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.

Assessment of spatial variation in lung cancer incidence and air pollutants: spatial regression modeling approach.

A notable finding is that Kerala's capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas.

[Trade-off and Synergy of Ecosystem Services in the Yangtze River Economic Belt and Its Driving Factors].

Ecosystem services (ESs) and their changes are complex processes driven by multiple factors. Understanding the trade-off and synergy between ESs and their driving factors is essential for achieving effective management of ESs and human well-being. Taking the Yangtze River Economic Belt as the research area, this study analyzed the temporal and spatial variation characteristics of four ESs including water yield, soil conservation, carbon sequestration, and food supply from 2000 to 2020. Correlation analysis and geographically weighted regression were used to identify and quantify the trade-off and synergy between ESs. On this basis, the partial least squares structural equation model was used to explore the impact of natural and human activities on ESs, and then the driving mechanism of ESs relationship change was analyzed via GeoDetector. The results showed that:① During the 20 years, the average annual carbon sequestration increased from 946.14 t·km-2 to 1 202.73 t·km-2, and the average food supply increased from 32.73×104 Yuan·km-2 to 127.22×104 Yuan·km-2. Water yield and soil conservation increased to a lesser degree. ② On the whole, carbon sequestration and soil conservation and food supply and water yield showed synergy, and other ESs were trade-offs. The relationship between ESs varied in different regions. ③ Terrain and climate were important driving factors for ESs and the trade-off and synergy of multiple ESs. Among them, structural equation model results showed that climate had a positive impact on water yield (S=0.73), and terrain had a negative impact on food supply (S=-0.57). GeoDetector results revealed that the main driving factors affecting the spatial relationship between carbon sequestration and water yield were elevation (q=0.38) and precipitation (q=0.19). The results of this study can provide a scientific reference for the sustainable management of ESs in the Yangtze River Economic Belt and the realization of the coordinated development of ecological environment protection and social economy in the region.

Digital financial inclusion and upgrading of consumption structure: Evidence from rural China.

Based on the perspective of spatial economy, this paper focuses on the primary effects and spatial characteristics of Digital Financial Inclusion (DFI) on the upgrading of rural consumption structure (URCS) in China, conducting a literature review and theoretical analysis. It then uses statistical data collected over the years and the Digital Financial Inclusion Index (DFII) of Peking University to prepare panel data for 31 provinces in China (aside from Hong Kong, Macao, and Taiwan) from 2011 to 2020 for empirical testing. The results are as follows: DFI can considerably boost URCS, and there is a strong spatial neighbor impact, that is, it is affected by random shocks in surrounding provinces via its spatial effect; DFI has nonlinear characteristics in the process of fostering URCS, with the threshold variables of income level and family sizes; the impact of DFI on URCS is spatially heterogeneous, and the promotion of the eastern region is better than other zones. These results can inform policymakers about rural development and provide valuable references to push forward rural vitalization.

Exploring the evolution of coupled natural-cultural ecosystem services and their geographically scaled driven modeling in a coastal city of Southeast China.

Rapid urban expansion and economic development challenges to the sustainability of ecosystem services (ESs), a solid understanding of the mechanisms that drive ESs helps policymakers to respond. However, few existing studies on ES-driven mechanisms emphasize the integration of natural and cultural services, with most neglecting spatial non-stationarity at the geographic scale. Here, we improved the ROS model to quantify cultural ecosystem services (CES) and developed a comprehensive ecosystem services index (CESI) by coupling CES with 6 typical natural ESs (carbon storage (CS), water yield (WY), nitrogen export (NE), soil conservation (SC), habitat quality (HQ), food supply (FS)), subsequently, Spearman's correlation and MGWR were employed to reveal the CESI-driven mechanism considering geographic scales. The results showed that: (1) From 2000 to 2020, CS, WY, SC, and HQ exhibited decline, which contrasts with the significant increase in CES. (2) The CESI showed a decreasing trend (3.28-3.70) while the coefficient of variation was increasing over time (0.11-0.15). The overall spatial distribution of CESI shows higher northwest than southeast, with strong spatial autocorrelation. (3) The CESI exhibits synergistic associations with CS, SC, HQ, and CES (0.54-0.83), and forms trade-offs with WY, NE, and FS. (4) Climate, vegetation, landscape, human, and topography have significant effects on CES and CESI with a significantly geographic scale differences, especially areas closer to the sea exhibit heightened sensitivity. Besides, the combined effects of multiple factors are stronger than any individual driver. The results emphasize the necessity of introducing ecological land in coastal cities and establishing natural reserves in high CESI areas to maintain diversity. The study improves the CES assessment methodology and proposes an integrated analytical framework that combines natural and cultural ESs with geographic-scale drivers, providing a new perspective on the analysis of ESs mechanisms.

Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression.

With the deepening linkage between housing and finance, the financial attributes of housing have been increasing. Thus, housing financialization has become a worldwide phenomenon that is gradually emerging in China's real estate market and thus cannot be ignored. The amount of urban capital is an important manifestation of financialization, but only a few studies have considered the spatial heterogeneity of impact of urban capital amount-represented by loan balances (LOAN) on housing prices. To fill this gap, this study builds a dataset of housing prices and influencing factors for county-level units using 2109 counties in China and analyzes the spatial scope and heterogeneity of housing financialization. Results show that globally, LOAN has a significant positive effect on housing prices, and the impact direction is in line with theoretical expectations. Locally, spatial heterogeneity exists for the impact of LOAN on housing prices, and the phenomenon of housing financialization is mainly observed in China's eastern coastal area. This study can help enhance the understanding of the spatial constraints on the impact of LOAN on housing prices and the spatial heterogeneity of housing financialization in China. Moreover, it provides a theoretical basis for policymakers to formulate spatially differentiated housing policies.

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region.

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000-2005), sharp decline (2005-2009), slow decline (2009-2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005-2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

Determinants and their spatial heterogeneity of carbon emissions in resource-based cities, China.

Global climate change associated with increased carbon emissions has become a global concern. Resource-based cities, by estimations, have emerged as major contributors to carbon emissions, accounting for approximately one-third of the national total. This underscores their pivotal role in the pursuit of carbon neutrality goals. Despite this, resource-based cities have long been neglected in current climate change mitigation policy discussions. Accordingly, using exploratory spatial data analysis and Geographical Weighted Regression method, this study investigates the determinants of carbon emissions and their spatial pattern in 113 resource-based cities in China. It can be concluded that: (1) The proportion of carbon emissions from resource-based cities in the national total has shown a marginal increase between 2003 and 2017, and the emissions from these cities have not yet reached their peak. (2) A relatively stable spatial pattern of "northeast high, southwest low" characterizes carbon emissions in resource-based cities, displaying significant spatial autocorrelation. (3) Population size, economic development level, carbon abatement technology, and the proportion of resource-based industries all contribute to the increase in carbon emissions in these cities, with carbon abatement technology playing a predominant role. (4) There is a spatial variation in the strength of the effects of the various influences.

Unveiling spatial disparities in basic medical and health services: insights from China's provincial analysis.

Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2012 to 2019, this paper constructs the evaluation index system of basic medical and health services in China from seven dimensions: medical and health facilities, health expenditure, medical services, traditional Chinese medicine hospital services, maternal and child health care, people's health and medical security, disease control and public health. The entropy method was used to measure the level of basic medical and health services in China, and its spatial differences and convergence characteristics were further investigated. In this study, we employ the entropy weight method, σ convergence, and ß convergence as our primary methodologies. The entropy weight method is used to evaluate the variability of each indicator, determine the weights of indicators, and quantify the information content of the data. σ convergence illustrates the process by which the variance of a sample decreases over time. ß convergence refers to the gradual approach of variables within an economic system towards their long-term equilibrium level over time. The results show that: (1) The scores of basic medical and health services in China's four major regions (including Northeast, East, Central and West) remain in a relatively stable state, with small fluctuations and great room for improvement; (2) There are significant regional differences in the level of basic medical and health services in China, and the intra-regional differences are much greater than the inter-regional differences; (3) There is no significant σ convergence observed in China and its four major regions; however, there is a notable presence of ß convergence.

Understanding the spatial disparity in socio-economic recovery of coastal communities following typhoon disasters.

