Search Results (735)

In the context of rapid urbanization and climate change, evaluating urban forest development and the effectiveness of related policies is of great significance. This study takes Chinese prefecture-level cities as the research object and constructs an evaluation system for Urban Forest Development Effectiveness (UFDE), encompassing forest networks, forest health, ecological welfare, and development coordination. The analytic hierarchy process–entropy weight method is employed to measure UFDE. On this basis, leveraging the quasi-natural experiment formed by the staggered implementation of the National Forest City Policy (NFCP), this paper applies double machine learning (DML) to identify the causal effects of the policy. The results show that NFCP significantly improves UFDE, and this conclusion remains robust across various model specifications and robustness checks. Meanwhile, the policy effects exhibit significant heterogeneity, being more pronounced in eastern and central regions, as well as in humid climate zones, while being relatively weaker in western and arid regions. Methodologically, this study introduces DML to enhance the precision of causal identification, and in terms of measurement, it achieves a multidimensional, comprehensive evaluation. It provides a new analytical framework for assessing environmental policy effectiveness and offers empirical evidence for optimizing urban ecological governance and promoting green development. Full article

Environmental policies play an important role in promoting corporate green innovation, yet existing studies often treat such policies as a single exogenous shock and pay limited attention to the institutional context in which firms respond. Using the Low-Carbon City Pilot (LCCP) policy in China as a quasi-natural experiment, this study examines how environmental policies influence corporate green innovation. Based on panel data of Chinese A-share listed firms from 2007 to 2023, a staggered difference-in-differences model is employed to identify the policy effect. The results show that the LCCP policy significantly promotes corporate green innovation and stimulates both substantive and strategic green innovation. From the perspective of institutional logics, capital market time orientation plays an important moderating role: long-term institutional investors strengthen the positive policy effect, while short-term institutional investors weaken it. Mechanism tests further show that the policy promotes green innovation mainly by increasing managerial attention to environmental and low-carbon issues, while its effect on temporal attention allocation is not significant. These findings highlight the importance of institutional contexts and managerial attention in shaping firms’ strategic responses to environmental policies and provide new empirical evidence on how environmental governance policies influence corporate green innovation. Full article

In the context of extreme climate events and increasingly stringent environmental regulation, insufficient corporate green resilience has become a micro-level bottleneck to achieving China’s “dual-carbon” targets. Using panel data on Chinese A-share listed firms from 2015 to 2023, this study treats the approval of the National Pilot Zone for Artificial Intelligence Innovation Applications as a quasi-natural experiment and employs a double machine learning (DML)–augmented difference-in-differences framework to estimate the causal impact of the policy on firms’ green resilience. We find that the pilot-zone policy significantly increases corporate green resilience by about 32%, with stronger effects among high-tech firms, non-heavily polluting industries, regulated sectors, and large enterprises. Mechanism analyses show that the policy improves green resilience through four channels—accelerating green innovation, enhancing supply-chain efficiency, alleviating financing constraints, and reducing operating costs—with innovation and supply-chain efficiency playing dominant roles. These findings provide firm-level causal evidence that AI-oriented place-based policies can strengthen firms’ capability to sustain green development under disturbances and inform the coordination of the “Digital China” and “Dual Carbon” agendas. Full article

As a strategic infrastructure supporting high-quality economic and social development, computing infrastructure plays a pivotal role in enabling green transitions. Using panel data from Chinese prefecture-level cities spanning 2007 to 2023 and leveraging the staggered commissioning of 12 National Supercomputing Centers as a quasi-natural experiment, this paper employs a time-varying difference-in-differences (DID) approach to estimate the effect of computing infrastructure on urban green total factor productivity (GTFP). The results indicate that the operation of supercomputing centers has a statistically significant positive effect on urban GTFP, with a magnitude equivalent to approximately 0.83 times the sample standard deviation of GTFP, a finding that remains robust to alternative dependent variable specifications, the exclusion of other policy shocks, and placebo tests. Mechanism analysis reveals that computing infrastructure facilitates green development through three channels: fostering green technological innovation, optimizing energy efficiency, and strengthening environmental regulation. Heterogeneity analysis shows that the positive effect is more pronounced in coastal cities, small-to-medium-sized cities, and regions with weaker digital infrastructure. Spatial analysis further uncovers a distance-decay pattern, with a siphoning effect within a 50 km radius and a spillover effect between 50 km and 200 km from the supercomputing center. This study provides empirical evidence on the environmental consequences of the computing economy and offers policy implications for optimizing computing infrastructure deployment to facilitate green transitions. Full article

