Search Results (25)

Against the backdrop of deepening global climate governance and the ongoing advancement of the dual carbon goals, clarifying whether climate risk management can effectively drive improvements in urban green energy efficiency holds significant importance for synergistically advancing climate adaptation and green transition. This study employs data from 284 prefecture-level and above cities in China spanning 2012–2023, utilising the climate-resilient city pilot policy as a quasi-natural experiment. By integrating dual machine learning models with spatial lag models, it systematically examines the causal effects, transmission mechanisms, and spatial spillover characteristics of climate risk governance on urban green energy efficiency. Findings reveal: ① Climate-resilient city development significantly enhances local green energy efficiency through three pathways: green finance development, green technological innovation, and industrial structure upgrading; ② The impact exhibits pronounced spatio-temporal heterogeneity, characterised by delayed and amplified effects, particularly pronounced in inland, central cities, and non-resource-based cities; ③ The impact exhibits a pronounced positive spatial correlation, not only catalysing local green transitions but also generating positive spatial spillovers to neighbouring regions, indicating potential for regional collaborative development. Additionally, green finance itself possesses positive cross-regional spillover effects. This study provides empirical evidence and policy recommendations for optimising climate adaptation policy design, strengthening green finance coordination, and advancing regionally linked green transitions. Full article

This study asks whether, and through which firm-level channels, the green data center (GDC) pilot improves corporate sustainability, and it aims to quantify the policy effect and identify its transmission pathways. Under the dual shift in digital transformation and low-carbon transition, the energy demand and emissions generated by data centers have become important constraints on the sustainability performance of firms. This study regards the 2015 “National Green Data Center Pilot Work Plan” as a quasi-experimental policy shock. In terms of methods, using panel observations of Chinese A-share companies listed in Shanghai or Shenzhen during 2010–2024, a difference-in-differences (DID) strategy is employed to estimate the effect of GDC policy and to identify its transmission pathways for corporate sustainability. In terms of results, the empirical estimates indicate that the GDC pilot improves corporate sustainability, and the effect is robust to fixed-effects specifications, an instrumental-variable strategy, propensity-score matching, and a placebo test. Mechanism tests show that the pilot works through three channels: greater green innovation output and technical value, reduced financing frictions, and enhanced green governance capacity. Heterogeneity tests further show that the effect is stronger among firms with a higher level of digital transformation, a larger share of skilled technical personnel, stronger internal control, and greater executive green awareness, and that it is clearer where regional environmental regulation is stricter and market competition is more intense. In terms of conclusions, by integrating institutional pressure theory with the resource-based view, this study explains how the GDC pilot is translated into a firm-level sustainability advantage, and it offers evidence for refining GDC policy design and advancing the coordinated digital and green transformation of enterprises. The novelty of the study lies in providing firm-level causal evidence within a unified “pressure-to-capability” framework, in opening the three transmission channels, and in specifying the technological, organizational, and environmental conditions under which the effect is stronger. Full article

To advance sustainable development, China has introduced low-carbon city pilots (LCCP) and new energy demonstration city pilots (NEDC) as important institutional innovations. Using 2006–2023 panel data for 274 Chinese cities, we treat the dual-pilot policy of LCCP and NEDC as a quasi-natural experiment. We measure carbon emission performance (CEP) via a super-efficiency SBM-GML index incorporating social welfare and undesirable outputs, and use double machine learning (DML) to estimate the policy’s impact on CEP. We find the dual-pilot policy is associated with significantly improved urban CEP, with a stronger effect than either single pilot alone. Mechanism tests suggest the policy may contribute to improved CEP by promoting green technology innovation, industrial structure upgrading, and energy efficiency. Heterogeneity test results demonstrate that the dual-pilot policy yields more pronounced impacts in cities characterized by higher economic development, weaker path dependence, and more stringent environmental governance. Additionally, negative cross-regional spatial spillovers are identified. Different from the existing literature, this study integrates social welfare dimensions into CEP’s measurement framework and further validates that the dual-pilot policy generates more outstanding efficiency benefits compared with separate LCCP and NEDC pilots Full article

