Spatial Clustering Characteristics Research Articles

Spatial and temporal distribution characteristics research of esophageal cancer in China

Objectives: To explore the spatial distribution characteristics, trend changes, and spatial clustering of esophageal cancer among residents in China at the county (city, district) scale, a spatial epidemiological approach was used, with the aim of providing localized evidence for the prevention and treatment of esophageal cancer in China. Methods: The data source was the incidence (crude rate) and mortality (crude rate) of esophageal cancer from 2005 to 2016 in the 2008-2019 edition of China Cancer Registration Annual Report published by the National Cancer Center. The Joinpoint model was used for time trend analysis. The tumor registration area in 2016 was selected as the study area for spatial feature analysis, with a total of 487 counties (cities and districts), covering 27.6% of the national population. Spatial autocorrelation analysis was performed to reveal spatial distribution characteristics by using Arcgis 10.6 software, and spatial scanning statistics was used to analyze spatial clustering characteristics by using SaTScan 9.5 software. The log-likelihood ratio (LLR) and relative risk (RR) were calculated in different windows, and the region with the largest LLR value represented the most likely cluster. Results: From 2005 to 2016, the incidence and mortality rate of esophageal cancer in China showed a trend of increasing at first and then decreasing. The incidence and mortality rate of esophageal cancer in 2016 were characterized by spatial positive correlation. High incidence and high mortality were mainly concentrated in the areas through which the Huaihe River flowed. The primary clusters (taking high incidence rate as an example LLR=6 374.41, RR=2.37, P＜0.001) were mainly distributed in Jiangsu, Anhui and Shandong in eastern China and eastern Henan and southern Hebei in central China, and secondary clusters (taking high incidence rate as an example LLR=1 971.19, RR=1.91, P＜0.001) in Gansu, Ningxia Hui Autonomous Region, Shaanxi, Sichuan and other central and western regions. Conclusions: The incidence and mortality of esophageal cancer in China have decreased since 2010. The disease burden of esophageal cancer has obvious spatial differences, and measures should be taken according to local conditions in high-risk cluster areas such as the Huaihe River basin.

Regional differences, dynamic evolution, and obstacle factors of cultivated land ecological security in China

Under the influence of geopolitical conflicts, extreme weather, and other factors, global food security has been severely impacted. Cultivated land ecological security in China is vital for guaranteeing local food security and sustainable development. Therefore, using provincial data from 2003 to 2020, this research establishes an index system based on the pressure-state-response (PSR) model and assesses its cultivated land ecological security via the entropy weight method. A series of measurements such as Dagum's Gini coefficient and spatial Markov chain are then adopted to reveal changes in regional differences and spatial-temporal characteristics of cultivated land ecological security in China. The findings indicate that this ecological security has risen from insecure to critically secure, but the overall gap has gradually widened, mainly stemming from interregional differences. Cultivated land ecological security exhibits positive spatial clustering characteristics and the phenomenon of club convergence. The amount of water resources, the degree of soil erosion control, and the strength of local financial expenditure on agriculture are the main factors hindering its cultivated land ecological security, and the threats mainly come from the response criterion layer. Therefore, it is essential to strengthen measures for cropland ecological protection, to formulate targeted strategies by combining the regional characteristics, to narrow the intra-regional gap, and to bring into play the spatial radiation effect of high-level provinces.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Investigating the spatial distribution mechanisms of traditional villages from the human geography region: A case study of Jiangnan, China

Investigating the spatial distribution and adaptation mechanisms of traditional villages is crucial for their development and human well-being. However, relatively few studies have focused on the interaction and comprehensive influence of historical-cultural, natural-geographic and socio-economic factors on traditional villages in Jiangnan region. Employing geographically weighted regression (GWR) and Geodetector methods, we developed an integrated framework. We found that (1) Traditional villages exhibit a “one-core, multiple-periphery” clustering pattern (nearest neighbor index <1), with significant high-high and low-low local spatial clustering characteristics and preferring gentle and relatively Complex terrains to adapt to the need to avoid warfare. (2) Urbanization negatively affects traditional village density patterns, while natural-geographic complexity has a positive impact. Their interaction nonlinearly amplifies this effect, with the urbanization-GDP interplay being particularly significant (q = 0.765). (3) Factors exhibit significant spatial heterogeneity in their impact on traditional village density, with the explanatory power increasing by 20% (GWR/R2 = 0.65). The urbanization rate and slope are the most critical socio-economic and natural-geographic factors, respectively (local regression coefficient: 23.31 and 11.58). Our study transcends administrative boundaries, revealing how historical-cultural, natural-geographical, and socio-economic factors collectively influence the distribution of traditional villages. This not only provides a new theoretical foundation for the conservation and development of traditional villages but also provides a new methodology basis for research on traditional villages.

