Search Results (610)

Scientifically evaluating net carbon dioxide (CO₂) emissions is the pivotal strategy for mitigating global climate change and fostering sustainable urban development. Shaanxi Province is situated in central China, and boasts robust energy resources in the north and a significant carbon-sink zone in the southern Qinling Mountains. Therefore, uncovering the spatial distributions of net CO₂ emissions and identifying its influencing factors across cities in Shaanxi Province would furnish a crucial theoretical foundation for advancing low-carbon development strategies. In this research, the net CO₂ emissions of cities in Shaanxi Province from 2005 to 2020 are calculated using the carbon-emission-factor calculation model, then the Geodetector is utilized to evaluate the single-factor explanatory power and two-factor interactions among the fourteen various influencing variables, and then the spatial econometric model is employed to analyze the spatial spillover effects of these key factors. The results show the following: (1) The net CO₂ emissions present significant regional differences among the ten cities of Shaanxi Province, notably Xi’an City, Yulin City, and Weinan City, which have recorded remarkable contributions with the respective totals reaching 72.2593 million tons, 76.3031 million tons, and 58.1646 million tons. (2) Regarding temporal trend changes, the aggregate net CO₂ emissions across whole province underwent a marked expansion from 2005 to 2019. Yulin City and Shangluo City exhibit remarkable surges, with respective average annual growth rates soaring at 7.38% and 7.39%. (3) From the perspective of influencing factors, GDP exhibits the most pronounced correlation spanning the entire province. Meanwhile, foreign investment emerges as a significant contributor specifically in Xi’an and Yulin City. Moreover, interaction detection reveals most factor combinations exhibit bi-enhancement, while a few exhibits intricate and non-linear enhancement. (4) The SDM regression and fixed-effect analysis reveal that city GDP had a positive spillover effect on neighboring cities’ net CO₂ emission, while investment in scientific research and technology services, along with per capita construction land, exhibit notable negative spillovers, suggesting potential emission reduction benefits across cities. Full article

The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region’s ecological restoration and protection. This study employs the Geographical Detector (Geodetector) model to quantitatively assess the impact of natural and human factors, such as temperature, precipitation, soil type, and land use, on vegetation growth. It aims to reveal the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau over the past 26 years. The results indicate that from 1995 to 2020, the vegetation coverage on the Loess Plateau shows an increasing trend, with a fitted slope of 0.01021 and an R² of 0.96466. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use. The highest average vegetation coverage is achieved when the temperature is between −4.8 and 2 °C or 12 and 16 °C, precipitation is between 630.64 and 935.51 mm, the soil type is leaching soil, and the land use type is forest. And the interaction between all factors has a greater effect on the vegetation cover than any single factor alone. This study reveals the factors influencing vegetation growth on the Loess Plateau, as well as their types and ranges, providing a scientific basis and guidance for improving vegetation coverage in this region. Full article

Strengthening the exploration of synergistic promotion mechanisms between ecosystem services (ESs) and new urbanization is of great significance for watershed development. In this work, we revealed the evolution mechanism of coupling coordination development degree (CCD) between ESs and new urbanization and its driving factors in the Huaihe River Basin (HRB) from 1980 to 2020 using a combination of the CCD model, Exploratory Spatial Data Analysis (ESDA) method, and GeoDetector model. Additionally, we employed the PLUS model to investigate multi-scenario simulations. The results demonstrate that ESs showed a decline initially, followed by an increase, while the urbanization index showed consistent annual growth over the four decades. Furthermore, the CCD between the ESs and urbanization showed a yearly optimization trend. The CCD demonstrated notable spatial clustering characteristics, with factors such as precipitation, distance from water body, elevation, and per area GDP emerged as the primary drivers. Under scenarios of ecological protection, comprehensive development, and natural protection, the value of ESs from 2020 to 2050 maintained an upward trend; however, it fell with the decrease under the scenario of cropland protection. These research findings offer valuable decision-making support for the differentiated regulation of ecosystem functions and promotion of high-quality urbanization development in the HRB. Full article

