County Scale Research Articles (Page 1)

In-depth exploration of the spatial heterogeneity patterns of urban carbon emissions holds significant scientific importance for regional sustainable development. However, few scholars have examined the spatiotemporal characteristics of county-level carbon emissions in Inner Mongolia. This study focuses on the three major cities of Hohhot, Baotou, and Ordos in Inner Mongolia. By integrating NPP-VIIRS nighttime light data, the CLCD (China Land Cover Dataset) dataset, and statistical yearbooks, it quantifies county-level carbon emissions and establishes a spatiotemporal analysis framework of urban morphology–carbon emissions from 2013 to 2021. Six morphological indicators—Class Area (CA), Landscape Shape Index (LSI), Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Patch Density (PD), and Interspersion Juxtaposition Index (IJI)—are employed to represent urban scale, complexity, centrality, compactness, fragmentation, and adjacency, respectively, and their impacts on regional carbon emissions are examined. Using a geographically and temporally weighted regression (GTWR) model, the results indicate the following: (1) from 2013 to 2021, The high-value areas of carbon emissions in the three cities show a clustered distribution centered on the urban districts. The total carbon emissions increased from 20,670 (104 t/CO2) to 37,788 (104 t/CO2). The overall spatial pattern exhibits a north-to-south increasing gradient, and most areas are projected to experience accelerated carbon emission growth in the future; (2) the global Moran’s I values were all greater than zero and passed the significance tests, indicating that carbon emissions exhibit clustering characteristics; (3) the GTWR analysis revealed significant spatiotemporal heterogeneity in influencing factors, with different cities exhibiting varying directions and strengths of influence at different development stages. The ranking of influencing factors by degree of impact is: CA > LSI > COHESION > LPI > IJI > PD. This study explores urban carbon emissions and their heterogeneity from both temporal and spatial dimensions, providing a novel, more detailed regional perspective for urban carbon emission analysis. The findings enrich research on carbon emissions in Inner Mongolia and offer theoretical support for regional carbon reduction strategies.

High-resolution CO2 fossil fuel emission data are critical for developing targeted mitigation policies. As a key approach for estimating spatial distributions of CO2 emissions, top–down methods typically rely upon spatial proxies to disaggregate administrative-level emission to finer spatial scales. However, conventional linear regression models may fail to capture complex non-linear relationships between proxies and emissions. Furthermore, methods relying on nighttime light data are mostly inadequate in representing emissions for both industrial and rural zones. To address these limitations, this study developed a multiple proxy framework integrating nighttime light, points of interest (POIs), population, road networks, and impervious surface area data. Seven machine learning algorithms—Extra-Trees, Random Forest, XGBoost, CatBoost, Gradient Boosting Decision Trees, LightGBM, and Support Vector Regression—were comprehensively incorporated to estimate high-resolution CO2 fossil fuel emissions. Comprehensive evaluation revealed that the multiple proxy Extra-Trees model significantly outperformed the single-proxy nighttime light linear regression model at the county scale, achieving R2 = 0.96 (RMSE = 0.52 MtCO2) in cross-validation and R2 = 0.92 (RMSE = 0.54 MtCO2) on the independent test set. Feature importance analysis identified brightness of nighttime light (40.70%) and heavy industrial density (21.11%) as the most critical spatial proxies. The proposed approach also showed strong spatial consistency with the Multi-resolution Emission Inventory for China, exhibiting correlation coefficients of 0.82–0.84. This study demonstrates that integrating local multiple proxy data with machine learning corrects spatial biases inherent in traditional top–down approaches, establishing a transferable framework for high-resolution emissions mapping.

Exploring the trade-offs and synergies among ecosystem services to support ecological management in the Yangtze River Delta Urban Agglomeration.

Atmosphere-based estimations of CO2 emissions at city and county scales and comparative analysis: a case study in Yangtze River Delta Urban Agglomerations (YRDUA).

Spatiotemporal variation and drivers of ecosystem service value: A case study in Ziwuling area, Loess plateau.

Regional carbon emission accounting and spatiotemporal characteristics analysis in small and medium scale - A case study of Jiangsu Province.

