Predicting carbon storage changes in coal mining regions: a remote sensing approach based on the PIM-PLUS-INVEST model

基于PIM-PLUS-INVEST模型的遥感方法预测煤矿区碳储量变化

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Abstract

Coal mining activities inevitably disturb the ecosystem and significantly affect the regional carbon pool and carbon storage function. In the context of global efforts to combat climate change and achieve carbon neutrality, accurately assessing the extent of coal mining's disturbance to regional carbon stocks is particularly important. However, existing research generally fails to fully incorporate the severe surface disturbance caused by mining into land-use change prediction. At the same time, the reclamation of coal mining subsidence land is regarded as an important means to restore ecological functions. However, its potential for carbon storage and restoration, and its contribution to regional carbon neutrality goals, have not been fully discussed. Therefore, this study constructed the PIM-PLUS-InVEST model framework to assess how coal mining and land reclamation affect the carbon storage function of 51 coal mines in Shandong Province. Through quantitative analysis, the following conclusions are drawn: (1) Mining of 51 coal mines in Shandong Province will cause 861,073.81 Mg of carbon storage loss, while reclamation can recover 62.12% of carbon storage loss. (2) The main reason for the decrease in carbon storage is that a large amount of cultivated land area is transformed into water body due to coal mining. (3) The variations in carbon storage disruption caused by mining and reclamation differ significantly across coal mining areas in Shandong Province, with most highly disrupted coal mines concentrated in Jining City. This study provides a quantitative framework for assessing carbon storage and reclamation in coal-mining areas. Specifically, by quantifying the 'ecological debt' associated with mining activities, the findings offer a scientific reference for clarifying ecological restoration responsibilities and supporting sustainable land management.

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