Synergistic disparities of pollution reduction and carbon mitigation in the industrial chain: Evidence from China's industrial sector.

The synergistic enhancement of pollution reduction and carbon mitigation (PRCM) is an inevitable requirement for China's ecological civilization construction. Existing studies primarily focus on macro-level research, and there is a relative lack of research specifically addressing the micro-level of industrial chains. Based on non-competitive IO tables, this study employed the structural path decomposition analysis method to analyze the synergistic disparities of the PRCM industry chain and its driving factors. The findings reveal: (1) The crucial emission industrial chains for CO2, SO2, and PM show a high overlap degree, accounting for 46.67 %, 46.67 %, 60.00 %, 50.00 %, and 56.67 % during 2002-2020. The PRCM industrial chains are operating at a low synergistic level, with proportions of only 13.33 %, 23.33 %, 20.00 %, and 16.67 %. PRCM exhibits a "similar origin with different paths" phenomenon. (2) China's carbon mitigation policies can reduce pollution, whereas pollution reduction policies have limited carbon mitigation effects. (3) The emission control effect is the primary disparate factor in PRCM synergy, while other factors exhibit consistent impact direction to three emissions. The study's conclusions and corresponding policy suggestions hold significant theoretical and practical implications for relevant authorities to systematically plan synergistic emission reduction pathways and establish targeted synergistic policies.

Assessing the impact of environmental regulations on environmental performance in Chinese cities: A spatial Stochastic Frontier Model with endogeneity analysis.

The Chinese government has implemented environmental regulations to address the deterioration of air quality associated with rapid industrialization. However, there is no consensus on whether environmental regulations are beneficial to environmental performance. The technical challenges related to endogeneity and spatial correlation may bias the estimation of the emission reduction effect of regulations. In this study, we comprehensively evaluate the environmental performance of sulfur dioxide regulations in Chinese cities using a novel stochastic frontier model that introduces the single control function to correct estimation errors caused by spatial spillovers and endogeneity. Our analysis emphasizes that insufficient resolution of endogeneity or spatial spillovers may lead to underestimation or neglect of the environmental performance improvements achieved by these regulations. On the contrary, our revised research results indicate that regulations aimed at reducing sulfur dioxide emissions not only successfully control sulfur dioxide emissions, but also have a positive impact on reducing carbon emissions. In addition, we conduct in-depth research on the mechanisms by which environmental regulations improve performance by stimulating green technology innovation and promoting industrial structure upgrading. Based on our research findings, we propose policy recommendations to establish a city cooperation mechanism of technology exchange to achieve synergistic emission reduction and strengthen regional factor circulation.

Assessing the environmental impacts of clean energy investment in Pakistan using a dynamic autoregressive distributed lag model.

In this study, the authors projected the impacts of clean energy investment on environmental degradation by applying a novel and dynamic Autoregressive Distributed Lag (DARDL) model for Pakistan from 1990 to 2022. Most researchers have used ecological footprint or CO2 emissions indicators to look at how clean energy investment affects environmental degradation, which primarily represents contamination induced by humans' consumption patterns and does not consider the impact of the supply side. Against this background, the study scrutinized the dynamic interaction between clean energy investment and environmental sustainability using the load capacity factor (LCF) as an ecological indicator in Pakistan, including economic growth, population density, trade openness, urbanization, and industrialization in the analysis. The long-run estimates from DARDL indicate that a 1 percent upsurge in clean energy investment mitigates environmental degradation by approximately 0.42 percent on average, controlling for other factors. Further, the study also revealed that a 1 percent increase in clean energy investment diminishes dirty energy consumption by approximately 0.45 percent. The validity of the findings is confirmed using alternate methods, i.e., KRLS. The study recommends that Pakistan prioritize investment in clean energy projects to promote environmental sustainability and enforce environmental regulations to reduce the adverse externalities associated with dirty energy activities.

How can green finance effectively promote low-carbon cities? Evidence from 237 cities in China.