The coupling coordination between health service supply and regional economy in China: spatio-temporal evolution and convergence.

The coordination of health service supply and regional economy is an integral path to promote China's prosperity. Based on the coupling mechanism of health service supply and regional economy, we sampled the data from 30 provinces in China from 2009 to 2021 in this study and constructed the evaluation index system. Additionally, we calculated the coupling coordination degree (HED) of the two through the coupling coordination degree model. We further used the kernel density estimation, Moran's I index, and spatial β convergence model to assess the dynamic evolution trends, spatial aggregation effect, and spatial convergence characteristics of coupling coordination. (1) HED in China showed a rising trend during the study period but with large regional differences, forming a gradient distribution pattern of "high in the east and low in the west." (2) The results of Kernel density estimation show that HED has formed a gradient differentiation phenomenon within each region in China. (3) HED has modeled spatial clustering characteristics during the study period, with high-value clusters mainly appearing in the eastern region and low-value clusters appearing in the northwestern region. (4) There are absolute β-convergence and conditional β-convergence trends in HED in China and the three major regions during the study period, but there is an obvious regional heterogeneity in the control factors. The research provides a reference for accurately implementing policies according to different levels of health service supply and economic development, in addition to narrowing the regional differences of the coupling coordination between the regional economy and health service supply.

Spatio-temporal patterns and population exposure risks of urban heat island in megacity Shanghai, China

Accompanied by global warming and rapid urbanization, the urban heat island (UHI) effect has been intensifying, which leads to an increased risk of population heat exposure, especially in mega-cities. However, there are fewer studies on the identification of UHI ranges and the associated population heat exposure risks, particularly on the fine scale. Therefore, this study took Shanghai as a case study and explored the spatio-temporal evolution characteristics of the UHI ranges and population heat exposure risks, based on five-period MODIS land surface temperature (LST) product, population density distribution data, and land use and cover change data from 2000 to 2020, combined with the temperature attenuation mutation (TAM) method, breakpoint regression model, population heat exposure relative risk assessment method, and spatial autocorrelation analysis. The results showed that: (1) From 2000 to 2020, the UHI range in Shanghai was continuously expanding, and the intensity of the surface UHI was gradually increasing. The overall pattern was characterized by a stable large heat island in the central urban area and new small heat islands that continued to emerge in the peripheral suburbs. (2) The population heat exposure risks in Shanghai intensified from 2000 to 2020, with an increasing number of people exposed to high-intensity UHI ranges, doubling the population coverage in these areas. By the end of 2020, the population exposed to UHI ranges accounted for approximately 81 % of the total population of Shanghai. (3) The spatial pattern of population heat exposure risks from 2000 to 2020 was generally similar. Population heat exposure regions of high risk level were primarily concentrated in the central urban areas, showing typical spatial clustering characteristics, while population heat exposure regions of low risk level were mainly dispersed in the peripheral suburbs, and showed a gradual transformation trend into population heat exposure regions of high risk level. Our findings can provide scientific basis and decision support for precise and refined urban heat risk control and planning management.

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data.

Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors. This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods. Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety. Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Synergistic Impacts of Built-Up Characteristics and Background Climate on Urban Vegetation Phenology: Evidence from Beijing, China

Vegetation is an important strategy for mitigating heat island effects, owed to its shading and evaporative cooling functions. However, urbanization has significantly affected regional vegetation phenology and can potentially weaken the cooling potential of vegetation. Previous studies have mainly focused on national and regional vegetation phenology, but local-scale vegetation phenology and dynamic variations in built-up areas remain unclear. Therefore, this study characterized the vegetation phenology in the densely built-up area of Beijing, China over the period of 2000–2020 based on high-resolution NDVI data using Savitzky–Golay filtering and explored its spatiotemporal characteristics and drivers. The results indicate that the vegetation phenology exhibits significant spatial heterogeneity and clustering characteristics. Compared with vegetation in peripheral blocks, vegetation in central urban blocks generally has an earlier start in the growing season (SOS), later end in the growing season (EOS), and a longer growing season length (GSL). However, the overall distribution of these parameters has experienced a process of decentralization along with urbanization. In terms of drivers, vegetation phenology indicators are mainly influenced by background climate. Specifically, SOS and GSL are mainly affected by temperature (TEP), whereas EOS is mainly influenced by annual precipitation (PRE). Additionally, local environmental factors, particularly the percentage of water body (WAP), also have an impact. Notably, the local environment and background climate have a synergistic effect on vegetation phenology, which is greater than their individual effects. Overall, this study extends the current knowledge on the response of vegetation phenology to urbanization by investigating long-term vegetation phenology dynamics in dense urban areas and provides new insights into the complex interactions between vegetation phenology and built environments.

An analysis of agglomeration structure for Beijing, Tianjin, and Hebei based on spatial-temporal big data

The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area’s inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH.

Evaluating water resource carrying capacity in Pearl River-West River economic Belt based on portfolio weights and GRA-TOPSIS-CCDM

Research on the spatial differences in the water resource carrying capacity (WRCC) and their causes can effectively address the imbalance of water resources and enhance the economic, social, and ecological properties of the basin. Sixteen cities in the Pearl River-West River Economic Belt (PR-WR EB) were selected for this study from 2017 to 2021. Based on the theoretical Driving-Pressure-State-Impact-Response model and related research, an evaluation model for WRCC was constructed. The weights obtained from the entropy method and CRITIC method were combined and assigned using the geometric method, after which they were applied to a model that combines the grey relational analysis (GRA), technique for order preference by similarity to ideal solution (TOPSIS), and coupling coordination degree model (CCDM) to calculate the evaluation value of the WRCC subsystem. Meanwhile, the spatial variability and spatial correlation of WRCC were analyzed using the natural breakpoint method, Moran's index, clustering characteristics, and outlier index. The overall WRCC of the PR-WR EB showed stable characteristics from 2017 to 2021. In terms of spatial correlation, significant spatial clustering characteristics were observed. The subsystem coupling degree ranged between 0.6 and 1.0 for all subsystems, thus exhibiting good coupling efficiency; meanwhile, the coordination degree was low and ranged between 0.17 and 0.36.

Multi-Objective Optimization of the Planting Industry in Jiangsu Province and Analysis of Its “Water-Energy-Carbon” Characteristics

The modern development of the planting industry needs to not only ensure food supply but also to consider social and environmental issues. This poses higher demands for rational planning of planting structures to achieve green development while meeting demands and conserving resources. Therefore, this paper takes Jiangsu Province as a representative case, accounting for and analyzing the water footprint, energy consumption, and carbon emissions of seven major crops. Based on this analysis, a multi-objective planning model is established to explore the optimization of its planting structure. The results show that: (1) from 2010 to 2020, the overall water footprint of these seven crops in Jiangsu Province initially increased and then decreased, while energy consumption showed a fluctuating upward trend; (2) carbon emissions from planting in Jiangsu Province initially increased and then fluctuated downward over time, and exhibited significant spatial clustering characteristics, with overall emissions being higher in northern Jiangsu, followed by central Jiangsu, and then southern Jiangsu; (3) the optimization results indicate that economic benefits increased by 4.06%, while the carbon emission and grey water footprints decreased by 3.78% and 7.62%, respectively, resulting in comprehensive improvements in economic and ecological benefits. This study can provide theoretical support for adjusting the planting structure in crop-producing areas and promoting the green and sustainable development of the planting industry.

Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications

Ensuring safety is crucial for promoting the sustainable growth of the construction industry. Assessing safety efficiency is of significant importance for optimizing safety management processes and improving the safety environment. However, the current mainstream methods for evaluating safety efficiency have limitations such as ignoring non-desired outputs and slack variables, the efficiency values being limited to the (0, 1) range, and a narrow perspective. To address these shortcomings, this study focuses on the characteristics of the construction industry and introduces the Super-SBM model and Malmquist index into the assessment of safety efficiency in the construction industry. The study analyzes the evolution characteristics of safety efficiency from both static and dynamic perspectives. Furthermore, using panel quantile regression models, the study identifies the factors influencing safety efficiency and analyzes their heterogeneity. Analyzing panel data from 30 provinces in China from 2015 to 2021, the results show that the overall safety efficiency of the construction industry in China is relatively low, with noticeable spatial clustering characteristics. Provinces in the eastern and central regions exhibit higher levels of construction safety efficiency. The Malmquist index demonstrates a declining trend, with technical efficiency being the primary factor limiting the improvement of safety efficiency in construction. Factors such as per capita GDP, urbanization rate, committed contract amounts, and the number of professionals engaged in survey and design, as well as engineering supervision, have an impact on construction safety efficiency, and the effects of these variables vary across different quantile levels of safety efficiency. This research can assist decision-makers in gaining a better understanding of the safety conditions in different regions of the construction industry. It can also assist in developing customized policies to enhance the health and safety environment, thereby promoting the stable development of the construction industry.

A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China.

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies.

Remediation Technologies of Contaminated Sites in China: Application and Spatial Clustering Characteristics

Screening remediation technologies through the lens of green, low-carbon, and sustainable development is crucial for contaminated land management. To better understand the applicability of remediation technologies, this paper explored their application in China based on a survey of 643 cases. By employing coupled analysis and local spatial autocorrelation methods, this study reveals the alignment between remediation technologies and pollutants, along with their spatial distribution and clustering patterns. Specifically, the four primary remediation technologies identified were cement kiln co-processing (CKCP), chemical oxidation/reduction (CO/CR), thermal desorption (TR), and solidification and stabilization (S/S), collectively accounting for over 90% of the cases. Additionally, our findings indicated significant variation in how different pollutants respond to remediation technologies, largely attributable to the characteristics of the pollutants. We observed High–High clustering patterns for CKCP, CO/CR, TR, and S/S. These were predominantly found in Jiangsu, Chongqing, Shandong, and Guizhou for CKCP and CO/CR and in Hebei, Jiangsu, Shanghai, and Chongqing for CO/CR. TR exhibited a High–High clustering in Shanghai, as did S/S. This research contributes to reducing the economic and resource costs associated with the trial-and-error of screening contaminated soil remediation technologies, offering valuable scientific and technological guidance for contaminated land regulation.

The Spatial–Temporal Characteristics of Land De-Urbanization in Metropolises: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

With a series of redevelopment activities, such as land consolidation and urban renewal, many cities in China have experienced land de-urbanization phenomena. These include the conversion of construction land into green spaces (such as parks, forests, and lawns), blue spaces (such as rivers, lakes, and wetlands), and farmland. However, there is currently limited research on diverse land de-urbanization types and pathways. This study focuses on investigating the land de-urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2014 to September 2023 using the Continuous Change Detection and Classification (CCDC) method. The results demonstrate that the GBA experienced 72.74 square kilometers of de-urbanization during the study period, primarily through the conversion of construction land to land with low plant coverage, including grassland and farmland. There were significant differences in the quantity and spatial agglomeration of de-urbanization between cities and within individual cities. Temporally, de-urbanization predominantly occurred in the period of 2016 to 2021, with a sharp decline in 2022. The temporal changes were significantly influenced by urban renewal policies and the impact of the COVID-19 pandemic. In terms of spatial clustering characteristics, the de-urbanization process in the GBA exhibited spatial agglomeration but was primarily characterized by low-level clustering. This study also examines the correlations between de-urbanization and factors including location and the stage of urbanization. The analysis showed that de-urbanization within cities tended to concentrate near the main urban roads within a range of 10–30 km from city centers. The trend of de-urbanization followed a pattern that is consistent with the Northam curve, where de-urbanization tends to increase during the rapid urbanization phase and decline as urbanization reaches a mature stage. Overall, this study provides valuable insights for the redevelopment of construction land within the context of ecological civilization construction. It also offers suggestions for urban land development and redevelopment in metropolitan areas.