In the context of global climate change and industrial low-carbon transition, whether environmental taxes can simultaneously promote environmental and economic benefits by stimulating corporate green investment remains a central issue in academic research. Existing studies have reached mixed conclusions regarding the effects of environmental taxes, emphasizing either the “innovation compensation” effect or the “crowding-out” effect. However, this binary perspective overlooks the internal boundary conditions under which environmental taxes operate, particularly the roles of market competition and firm-level resource endowments. In particular, limited attention has been paid to how competitive market environments shape firms’ responses to environmental regulation. To address this gap, this study develops an integrated analytical framework that combines external market competition with internal firm endowments. Using China’s 2018 Environmental Protection Tax Law as a quasi-natural experiment and a panel dataset of Chinese listed firms from 2009 to 2024, this study employs a Difference-in-Differences (DID) approach to examine the impact of environmental taxation on corporate green investment. The results show that: (1) the environmental protection tax significantly promotes corporate green investment, with substantial heterogeneity across firm size, ownership structure, and regional institutional environments; (2) market competition serves as an important external moderating mechanism, as intensified competition strengthens firms’ incentives to pursue technological differentiation through green investment, thereby generating an “escape-competition effect”; and (3) from an internal perspective, the effectiveness of environmental taxation is also shaped by firm endowments. High investment activity provides the necessary resource buffer to support strategic pivots, whereas rapid revenue growth and high financial slack (excessive cash ratio) generate strategic inertia, thereby attenuating firms’ responsiveness to the tax shock. This study not only provides empirical evidence from China on the mechanisms through which environmental taxes influence corporate green transformation, but also offers important policy implications for improving environmental tax systems in other countries. Full article

Sino–U.S. trade friction (SUTF) has imposed significant shocks on economic systems and firm operations, attracting growing scholarly attention. This study investigates the impact of SUTF on corporate innovation quality and its underlying mechanism. Using the U.S. Section 301 investigation as a quasi-natural experiment, we adopt a difference-in-differences (DID) research design. The results indicate that SUTF significantly enhances corporate innovation quality, and this positive effect is partially mediated by the adoption of artificial intelligence (AI)—a general-purpose technology that reshapes traditional organizational and management systems. Moreover, the innovation-enhancing effect of SUTF is more pronounced among firms with a higher proportion of executives with IT experience and those with stronger corporate governance. These findings contribute to the literature on the economic consequences of SUTF by revealing AI adoption as a novel mechanism. This study also offers practical insights for firms navigating an era of heightened trade tensions and can inform policies aimed at fostering high-quality innovation. Full article

The digital economy has changed the way agricultural production is organized and how rural households access markets, jobs, and information. Yet it remains unclear whether these changes translate into higher income for rural residents, especially in major agricultural regions. This study examines the income effect of digital infrastructure development by using the rollout of the Broadband China policy as a quasi-natural experiment. The analysis draws on panel data for 77 prefecture-level administrative units in the Yellow River Basin, one of China’s major agricultural regions, from 2009 to 2021. A staggered difference in differences model is used to estimate the policy effect. The results show that digital infrastructure development significantly increases rural residents’ income. Under the log income specification, the baseline coefficient indicates an average income increase of about 8.33%. The mechanism analysis shows that innovation capacity and nonfarm employment both serve as positive partial transmission channels, with innovation capacity explaining a larger share of the total effect. The heterogeneity results suggest that the income effect is stronger in regions with higher GDP and larger population size. These findings indicate that digital infrastructure can support rural income growth when it is linked with local innovation capacity, employment opportunities outside agriculture, and rural development policies suited to local conditions. Full article