To address the challenges of generating autonomous, high-quality control laws for high-performance Unmanned Aerial Vehicles (UAVs) performing long-horizon complex aerobatic maneuvers, specifically the difficulty of achieving energy-altitude closure, low-level control chattering, and low utilization of high-quality experience, this paper proposes an improved Soft Actor-Critic (SAC) algorithm incorporating a Quality-Aware Expert Pool (QAEP). Using the aerobatic loop maneuver as a representative scenario, this study explores the autonomous generation of control-surface manipulation policies comparable to those of skilled human pilots for agile fixed-wing UAVs. First, a singularity-free feature state representation and a rate-integrated action space are constructed. Combined with a symmetric shaping reward, these suppress control chattering at the physical level and achieve energy-altitude closure throughout the maneuver. Second, a dual-threshold expert pool driven by task reward and trajectory quality, together with a progressive mixed-sampling mechanism, is designed to effectively filter out low-quality samples and improve algorithmic convergence stability. Simulation experiments based on JSBSim with a high-fidelity F-16 model, which serves as a representative surrogate for a high-performance UAV, demonstrate that the proposed method generates maneuver strategies with manipulation quality comparable to that of skilled human pilots. The Dynamic Time Warping (DTW) similarity between the generated control commands and human expert demonstration data exceeds 0.97, the Manipulation Smoothness Index (MSI) is improved by 7.3%, and the loop completion rate under randomized initial conditions reaches 96.2%. These results suggest that the proposed framework enables human-like energy coordination and fine-grained control sequence generation in complex simulation environments, offering a promising approach to advancing maneuver intelligence and autonomous control capability in UAV systems. Full article

Under China’s dual-carbon agenda, a central question is whether the Low-Carbon City (LCC) pilot policy reduces energy intensity, whether this effect can be credibly interpreted as causal, and under which conditions and through which channels it operates. Using a balanced panel of 282 prefecture-level and higher-level cities from 2006 to 2023, this study develops a problem-oriented framework that integrates effect identification, credibility validation, and heterogeneity and mechanism analysis. The average treatment effect is estimated using staggered difference-in-differences, while dynamic effects are identified with interaction-weighted and imputation-based event-study estimators, and selection concerns are further addressed through propensity score matching difference-in-differences and a battery of stability checks. The results show that the LCC pilot policy reduces urban energy intensity, with the baseline estimate implying a decline of about 15–16%, and that the policy effect accumulates over time rather than appearing immediately. This finding remains stable across alternative specifications, placebo tests, and matched-sample estimation. The policy effect is stronger in cities with higher initial energy intensity and higher levels of economic development. Mechanistic evidence indicates that adjustments in fixed asset investment and changes in AI-related resource allocation are two observable channels associated with the decline in energy intensity. By focusing on energy intensity as a process-oriented performance indicator, this study provides more direct evidence on the energy-efficiency consequences of low-carbon urban governance and clarifies the conditional and structural foundations of policy effectiveness. Full article

Under the “dual carbon” strategic framework, the low-carbon transition of the logistics sector—a major source of carbon emissions in the national economy—has become imperative for achieving green development. The adoption of new-energy vehicles (NEVs) represents a critical pathway for decarbonizing logistics operations. Initiated in 2009, China’s “Ten Cities, Thousand Vehicles” Demonstration Project served as a pioneering policy to accelerate NEV deployment, offering a valuable use case for reducing emissions in urban logistics. Using this initiative as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) approach and panel data from 275 Chinese prefecture-level cities (2000–2021) to evaluate the causal effect of the policy on urban logistics CO₂ emissions. The robustness of the findings is confirmed through parallel trend tests, placebo tests with reassigned treatment timing, alternative dependent variable construction, and instrumental variable estimation. Mechanism and heterogeneity analyses are further conducted to uncover underlying channels and contextual variations. The results indicate a statistically significant reduction in logistics carbon emissions in pilot cities, which remains consistent across multiple robustness checks. Mediation analysis reveals that the policy effect is partially transmitted through increased NEV stock. Moreover, the emission reduction effect is more pronounced in cities with lower logistics dependency and non-consumer-oriented economic structures, while it is weaker in consumer and highly logistics-dependent cities. These findings confirm the sustainable contribution of early NEV policies through advancing the transition to low-carbon logistics and supporting dual carbon goals, fill the empirical gap in developing countries’ freight decarbonization, and offer actionable insights for targeted regional sustainable logistics strategies. Full article

The low-carbon city pilot (LCCP) and carbon emissions trading (CET) represent two critical policies for reducing carbon emissions. Accurately evaluating their synergistic effects on the reduction in pollution and carbon emissions (RPCE) is of utmost importance for advancing China’s low-carbon economic growth and achieving the dual-carbon objectives. Utilizing data from 279 prefecture-level cities during 2008 to 2021, this study employed a multi-phase differences-in-differences model to investigate the synergistic effects of the concurrent implementation of LCCP and CET (referred to as the “dual pilot” policy) on RPCE. The findings revealed that (1) the dual pilot policies reduced per capita CO₂ emissions by 0.644% and PM_2.5 concentration by 0.114%, with the dual effect being significantly superior to that of single pilot policies; (2) through mechanism analysis, it was found that technological innovation and clean energy transition served as the principal channels through which the “dual pilot” policy exerted its influence on RPCE; and (3) heterogeneity analysis demonstrated that the “dual pilot” policy was particularly effective in the RPCE in big cities, non-resource-based cities, and highly urbanized cities. This study provides novel empirical evidence supporting the integration of active government intervention with effective market mechanisms to maximize synergies in carbon emission reduction policies and achieve RPCE. Full article

