Impact of environmental regulation on green total factor productivity: a new perspective of green technological innovation

Measurement of eco-efficiency in the horse industry, spatiotemporal evolution and convergence analysis

The horse industry constitutes a vital economic sector in Xinjiang, China. This study quantitatively assesses the sector's sustainable development through eco-efficiency analysis across northern Xinjiang counties from 2001 to 2021. The research employs four analytical methods: the S-SBM model for efficiency measurement, kernel density estimation for distribution analysis, Moran's index for spatial autocorrelation examination, and convergence tests for long-term trend assessment. Results demonstrate a consistent decline in eco-efficiency, decreasing from 0.821 in 2001 to 0.444 in 2021, with an average value of 0.557. Significant regional disparities emerge, with efficiency scores ranging from 0.499 to 1.285 across different prefectures. Spatial analysis reveals pronounced clustering effects, particularly in Yili Prefecture. Convergence tests indicate the presence of β-convergence but the absence of σ-convergence, suggesting narrowing efficiency gaps over time despite persistent regional inequalities. These empirical findings provide substantive evidence for policymakers seeking to enhance Xinjiang's equine economy sustainability and resource efficiency. The study contributes to the limited literature on ecological efficiency measurement in animal husbandry sectors.

Analyzing the influence of financial technology and environmental taxation on energy transformation in BRI nations

This research examined the management, financial technology, and environmental taxation elements impacting energy transformation in Belt and Road Initiative (BRI) countries concerning foreign direct investment (FDI). The study aims to analyze data from 2014 to 2022, encompassing a balanced group of 148 BRI member nations-72 from minimal and lower-middle-class countries and 78 from significant and middle-income industrialized nations. Utilizing the two-step systems generalized method of moments (GMM) framework and verifying with the two-stage least squares (2SLS) approach, the study identified critical drivers and barriers to energy transformation in these countries. The findings indicate that effective management practices, advanced financial technologies, and appropriate environmental taxation policies significantly influence energy transformation and FDI inflows. Additionally, the research reviewed the impact of cruise tourism on human health and environmental ecosystems, highlighting severe environmental consequences such as habitat deterioration, marine pollution, and ecosystem disturbances. Human health issues linked to air and water pollution, including respiratory disorders and water-borne illnesses, were also identified. The socioeconomic effects on nearby communities were significant, underscoring the need for stringent regulations and sustainable practices. This comprehensive analysis provides essential insights for policymakers, industry stakeholders, and scholars, advocating for integrated policies to promote sustainable energy transformation and mitigate the adverse effects of cruise tourism.

Navigating Asia's sustainable growth trajectory: a cross-regional comparative analysis

This study examined the green total factor productivity (GTFP), technical progress change (GTC), and technical efficiency change (GEC) trajectories across 39 Asian countries from 2006 to 2021. GTFP was assessed across five Asian regions using the global Malmquist-Luenberger (GML) index with a slacks-based measure model and directional distance function. The results of the study show that West Asia, with a mean GTFP value of 1.0011, is ahead of Southeast Asia and South Asia, while Central and East Asia show room for improvement. Gini index analysis further shows that GTFP varies significantly within and outside the region, with large fluctuations especially in Southeast Asia and South Asia. GTC stagnated significantly in 2009 and 2015, indicating a bottleneck in technological progress, while GEC showed a positive upward trend, reflecting the improvement in resource efficiency. The 'decoupling' of GTC and GEC highlights the inconsistency between efficiency growth and the pace of technological progress, particularly in East and Central Asia. In 2021, Asia's average GTFP reaches 1.0048, signaling the region's progress towards sustainable productivity. This study provides important theoretical and practical implications by quantifying the regional differences in GTFP, GTC, and GEC, which provide a quantitative basis and decision-making reference for the formulation of green development policies in the Asian region.