Against the backdrop of China’s “dual carbon” goals and urban green transition, improving urban land use efficiency is essential for shifting land development from extensive expansion to intensive and low-carbon use. Using the Low-Carbon City Pilot Program as a quasi-natural experiment, this study examines panel data from 285 prefecture-level cities in China from 2007 to 2023. We apply a multi-period difference-in-differences model, a threshold regression model, and a spatial Durbin model to assess the program’s impact on urban land use efficiency. The results show that the pilot program significantly improves urban land use efficiency, and the effect persists over time. This finding remains robust across a series of robustness checks. Heterogeneity analysis shows that the efficiency gains are stronger in cities with lower air pollution control pressure, higher industrial pollution control pressure, and lower fiscal pressure. Further threshold analysis shows that digital connectivity is a key condition for strengthening the policy effect. The spatial analysis suggests that the policy effect shows some spatial association. However, the decomposed indirect and total effects are not robust, so the spatial results should be interpreted with caution. This study provides empirical evidence on how low-carbon city pilots affect urban land governance and land use efficiency. Its conclusions, however, remain subject to limitations related to efficiency measurement, policy identification, and the availability of city-level data. Full article

Rural e-commerce is reshaping agricultural markets, yet its environmental consequences remain insufficiently understood. This study examines how the Rural E-commerce Comprehensive Demonstration (RECD) program affects agricultural carbon outcomes in China. Using a balanced panel of 2152 counties from 2010 to 2022, we employ a multi-period difference-in-differences (DID) model to identify the effect of the RECD policy. The results show that the RECD policy significantly increases total agricultural carbon emissions. Evidence for production expansion and production restructuring suggests that improved market access and stronger price incentives encourage output expansion and a shift toward more market-oriented production, thereby raising aggregate emissions. At the same time, the RECD policy significantly reduces the carbon emission intensity and improves the carbon emission efficiency, indicating better carbon performance per unit of agricultural output. Further analysis shows that this dual result reflects the coexistence of efficiency gains and scale expansion, with the scale effect dominating the technical effect at the current stage. The emission-increasing effect is more pronounced in balanced agricultural areas, poverty-designated counties, counties with weaker initial e-commerce foundations, and counties with higher initial emission levels, while stronger environmental regulation and green technological innovation significantly mitigate this effect. In addition, the RECD policy generates spillover effects on neighboring counties within 50 km. These findings provide empirical evidence on the effects of the RECD policy on agricultural carbon emissions and offer policy guidance for integrating rural e-commerce policies with low-carbon agricultural transformation. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

Comprehensive utilization of crop straw (CUCS) is a critical pathway toward sustainable agricultural development, synergizing food security and carbon neutrality goals. However, there remains a lack of systematic empirical evidence regarding its macro-level productivity associations and the conditions under which they materialize. Based on China’s provincial panel data from 2011 to 2023, this paper takes the CUCS pilot policy launched in 2016 as a quasi-natural experiment and employs the difference-in-differences (DID) model to examine the association between CUCS and grain yield, along with its moderating factors and environmental co-benefits. This study yields four main findings. First, CUCS is associated with higher grain yield in pilot regions, and this finding remains robust after a series of endogeneity and robustness checks. Second, the positive association between CUCS and grain output appears to be moderated by fiscal support and innovation–entrepreneurship. The relationship is more pronounced in regions with higher fiscal expenditures on agriculture and environmental protection, as well as more agricultural patents and agricultural enterprises. Third, heterogeneity analysis suggests that the CUCS–grain output association tends to be stronger in regions with richer groundwater resources and more agricultural meteorological observation stations. Fourth, extended analysis indicates that CUCS is also associated with lower particulate matter and agricultural carbon emissions, a pattern consistent with synergistic environmental benefits. By integrating economic and environmental dimensions into a unified analytical framework, this study provides empirical evidence on the contribution of comprehensive straw utilization to grain output and highlights the enabling role of fiscal and innovation environments. These findings offer integrated evidence from China for the policy evaluation of climate-smart agriculture and contribute to the broader sustainable development agenda. Full article

