Spatial Agglomeration Effect Research Articles

A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.

Is fair carbon mitigation practicable in China? Insights from digital technology innovation and carbon inequality

The uneven matching of distribution and targets in practicing carbon emission reduction has led to emission reduction inequality, while digital technology innovation (DTI) is expected to reduce these gaps. Therefore, this study casts new insights into modeling the nexus between DTI and carbon inequality using China's 286 prefecture-level cities from 2000 to 2021. Remarkably, city-level DTI performance is innovatively proxied by the related patent application volume, while the carbon inequality index within the region is measured using the environmental Gini coefficient. The main findings of this study include: (1) DTI robustly mitigates China's carbon inequality. (2) The effect of DTI is asymmetrically more significant in regions with a higher carbon inequality index. (3) DTI heterogeneously reduces carbon inequality more obviously in the eastern and central regions, and technology innovation in digital product manufacturing and digital technology application industries reduces carbon inequality more significantly. (4) More precisely, DTI strengthens carbon justice through income equalization, spatial agglomeration, and digital inclusion effects. Specifically, DTI significantly reduces income inequality and the digital economy's spatial agglomeration level and enhances digital financial inclusion, thereby reducing carbon inequality. This study also provides the policy implications for China.

Assessing the spatiotemporal evolution and socioeconomic determinants of PM2.5-related premature deaths in China from 2000 to 2021

China's swift socioeconomic development has led to extremely severe ambient PM2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM2.5-related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action.

Land Green Utilization Efficiency and Its Driving Mechanisms in the Zhengzhou Metropolitan Area

Improving land green use efficiency is of great significance for promoting high-quality economic development and promoting the modernization of harmonious coexistence between humans and nature. In this study, the super-efficiency SBM model with non-expected output was used to measure the level of land green use efficiency at county scale in the Zhengzhou metropolitan area from 2005 to 2020. Based on this, the spatio-temporal evolution and spatial agglomeration characteristics were analyzed. Finally, the driving mechanisms were revealed by using the geographical detector model. The results were as follows: (1) From 2005 to 2020, the land green use efficiency of the Zhengzhou metropolitan area fluctuated from 0.5329 to 0.5164, with an average annual decline rate of 0.21%, exhibiting three stages: decline, rise, then another slight decline. At the city level, Luohe City had the highest land green use efficiency, while Zhengzhou City had the lowest. (2) The land green use efficiency of the Zhengzhou metropolitan area showed a significant spatial positive correlation, Moran’s I index increased from 0.1472 to 0.2991, and the spatial agglomeration effect was continuously enhanced. On the local scale, high-high (H-H) aggregation and low-low (L-L) aggregation were dominant, high-high (H-H) aggregation areas were mainly distributed in the southwest and southeast of the Zhengzhou metropolitan area, and low-low (L-L) aggregation areas were mainly distributed in the central and western parts of the Zhengzhou metropolitan area. (3) There is heterogeneity in the degree of influence of different driving factors on land green use efficiency in the Zhengzhou metropolitan area, which is ranked as topographic relief (X7) > forest coverage rate (X8) > social consumption (X6) > industrial structure (X3) > urbanization rate (X2) > economic development (X1) > industrial added value scale (X5) > financial expenditure (X4). q values were 0.1856, 0.1119, 0.1082, 0.0741, 0.0673, 0.0589, 0.0492 and 0.0430, respectively. The interaction of two factors can enhance the explanatory power of land green use efficiency in the Zhengzhou metropolitan area. Except for the interaction of topographic relief and forest coverage rate, the other factors all show double factor enhancement. The explanatory power of the interaction between topographic relief and urbanization rate is the strongest, at 0.3513. In the future, policy regulation should be carried out from the perspectives of the interaction of social and economic conditions such as improving forest coverage rate, improving consumption power, optimizing industrial structure and improving land green use mechanisms to promote the improvement of land green use efficiency.

