Enhancing urban circular economy efficiency (UCEE) is crucial for sustainable development, yet the roles of artificial intelligence and environmental regulation remain unclear. Using panel data for 30 Chinese provinces from 2013–2022, we measure UCEE with a Super-SBM model including undesirable outputs, track dynamics via the Global Malmquist–Luenberger index, and estimate spatial effects with a spatial Durbin model. Nationally, the average UCEE index rises from about 0.3 to above 0.7, but large gaps persist, with leading eastern provinces approaching 1.0 and some northeastern provinces remaining below 0.1. The GML index and its EC and TC components stay above 1, indicating sustained efficiency gains dominated by technological progress. Spatial results show that both artificial intelligence and environmental regulation significantly inhibit local UCEE, while artificial intelligence generates positive spillovers; their interaction produces a “1 + 1<2” crowding-out effect, underscoring the need for smarter, better-coordinated green governance.

Identifying key drivers of low-carbon development (LCD) in the Yangtze River Delta is essential for advancing China’s sustainable development agenda. This study introduces an interpretable machine learning framework based on the SMOTE-XGBoost model, which achieves high predictive accuracy (AUC = 0.988) in evaluating regional LCD. SHapley Additive Explanations and partial dependence plots are employed to quantify the effects of input variables. Fiscal revenue, ventilation coefficient (VC), and agricultural value added emerge as the most influential determinants. Moving beyond linear assumptions, the analysis reveals complex non-linear interactions. Specifically, all three features follow a similar pattern whereby, upon reaching a feature-specific threshold, their marginal effects on regional LCD plateau. In addition, we identify an interactive mechanism under which LCD is most sensitive to fiscal revenue in settings with limited ventilation (VC ≤ 1223 m²/s). As fiscal capacity strengthens (fiscal revenue > 2.3 billion yuan), LCD becomes relatively more sensitive to agricultural value added, warranting a reallocation of resources toward agricultural mitigation. This shift is particularly salient in low-ventilation counties (VC < 1338 m²/s). Overall, the findings provide empirical evidence of complex, non-additive relationships among socioeconomic and environmental factors and offer actionable, data-driven insights for designing synergistic, targeted policies to support high-quality, sustainable regional development.

There is a growing recognition of the importance of sustainable development. Political economy factors and natural resources are considered important sources of economic prosperity. These are the fundamental primary resources utilized in the production process, which subsequently drives economic activities and results in sustained growth. Nevertheless, resource-rich countries often experience sustainability challenges. This study examines the impact of mineral resource rents on sustainable development in selected developed countries from a political economy perspective. Moreover, we include renewable energy deployment and fiscal decentralization as additional determinants of sustainable development.The study uses random-effects and fixed-effects estimation methods to estimate the model. The study also uses quantile regression econometric techniques. A sustainable development index (SDI), integrating economic, social and environmental dimensions, is constructed as the dependent variable. The results reveal that mineral resource rents and renewable energy electricity output negatively affect sustainable development. Nevertheless, environment-related taxes and fiscal decentralization positively affect sustainable development.These results provide valuable insights for academia, researchers and policy makers aiming to achieve environmental sustainability through institutional and fiscal reforms.

Against the backdrop of the “dual carbon” goals, improving total factor energy efficiency (TFEE) has become a core task for low-carbon transformation. Using a panel dataset covering 30 provinces in China from 2011 to 2024, this paper employs the super-efficiency SBM model to measure regional TFEE, and applies the spatial Durbin model and threshold regression model to examine the impact of the digital economy, environmental regulation, and their interaction on TFEE. Different from traditional linear analysis, this study reveals the nonlinear mechanism through which the digital economy unlocks the potential of environmental regulation and transforms its effect from “compliance cost pressure” to “innovation compensation gain”. The findings show that: First, the digital economy significantly promotes TFEE, with 56.68% of its energy-saving effect realized through spatial spillover. Second, environmental regulation alone shows a “cost-following effect” and inhibits TFEE, but its interaction with the digital economy generates a strong synergistic effect with a coefficient of 0.299. Third, the digital economy acts as a critical threshold: only when its level exceeds 0.435 can environmental regulation effectively boost TFEE via the innovation compensation effect. Fourth, the synergistic effect has strong spatial spillovers, with the indirect effect (0.383) exceeding the direct effect (0.282). This study contributes new evidence that digital infrastructure construction should be prioritized in regions with strict environmental regulation to avoid efficiency losses, and provides targeted implications for implementing regionally differentiated policies that prioritize digital upgrading to match environmental regulation intensity to coordinate digital development, environmental governance, and energy efficiency improvement.

