production efficiency and competitiveness. This enables it to effectively address the dilemma between economic development and environmental protection [5]. In terms of assessing the efficiency of green technology innovation, the mainstream methods in the academic community can be categorized into two groups: parametric methods, such as the stochastic frontier approach (SFA), and non-parametric methods, such as data envelopment analysis (DEA). For instance, Li et al. measured China’s green innovation efficiency and examined the coupling coordination degree between its green innovation efficiency and ecological welfare performance using a coupling coordination degree model [6]. Jiang et al. evaluated the technical efficiency levels across different regions in China and analyzed their changing trends [7]. Zhang et al. utilized the SFA model to individually measure and analyze the two main components of green technology innovation efficiency, namely technological change and technical efficiency, in 33 countries along the “One Belt and One Road” initiative [8]. However, the SFA method requires the formulation of hypotheses in advance and cannot simultaneously link positive and negative outputs. This study addresses this limitation by incorporating the distance function (DDF) to capture the hindering effect of market factor mismatch. Du et al. analyzed the efficiency of green technology innovation in China’s industrial enterprises in 2014 using DEA models [9]. Their findings reveal that the nationwide technological innovation efficiency has improved, with the highest efficiency observed in the eastern region. Guan et al. focused on industrial enterprises as their research subject and employed a two-stage DEA model to empirically test green technology innovation efficiency [10]. The results highlighted significant variations in efficiency between provinces and regions, indicating the need for improvement. Nasie et al. examined the impact of financial investment and environmental regulations on corporate green technology innovation efficiency using the DEA-Tobit model [11]. They concluded that government actions do not significantly promote positive advancements in corporate green technology. Scholars generally favor the DEA model for calculation methods [12]. For instance, Nasierowski et al. [13] employed the DEA method to quantify green innovation efficiency and examined the outcomes of investment in green innovation processes and their efficiency in 2005 and 2009.
However, while there have been some studies focusing on green technology development, there is a lack of research specifically addressing water environmental regulation. Moreover, many of the existing studies primarily focus on the theoretical aspects and lack practical implementation and efficiency assessments [14]. Additionally, the endogenous logic in several empirical studies has not been fully demonstrated, leading to the low credibility of the conclusions. Our current understanding of the influencing mechanisms for corporate technological innovation is still incomplete. Therefore, this study aims to fill this gap by focusing on the water governance sector in China and utilizing the Malmquist DEA methodology to examine the efficiency of listed water governance companies and the factors influencing their efficiency.
This study focuses on the regulation of the water environment and also evaluates the efficiency of green technology innovation (GTIE) from a new perspective. The aim of this research is to assess how effective GTIE is in water environment management enterprises in our country. The sample for this assessment consists of publicly listed companies in the water management industry. The study primarily examines the relationship between GTIE and various characteristics, such as Research & Development investment and other financial indicators. This research is unique compared to previous studies, as it introduces a novel research subject. Previous studies on corporate innovation efficiency have typically focused on either an entire listed company or an entire city, rather than concentrating on a specific industry. In the traditional research, the focus of the GTIE index has mainly been on the new energy sector, with firms in high-tech industries taking precedence and manufacturing being a secondary component. There has been a lack of research in the water environment management industry. Furthermore, previous research has primarily concentrated on the treatment efficiency of domestic municipal or standard sewage treatment plants, overlooking the examination of water treatment enterprises with corporate organizational structures. Given the broad scope of the research subject, this article primarily evaluates the efficiency of green technology innovation in water treatment firms and its determining factors. Additionally, it explores the reasons for the significant variations observed across different organizations over different years. In the face of significant environmental challenges and emerging economic development scenarios, the adoption of green technology is essential for organizations to ensure their long-term sustainability. Prioritizing technical innovation is crucial for achieving transformation and development more effectively. By measuring and researching the GTIE of firms at the micro-level, more precise management models and strategies to enhance GTIE can be developed.