Can market-oriented and government-led spatial agglomeration of factories reduce carbon emission intensity? Evidence from China

Both market-oriented and government-led spatial agglomeration of factories profoundly affect China's carbon emission intensity. However, few studies have comprehensively examined the effect of spatial agglomeration of factories driven by different motivations on carbon emission intensity, and often ignore the moderating effect of local government attention to the environment. This study attempts to incorporate market-oriented and government-led spatial agglomeration of factories into the same analytical framework and examine their interaction effects on carbon emission intensity. We first calculate an Agglomeration Index based on the geographic coordinates of micro-factories to measure market-oriented agglomeration of factories, and take development zone policies as a proxy for government-led agglomeration of factories. Based on city-level data from 1998 to 2013, we empirically analyze their impacts on carbon emission intensity and further explore regional heterogeneity and mechanisms. The results show that both the market-oriented and government-led spatial agglomeration of factories can significantly reduce carbon emission intensity through scale effect, innovation effect and structure effect. However, the agglomeration of factories caused by government forces has weakened the original effect of market-oriented agglomeration of factories in reducing carbon emission intensity. Heterogeneity analysis indicates that government-led agglomeration of factories is more effective in reducing carbon emission intensity in less-developed regions. Furthermore, the improvement of the governments' attention to the environment can further exert the positive effect of spatial agglomeration of factories on carbon emission intensity. Our study deepens the understanding of the effects of spatial agglomeration of factories caused by different motivations, and provides a reference for China and other developing countries to formulate relevant policies for reducing carbon emission intensity.

Impacts of Low-Carbon City Pilot Policy on Urban Land Green Use Efficiency: Evidence from 283 Cities in China

On the global scale, the low-carbon city pilot policy (LCCPP) has important significance for and influence on the study of urban land green use efficiency (ULGUE). Based on the panel data of 283 cities in China from 2007 to 2019, this study uses the super-SBM model, multi-period DID model, spatial econometric model, intermediary effect model, and heterogeneity analysis methods to deeply explore the specific impact mechanism of LCCPP on ULGUE. The results show the following: (1) During the study period, the average ULGUE of the selected samples increased by 11.71 percentage points overall and showed a certain spatial agglomeration effect. (2) LCCPP has a significant promoting effect on the improvement of ULGUE, and there is a positive spatial spillover effect. (3) The impact of LCCPP on ULGUE is mainly achieved through two paths: reducing energy utilization intensity and improving urban innovation level. (4) In cities with different levels of land green use efficiency, geographical location, and resource endowment, there are significant differences in policy effects. This paper puts forward countermeasures and suggestions to comprehensively promote the sustainable development of global cities and the improvement of land green use efficiency.

Spatiotemporal Changes in China’s Tourism Industry Development

As a strategic pillar of the Chinese economy, high-quality development of the tourism industry has become a pressing demand in the contemporary era. Following the new developmental concept advocated by the Chinese government, this study has constructed a comprehensive set of indicators for high-quality tourism development. A thorough spatial analysis of the developmental quality of China’s provincial tourism industry from 2000 to 2021, as well as the developmental conditions of various subsystems, was conducted by employing the entropy–value–TOPSIS method, Dagum Gini coefficient, and ESTDA approach. The research has found that (1) the overall quality of China’s tourism industry has significantly improved, (2) the determinants of differences in tourism-development quality in China transitioned from endowment advantages to innovation capabilities. (3) positive spatial-agglomeration effects are evident in the developmental landscape of China’s tourism industry, (4) the quality of regional tourism development is closely related to the economic development of urban agglomerations, (5) China’s tourism industry demonstrates resilience and capacity for restructuring in the post-pandemic era.

Industrial fluoride emissions and their spatial characteristics in the Nansi Lake Basin, Eastern China.

