Spatial Agglomeration Characteristics Research Articles

Rural Industrial Integration and New Urbanization in China: Coupling Coordination, Spatial–Temporal Differentiation, and Driving Factors

Understanding the coupling coordination status between rural industrial integration (RII) and new urbanization (NU) is critical for the Chinese government to optimize policies that promote the synergetic and sustainable development of RII and NU. Based on constructing evaluation index systems, this paper uses the entropy value method, coupling coordination degree model, exploratory spatial analysis method, gravity center model, and geographical detector model to reveal the characteristics of the spatial–temporal differentiation of the coupling coordination and its driving factors in China. The results show that: (1) The regional differences in the coupling coordination degree between RII and NU are obvious, and the coupling coordination degree of China’s three economic regions are all in an increasing trend, but the mean values in the central and western regions are always below the national average. (2) The coupling coordination degree has significant spatial agglomeration characteristics, but the regional differences are gradually decreasing. The whole country is still dominated by the low–low agglomeration, and the provinces with the high–high agglomeration are mainly located in the eastern region. (3) The gravity center of the coupling coordination degree has moved in both the east–west and north–south directions from 2011 to 2021, but the movement trend in the north–south direction is more obvious. (4) The economic development level, industrial structure, transportation conditions, government support capacity, financial support level, and agricultural mechanization level are important factors driving the spatial–temporal variation, and their interactions will enhance the differentiation. The results can provide a theoretical basis and decision-making reference for relevant government departments in China to promote the sustainable development of RII and NU.

Assessing the ecological security of tourism in Northeast China

Abstract The ecological security of tourism provides an important guarantee of the sustainable development of regional tourism. In this study, the authors use the Driving Force-Pressure-State-Impact-Response model to construct a system to assess the ecological security of tourism in three provinces of Northeast China. We analyze the characteristics of the dynamic spatiotemporal evolution of the ecological security of tourism in 34 prefecture-level cities, one prefecture, and one region in three provinces of Northeast China from 2010 to 2019 and identify obstacles to this evolution. The results show the following: (1) The ecological security of tourism in Northeast China fluctuated and grew during the study period. Significant changes were observed in the rankings of the provinces in the ecological security of tourism. The pattern of spatial distribution has gradually shifted, from “high in the west and low in the middle” in 2010 to “slightly high in the middle-east and slightly low in the west” in 2019. (2) The ecological security of tourism exhibited prominent characteristics of spatial agglomeration on the whole, where the degree of agglomeration has gradually decreased over time. Economically developed cities in Central China exhibited strong local autocorrelation, and the majority had the characteristics of spatial agglomeration and dependence. (3) There were significant differences in obstacles to the ecological security of tourism among the cities (prefectures and regions) and provinces. Industrial sulfur dioxide emissions and urban population density are the main obstacles to the ecological security of tourism, while the pressure system has emerged as the main hindrance to improvements in it in the three provinces considered here. These results provide insights into the ecological security of tourism in the three provinces of Northeast China, as well as a theoretical reference for formulating and implementing the relevant measures of risk prevention and control. This study shows that increasing the protection of the tourism ecological environment and the construction of new energy sources, building complete tourism infrastructure, coordinating the relationship between tourists and the natural environment, adopting differentiated tourism development measures, and overcoming the obstacles will help to gradually improve the level of tourism ecological security in three provinces of Northeast China.

Research on the spatiotemporal evolution and non-stationarity effect of urban carbon balance: Evidence from representative cities in China

Accelerating the attainment of carbon balance in Chinese cities has become pivotal in addressing global climate change and promoting green, low-carbon development. This study, encompassing 277 prefecture-level and above cities from 2007 to 2020, reveals a positive overall trend in China's urban carbon balance index. The evolution unfolds in two stages, demonstrating a distinct “tiered development” pattern across the eastern, central and western regions. Moreover, significant spatial agglomeration characteristics characterize China's carbon balance hot and cold spots throughout the study period, with their spatial agglomeration degree remaining stable. The standard deviation ellipse analysis confirms these hot and cold spots' alignment with China's economic development level and population distribution. The GTWR test results highlight the pronounced non-stationary characteristics of different driving factors in space and time, exhibiting variations in strength and direction among regions. Consequently, enhancing China's urban carbon balance requires tailored measures based on different areas' unique conditions and development characteristics, emphasizing a hierarchical and classified approach to leverage distinct driving factors and foster a green development system in China.