As a natural disaster, drought can endanger global ecology, socio-economic systems, and sustainable development. To address sudden droughts in the future, assess drought disasters, and propose mitigation measures, in-depth research on the spatiotemporal variations in and driving factors of meteorological drought is essential. To study drought in the Yellow River Basin, we calculated the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI), derived from monthly meteorological data recorded at weather stations from 1968 to 2019. We examined the features of drought and its driving factors using the trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Sen’s slope estimator, as well as a drought frequency analysis, center of gravity migration model, standard deviation ellipse model, and geographic detector. Our analysis shows that (1) from 1968 to 2019, the Yellow River Basin exhibited a shift from aridity to increased moisture on an annual basis, with the smallest SPEI of −1.47 in 2002 indicating a moderate drought; SPEI3 showed a growing tendency in all seasons, particularly in winter (0.00388/year), followed by spring (0.00214/year), summer (0.00232/year), and fall (0.00196/year). The SPEI3 exhibited higher fluctuations in frequency compared to the annual-scale SPEI12; (2) in terms of spatial variability, there was no significant change in drought conditions at any scale, with the probability of a drought event being greater in the eastern and northwestern portions of the watershed. The epicenter of the drought exhibited a tendency to migrate southwestward; (3) among the seven driving factors, land use and night lighting were the dominant factors affecting drought conditions, with driving force values of 0.75 and 0.63, respectively. Full article

Accurately identifying the impact of different factors on water conservation is influenced by the spatial grid scale. However, existing studies on water conservation often overlook the Modifiable Areal Unit Problem (MAUP). MAUP is one of the key factors contributing to the uncertainty in spatial analysis results. The Qinling Mountains are a critical water conservation area, with the Yiluo River Basin (YLRB) as a key sub-basin. This study uses the Optimized Parameter GeoDetector (OPGD) model to analyze water conservation changes and influencing factors in the YLRB from 1990 to 2020. By optimizing spatial scale (2 km grid) and driving factor discretization, the OPGD model addresses spatial heterogeneity and the MAUP, enhancing analysis accuracy. Results show a fluctuating upward trend in water conservation depth, averaging 0.94 mm yearly, with a spatial decline from southwest to northeast. High–high and low–low clusters dominate the region, with some areas consistently showing high or low values. Key conservation zones expanded by 2748 km², reflecting significant enhancement. Natural factors, particularly precipitation, predominantly influence water conservation, outweighing human activities. The interaction between precipitation and temperature notably affects dynamic changes, while human impacts, such as land use, play a secondary role. The findings suggest water management should prioritize climatic factors and integrate land-use policies to enhance conservation. The OPGD model’s application improves factor identification and supports targeted ecological and water management strategies. Full article

Coal fires are disasters that occur when underground coal seams are subjected to combustion conditions induced by natural or human factors. This study attempts to investigate the impact of coal fires on the surrounding environment by assessing the eco-environmental quality and its dynamic changes in the Surablak coal fire area. To achieve this, an improved remote sensing ecological index (termed RSEIds) is introduced to assess and track the quality and dynamics of eco-environmental conditions in the Surablak coal fire area from 1990 to 2022. Subsequently, this index is combined with a geographic detector (GeoDetector) model to identify potential factors influencing eco-environmental quality. The findings indicate that (1) compared with the established Remote Sensing Ecological Index (RSEI), the RSEIds provides a high degree of precision in reflecting the eco-environmental conditions within the regions affected by coal fires, (2) the eco-environmental quality within the Surablak coal fire area underwent a continuous deterioration from 1990 to 2022, with the area of ecological degradation constituting 53.41% of the study region, (3) regions with excellent and good RSEIds values are mainly found in the forested mountainous regions located in the northern section of the coal fire area, whereas regions with poor and fair RSEIds values largely coincide with the coal fire locations, and (4) since 2006, the distance to the coal fire has become the key factor influencing eco-environmental quality in the Surablak area, while temperature and precipitation remained important factors. The outcomes of this study will provide essential references for guiding ecological restoration and promoting sustainable development in coal fire areas. Full article

