Lei Jiang

The urban heat island (UHI) effect represents a critical urban climate phenomenon arising from the combined pressures of rapid urbanization and climate warming. Although its association with carbon emissions has received increasing scholarly attention, the underlying behavior-mediated pathways and cross-regional spillover patterns remain insufficiently understood. Using multi-source geospatial data for the Yangtze River Delta urban agglomeration from 2014 to 2023, this study develops a multi-scale analytical framework integrating 1 km urban agglomeration exploratory analysis and 5 km spatial econometric modeling. Anthropogenic Energy Activity Intensity (AEAI) is constructed as a proxy for energy-related human activities, and a spatial Durbin model, combined with a spatial mediation approach, is employed to examine the spatial associations and statistically mediated pathways within the “heat-energy-carbon” nexus. The results indicate that: (1) carbon emissions exhibit significant positive spatial spillover effects, consistent with thermal diffusion processes and socioeconomic network interactions; (2) AEAI represents a substantial partial statistical mediation pathway in the association between UHI and carbon emissions, accounting for 44.63% of the total association. This suggests that the UHI–carbon emission linkage is partly embedded in spatial patterns of energy-intensive human activities rather than reflecting a purely direct thermal effect. These findings suggest that regional climate governance may need to move beyond single-city interventions and purely physical cooling strategies toward integrated approaches that combine cross-regional coordination with behavioral regulation. Promoting passive cooling-oriented urban planning and demand-side energy transitions may help reduce carbon lock-in risks and support the development of climate-resilient urban agglomerations.

The spatial equity of kindergarten distribution is a critical concern for China’s educational policy. This study investigates the socioeconomic determinants underlying the spatial stratification of kindergartens across 323 Chinese cities. Utilizing a spatial stratified heterogeneity framework, we employed web-crawling techniques to gather kindergarten location data and applied Geographical Detector (GD) analysis complemented by Pearson correlation and Grey relational degree methods to quantify the influence and interactions of key factors. Our principal results indicate that: (1) The distribution of kindergartens is highly aggregated in affluent eastern coastal regions, but this pattern becomes more scattered when normalized per capita, revealing disparities in access. (2) Economic capacity (per capita income) and educational attainment (average years of education) are the two most powerful single factors (q-statistics of 0.351 and 0.343, respectively) explaining the spatial distribution. (3) Critically, interaction effects between factors—particularly between income and marriageable population or education levels—are shown to be non-linearly enhanced, meaning their combined effect is stronger than the sum of their individual parts. This study’s main contribution is a robust, methodologically novel empirical analysis that moves beyond linear assumptions to identify the synergistic drivers of kindergarten distribution. We conclude that policy makers must prioritize interventional strategies that simultaneously address economic and educational development to effectively reduce spatial inequities in preschool education access. Moreover, regional economic support policies or targeted public investment in kindergartens could bridge the gaps between urban and rural areas and promote more equitable access to preschool education.

Rural revitalization relies on both industrial upgrading and cultural reinvigoration. Tea-culture–tourism integration has been regarded as a potentially effective route for advancing rural revitalization, yet its interactive relationship and dynamic evolution remain insufficiently understood. Based on provincial-level panel data from 18 major tea-producing provinces in China between 2013 and 2022, this study constructs two comprehensive evaluation index systems for tea–culture–tourism integration and rural revitalization. It employs the entropy weight method, the coupling coordination degree model, the Dagum Gini coefficient decomposition, and spatial econometric models to measure their coupling coordination degree and analyze its spatiotemporal pattern, regional disparities, and driving factors. The main findings are as follows: (1) Both tea–culture–tourism integration and rural revitalization exhibited upward trends, with the latter being consistently higher. (2) The coupling coordination degree transitioned from dissonance to coordination, spatially forming a gradient progression pattern of “coastal-river-inland”. (3) Regional disparities were primarily dominated by transvariation density and inter-regional differences. (4) Results of spatial econometric models indicated that fiscal conditions exerted a significant positive direct effect, while urban–rural social development, employment structure, and human capital generated significant positive direct and indirect effects.