Administration or marketization: Environmental regulation, marketization and agricultural green total factor productivity

The mature market can effectively reflect the value of green agricultural products, but the market in developing countries is developing slowly, so how to implement administrative environmental policies while cultivating the market has become the key to the green development of agriculture. In this paper, the government and the market are discussed under the common goal of agricultural green development. Based on the provincial panel data of China, taking agricultural green total factor productivity (AGTFP) as a breakthrough point, the relationship between environmental regulation tools, marketization processes and agricultural green development, specific applicable conditions and transmission mechanisms are explored, and further subdivided into factor and product markets to verify their correlation with AGTFP. The results show that environmental regulation and marketization processes and AGTFP are positively correlated. The former can establish a positive relationship with AGTFP through resource reallocation and technological innovation, and the latter can do so by improving the level of information and land transfer and perfecting infrastructure construction. These findings will provide new ideas for developing countries similar to China's agricultural development and enlighten developing countries pay full attention to and cultivate the market while formulating appropriate environmental regulation policies. In addition, they also need to coordinate the development of technology and organizations.

The impact of economic growth targets on the level of green development-A perspective on officials' promotion incentives and environmental regulations

Currently, China is at a critical stage of accelerating the green transformation of its economic development model, with considerable attention being paid to achieving this transformation while maintaining moderate economic growth. This study uses 271 prefecture-level cities in China from 2006 to 2020 to examine the impact of local government economic growth target constraints on the level of green economic development and to elucidate the underlying mechanisms. The results show that economic growth targets significantly inhibit the level of urban green development, with this effect being more pronounced in the economically developed eastern regions of China. Hard constraints on economic growth targets have a greater inhibitory effect on green development than soft constraints. The greater the promotion pressure on local officials, the stronger the inhibitory effect of economic growth target constraints on green development. The test of the mediation effect model reveals that economic growth targets can inhibit green development by affecting the degree of regional marketization, leading to mismatches in the capital and labor markets. Moreover, environmental regulations can mitigate the inhibitory effect of economic growth targets on green development levels. The conclusions of this study provide useful insights for local governments to optimize economic development target constraint mechanisms and accelerate high-quality green economic development.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist-Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Research on the coupling coordination characteristics and convergence of digital finance and regional sustainable development: evidence from Chinese city clusters

The study examines the digital finance (DF) and regional sustainable development (RSD) across 90 cities within six major city clusters in China over the period from 2011 to 2020. By constructing an evaluation index system for DF and RSD, we employed the entropy value method to assess their levels, and the coupling coordination degree (CCD) model to evaluate their interplay. Our analysis extended to temporal and spatial disparities, distribution dynamics, and the convergence of CCD through kernel density estimation, Markov chain analysis, σ -convergence, and β -convergence techniques. The results indicate a consistent upward trend in CCD, yet it remains at a low level with pronounced regional disparities and temporal characteristics. The kernel density distribution's central tendency has shifted rightward progressively, albeit with a decelerating pace annually. The Markov transition probability matrix suggests a stable CCD across various levels, hinting at "club convergence". Furthermore, both σ -convergence and β -convergence analyses reveal significant convergence trends in CCD, enhanced by economic growth factors. Using the Quadratic Assignment Procedure (QAP) method, we found that regional economic growth disparities significantly influence the CCD's regional variances.

The impact of peer effect of industrial robot application on enterprise carbon emission reduction

The application of intelligent technology, such as industrial robots, is related to the environmental governance effectiveness of enterprises and the realization of the goal of "carbon peak and carbon neutrality". Due to their similar external environments, driven by economic rationality, peer enterprises will mimic the robotics applications of other enterprises, which in turn will affect the enterprises' carbon emissions. However, little literature has explored the impact of industrial robot application on enterprise carbon emissions from the perspective of peer effect. Based on the data of Shanghai and Shenzhen A-share manufacturing listed enterprises in China from 2011 to 2021, this paper explores the impact of industrial robot application on carbon emission reduction of manufacturing enterprises from the perspective of peer effect. It is found that the industry peer effect and regional peer effect brought by the application of industrial robots are conducive to promoting the carbon emission reduction of enterprises. Among them, the industry peer effect of industrial robot applications promotes carbon emission reduction by enhancing the green innovation ability of enterprises, while the regional peer effect promotes carbon emission reduction by improving the service level of enterprises. It is further found that the degree of industry competition and the level of environmental regulation have inverted U-shaped moderating effects on the industrial robot application industry peer effect, regional peer effect, and enterprises' carbon emission reduction, respectively. The results enrich the research on the impact of industrial robot application on carbon emission reduction of manufacturing enterprises and provide policy implications for improving the environmental performance of enterprises.

