Geodetector Model Research Articles

Local carbon emission zone construction in the highly urbanized regions: Application of residential and transport CO2 emissions in Shanghai, China

Optimizing urban morphology can help city managers deploy internal carbon reduction strategies. However, due to the numerous factors that influence urban carbon emissions and the complex paths of their effects, existing research findings are diverse and difficult to compare. This study constructed the local carbon emission zone (LCEZ) which links the highly urbanization morphology profiles and CO2 emission intensity to favor summarizing the form characteristics for the low CO2 emission control in new communities. The approach involved selecting six indicators from the perspectives of urban external morphology, internal characteristics, and development intensity, and using geographically weighted regression (GWR) to identify three key factors. The selected factors were divided into low(1) medium(2) and high(3) categories and were combined with each other to construct the LCEZ. The nonparametric test and GeoDetector model were used to test the difference and similarity of LCEZs, respectively. The results showed that 1) The impacts of functional mixing entropy (FME), building height (BH) and road density (RD) on residential and transport CO2 emissions (RTCE) were relatively large and positive; 2) The low- and medium-FME areas were the major types of LCEZ; 3) The area proportions of the low FME LCEZs in different population areas of the RTCE cold spot regions were the highest (81.84%–88.25%); 4) It is recommended that the morphological feature values of compact-low rise-low network (1-1-1), mid compact-low rise-mid network (1-1-2), compact-mid rise-low network (1-2-1) and mid compact-mid rise-mid network (1-2-2) were taken as the low CO2 emission control of highly urbanized regions.

Spatiotemporal variation and driving factor of vegetation coverage from 2000 to 2020 in southern Jiangxi Province, China.

Vegetation plays a critical role in the water and carbon cycling and energy flow, serving as an indicator for regulating land carbon balance and reflecting climate change and human activities. We analyzed the spatiotemporal variations of normalized difference vegetation index (NDVI) during the growing season in southern Jiangxi from 2000 to 2020, using statistical methods, including the Mann-Kendall test, Theil-Sen Median analysis, Hurst index, and coefficient of variation. We employed the geodetector model to comprehensively assess the impacts of climate, topography, soil and human factors on spatial differentiation of vegetation NDVI. The results showed NDVI exhibited an upward fluctuating trend with a rate of 0.003 per year from 2000 to 2020. The proportion of high-grade and medium-high-grade NDVI areas were 55.8% and 41.9%, respectively, while the areas with low and relatively low fluctuations accounted for 92.3%. The proportions of areas showing extremely significant improvement and significant improvement were 40.4% and 19.4%, respectively. In contrast, the combined proportion of areas displaying extremely significant degradation and significant degradation was only 2.2%. The proportions of areas demonstrating continuous improvement and future improvement were 28.0% and 60.2%, respectively. Elevation, precipitation, relative humidity, temperature, landform type, land use type, population density, and nighttime light were identified as the major factors for the vairations of NDVI in the study area, followed by slope, soil type, and GDP, while slope aspect and vegetation type had indirect influence. Throughout the study period, NDVI in southern Jiangxi was overall stable, with future changes primarily indicating improvement. Notably, human factors such as land use type, population density, and nighttime light index exhibited an upward trend in their impacts on NDVI.

A downscaling-calibrating framework for generating gridded daily precipitation estimates with a high spatial resolution

Accurate precipitation datasets with a high spatiotemporal resolution are crucial for regional hydrological simulation and water resources management. Although various global satellite precipitation products (SPPs) are available, they cannot be directly applied to small-scale research due to their low spatial resolution and significant bias. To solve these problems, based on the consideration of spatiotemporal relationships, this paper presented a comprehensive downscaling-calibrating framework that consists of spatial downscaling procedures and calibrating processes. Specifically, a geographically weighted regression (GWR) model, coupled with the environmental variables selected by the Geodetector model, was applied to generate uncorrected daily precipitation data with high-spatial resolution (1 km × 1 km). Then, the preliminary downscaled daily precipitation was calibrated using a bidirectional long short-term memory (Bi-LSTM) network. The proposed framework was applied to downscale and calibrate the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product of the Jiulong River Basin in southeast China from 2010 to 2016. The results indicate that: (1) the spatial downscaling procedures improved the spatial resolution of the SPPs while maintaining its accuracy; (2) the calibrating processes can effectively correct the bias of preliminary downscaled daily precipitation. Meanwhile, the accuracy evaluation showed that the proposed GWR_Bi-LSTM framework outperformed the comparative methods, and the generated precipitation dataset exhibited a better performance under various evaluation conditions. Generally, based on the proposed framework, gridded daily precipitation data with high quality and spatial resolution can be generated for hydrological and meteorological research.