In the contemporary economic landscape shaped by globalization and digital transformation, patent-intensive industries have emerged as critical engines for enhancing national competitiveness. This study analyzed 98,464 collaborative patent application records (2012–2023) from listed companies in patent-intensive sectors, sourced from the China National Intellectual Property Administration (CNIPA) database. Through kernel density estimation, social network analysis, and community detection techniques, we examined the evolutionary trajectories of innovation networks and spatial patterns within these industrial clusters. Our findings indicate a notable spatial agglomeration trend in patent-intensive industries, exhibiting a prominent “core-periphery” structural feature. The core nodes of this cluster network closely align with economically developed regions, and the network structure has gradually shifted from a triangular framework supported by Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta to a diversified multilateral framework. Moreover, the community structure of the collaborative network within China’s patent-intensive industrial clusters exhibits distinct characteristics driven by technological relevance and strategic synergy, rather than strictly adhering to the principle of geographical proximity. These discoveries not only enrich the application of innovation network theory in the specific context of China, but also provide valuable guidance for cluster enterprises in selecting partners and achieving collaborative innovation.

This research aims to investigate whether fiscal efficiency promotes green total factor productivity (GTFP) in China. To this end, we used multi‐source data and adopted productivity index approaches to measure GTFP and fiscal efficiency scores of 284 Chinese cities and then applied a spatial econometric model to examine the impact of fiscal efficiency on GTFP. The main findings of this study are as follows: (1) Overall GTFP scores showed an N‐shaped trend during the sample period. Specifically, they first rose, then decreased, and finally went up. (2) The dynamic average fiscal efficiency scores of Chinese cities exhibited an upward trend with fluctuations. The spatial distribution of the fiscal efficiency scores of 284 cities showed that overall the eastern and central cities had the highest scores, followed by the western and northeastern cities. (3) The results of the spatial lag panel data model revealed that the spatially lagged term of GTFP was significantly positive, indicating that spatial spillovers of GTFP could not be ignored. Otherwise, it may lead to biased conclusions. It implies that improvements in the GTFP of neighboring cities contribute to promoting the GTFP growth of the own city. Moreover, the impact of fiscal efficiency on GTFP was verified to be significant and positive, indicating that fiscal efficiency improvement could significantly promote China's high‐quality development measured by GTFP. In addition, increases in urbanization level, economic level, education level, industrial structure, and innovation level had positive effects on GTFP, while there was a U‐shaped relationship between environmental regulation and GTFP. Last, several tailored policy recommendations were proposed.

The Chinese governments have reinforced environmental regulation to solve the problem of ever-growing environmental degradation by increasing expenditure for environmental protection. We observe that provincial expenditure for environmental protection is spatially clustered, implying potential spatial spillovers of environmental policies proxied by the expenditure. Hence, we plan to investigate the direct and indirect spillover effects of environmental policies based on an extended spatial STIRPAT Durbin model. The findings are as follows: (1) Results reveal that the variable of expenditure for environmental protection has a significant and negative impact on SO2 pollution, indicating that an increase in environmental awareness of governments contributes to mitigating SO2 pollution; (2) An indirect spatial spillover effect of environmental protection expenditure is also verified since significant and negative coefficients are obtained, which indicates that increasing environmental protection expenditure of neighboring provinces can also help reduce the SO2 pollution of a province. The possible explanation is that provinces will follow suit if neighboring provinces increase spending on environmental protection, presenting the demonstration and competition effects. To conclude, it can be verified that the effect of environmental policy works. Hence the need for more environmental protection expenditure cannot be overstated for China’s sustainable development.

China faces a multi-objective environmental governance dilemma involving achieving fundamental ecological environment improvement, carbon peaking, and carbon neutrality. In this study, we constructed indicators of the level of green finance development through the entropy-weighted TOPSIS evaluation model, and adopted the two-way fixed-effect spatial Durbin model (SDM) and the multiple spatial mediation effect model to explore the impacts and paths of green finance as a policy tool to promote environmental sustainability in carbon emissions and environmental pollution using panel data from 30 provinces in China between 2007 and 2019. The research results show that green finance (GF) can significantly reduce carbon emission intensity (CEI) and air pollution (AP). Moreover, the role of GF in reducing AP becomes weaker due to increased levels of green finance in neighboring regions negatively affecting local air quality. Mediation path analysis shows that GF reduces CEI and AP mainly by promoting the optimization of energy structures and technological progress. The results of regional heterogeneity analysis show that there is variability in the pathway of the impact of GF on CEI and AP in different regions, and the Western region is more sensitive to GF policies. Therefore, policymakers should give their full attention to the functions of GF to mitigate China’s carbon emissions and environmental pollution.