Search Results (189)

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability. Full article

In the context of global climate change and intensifying resource and environmental constraints, improving agricultural eco-efficiency (AEE) has become critical to achieving the green transformation of agriculture. This study develops a comprehensive evaluation index system for AEE that incorporates factor inputs, expected outputs, and undesirable outputs. Using county-level panel data from 2010 to 2022 for the Yangtze River Economic Belt (YEB), it applied the super-efficiency slacks-based measure (SBM) model to quantify AEE. Furthermore, spatial autocorrelation analysis and the spatial Durbin model (SDM) are employed to reveal its spatiotemporal characteristics and influencing factors of AEE. The results indicate that the overall AEE of the YEB exhibited a fluctuating upward trend over the study period, yet significant regional heterogeneity persisted. AEE showed pronounced positive spatial correlations, with regional disparities primarily stemming from hyper-variance intensity, suggesting that high- and low-efficiency counties are spatially interwoven. The SDM results indicate that local temperature, economic development, urbanization, fiscal support for agriculture, and agricultural production structure positively influence local AEE, while rural residents’ income and educational attainment exert negative effects. These factors also demonstrate significant spatial spillover effects, with economic development and ecological conditions in adjacent regions generating positive externalities, while neighboring urbanization and temperature producing negative impacts. This study deepens the understanding of the driving mechanisms underlying AEE from a spatial interdependence perspective, providing a scientific basis for formulating cross-regional collaborative policies aimed at promoting green agricultural development in major river basins. Full article

Against the backdrop of China’s strategic goals of achieving carbon peaking and carbon neutrality, a key question is whether renewable electricity generation (REG) is associated with lower carbon emission intensity (CEI). To address this issue, this study employs panel data from 30 Chinese provinces from 2005 to 2024 and combines the Tapio decoupling model, Moran’s I test, and the spatial Durbin model (SDM), with the ordinary least squares (OLS) used as a benchmark to analyze the decoupling evolution, spatial spillover associations, and potential transmission channels between REG and CEI. The findings show that: (1) the relationship between REG and CEI evolves from weak decoupling to strong decoupling, suggesting a potentially nonlinear relationship; (2) CEI exhibits significant spatial autocorrelation and regional clustering; (3) REG is significantly associated with lower CEI, with both local and spatial spillover associations; (4) the local mitigation association is stronger in eastern and higher-CEI provinces, while spillover effects are more pronounced in western, northeastern, and resource-based provinces; and (5) the REG-CEI association may operate through energy structure (ES) optimization and energy intensity (EI) reduction, while environmental regulation (ER) may strengthen this association. The endogeneity tests provide supplementary evidence consistent with these findings, although they should not be interpreted as definitive causal proof. Overall, this study contributes to the sustainability literature by showing that the REG-CEI relationship is not merely a static local association, but a dynamic and spatially differentiated pattern shaped by regional coordination and energy-system adjustment. These findings provide evidence relevant to sustainability-oriented energy policy by suggesting that renewable electricity development should be assessed not only by generation scale, but also by its association with carbon-intensity reduction, spatial coordination, and energy-system efficiency. Full article

Against the backdrop of widening regional disparities and the rapid expansion of digital connectivity, understanding the relationship between new quality productive forces (NQPF) and urban–rural integration requires a systemic and network-based perspective. This study approaches urban–rural integration from a complex adaptive system perspective embedded in dynamic interregional networks. Using panel data from 31 Chinese provinces from 2014 to 2024, we construct composite indices for NQPF and urban–rural integration and combine two-way fixed-effects models, static Spatial Durbin Models (SDM), and dynamic-network two-way fixed-effects spatial-lag specifications. This framework helps examine local associations, network-based spillover patterns, and heterogeneous system responses. The results show that: (1) urban–rural integration exhibits significant spatial clustering, with Moran’s I becoming positive and statistically significant after 2016, reflecting persistent structural imbalances within the regional system; (2) the static SDM results show that NQPF is positively associated with urban–rural integration both locally and through spatial indirect linkages; (3) compared with conventional static geographic matrices, the dynamic network-based spatial weights provide additional information on evolving interregional linkages shaped by economic proximity, digital capability similarity, and factor mobility; and (4) under the dynamic network-based specification, NQPF remains positively associated with network exposure in connected provinces, with heterogeneous patterns across regions. More stable local associations are observed in high-connectivity and eastern regions, while the low-connectivity group and central–western regions appear to benefit more from network-based linkages. These findings suggest that the relationship between NQPF and urban–rural integration is embedded in a spatially connected and network-conditioned regional system. By integrating spatial econometrics with a complex systems perspective, this study provides a complementary framework for understanding regional transformation in the digital era. Full article