Against the background of the global low-carbon transformation, battery charging and battery swap (BaaS) have become two mainstream energy replenishment modes for battery electric vehicles (BEVs). Existing studies lack comparative Stackelberg game analysis covering fuel vehicle manufacturers, BEV manufacturers, and independent battery operators under heterogeneous consumer preferences including purchase price sensitivity, mileage cost perception, and vehicle depreciation attention. This study establishes a tripartite game framework covering three industrial scenarios: independent charging operation, third-party exclusive battery-swap R&D, and vehicle battery joint swap station construction. By solving closed-form equilibrium solutions and conducting parameter comparative statics plus numerical surface simulation, this paper systematically identifies how multi-dimensional consumer preferences affect product pricing, market demand, battery service level, and supply chain profit distribution. The results indicate that under the given baseline parameter settings, battery swap mode yields higher vehicle prices but lower market demand relative to charging mode; joint R&D only achieves bilateral profit win–win when consumers attach high importance to driving cost and automakers undertake low cost-sharing ratios. This study expands the theoretical framework of BEV energy replenishment supply chain games and provides quantitative decision references for vehicle and battery enterprises to select optimal operational cooperation paths.

The transition toward low-carbon transportation represents a critical strategy for mitigating climate change and reducing greenhouse gas emissions. New energy vehicles (NEVs), powered by advanced battery technologies, have emerged as a pivotal solution to address the dual challenges of energy security and environmental degradation. However, the high costs and technological uncertainties associated with battery research and development (R&D) present significant strategic dilemmas for NEV manufacturers in selecting optimal production modes. This study investigates the environmental and economic implications of three distinct battery production strategies—in-house R&D, outsourcing, and joint R&D collaboration—within a supply chain framework comprising traditional fuel vehicle manufacturers, NEV manufacturers, suppliers, and third-party battery producers. By developing game-theoretic models, we analyze how R&D efficiency, market distribution preferences, and R&D cost-sharing ratios influence equilibrium outcomes including pricing, green performance, market demand, and profitability across supply chain participants.Key findings reveal that: (1) reduced R&D efficiency under in-house production negatively impacts NEV manufacturers' environmental performance and profitability while benefiting traditional automakers; (2) consumer market distribution significantly shapes strategic decisions, with traditional vehicle market dominance adversely affecting green innovation; (3) outsourcing proves superior only when battery suppliers demonstrate clear technological leadership; and (4) joint R&D strategies enhance supply chain profitability but are most effective in markets with high traditional vehicle demand. This research provides actionable insights for environmental management practitioners and policymakers seeking to promote sustainable manufacturing practices and accelerate the decarbonization of the automotive industry.

This paper examines the impacts of consumers’ and manufacturers’ fairness concerns on supply chain decisions and compares alternative government subsidy policies. A two-stage supply chain model is developed under three scenarios: benchmark, subsidies to green manufacturers, and subsidies to green consumers. The results indicate that fairness concerns significantly affect market outcomes. Consumers’ fairness concerns can enhance the competitiveness of green products, while manufacturers’ fairness concerns tend to intensify competition and reduce retailer profits. Although both subsidy schemes have identical feasible subsidy ranges, they operate through different mechanisms. Both subsidies can improve green product competitiveness and social welfare under the proposed model, while consumer subsidies generate stronger demand expansion effects. This study incorporates bilateral fairness concerns into green supply chain analysis and provides theoretical and policy insights for designing subsidy schemes that promote sustainable development.

ABSTRACT In contemporary society, improving energy transition efficiency (ETE) has become a necessary measure to ensure the normal and sustainable functioning of society. Aiming to inform region‐specific “dual‐carbon” governance, this study quantifies the drivers of energy‐transition efficiency across 30 Chinese provinces from 2013 to 2022. Applying Super‐SBM, GML decomposition and a spatial Durbin model, we test how resource endowment and digital transformation affect ETE and whether spatial spill‐overs call for differentiated policies. The results are: (1) National ETE exhibits an “east‐high, west‐low” pattern yet inter‐regional gaps narrowed significantly after 2019, with technological progress contributing most; (2) resource abundance raises local ETE and exerts an even stronger positive indirect effect on neighboring provinces, indicating an “endowment advantage” rather than a curse; (3) digitalization enhances local efficiency but generates negative spill‐overs, suggesting a competitive “digital siphoning”; (4) regional heterogeneity is pronounced: eastern digital benefits spill over weakly while resource effects are modest, central provinces gain most from resource‐driven diffusion, and western regions experience strong spill‐overs from both factors. These findings affirm that one‐size‐fits‐all energy policies are inadequate; instead, tailored strategies that leverage local resource strengths, coordinate digital investment, and manage cross‐regional externalities are essential for accelerating an equitable and efficient national energy transition.