Spatio-Temporal Variation in Soil Salinity and Its Influencing Factors in Desert Natural Protected Forest Areas

Soil salinity is a crucial parameter affecting soil health. Excessive surface salt accumulation degrades soil structure, inhibits vegetation growth, and diminishes plant diversity. Such increases in salinity can accelerate desertification, leading to soil resource loss, hampering agricultural progress, and compromising ecological security. However, the vastness of arid regions and data acquisition challenges often hinder efficient SSC monitoring and modeling. In this study, we leveraged remote sensing data coupled with machine learning techniques to investigate the spatio-temporal dynamics of SSC in a representative desert natural forest area, the Alxa National Public Welfare Forest. Utilizing the geodetector model, we also delved into the factors influencing SSC. Our results underscored the effectiveness of the Convolutional Neural Networks (CNN) model in predicting SSC, achieving an accuracy of 0.745. Based on this model, we mapped the spatial distribution of SSC, revealing hydrothermal conditions as pivotal determinants of salt accumulation. From 2016 to 2021, soils impacted by salinity in the research area exhibited a rising trend, attributed to the prevailing dry climate and low precipitation. Such intensified salinity accumulation poses threats to the healthy growth of protective forest vegetation. This study can provide a theoretical reference for salinization management and ecological protection in desert natural forest areas.

The Spatiotemporal Pattern Evolution and Driving Force of Tourism Information Flow in the Chengdu–Chongqing City Cluster

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya’an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations.

Differential effects of climatic and non-climatic factors on the distribution of vegetation phenology trends on the Tibetan plateau

The study of vegetation phenology changes is important because it is a sensitive indicator of climate change, affecting the exchange of carbon, energy and water fluxes between the land and the atmosphere. Previous studies have focused on the effects of climatic factors among environmental factors on vegetation phenology, thus the effects of non-climatic factors among environmental factors have not been well quantified. This study endeavors to scrutinize the spatiotemporal inconsistency in the start-of-season (SOS) and the end-of-season (EOS) on the Tibetan Plateau (TP) and to quantify the effects of environmental factors on phenology. To this end, the Moderate-resolution Imaging Spectroradiomater (MODIS) Normalized Difference Vegetation Index (NDVI) data from 2001 to 2018 and four common used methods were employed to extract SOS and EOS, and the site data was used to select the most appropriate phenology results. The Geodetector model was used to assess and measure the explanatory power of different environmental factors. The research results indicate that temperature exerts a more substantial impact on phenology than precipitation on TP. non-climatic factors such as longitude, latitude, and elevation are more influential in determining the distribution of phenological trends than climatic factors. Among these non-climatic factors, latitude has the most prominent effect on the trends of SOS. Furthermore, non-climatic factors exhibit a stronger effect on SOS, whereas EOS is more susceptible to climatic factors and less influenced by non-climatic factors. These discoveries bear great significance in comprehending the intricate outcomes of regional changes on vegetation phenology and enhancing phenology models.

Identifying the driving forces of global ecosystem services balance, 2000–2020

In 2019, the United Nations (UN) General Assembly announced the implementation of the “UN Decade on Ecosystem Restoration (2021–2030)” initiative. However, overexploitation continues to severely impact the global ecosystem services (ESs) balance. Identifying the global ESs balance and its driving forces can effectively identify sensitive areas under such high ecological stress and corresponding governance responses. The world is currently facing increasing pressure on natural resources, while research on this issue remains controversial at global level. To address the gap, we portrayed the spatial characteristics of global ESs balance across county units worldwide in 2000, 2010, and 2020, and we further examined the driving forces of ESs balance covering six continents of the world (excluding Antarctica) by Geodetector model. Our results showed that the deficit areas of ESs balance were concentrated in urban and barren areas, while the surplus areas were distributed in areas with dense vegetation, especially primary forest communities. Furthermore, the global Moran's I value of ESs balance averaged 0.719 over three years, demonstrating a significant spatial dependence of its spatial distribution. According to the spatial statistical analysis, the factors that affected ESs balance varied among continents and had evolved over the last two decades. Nevertheless, nighttime light intensity and forest proportion consistently showed high contributions, with maximum values of 0.569 and 0.546, respectively. The contribution of socio-economic factors is higher in the developed regions and lower in the less developed regions. This work provides a framework for future global large-scale ESs balance assessments. It identifies similarities in impact mechanisms across continents and the dominance of individual drivers to encourage contextualized, collaborative governance in environmental policy.