Excessive fluoride emissions threaten ecological stability and human health. Previous studies have noted that industrial sources could be significant. However, quantifying industrial fluoride emissions has not been yet reported. In this study, both bottom-up and top-down approaches were used to estimate the fluoride emissions in the Nansi Lake Basin. Global and local spatial autocorrelation were adopted to reveal the spatial agglomeration effects. The fluoride emissions calculated by the bottom-up approach were larger than those calculated by the top-down method. The highest fluoride input mainly occurred in Zoucheng and Mudan. The highest fluoride emissions mainly occurred in Zoucheng and Rencheng using the bottom-up approach. The highest fluoride emissions mainly occurred in Zoucheng and Yanzhou using the top-down approach. Mining and washing of bituminous coal and anthracite (BAW) was the most significant source of fluoride input and emissions. A significant spatial agglomeration effect of fluoride emissions was found. These findings could provide a method for accurate industrial fluoride emission estimation, complement the critical data on the fluoride emissions of main industrial sectors, and provide a scientific basis for tracing fluoride sources.

Coupling coordination and interactive effects of new urbanization efficiency and eco-efficiency-A case study of Fujian Province.

This study explores the coupling and coordinated development of new urbanization and eco-efficiency and their interaction mechanisms from the perspective of efficiency, and it has significant implications for promoting high-quality development and surpassing in China's regional development. The study aims to investigate the spatiotemporal distribution pattern of new urbanization efficiency and eco-efficiency and its coupling and coordinated development relationship. Using panel data from 2010 to 2020 for nine cities in Fujian Province, this study employs the undesired super-efficiency SBM model to measure the efficiency of new urbanization and eco-efficiency. Additionally, a spatial panel Durbin model is constructed to analyze the interaction effect between the two efficiencies. During the study period, both new urbanization efficiency and eco-efficiency in Fujian Province showed a fluctuating upward trend, with higher efficiency in the southeast than the northwest, exhibiting significant spatial agglomeration effects. Despite high double efficiency coupling, coordinated development was low, evolving from near-disorder to barely coordinated. The high coupling and coordination areas were mainly concentrated in the southeast, with gaps between different regions gradually narrowing. There was positive interaction between dual efficiency in the same region, with new urbanization efficiency showing a positive spatial spillover effect on eco-efficiency. Conversely, the spatial spillover effect of eco-efficiency on new urbanization efficiency was not significant.

Can Spatial Agglomeration Promote Exports? The Evidence from China’s Wood-Processing Industry

Wood processing products can be used as a substitute for some high carbon emission materials, thus contributing significantly to climate change mitigation. China has significant importance in guaranteeing a stable global supply of wood products. However, China has experienced a decline in wood product exports recently, which could affect the world’s progress toward sustainable development. This study focuses on the spatial agglomeration of China’s wood-processing industry and explores the potential of integrating spatial agglomeration to address the diminishing export growth. We found that the spatial agglomeration of China’s wood-processing industry generally increased rapidly between 1999 and 2004, but decreased after 2004. The centroid of China’s wood-processing is shifting from the northeastern region toward the eastern coastal areas. The industry is more concentrated in the north–south direction and slightly dispersed in the east–west direction. Most sub-sectors of China’s wood-processing industry operate at the primary agglomeration level, indicating significant potential for improvement and further development. The empirical results unveil the substantial positive influence of spatial agglomeration on exports within China’s wood-processing industry, indicating the prospects for enhancing exports through the deliberate exploitation of agglomeration effects. The results advocate the view that spatial agglomeration acts as an essential accelerator for export growth and firms’ international competitiveness. Therefore, spatial agglomeration can alleviate the export constraints of China’s wood-processing industry. The results also show that exports are hampered by increased enterprise productivity. The ‘productivity paradox’ phenomenon has also surfaced in low-tech industries, like the wood-processing industry, as a result of issues with transaction costs, the development path, and policy orientation. Therefore, we should strategically leverage the spatial agglomeration effect, especially in the western region, and guide the moderate competition of enterprises to enhance the competitiveness of low-tech enterprises. China’s wood-processing industry can provide advanced experience and lessons for other countries regarding industrial cluster allocation and sustainable development models.

Index construction and application of digital transformation in the insurance industry: Evidence from China.