Soil erosion poses a severe threat to agricultural productivity and ecological security on the Loess Plateau. However, previous studies have rarely integrated physical modeling, elasticity coefficients, and spillover effects into a unified framework at the county level. To address this gap, this study coupled the Revised Universal Soil Loss Equation (RUSLE) with the Spatial Durbin Model (SDM) to systematically investigate the spatiotemporal dynamics, factor elasticity characteristics, and spatial dependence mechanisms of soil erosion on the Loess Plateau from 1990 to 2024. Results show that the annual average erosion rate decreased by 15.5%, with a highly volatile phase before 2001 and a stabilized, low-erosion phase thereafter. The driving factors exhibited marked heterogeneity in direction and strength. The land cover and management factor (C) was the strongest erosion-reducing factor, whereas annual precipitation (PRE) was the primary natural erosion-enhancing factor. County-level erosion also displayed significant positive spatial dependence. PRE had a stable positive indirect effect, whereas C and the support practice factor (P) mainly contained erosion within local jurisdictions. These findings of a unified RUSLE–SDM framework reveal a joint driving mechanism of localized human interventions and climate-driven cross-regional spillovers, providing quantitative support for differentiated soil and water conservation strategies on the Loess Plateau. Full article

The deep integration of the digital economy and education is a critical pathway to addressing the common challenges in less-developed regions, such as human capital shortages, unequal public service provision, and low developmental inclusiveness during new-type urbanization. Using panel data from 11 prefecture-level cities in Jiangxi Province from 2017 to 2024, this study first constructs a comprehensive index system to measure the integration level of the digital economy and education, as well as the inclusive development level of new-type urbanization. The entropy method is employed for objective weighting and composite score calculation. The spatiotemporal patterns of these two variables are visualized using hot spot analysis. A spatial Durbin model (SDM) with dual fixed effects is then applied to empirically examine the direct effect, spatial spillover effects, and regional heterogeneity of the digital-education integration. The main findings are as follows: (1) Both the integration level of the digital economy and education and the inclusive development of new-type urbanization in Jiangxi Province exhibit a distinct spatial pattern characterized as “high in the north, low in the south, and weak in the central region”, with significant spatiotemporal coupling between the two. (2) The digital-education integration exerts a significant positive direct effect on the local inclusive development of new-type urbanization. The core transmission mechanisms are the inclusive sharing of digital educational resources and the effective enhancement of human capital. (3) The integration generates a positive, albeit relatively weak, spatial spillover effect on neighboring areas. The strength of this spillover effect shows pronounced regional heterogeneity, being strongest in Northern Jiangxi, followed by Southern Jiangxi, and weakest in Central Jiangxi. (4) Economic development and industrial upgrading synergistically drive inclusive development alongside the digital-education integration. However, unequal social security provision remains a significant inhibiting factor for inclusive development. Full article

For old industrial urban agglomerations, low-carbon planning requires emission information at a finer spatial scale, but county-level energy statistics are often incomplete. This study focuses on the Central-Southern Liaoning Urban Agglomeration, a typical heavy-industrial region in Northeast China. County-level energy-related carbon emissions for 73 units from 2005 to 2024 are reconstructed by combining socioeconomic panel data with harmonized DMSP-OLS-like nighttime light data. On this basis, global and local spatial autocorrelation, Moran scatterplots, Markov and spatial Markov transition matrices, and a spatial STIRPAT-based Spatial Durbin Model are used to examine the spatial pattern, transition process, and driving factors of emissions. The results show that emissions continued to increase during the study period, although the growth rate became slower and no clear regional peak was observed. Moran’s I rose from 0.627 in 2005 to 0.675 in 2024, which means that county-level emissions became more spatially clustered. The traditional Markov matrix shows strong state persistence, with diagonal probabilities ranging from 0.8793 to 0.9852. The spatial Markov results further suggest that counties surrounded by high-emission neighbors face greater pressure to move upward. In the SDM results, the spatial autoregressive coefficient is significant at the 1% level, with rho = 0.537. GDPPC and POP show negative direct effects, SEC increases local emissions but has a negative indirect effect, and PE is positively related to local emissions. Spatially, high-emission counties are mainly distributed around Shenyang, Anshan, Liaoyang, Dalian, and other industrial cores, while eastern ecological counties remain at relatively low emission levels. These findings provide county-scale evidence for differentiated low-carbon governance in old industrial regions. Full article