Assessment of Urban Resilience and Detection of Impact Factors Based on Spatial Autocorrelation Analysis and GeoDetector Model: A Case of Hunan Province

The rapid development of urbanization has led to increasing uncertainties related to urban safety risks, which has brought certain challenges to the sustainable development of cities. The concept of urban resilience has found a new way to improve the ability of a city to absorb and resolve risks. However, the existing literature on the evaluation of urban resilience is mostly developed from a static perspective, lacking a systematic and dynamic understanding of the level of urban resilience. Therefore, this paper takes Hunan Province as the research object, determines the resilience evaluation indicators, collects the data of each indicator by using the observation method and the literature method, then chooses the comprehensive index method and other methods to measure the urban resilience level of Hunan Province in the years of 2010–2021, and observes the dynamic changes in the resilience level. And, we use the GeoDetector model to detect the dominant factors affecting the urban resilience level and the interaction between these factors. The results of this study show that: (1) The level of urban resilience in Hunan Province shows a steady upward trend from 2010 to 2021, but cities with low resilience levels hold a dominant position. Among all subsystems, the level of urban economic resilience is the highest. (2) From 2010 to 2021, the level of urban resilience in Hunan Province indicates a stepwise spatial structure in the spatial pattern, gradually decreasing from east to west. (3) The urban resilience of Hunan Province from 2010 to 2021 has a significant spatial agglomeration effect, mainly manifested as “L-H type” agglomeration and “L-L type” agglomeration. (4) The spatio-temporal differentiation of urban resilience is mainly caused by economic and social factors, while ecological, institutional, and infrastructure factors have a relatively small influence on the level of urban resilience. The interaction of impact factors will have a more significant influence on urban resilience. The research results of this article are of great significance for urban resilience construction in Hunan Province and even the whole country.

Integrating ecological risk, ecosystem health, and ecosystem services for assessing regional ecological security and its driving factors: Insights from a large river basin in China

Watershed ecological security (ES), which is fundamental to regional sustainable development, consists of ecological risk (ER), ecosystem health (EH), and ecosystem services (ESs). It remains unclear how to describe the status of regional ES by integrating the three key characteristics, and little is known about the differences among the factors driving ER, EH, ESs, and ES. Here, we established an integrated assessment system of regional ES based on the cause–effect relationship of ER, EH, and ESs. We quantified the ES characteristics in the Huaihe River Basin of China in 2020 and pinpointed the factors (i.e., natural environment, urbanization, accessibility) driving their spatial differentiation using the Geodetector model. Results showed that: (1) in the study area, ER and EH were at the average level, and ESs and ES were at the high level. (2) ER, EH, ESs, and ES had positive spatial correlations (Moran’ I > 0.60), with ER and EH having the strongest spatial dependence. (3) Natural environmental factors predominantly influenced ER, EH, and ESs. Slope was the primary driver for ER and ESs (q-value = 0.492 and 0.395, respectively), and vegetation coverage was the major driver for EH (q-value = 0.506). (4) ES was mainly influenced by urbanization factors, the proportion of construction land area exhibiting the highest explanatory power (q-value = 0.751), and the interaction with vegetation coverage being the strongest (q-value = 0.836). This study provides new ideas and methods for the assessment of regional ES, and holds positive implications for the protection and management of regional ecosystems.