A study on the impact of ESG rating on green technology innovation in enterprises: An empirical study based on informal environmental governance

Improving corporate green technology innovation is a key link in achieving green transformation and development. Compared with formal environmental regulations that force companies to carry out green innovation passively, ESG ratings under soft environmental regulations can better stimulate the internal motivation of companies. This study uses the ESG ratings of listed companies published for the first time by SynTao Green Finance as an exogenous impact. Taking China's A-share listed companies from 2011 to 2022 as a research sample, the multi-period differences-in-differences model was used to empirically test the impact of ESG rating soft supervision on corporate green technology innovation. The results show that the impact of ESG rating events as a soft market regulation has a significant positive impact on the improvement of corporate green technology innovation. This conclusion still holds after a series of robustness tests. Meanwhile, enterprise digital transformation plays a positive regulatory role. The heterogeneity test shows that the green technology innovation of state-owned enterprises is more affected by ESG ratings. Mechanism research has found that ESG rating events promote corporate green technology innovation by easing corporate financing constraints and reducing managerial myopia. Further research found that under the influence of the external environment, intensified market competition and increased attention from the capital market are also conducive to the improvement of corporate green technology innovation. This study strengthens the corporate ESG concept under the guidance of green development and provides empirical evidence for promoting corporate green transformation.

How does artificial intelligence affect the transformation of China's green economic growth? An analysis from internal-structure perspective

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.

Heterogeneity effects of environmental regulation policy synergy on ecological resilience: considering the moderating role of industrial structure

Given the great importance attached to ecological civilization and green development, exploring the heterogeneous effects of environmental regulation policy synergy on ecological resilience holds significance for improving environmental protection and the design of environmental policies. Based on the policy synergy perspective, this paper uses 30 provinces (municipalities and autonomous) in China as the research sample. Bayesian quantile regression is employed to explore the heterogeneous effects of environmental regulation policy synergy on ecological resilience from 2007 to 2021, and the moderating effect of the industrial structure is examined. The results indicate the following: (1) there is significant heterogeneity and variability in the effect of environmental regulation policy synergy on ecological resilience. Specifically, the effects of policy mixes 12, 13, and 23 on ecological resilience shows a U-shaped trend, while the impact of policy mix 123 on ecological resilience shows a positive effect. (2) There are significant differences in the effects of environmental regulation policy synergy under different quantiles of ecological resilience. Taking policy mix 12 as an example, we find that the effect of policy synergy on ecological resilience tends to decrease and then increase at a lower quantile. Additionally, the effect of policy synergy on ecological resilience tends to be positive at a higher quantile. (3) There are significant differences in the moderating effects of the industrial structure. Policy mix 12 can effectively enhance ecological resilience through industrial structure upgrading, while the moderating effects of alternative policy combinations are deemed insufficient. Finally, relevant policy recommendations are proposed to effectively improve ecological resilience.

Population agglomeration in Chinese cities: is it benefit or damage for the quality of economic development?

This paper explores the impact of population agglomeration on urban economic development quality in various cities of China. The results show that population agglomeration significantly contributes to the improvement of urban green total factor productivity by increasing population diversification, promoting knowledge spillovers, and reducing pollution emission intensity. Moreover, we find that population agglomeration in type II big cities and type I large cities significantly improves green total factor productivity, while the impact of population agglomeration in metropolises and mega-cities on green total factor productivity is not significant. On the one hand, type II big cities and type I large cities are in the period of rising economic development, the population has not yet reached saturation, and there is still a large demographic dividend space. On the other hand, excessive population agglomeration also brings about "urban diseases" such as population congestion and traffic congestion, especially in the metropolises and mega-cities. Finally, using data on producer services and its sub-sectors, we identify a more significant driving effect of high-end talent agglomeration on green total factor productivity.