Urban–rural integration (URI) is essential for achieving sustainable regional development and addressing the long-standing urban–rural dual-structure divide. This study investigates the impact of factor mobility—specifically labor, capital, and land—on URI across 1712 Chinese counties. By constructing a multidimensional evaluation system for URI and employing a Spatial Durbin Model (SDM), we unpack the direct and indirect effects, as well as the spatial spillover effects of these factors. The results indicate that URI levels in China exhibit significant positive spatial autocorrelation and distinct regional disparities. Labor and capital mobility significantly promote URI, manifesting robust positive direct effects and spatial spillovers that benefit neighboring counties. By contrast, land mobility reveals a “structural mismatch,” whereby inefficient land-use conversion can hinder integration, particularly in less-developed regions. Heterogeneity analysis further shows that the effects of factor mobility are strongest in Eastern China, while Western regions face structural constraints. These findings suggest that sustainable urban–rural transformation requires not only the free flow of production factors but also a coordinated spatial strategy to mitigate regional imbalances. This study provides policy-relevant insights for policymakers aiming to optimize factor allocation and enhance grassroots-level sustainability within the framework of rural revitalization and integrated regional development. Full article

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. Full article

Against the global backdrop of the manufacturing industry (MFI)’s transition toward sustainability, we investigated the impact mechanisms and spatial effects of digital elements on the green upgrading of manufacturing industries. Based on an innovation ecosystem perspective, we utilize panel data from 13 prefecture-level cities in the Beijing–Tianjin–Hebei (BTH) region of China spanning 2003 to 2023, employing a spatial Durbin model (SDM) for empirical analysis. The findings reveal the following: (1) Both digital element inputs and manufacturing green upgrading in the BTH region exhibit significant positive spatial correlation, with the latter demonstrating notable path dependence. (2) While digital elements significantly drive local manufacturing green upgrading, they also generate a spatial siphon effect at the regional level, exerting a certain inhibitory impact on the green upgrading of neighboring areas. (3) Mechanism analysis indicates that local digital elements facilitate manufacturing green upgrading by enhancing firms’ digital innovation capabilities, stimulating consumer digital demand, and optimizing corporate resource allocation efficiency. This research provides theoretical support and empirical evidence for governments to formulate targeted digital economy policies and promote low-carbon, green development in the manufacturing industry (MFI). Full article

Enhancing green development efficiency (GDE) is crucial for promoting ecological protection and high-quality growth in the Yellow River Basin (YRB). Using panel data from 48 prefecture-level cities in the YRB from 2010 to 2022, this study applies a Super-SBM model that accounts for undesirable outputs to measure GDE. Then, a modified gravity model and social network analysis (SNA) are used to identify the evolution of its spatial correlation. Additionally, a spatial Durbin model (SDM) is employed to examine the driving mechanisms from the dual perspectives of the innovation rebound effect and external micro-ESG (Environmental, Social, and Governance) reverse-forcing pressure. The results reveal the following: First, the spatial pattern of GDE in the YRB has changed significantly, showing an overall spatial imbalance, with efficiency improvements in the middle reaches and declines in the lower reaches. Notably, resource-based cities have improved GDE due to environmental regulations. Second, the spatial correlation network has evolved from a point-axis layout to a more complex network structure. However, spatial links among cities are mainly driven by geographic proximity, while collaborative ties between cities with similar economic features remain weak. Third, technological innovation has a significant negative effect on local GDE, likely due to the energy rebound effect. Meanwhile, the cross-regional transmission of the external supply chain ESG reverse-forcing mechanism remains weak, constrained by the carbon lock-in effect in the middle and upper reaches. These findings suggest that internal technological structures and external market constraints both influence GDE in the YRB. This research offers an empirical foundation for developing targeted, cross-regional collaborative governance policies. Full article