In the context of digitization, the insurance industry's value chain is undergoing significant shifts. However, the existing research on its comprehension and measurement remains relatively limited. This study constructs an index system for digital transformation in the insurance industry (DTII) on three components: digital infrastructure, digital platform, and digital applications. Utilizing data from 31 provinces in China, this study employs the entropy weight method, analytic hierarchy process method and minimum relative entropy method to measure the weights of indicators, empirically applying this index system. The results show that DTII in China experiences rapid advancement with an average annual growth rate of 20.46% from 2014 to 2020 and there exists strong regional convergence. In addition, the spatial agglomeration and spatial effects of DTII are mainly concentrated in the life insurance industry and the eastern region. This study provides an index system and empirical evidence for evaluating the DTII, providing policy insights for exploring the sustainable development path of the insurance industry in the digital era.

Industrial agglomeration and PM2.5 pollution in Yangtze River Economic Belt in China: non-linear estimation and mechanism analysis

Introduction: The uncertainty associated with PM2.5 pollution hinders the economic high-quality development and threatens public health. Industrial agglomeration stands as a critical factor in regional economic and environmental governance, and the current studies about its impact on PM2.5 pollution are mostly limited to a specific industry or unidirectional influence.Methods: Our study constructed spatial econometric models to analyze the effect of three major industrial agglomerations on PM2.5 pollution, based on evidence from 110 prefecture-level cities of the Yangtze River Economic Belt in 2005–2019.Results: The results show that: 1) The three major industrial agglomerations and PM2.5 pollution present different spatiotemporal characteristics and show prominent positive spatial autocorrelation and agglomeration effect. 2) The primary industrial agglomeration contributes to a decrease in PM2.5 pollution and exhibits negative spatial spillover effects. A nonlinear relationship is observed between the secondary industrial agglomeration and PM2.5 pollution. The tertiary industrial agglomeration results in an increase in PM2.5 pollution. 3) The effects of secondary industrial agglomeration on PM2.5 pollution exhibit varying degrees of ‘inverted U-shape’ curves in the upstream, midstream, and downstream cities. The midstream cities are the first to reach the inflection point of agglomeration. 4) Industrial agglomeration affects PM2.5 pollution through three mechanisms, including scale expansion effect, technological spillover effect, and population scale effect.Discussion: Based on the empirical findings, this study provides scientific support and decision-making reference to improve the positive impacts of industrial agglomerations on PM2.5 pollution.

Dynamic Evolution Analysis of Spatial Differences of Carbon Emissions in Beijing-Tianjin-Hebei Region from 1997 to 2017

Carbon emission is the main cause of climate change, and Beijing-Tianjin-Hebei (BTH), is one of the main region of carbon emission in China. In order to study the carbon emission characteristics in the BTH region, this paper uses the Geostatistical method to analyze the dynamic evolution of the spatial variations of carbon emissions from 1997 to 2017. (1) The global Moran’s I analysis indicates that carbon emissions take on a spatial agglomeration effect in BTH region. The global Moran’s I show a decreasing trend from 1997 to 2012, indicating that there is gradually weakening pressure between cities. However, from 2012 to 2017, the global Moran’s I show an increasing trend, indicating that there is gradually strengthening pressure between cities. (2) The hotspot areas have remained largely unchanged except Beijing. Beijing has transitioned from a hotspot area to a non-hotspot area. (3) Standard deviational ellipse analysis showed that that the carbon emission distribution in the BTH region basically exhibits a northeastsouthwest distribution pattern and has a very small variation.

Analysis of coupled coordination and spatial interaction effects between digital and tourism economy in the Yangtze River Delta region.

The coupled and coordinated development of the digital economy and tourism economy has become an inevitable choice for achieving high-quality development in both sectors. This paper proposes a conceptual index system using entropy weight method and coupling coordination degree model for analysis of the coupling coordination relationship between digital economy and tourism economy. After that, the paper introduces the Moran's Index to examines the spatial heterogeneous effects of coupling coordination degree. This framework is applied to 41 cities in the Yangtze River Delta region during 2011-2021. The results show that the temporal changes of the coupling coordination relationship between digital economy and tourism economy in 41 cities are quite satisfactory, while the regional differences are large. Furthermore, the coupling degree between digital economy and tourism economy shows the strong spatial agglomeration effect, and the spatial proximity of regions with similar integration indicates that the "Matthew Effect" gradually highlights the clustering of high and low levels. Based on the results of analysis, this paper finally puts forward several policy recommendations to provide a referential path for the integration of digital economy and tourism.