Enhancing energy transition efficiency (ETE) is vital for sustainable development and climate mitigation. This study measures national ETE from 2008 to 2022 using the Super-SBM model and analyzes its spatiotemporal patterns. The GML index is decomposed to isolate the effects of technological progress and technical efficiency changes on ETE. The Spatial Durbin Model (SDM) examines how green finance and high-quality development interact across regions. Results show that the national average ETE increased with fluctuations over the study period, exhibiting a spatial pattern of relatively higher efficiency in the west and lower efficiency in parts of the east and north, mainly driven by large-scale clean energy deployment in western provinces. GML decomposition indicates that ETE growth stems primarily from technological advancement, whereas technical efficiency contributes marginally. HQD shows a significant positive association with ETE, yet its spatial spillover remains weak nationally. Conversely, green finance generates notable negative externalities with pronounced regional heterogeneity; resource competition or policy misalignment may erode efficiency in adjacent areas. These findings underscore the need for coordinated regional green finance strategies and balanced clean energy transition policies.

To enhance energy efficiency (EE) and achieve sustainable development. This study measures EE through super-efficiency SBM model, and verifies artificial intelligence (AI) and green finance (GF) impact on EE by Tobit model, conclusions as follows: (1) The EE of each region and the country is the spread of the low, with a lot of opportunity for improvement. The EE decreases in the following order: the regions in eastern, central, and western. (2) At the national level, AI has a significant positive effect on EE, implying that advances in AI can effectively improve EE. From different regions, AI impact on EE in both the eastern and central regions shows positive effect, and the effect in the central is larger than that eastern, while in the western region is positive but statistically insignificant. (3) At the national level, GF promotes EE but the elasticity coefficient is small; in the eastern region, GF has the biggest effect on EE, while in the central and western regions, it has weaker effects on EE. (4) Energy endowment inhibits EE; environmental regulation can promote EE at the national and regional levels, with the biggest effect in the eastern region and the least effect in the western region. The industrial structure coefficient in all regions reduces the EE. The technology level inhibits EE only in the central region. The thesis through the analysis of the relationship between the three and the reliability of the conclusions drawn from the analysis, to be able to better play the GF and AI in the energy sector of the policy implementation effect, effectively improve EE, improve the energy structure, for the comprehensive promotion of the energy transition is of great significance.

As the climate problem is getting more and more serious and the “low-carbon revolution” of globalization is emerging, the logistics industry, as a high-end service industry, must also take the road of low-carbon development. Improving logistics carbon emission efficiency (LCEE) is gradually becoming an inevitable choice to maintain sustainable social development. The study uses the Super-SBM (Super-Slack-Based Measure) model to evaluate the urban LCEE from 2013 to 2022, explores the contribution of efficiency changes and technological progress to LCEE through the decomposition of the GML (Global Malmquist–Luenberger) index, and reveals the influence of digital transformation and energy consumption structure on LCEE by using the Spatial Durbin Model, concluding as follows: (1) LCEE declines from east to west, with large regional differences. (2) LCEE has steadily increased over the past decade, with slower growth from east to west. It fell in 2020 due to COVID-19 but has since recovered. (3) LCEE shows a catching-up effect among the three major regions, with technological progress being a key driver of improvement. (4) LCEE has significant spatial dependence. Energy consumption structure has a short-term negative spillover effect, while digital transformation has a positive spillover effect.

Achieving energy conservation (EC) and carbon emissions (CE) reduction through effective policies and economic instruments has become an important issue in China. Using China's provincial panel data, this paper quantitatively analyzes the impact of the Chinese government's EC assessment policy and financial development on EC and CE reduction. The results show that the government EC assessment policy can effectively promote EC, but have no significant impact on CE. Financial development has a significant positive effect on CE; however, it does not promote EC due to its significant positive effect on EC intensity. We found that the increasing proportion of tertiary industry does not effectively reduce total CE and CE intensity. Based on the results, to achieve the CE peak goal in 2030, we suggest: (1) To promote the development of green finance and provide financing support for green projects with CE reduction benefits; (2) To innovate and improve the energy pricing system, the green development and the energy consumption structure should be enhanced and restructured through the energy price mechanism; (3) Strict CE reduction system, energy consumption control, and other measures to strengthen the control of CE growth; (4) With higher market access standards and low-carbon product certification, it is mandatory for enterprises to save energy and reduce CE in the production process. The implementation of these policies will accelerate the process of transforming the economy into a low-carbon development model and achieve the goals of EC and CE reduction.