Analysis of Vegetation Dynamics and Driving Mechanisms on the Qinghai-Tibet Plateau in the Context of Climate Change

The Qinghai-Tibet Plateau (TP) is susceptible to climate change and human activities, which brought about drastic alterations in vegetation on the plateau. However, the trends and driving mechanisms of vegetation changes remain unclear. Therefore, the normalized difference vegetation index (NDVI) was used to analyze the spatiotemporal distribution of vegetation and the consistency of dynamic trends in the TP from 2000 to 2020 in this study. The independent contributions and interactive factors of natural and human activities on vegetation changes were investigated through the Geodetector model. The drivers of vegetation under different dry–wet zones and precipitation gradients were quantitatively separated, and the internal mechanisms of vegetation changes were discussed from multiple perspectives. The results showed that from 2000 to 2020, the NDVI had an overall increasing trend, with an increasing rate of 0.0027 a−1, and the spatial pattern was different, increasing gradually from the northwest to the southeast. Consistent improvement occurred in the central and southeastern parts of the TP, while the western and northern parts consistently deteriorated. The annual mean precipitation had the greatest explanatory power for vegetation changes (0.781). The explanatory power of the integrated effects between two factors was greater than that of individual factors. The integrated effects between annual mean precipitation and other driving factors had the strongest explanatory power on vegetation variations. The driving mechanisms of vegetation dynamics varied among different dry–wet zones, and the vegetation growth was more sensitive to the response of precipitation in arid and semi-arid climate zones. This study enhances our understanding of the intrinsic mechanisms of vegetation changes on the plateau, which can provide a reference for ecological conservation, and has implications for further prediction and assessment of vegetation ecosystem stability.

Analysis of Spatial-Temporal Changes and Driving Factors of Vegetation Coverage in Jiamusi City

This study of vegetation coverage in Jiamusi City provides theoretical support for local urban development, land use, and ecological environmental protection. Based on the land cover data and Landsat remote sensing image data from 2000 to 2020, the vegetation cover and land use data of Jiamusi City were extracted. The study includes the following aspects: (1) an analysis of the spatio-temporal changes in vegetation coverage; (2) analysis of the land use situation in Jiamusi City; and (3) investigation of the impact of natural and human factors on vegetation coverage in Jiamusi City using the Geodetector model. The results show that (1) over the past 20 years, the vegetation coverage of Jiamusi has shown a decreasing trend, declining from 25.22% in 2000 to 17.13% in 2020, representing a decrease of 32%. In terms of spatial distribution, the areas of Fuyuan City and Tongjiang City have experienced more significant decreases in vegetation coverage, decreasing by 73.6% and 54.0%, respectively. (2) The land use pattern of Jiamusi City has undergone significant changes during the study period; except for paddy fields (PF), unused land (UL), and construction land (CL), the areas of all the land categories have decreased. The ranking of the single land use dynamic degree in terms of magnitude is as follows: PF, UL, CL, dry farmland (DF), vegetation coverage land (VCL), and wetland (WET). (3) The changes in vegetation coverage were influenced by both natural and human activities and, according to the Geodetector results, the main influencing factors were CL and DF. The key findings of this study emphasize the need for comprehensive land use planning and ecological environmental protection that focus on sustainable development and conservation practices, and lay the groundwork for future ecosystem management and urban planning efforts in Jiamusi City.

Coupling analysis and driving factors between carbon emission intensity and high-quality economic development: Evidence from the Yellow River Basin, China

This study aims to explore the complex dynamic relationship between carbon emission intensity (CEI) and high-quality economic development (HQED) in the Yellow River Basin (YRB), with the goal of fostering ecological protection and high-quality development strategies. However, there is limited knowledge on the comprehensive development level, evolutionary trends, and driving factors of these two systems in the YRB. To address this, the study uses multiple methodologies, including nighttime light data, the entropy-topsis method, the coupling coordination degree model (CCDM), exploratory spatial data analysis (ESDA), and the geodetector model, to examine the coupling coordination relationship and driving factors between CEI and HQED across sixty cities in the YRB from 2010 to 2019. The results reveal a decreasing trend in CEI and a “U”-shaped development trend for HQED, characterized by low coordination and significant spatial imbalance. Moreover, per capita GDP, population density, urbanization level, industrial structure, and energy intensity are found to have significant driving effects on the CCD. Based on the results, the study suggests policies to boost the CCD between CEI and HQED. These involve promoting green transitions, endorsing spatially tailored development strategies, and implementing interventions on major CCD drivers for balanced growth. It also proposes refining regional governance and establishing an innovation fund for green technologies, particularly beneficial for underdeveloped regions. These policy implications could guide economic development and emission reduction strategies in ecologically fragile and economically underdeveloped regions.