The impacts of China's dual carbon policy on green innovation: Evidence from Chinese heavy-polluting enterprises

Encouraging enterprises to engage in green innovation is a potent strategy for reducing carbon emissions from production. As one of the largest carbon emitters, China has launched a series of policies to achieve carbon peaking and neutrality collectively referred to as China's dual carbon policy. However, existing research on the impact of China's dual carbon policy on green innovation by heavy-polluting enterprises is insufficient. To fill this gap, this study constructed a theoretical model to draw hypotheses about the impact of the dual carbon policy on enterprises' green innovation and verified this impact using a difference-in-differences model to conduct a quasi-natural experiment based on data from 2010 to 2022 from Chinese A-share-listed enterprises. The results indicate that the dual carbon policy had a significantly positive influence on green innovation in heavy-polluting enterprises. Moreover, environmental tax mediated this effect, while enterprises' total costs and subsidies positively moderated it. Additionally, the impact exhibited variations based on several key factors, including green patent type, carbon emissions, enterprise ownership structure, and Environmental, Social, and Governance ratings. This study supplements related research on the effects of environmental policy on green innovation and provides both theoretical and empirical support for adapting subsequent environmental policies.

Does environmental regulation lessen health risks? Evidence from Chinese cities

Introduction

Atmospheric pollution is a severe problem confronting the world today, endangering not only natural ecosystem equilibrium but also human life and health. As a result, governments have enacted environmental regulations to minimize pollutant emissions, enhance air quality and protect public health. In this setting, it is critical to explore the health implications of environmental regulation.

Methods

Based on city panel data from 2009 to 2020, the influence of environmental regulatory intensity on health risks in China is examined in this study.

Results

It is discovered that enhanced environmental regulation significantly reduces health risks in cities, with each 1-unit increase in the degree of environmental regulation lowering the total number of local premature deaths from stroke, ischemic heart disease, and lung cancer by approximately 15.4%, a finding that remains true after multiple robustness tests. Furthermore, advances in science and technology are shown to boost the health benefits from environmental regulation. We also discover that inland cities, southern cities, and non-low-carbon pilot cities benefit more from environmental regulation.

Discussion

The results of this research can serve as a theoretical and empirical foundation for comprehending the social welfare consequences of environmental regulation and for guiding environmental regulation decision-making.

Research on the spatial patterns and evolution trends of the coupling coordination between digital finance and sustainable economic development in the Yellow River Basin, China

In the current global context, digital finance (DF) and sustainable economic development (SED) are important topics. The synergies between DF and SED have already been proven. However, the measurement and quantitative analysis of the coupling coordination degree (CCD) of DF and SED have not received sufficient attention to date. Based on data from 55 cities in the Yellow River Basin (YRB) from 2011 to 2021, this study constructs an evaluation index system of DF and SED and measures their level, respectively. The proposed CCD model is then used to measure the CCD between the two systems. In addition, kernel density estimation, Markov chain, σ-convergence, β-convergence, and the quadratic assignment procedure (QAP) method are used to study the spatial pattern, distribution dynamic evolution trend, convergence, and influencing factors of the regional differences in the CCD. The results show that: (1) From 2011 to 2021, the CCD level showed a stable upward trend and regional heterogeneity, and the time stage characteristics were more obvious. (2) The center position and change interval of the overall distribution curve of the kernel density estimation gradually shifted to the right. The Markov transfer probability matrix shows that the CCD is more stable among different levels, indicating a phenomenon of "club convergence". (3) A convergence analysis shows that there are significant σ-convergence, absolute β-convergence, and conditional β-convergence. (4) The QAP regression shows that factors such as the regional differences in GDP per capita have a significant impact on the regional differences in the CCD. This study offers a comprehensive structure that can be used to examine the synergistic effects between DF and SED; the research findings can also provide perspectives for other areas.

