Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River Basin
Understanding the spatiotemporal dynamics of resource–environment carrying capacity (RECC) is essential for balancing ecological protection and socioeconomic development in river basins. This study applied various methodologies, including Panel Vector Autoregression (PVAR), Geographically Temporally...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2073-445X/14/6/1289 |
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| author | Xin Xiang Yi Xiao Yongxiang Chen Huan Huang |
| author_facet | Xin Xiang Yi Xiao Yongxiang Chen Huan Huang |
| author_sort | Xin Xiang |
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| description | Understanding the spatiotemporal dynamics of resource–environment carrying capacity (RECC) is essential for balancing ecological protection and socioeconomic development in river basins. This study applied various methodologies, including Panel Vector Autoregression (PVAR), Geographically Temporally Weighted Regression (GTWR), and Random Forest, to analyze in the Yellow River Basin from 2011 to 2021. PVAR quantifies dynamic interactions among RECC subsystems (population, resources, environment, and economy), while Random Forest identifies nonlinear drivers, and GTWR captures spatiotemporal heterogeneity. Results show RECC performance has continually improved, while subsystem and regional differences have been observed. Downstream regions exhibit higher RECC due to advanced infrastructure, whereas upstream areas face ecological constraints. PVAR results reveal bidirectional relationship among population, resource and economy subsystems, with unidirectional environmental pressure from economic activities. In terms of influencing factors analysis, which are classified into three sections, including geography, socioeconomic, and technological innovation. The random forest model identified that the economic development level has higher importance. The GTWR results expanded the spatiotemporal heterogeneity analysis: socioeconomic factors show significant regional variation. These findings provide a transferable paradigm for complex human–environment system analysis, offering policy-responsive zoning strategies that balance SDG implementation with basin-specific ecological constraints. |
| format | Article |
| id | doaj-art-997134d96f0f4e13ae69b21c0115618f |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj-art-997134d96f0f4e13ae69b21c0115618f2025-08-20T03:27:37ZengMDPI AGLand2073-445X2025-06-01146128910.3390/land14061289Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River BasinXin Xiang0Yi Xiao1Yongxiang Chen2Huan Huang3College of Management Science, Chengdu University of Technology, Chengdu 610059, ChinaBusiness School, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geography and Planning, Chengdu University of Technology, Chengdu 610059, ChinaBusiness School, Chengdu University of Technology, Chengdu 610059, ChinaUnderstanding the spatiotemporal dynamics of resource–environment carrying capacity (RECC) is essential for balancing ecological protection and socioeconomic development in river basins. This study applied various methodologies, including Panel Vector Autoregression (PVAR), Geographically Temporally Weighted Regression (GTWR), and Random Forest, to analyze in the Yellow River Basin from 2011 to 2021. PVAR quantifies dynamic interactions among RECC subsystems (population, resources, environment, and economy), while Random Forest identifies nonlinear drivers, and GTWR captures spatiotemporal heterogeneity. Results show RECC performance has continually improved, while subsystem and regional differences have been observed. Downstream regions exhibit higher RECC due to advanced infrastructure, whereas upstream areas face ecological constraints. PVAR results reveal bidirectional relationship among population, resource and economy subsystems, with unidirectional environmental pressure from economic activities. In terms of influencing factors analysis, which are classified into three sections, including geography, socioeconomic, and technological innovation. The random forest model identified that the economic development level has higher importance. The GTWR results expanded the spatiotemporal heterogeneity analysis: socioeconomic factors show significant regional variation. These findings provide a transferable paradigm for complex human–environment system analysis, offering policy-responsive zoning strategies that balance SDG implementation with basin-specific ecological constraints.https://www.mdpi.com/2073-445X/14/6/1289resource–environment carrying capacityPVARrandom forest regressionGTWRYellow River Basin |
| spellingShingle | Xin Xiang Yi Xiao Yongxiang Chen Huan Huang Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River Basin Land resource–environment carrying capacity PVAR random forest regression GTWR Yellow River Basin |
| title | Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River Basin |
| title_full | Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River Basin |
| title_fullStr | Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River Basin |
| title_full_unstemmed | Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River Basin |
| title_short | Spatiotemporal Dynamics and Driving Mechanisms of Resource–Environment Carrying Capacity in the Yellow River Basin |
| title_sort | spatiotemporal dynamics and driving mechanisms of resource environment carrying capacity in the yellow river basin |
| topic | resource–environment carrying capacity PVAR random forest regression GTWR Yellow River Basin |
| url | https://www.mdpi.com/2073-445X/14/6/1289 |
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