Spatial pattern and influencing factors of land border cultural heritage in China

This paper provides an analysis of China’s land border cultural heritage, aiming to offer guidance for its protection and development. The study compiled a comprehensive database of heritage sites and analyzed their mathematical characteristics based on quantity, type, and quality. The spatial pattern of the cultural heritage was described using kernel density analysis, spatial variability, and spatial correlation, while the driving mechanism was measured based on Geo-Detector model. The findings revealed that the land border areas of China have a total of 1227 cultural heritage, comprising two main types and 18 subtypes. National cultural heritage accounted for 22.74%, with ancient city ruins being the largest type of cultural heritage. The Southwest and Northeast regions of China exhibited high-high clusters and low-low clusters, respectively. Moreover, the study identified history of administrative succession, evolution of historical and cultural, altitude, distance from the city, population density, minority population ratio, urbanization process, and per capita GDP as significant factors impacting the spatial pattern of land border cultural heritage in China. Based on these findings, the study suggests that the government should invest more in border cultural heritage, improve transportation infrastructure in border areas, ensure dynamic inheritance and protection of cultural heritage, and promote the joint development of different types of cultural heritage. The research results will provide data support and decision-making reference for the sustainable development of cultural heritage in land border areas, and can provide a new perspective for the management of cultural heritage in similar areas.

Study on the impact of vegetation change on ecosystem services in the Loess Plateau, China

Vegetation change has a non-negligible role in the impact of ecosystem services (ESs). Quantitative assessment of vegetation ecosystem services is the basis for sustainable regional development. Using the Chinese Loess Plateau (LP) as the study area, this study firstly analysed the spatial and temporal characteristics of vegetation change and used a Geodetector model to reveal the driving mechanism. Next, this study applied the InVEST model to quantitatively assess the four services of carbon storage, habitat quality, soil conservation and water yield, and explored the vegetation ecosystem service balance through vegetation type transfer. The results showed that (1) there was a trend towards an increase in the area of all vegetation types, with sparse vegetation being the most pronounced. Areas of damaged vegetation were concentrated in urban and peripheral areas, as well as wasteland and bare ground. (2) Annual precipitation, soil types and cumulative afforestation area ratio were the main drivers of vegetation change on the LP. (3) Restoration of woodland enhanced carbon storage, habitat quality and soil retention services for vegetation on the LP, while water yield decreased by 31.66%. The restoration of vegetation area did not enhance all ecosystem services. This study may provide a reference and basis for the sustainable management of regional ecosystems.

Spatial heterogeneity of human activities and its driving factors in karst areas of Southwest China over the past 20 years

Guizhou Province is located in the karst mountain regions of Southwest China, where the ecological environment is extremely fragile and particularly sensitive to human activities. Therefore, understanding the changing characteristics and driving factors of human activity in recent decades is urgent. In this study, least squares, correlation analysis, spatial autocorrelation analysis, and GeoDetector model were used. Based on a large collection of nightlight, topography, and meteorological data, combined with geographical information technology, we investigated the spatial distribution, intensity change, and degree of impact of human activity from 2000 to 2020. The results showed that during the study period, human activities intensity and range were expanding. The human activity areas expanded from 15,963 to 86,923 km2 at an increasing rate of 4,279.2 km2/a, which was 1,118.4 km2/a from 2000 to 2010 and 6,375.3 km2/a from 2010 to 2020. The human activity intensity increased from 0.009 to 0.018, with an increasing rate of 0.0006/a, which was 0.00028/a from 2000 to 2010 and 0.00073/a from 2010 to 2020. The rate of increase in the human activity intensity and the rate of expansion in the human activity range from 2010–2020 were significantly higher than that from 2000–2010, becoming more notable (p ≤ 0.01), especially after 2012. With the expansion of human activities, the center of gravity of human activity moved towards the northeast by 20.71 km. The human activities were primarily distributed in areas with the gentlest slopes (6–15°), middle and low altitudes (489–1,982 m), suitable temperatures (12.36–17.74°C), and abundant precipitation (1,001.99–1,276.99 mm). The research results using the GeoDetector model indicate that slope had the greatest impact on human activities with a q value of 0.1338; precipitation, elevation, and temperature had q values of 0.0626, 0.0253, and 0.0136, respectively. The combined impact between the precipitation and slope was the greatest with a q value of 0.1803. In Guizhou Province, under policy guidance, human activities that promoted vegetation change accounted for 79.60%. This study attempts to enhance sustainable development and provides valuable information on the environmental protection of karst mountain regions.