Financial supply side structural reform (FSSR) serves as a key for advancing the low-carbon transformation of industrial energy (LTIE) and supporting the dual carbon strategic goals. By using provincial panel data from China for the period of 2008–2022 and leveraging the national financial comprehensive reform pilot zones as a quasi-natural experiment, this study uses the difference-in-differences method to examine empirically the effect of FSSR on the LTIE and the underlying mechanisms. Research findings indicate that, first, FSSR can significantly advance the LTIE, which remained unchanged after other policies, omitted variables, and other potential influencing factors were controlled. Second, the mechanism tests indicate that FSSR can drive the LTIE by increasing green financial support, fostering green industrial development, and promoting green technological innovation. Third, the heterogeneity tests reveal that the benchmark effect is pronounced in regions with weak environmental regulation and a low level of financial development. This study provides theoretical and empirical evidence to understand the crucial role of FSSR in advancing the LTIE and insights for relevant policy formulation. Full article

China’s ecological civilization construction and the “dual-carbon” strategy highlight the urgent need for coordinated governance of pollution and carbon reduction. Whether data elements and artificial intelligence integration (DEAII) can serve as a new pathway to achieve this goal remains to be explored. This study investigates the dynamic effects of DEAII on pollution and carbon reduction using panel data from 275 prefecture-level cities in China during 2009–2021. An evaluation index system and a modified coupled coordination degree model are developed to measure DEAII, while an ordinary least squares (OLS) fixed effects model is applied to assess its impacts. The results show stage-specific effects of DEAII, including the phenomenon of “pollution reduction but carbon increase”. Mechanism analysis indicates that improvements in green energy technology efficiency (GETE) and optimization of urban spatial structure are the main channels for achieving synergy. Heterogeneity analysis reveals that although government attention to environmental protection strengthens pollution control, it has limited effects on short-term carbon reduction. Moreover, the carbon reduction benefits of green energy transition pilots exhibit a time lag, and the “digital intelligence divide” generates negative spatial spillovers. These findings provide new evidence for the dilemma of “environmental protection without low-carbon benefits” and suggest policy directions for enhancing the coordinated governance of pollution and carbon reduction. Full article

Global efforts to achieve carbon neutrality increasingly rely on institutional green policy that reshape corporate environmental behavior. This study examines whether green policy improves corporate environmental performance (EP). Using panel data of the A-share listed firms from 2010 to 2022, we exploit the rollout of pilot cities as a quasi-natural experiment and apply a difference-in-differences (DID) framework, supplemented by double machine learning (DML) and robustness tests. The results show that the New Energy Demonstration City (NEDC) policy notably increases EP, with stronger effects for state-owned enterprises, large firms, and regulated industries. Mechanism analysis indicates that artificial intelligence innovation capacity and the stringency of regional environmental regulation amplify the policy’s effectiveness, revealing a “innovation–regulation” dual mechanism. By focusing on integrated EP rather than single outcomes, this paper extends the literature on green policy instruments. It demonstrates that structural policies combining fiscal incentives and regulatory constraints can correct market failures and foster long-term green transition. Beyond China, the findings provide insights for other developing economies where market-based instruments alone may be insufficient to trigger low-carbon transformation. Full article

As global climate change intensifies with unprecedented urgency, nations worldwide have increasingly adopted market-based environmental regulatory instruments to advance carbon reduction objectives. In 2017, China launched energy rights trading pilots, thereby providing a crucial policy instrument for controlling total energy consumption at its source. However, the specific impacts and transmission pathways through which this system influences corporate carbon reduction behavior remain insufficiently explored through rigorous empirical investigation. Drawing upon panel data from heavy-polluting companies listed on the Shanghai and Shenzhen A-share markets, this study employs a difference-in-differences methodology to identify the causal effects of energy rights trading systems on corporate carbon reduction. Our findings reveal that energy rights trading systems significantly reduce corporate carbon emission intensity, generating pronounced emission reduction effects. Further mechanism analysis demonstrates that this system operates through two principal pathways: first, by promoting increased green investment among enterprises, whereby short-term emission reductions are achieved through procurement of energy-saving equipment and environmental protection facilities, and second, by stimulating corporate green technological innovation, whereby long-term sustainable emission reductions are realized through the development of energy-saving technologies and clean processes. Additionally, the research reveals that enterprises with lower financing constraints and stronger supply chain bargaining power respond more actively to policy implementation, with policy effects exhibiting significant heterogeneity. This study not only enriches the theoretical understanding of market-based environmental regulatory policy effects but also provides crucial empirical evidence for improving the energy rights trading system design and enhancing policy implementation effectiveness, thereby offering important policy insights for promoting corporate green transformation and achieving “dual carbon” objectives. Full article