Spatiotemporal Variability and Impact Factors of Domestic Water Prices in China

Given China’s status as one of the most water-scarce countries globally, its rapid development of urbanization and sustained economic growth have led to increasing pressure on the urban water supply. Water pricing is also receiving increasing attention as an important tool for water resource management. This study analyzes the spatial and temporal characteristics of domestic water prices in China and their drivers. To this end, domestic water price data from 285 cities in China were collected. Spatial statistical analysis and geodetector were used to examine the spatial distribution and temporal patterns of domestic water prices from 2007 to 2020, as well as to identify the primary factors influencing these prices. The following results are noted: (1) The national average domestic water price has increased from 2 RMB/m3 to 3.12 RMB/m3, where the northeast and eastern regions have higher prices than the national average, while the central and western regions have lower prices. (2) The spatial distribution of urban domestic water prices presents clear differences characteristic of north–south and spatial agglomeration effects; the high-value area of domestic water prices is mainly concentrated in Beijing–Tianjin–Hebei. (3) On a national and regional scale, the price of domestic water is closely related to economic development, water resources, and resident’s income level. Furthermore, this study revealed that the interaction between pairwise factors played a more significant role in influencing domestic water prices compared with the individual impact of each factor. This finding contributes to a deeper understanding of the spatiotemporal heterogeneity in domestic water pricing and offers valuable insights and guidance for water pricing reform in China.

The coupling relationship and driving mechanism between urbanization and ecosystem services in the Yellow River Basin from a multi-spatial scale perspective

Rapid urbanization has led to ecological destruction and associated issues. Macro policies wield substantial influence over urbanization and its relationship with the environment. Without considering the differences in scale, macro policies may be ineffective at addressing urbanization’s adverse impacts on the environment, and even worsen this relationship. We used data on 622 counties, 76 prefectures, and 7 urban agglomerations in the Yellow River Basin to examine the development level, coupling coordination degree, and spatial patterns of urbanization and ecosystem services at three scales during 2000–2020. Further, we explored the driving mechanisms in the relationship between urbanization and ecosystem services. We found that: First, the coupling coordination was relatively low but showed an upward trend. A sizeable spatiotemporal difference existed, with higher (lower) coordination in the east (west). Second, the coupling coordination of each scale exhibited significant spatial positive correlations. The low-value heterogeneous region was embedded around the agglomeration region, and polarization was significant. The larger the scale, the stronger the agglomeration effect. Further, the coupling coordination spatial agglomeration effect of each scale gradually weakened over time. Third, the spatial and temporal distributions of coupling coordination and its agglomeration characteristics at different scales differed. The urban agglomeration scale showed significant overall coordination or agglomeration characteristics, and prefecture and county regions showed local and unique characteristics within urban agglomerations. Fourth, the dominant factors influencing the spatial patterns of the coupling coordination at the county, prefecture, and urban agglomeration scales differed. The interaction and factor detection showed linear and double-factor enhancements. We find that economic development, government policies, environmental protection, and natural factors are the combined effects of urbanization and ecosystem services. Our research method can provide a reference for other river basins, and the results can help governments in formulating policies for sustainable development at different spatial scales.

Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China

China is a traditional agricultural country, and the integration of rural industries has become an essential policy direction for the current strategy of agricultural modernization in China. The purpose of this study is to evaluate the level of integrated rural industry development in China, identify problems in the development process, and combine analysis of related theories and practices of rural industry integration to propose strategies to enhance the level of integrated rural industry development in China. Hence, this paper, rooted in the integration of rural industries, cross-industry, industry extension, industry agglomeration, and industry penetration, sets up a measurement index system. Using provincial panel data from 2011 to 2020, the paper measures the level of integrated development in rural industries across China and its 31 provinces. Research indicates that during the sample period, the overall level of integrated development in rural industries in various provinces in China has shown an upward trend. Development among the eastern, central, and western regions is highly uneven, but this disparity has been gradually narrowing in recent years. Furthermore, the levels of integrated development in different provinces exhibit significant spatial agglomeration effects. The development level of neighboring regions significantly impacts the province’s development status.