Study on the Spatial and Temporal Evolution of Ecosystem Service Values and Driving Mechanism in the Yan River Basin from 1990 to 2020

Ecological and environmental degradation are among the major challenges facing humanity today. The analysis of ecosystem service value assessments can therefore serve as a means to guide ecosystem restoration, as well as provide indications for sustainable land use and land management decisions. The present study examines changes in land use and the associated ecosystem service values in the Yan River Basin in China for the period of 1990–2020. Based on high-resolution Landsat satellite data, we obtained detailed land type distribution data for the basin, allowing the analysis of the internal structure and the degree of influence of the land use by using information entropy and elasticity coefficient. We also explored the spatiotemporal differentiation of ESVs by applying the method of equivalent factors and hotspot analysis. Finally, we identified possible drivers for development patterns observed in the watershed using geodetector models. During the study period, the area of arable land dropped continuously, while the scope of forest land, grassland, and construction land increased. The land type layout developed in the direction of reduced uniformity. ESVs measured in monetary terms first rose and later fell, but nevertheless increased by 1.152 billion yuan overall. The decrease was mainly due to the accelerated urbanization construction in the later stage. Spatially, ESV distribution coincided with the land-use pattern, showing a growing pattern from north to south. The changes were due not to the role of a single factor but the joint interactions between multiple factors such as human activities, natural factors, and landscape patterns. The results can provide a basis for constructive suggestions to connect and promote the basin’s natural and socio-economic surroundings, and also reflect the effectiveness of the policy of systematically stopping cultivation and planting trees and grass on stunted cultivated land.

Evaluation and Driving Determinants of the Coordination between Ecosystem Service Supply and Demand: A Case Study in Shanxi Province

Understanding the coordination relationship between ecosystem service (ES) supply and demand and elucidating the impact of driving factors is critical for regional land use planning and ecological sustainability. We use a large watershed area as a case to map and analyze ES supply, demand and the coordination relationship, and identify the associated socio-ecological driving variables. This study assessed the supply and demand of five ESs (crop production, water retention, soil conservation, carbon sequestration, and outdoor recreation) in 2000 and 2020, and evaluated the coordination between them employing the coupling coordination degree model (CCDM). Additionally, we utilized the geo-detector model (GDM) to identify driving determinants and their interactive effects on the spatial pattern of the coupling coordination degree (CCD) between ES supply and demand. The results showed that mountainous regions with abundant forest coverage were high-value areas for ES supply, while the ESs were predominantly required in city center areas within each basin area. From 2000 to 2020, there was a slight decline in ES supply and a significant increase in ES demand. Counties were grouped into four coordination zones in the study area: extreme incoordination, moderate incoordination, reluctant coordination, and moderate coordination. The number of counties with extreme incoordination linked to regions with a mountain ecosystem is increasing, where the ES supply is much greater than the demand. The moderate incoordination counties dominated by a cropland ecosystem exhibited slightly higher levels of ES supply than demand. The moderate and reluctant coordination were linked to counties with distinct ecological characteristics. Construction land played a major role in the characteristics of the CCD, followed by grassland. The interaction between construction land and all other factors significantly increased the influence on the CCD. These findings offered valuable insights for land managers to identify areas characterized by incoordination between ES supply and demand and understand associated factors to develop optimal ES management strategies.

The continuing decrease of sandy desert and sandy land in northern China in the latest 10 years