3. Analysis of the Results
This article employs the DEA-SOLVER Pro5.0 made by Cabit Information Technology Co., Ltd. in China to compute the BCC model with variable returns to scale. It selects input orientation optimization with fixed output to calculate the GTIE of 24 A-share listed enterprises in the water environment treatment industry, resulting in the GTIE values for each enterprise, as shown in Table 4.
In Table 4, EFFCH represents the combined technical efficiency, which indicates how close the decision-making unit is to the optimal production boundary or technological frontier. The greater the technical efficiency, the closer the decision-making unit is to the optimal production boundary, and the higher its efficiency. EFFCH can be further decomposed into pure technical efficiency (PECH) and scale efficiency (SECH). TECHCH represents technological progress, which refers to the outward shift of the technological frontier and the maximum output increase under the current technological level. PECH represents pure technical efficiency, which takes into account factors like corporate management and technology that affect the production efficiency. SECH represents the scale efficiency, which represents the production efficiency as it is affected by the enterprise’s size. TFPCH represents the total factor productivity change and is calculated as TFPCH = TECHCH * EFFCH. The EFFCH value can also be decomposed into pure technical efficiency changes (PECH) and scale efficiency changes (SECH). Unlike the comprehensive technical efficiency index, the Malmquist DEA index model reflects dynamic changes in efficiency, and the EFFCH represents the change in efficiency caused by changes in the relative efficiency.
The average EFFCH value of the 24 water environment governance enterprises as a whole between 2019 and 2022 is below 0.8, specifically 0.672, indicating a low overall GTIE. The ability to convert R&D inputs into technological innovation outputs is poor, highlighting the need for improvement in the overall GTIE of these enterprises. There is significant room for enhancement in their overall GTIE. On the other hand, the mean PECH value of these enterprises over the four-year period is 0.815, indicating a good resource allocation capacity and management level in terms of GTIE inputs and outputs. Furthermore, the mean SECH value is 0.827, suggesting that the 24 water environment treatment enterprises as a whole have a reasonable development scale in green technology innovation. However, the mean PECH value is lower than the mean SECH value, indicating that the low PECH is the primary reason for the low EFFCH. This implies that the water environment management enterprises should prioritize improving their resource allocation capacity and management level (PECH) to effectively enhance their own GTIE.
Considering the average EFFCH values for each enterprise in the water environment management sector, it is apparent that there are significant efficiency differences among the 24 enterprises. The average EFFCH falls between 0.221 and 1, indicating varying levels of efficiency. Out of the 24 enterprises, 11 have an EFFCH higher than the overall average level. Additionally, six enterprises (A3, A8, A2, A20, A17, and A12) have an average EFFCH exceeding 0.8. These enterprises, especially A3 and A8, consistently maintained an EFFCH value of 1 between 2019 and 2022, establishing themselves as benchmark entities in the water environment management sector. The higher mean EFFCH suggests that these enterprises possess a high level of general technical and innovative efficiency (GTIE). On the other hand, 10 out of the 24 enterprises have an average EFFCH below 0.6 (A21, A23, A16, A1, A11, A10, A22, A5, A13, and A4). Among them, four enterprises (A22, A5, A13, and A4) exhibit a comprehensive technical efficiency index below 0.5. The lower average EFFCH values indicate a poor GTIE for these 10 enterprises, which hampers industry-wide efficiency improvement.
The PECH values of the 24 water environment management enterprises range from 0.27 to 1, indicating significant disparities in the resource allocation capability for green technological innovation among the enterprises. Out of the 24 enterprises, 13 have a PECH value higher than 0.8. Moreover, five enterprises (A3, A8, A20, A17, and A7) exhibit a PECH average value of 1 between 2019 and 2022, indicating consistently high levels of resource allocation capability and management proficiency. These enterprises possess robust resource allocation capabilities, facilitating their efficiency in green technology innovation. On the other hand, the mean value of PECH for the 24 enterprises, including A4 and A10, is lower than 0.6, suggesting a poorer resource allocation ability and low management levels for green technological innovation. These enterprises fail to transform R&D investment into green technology innovation output effectively.