The relationship between the geographic environment and human health has been a long-standing focus of scientific inquiry. Sn as an essential trace element for the human body, play vital roles in individual health and may influence longevity. However, the extent to which the statistical characteristics of population longevity are associated with elemental geochemical background values at a regional scale remains an important question. Based on the geochemical survey data of Yunnan Province and Chinese census data, the article utilizes Arcgis spatial analysis and mathematical statistics to explore the relationship between ω(Sn) and regional longevity level. The results of the study show that: (1) There is a close correlation between ω(Sn) and regional longevity levels. Within Yunnan Province, regions with high ω(Sn) have higher levels of longevity index and Ultra-octogenarian Index. (2) Spearman’s correlation coefficient shows that ω(Sn) is significantly positively (P < 0.01) correlated with both the longevity index and the Ultra-octogenarian Index; Linear regression further reveals that ω(Sn) always has a significant positive influence on the longevity index. For the Ultra-octogenarian Index, although the strength of the influence of ω(Sn) is not as significant as that of the longevity index, its influence on the healthy longevity of the population cannot be ignored. At the county scale in Yunnan Province, there is a significant positive correlation between ω(Sn) and longevity index, which may be related to the exposure of Sn in the natural environmental background into the human body and thus affecting the incidence of cancer, but the biogeochemical cycling mechanism of its association with longevity still needs to be further investigated.

To address two critical gaps in vegetation carbon sink (VCS) research---its limited policy relevance at the county scale and the insufficient identification of nonlinear interactive effects within driving mechanisms---this study focuses on the Chengdu-Chongqing Economic Circle (CCEC). Using MODIS NPP data (2002--2022), we examined the spatiotemporal dynamics of VCS through time-series analysis, standard deviational ellipse, and spatial autocorrelation analysis. Crucially, we applied the Geodetector model to quantitatively disentangle the roles of natural and anthropogenic drivers. The results show that: (1) VCS followed a fluctuating upward trajectory, peaking in 2019, but declined sharply in 2006 due to an extreme drought; (2) spatially, a ``three-belt agglomeration'' pattern was identified, with high-value clusters in mountainous areas (Southwestern Sichuan, Southeastern Chongqing, Southeastern Sichuan) and low-value diffusion in plains (Chengdu Plain, Chongqing Valley). The VCS centroid remained consistently located in Anyue County, while spatial clustering gradually weakened; (3) single-factor detection highlighted natural factors---especially elevation (q &gt; 0.76)---as dominant drivers of spatial heterogeneity, whereas interaction detection revealed widespread ``nonlinear enhancement'' between natural and anthropogenic factors. These interactions explained far more variance than individual factors and amplified spatial heterogeneity synergistically. By integrating county-scale analysis with the identification of nonlinear interaction mechanisms, this study provides a scientific foundation for differentiated ecological governance and the precise implementation of China's ``Dual Carbon'' (carbon peaking and carbon neutrality) goals in the CCEC.

Abstract Farmland carbon pools are pivotal to the terrestrial carbon cycle. Studying soil organic carbon density (SOCD) spatial heterogeneity is crucial for uncovering regional carbon balance mechanisms and optimizing sequestration strategies. Focusing on county-scale SOCD heterogeneity, this study aids sustainable farmland management, carbon sequestration, and spatial carbon pool modeling. Based on the 2020 farmland soil organic carbon (SOC) analysis survey data. The SOCD and spatial pattern of farmland in Chengcheng County, Shaanxi Province were estimated and analysed by combining GIS and GPS technologies. Optimal Parameter Geodetector (OPGD) was used to detect spatial heterogeneity and quantitatively attribute the influencing factors of SOCD in farmland in Chengcheng. The results showed that the SOCD of farmland in Chengcheng, ranged from 1.10 to 1.79 kg m−2, with an average of 1.39 kg m−2. The SOC stock was 151.96 × 104 t. Both values were lower than the national average and much lower than those in European and American countries. Between the north-west and south-east of the plough layer, Chengcheng’s farmland’s SOCD clearly distinguishes two distinct sections. The average farmland SOCD in the north-west is lower than that in the south-east by 0.27 kg m−2. The explanatory power of temperature, population density and DEM all exceeded 0.3, and the explanatory power of the interaction between them exceeded 0.6. This implies that the combined influences of nature and human activity are responsible for the spatial patterns of SOCD in farming at the county scale in the Weibei dry loess plateau.