Multidimensional measurement of the High-Quality development of city Clusters: Dynamic Evolution, regional differences and trend forecasting--based on the basic connotation of Chinese-style modernization

Chinese-style modernization has led to new requirements for city clusters strategies, which is highly important for the high-quality development (HQD) of city clusters and new urbanization. Based on the basic connotation of Chinese-style modernization, an evaluation index system for the HQD of city clusters is constructed from five dimensions, namely, demographic advantages, coordinated regional development, material and cultural-ethical advancement, ecological sustainability, and opening-up. The HQD of 19 city clusters was measured from 2011 to 2021 using the entropy weight-CRITIC method, and the dynamic evolution, spatial correlation and long-term trend were analyzed. The study revealed that (1) the overall HQD of Chinese city clusters has shown continuous growth, and the spatial distribution of the gradient of HQD in the 'East, Center and West' has decreased, but the spatial pattern of 'South High and North Low' has further emerged. (2) The overall and intra-regional differences in the HQD of city clusters gradually narrowed, while the differences between city clusters remained significant. (3) The HQD of city clusters shows obvious positive spatial correlation and obvious spatial agglomeration characteristics, with high-value agglomerations mostly in mature-stage city clusters along the east coast. (4) There is an obvious 'club convergence' phenomenon in the HQD of city clusters; the higher the HQD of neighboring regions is, the greater the probability of upward HQD of city clusters, and the higher the quality of Chinese city clusters shows an upward trend in the long run.

Study on the differences and influencing factors of spatial distribution of population aging at township scale: a case study of township research units in Anshun City, China.

An aging population is one of the main features of China's current population structure, and it is a key area that needs attention to achieve high-quality population development. Because of its unique geographical environment, economic conditions, and sociocultural background, the study of population aging in the karst region of southwest China is particularly important. However, there is a lack of research exploring the regional differentiation of population aging and its influencing factors in the karst regions of southwest China. In light of this, we chose Anshun City, located in Guizhou Province's southwest area, as the case study area. We used the Lorenz curve and spatial autocorrelation to study the differences in the spatial distribution pattern of population aging and introduced multi-scale geographical weighted regression to explore its influencing factors. The results show that Anshun City's older people population proportion (OPP) is generally high with more than 7% of the older people there, making it part of an aging society. The OPP appeared high in the east and low in the west in spatial distribution; the older people population density (OPD) revealed a gradually increasing trend from south to north. At the township scale, both the OPP and the OPD showed significant spatial positive correlation, and the spatial agglomeration characteristics were obvious. OPD and OPP have a positive spatial correlation at the global level, and townships with similar OPP or OPD were spatially adjacent. The spatial distribution characteristics of population aging are the consequence of complex contributions such as natural, social, economic, and karst factors. Further, the spatial distribution pattern of aging is determined by a variety of influencing factors, which have different directions and intensities. Therefore, it is necessary to formulate and implement corresponding policies and strategies to deal with the aging problem in the future.

Regional differences and dynamic evolution of high-quality development in service industry: A case study of the Chengdu-Chongqing economic circle.

The high-quality development of service industry has become an important engine for promoting sustainable economic development. This paper first constructed the evaluation index system of high-quality development of service industry, based on panel data from 2005 to 2020. Second, Kernel density, Markov chain and Dagum Gini coefficient were used to represent the regional differences and dynamic evolution of service industry, and the Koo method was used to explore the characteristics of spatial agglomeration. Finally, social network analysis was used to identify core indicators. The study found that: (1) From 2005 to 2020, the overall level of service industry first decreases and then increases, with Chengdu and Chongqing leading other cities. (2) The development of service industry in the CCEC has large spatial differences, mainly due to inter-regional differences. (3) The level of spatial agglomeration is less variable, with high agglomeration mainly in Chengdu. (4) Indicators such as the level of human capital are the core factors of its high-quality development. This study is of great theoretical and practical significance for the optimization and upgrading of service industry in the CCEC and the synergetic development of the region.

Can Financial Agglomeration Development Reduce Carbon Emissions? Evidence from the Yangtze River Delta Region of China

Reducing carbon emissions is a crucial measure for achieving sustainable development. The financial industry exhibits remarkable spatial agglomeration characteristics, which play a pivotal role in advancing carbon emission reduction and facilitating energy transformation. Using panel data from 41 cities in the Yangtze River Delta from 2008 to 2019, this study employed a spatial econometrics model to investigate the impacts and spatial spillover effects from the development of financial agglomeration on carbon emissions and the associated underlying mechanisms. The research shows that (1) there is an uneven spatial distribution of carbon emissions and financial development within the Yangtze River Delta region of China; (2) an inverted U-shaped relationship exists between financial agglomeration and carbon emissions, and only Shanghai’s level of financial agglomeration exceeds the extremum point; (3) financial agglomeration shows a negative spatial spillover effect on carbon emissions; and (4) financial agglomeration can promote industrial movement toward reducing carbon emissions. The study suggests some strategies for carbon reduction in China.