The increasing risk of climate change in the Anthropocene underscores the importance and urgency of enhancing resilience to climate-related disasters. However, the assessment of resilience to disasters with traditional statistical data is spatially inexplicit and timeliness inadequate, and the determinants of resilience remain unclear. In this study, we employed spatially detailed daily nighttime light images to assess socio-economic disturbance and track near real-time recovery of coastal communities in Southeast China following super typhoon Meranti. Furthermore, we constructed a "exposure-sensitivity-adaptive capacity" framework to explore the role of key factors in shaping spatiotemporal patterns of recovery. Our case study showed a significant spatial disparity in socio-economic recovery in the post-typhoon period. Low-urbanized areas recovered relatively rapidly with the weakest socio-economic disturbance they suffered, and middle-urbanized areas experienced the slowest recovery despite the disruption being moderate. Remarkably, high-urbanized areas were the most severely impacted by the typhoon but recovered fast. The exposure to hazard, socio-economic sensitivity, and adaptive capacity in communities explained well the spatial disparity of resilience to the typhoon. Maximum wind speed, percentage of the elderly, and percentage of low-income population significantly negatively correlated with resilience, whereas commercial activity intensity, spatial accessibility of hospitals, drainage capacity, and percentage of green open space showed significantly positive relationships with resilience. Notably, the effects of key factors on resilience were spatially heterogeneous. For instance, maximum wind speed exhibited the strongest influence on resilience in middle-urbanized areas, while the effect of commercial activity intensity was most pronounced in low-urbanized areas. Conversely, spatial accessibility of hospitals and drainage capacity showed the strongest influence in high-urbanized areas. Our study highlights the necessity of linking post-disaster recovery with intensity of hazard, socio-economic sensitivity, and adaptive capacity to understand community resilience for better disaster risk reduction.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data.

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

Tracking household carbon inequality in China: Composition effect or coefficient effect?

Reasonable allocation of carbon emission reduction tasks requires addressing household carbon inequality. This study aims to track characteristics of household carbon inequality in China using the recentered influence function (RIF) based on the Household Tracking Survey data in 2018 and the multi-regional input-output table. The Oaxaca-Binder decomposition based on RIF further decomposes household carbon inequality based on spatial heterogeneity into composition and coefficient effects. The results indicate that (1) household carbon inequality is widespread in China, generally close to the 60/30 distribution, favouring high-income families. Furthermore, (2) increases in income, wealth and economic burden and declining marriage rate promote household carbon inequality, which is suppressed by the development of education and the Internet and the increase in car ownership. Additionally, (3) the carbon inequality of urban households is smaller than that of rural households, which is contributed by the composition effects of family size, education, car ownership, Internet development and the coefficient effect of income and housing. Finally, (4) under the composition effect of family size and the coefficient effect of income, the household carbon inequality in the eastern region is smaller than in the central and western regions.

Assessing habitat quality at Poyang Lake based on InVEST and Geodetector modeling.

Poyang Lake is an essential natural wetland in the Yangtze River basin and plays a vital role in maintaining the ecosystem function and ecological security in the middle and lower reaches of the Yangtze River. However, the relative importance and spatial heterogeneity of the impacts of human activities and land use changes on ecological security needs to be further explored. Here, we analyzed the habitat quality level around Poyang Lake in 2022 and explored the factors of habitat quality change from a geographical perspective. The land use structure changes around the Poyang Lake basin from 2000 to 2022 were quantitatively analyzed, and then the relative importance and spatial heterogeneity of each factor on ecological security changes were investigated using geographic probes. The results show that (1) The worst quality habitat (0-0.1) consists mainly of construction land (1624.9 km2) with an area of 1634.64 km2; (2) Construction land continues to increase with the most significant change, and the dynamic land use attitude is 0.47. Grassland and mudflats have the greatest decrease. The increase in cultivated land in different periods is mainly due to the shift of water surface and forest land; (3) The drivers of habitat quality in Poyang Lake were significantly influenced by the interaction of socioeconomic factors. The explanatory power of population density interacting with the total year-end population and population density interacting with administrative area exceeded 0.84. These values were higher than the explanatory power of each individual factor, indicating that habitat quality was primarily associated with population density, total year-end population, and administrative area. These results suggest that human activities contribute to the degradation of wetlands around Poyang Lake. This study has significant reference value for coordinating human-land relationships in Poyang Lake, optimizing land management policy, and improving the sustainable development of cities.

Spatiotemporal characteristics and influencing factors of carbon emissions from land-use change in Shaanxi Province, China.

Due to global warming, there evolves a global consensus and urgent need on carbon emission mitigations, especially in developing countries. We investigated the spatiotemporal characteristics of carbon emissions induced by land use change in Shaanxi at the city level, from 2000 to 2020, by combining direct and indirect emission calculation methods with correction coefficients. In addition, we evaluated the impact of 10 different factors through the geodetector model and their spatial heterogeneity with the geographic weighted regression (GWR) model. Our results showed that the carbon emissions and carbon intensity of Shaanxi had increased overall in the study period but with a decreased growth rate during each 5-year period: 2000-2005, 2005-2010, 2010-2015, and 2015-2020. In terms of carbon emissions, the conversion of croplands into built-up land contributed the most. The spatial distribution of carbon emissions in Shaanxi was ranked as follows: Central Shaanxi > Northern Shaanxi > Southern Shaanxi. Local spatial agglomeration was reflected in the cold spots around Xi'an, and hot spots around Yulin. With respect to the principal driving factors, the gross domestic product (GDP) was the dominant factor affecting most of the carbon emissions induced by land cover and land use change in Shaanxi, and socioeconomic factors generally had a greater influence than natural factors. Socioeconomic variables also showed evident spatial heterogeneity in carbon emissions. The results of this study may aid in the formulation of land use policy that is based on reducing carbon emissions in developing areas of China, as well as contribute to transitioning into a "low-carbon" economy.