Winter wheat yield prediction at a county scale using time series variation features of remote sensing spectra and machine learning

Ecotourism suitability at county scale in China: Spatial pattern, obstacle factors, and driving factors

It is one of the goals of current social development to achieve regional high-quality development and meet people's needs for an ecological environment. Taking Gansu Province as an example, the spatial and temporal changes in carbon emissions in Gansu Province from 1992 to 2021 and their influencing factors are analyzed based on a multi-scale perspective using spatial autocorrelation, cold hotspots, and geographic probes. The results showed that： ① The total carbon emissions in Gansu Province fluctuated and increased from 1992 to 2021, but the growth rate was decreasing. ② In terms of the type of growth, low growth was mainly concentrated in the relatively economically developed districts and counties, and high growth was mainly concentrated in the economically underdeveloped districts and counties, regardless of the city and state scales or county scales. ③ Carbon emissions in Gansu Province showed a significant global spatial positive correlation at the county scale, with the aggregation capacity weakening and then increasing, the high-high aggregation area moving from Longzhong to Longdong, and the low-low aggregation area distributed in the south of Gansu. ④ The influence of factors on carbon emissions tended to grow with the enlargement of the study scale, and the influence of regional GDP on the spatial differentiation of carbon emissions has been maintained at a high level. Whether at the city-state scale or the county scale, the explanatory power of a single factor on carbon emissions was weaker than the interaction of the factors.

ABSTRACTEcological Security Pattern (ESP) coordinates urban expansion and ecological protection. Previous single‐scale ESP studies ignored the differences between regional and local conservation needs, resulting in policy conflicts across administrative scales. This study proposed a multi‐scale ESP framework with Zhangye, a typical arid city, as the study area. Guided by the United Nations Sustainable Development Goals (SDGs), relevant ecosystem services (ESs) were selected and ecological sources were identified at both city and county scales. A multi‐scale integration was then proposed to classify ecological sources into hierarchical scales. Subsequently, the aridity index was applied to modify the resistance surface, and the circuit theory model was employed to quantify ecological corridors and strategic points. The results showed that the ecological sources in Zhangye spanned 18,367.93 km2, occupying 47.6% of the city's total area. Among them, the first‐level source area covered 12,674.73 km2 (69%), the second level covered 2274.05 km2 (12.38%) and the third level covered 3419.15 km2 (18.62%). Notably, 31% of these ecological sources exhibited scale‐dependent variations in their protection requirements. In total, 35 ecological corridors were delineated, totaling 211.65 km. These included 11 at the first level, 7 at the second level, and 17 at the third level, along with 11 pinch points and 41 ecological barriers. Finally, the ESP was optimized into a “three zones and three belts” spatial pattern, and targeted suggestions were proposed for the ecological planning of Zhangye. This study demonstrated an effective approach to harmonizing macro‐regional conservation objectives with micro‐local ecological protection needs, providing a transferable solution for inter‐regional planning challenges.

A comprehensive exploration of the trade-offs/synergies and drivers of ecosystem services (ESs) is essential for formulating ecological plans. However, owing to the limited attention given to multiple scales, the relationship of ESs still needs to be further explored. Taking the Yangtze River Delta region of China as the study area, a multiscale data framework with a 1 km grid and 10 km grid and county was established, and six ESs were evaluated for 2000, 2010, and 2020. Then, the trade-offs and synergies between ESs were explored by Spearman’s correlation analysis and geographically weighted regression (GWR), and the ecosystem service bundles (ESBs) were identified by self-organizing maps (SOMs). Finally, the socioecological drivers of ESs were further analyzed via GeoDetector. The results showed that (1) the distribution of ESs exhibited spatial heterogeneity. (2) At the grid scale, there were very strong trade-off effects between crop production and the other ESs. The synergistic effects between ESs at the county level were further strengthened. (3) The ESBs identified at different temporal and spatial scales were different. (4) Land use had the strongest explanatory power for all the ESs. At the grid scale, climatic and biophysical factors had great impacts on ESs, whereas population density and night light remote sensing had significant impacts on crop production, carbon storage, and water yield at the county scale.