Does rural infrastructure resilience affect population loss? Empirical research based on spatial measurements

PurposeBy analyzing the mechanisms by which rural infrastructure resilience (RIR) impacted population loss in Longxi County, this study proposes measures to improve RIR, which provides a practical reference for realizing China's rural revitalization strategy, besides providing ideas for alleviating population loss in similar regions around the world.Design/methodology/approachThis study considered 213 administrative villages in Longxi County in the Longzhong loess hilly region as the evaluation unit. Based on the construction of a multidimensional RIR evaluation system, the spatial spillover effect of RIR on population loss was determined using the spatial Durbin model (SDM).FindingsThe average resilience of each subsystem of rural infrastructure in Longxi County was low, and there were large differences in the spatial distribution. The mean RIR index value was 0.2258, with obvious spatial directivity and agglomeration characteristics. The population loss index of Longxi County had a value of 0.1759, with 26.29 of villages having a high loss level. The population loss was relatively serious and was correlated with the spatial distribution of RIR. The villages with larger RIR index values had lower population loss. The RIR had a significant spatial spillover effect on population loss. Productive infrastructure resilience and living infrastructure resilience (LIR) had negative spillover effects on population loss, and social service infrastructure resilience (SSIR) had a positive spillover effect on population loss.Originality/valueBy analyzing the mechanisms by which RIR impacted on population loss in Longxi County, this study proposes measures to improve RIR, which provides a practical reference for realizing China's rural revitalization strategy, besides providing ideas for alleviating population loss in similar regions around the world.

County-Level Land Use Carbon Budget in the Yangtze River Economic Belt, China: Spatiotemporal Differentiation and Coordination Zoning

The local land use carbon budget (LUCB) balance is an important factor in achieving regional carbon neutrality. As the basic unit of China’s economic development and social governance, the county level is an important part of the realization of the “double carbon” goal. This paper focuses on 1069 county units within the Yangtze River Economic Belt (YREB). It utilizes data on land use, nighttime light, energy consumption, and social and economic factors to construct carbon emission models. The spatiotemporal characteristics of LUCB in these county units are analyzed using standard deviational ellipse (SDE) and spatial autocorrelation methods. Additionally, a zoning study is conducted by examining the economic contribution coefficient (ECC) of carbon emissions, the ecological support coefficient (ESC), and their coupling relationship. The results show that (1) the total land use carbon emissions (LUCE) increased significantly during the research period, and the total carbon sink was relatively stable. (2) The LUCB is spatially high in the east and low in the west, with the center of gravity moving to the southwest as a whole. (3) The LUCB shows positive spatial autocorrelation and has significant spatial agglomeration characteristics, which are mainly high–high and low–low regional agglomeration types. (4) The ECC is high in the east and low in the west, the ESC is high in the west and low in the east, and the coordination and coupling degrees of the two are low. (5) According to the ECC and ESC, the county unit is divided into a low-carbon conservation area, an economic development area, a carbon sink development area, and a comprehensive optimization area. This study is helpful in promoting the sustainable development of carbon neutrality and low carbon in the YREB.

Evaluation of China’s provincial eco-efficiency with the explainable boosting machine (EBM) model and Tobit regression

The explainable boosting machine (EBM) model measures China’s provincial eco-efficiency, and the Tobit regression model reveals internal driving factors to provide consultation for promoting China’s green development based on the panel data of 30 provinces from 2000 to 2018. The findings of this article are as follows: provincial-level regional ecological efficiency is low, growth is slow, regional differentiation is significant, and development still has a trend of incoordination and multi-polarization. From the perspective of global autocorrelation, the Moran index is significantly positive, and the province eco-efficiency of the first grade presents a positive spatial correlation and has the characteristics of spatial agglomeration. The regression analysis results show that the economic level, the FDI, the level of technological innovation, and the level of human capital are the main influencing factors of eco-efficiency, and there are spatial differences. Relevant suggestions are based on the status and influencing factors of the unbalanced development heterogeneity of provincial eco-efficiency in China.