Does digital economy curb carbon intensity? New insights from China

China is in the period of vigorously developing the "digital economy" and "low-carbon economy," facing the double pressure of realizing the "dual-carbon" target and maintaining stable economic growth. This paper tests the role of the digital economy (DIEC) in the process of carbon emission reduction for the advancement of low carbon economy based on this issue from the perspective of carbon intensity (CI) by constructing a fixed effects and mediation effects model using data from 30 areas from 2011 to 2021. The study results show that at the national level, the advancement of DIEC significantly inhibits CI, and the conclusion still holds after various robustness tests. From the geographic region level, the suppression of CI by digital economic advancement has the most substantial impact in the central region. Although the eastern and western areas have similar results, the significance level is higher in the east region. When considering the economic development level, the impact of DIEC on CI is more significant in areas with lower economic development than those with higher economic growth. In analyzing the path of the DIEC affecting CI, it is found that the DIEC mainly inhibits CI by promoting technological advancement and reducing energy consumption intensity.

Towards sustainable development: what carbon trading pilot policy has been done for mitigating carbon emissions and air pollution?

This study examines the impact of carbon trading pilot policy (CTPP) on carbon emissions (CO2) and air pollution (Ap) using the difference in differences method (DID) utilizing panel data from 30 Chinese areas spanning from 2008 to 2020. The results indicate that CTPP implementation can effectively decrease CO2 and Ap. CTPP can reduce CO2 and Ap through positive incentive effects that promote industrial structure upgrading and drive technological progress. Moreover, CTPP exhibits significant regional variation, with CTPP significantly reducing CO2 in both the eastern and central and western regions. CTPP do not show an effective reduction in Ap in eastern region, while effectively reduce Ap in central and western regions.

Digital infrastructure construction, diversified environmental regulation, and dual control of urban carbon emissions-quasi-natural experiment from "broadband China" strategy

This study used three batches of "broadband China" strategies (BCS) implemented from 2014 to 2016 as quasi-natural experiments (QE) to distinguish the level of urban digital infrastructure construction (DIC). Using 231 prefecture-level cities in China from 2011 to 2019 as research samples, a progressive differences-in-differences (DID) model was used to empirically test the relationship characteristics between DIC and urban carbon emission (CE) total and intensity. The results show that (1) DIC has a significant negative correlation with total carbon emissions (TCE) and carbon emission intensity (CI), which is conducive to the "dual control" of urban CE, which is still valid after the five robustness tests. (2) The mechanism test shows that the DIC is conducive to improving the level of formal environmental regulation (FER) and informal environmental regulation (IER) and strengthening the synergy between formal and informal environmental regulation (ER), so as to achieve "dual control" of carbon emissions, of which the synergy is the most significant, followed by FER. (3) In heterogeneity research findings, only the third batch of pilot projects can achieve "dual control" of CE, which has the advantage of latecomers. The "dual control" effect of carbon emissions in DIC is more significant in cities with higher levels of marketization and eastern cities. Therefore, DIC plays the role of "icing on the cake," not "providing charcoal in the snow."

Environmental taxes, enterprise innovation, and environmental total factor productivity-effect test based on Porter's hypothesis

Under the increasingly severe environmental constraints, improving environmental total factor productivity (ETFP) is the fundamental way for the sustainable development of heavily polluting enterprises. Based on 3463 panel data of A-share listed companies in China from 2011 to 2019, this paper employs Porter's hypothesis (PH) framework to explore the impact of environmental tax (EN_T) on enterprise innovation and environmental total factor productivity for the heavily polluting manufacturing industry using the propensity score matching (PSM) method. The empirical results show the following. (i) Environmental taxes positively affect enterprise innovation (EI) and environmental total factor productivity (ETFP). (ii) Mechanism analysis verifies a partial mediating effect for EI between EN_T and ETFP. (iii) Regional heterogeneity analysis illustrates the differences in the impact of environmental taxes on innovation quality. (iv) Individual heterogeneity analysis shows that the "strong Porter hypothesis" is only valid for large-scale enterprises. The results are of great importance for both government and enterprises to improve the EN_T system and optimize the allocation of resources in realistic practice.