From the perspective of SECH, the mean value of SECH for the 24 water environment management enterprises falls between 0.452 and 1, surpassing the mean value of PECH, which represents the minimum land efficiency value for enterprises, indicating a superior performance compared to PECH. A total of 15 of the enterprises exhibit a scale efficiency mean value exceeding 0.8, suggesting that the collective scale development of the 24 enterprises in green technology innovation is favorable. However, the scale efficiency of one enterprise, A5, is below 0.6, indicating that its development scale in green technology innovation is unreasonable, thus impeding the enhancement of its GTIE.
Based on the EFFCH decomposition results mentioned above, out of the 24 enterprises, there are 11 whose scale efficiency is lower than their PECH. These enterprises are A5, A13, A7, A18, A21, A23, A1, A16, A17, A12, and A20. Among these, the average PECH of A5 is higher than the scale efficiency by 0.5. The SECH of these 11 enterprises is lower than their PECH, indicating that the main hindrance to the development of GTIE in these enterprises is low scale efficiency, which means their scale is not optimal. Therefore, these enterprises need to make timely adjustments to their development scale based on their own characteristics and environmental changes in order to effectively enhance their innovation efficiency. In addition to A3 and A8, which have the highest PECH and SECH values, the other types of enterprises (11 in total) have higher SECH values than PECH values. These enterprises are A22, A4, A6, A19, A9, A10, A11, A14, A24, A15, and A2. Compared to scale efficiency, the main reason for the low EFFCH in these types of enterprises is a low PECH, indicating an insufficient resource allocation capacity and a low management level. According to the data results, it is clear that the number of patent applications from water environment treatment firms has significantly increased in recent years. However, the conversion rate of these applications is notably low, limiting the impact on improving their innovation efficiency. To address this issue, enterprises need to adapt to ever-changing market demands by focusing on specific segments of innovation and research and development. This will enable them to develop core technologies and continuously enhance their practical application, thereby creating a distinct competitive advantage. In particular, digital applications play a crucial role in fostering innovation and improving efficiency. Through the utilization of digital information technology, enterprises can automatically collect data on water quality indicators, flow rates, water temperature, and more. By employing machine learning and big data analysis techniques, real-time monitoring of the water environment becomes possible, enabling the prediction of future trends and supporting decision-making processes. Furthermore, artificial intelligence can be utilized to analyze the water treatment process parameters and identify the optimal treatment conditions using optimization algorithms. This approach enhances the water treatment efficiency and effectiveness while reducing energy and resource consumption [17].
Among the 24 enterprises, A21, A16, A9, A11, and A25 exhibit increasing returns to scale, indicating that allocating more resources for research and development will lead to a greater production of environmentally friendly technological advancements. This suggests a growing market demand for eco-friendly innovations. However, these firms face challenges in meeting this demand due to limitations in their current input and output capacities. On the other hand, A22, A20, A3, and A84 enterprises show no change in returns to scale, with A3 and A84 demonstrating constant returns to scale. This implies that increasing R&D inputs will proportionally increase the technological innovation output. The remaining 15 enterprises exhibit diminishing returns to scale, which means that blindly increasing their R&D investment may lead to a decline in the efficiency of their green technological innovation.
In the model construction and analysis above, corporate governance and internal governance models evidently have a significant impact on firms’ R&D efficiency and effectiveness. Both too small and too large of a scale can affect the improvement of the innovation efficiency [18]. Therefore, enterprises should enhance their resource allocation capacity and establish a reasonable enterprise scale and internal management mechanism. They should also optimize shareholders’ equity structure and the organizational scale. Water environment governance enterprises need to reassess their organizational scale and management mechanisms to ensure that all production factors can maximize their benefits. Furthermore, they should adhere to market-demand-oriented strategies and optimize investment into and the allocation of funds, technology, talents, and other resources [19]. Additionally, in the context of carbon reduction efforts, water environment governance enterprises need to upgrade their operational capabilities in two key areas: (1) the ability to conserve energy and minimize consumption to achieve low carbon and low energy consumption; (2) the ability for intelligent operation. For heavy-asset enterprises involved in water governance, improving the intelligent asset operation efficiency and optimizing the investment of funds into appropriate areas and technological research and development are essential.