Under the low-carbon background, regional talent allocation and transformation and upgrading of export trade are all important issues of common concern among academic circles in the stage of sustainable economic and environmental development. This paper explores talent allocation’s impact on the transformation and upgrading of export trade. Based on the results, the improvement in regional talent allocation level has significantly increased the percentage of regional general trade exports. It is conducive to the trans- formation and upgrading of export trade, and such an influence also shows the nonlinear characteristic of increasing “marginal effect”. This conclusion still stands after a series of robustness tests. According to the influencing mechanism test results, the technological innovation brought by the improvement in regional talent allocation level is an important transmission channel for such improvement to facilitate the transformation and upgrading of export trade. Based on the multi-dimensional analysis of the results of transformation and upgrading of export trade, regional talent allocation has significantly enhanced the complexity of exported technologies and actively promoted innovation among exporters. The research provides important inspiration for further promoting the structural reform of regional talent allocation and facilitating the transformation and upgrading of export trade patterns. First published online 12 February 2025

The purpose of this paper is to quantitatively study the impact mechanism of urbanization, water resources, and forestry system coupling on carbon emissions, and explore new ways to reduce carbon emissions, as complex relationships exist among urbanization, water resources, and forestry systems. Based on the data of provincial regions in mainland China from 2015 to 2024, this paper analyzes the impact of urbanization, water resources, and forestry system coupling on carbon emissions by constructing the STIRPAT model. The findings reveal significant heterogeneity in the impact of the coupling degree among urbanization, water resources, and forestry systems on carbon emissions across Chinese provinces. Most regions exhibit insufficient carbon reduction effects. Enhancing the carbon mitigation effect through improving the coupling coordination of urbanization, water resources, and forestry systems presents a novel pathway toward achieving carbon neutrality during urbanization processes. Heterogeneity analysis further indicates that disparities in economic aggregate alter the mechanisms through which the STIRPAT model influences carbon emissions. The main contribution of this paper is to establish the evaluation index system of urbanization, water resources, and forestry development, analyze the mechanism of urbanization, water resources, and forestry coupling system affecting carbon emissions with the STIRPAT model, and explore new pathways for achieving carbon neutrality within urbanizing systems.

Considering the uncertainty of green technology research and development (R&D) investment and channel members’ fairness concern behavior, this article respectively analyzes participants’ optimal decision-making under the manufacturer-dominated and the retailer-dominated structures. Given the probability of high efficiency and low efficiency of green R&D, this article uses the expected values under different probabilities to represent the equilibrium results. Through numerical simulations, we further comparatively discuss the equilibria under different scenarios. The results indicate that regardless of whether the manufacturer’s green R&D is efficient or inefficient, it always will lead to an increase in products’ pricing, greenness, demand, retailer’s profit, and supply chain’s social welfare. However, the change in the manufacturer’s profit is uncertain, which is also related to its fixed R&D costs and R&D efficiency. Under the manufacturer-dominated structure, fairness concern behavior can lead to a decrease in products’ pricing, greenness, demand, and profits for both the manufacturer and retailer; But under the retailer-dominated structure, the manufacturer’s fairness concern has no impact on products’ retail price, greenness, and demand. It only leads to the retailer raising markup price and reducing marginal profit, while the manufacturer’s wholesale price and profit will increase. In addition, whether under the manufacturer-dominated and the retailer-dominated structures, the supply chain’s social welfare is greater than that without green R&D and increases with the increase of the probability of efficient R&D. Nevertheless, the impact of the manufacturer’s fairness concern on social welfare is not the same. It leads to a decrease in social welfare under the manufacturer-dominated structure, while leads to an increase under the retailer-dominated structure.

The automotive industry's low-carbon transformation is crucial to a nation's ability to fulfill its "Carbon Peaking and Carbon Neutrality" (CPDN) commitment. However, China's automotive industry still has a number of issues that have sparked debate. Based on the fact that people use social Q&A platforms to get information, solve problems, and aid in decision-making, negative answers in massive amounts of information typically have a higher degree of information perception and are easier to spread. This work constructed the algorithm of emotion calculation and classification, negative network construction for social Q&A platforms, and carried out empirical research with Zhihu. The 175 questions and 5220 corresponding answers for new energy automobiles were organized as a database to search the development path for the new energy automobile industry. The new energy vehicle industry's development path primarily entails: resolving the issue of charging difficulty and popularizing charging heaps; attending to the battery safety issue and the head brand of new energy vehicles concentrating notably on quality control. The empirical findings also demonstrate that algorithms developed can more effectively complete the task of sentiment analysis, aid users in making decisions, and contribute to realizing the CPDN goal.