Have public environmental appeals inspired green total factor productivity? empirical evidence from Baidu Environmental Search Index

Literatures focus on whether formal environmental regulatory policies can effectively stimulate green total factor productivity (GTFP), but generally ignore the role of informal institutions such as public environmental appeals. This paper uses inter-provincial panel data and Non-Radial Slacks-Based Measure (SBM) model to measure GTFP. With the Baidu Environmental Search Index presenting the public environmental appeals, the dynamic panel System Generalized Method of Moments (SYS-GMM) and Differenced Generalized Method of Moments (DIFF-GMM) methods are used to explore the effect, transmission mechanism and conditions of the public's environmental demands on green total factor productivity. The study concludes that public environmental appeals are beneficial to improve GTFP, and this result still holds after endogenous and robustness tests. The mechanism results show that the green technology innovation effect is an important mechanism for public environmental appeals to improve GTFP. In addition, public environmental appeals of different pollutants and different locations have heterogeneous effects on GTFP. In addition, the public wealth level, human capital level, and government institutional environment play an important role in the impact of public environmental appeals on GTFP.

How does digital technology empower urban green development efficiency in the Beijing-Tianjin-Hebei region-mechanism analysis and spatial effects

Under the strategy of "Digital China" and "Sustainable Development," the synergistic development of digital economy and green economy has become a crucial topic. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study investigates the direct effect, intrinsic mechanism, and spatial spillover effect of digital technology development (DTD) on urban green development efficiency (GDE). The empirical results show that (1) DTD significantly improves urban GDE in the BTH region, and it passes the endogeneity test, (2) DTD can enhance urban GDE by improving the environmental regulation intensity and technological innovation level in the BTH region; however, the industrial structure optimization weakens the promotion effect of DTD on urban GDE in the BTH region, which shows a "masking effect," (3) the kernel density estimation method and ArcGIS technology reveal the existence of "digital divide" and GDE differences among cities in the BTH region. Moreover, the spatial distribution pattern of DTD gradually forms "H-H" and "L-L" clusters in the BTH region, and (4) DTD also increases the GDE of neighboring cities through spatial spillover effects in the BTH region, and it passes the robustness test of replacing the spatial weight matrix. This study is important for the BTH region to simultaneously solve economic development and environmental problems in the context of digitalization.

Does the US regional greenhouse gas initiative affect green innovation?

This study measures the impact of the implementation of the Regional Greenhouse Gas Initiative (RGGI) on firms' green innovation initiatives. We used 20 years of panel data from the Fortune 500 list of the US largest companies. Based on DID, a benchmark regression, the RGGI has a significant adverse effect on the green innovation of Fortune 500 companies, and we verified these findings with multiple robustness tests. As we investigate how energy-intensive industries were affected by RGGI, we found that it slowed down green innovation, but it was not statistically significant. This study provides a novel perspective on how the RGGI influences green innovation in firms and how different types of sectors respond to the policy. The findings indicate that the "weak" Porter Hypothesis has not been confirmed in the present carbon trading market (particularly the RGGI) for Fortune 500 firms in the USA. In terms of policy, we believe that a well-covered and differentiated legislation that fosters green innovation while being realistic about the policy's goal and the firm's environmental attitude, like emissions reduction through green innovation, is essential.