Table 4 exclusively focuses on static efficiency and neglects analysis of the dynamic perspective regarding the GTIE of each enterprise. Hence, this research utilizes the DEAP2.1 software to calculate and assess the total factor productivity index of 24 companies from 2019 to 2022, considering both dynamic-level and development trends, using the Malmquist index model. The findings are displayed in Table 5 and Table 6.
The TFPCH in the results represents the full factor productivity, which reflects the effectiveness of changes in production and operation over time. It quantifies the total output of each unit or the ratio of total output to all factors input. As shown in Table 4, the mean value of the total factor productivity index for the 24 enterprises in green technological innovation from 2019 to 2022 is 1.022, indicating an upward trend in the 4-year period with a 2.2% increase. This suggests that the efficiency of the 24 enterprises as a whole in green technological innovation has improved. The decomposition of the total factor productivity index shows that the mean value of the technical efficiency change index is 0.88, indicating a 12% decrease in the technical efficiency change index for the 24 enterprises as a whole. Furthermore, the mean value of the technical progress index is 1.162, indicating a 16.2% increase in the technical progress index. This underscores that the improvement in technical progress serves as the primary driving force behind the enhancement of the total factor productivity index.
Analyzing the total factor productivity index for each time period, the index for the four-year period from 2019 to 2022 demonstrates an upward trend from 2019 to 2020, with a 17.8% increase. However, it subsequently declines from 2020 to 2022, with a decrease of 0.7% from 2020 to 2021 and a further decrease of 8.7% from 2021 to 2022. The decomposition of the total factor productivity index reveals that the main reason for the decline in the index between 2020 and 2021 is the decrease in the technical progress index, while the decline between 2021 and 2022 is attributed to the decrease in the technical efficiency index. Further analysis shows that the decline in the technical efficiency index can be traced back to both the PECH index and the SECH index. It becomes evident upon closer examination that the decrease in the technical efficiency index is linked to the simultaneous decrease in both the PECH index and the SECH index.
Out of all the enterprises, 14 of them, accounting for 58.3% of the total, have shown a growth trend in their total factor productivity index between 2019 and 2022. Seven of these enterprises have experienced a growth rate of more than 10%, with A23 exhibiting the largest growth rate at 37.1% over the 4-year period. However, 10 out of the 24 enterprises have demonstrated a decreasing trend in their total factor productivity index: A21, A17, A2, A13, A4, A19, A15, A18, A22, and A6, respectively. An analysis of the reasons for the declining trend in these 10 enterprises, conducted by decomposing the total factor productivity, indicates that the technical efficiency change index of these enterprises is lower than 1, reflecting a declining trend. This suggests that the primary factor contributing to the decrease in the total factor productivity index of these 10 enterprises is the decline in their technical efficiency. Specifically, the technical efficiency change index and the technical progress index of A2 are both lower than 1, and the decline in the total factor productivity index of A2 can be attributed to the simultaneous decline in the technical efficiency change index and the technical progress index. Further decomposition of the technical efficiency change index reveals that the PECH change index and scale efficiency change index of enterprises A21, A2, A4, A15, and A65 demonstrate a declining trend. This indicates that the technical efficiency change index of these five businesses has decreased due to an inadequate resource allocation capacity and an unreasonable scope of operations. On the other hand, the SECH change index of enterprises A17, A19, A18, A13, and A22 is attributed to the decline in the total factor productivity index caused by the decrease in the PECH change index. Based on the above conclusions, enterprises A13 and A22 should prioritize improving their resource allocation capacity and management level to prevent a further decline in the PECH index and effectively enhance their total factor productivity index. Enterprises A17, A19, and A18, on the other hand, need to focus on adjusting their development scale and improving their SECH to prevent a further decline in the SECH index and maintain their output efficiency. Additionally, enterprises A21, A2, A4, A15, and A6 should consider adjusting their resource allocation capacity, management level, and development scale. It is crucial for these enterprises to recognize the interplay between R&D inputs and outputs and strengthen their technological capabilities and the efficiency of technological transformation.