Impact of environmental regulation intensity on the efficiency of sustainable economic growth in the European Union

Environmental pollution is an essential constraint to the sustainable growth of regional economy. This paper uses the super efficiency slacks-based measure data envelopment analysis (SBM-DEA) model to measure the efficiency of sustainable economic growth (ESEG) in the European Union (EU) and applies the spatial econometric model to explore the impact of environmental regulation intensity (ERI) on ESEG in the EU. The results show that the ESEG in the EU shows an N-shaped growth trend and the spatial pattern of “Western Europe > Eastern Europe > Central Europe > Northern Europe > Southern Europe” from 2001 to 2019. The impact of ERI on the ESEG in the EU presents a “U" shape, indicating that increasing ERI first reduces ESGE, and after reaching the turning point of ESGE, continuing to increase ERI will promote ESGE. Further analysis shows the ERI have spatial spillover effect on the ESEG, while the spatial spillover effect in the eastern and western regions of the EU is different. Improving the ERI in the western region is not only conducive to the ESEG of the country but also has a positive spillover effect on neighboring countries. However, the spatial agglomeration effect in the eastern region is not significant. In addition to environmental regulation, labor & capital structure, research and development (R&D) expenditure, GDP per capita and trade value have a significant impact on the ESGE of a country and neighboring countries.

Distribution characteristics and influencing factors of household consumption carbon emissions in China from a spatial perspective

Household consumption carbon emissions (HCCEs) have become the main growth point of China's carbon emissions in the future. It is important to investigate the factors affecting the demand-side carbon emissions in order to find the accurate entry point of emission reduction and achieve carbon peaking and carbon neutrality goals. Different from previous studies, this study analyzed the spatial and temporal evolution characteristics of provincial HCCEs in China from a spatial perspective by using the Theil index and spatial auto-correlation and explored the key influencing factors and spatial spillover effects of HCCEs in different regions by using an econometric model. The results of the study showed that: (1) Per capita HCCEs increased by 11.90% annually, and the eastern region > northeastern region > western region > central region. (2) There were regional differences in per capita HCCEs, but the decrease was significant at 40.32%. (3) The spatial agglomeration effect of per capita HCCEs was significant, and the hot spots were mainly concentrated in the eastern coastal areas. (4) From the national level, every 1% increase in residents' consumption power would increase HCCEs by 2.489%. Which was the main factor for the growth of HCCEs, while the increase in fixed asset investment would restrain HCCEs. At the regional level, the change in population size significantly increased the HCCEs in the eastern and central regions. While for the western region, a 1% increase in population would reduce the HCCEs by 0.542%. For the eastern and central regions, the degree of aging and the consumption structure of residents could suppress regional HCCEs. However, the consumption structure of residents drove the growth of HCCEs in the western region. For the Northeast region, residents' consumption capacity and cooling degree days were the main factors for the growth of residents' consumption, while fixed asset investment could inhibit the growth of HCCEs.

Spatial spillover effect of environmental regulation on environmental efficiency of electric power industry in China

The electricity power (EP) industry, a primary source of emissions, significantly affects China's energy conservation and reduction efforts. The Chinese government has introduced various regulatory policies to limit the emissions and waste generated by the EP industry. This paper intends to assess the influence of environmental regulation (ER) on the environmental efficiency (EE) within China's EP sector, particularly concerning spatial spillover effects. The slack-based and the spatial Durbin models are applied to quantify EE and analyze the nonlinear connection between ER and EE. Some findings are summarized below. First, the EE of China's EP industry has significant regional heterogeneity and exhibits a positive spatial agglomeration effect of EE. Second, the influence of ER on EE in local provinces shows a U-shaped relationship, while it exhibits an inverted U-shaped effect in neighboring provinces. Third, ER can indirectly drive EE in the local province by affecting technological innovation. Finally, policy recommendations are offered to enhance the EE in China's EP industry.