The Effect of Farmland Transfer on Agricultural Green Total Factor Productivity: Evidence from Rural China

Exploring the effect and mechanism of farmland transfer on agricultural green total factor productivity (AGTFP) in China is of great significance for exerting the effectiveness of China's farmland transfer policy and promoting green agricultural development. Based on panel data from 30 provinces from 2005 to 2020, this paper applies a two-way fixed effects model to analyze the impact of farmland transfer on AGTFP, and the mechanism of farmland transfer on AGTFP is also investigated. We find that farmland transfer has a significant and sound promoting effect on AGTFP, with respect to multiple robustness checks; there is heterogeneity regarding the impact of farmland transfer on AGTFP in terms of food functions, and farmland transfer can promote regional AGTFP through nonagricultural labor transfer and agricultural technology utilization. When considering the fact that farmland transfer has increased China's AGTFP, the Chinese government should continue to adhere to the farmland transfer policy, accelerate nonagricultural labor transfer, improve the level of agricultural technology utilization, and ultimately promote green agricultural development.

Study on the impact of informal environmental regulation on substantive green innovation in China: evidence from PITI disclosure

Informal environmental regulation plays an important role in green innovation, which is of great significance to realize the win-win situation between environmental protection and economic development. However, few studies have focused on the incentive effect of informal environmental regulation on innovation motivation. Based on the panel data of 286 cities from 2003 to 2018, the DID (difference-in-differences) model is used to investigate the impact of pollution Information Transparency Index (PITI) published by ENGOs (Environmental Non-governmental Organizations) on substantive green innovation. The results show that PITI disclosure has a significant positive and robust effect on substantive green innovation in China. Additionally, heterogeneous analysis shows that key environmental protection and highly innovative cities benefit more from PITI disclosure. The effect in the second stage is stronger than in the first stage. Mechanism analysis shows that PITI disclosure enhances substantive green innovation through FDI (Foreign Direct Investment) and human capital channels. Based on the above conclusions, three policy implications are put forward in this paper.

China's logistics green competitiveness promotion path: a fuzzy-set qualitative comparative analysis approach

Increasingly serious environmental problems are forcing many industries to gradually turn to green development, and green competitiveness has become the vision of policymakers and industry practitioners. The impact of the development of the logistics industry on the environment cannot be ignored, but the promotion path of its green competitiveness has not been fully studied. Based on Porter's diamond model, this study uses fuzzy-set qualitative comparative analysis to explore the condition configuration of the logistics industry in 30 provincial regions in mainland China to enhance green competitiveness and effectively combine theory with practice. Factors considered include logistics technology innovation ability, logistics market demand, proportion of tertiary industry, management level of logistics enterprises, environmental regulation, and the internet. The results show that the green competitiveness of the logistics industry is influenced by many factors and cannot be sufficiently explained by a single factor. The findings point to three paths with equal authenticity to improve the green competitiveness of the logistics industry. By comparing the same types of high configuration and low configuration, this study finds that environmental regulation is an important external driving force for regions with low added value of output products, and for regions with certain geographical advantages and numerous logistics enterprises, in addition to the management level of logistics enterprises, support from the internet is also particularly important in improving the green competitiveness of logistics. These findings can provide a practical reference for the promotion of green competitiveness in the logistics industry in different regions.

The Impact of Foreign Direct Investment on Urban Green Total Factor Productivity and the Mechanism Test

This paper employs the slack-based model directional distance function to measure the green total factor productivity of each city, using the panel data of 284 prefecture-level cities in China from 2004 to 2019 and considering the unexpected output. The results are as follows: ① Foreign direct investment significantly suppresses the improvement of urban green total factor productivity, and the negative impact on the green technology progress index is the main reason to inhibit the increase of the green total factor productivity. The results are still significant through a series of robustness tests such as replacing variables and eliminating outliers; the positive intermediary effect of scientific and technological innovation exists, and the Sobel test and bootstrap random sampling test are passed. The upgrading of industrial structure has a positive regulating effect on the improvement of urban green total factor productivity. ② The impact of foreign direct investment on urban green total factor productivity has regional heterogeneity. The inhibitory effect of foreign direct investment on resource-based cities and non-coastal cities is greater than that on non-resource-based cities and coastal cities, and the negative impact on China-Europe train opening cities is greater than that on non-opening cities. Accordingly, the paper puts forward policy suggestions from the aspects of improving the quality of foreign direct investment and implementing differentiated management.