Timely monitoring of the sandy desert and sandy land (SDSL) dynamics and their driving factors is vital for ecological restoration policy-making to combat the expansion of the SDSL. Northern China, being the principal implementation area of ecological restoration projects, is ecologically fragile and encompasses almost all of the SDSL regions in China. To explore and compare the dominant driving factors for SDSL changes by extracting both aeolian activity information and vegetation indices, the SDSL datasets in northern China from 2010 to 2020 were first constructed in this study by the human–computer interactive interpretation method based on the series Landsat image data. The spatiotemporal dynamic characteristics of SDSL were subsequently analyzed, and the dominant driving factors of SDSL changes were further explored via the geodetector model by the fractional vegetation cover trend and the difference of aeolian activity index. The results showed a net decrease of 3,780.25 km2 in the total SDSL area between 2010 and 2020, and the decrease rate was 2.44 times higher in the shorter period of 2015–2020 compared to 2010–2015. The spatial differentiation between the western and eastern parts of the Helan Mountains was prominently exhibited by the relatively higher changed SDSL area in the eastern part (62.74% for aeolian activity weakened and 83.32% for aeolian activity intensified) and the increasingly changed SDSL area in the western part (from 33.09% to 39.60% in aeolian activity weakened and from 13.02% to 19.60% in aeolian activity intensified). Especially, the increased aeolian activity intensified SDSL area has happened on the southern edge of the Gurbantunggut Desert and along both sides of the Tarim River from 2015 to 2020 deserve attention. The co-driving of the multiple driving factors dominated the change of SDSL from 2010 to 2020, with the county mean value of the difference in aeolian activity index (MDAAI) and the county mean value of the trend in fractional vegetation cover (MFVCT) as dependent variables, respectively. Climatic factors of the mean annual average temperature, the mean annual cumulative precipitation, and the mean annual aridity index have a relatively larger explanatory power for the change of SDSL in both MDAAI and MFVCT. Anthropogenic factors of the mean annual afforestation area and the mean annual number of large livestock and sheep and goats (year-end) have a greater explanatory power in MDAAI, while the mean annual number of large livestock and sheep and goats (year-end) and the mean annual population density in MFVCT. In particular, the dependent variable MDAAI is better at identifing the dominant drivers of aeolian activity intensified SDSL area compared to MFVCT, with anthropogenic factors playing a stronger dominant role.

The Spatial Association of Rural Human Settlement System Resilience with Land Use in Hunan Province, China, 2000–2020

In China, the rural human settlement system (RHSS) reflects the relationship between rural people and land but is affected by land use and land cover change (LUCC). Maintaining a harmonious development between RHSS resilience (RHSSR) and LUCC is an important rural development issue. However, the spatial association between LUCC and RHSSR remains unclear, constraining effective land use and rural policymaking. The association between RHSSR and LUCC was assessed from a spatial perspective. Using county-level spatial panel data for Hunan Province, China (2000–2020), an evaluation indicator system was established to measure RHSS and analyze the spatiotemporal evolution of RHSSR using a geospatial analysis and geodetector model. The average RHSSR level increased from 0.158 to 1.406. The RHSSR generally presented a three-level stepped spatial distribution feature of high in the east and low in the west, with a belt-like distribution. Additionally, the RHSSR was consistent with land use intensity (LUI), but there was significant spatial heterogeneity in the spatial relationship between LUI and RHSSR. The evolution of RHSSR occurred through industrial development, medical service improvement, increased income, and environmental protection. This has important implications for future rural development strategies, the sustainable development of rural land, and the integration of RHSS into regional planning.

Spatial-temporal evolution patterns and drivers of PM2.5 chemical fraction concentrations in China over the past 20 years.

The quantitative assessment of the spatial and temporal variability and drivers of fine particulate matter (PM2.5) fraction concentrations are important for pollution control and public health preservation in China. In this study, we investigated the spatial temporal variation of PM2.5 chemical component based on the PM2.5 chemical component datasets from 2000 to 2019 and revealed the driving forces of the differences in the spatial distribution using geodetector model (GD), multi-scale geographically weighted regression model (MGWR), and a two-step clustering approach. The results show that: the PM2.5 chemical fraction concentrations show a trend of first increasing (2000-2007) and then decreasing (2007-2019). From 2000 to 2019, the change rates of PM2.5, organic matter (OM), black carbon (BC), sulfates (SO2- 4), ammonium (NH+ 4), and nitrates (NO- 3) were -0.59, -0.23, -0.07, -0.15, -0.02, and 0.04μg/m3/yr in the entirety of China. The secondary aerosol (i.e., SO2- 4, NO- 3, and NH+ 4; SNA) had the highest fraction in PM2.5 concentrations (55.6-68.1% in different provinces), followed by OM and BC. Spatially, North, Central, and East China are the regions with the highest PM2.5 chemical component concentrations in China; meanwhile, they are also the regions with the most significant decrease in PM2.5 chemical fraction concentrations. The GD and MGWR model shows that among all variables, the number of enterprises, disposable income, private car ownership, and the share of secondary industry non-linearly enhance the differences in the spatial distribution of PM2.5 component concentrations. Electricity consumption has the strongest influence on NH+ 4 emissions in Northwest China and BC and OM emissions in Northeast China.