IntroductionThe ecosystem service value (ESV) is a critical element in the preservation of ecological barriers. The objective of this study is to elucidate the nonlinear correlation between ESV and the key factors that contribute to enhancing the accuracy and reliability of ecosystem service value assessment.MethodsIn this study, ESV were evaluated based on grid and county scales. Furthermore, the driving factors of ESV were explored using the explainable machine learning method.ResultsThe findings are as follows: (1) The net ESV of the Gangjiang Upstream Basin (GUB) has undergone a decline from 1990 to 2000, with climate regulation and hydrological regulation collectively accounting for approximately 50% of all functions. (2) A mere 0.69% of the areas exhibited an increase in the level of ESV, while 11.19% demonstrated a decline by 2020, based on the grid scale. The ESV exhibited a slight increase in two counties, while it demonstrated a decrease in the remaining 16 counties at the county scale. The ESV exhibited a substantial positive spatial correlation, manifesting as the presence of considerable high-high and low-low clustering regions. (3) The interpretable machine learning analysis revealed a consistently strong negative correlation between ESV and human activity intensity (HAI), fractional vegetation cover (FVC), and elevation across the entire observed range. In contrast, the soil organic matter (SOC) demonstrated a non-linear, highly significant positive correlation with ESV.DiscussionThis paper addresses the observed decline in the value of ecosystem services of GUB by proposing a series of strategies designed to enhance ESV in the region. Furthermore, drawing on research findings related to the driving factors and thresholds of ESV, this paper presents specific measures that can serve as references for managers.

Under the dual backdrop of the innovation-driven development strategy and the coordinated development of the Beijing–Tianjin–Hebei region, the growth of county-level technology-based small and medium-sized enterprises (SMEs) in Hebei Province has been constrained by regional heterogeneity in resource endowments and industrial foundations, which conflicts with the uniform application of support policies. Accurately identifying spatial differentiation patterns in policy effectiveness has become essential for optimizing policy implementation. Existing studies have primarily employed traditional models such as ordinary least squares (OLS), overlooking the spatial correlation of county-level economic systems. Moreover, limited attention has been paid to the local spatial heterogeneity of policy variables and enterprise development indicators, the nonlinear characteristics of policy effects at the county scale, and the quantification of spatial spillover effects of support policies. To address these limitations, moderation models, threshold regression models, and the Spatial Durbin Model (SDM) were integrated in this study to construct a moderating effect measurement model of support policies. Through this approach, the spatially differentiated effects of support policies for technology-based SMEs across counties in Hebei Province were quantified. The nonlinear moderating mechanisms of policy instruments and their spatial transmission patterns were systematically examined. By integrating spatial econometric techniques with policy effect evaluation frameworks, this study proposes a novel paradigm for regional policy research and offers evidence-based guidance for designing differentiated policy measures aimed at enhancing the effectiveness of support initiatives in Hebei Province.

Gully erosion refers to the landform formed by soil and water loss through gully development, which is a critical manifestation of soil degradation. However, research on the spatio-temporal variations in erosion gullies at the county scale remains insufficient, particularly regarding changes in gully aggregation and their driving factors. This study utilized high-resolution remote sensing imagery, gully interpretation information, topographic data, meteorological records, vegetation coverage, soil texture, and land use datasets to analyze the spatio-temporal patterns and influencing factors of erosion gully evolution in Bin County, Heilongjiang Province of China, from 2012 to 2022. Kernel density evaluation (KDE) analysis was also employed to explore these dynamics. The results indicate that the gully number in Bin County has significantly increased over the past decade. Gully development involves not only headward erosion of gully heads but also lateral expansion of gully channels. Gully evolution is most pronounced in slope intervals. While gentle slopes and slope intervals host the highest density of gullies, the aspect does not significantly influence gully development. Vegetation coverage exhibits a clear threshold effect of 0.6 in inhibiting erosion gully formation. Additionally, cultivated areas contain the largest number of gullies and experience the most intense changes; gully aggregation in forested and grassland regions shows an upward trend; the central part of the black soil region has witnessed a marked decrease in gully aggregation; and meadow soil areas exhibit relatively stable spatio-temporal variations in gully distribution. These findings provide valuable data and decision-making support for soil erosion control and transformation efforts.