Environmental Regulation, Urban-Rural Income Gap and Agricultural Green Total Factor Productivity

Environmental regulation is the basis for achieving green agricultural development, and urban-rural integration is the key to optimizing the allocation of agricultural elements and achieving sustainable agricultural development. This paper aims to investigate the spatial spillover effect of environmental regulation on China’s agricultural green total factor productivity (AGTFP) and examine the mediating effect of the urban-rural income gap. Both the Super-SBM-DEA model and the Global Malmquist–Luenberger productivity index are used to account for the AGTFP of China’s 30 provinces, and the spatial Durbin model and the mediating effect model are used to analyze the impact of environmental regulation. We found that firstly, during the sample period, China’s AGTFP has increased with an average annual growth rate of 3.27%, which is mainly promoted by agricultural green technology progress (AGTC). Secondly, both the direct and spatial effects of environmental regulation on AGTFP show a significant “U”-shaped feature and have regional heterogeneity based on differences in economic development levels and factor endowments. Thirdly, there is an “inverted U”-shaped relationship between environmental regulation and the urban-rural income gap, and the urban-rural income gap negatively affects AGTFP. Based on the empirical results, we propose that the Chinese government should pay attention to green technology innovation, break the market segmentation, promote urban-rural integration, and then promote the AGTFP.

Research on the Policy Effect and Mechanism of Carbon Emission Trading on the Total Factor Productivity of Agricultural Enterprises

Given the rural revitalization strategy in the new era, agricultural development is faced with the dual constraints of resources and the environment. Promoting the green development of agriculture is one of the important missions to solve major social issues in the new era. The implementation goal of the carbon emission trading system is to achieve a win-win situation between carbon emission reduction and green development. To evaluate the effectiveness of the carbon emission trading system on agricultural enterprises, this paper uses a double-difference model to analyze the policy effect and mechanism research path of the impact of the carbon emission trading system on the total factor productivity of agricultural enterprises. The results based on the panel data of listed agricultural companies from 2010 to 2020 show that (1) carbon emission trading rights have significantly improved the total factor productivity of agricultural enterprises; (2) green innovation in carbon emission trading rights have an impact on the total factor productivity of agricultural enterprises; and (3) heterogeneity analysis shows that the effect of carbon emission trading rights on the total factor productivity of agricultural enterprises mainly exists in large-scale, nonstate-owned, high-debt enterprises, enterprises in the eastern region, and enterprises with government subsidies. Therefore, in the future, China should continue to implement the current carbon emission trading rights system in air pollution control, and at the same time, it needs to be supplemented by government intervention and other means for long-term governance. In conclusion, the study provides a reference value for promoting the realization of the long-term goal of “low carbon” and “high quality” green development of agricultural economy and for making reasonable and effective behavioral decisions for the survival and development of enterprises.

Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China

Environmental regulation is essential to promote green and sustainable development in dairy farming. Nevertheless, limited studies have focused on the impact of environmental regulation on the green total factor productivity (GTFP) of dairy farming. This study measures the GTFP of dairy farming in 27 provinces in China during 2009–2020 using the Slack Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index. In addition, random effects and threshold regression models are used to measure the impact of environmental regulations on the GTFP of dairy farming. The results demonstrate the fluctuating growth of the GTFP of dairy farming and that technical efficiency is the primary driver of the GTFP growth. The annual growth rate of GTFP is the highest in large-scale dairy farming (3.27%), followed by medium-scale dairy farming (2.73%) and small-scale dairy farming (1.98%). Furthermore, environmental regulation positively affects the GTFP and has a threshold effect on the GTFP, with the urban–rural income gap as the threshold variable in medium-scale dairy farming and small-scale dairy farming. The impact on the GTFP can be significantly negative if the urban–rural income gap crosses the threshold value. Overall, this study provides some policy recommendations for attaining green and sustainable dairy farming development in China.