Analysis of spatial spillover effects and influencing factors of transportation carbon emission efficiency from a provincial perspective in China.

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation.

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley’s K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region.

Dynamic Analysis of Regional Integration Development: Comprehensive Evaluation, Evolutionary Trend, and Driving Factors

Regional integration, as an essential measurement for solving unbalanced and uncoordinated regional development, plays an important role in achieving regional sustainable development. In this study, we aimed to construct a systematic research framework to facilitate the development of regional integration. Using 31 prefecture-level cities in the urban agglomeration in the Middle Reaches of the Yangtze River (MYR-UA) as case sites, this study applied box plots, kernel density estimation, GIS visualization tools, Markov chains, and geographic detectors to reveal the dynamic spatiotemporal evolution and factors influencing regional integration from 2009 to 2018. The results indicate that the level of regional integration and its subsystem development is suboptimal in MYR-UA; the temporal characteristic shows an upward fluctuating trend, and the spatial distribution shows remarkable spatial correlation and clustering characteristics. Additionally, we found that the level of regional integration development in MYR-UA has both “path dependence” and “self-locking” effects, and the spatial lag type has a crucial impact on the degree of regional transfer stability. The dominant factors affecting regional integration development include the GDP per capita, economic openness, industrial structure, proportion of education expenditure in fiscal expenditure, urbanization rate, proportion of environmental investment in fiscal expenditure, population density, capital flow, information flow, and technology flow. Finally, based on the findings of this study, policy recommendations for promoting regional integration are proposed.

Impact of Heterogeneous Environmental Regulations on Green Innovation Efficiency in China’s Industry

Innovation is the primary driving force for development, and green innovation efficiency (GIE) plays a key role in regional sustainable development. Moreover, environmental regulations (ERs) are also crucial for innovation and green transformation. Considering the heterogeneity of ERs, we assess the dynamic GIE in the industrial sectors of China. We detect their spatial clustering characteristics, and distinguish the impacts of ERs. Results suggest that there exist significant differences in GIE. Provinces such as Hainan, Guangdong and Zhejiang are ranked high, while Gansu, Inner Mongolia and Ningxia are ranked at the bottom, which shows some spatial dependence. The relationship between the administrative regulation and GIE demonstrates a U-shape, and has not reached a critical point, whereas the relationship between the market-based regulation and GIE possesses an inverted U-shape, which is highly significant. Furthermore, a positive linear relationship exists between the lagged public participation regulation and GIE. This paper also proposes that the economic development level and industrial structure are vital factors in accelerating industrial GIE. These conclusions provide scientific support for formulating regional transformation strategies.

Evaluation of the effect of a low-carbon green city policy on carbon abatement in South Korea: A city-level analysis based on PSM-DID and LSA models

Climate change response is a long-term, stage-specific global challenge that necessitates ongoing research on its routes. Although previous research has examined policy planning and indicator development for a low-carbon transition based on a variety of macropolicies, more research is needed to determine whether such policies can help reduce carbon emissions and accelerate urban transitions. Based on 2015–2021 panel data covering 17 South Korean first-level administrative districts, we employ a propensity score matching-difference in difference (PSM-DID) model and a mediating effect model to address issues of interregional development faults and policy feasibility and to investigate the impacts of the low-carbon green city (LCGC) policy in different regions on urban carbon emissions. To address the development gap and policy feasibility across regions, this study investigates the mechanism and path of the effect of the LCGC policy on urban carbon dioxide emissions in various regions, ultimately incorporating local spatial autocorrelation (LSA) to investigate the characteristics of the differences at the spatial level. The findings demonstrate that (1) the present regulations effectively limit total urban carbon emissions but are characterized by strong rebound elasticity and a short period. (2) Energy use and renewable energy production have the greatest influence on reducing total urban carbon emissions. (3) The local spatial autocorrelation clustering characteristics present the clustering characteristics of being high in the east and low in the north. The HH agglomerations have the largest coverage and are mainly distributed in the southeast and southwest directions of the study area. The results of the study provide a decision-making basis and theoretical support for relevant decision-makers to formulate differentiated carbon emission reduction policies and to eliminate the current transition dilemma with a new research methodology.