Developing industries and promoting employment are essential for rural revitalization. This study establishes a theoretical framework to support the coordinated development of rural industry and employment (RIE) with Chongqing, China as the study area. Methods include GIS spatial analysis, the entropy-weighted TOPSIS method, a coupled coordination degree model, and an optimal-parameter-based GeoDetector. The analysis examines the spatio-temporal evolution and driving mechanisms of the coordinated development of RIE. The main findings are as follows. (1) During the study period, Chongqing’s RIE improved significantly overall, although rural industry is relatively lagging. (2) The evolution characteristics of the coordinated development of RIE exhibit “spatio-temporal ripple” and “spindle-shaped” patterns, and the spatial agglomeration has been enhanced. The growth of RIE is accompanied by the spatial diffusion of rural industry and the spatial echo of rural employment. (3) The primary driving mechanism for the coordinated development of RIE is “human-centered, natural resource-based socio-economic development.” Finally, this study discusses employment-centered strategies for rural industrial development, providing a theoretical foundation for policy-making in rural industrial development. Full article

Given the constraints of limited cultivated land resources, ensuring and enhancing crop productivity are crucial for food security. This study takes Chengdu as a case study. Using the cultivated land productivity (CLP) evaluation model, we calculated the cultivated land productivity index (CLPI) and analyzed its spatial distribution characteristics. The Geographical Detector model was employed to identify the main factors influencing CLP, and corresponding countermeasures and measures were proposed based on the limiting degrees of these factors. The findings reveal that Chengdu’s CLP index ranges from 1231 to 3053. Global spatial autocorrelation analysis indicates a spatial agglomeration pattern in Chengdu’s overall crop productivity distribution. The local spatial autocorrelation analysis demonstrates that township (street)-level crop productivity in Chengdu is primarily characterized by “high–high”, “low–low”, and “low–high” clusters. Key factors influencing the spatial differentiation of CLP in Chengdu include the agronomic management level, soil bulk density, irrigation guarantee rate, soil body configuration, field slope, and farmland flood control standard. Interaction detection shows that there are both double-factor and nonlinear enhancements among the factors. Specifically, the interaction between soil bulk density and the agronomic management level among other factors have the most explanatory power for the spatial differentiation of CLP. The CLP in Chengdu is highly restricted by its technical level, with the agronomic management level severely limiting CLP by more than 50%. These research results provide a theoretical reference for regional high-standard farmland construction and the protection and utilization of cultivated land resources. Full article

Effective production, living, and ecological space allocation is essential for improving and optimizing urban space development. In this study, we proposed a production–living–ecological space (PLES) identification method based on Point of Interest (POI) data and China Land Cover Dataset (CLCD) to identify PLESs in Xuzhou City for the years 2012, 2018, and 2022, with an average recognition accuracy of 89.81%. Moreover, the land-use transfer matrix, center of gravity migration, and Geo-detector were used to reveal the spatiotemporal pattern evolution of PLESs. The results showed that: (1) The distribution of PLESs presented significant differentiation between Urban Built-Up Area (UBUA) and Non-Urban Built-Up Area (NUBUA). UBUA was mainly composed of living spaces, while NUBUA was primarily characterized by production–ecological spaces. (2) The intensive utilization of urban land led to an increase in the area of multifunctional spaces, while the complexity of urban space increased. (3) During 2012 to 2022, the center of gravity of PLESs remained relatively stable. The moving distances were all less than 1 km (except for ecological space from 2012 to 2018). (4) The evolution of PLESs was closely linked with socio-economic factors, and the interactions between the factors also had a significant driving effect on PLESs. Full article

