Analysis of Evolving Carbon Stock Trends and Influencing Factors in Chongqing under Future Scenarios

Kangwen Zhu, Jun He (), Xiaosong Tian, Peng Hou (), Longjiang Wu, Dongjie Guan, Tianyu Wang and Sheng Huang
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Kangwen Zhu: Satellite Applicsticn Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China
Jun He: No. 107 Geological Team of the Chongqing Bureau of Geology and Mineral Exploration, Chongqing 401120, China
Xiaosong Tian: School of Resources and Safety Engineering, Chongqing Vocational Institute of Engineering, Chongqing 402260, China
Peng Hou: Satellite Applicsticn Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China
Longjiang Wu: No. 107 Geological Team of the Chongqing Bureau of Geology and Mineral Exploration, Chongqing 401120, China
Dongjie Guan: School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
Tianyu Wang: School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
Sheng Huang: No. 107 Geological Team of the Chongqing Bureau of Geology and Mineral Exploration, Chongqing 401120, China

Land, 2024, vol. 13, issue 4, 1-16

Abstract: The relationship between land use changes and regional carbon storage is closely linked. Identifying evolving trends concerning and influencing factors on carbon storage under future scenarios is key in order to achieve the “dual carbon” goals. Using Chongqing as a case study, this study integrated the advantages of the PLUS model, InVEST model, and a geographic detector model. It conducted simulations of land use type data under scenarios of natural development (ND) and ecological protection (EP), and identified evolving trends and influencing factors regarding carbon storage. The results were as follows: (1) the PLUS model demonstrated excellent simulation performance, with a Kappa coefficient above 0.85 and an overall accuracy above 0.90. During the study period, significant changes occurred for cultivated land, forested land, water bodies, and construction, which were closely related to carbon storage; (2) carbon storage in Chongqing showed a decreasing trend, with a decrease of 10.07 × 10 6 t C from 2000 to 2020. Under the ND scenario, carbon storage was projected to decrease by 10.54 × 10 6 t C in 2030 compared to 2020, and it was expected to stabilize from 2030 to 2050. At the county level, Youyang, Fengjie, and Wuxi had the highest carbon storage, while Nanchuan, Jiangbei, and Dadukou had the lowest; (3) the spatial distribution of carbon storage presented an “eastern hotspot western cold spot aggregation” pattern. The proportions of regions with a decreased, unchanged, and increased aggregation of carbon storage in Chongqing during 2000–2010 and 2010–2020 were 2.99%, 95.95%, 1.06%; and 4.39%, 92.40%, 3.21%, respectively. The trend indicated a decrease in the aggregation of carbon storage, and future carbon storage was expected to stabilize; (4) elevation, terrain fluctuation, NDVI, annual average temperature, annual average precipitation, and nighttime light index had influence values of 0.88, 0.81, 0.61, 0.86, 0.77, and 0.81 on carbon storage, respectively, with different combinations of influencing factors having a greater impact. In the future, ecological priority and green development concepts should be followed, and comprehensive improvement of regional development conditions should be pursued to enhance carbon storage, thereby promoting the achievement of the “dual carbon” goals. This study provided an analytical path and data support for formulating optimized carbon storage policies at the regional level.

Keywords: InVEST model; PLUS model; geographic detector; carbon storage; influencing factors (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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