Under global pressure to reduce carbon emissions, understanding how the level of artificial intelligence (AI) use affects corporate carbon emissions (CCE) is crucial for achieving a green transition. This study, based on provincial panel data from China, employs a combination of two-way fixed effects (TWFE) empirical analysis and structural equation modeling (SEM) to identify the nonlinear effects of AI use levels on corporate carbon emission intensity and their transmission pathways, and conducts robustness and regional heterogeneity tests. The empirical results show that: first, the relationship between AI use levels and corporate carbon emissions exhibits a significant inverted U-shaped curve-in the early stages of development, due to high energy consumption in computing and deployment, AI adoption may temporarily increase carbon emissions; however, after exceeding a critical point, further deepening of use significantly reduces carbon emissions. Second, SEM analysis reveals several key mediating channels: improving green innovation efficiency (GIE), enhancing energy utilization efficiency (EUE), promoting Scientific innovation (SI), and driving industrial structure upgrading (ISU). These pathways collectively amplify the emission reduction effect of AI. Third, regional heterogeneity analysis shows that the AI emission reduction effect is significantly stronger in the eastern region than in the central and western regions. Finally, this study emphasizes the policy implications: while promoting the use of AI, energy structure and incentive mechanisms should be optimized, and differentiated policies should be formulated according to regional characteristics to achieve an AI-driven sustainable low-carbon transformation.

Enhancing agricultural economic resilience (AER) is essential for global food security. As a key policy tool for stabilizing agricultural production, policy-based agricultural insurance lacks rigorous causal evidence on its impact on resilience. In this study, AER is operationalized as a composite index capturing resistance and recovery capacities across pressure, state, and response dimensions. Using 2012–2023 provincial panel data from China (31 provinces × 12 years = 372 observations), we measure AER via the entropy method and identify policy effects using a staggered multi-timepoint difference-in-differences (DID) model. We find that policy-based staple crop insurance significantly increases AER by approximately 2.5 percentage points, primarily by promoting agricultural technological innovation (ATI) and regional industrial structure upgrading (RIS). The improvement effects are more pronounced in central and western regions, non-major staple-crop producing areas, and regions with higher natural risks. Robustness is confirmed via event study, alternative weighting schemes (PCA and equal weighting), and placebo tests. This study provides reliable causal evidence for the resilience-enhancing effect of agricultural insurance and clarifies its internal transmission mechanisms, offering empirical support for the optimization of agricultural risk governance policies. Limitations include the use of provincial-level aggregate data and the lack of analysis of spatial spillover effects between regions. Our findings suggest that differentiated policy implementation can support more sustainable and targeted agricultural risk governance.

Toward a sustainable energy future: Can green data center construction promote energy efficiency?

The digital economy serves as a crucial engine driving the upgrading of industrial structure and the high-quality development of the economy. Based on the panel data of 31 provinces in China from 2011 to 2023, this paper constructs individual fixed effect models and mediating effect models to systematically examine the impact mechanism of the digital economy on the upgrading of industrial structure. The empirical results show: First, the digital economy can significantly promote the upgrading of industrial structure, and the driving effect exhibits regional heterogeneity with the central region > western region > eastern region. Second, the digital economy can indirectly promote the upgrading of industrial structure by enhancing human capital, and the mediating effect of human capital also shows regional heterogeneity. Specifically, the central region has a complete mediating effect, the western region has a partial mediating effect, and the eastern region has no mediating effect. Therefore, this paper proposes that each region should implement differentiated digital economic development strategies and strengthen talent cultivation and allocation, and improve the regional coordinated development mechanism to achieve the coordinated upgrading of regional industrial structure through the empowerment of the digital economy, and contribute to the high-quality development of the economy.

Optimizing and adjusting the energy structure is not only an important task for China's energy development but also a key support for achieving the 'double carbon' goal and an important tool for accelerating the overall green transformation of the economy and society. Based on the panel data of 30 provinces in China from 1990 to 2021, this study explores the impact of energy restructuring on the coupled and coordinated development of the economy and carbon emission reduction by using the fixed-effects model and the instrumental variables method. The results of the study show that: ① The degree of economic and carbon emission reduction coupling and coordination in the study period as a whole showed an upward trend, but there were obvious characteristics of inter-regional and provincial differentiation. ② The transformation of the energy structure significantly promoted the coupling and coordinated development of the economy and carbon emission reduction, and this conclusion remained valid after a series of robustness tests. ③ Heterogeneity analysis showed that since China first put forward the "carbon emission reduction" goal, the positive effect of energy structure transformation has been on the rise. Meanwhile, in eastern regions as well as regions with high human capital and high government governance level, the energy structure transformation played a more significant role in promoting the coupled and coordinated development of economy and carbon emission reduction. ④ Mechanism analysis showed that the energy structure transformation could promote the upgrading of the industrial structure and then enhance the level of the coupled and coordinated development of the economy and carbon emission reduction. Accordingly, it is necessary to promote the process of energy structure transformation according to the time and place, to improve the level of human capital and government governance, and to improve the transmission mechanism of industrial structure upgrading.