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

To achieve high-quality development, the Chinese government has proposed the establishment of Green Finance Reform and Innovation Pilot Zones (GFRIPZs). Green finance enhances environmental governance, guides capital flows, and supports sustainable economic growth. Against the backdrop of the “dual carbon” goals (carbon peaking and carbon neutrality), this study examines whether green finance significantly drives urban energy structure transition and explores its underlying mechanisms. Leveraging the GFRIPZ policy as a quasi-natural experiment, we construct a multi-period Difference-in-Differences (DID) model to systematically analyze the impact of green finance on energy structure transition. The findings reveal that the GFRIPZ policy significantly accelerates urban energy structure transition, with stronger effects observed in resource-based cities, central and western regions, and cities with higher administrative levels. Green finance drives this transition through the mediating effects of green technological innovation and resource allocation efficiency, while the digital economy further amplifies its positive impact. This study concludes that green finance effectively promotes urban energy structure transition and supports high-quality economic development. These findings provide empirical evidence and policy insights for advancing China’s energy revolution and achieving its climate objectives. Full article

Against the backdrop of the structural transition in China’s economic landscape, the implementation of digital economy policies—particularly through the Broadband China Demonstration Cities initiatives—has significantly enhanced urban ecological resilience. Based on panel data from 280 prefecture-level cities in China over the period 2013–2022, this study employs the national big data comprehensive pilot zone as a quasi-natural experiment and utilizes the dual machine learning method to examine how pilot zone construction influences urban ecological resilience. This analysis provides theoretical support for fostering green urban development. The results are summarized as follows. (1) The construction of national big data comprehensive pilot zones significantly enhances urban ecological resilience. The conclusion is robust to various tests, including the removal of outliers, changes in sample splitting ratios, and alterations in machine learning algorithms. (2) The construction of national big data comprehensive pilot zones indirectly improves urban ecological resilience through pathways of green innovation and energy efficiency. (3) This study assesses the heterogeneity of policy effects based on the generalized random forest (GRF) model to identify the sources of heterogeneity in policy effects, and conducts a comprehensive heterogeneity analysis from the three dimensions of resource endowments, geographical location characteristics, and the attributes of environmental protection zones. These findings enrich the analysis of the consequences of national big data comprehensive pilot zone policies and offer a theoretical basis and policy reference for how constructing big data pilot zones can better serve urban ecological development. Full article

China faces issues such as air pollution and global climate change, and the Carbon Emission Trading Policy (CETP) has attracted considerable attention as a core policy tool for achieving the “dual carbon” goals. Based on panel data from the Pearl River–West River Economic Belt (PRWREB) from 2008 to 2021, we use the Synthetic Control Method (SCM) and Spatial Difference-in-Differences (S-DID) models to explore the pollution reduction and carbon reduction effects of the CETP and its spatial heterogeneity. Our analysis reveals several interesting insights. First, the CETP has promoted a 34.1% overall reduction in pollution and carbon levels in the pilot areas, with sustained effects. Moreover, spatial spillover effects can reduce the pollution and carbon levels in the economic belt by 29.9%. Second, the pollution and carbon reduction effects of the CETP are more significant in regions with better economic development and active carbon trading. It has the best synergistic reduction effects on CO₂ and SO₂ but is less effective in reducing PM_2.5. Third, the spillover effects of the CETP on technological innovation are greater than the direct effects, with the most noticeable pollution and carbon reduction outcomes. The overall negative effect on industrial structure is that it fails to promote pollution and carbon reduction. The emission reduction mechanisms vary for different targets: CO₂ and PM_2.5 are related to energy efficiency, SO₂ to advancing industrial structure, and smoke and dust to technological innovation. Based on the research conclusions, we propose to improve the coordinated governance system for carbon and pollution, advance pollution and carbon reduction according to local conditions, and implement targeted emission reduction and efficiency enhancement. Full article