Public data openness (PDO) is critical for advancing digital government initiatives and sustainable development. This study investigates the impact and underlying mechanisms of PDO on carbon emission efficiency (CEE) using a staggered difference-in-differences (DID) approach. The results reveal that the PDO significantly improves CEE. Mechanism analysis demonstrates that PDO enhances CEE by facilitating digital technology innovation, improving capacity utilization, and fostering industrial structure upgrading. The positive effect of PDO on CEE exhibits heterogeneity across the dimensions of data themes, human capital, green finance development, and land marketization. Furthermore, the Broadband China Strategy (BCS) and the New Energy Demonstration City (NEDC) policy amplify PDO’s positive effect on CEE. This study quantitatively evaluates the economic and environmental effects of data resource openness and sharing, offering insights into deepening data infrastructure development and unleashing data’s potential to promote sustainable development. Full article

The circular economy offers a key pathway to achieve the joint improvement of resource conservation and carbon reduction, yet its causal effect on urban energy efficiency remains insufficiently examined. This paper takes China’s Zero-Waste City (ZWC) policy as a quasi-natural experiment and uses panel data from prefecture-level cities between 2006 and 2023. By applying staggered difference-in-differences and double machine learning methods, we evaluate the effect of urban circular economy transformation on energy efficiency. The results reveal four main findings: (1) The ZWC policy significantly improves energy efficiency in pilot cities. (2) The policy operates through three mechanisms: resource circulation, structural optimization, and innovation compensation. (3) Policy effects are stronger in environmentally regulated cities, large cities, and regions with higher artificial intelligence development. (4) The policy also generates broader benefits beyond energy savings, including coordinated fiscal, economic, and environmental gains. Overall, this paper highlights the spillover benefits of the circular economy from waste reduction to energy conservation and provides policy implications for coordinating waste management and energy transition at the urban level. Full article

Public data openness refers to the practice whereby governments make the raw data collected, generated, and managed during public administration freely available to society through unified platforms for public development and use. As a government-led reform in digital governance, public data openness offers a new approach to strengthen urban export resilience. Using panel data from 283 cities at the prefecture level and above in China from 2009 to 2023, we construct a comprehensive evaluation system to measure urban export resilience. Taking the launch of local public data platforms as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) model and a mediating effect model to examine the impact of public data openness on urban export resilience and its underlying mechanisms. The results show that public data openness significantly improves urban export resilience by improving the institutional environment and enhancing information acquisition efficiency. Heterogeneity analysis further shows that the effect is stronger in eastern cities with higher levels of economic development, higher administrative ranks, and larger urban size. These findings enrich the literature on the economic consequences of public data openness and provide policy recommendations for strengthening urban resilience through digital governance. Full article

How AI can contribute to green sustainable development (GSD) in China is a critical yet underexplored question. Leveraging the staggered implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) as a quasi-natural experiment, this study employs a difference-in-differences approach with panel data from 285 prefecture-level cities (2017–2022). The main findings are threefold. First, AI directly promotes GSD and, more importantly, indirectly enhances GSD by upgrading new-quality productivity (NQP)—a novel mechanism that distinguishes this study from conventional environmental policy evaluations. Second, the facilitating effect is not uniform: significant positive effects are detected in the western, eastern, and central regions, but not in the northeastern region; among major urban agglomerations, the Pearl River Delta, Chengdu-Chongqing, and Yangtze River Deltaexhibit significant effects, whereas the Middle Reaches of the Yangtze River and Beijing-Tianjin-Hebei region does not. Third, spatial spillover analysis reveals that AI’s favorable effect on GSD spreads primarily through intercity similarity in economic development level. These findings provide actionable insights for policymakers aiming to harness AI for sustainable development, highlighting the importance of fostering NQP and designing regionally differentiated strategies. Full article