Grain security is the foundation of national security, and non-grain production of cultivated land (NGPCL) poses a challenge to grain security. Existing research on the NGPCL has mainly focused on large-scale studies, with relatively few analyses at smaller scales, such as county-level units. Therefore, we selected Jilin Province, one of China’s most important grain-producing areas, as the study region. We extracted data on NGPCL from 2000, 2005, 2010, 2015, and 2020, and calculated the non-grain production rate of cultivated land for each of the province’s counties. Based on this, we used the gravity center and standard deviation ellipse models, and spatial autocorrelation analysis tools to reveal the spatiotemporal evolution characteristics of the non-grain production rate of the planting structure (NGPRPS) in Jilin Province. Finally, we applied the geographic detector to analyze the impact of 10 factors on the changes in the NGPRPS. The results show that: (1) From 2000 to 2020, the NGPRPS in Jilin Province generally showed a downward trend, which can be divided into three phases: fluctuation, decline, and an initial increase followed by a decrease. (2) There is a clear spatial differentiation in the non-grain production of planting structure (NGPPS) in Jilin Province, with the spatial pattern being generally low in the center and higher at the periphery. In the early stage, the non-grain production rate (NGPR) increased rapidly, while in the later stage, the spatial distribution of NGPPS became more pronounced in the southeastern direction. (3) In the short term, policy factors played a significant role in the changes in the NGPRPS. In the long term, however, natural environment, production resources, economic level, and social development showed interactive effects on the changes in the NGPR in the region. Based on these findings, the government can adopt corresponding measures and management policies considering the impact of these factors, the research results, and the proposed strategies. These include the rational implementation of land use planning, delineating the baseline for cultivated land protection, and controlling the use of cultivated land. Full article

Accurately assessing the impact of land use patterns on total phosphorus (TP) concentration in surface water is crucial for protecting the water environment of the Yangtze River Basin (YRB). However, due to the heterogeneity of land use patterns, the regional differences in the intensity and direction of their impacts on TP concentrations in the YRB remain insufficiently understood. This study utilizes water quality monitoring data from state-controlled sections in 2021 and employs spatial autocorrelation analysis, geographic detectors, and Pearson correlation models to identify the impacts of land use on TP concentrations at multiple scales across the YRB. The results indicate that TP concentrations at 98.8% of the monitoring stations in the YRB exceed the Class III standard, with high concentrations of TP concentrated in the Pudu River Basin, Chengdu Plain, Jianghan Plain, and Yangtze River Delta regions. At the YRB scale, the spatial pattern of built-up land, cropland, and industrial and mining land significantly increases TP concentrations, while the pattern of forest and grassland areas exert mitigating effects. At the sub-basin scale, the impact of land use patterns on TP concentrations varies regionally. Specifically, TP concentrations in the Pudu River Basin are primarily attributed to the spatial pattern of industrial and mining land, in the Chengdu Plain to the spatial pattern of cropland and industrial and mining land, and in the Jianghan Plain to the spatial pattern of cropland, built-up land, and industrial and mining land. These results provided decision support for TP concentration control strategies and effective mitigation measures. Full article

China’s Nationally Important Agricultural Heritage Systems (China-NIAHS) are agricultural systems with deep historical and cultural roots that exhibit temporal continuity and spatial heterogeneity in their formation and distribution. As modern and industrialized agriculture have developed, traditional agricultural systems are facing unprecedented challenges and pressures. This study investigates the spatiotemporal distribution and influencing factors of 196 China-NIAHS sites, categorized into five categories. Using spatial analysis techniques and Geographical Detectors, this study identifies key natural, socioeconomic, and cultural drivers shaping their distribution. The results reveal a predominantly clustered spatial distribution of China-NIAHS, centered around the Yangtze River Basin, with significant influences from population density, tourism development, and industrialization. Historical analysis highlights a west-to-east and northward migration of agricultural activity, driven by political stability and technological advancements. Further findings indicate that the spatial distribution of China-NIAHS is primarily determined by population density, tourism development, and river network density. Population density plays a pivotal role in heritage preservation, tourism development generates economic benefits and facilitates cultural dissemination, and river network density supports the formation and sustainability of heritage sites. Conversely, urbanization and economic development have limited influence, emphasizing the need to prioritize socioeconomic and natural factors in conservation strategies. This study provides a comprehensive understanding of the spatial and temporal dynamics of China-NIAHS, offering valuable insights for sustainable heritage conservation and the strategic integration of natural and socioeconomic factors into modern agricultural policies. These findings deepen the understanding of China-NIAHS, highlighting their role in ecological and cultural sustainability while supporting value assessment, region-specific protection, and sustainable utilization strategies. Full article