Urban areas contribute 85% of China's CO2 emissions. Green finance is an important means to support green energy development and achieve the low-carbon transformation of high-energy-consuming industries. The motivation of this article is to investigate the impact and mechanism of green finance on urban carbon intensity. Most existing literature uses linear models to investigate urban carbon intensity, ignoring the nonlinear relationships between economic variables. The nonparametric models can fill the inherent shortcomings of linear models and effectively simulate the nonlinear nexus between economic variables. Based on the 2011-2021 panel data of 237 cities in China, this paper applies the nonparametric additive model to survey the influence of green finance on urban carbon intensity. Empirical findings exhibit that green finance exerts an inverted U-shaped effect on urban carbon intensity, indicating that the carbon reduction effect of green finance has gradually shifted from inconspicuous in the early stages to prominent in the later stages. Then, from the perspectives of region, city size, and carbon intensity, this article conducts heterogeneity analysis. The results show that the impact of green finance on various carbon intensities all exhibits obvious nonlinear feature. Furthermore, this article employs a mediation effect model to conduct mechanism analysis. The results display that technological progress and industrial structure are two important mediating variables, both of which produce an inverted U-shaped nonlinear impact on urban carbon intensity.

Internet development, information availability, and Chinese enterprises' cooperative green technology innovation.

Cooperative green technology innovation is potentially the answer to the lack of motivation for independent green innovation, which can effectively alleviate many difficulties faced by enterprises engaging in independent green innovation. Internet development provides new opportunities to stimulate innovative cooperation of enterprises. However, little literature has studied the impact of Internet development on enterprises' cooperative green technology innovation. Based on the data of Chinese A-share 3284 listed companies from 2010 to 2019, this paper uses a panel two-way fixed effects model to assess the effect of Internet development on enterprises' cooperative green innovation. The findings are: (1) Internet development significantly drives firms' collaborative green innovation behavior. The result remains robust even after performing a series of robust tests. (2) The Internet empowers green innovation cooperation among firms by improving information availability, including market information availability and technical information availability. (3) The heterogeneous results show enterprises use the Internet to accomplish high-quality collaborative green innovation. Internet development is more helpful in encouraging the cooperative green innovation of enterprises located in central cities and state-owned enterprises. This study provides novel and targeted policy implications to empower enterprises' green innovation cooperation and drive sustainable economic development through Internet development.

Toward sustainable environmental management: Insights from Chinese first-tier cities' consumers' response to green electricity payment policies.

China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay.

Evaluating forest depletion and structural change effects on environmental sustainability in Pakistan: Through the lens of the load capacity factor.

The pace of species extinction and deforestation has increased dramatically due to the substantial increase in global environmental degradation. This trend is approaching the crucial temperature threshold of 2 °C and calls for more attention. Although previous research has observed the individual impacts of forest depletion, structural change, economic growth, and urbanization on various sustainability outcomes, there has been no previous research into their interrelationships with an emphasis on the load capacity factor (LCF). Furthermore, no previous study has examined the environmental impacts of the abovementioned variables by contrasting the results of LCF and CO2 emissions in Pakistan. Therefore, this research suggests a theoretical framework that integrates these concepts, provides a roadmap for an effective and sustainable mitigation strategy for Pakistan and compares LCF results with CO2 emissions. Using the time-series data from 1970 to 2021, a unique and sophisticated dynamic Autoregressive Distributed Lag (DARDL) technique, the authors found that (i) a 1 % rise in forest depletion leads to a decline in load capacity factor by 0.026 %. (ii) A one per cent upsurge in structural change fosters environmental sustainability by raising the load capacity factor by 0.084 %. (iii) An increase of 1 % in economic growth dwindles the load capacity factor by 0.027 %. (iv) A one per cent surge in urbanization enhances the load capacity factor by 0.029 %. The findings suggest that Pakistan's Government should promote afforestation by emphasizing the constructive role of structural change in achieving environmental sustainability.

Exploring residents' willingness to pay for the research and development of renewable energy: A survey from first-tier cities in China.

The research and development (R&D) of renewable energy (RE) is crucial for cost reduction in electricity generation and enhancing power system stability. Compared to traditional fossil fuels, it demands more financial support. To investigate Chinese residents' willingness to pay (WTP) for the R&D of RE and its influencing factors, we conducted a large-scale online survey in four first-tier cities in China in 2023. The research findings indicate that (1) Chinese residents are willing to pay approximately 31.20 yuan (4.34 USD) per month for the R&D of RE. (2) WTP is higher under a mandatory payment model than a voluntary one. (3) Electricity consumption, environmental concern, environmental behavior, willingness to participate, satisfaction with government RE policies, and trust in the government's environmental governance capability significantly influence WTP. (4) Younger, male, and larger household residents exhibit higher WTP. Based on these findings, targeted policy recommendations were proposed.