Study on the Spatiotemporal Evolution and Influencing Factors of Forest Coverage Rate (FCR): A Case Study on Yunnan Province Based on Remote Sensing Image Interpretation

The study of the forest coverage rate (FCR) is related to the ecological environment and sustainable development goals (SDGs) of a region. In light of the lack of an organic integration method of “spatiotemporal evolution, correlation analysis, and change prediction” and the lack of a methodology that integrates methods of “remote sensing (RS) and GIS, multi-phase LUCC, and construction of econometric models” in the research methods at present, this study focus on Yunnan, a typical border province located in China with a relatively fragile “innate” ecological environment, as the research area. Based on the interpretation of land use/land cover (LULC) data retrieved from seven periods RS images (1990, 1995, 2000, 2005, 2010, 2015, and 2020), the spatiotemporal evolution of FCR in 129 counties was analyzed. Complementary research methods, such as the spatial econometric model, geographically weighted regression (GWR), and the geographic detector (GD), are used to reveal the influencing factors of FCR. Finally, this study predicts the FCRs of 129 counties in Yunnan from 2025 to 2050. The FCR in Yunnan presents an increasing trend year by year, increasing from 28.96% in 1990 to 49.05% in 2020. In addition, it exhibits spatial agglomeration characteristics with fewer values in the east and more in the west. The analysis of influencing factors show that the increases in the per capita GDP, land utilization rate, and annual average temperature, and the implementation of the Conversion of Cultivated Land into Forest Project (CCFP) will significantly improve the FCR, while the increases in the population density land reclamation rate, the proportion of construction land area, and the proportion of soil erosion land area will significantly reduce the FCR. Furthermore, the FCR is influenced by multiple factors, and the relative factors observed not only show significant spatial differences, but also present complex and diverse patterns, with the additional characteristics of being interwoven and overlapping. This study contributes to expanding and improving the methods and pathways of exploring the spatiotemporal evolution characteristics of FCR in ecologically fragile areas using RS methods, providing a reference for increasing FCR and improving the ecological environment’s quality in Yunnan Province and other ecologically fragile areas.

Study on function evolution and coupling coordination degree of "Three Lives Space" in the upper reaches of Yangtze River in China.

Analyzing the functional evolution characteristics and coupling coordination of the "Three Lives Space" in the upper reaches of Yangtze River (URYR) is crucial for achieving high-quality, coordinated development and ecological protection in the region. Based on the panel data of the production function, living function, and ecological function of 31 cities in the URYR from 2010 to 2020, this study establishes the evaluation index system of the "Three Lives Space" function and analyzes its spatio-temporal evolution characteristics and spatial correlation by utilizing the coupling coordination degree model and spatial autocorrelation test. The results indicate that (1) the functional coupling coordination of 31 cities in the URYR exhibits a steady upward trend as a whole, which can be characterized by a sequence of stages, including the disordering decline in disorder, barely coordination, primary coordination, and intermediate coordination. (2) The 31 cities in the URYR have significant spatial agglomeration characteristics. Among them, most cities in Sichuan Province and Chongqing City have high-high gathering effects, and most cities in Guizhou Province and Yunnan Province have low-low gathering effects. Therefore, it is imperative to scientifically and rationally organize the production, living, and ecological space, collectively safeguard the crucial ecological barrier of the URYR, and promote its integrated development.

A framework for analyzing energy consumption in urban built-up areas based on single photonic radar and spatial big data

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide.

Spatiotemporal variations and spillover effects of regional water resources metabolism efficiency with heterogeneous resource endowments

Abstract Scientific evaluation of water resources metabolism efficiency (WME) has essential theoretical and practical value for regional water resources security and sustainable utilization. Based on the framework of the water resources metabolism process and construction of the indicator system of WME, the spatiotemporal variations of WME were analyzed using the weighted summation method in the Eastern, Central, and Western regions of China during the period from 1990 to 2020 with heterogeneous resource endowments. The exploratory spatial data analysis (ESDA) and spatial Durbin model were applied to analyze the spatial autocorrelation patterns and reveal spillovers of the WME. The results show that (1) The WME increased at different speeds in the Eastern, Central, and Western regions from 1990 to 2020 with heterogeneous resource endowment, and the spatial variation of WME in the Eastern and Central regions was significantly higher than in the Western regions. (2) The WME had significant spatial autocorrelation except in 2015, indicating obvious spatial agglomeration characteristics and spatial heterogeneity simultaneously. (3) The spatial spillover effects analysis indicated that the waste virtual recycling use ratio, the ratio of output virtual water and input virtual water, and industrial water ratio were the primary factors influencing WME.

