Spatiotemporal dynamics of ecological quality and its drivers in Shanxi Province and its planned mining areas
Abstract As a major coal-producing province, understanding the spatiotemporal evolution of ecological quality and its driving factors in Shanxi is essential for promoting environmental protection and sustainable development. This study employs MODIS data to calculate the Remote Sensing Ecological In...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-15550-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract As a major coal-producing province, understanding the spatiotemporal evolution of ecological quality and its driving factors in Shanxi is essential for promoting environmental protection and sustainable development. This study employs MODIS data to calculate the Remote Sensing Ecological Index (RSEI) for Shanxi Province and its designated mining areas from 2000 to 2023, aiming to investigate the spatial and temporal dynamics of ecological quality. The CatBoost model and Geographically Weighted Regression (GWR) are applied to identify and analyze the underlying driving factors. The results show that ecological quality in both Shanxi Province and its planned mining regions exhibited an overall upward trend between 2000 and 2020, with varying levels of improvement observed across different mining zones. Trend analysis indicates a general enhancement in ecological conditions over the past two decades. RSEI displays significant spatial autocorrelation, characterized by high-value clustering in the southern regions and low-value clustering in the northern and western mining zones and areas with intensive human activity. Key influencing factors include elevation, net primary productivity (NPP), precipitation, and population density. The CatBoost model, supplemented with SHAP (SHapley Additive exPlanations) values, quantifies the relative importance and predictive contribution of each factor to RSEI outcomes. The GWR model further reveals spatial heterogeneity in these relationships, uncovering localized effects, spatial gradient patterns, and clustering phenomena. Additionally, the Hurst index analysis indicates that most areas within Shanxi Province and its designated mining zones are likely to maintain an upward trend in ecological quality in the future. As a comprehensive large-scale and long-term assessment, this study provides valuable theoretical and empirical support for regional planning, ecological monitoring, and the management of mining areas, thereby contributing to sustainable development and ecological conservation efforts. |
|---|---|
| ISSN: | 2045-2322 |