ABSTRACT This study examines the asynchronous impact of digital financial development (DFI) on industrial structure upgrading (ISU) – rationalization (Theil) and advancement (WAI) – using a macro panel of China and ASEAN-6 (2011–2023). Addressing the small-N constraint, we employ static two-way fixed effects (TWFE) with Driscoll-Kraay standard errors. Endogeneity is isolated using shift-share instrumental variables via limited information maximum likelihood (LIML) and a partialing-out technique. Results reveal a profound divergence: DFI significantly drives rationalization (Theil) by mitigating information asymmetry, but fails to facilitate WAI due to maturity mismatch inherent in its short-duration nature. Mechanism analysis confirms a structural substitution effect, where DFI bypasses traditional collateral reliance, exhibiting stronger corrective effects in economies with severe credit rationing. These findings suggest implementing differentiated macro-prudential regulations and algorithmic regulatory sandboxes in emerging markets.

ABSTRACT This paper examines digital finance’s impact on manufacturing value-added using 2011–2022 panel data for China and ten Association of Southeast Asian Nations (ASEAN) economies. We identify significant Heterogeneous Treatment Effects (HTE): divergent forces across development gradients result in a statistical offset, rendering the average treatment effect (ATE) insignificant. In underdeveloped economies, while the direct impact is statistically insignificant (0.104), digital finance triggers conditional empowerment by significantly mitigating credit friction (0.022*). Developed economies experience significant structural upgrading and servitization (−0.052*). Results are robust to Bartik instrumental variable (IV) and PWT-based human capital estimations.

Performance evaluation of Natural Fiber Textile Reinforced Mortar (NFTRM) in masonry structural upgrading

The impact of the digital economy on the forest food industry: Mechanisms and evidence from China

With the help of information from the CGSS 2024 survey, the paper explores the impact of sports consumption structure upgrade of Chinese residents on their subjective well-being and the processes through which this is achieved. The construction of an ordered classification logistic regression model and an ordinary least squares (OLS) model reveals the direct influence of the upgrade of the sports consumption structure. In this context, the upgrading of the sports consumption structure significantly and positively affects the subjective well-being of residents in the sense that the higher is the degree of structure upgrade, the better subjective well-being the residents would have. Furthermore, the paper finds some differences depending on different income groups. The paper shows that the influence of the sports consumption structure upgrading on subjective well-being is statistically significant at the 1 percent confidence interval level for residents whose yearly income does not exceed 20,000 yuan and at the 5 percent confidence interval level for residents who earn between 20,000 and 50,000 yuan annually.

This research studies the effect of the Accelerated Depreciation Policy (ADP) on the corporate sustainability of Chinese A-share firms between 2012 and 2017. We employ difference-in-differences estimation and reveal that ADP has a significant positive effect on corporate sustainability, particularly regarding employment, remuneration, and stakeholder rights. The effect is mostly attributed to increases in total factor productivity and short-term leverage, with a lesser role played by workforce skill structure upgrades. This effect is consistent and particularly prominent in firms with higher visibility and labor intensity, lower probability of obtaining long-term bank loans, and firms that are not state-controlled or politically connected. Our findings demonstrate that tax policy is vital in sustainability-related corporate decision-making.

Digital trade is reshaping innovation incentives in emerging economies, yet whether digital trade policy can promote a sustainable upgrading of urban innovation structure—one that shifts the composition of innovative activity toward higher quality while contributing to broader societal goals—remains unclear. Exploiting the staggered rollout of China’s Cross-Border E-Commerce (CBEC) Comprehensive Pilot Zones as a quasi-natural experiment, this study examines how the policy is associated with changes in urban innovation composition using city-level panel data from 2010 to 2023. We construct a patent-text-based proxy for the disruptive orientation of urban innovation using TF-IDF analysis of patent abstracts—a methodological approach that captures textual distinctiveness as a dimension of innovation quality—and validate it against invention-patent intensity and highly cited patents. Using both conventional two-way fixed-effects and modern heterogeneity-robust estimators, we find that CBEC pilot-zone adoption is associated with a higher disruptive-innovation proxy share and a lower share of low-quality (strategic) patenting. The estimated increase in the disruptive-innovation proxy share is about 1.3 percentage points, equivalent to 40.6% of the sample mean. The effect is stronger in cities with weaker business environments, consistent with an institutions-as-substitutes interpretation, and is amplified by pre-existing local government attention to digital trade. Exploratory channel tests point to entrepreneurship, digital payment, and digitization as intermediate outcomes that respond to the policy. These findings contribute to the literature on digital trade policy, innovation composition, and sustainable upgrading by showing that a policy-induced compositional reallocation—rather than a simple increase in patent volume—may support a more quality-oriented, inclusive, and potentially more sustainable pattern of urban innovation in emerging economies.

