Haojuan Li , Kun Zhang , Yongqiang Liu , Yan Qin , Weiping Wang , Mingyu Wang , Yongnan Liu , Yaqian Li
{"title":"中国土地利用与碳储量时空演变:基于PLUS-InVEST模型和SHAP的多情景模拟与驱动因素分析","authors":"Haojuan Li , Kun Zhang , Yongqiang Liu , Yan Qin , Weiping Wang , Mingyu Wang , Yongnan Liu , Yaqian Li","doi":"10.1016/j.envres.2025.121860","DOIUrl":null,"url":null,"abstract":"<div><div>The spatiotemporal distribution of land use/cover changes (LUCCs) and carbon storage (CS), as well as their driving factors under global climate change, have become key issues in ecological and environmental sciences. As a major contributor to global CS, understanding China's CS changes and the driving forces is crucial for addressing climate change and achieving carbon neutrality. In the study, China is split into seven major ecological zones, and a combined model is suggested that uses the CMIP6 climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) along with the PLUS and InVEST models. The study systematically analyzes the spatiotemporal evolution of land use and CS from 1990 through 2020 and predicts the changes under three future scenarios for 2030 and 2050. Using Random Forest and SHAP methods, the study quantifies the impact weights of natural and anthropogenic factors on CS. The main findings are as follows: (1) From 1990 to 2020, China's CS showed a steadily increasing trend, but with significant regional differences. The Qinghai-Tibet Plateau is the largest CS area, accounting for 26.96 % of the national total CS in 2020, while the highly urbanized and densely populated South China region has the lowest CS share, only 4.39 %. (2) Under the SSP1-2.6 scenario, CS will be highest in 2030 and 2050, reaching 1.003 × 10<sup>11</sup> t and 1.026 × 10<sup>11</sup> t, respectively, with growth rates of 3.33 % and 5.79 % compared to 2020. Under the SSP5-8.5 scenario, CS shows a downward trend, with 9.31 × 10<sup>10</sup> t and 9.32 × 10<sup>10</sup> t in 2030 and 2050, respectively, corresponding to a decrease of 4.01 % and 3.91 % compared to 2020. The SSP2-4.5 scenario predicts relatively stable CS. (3) Natural and anthropogenic factors are the primary drivers of the spatiotemporal changes in CS. The importance of these factors varies across different regions. The study provides scientific insights for ecological protection and carbon neutrality policy formulation.</div></div>","PeriodicalId":312,"journal":{"name":"Environmental Research","volume":"279 ","pages":"Article 121860"},"PeriodicalIF":7.7000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal evolution of land use and carbon storage in China: Multi-Scenario simulation and driving factor analysis based on the PLUS-InVEST model and SHAP\",\"authors\":\"Haojuan Li , Kun Zhang , Yongqiang Liu , Yan Qin , Weiping Wang , Mingyu Wang , Yongnan Liu , Yaqian Li\",\"doi\":\"10.1016/j.envres.2025.121860\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The spatiotemporal distribution of land use/cover changes (LUCCs) and carbon storage (CS), as well as their driving factors under global climate change, have become key issues in ecological and environmental sciences. As a major contributor to global CS, understanding China's CS changes and the driving forces is crucial for addressing climate change and achieving carbon neutrality. In the study, China is split into seven major ecological zones, and a combined model is suggested that uses the CMIP6 climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) along with the PLUS and InVEST models. The study systematically analyzes the spatiotemporal evolution of land use and CS from 1990 through 2020 and predicts the changes under three future scenarios for 2030 and 2050. Using Random Forest and SHAP methods, the study quantifies the impact weights of natural and anthropogenic factors on CS. The main findings are as follows: (1) From 1990 to 2020, China's CS showed a steadily increasing trend, but with significant regional differences. The Qinghai-Tibet Plateau is the largest CS area, accounting for 26.96 % of the national total CS in 2020, while the highly urbanized and densely populated South China region has the lowest CS share, only 4.39 %. (2) Under the SSP1-2.6 scenario, CS will be highest in 2030 and 2050, reaching 1.003 × 10<sup>11</sup> t and 1.026 × 10<sup>11</sup> t, respectively, with growth rates of 3.33 % and 5.79 % compared to 2020. Under the SSP5-8.5 scenario, CS shows a downward trend, with 9.31 × 10<sup>10</sup> t and 9.32 × 10<sup>10</sup> t in 2030 and 2050, respectively, corresponding to a decrease of 4.01 % and 3.91 % compared to 2020. The SSP2-4.5 scenario predicts relatively stable CS. (3) Natural and anthropogenic factors are the primary drivers of the spatiotemporal changes in CS. The importance of these factors varies across different regions. 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Spatiotemporal evolution of land use and carbon storage in China: Multi-Scenario simulation and driving factor analysis based on the PLUS-InVEST model and SHAP
The spatiotemporal distribution of land use/cover changes (LUCCs) and carbon storage (CS), as well as their driving factors under global climate change, have become key issues in ecological and environmental sciences. As a major contributor to global CS, understanding China's CS changes and the driving forces is crucial for addressing climate change and achieving carbon neutrality. In the study, China is split into seven major ecological zones, and a combined model is suggested that uses the CMIP6 climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) along with the PLUS and InVEST models. The study systematically analyzes the spatiotemporal evolution of land use and CS from 1990 through 2020 and predicts the changes under three future scenarios for 2030 and 2050. Using Random Forest and SHAP methods, the study quantifies the impact weights of natural and anthropogenic factors on CS. The main findings are as follows: (1) From 1990 to 2020, China's CS showed a steadily increasing trend, but with significant regional differences. The Qinghai-Tibet Plateau is the largest CS area, accounting for 26.96 % of the national total CS in 2020, while the highly urbanized and densely populated South China region has the lowest CS share, only 4.39 %. (2) Under the SSP1-2.6 scenario, CS will be highest in 2030 and 2050, reaching 1.003 × 1011 t and 1.026 × 1011 t, respectively, with growth rates of 3.33 % and 5.79 % compared to 2020. Under the SSP5-8.5 scenario, CS shows a downward trend, with 9.31 × 1010 t and 9.32 × 1010 t in 2030 and 2050, respectively, corresponding to a decrease of 4.01 % and 3.91 % compared to 2020. The SSP2-4.5 scenario predicts relatively stable CS. (3) Natural and anthropogenic factors are the primary drivers of the spatiotemporal changes in CS. The importance of these factors varies across different regions. The study provides scientific insights for ecological protection and carbon neutrality policy formulation.
期刊介绍:
The Environmental Research journal presents a broad range of interdisciplinary research, focused on addressing worldwide environmental concerns and featuring innovative findings. Our publication strives to explore relevant anthropogenic issues across various environmental sectors, showcasing practical applications in real-life settings.