Measurement and characteristics of the temporal-spatial evolution of China’s healthcare services efficiency

BackgroundHealthcare services efficiency (HSE) is directly related to the healthcare demands of the general public and also plays an essential role in the country’s coordinated economic and social development.MethodsIn this study, the stochastic frontier approach (SFA)-Malmquist model was applied to measure the HSE of 31 Chinese provinces based on panel data from 2010–2020. Then, kernel density estimation, Markov chain, and exploratory spatial data analysis were adopted to study the temporal-spatial dynamic evolution characteristics of the HSE.ResultsThe study found that China’s HSE showed an average value of approximately 0.841, indicating room for improvement. The HSE varied significantly across regions, presenting an “East > Central > West” distribution layout. The TFP of healthcare services in China grew by 1.6% per year, driven mainly by technological progress of 1.8% per year. The trend of the HSE shifting to a high level in China was significant, but its evolution exhibited stability of maintaining the original state, and it was harder to achieve leapfrog transfer. The temporal-spatial evolution of the HSE was also significantly affected by geospatial factors, with a clear spatial spillover effect and spatial agglomeration characteristics. Provinces with high-level HSE exhibited positive spatial spillover effects, while provinces with low-level HSE had negative spatial spillover effects. Thus, the “club convergence” phenomenon of “high efficiency concentration, low efficiency agglomeration, high levels of radiation, and low levels of suppression” was formed in the spatial distribution.ConclusionsThe results indicate that countermeasures should be taken to improve the HSE in China. Theoretical support for the improvement of HSE is provided in this paper.

Evolution and spatial reconstruction of rural settlements based on composite features of agglomeration effect and ecological effects in the Hexi Corridor, Northwest China.

Rural reconstruction plays a pivotal role in the revitalization of rural areas and the development of regions. Understanding the pattern and direction of rural settlement reconstruction is crucial for effectively coordinating urban and rural development, as well as promoting regional rural revitalization. The present study proposes a novel approach to elucidate the evolution and spatial reconstruction of rural settlements by integrating features of agglomeration effect and ecological effect. By employing GIS spatial analysis technology and ecosystem service value modeling, the research analyzes the combined spatial agglomeration and ecological value characteristics of rural settlements in an arid oasis area, specifically focusing on the Hexi Corridor. Based on the analysis, the study identifies specific rural settlement reconstruction zoning and directions for optimization, considering rural settlement accessibility. The study reveals three key findings: (1) There are significant differences in the scale density and spatial distribution of rural settlements across the Hexi Corridor. (2) The overall ecological environment quality is good, and there is significant spatial differentiation in the ecosystem service value, influenced by topographic factors. (3) The optimal layout mode for rural settlements in the Hexi Corridor is the combination type of 'higher-ecological higher-density'. Based on the combined agglomeration effect and ecological effect features, the research determines the reconstruction scope of alienated rural settlements. Additionally, four predominant reconstruction modes are identified: urban agglomeration type, central village construction type, internal coordination type, and ecological protection type. The study proposes viable reconstruction paths for rural settlements based on these modes.

The impact of rice-crayfish field on socio-ecological system in traditional farming areas: Implications for sustainable agricultural landscape transformation

Agricultural landscape transformation has profound impacts on regional sustainable development. Understanding the relationship between agricultural landscape transformation and socio-ecological system is fundamental to both social prosperity and ecological environment. Rice-crayfish field (RCF) as a unique agricultural model is widely practiced in traditional farming areas and causes significant landscape transformation. However, the RCF remains less understood and its impacts on socio-ecological systems need to be comprehensively investigated. China has a developed crayfish industry with over 90% of the world's crayfish production, which provides an interesting lens to examine the RCF-led agricultural landscape transformation. This research proposes a novel research framework to analyse the spatial dynamic of RCF and its complex impact on socio-ecological system. Using remote sensing and geospatial data analysis, this study explores the spatiotemporal heterogeneity of RCF-led landscape transformation and socio-ecological system. In addition, the impacts of RCF on socio-ecological system are examined by the Pearson correlation analysis, root mean square error and regression model. The results reveal that RCF has dramatically expanded and transformed from paddy fields. The socio-ecological system presents obvious spatial agglomeration characteristics. RCF has the potential to increase farmers' income and alleviate rural poverty, but it is not conducive to grain production and water consumption. The RCF-led agricultural landscape transformation could be a double-edged sword to socio-ecological system. To balance social and ecological systems, it is necessary to manage and design the rational development of RCF to support sustainable agricultural landscape transformation.