This paper examines how green finance and environmental regulation jointly promote the green transformation and upgrading of industrial structure. Based on data from 31 provinces during 2014–2023, the results show that both green finance and environmental regulation significantly drive the greening of industrial structure, and environmental regulation plays a mediating role in their relationship. The study also finds that the impact of green finance is more pronounced in western and northeastern regions, while it is relatively moderate in eastern and central regions. This paper provides implications for policy-making, suggesting the enhancement of green finance support and optimization of environmental regulation, as well as the formulation of differentiated policies according to regional heterogeneity.

Power battery enterprises are a key link in the new energy vehicle (NEV) industry chain. However, studies analyzing the investment layout of power battery enterprises from a micro perspective are relatively scarce. This study takes Contemporary Amperex Technology Co. Limited (CATL) as a case and employs various spatial analysis methods and an optimal parameter-based geographical detector (OPGD) to analyze the spatiotemporal evolution and driving mechanisms of its investment layout from 2020 to 2024. The results indicate that CATL’s investment center has shifted from Jiangxi to Hubei, and the spatial expansion axis has changed from a northwest–southeast to a southwest–northeast direction. The investment layout has evolved from a “one core with two secondary cores” structure to a “provincial dual core, multi-core outside the province” structure and, ultimately, to a nationwide networked pattern. By 2024, CATL’s investment network covered the southeastern coast, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), central China, and southwestern regions. County-level spatial autocorrelation analysis shows that the investment agglomeration effect has continuously strengthened (with the global Moran’s I increasing from 0.006 to 0.025). High–high agglomeration areas gradually expanded from the southeastern coast to Xiamen and several provinces in central and western China, while high–low agglomeration areas, as early signals of investment diffusion, initially expanded and then contracted. The driving mechanism analysis reveals that fiscal support (q = 0.668), industrial structure upgrading (q = 0.585), tax burden (q = 0.543), and economic development (q = 0.536) are the primary factors driving investment layout, with significant synergistic effects between these factors. The synergy between industrial structure upgrading and clean energy supply stands out as particularly prominent. These findings contribute to optimizing the spatial layout of the NEV industry and promoting regional economic development.

Atmospheric pollutants and CO2 share common origins in fossil fuel combustion, raising the question of whether fiscal incentives targeting air quality alone can indirectly reduce carbon emissions. This study examines this question by evaluating China’s air quality ecological compensation policy, a provincial-level horizontal fiscal transfer mechanism under which cities are rewarded or penalized according to changes in ambient air quality indicators, without incorporating any explicit carbon-related assessment criteria. Using panel data from 268 prefecture-level cities over 2007–2023 and a multi-period difference-in-differences design, we find that the policy significantly reduces the composite pollution carbon index (β = −0.213, p < 0.01), with the effect confirmed by an alternative weighted-average specification (β = −0.153, p < 0.01) and robust to propensity score matching, one-period lagged regression, exclusion of provincial-level municipalities, and exclusion of the COVID-19 period. A two-step mechanism analysis, adopted to avoid post-treatment bias from “bad controls,” reveals that the policy promotes industrial structure upgrading (β = 0.253, p < 0.01), enhances green technological innovation capacity (β = 0.047, p < 0.10), and reduces energy consumption intensity (β = −0.012, p < 0.01). Heterogeneity analysis based on quartile subsamples shows that the synergistic benefits concentrate in cities with stronger fiscal capacity (β = −0.349, p < 0.01 versus insignificant for low-support cities), higher economic development, and greater urbanization (β = −1.558, p < 0.01 for highly urbanized cities), while the policy effect is statistically insignificant in the least-advantaged subgroups across these three dimensions. In contrast, the green coverage dimension reveals an opposite pattern: the effect is strongest in cities with lower green coverage (β = −0.378, p < 0.05) and insignificant in high-coverage cities, indicating diminishing marginal returns where environmental baselines are already favorable. These findings highlight the need for differentiated compensation standards, including tiered compensation coefficients and targeted fiscal support for resource-constrained regions, to ensure equitable governance outcomes.