The impacts of land use transition on ecological environment quality (EEQ) during China’s rapid urbanization have attracted growing concern. However, existing studies predominantly focus on single-scale analyses, neglecting scale effects and driving mechanisms of EEQ changes under the coupling of administrative units and grid scales. Therefore, this study selects Zhejiang Province—a representative rapidly transforming region in China—to establish a “type-process-ecological effect” analytical framework. Utilizing four-period (2005–2020) 30 m resolution land use data alongside natural and socio-economic factors, four spatial scales (city, county, township, and 5 km grid) were selected to systematically evaluate multi-scale impacts of land use transition on EEQ and their driving mechanisms. The research reveals that the spatial distribution, changing trends, and driving factors of EEQ all exhibit significant scale dependence. The county scale demonstrates the strongest spatial agglomeration and heterogeneity, making it the most appropriate core unit for EEQ management and planning. City and county scales generally show degradation trends, while township and grid scales reveal heterogeneous patterns of local improvement, reflecting micro-scale changes obscured at coarse resolutions. Expansive land transition including conversions of forest ecological land (FEL), water ecological land (WEL), and agricultural production land (APL) to industrial and mining land (IML) primarily drove EEQ degradation, whereas restorative ecological transition such as transformation of WEL and IML to grassland ecological land (GEL) significantly enhanced EEQ. Regarding driving mechanisms, natural factors (particularly NDVI and precipitation) dominate across all scales with significant interactive effects, while socio-economic factors primarily operate at macro scales. This study elucidates the scale complexity of land use transition impacts on ecological environments, providing theoretical and empirical support for developing scale-specific, typology-differentiated ecological governance and spatial planning policies.

Abstract Rapid urbanization, while enhancing the living standards, posed significant challenges to food security (FS) and water quality security (WQS). Previous research mainly focused on assessing FS and WQS, remaining their trade‐off and driving pathway unclear. Taking Yangtze River Delta as study area, this study quantified FS and WQS from 2000 to 2020, and explored their trade‐off and driving pathways at both county and regional scales. The results indicated that FS and WQS showed opposite spatial patterns, while both of them experienced significant declining trends across county and regional scales. Although both synergy potential and trade‐off intensity showed increasing trends, the mitigation of FS‐WQS trade‐off remained challenging due to the substantially greater rise in trade‐off intensity compared to synergy potential. “Win‐win” combinations were identified with high FS, high WQS and the lowest opportunity cost. Urbanization, especially urban expansion and population growth, significantly intensified the trade‐off by reducing food production while increasing demands for both food and water quality. This study finally established a conceptual framework for characteristics and driving mechanisms of FS, WQS and their trade‐off at different urbanization stages, offering scientific insights for sustainable development in rapidly urbanizing regions.

Land use carbon emissions (LUCE) contribute significantly to global warming. Recognizing the influence of regional heterogeneity and geographical scale on socioeconomic development, studying LUCE at various scales is crucial for devising more effective emission reduction measures. However, previous studies have predominantly focused on a single scale. This study focuses on the Yangtze River Economic Belt (YREB), utilizing land use, nighttime light, and energy consumption data to compute LUCE at provincial, prefectural, and county scales, employing spatial autocorrelation, geographic detectors, and the Multiscale Geographically Weighted Regression (MGWR) model to analyze the spatiotemporal dynamics and impact factors of LUCE across different scales. Our results show: (1) Throughout the study period, LUCE in the YREB exhibited a steady increase, rising from 28,434.32 × 104 t to 86,581.79 × 104 t. (2) Positive spatial autocorrelation was observed in LUCE at all three scales. Notably, spatial clustering intensified at the provincial and prefectural levels, while a diminishing trend in clustering was noted at the county scale. (3) Predominant clustering patterns at the prefectural and county scales included H–H and L–L types, with the county scale displaying more pronounced clustering characteristics. (4) Economic development emerged as the primary influencing factor on LUCE at both the prefectural and county scales. Nevertheless, the intensity of impact from carbon emission intensity, industrial structure, population size, government intervention, and land use degree differs between the two levels. This research underscores the high sensitivity of LUCE to administrative scales, emphasizing the necessity of considering these scales when formulating emission reduction strategies.