This study examines the relationship between rural land circulation and common prosperity across 30 Chinese provinces over the period 2010–2022. We construct a multidimensional common prosperity index based on economic development, income distribution, public services, and social security using the entropy weight method. A Spatial Durbin Model (SDM) is employed to capture both local effects and interregional spillovers. The results show that rural land circulation exerts a positive and statistically significant impact on common prosperity. Effect decomposition further indicates that the influence is primarily driven by local (direct) effects, while spatial spillovers also play a meaningful role, suggesting that improvements in one region can generate positive externalities for neighboring areas. Additional analysis reveals three key channels through which land circulation is associated with common prosperity: improvements in agricultural productivity, increases in farmer income, and urbanization advancement. The effects exhibit clear regional heterogeneity, being strongest in central China, moderate in western regions, and statistically insignificant in the eastern provinces, reflecting diminishing marginal returns as land markets mature. Moreover, the impact of land circulation is more pronounced in regions with higher levels of digital economy development, indicating that digitalization enhances the efficiency and inclusiveness of land market transactions. These findings are robust to alternative spatial weight matrices, variable definitions, and sample adjustments. Overall, the results highlight the importance of regionally differentiated land circulation policies and the role of market integration in promoting balanced and inclusive development. Full article

This study investigates the spatial effects of artificial intelligence (AI) innovation on carbon intensity using provincial panel data from 30 Chinese provinces over 2010–2023. Employing the Spatial Durbin Model (SDM), we find that a 1% increase in AI patent count reduces local carbon intensity by 0.034% (direct effect, p < 0.01) but increases carbon intensity in neighboring regions by 0.069% (indirect effect, p < 0.05). Heterogeneity analysis shows that AI innovation reduces local carbon intensity by 0.069% in non-western regions (p < 0.01) but has no significant effect in the western region. In regions with above-median R&D intensity, both direct and indirect effects become negative (−0.094% and −0.069%, respectively), indicating that AI innovation reduces carbon intensity locally and in neighboring areas. Mechanism tests confirm that industrial structure upgrading mediates this relationship, with AI innovation increasing the industrial structure hierarchy coefficient by 0.004 (p < 0.05). These findings provide quantitative evidence that AI innovation has opposing local and spillover effects on carbon intensity, and that high R&D intensity can reverse negative spillovers into positive ones. The results offer empirically grounded policy recommendations for China’s dual-carbon targets and sustainable development. Full article

The relationship between forest ecosystem services and human health has emerged as a key topic in forest economics and health policy research. This study develops a spatial modelling framework to quantify the health benefits of forest ecosystem services and proposes policy mechanisms to incorporate these benefits into governmental health strategies. Using county-level panel data from 66 administrative units in Zhejiang Province, China, covering the period 2013–2023, we analyse the relationship between forest-mediated air purification services and two population health outcomes: the incidence of respiratory diseases and cardiovascular disease mortality. We employ a Spatial Durbin Model (SDM) to estimate both direct and spatial spillover effects across county boundaries. The findings indicate that forest ecosystem services exert significant negative effects on adverse health outcomes, with spillover effects extending beyond administrative boundaries. The monetised health benefit of forests is estimated at approximately RMB 1108.6 per hectare per year, substantially exceeding current ecological compensation standards and suggesting systematic undervaluation of forest health services. Heterogeneity analysis reveals that health benefits are greater in urbanised regions and among vulnerable population groups, including the elderly. These findings provide an empirical basis for reforming health-oriented ecological compensation mechanisms and offer implications for sustainable land use governance aligned with SDG 3 (Good Health and Well-being) and SDG 15 (Life on Land). Full article

Digitalization is the driver of regional competitiveness and sustainable development, but its geographical impacts differ significantly across Europe. This study was conducted to determine if digital transformation results in regional sustainability or if it increases spatial inequalities, concentrating on European NUTS-1 regions for the period 2021–2025. A composite Regional Digitalization Index was developed by means of Principal Component Analysis (PCA) based on indicators measuring internet access, internet usage, and the availability of digital public services. Dynamic spatial panel econometric models were used for empirical investigation, including a Spatial Autoregressive (SAR) model and a Spatial Durbin model (SDM), which facilitated the exploration of both temporal dependence and spatial spillover. Three main conclusions can be derived from the results, as follows: The level of digitalization in a region is highly stable over time, whereby the development depends most on previous paths. Subsequently, human capital is highly significant for digital development, and its effects are not only local but also spill over to neighboring regions. Lastly, spatial interactions consist of two opposite forces—the positive diffusion from digitally advanced neighboring regions and the competitive effects related to the economic strength of neighboring regions—that further intensify the core–periphery divide. Full article