Green innovation is the key endogenous driving force for enhancing the level of coordinated pollution reduction and carbon emission reduction governance. Based on the panel data of 273 prefecture-level and above cities in China from 2014 to 2023, this article constructs a city-level pollution reduction and carbon emission synergy index. Using a two-way fixed effects model, a mediating effect model, a threshold regression model, and a spatial Durbin model, this study systematically examines the impact of green innovation on pollution-carbon synergy and explores its underlying mechanisms. The results show that: (1) Green innovation significantly promotes the enhancement of the synergy effect of urban pollution reduction and carbon emission reduction, and the conclusion remains valid in multiple robustness tests. (2) The upgrading of industrial structure and the optimization of energy consumption structure play a significant mediating role in the process of green innovation influencing the synergistic effect of pollution reduction and carbon emission reduction. Green innovation, by promoting the high-endization of industries and the low-carbonization of energy, enhances the synergistic effect of emission reduction at a deeper level. (3) Green innovation has a significant dual threshold feature for the synergistic effect of pollution reduction and carbon emission reduction. Its promoting effect depends on the improvement of the cleanliness level of the industrial structure and energy consumption structure. (4) Green innovation has a significant spatial spillover effect. It not only enhances the collaborative governance level of local cities but also has a positive impact on surrounding cities through technology diffusion and regional linkage. Therefore, it is necessary to adhere to the governance approach of adapting measures to local conditions and advancing in phases, by improving the green innovation support system, consolidating structural basic conditions and strengthening regional coordination.

Digital financial inclusion and household consumption structure upgrading.

This study systematically investigates the spatiotemporal differentiation patterns and natural-socioeconomic interactive effects between industrial structure upgrading and ecological environment coupling coordination in China， utilizing provincial panel data from 2011 to 2021. A comprehensive methodology was employed， including the coupling coordination degree model， Dagum Gini coefficient decomposition， and geographical detector analysis. Key findings revealed that： ① The coupling coordination degree between industrial structure upgrading and ecological environment exhibited an upward trend nationwide during the study period yet remained predominantly at a primary coordination stage. Significant regional disparities persisted， with eastern China demonstrating markedly higher coordination levels than in other regions. ② Spatial patterns followed a distinct stepped distribution of "east-high vs. west-low，" accompanied by an evolutionary trajectory of "eastern leadership and western catch-up." ③ Inter-regional differences constituted the primary source of overall coordination disparity， particularly highlighting development gaps between eastern China and western/northeastern regions as core contradictions. ④ Geographical detector analysis identified research and development （R&D） intensity and openness level as dominant socioeconomic drivers， while vegetation coverage and annual precipitation emerged as critical natural constraints. Notably， the interaction between annual precipitation and R&D intensity exerted decisive influence on coupling coordination. This research elucidates the spatiotemporal co-evolution mechanisms and driving factors between industrial transformation and ecological sustainability， providing scientific support for formulating regionally differentiated strategies to promote green industrial transition in China.

To reconcile the tension between developmental priorities and environmental sustainability， transitioning toward eco-friendly decarbonized economic systems has emerged as a crucial strategic imperative. By applying the system dynamics method， a dynamic simulation model including five subsystems（economy， population， science and technology， energy， and environment）was constructed to systematically analyze the interaction mechanisms among these subsystems and to explore the driving effect of the digital economy on the green and low-carbon transformation from 2011 to 2030. Through the simulation analysis of the benchmark scenario， core scenario （driven by the digital economy）， auxiliary scenario （driven by technological innovation and industrial structure upgrading）， and comprehensive scenario （driven by multiple factors）， the results showed that： ① In the single driving scenario， the green and low-carbon transformation oriented by the digital economy had the best effect， with the carbon emissions per unit of GDP decreasing by 40.79% compared to the benchmark scenario and the green GDP increasing by 53.30×1012 trillion yuan. ② The comprehensive scenario generated a multiplier effect， with the reduction in carbon emissions per unit of GDP expanding to 46.38% and the green GDP exceeding 294.95×1012 yuan. This study not only verified the "1+1>2" transformation rule of multi-factor synergy but also constructed a systematic transformation framework of "digital traction-innovation drive-industrial restructuring，" providing a decision-making basis for formulating an integrated policy system for green and low-carbon transformation.

Digital infrastructure and urban-rural income gap: Empirical evidence from China