Carbon emissions trading scheme and regional total factor carbon productivity: based on temporal-spatial dual perspectives.

The carbon emissions trading scheme (CETS) in China is an important market-based environmental policy mechanism for decreasing carbon emissions. This paper calculates the total factor carbon productivity (TFCP) based on data from 275 cities in China from 2007 to 2020 using the DEA method and investigates the impact of the CETS on regional TFCP using the differences-in-differences (DID) method, all against the backdrop of carbon peaking and carbon neutrality. The research findings reveal that CETS has consistently improved TFCP in pilot cities, and this conclusion has held up following a number of robustness tests. Temporal heterogeneity experiments demonstrate that as implementation time increases, the enhancing effect takes on an inverted "U-shaped" structure with a 7-year effective lifetime. Spatial heterogeneity studies reveal that as one moves away from the pilot cities, the policy effect on surrounding cities' TFCP is inhibited, followed by facilitation. CETS policies can influence regional TFCP through the effects of green innovation and industry upgrading, according to mediation mechanism testing. We present policy recommendations based on the research findings for meeting the "dual" carbon goals and strengthening the carbon trading mechanism.

Study on the structural characteristics of China's high-speed railway network and its coordination with economic growth based on Fractal theory.

As one of the modern transportation modes, the high-speed railway network system has been a robust part of the comprehensive transportation system in China. An important topic emerges the exploration and optimization of its structural organization and coordinated relationship with the regional development, including urban form, land use, and economy. Therefore, supported by the integration of geographical information system (GIS) and fractal theory, this paper aims to carry out an investigation and discussion on the structural characteristics, including intensity (density), complexity, nonstationarity, and heterogeneity of the high-speed railway network in China (HSRNC) from the perspective of the whole country and specific regions, i.e., urban agglomerations. Moreover, based on the time-series data of network mileage expansion and economic output analysis, this study aims to evaluate and characterize the coordinated relationships between network development and economic growth in the context of the nationwide area and urban agglomerations. This study aims to explore and promote the spatial structural organization and morphology of the high-speed railway network in China, thus improving the coordinated development with the regional economic growth, for giving a new perspective to the future planning and evolution of the high-speed railway network in China.

Comprehensive evaluation of community human settlement resilience and spatial characteristics based on the supply-demand mismatch between health activities and environment: a case study of downtown Shanghai, China.

INTRODUCTION: Under globalization, human settlement has become a major risk factor affecting life. The relationship between humans and the environment is crucial for improving community resilience and coping with globalization. This study focuses on the key contradictions of community development under globalization, exploring community resilience by analyzing the mismatch between residents' health activities and the environment. METHODS: Using data from Shanghai downtown, including land use, Sports app, geospatial and urban statistics, this paper constructs a comprehensive community resilience index (CRI) model based on the DPSIR model. This model enables quantitative analysis of the spatial and temporal distribution of Community Human Settlement Resilience (CR). Additionally, the paper uses geodetector and Origin software to analyze the coupling relationship between drivers and human settlement resilience. RESULTS: i) The scores of CR showed a "slide-shaped" fluctuation difference situation; ii) The spatial pattern of CR showed a "pole-core agglomeration and radiation" type and a "ring-like agglomeration and radiation" type. iii) Distance to bus stops, average annual temperature, CO2 emissions, building density and number of jogging trajectories are the dominant factors affecting the resilience level of community human settlement. CONCLUSION: This paper contributes to the compilation of human settlement evaluation systems globally, offering insights into healthy community and city assessments worldwide. The findings can guide the creation of similar evaluation systems and provide valuable references for building healthy communities worldwide.

Spatiotemporal dynamics and influencing factors of carbon productivity in counties of Shandong Province, China.