Research on monitoring forest disturbances and analyzing its driving factors is crucial for the sustainable management of forest ecosystems. To quantitatively identify the spatial distribution and dynamic changes of forest disturbance and its driving factors in Guangdong Province from 1990 to 2019, the long-term Landsat time series imagery and the LandTrendr change detection algorithm were utilized. The impact of forest disturbances on four types of landscape fragmentation (attrition, perforation, shrinkage, and subdivision) was analyzed using the Forman index. The Geodetector model was used to analyze the driving factors of forest disturbance from human activity and the natural environment. The results showed that the LandTrendr algorithm achieved a Kappa coefficient of 0.79, with an overall accuracy of approximately 82.59%. The findings indicate a consistent increase in shrinkage patches, both in quantity and area. Spatially, the centroids of forest fragmentation processes exhibited a clear inland migration trend, reflecting the growing ecological pressures faced by inland forest ecosystems. Furthermore, interactions among driving factors, particularly between population density and economic factors, significantly amplified their combined impacts. The correlation between forest disturbances and socio-economic factors revealed distinct regional variations, highlighting significant differences in forest disturbance dynamics across cities with varying levels of economic development. This study provides critical insights into the spatiotemporal dynamics of forest disturbances under rapid urbanization and economic development. It lays the groundwork for sustainable forest management strategies in Guangdong Province and may contribute to global discussions on managing forest ecosystems during periods of rapid socio-economic transformation. Full article

As one of the 19 key regions for comprehensive land development in China, the Tianshan North Slope urban agglomeration is significant for China’s urban development when calculating the land subsidence and analyzing the impact factors. This study focused on eight cities in the Tianshan North Slope urban agglomeration, calculating the land subsidence rate from 18 January 2018 to 12 April 2023 using Sentinel-1A data and analyzing the spatiotemporal patterns and impact factors of land subsidence. The results showed that (1) the average land subsidence rate is mainly distributed between −30 and 10 mm/a, and the maximum subsidence rate can reach −358 mm/a. Land uplift mainly occurs in Hutubi County and Manas County. (2) From the transition matrix, landscape pattern index, and Moran’s I, the spatiotemporal patterns of the land subsidence rate are obvious, with a spatial positive correlation. During the monitoring period, each administration experienced varying degrees of land subsidence or uplift processes. (3) Using GeoDetector to perform quantitative analyses, it was found that the hydrological environment is significant to land subsidence, and human activities, such as road network density and nighttime lighting, contribute the least to land subsidence, suggesting that it is related to the arid climate of the study area. This paper aims to provide theoretical support for the stable development of and production activities in the study area. This approach not only offers technical support but also provides guidance for evaluating, monitoring, and the early warning of land subsidence in the region. Full article

Understanding the tourism resource network attention is crucial for promoting sustainable tourism development. This study utilized multi-source data to assess tourism resource network attention in Western Hunan, with GIS spatial analysis and the Geodetector method applied to identify spatial patterns and influencing factors. The results indicate a distinct “dual-core” spatial clustering in network attention, with natural landscape resources centralized in Zhangjiajie and cultural landscape resources in Xiangxi Prefecture. Recreational tourism resources exhibit a similar clustering pattern around these primary and secondary centers. The factors and intensities influencing network attention differ by tourism resource type. For overall tourism resources, natural landscapes, and cultural landscapes, tourist attractions rating (X₁₁) and attraction clustering degree (X₁₂) are the primary drivers, with the strongest impact on natural landscapes (q = 0.648, 0.373), followed by overall resources (q = 0.361, 0.216) and cultural landscapes (q = 0.311, 0.206). In contrast, recreational resources are most influenced by nearby attractions and tourism service capacity (q(X₁₂) = 0.743, q(X₁₅) = 0.620), alongside notable effects from regional factors related to economic development, industrial structure, and tourism development (X₁–X₉). The interaction between inherent tourism resource characteristics (X₁₀–X₁₅) and regional environmental factors (X₁–X₉) enhances the driving effect on tourism resource network attention. These findings inform differentiated, resource-specific tourism planning strategies for sustainable development in Western Hunan, promoting balanced regional growth and optimized resource management through a data-driven approach. Full article