{"title":"Spatial mismatch and drivers of carbon sequestration services supply-demand in China","authors":"Qi Pang, Jie Xu, Ying Zhou, Meixin He","doi":"10.1016/j.ecolind.2025.113389","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates the spatial mismatch between the supply and demand of carbon sequestration services in China, a critical issue for achieving the country’s “dual carbon” goals of peak carbon emissions by 2030 and carbon neutrality by 2060. Using the Carnegie-Ames Stanford Approach (CASA) model and the carbon emission coefficient method, the supply and demand of carbon sequestration services from 2000 to 2020 were quantitatively assessed. The supply–demand ratio index (SDI) and Local Indicators of Spatial Association (LISA) were employed to analyze the spatial distribution and intensity of the supply–demand relationship. The results reveal that while the supply of carbon sequestration services exhibited an overall increasing trend, demand steadily grew, leading to a widening mismatch, particularly in the economically developed eastern regions and urban agglomerations. The supply was primarily influenced by the Normalized Difference Vegetation Index (NDVI) and land use/land cover change (LUCC), while demand was closely linked to population density (POP) and Gross Domestic Product (GDP). To further analyze the factors driving this mismatch, the Structural Equation Model (SEM) and Random Forest (RF) model were applied to identify the impact of both natural and human activity factors. SEM highlighted complex causal relationships, while RF captured nonlinear interactions between these variables. The findings emphasize the growing tension between insufficient supply and rising demand for carbon sequestration services in key regions. To mitigate this spatial mismatch and support the “dual carbon” goals, several policy recommendations are proposed, including strengthening ecosystem protection, promoting sustainable land management practices, establishing carbon markets, and encouraging broader participation in carbon trading. These strategies aim to address the supply–demand imbalance and contribute to sustainable carbon management in China.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113389"},"PeriodicalIF":7.0000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X2500319X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigates the spatial mismatch between the supply and demand of carbon sequestration services in China, a critical issue for achieving the country’s “dual carbon” goals of peak carbon emissions by 2030 and carbon neutrality by 2060. Using the Carnegie-Ames Stanford Approach (CASA) model and the carbon emission coefficient method, the supply and demand of carbon sequestration services from 2000 to 2020 were quantitatively assessed. The supply–demand ratio index (SDI) and Local Indicators of Spatial Association (LISA) were employed to analyze the spatial distribution and intensity of the supply–demand relationship. The results reveal that while the supply of carbon sequestration services exhibited an overall increasing trend, demand steadily grew, leading to a widening mismatch, particularly in the economically developed eastern regions and urban agglomerations. The supply was primarily influenced by the Normalized Difference Vegetation Index (NDVI) and land use/land cover change (LUCC), while demand was closely linked to population density (POP) and Gross Domestic Product (GDP). To further analyze the factors driving this mismatch, the Structural Equation Model (SEM) and Random Forest (RF) model were applied to identify the impact of both natural and human activity factors. SEM highlighted complex causal relationships, while RF captured nonlinear interactions between these variables. The findings emphasize the growing tension between insufficient supply and rising demand for carbon sequestration services in key regions. To mitigate this spatial mismatch and support the “dual carbon” goals, several policy recommendations are proposed, including strengthening ecosystem protection, promoting sustainable land management practices, establishing carbon markets, and encouraging broader participation in carbon trading. These strategies aim to address the supply–demand imbalance and contribute to sustainable carbon management in China.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.