Since the reform and opening up, the rough development mode has provided a strong impetus for China's economic leap forward, however, with the economic development, the protection of the ecological environment has become more and more important. In order to achieve high -quality development with maximized benefits and minimized pollution, measuring the value of urban ecoefficiency by constructing an urban eco-efficiency evaluation system in combination with regional realities has become an important indicator of the quality of regional development. In this study, the super -efficiency SBM model with non -desired outputs was used to measure the urban eco-efficiency of 11 provincial -level administrative regions in the Yangtze River Economic Belt from 2012 to 2020, with Shanghai and Guizhou provinces at the top and bottom of the list with 0.901 and 0.160, respectively, and the overall trend of increasing year by year. The results were analyzed by the Malmquist index and the Tobit model, and significant differences in eco-efficiency were found in different regions. In terms of spatial and temporal patterns, the downstream has obvious advantages over the middle and upstream, with 0.562, 0.302, and 0.229, respectively, showing obvious spatial clustering effects. From the perspective of influencing factors, scientific and technological investment is the core growth point of urban eco-efficiency in the Yangtze River Delta region, and the influence of GDP per capita on urban eco-efficiency passes the significance test of 1%, which has a significant impact; meanwhile, the improvement of industrial structure and the level of urbanization can effectively improve the level of urban eco-efficiency. These findings are of great significance in promoting high -quality regional development. On the one hand, we must strike a balance between development and ecosystems while reducing pollution from agriculture, industry, and households. On the other hand, we must accelerate the transformation and upgrading of traditional industries and strengthen the development of industrial automation while focusing on the green, environmental, and sustainable development of cities. Finally, we must follow the development concept of ecological priority and vigorously research and develop technology to improve the output efficiency of natural resources, labor resources, and capital.

Green development is an inevitable choice for sustainable development under the constraints of environmental resources. This paper attempts to explore the connotation of urban land green utilization efficiency (LGUE) and reveal its spatial differentiation characteristics. This study adopts the super-SBM model to measure LGUE from 2009 to 2022 and analyzes the spatiotemporal variation rules. Then, the study reveals the spatial influencing factors of LGUE, drawing the following conclusions: (1) the average efficiency value of LGUE at the national level is still at a low level, but it is on an upward trend. There are significant differences in LGUE among the eastern, central, and western regions, with the highest LGUE in the eastern region and the lowest in the western region. (2) The spatial distribution of LGUE in various cities across the country is not entirely random but shows significant spatial autocorrelation characteristics. The improvement in LGUE in a region can improve the surrounding region’s LGUE. (3) Economic development level promotes the improvement of local city LGUE, but its impact on LGUE of surrounding neighboring cities is not significant; local city industrial structure upgrading can improve LGUE in both local and neighboring cities; foreign investment in local cities can promote LGUE in both local and neighboring cities; the increase in population density will hinder LGUE in local cities but improve surrounding cities LGUE. The intervention degree of local city government will suppress the improvement of LGUE in both local and neighboring cities.

The ecological and environmental subsystem is a core link in the regional sustainable development system and an important path for regional green transformation. From data availability, this paper selects 30 provinces and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2005 to 2022 and adopts the system Generalized Method of Moments (GMM) to explore the impacts of green financial policies and urban renewal policies on regional environmental sustainable development, expecting to provide theoretical reference for China's environmentally sustainable development and dual-carbon goal, and draws the conclusions as follows: (1) Overall, green financial development is conducive to regional environmental sustainable development; from a regional perspective, green finance significantly contributes to environmental sustainability in the eastern region, but not in the central and western regions. (2) Urban renewal can improve environmentally sustainable development at the national level, and the implementation of urban renewal policies in the east and central can also improve environmentally sustainable development, but it does not have a significant impact in the west of the country, which is still insufficient to play a positive role. (3) Economic development can promote environmental sustainability, but is not significant in the west. Resource consumption in all regions has a significant inhibitory effect on ecological and environmental sustainability; the increase of the proportion of the secondary industry has a significant inhibitory effect on ecological and environmental sustainability, except for the eastern region; environmental regulation on regional environmental sustainability at the national level and the eastern region can promote environmental sustainability, while the central and western environmental sustainability the greater the negative effect.