In the context of the increasing global greenhouse effect, the Chinese government has proposed a "dual carbon" target. As a major carbon-emitting province in China, Shandong Province needs to improve its carbon productivity to coordinate carbon emission reductions and sustainable economic growth. This study analyzes the spatial and temporal evolution of carbon productivity at the county scale and the factors influencing it in Shandong Province from 2000 to 2017. The study uses the Dagum Gini coefficient, kernel density analysis, spatial autocorrelation model, and geographically and temporally weighted regression model. The results indicate that the carbon productivity in Shandong Province nearly doubled during the study period, revealing a spatial distribution characteristic of "high in the east and low in the west," together with a significant positive spatial autocorrelation. Intra-regional differences, the most important source of development differences among the three economic circles, rose to 32.11% during the study period, whereas inter-regional differences declined to 26.6%. Gross domestic product per capita and population density play a significant positive role in the development of carbon productivity. The balance of deposits in financial institutions at the end of the year has a weak positive effect, and the local average public finance expenditure and secondary industry structure on carbon productivity are negative in general. Shandong Province should identify specific regions with weak carbon productivity levels and understand the key factors to improve carbon productivity to promote the achievement of the "dual carbon" goal.

Immunohistochemical Assessment of Microvessel Density in OSCC: Spatial Heterogeneity of Angiogenesis and Its Impact on Survival.

(1) Background Oral squamous cell carcinomas (OSCC) are a common malignancy of the oral cavity and are often diagnosed when they have already spread to the regional lymph nodes. Advanced stages of cancer are characterized by the development of distant metastases. Angiogenesis, a hallmark of cancer, is known to contribute to cancer progression and metastasis. High microvessel density (MVD) has been linked to poor clinical outcomes in various types of cancer. (2) Methods: In this study, we aimed to investigate the spatial heterogeneity of blood vessels by comparing the tumor center and invasion front and to evaluate its prognostic value in OSCC. A total of 71 OSCC patient specimens were collected. The tissue was immunohistochemically stained using CD31 antibody to assess the MVD in the tumor center and the invasion front. Furthermore, the associations between the histopathological parameters, including MVD, disease-free survival (DFS), and overall survival (OS) were computed. (3) Results: In our study, we found a significantly higher presence of blood vessels at the invasion front of OSCCs compared to the tumor center. However, we did not observe any significant differences in MVD between different tumor stages. High intratumoral MVD was shown to be a positive prognostic factor for DFS (p = 0.047). (4) Conclusions: To the best of our knowledge, we were the first to analyze MVD as a prognostic factor by considering its spatial heterogeneity in OSCC. However, further studies are warranted to further elucidate the complexity of microvascular spatial heterogeneity and its influence on prognosis.

What drives the spatial heterogeneity of cropping patterns in the Northeast China: The natural environment, the agricultural economy, or policy?

Understanding the spatiotemporal dynamic of crop cover types and the driving forces of cropping patterns in the Northeast China (NEC) is essential for establishing suitable and sustainable cropping patterns that are adapted to local conditions, and for promoting the optimal use of black soil resources. Here, we classified the major grain crop cover types and investigated their spatiotemporal dynamic in the NEC by combining multi-source remote sensing imagery and phenological information based on the Google Earth Engine (GEE) platform. A number of typical cropping patterns from 2017 to 2021 were defined and extracted, and the characteristics of their spatial heterogeneity were analyzed. Driving mechanisms for the spatial heterogeneity of cropping patterns were revealed using Geodetector. The results concluded that over the past five years (2017-2021), there has been a shift from soybean to maize in the NEC, while rice has remained stable in terms of spatiotemporal dynamics. Seven dominant cropping patterns showed high spatial heterogeneity and positive spatial agglomeration. The center of gravity of the cropping pattern shifted southwards as the frequency of maize planting increased, while the center of gravity shifted northwards as the frequency of soybean planting increased, while the rice cropping pattern remained stable. The interaction between black-soil productivity index (BPI) and total grain income trend (TGIT) exhibits the most pronounced impact on the spatial heterogeneity of cropping patterns, with a q statistic of 0.523. Following closely are the interactions of soybean subsidies trend (SST), rice subsidies trend (RST), and maize subsidies trend (MST) with TGIT, with q statistics of 0.481, 0.472, and 0.452, respectively. Among the seven dominant cropping patterns, the soybean-based cropping pattern had the highest level of TGIT and BPI, followed by the maize-based cropping pattern, while the rice-based cropping pattern had the lowest level. All of the natural environmental, agri-economic and policy factors have a synergistic effect in contributing to the spatial heterogeneity of cropping patterns. Natural environmental factors determine the overall spatial distribution of cropping patterns in the NEC, while economic and policy factors combine to influence farmers' decisions, resulting in diverse regional cropping patterns. It is recommended that maize-soybean rotations such as Maize-Soybean Alternate Cropping (MSAC) and Maize-Soybean Rotational Cropping (MSRC) should be promoted, especially in the central and southern regions of the NEC, to meet agricultural market demand and stabilize soil productivity.