How to Account for Changes in Carbon Storage from Coal Mining and Reclamation in Eastern China? Taking Yanzhou Coalfield as an Example to Simulate and Estimate

Abstract

Carbon sequestration in terrestrial ecosystems plays an essential role in coping with global climate change and achieving regional carbon neutrality. In mining areas with high groundwater levels in eastern China, underground coal mining has caused severe damage to surface ecology. It is of practical significance to evaluate and predict the positive and negative effects of coal mining and land reclamation on carbon pools. This study set up three scenarios for the development of the Yanzhou coalfield (YZC) in 2030, including: (1) no mining activities (NMA); (2) no reclamation after mining (NRM); (3) mining and reclamation (MR). The probability integral model (PIM) was used to predict the subsidence caused by mining in YZC in 2030, and land use and land cover (LULC) of 2010 and 2020 were interpreted by remote sensing images. Based on the classification of land damage, the LULC of different scenarios in the future was simulated by integrating various social and natural factors. Under different scenarios, the InVEST model evaluated carbon storage and its temporal and spatial distribution characteristics. The results indicated that: (1) By 2030, YZC would have 4341.13 ha of land disturbed by coal mining activities. (2) Carbon storage in the NRM scenario would be 37,647.11 Mg lower than that in the NMA scenario, while carbon storage in the MR scenario would be 18,151.03 Mg higher than that in the NRM scenario. Significantly, the Nantun mine would reduce carbon sequestration loss by 72.29% due to reclamation measures. (3) Carbon storage has a significant positive spatial correlation, and coal mining would lead to the fragmentation of the carbon sink. The method of accounting for and predicting carbon storage proposed in this study can provide data support for mining and reclamation planning of coal mine enterprises and carbon-neutral planning of government departments.

Blackland Conservation and Utilization, Carbon Storage and Ecological Risk in Green Space: A Case Study from Heilongjiang Province in China

“…The study obtained carbon density by referring to the average value of carbon density data for each category, which limits the accuracy of the study. In the future, long-term surveys and extensive field measurements are required to obtain more detailed data and to account for the heterogeneity of carbon density using a variety of remote sensing techniques [ 81 ]. The landscape pattern is influenced by scale effects, and the study divided the evaluation grid of CS and LER only by referencing previous studies, even though the correlations met the study’s criteria.…”

Progress of Mine Land Reclamation and Ecological Restoration Research Based on Bibliometric Analysis

“…The publications of the top five countries accounted for 28.26%, 10.82%, 9.63%, 6.07%, and 3.52%, respectively, indicating those countries made the greatest contributions to the research on LRER. For example, in China, there are many studies on LRER in coal mining areas, and the research areas are mainly concentrated in ecologically fragile areas in Northwest China [4,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] and high water level areas in Eastern China [6,[45][46][47][48][49][50][51][52]. The research focuses include reclamation of open-pit coal mines [8,38,39,[53][54][55][56][57][58], reclamation of coal mining subsidence sites [7,51,[59][60][61][62][63][64]…”

Spatio-Temporal Dynamic of the Land Use/Cover Change and Scenario Simulation in the Southeast Coastal Shelterbelt System Construction Project Region of China

“…The PLUS model combines the advantages of the transformation analysis strategy and the pattern analysis strategy to form the Land Use Expansion Analysis Strategy (LEAS) [40]. By extracting part of the land use expansion with two phases of the LUCC images, LEAS digs deep into the land-use conversion rules and obtains the conversion inertia probability of each category [41]. A CA model based on multi-type random patch seeds (CARS) [42] is proposed to automatically simulate and generate land use patches under the spatiotemporal state with the constraints of development land.…”