Author
Listed:
- Jingyang Liu
(Institute of Ecology, School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
Reading Academy, Nanjing University of Information Science & Technology, Nanjing 210044, China)
- Jia Wan
(Institute of Ecology, School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China)
- Shirong Li
(Institute of Ecology, School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China)
- Yuzhe Shen
(School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)
- Wangya Han
(Institute of Ecology, School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China)
- Guohua Liu
(State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)
Abstract
Quantifying the spatiotemporal patterns of the coordination between ecosystem service supply and demand is vital for regional sustainable development. To reveal the dynamic pattern of the coordination of ecosystem service (ES) supply and demand in the Lhasa River Basin, we quantified the supply of the following four ESs using the InVEST model from 2000 to 2018: carbon sequestration (CS), water conservation (WC), habitat quality (HQ), and soil conservation (SC). Using socio-economic data, including land development degree, GDP, and population density, the ES demand was quantified. The ES supply–demand ratio (ESDR) and coupling coordination degree (CCD) model were used to evaluate the coupling relationship and coordination of ES supply and demand. The spatial autocorrelation analysis was used to determine the spatial correlation and changes in the ES supply–demand coupling coordination degree. The results indicate that the distribution of ESDR exhibited significant spatial heterogeneity. The area with ES supply far greater than demand was always in the upstream area of the Lhasa River, while the ES demand of Chengguan District far exceeded supply. Grasslands and forests were the main contributors to ESDRs, providing positive ESDRs for three services, SC, HQ, and WC, with a total proportion above 80%. From 2000 to 2018, the mismatch between ES supply and demand was gradually spreading upstream, while the upstream areas had a relatively high CCD. The spatial correlations of the CCD in the Lhasa River basin all showed statistically significant differences ( p < 0.01). The high–high aggregation areas were concentrated in the northeast of the Lhasa River basin, while the low–low aggregation areas were centered around Chengguan District. This study provides reference values for optimizing the land use spatial patterns in ecologically vulnerable areas with the goal of sustainable development.
Suggested Citation
Jingyang Liu & Jia Wan & Shirong Li & Yuzhe Shen & Wangya Han & Guohua Liu, 2024.
"Spatial–Temporal Pattern of Coordination between the Supply and Demand for Ecosystem Services in the Lhasa River Basin,"
Land, MDPI, vol. 13(4), pages 1-17, April.
Handle:
RePEc:gam:jlands:v:13:y:2024:i:4:p:510-:d:1375059
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