Towards a low-carbon society: spatial distribution, characteristics and implications of digital economy and carbon emissions decoupling

Reducing carbon emissions and attaining sustainable economic, social, and environmental development are vital components of the 2030 Agenda for Sustainable Development. Previous research has overlooked the influence of the digital economy on urban carbon decoupling. To bridge this gap, this study employs GIS software and econometric models for analyzing the spatial distribution and characteristics of the digital economy and carbon decoupling and also investigates the direct, heterogeneous, and spatial effects of the digital economy on carbon decoupling. This study reveals: (1) A lesser digital economy presence in the northeast and northwest, while more developed in the Southeast Coast and municipalities directly under the Central Government. Cities with weak carbon decoupling are concentrated in Northeast and North China. (2) The digital economy and the decoupling of urban carbon emissions have spatial correlation and agglomeration characteristics. (3) The digital economy can contribute to decoupling carbon emissions in cities. (4) Improved urban carbon decoupling by the digital economy in central, eastern, and non-resource-based cities. (5) Spatial spillover effect in urban carbon emissions decoupling, yet the digital economy worsens nearby cities’ carbon decoupling due to a siphon effect. That research indicates that the digital economy holds significant promise not only in advancing human progress, bridging the digital divide, and fostering social development but also in driving the decoupling of urban carbon emissions.

Mechanism and evolution trend of digital green fusion in China's regional advanced manufacturing industry

This paper has observed pivotal changes in innovative frameworks at the intersection of the digital revolution and sustainable initiatives. Within Industry 4.0, the fusion between intelligent manufacturing and green innovation has become a central driver for sustainability. Integrating these two pivotal forces to harness their collective efficiency is crucial. However, current literature offers limited insights into their combined impact, and the direction of their evolving trends still needs to be defined. Considering this, the research elucidates the core concepts and traits of digital-green fusion (DGF) within regional advanced manufacturing (RAMIDGF). Moreover, this research proposes a double-helix fusion mechanism model. Utilizing data from the advanced manufacturing sectors in six key Chinese urban clusters, this paper adopts a comprehensive analytical toolkit that includes the Coupling Modified Model, Kernel Density Estimation, Markov Chain algorithms, and the Dagum Gini Coefficient for trend analysis. The findings indicate that between 2009 and 2021, RAMIDGF exhibited a trend of initial growth followed by a decline, showing a strong correlation with per capita GDP (RGDP). The principal source of variation in RAMIDGF stems from differences between urban agglomerations, with the Yangtze River Delta and the Beijing-Tianjin-Hebei clusters exhibiting transitional behavior and a ‘ladder-type trend’ in RAMIDGF. As metropolitan clusters become more economically integrated, the probability of transitional behavior in RAMIDGF heightens. However, achieving elevated leapfrog development proves challenging, with an inherent risk of decline. Spatial spillover implications, agglomeration characteristics, and club convergence tendencies mark the RAMIDGF process. These findings expand upon new paradigms for regional sustainable development, offering novel avenues for realizing high-quality growth.

The Impact of Agricultural Insurance on Urban–Rural Income Gap: Empirical Evidence from China

Based on the panel data of 31 provinces in China from 2005 to 2020, this paper analyzes the mechanism and spatial spillover effect of agricultural insurance on the urban–rural income gap using a fixed effect model, an intermediary effect model, and a two-stage least square method. The results show that agricultural insurance has a significant inhibitory effect on the income gap between urban and rural areas. This inhibitory effect is realized through the path of “improving the development level of agricultural insurance-improving agricultural total factor productivity-reducing the income gap between urban and rural areas”, in which the intermediary effect of agricultural total factor productivity accounts for 19.74% of the total effect. At the same time, the income gap between urban and rural areas in China exhibits typical spatial agglomeration characteristics. The western region has always been the region with the largest income gap between urban and rural areas, while the eastern region is the region with the smallest income gap between urban and rural areas. The development of agricultural insurance has had a spatial spillover effect on the income gap between urban and rural areas, and the development of agricultural insurance in neighboring areas expands the income gap between urban and rural areas in this region. In order to prevent the siphon effect, agricultural insurance investment should be increased in the neighboring regions. The results of this paper support the view of the resource flow model. Finally, we put forward some suggestions for the development of agricultural insurance, improvement in agricultural total factor productivity, and the narrowing of the income gap between urban and rural areas.