A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê Plateau

Under climate change and human activities, ecosystem service (ES) research lacks systematic approaches and scientific depth. This study develops a comprehensive framework integrating advanced models to predict ESs, analyze interactions, identify key drivers, and assess spatial effects on the Zoigê P...

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Main Authors: Wanting Zeng, Li He, Zhengwei He, Yang Zhao, Yan Yuan, Jintai Pang, Jiahua Zhao
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Land
Subjects:
Online Access:https://www.mdpi.com/2073-445X/14/3/441
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author Wanting Zeng
Li He
Zhengwei He
Yang Zhao
Yan Yuan
Jintai Pang
Jiahua Zhao
author_facet Wanting Zeng
Li He
Zhengwei He
Yang Zhao
Yan Yuan
Jintai Pang
Jiahua Zhao
author_sort Wanting Zeng
collection DOAJ
description Under climate change and human activities, ecosystem service (ES) research lacks systematic approaches and scientific depth. This study develops a comprehensive framework integrating advanced models to predict ESs, analyze interactions, identify key drivers, and assess spatial effects on the Zoigê Plateau. The results indicate the following: (1) From 2000 to 2020 and across three 2040 scenarios, water conservation (WC) improves, while carbon storage (CS) and habitat quality (HQ) decline, leading to overall ES degradation. Core ES areas face rising degradation risks from 9% to 29% under increasing environmental stress (SSP119 to SSP585). (2) ES importance follows HQ > CS > SC > WC, with bivariate interactions outperforming single-factor effects. Future scenarios show weakened interactions, correlating with higher ecological stress, indicating ES stability risks. (3) Land use (>40% explanatory power) is the primary driver, while urban expansion, slope, evapotranspiration, and precipitation contribute (6–12%). (4) ES drivers showed weak spatial patterns from 2000 to 2020 but became more stable under future scenarios, suggesting stronger environmental control. This study provides a methodological paradigm for ES analysis and supports ecological planning in alpine wetland–grassland regions.
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spelling doaj-art-42c16b8cc128422bba43975ce0dcf63f2025-08-20T02:42:32ZengMDPI AGLand2073-445X2025-02-0114344110.3390/land14030441A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê PlateauWanting Zeng0Li He1Zhengwei He2Yang Zhao3Yan Yuan4Jintai Pang5Jiahua Zhao6State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaUnder climate change and human activities, ecosystem service (ES) research lacks systematic approaches and scientific depth. This study develops a comprehensive framework integrating advanced models to predict ESs, analyze interactions, identify key drivers, and assess spatial effects on the Zoigê Plateau. The results indicate the following: (1) From 2000 to 2020 and across three 2040 scenarios, water conservation (WC) improves, while carbon storage (CS) and habitat quality (HQ) decline, leading to overall ES degradation. Core ES areas face rising degradation risks from 9% to 29% under increasing environmental stress (SSP119 to SSP585). (2) ES importance follows HQ > CS > SC > WC, with bivariate interactions outperforming single-factor effects. Future scenarios show weakened interactions, correlating with higher ecological stress, indicating ES stability risks. (3) Land use (>40% explanatory power) is the primary driver, while urban expansion, slope, evapotranspiration, and precipitation contribute (6–12%). (4) ES drivers showed weak spatial patterns from 2000 to 2020 but became more stable under future scenarios, suggesting stronger environmental control. This study provides a methodological paradigm for ES analysis and supports ecological planning in alpine wetland–grassland regions.https://www.mdpi.com/2073-445X/14/3/441ecosystem servicesscenario simulationmachine learningMGWRZoigê Plateau
spellingShingle Wanting Zeng
Li He
Zhengwei He
Yang Zhao
Yan Yuan
Jintai Pang
Jiahua Zhao
A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê Plateau
Land
ecosystem services
scenario simulation
machine learning
MGWR
Zoigê Plateau
title A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê Plateau
title_full A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê Plateau
title_fullStr A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê Plateau
title_full_unstemmed A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê Plateau
title_short A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê Plateau
title_sort prediction interaction driving framework for ecosystem services under climate change and human activities a case study of zoige plateau
topic ecosystem services
scenario simulation
machine learning
MGWR
Zoigê Plateau
url https://www.mdpi.com/2073-445X/14/3/441
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