In the aforementioned study, although the overall total factor productivity showed an upward trend, the effectiveness of green technology innovation was impeded by an insufficient technical efficiency within the firms and a low input–output ratio. Consequently, water environment governance enterprises should prioritize the rationality of their research and development investment, optimize the input–output ratio, and establish clear targets and expected conversion rates for technological transformation [20].
4. Conclusions
In this paper, we analyzed the GTIE of 24 listed Chinese water treatment companies by selecting micro enterprise data from 2019 to 2022 and processed the relevant indicator data. We then applied the Malmquist DEA model to quantitatively analyze the factors that influence these companies and constructed a theoretical framework to further investigate their influencing mechanisms.
Our research findings indicate that the average PECH of these 24 companies during the aforementioned period is 0.912; the average value of EFFCH is 0.88; the average SECH is 0.964; and the average TFPCH is 1.022. While the average values are relatively high and the TFPCH increased by 2.2% during the studied period, most companies’ indices do not exceed the average.
Currently, China’s water environment treatment enterprises are experiencing an overall upward trend in their GTIE. However, there is still significant room for improvement, particularly in terms of their ability to translate R&D inputs into technological innovation outputs. Specifically, these enterprises demonstrate a commendable level of PECH and SECH. However, there is a noticeable disparity in efficiency among them, and a strong positive correlation exists between PECH, SECH, and EFFCH. This suggests that technological progress and the development scale are the primary factors influencing EFFCH. Furthermore, their total factor productivity is generally increasing, but it has shown a tendency similar to an “inverted U” over the past four years. There is a considerable variation in the total factor productivity across enterprises, with those performing better in this aspect typically exhibiting higher levels of innovation. Additionally, in the analysis of the causes of innovation efficiency, R&D inputs, the resource allocation capacity, and the management level have been found to have a significant impact on EFFCH. Increasing R&D inputs can lead to higher outputs in green technology innovation, indicating a growing market demand for such innovations. However, the existing input and output levels of these enterprises often fall short of meeting the market demand. This suggests that while increasing R&D inputs can boost technological innovation outputs proportionally, blindly increasing these inputs may lead to a decline in GTIE. Moreover, a low resource allocation capacity and an excessive development magnitude have a detrimental effect on the innovation indices.
This study offers the following recommendations: Firstly, in terms of internal enterprise management, there should be a focus on prioritizing the training and recruitment of skilled individuals in the field of water governance. This will involve establishing a team with professional skills and expertise, enhancing the professionalism of employees, and providing robust talent support to drive the transformation and development of enterprises. Additionally, companies should actively cultivate collaborative partnerships with the government, universities, scientific research institutions, and other entities to collectively advance the development and implementation of water treatment technologies. Through industry–academia–research cooperation, there can be resource sharing, complementary strengths, and the promotion of technological innovation and the transformation of achievements. Secondly, enterprises need to improve their awareness of technological innovation and enhance the effectiveness of water environment management. To achieve dynamic efficiency, organizations should develop a dynamic intelligent decision support system that utilizes artificial intelligence’s self-learning and optimization capabilities to identify and rectify product operational deficiencies, thereby improving and optimizing their products. Continuous data collection, feedback analysis, and strategy adjustments can enhance the efficiency of water environment management. Furthermore, although the aforementioned study did not research and analyze government policies and subsidies, it is clear that the water environment treatment industry has significant policy-oriented characteristics, and its development status and prospects are closely linked to the government’s macro-policy orientation and measures. The government should facilitate the transformation of sewage treatment enterprises into water governance enterprises using financial subsidies, tax incentives, and other policy measures, reducing the enterprises’ transformation expenses and increasing their enthusiasm. Additionally, the government should increase financial investment into the field of water treatment and provide financial support for original and breakthrough technological innovations and project implementation on the part of enterprises [21]. By setting up special funds and guiding social capital investment, the government can promote the development and application of water governance technologies. At the same time, the government should focus on improving the regulatory mechanisms and strengthen the supervision and assessment of water governance enterprises by formulating relevant regulations and standards to ensure that the capital operation norms and technical levels of water governance enterprises meet the required standards.
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