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碳中和背景下碳盈余与碳赤字的多情景模拟与预测——以中国长株潭都市圈为例

Multi-scenario simulation and prediction of carbon surplus and deficit under the background of carbon neutrality: a case study of Chang-Zhu-Tan metropolitan area in China.

作者信息

Sun Weiyi, Liu Jiaxi, Liu Xianzhao, Wang Tianhao

机构信息

School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China.

School of Economics, Guangxi Minzu University, Nanning, 530006, China.

出版信息

Carbon Balance Manag. 2025 Jul 18;20(1):23. doi: 10.1186/s13021-025-00314-3.

DOI:10.1186/s13021-025-00314-3
PMID:40681806
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12275343/
Abstract

BACKGROUND

Global climate change, marked by persistent warming trends, has emerged as one of the foremost challenges confronting human society in the 21st century. Systematically promoting carbon peak and neutrality has become a critical priority for governments in China. As the most active urbanization region in the country, metropolitan areas assume a pivotal leadership and exemplary role in executing carbon peak and neutrality initiatives. Consequently, we focus our research on the Chang-Zhu-Tan Metropolitan Area (CMA). The STIRPAT and CA-Markov models are employed to forecast carbon sinks and carbon emissions under various scenarios in 2030 and 2060, respectively, to explore pathways to carbon neutrality under various conditions.

RESULTS

The findings indicate that the carbon surplus and deficit (CSD) values have consistently been negative from 2000 to 2020, signifying a persistent carbon deficit in the region, which has exhibited an upward trend. Notably, the CSD in Yuelu, Ningxiang, and Changsha experienced the most significant increases, particularly in Yuelu, where it reached - 11.22 × 10 t by 2020. Depending on the combinations of scenarios, the CSD values are anticipated to range from - 130.75 × 10 t to - 98.22 × 10 t in 2030, and from - 63.28 × 10 t to - 21.22 × 10 t in 2060. Furthermore, the carbon emissions under different scenarios are projected to reach peaks in 2030, with a maximum of 66.54 × 10 t in 2060.

CONCLUSIONS

The prediction results of carbon neutrality in the CMA indicate that carbon emission is expected to reach peaks before 2030 across various scenarios. However, carbon emissions will significantly exceed the carbon sink capacity by 2060, and there is still a carbon emission gap of at least 2122.44 × 10 t from achieving carbon neutrality, highlighting the necessity of accelerating emission reduction in the industrial and energy sectors. Consequently, the critical challenge to achieve carbon neutrality lies in the substantial reduction of carbon emissions.

摘要

背景

以持续变暖趋势为特征的全球气候变化已成为21世纪人类社会面临的首要挑战之一。系统推进碳达峰和碳中和已成为中国政府的关键优先事项。作为中国最活跃的城市化地区,大都市区在实施碳达峰和碳中和举措方面发挥着关键的引领和示范作用。因此,我们将研究重点聚焦于长株潭都市圈(CMA)。分别运用STIRPAT模型和CA - Markov模型预测2030年和2060年不同情景下的碳汇和碳排放,以探索不同条件下实现碳中和的路径。

结果

研究结果表明,2000年至2020年碳盈余与赤字(CSD)值一直为负,表明该地区持续存在碳赤字,且呈上升趋势。值得注意的是,岳麓、宁乡和长沙的CSD增长最为显著,尤其是岳麓,到2020年达到 - 11.22×10 t。根据情景组合,预计2030年CSD值在 - 130.75×10 t至 - 98.22×10 t之间,2060年在 - 63.28×10 t至 - 21.22×10 t之间。此外,不同情景下的碳排放在2030年预计达到峰值,2060年最高为66.54×10 t。

结论

长株潭都市圈碳中和预测结果表明,各情景下碳排放预计在2030年前达到峰值。然而,到2060年碳排放将大幅超过碳汇能力,实现碳中和仍存在至少2122.44×10 t的碳排放缺口,凸显了加快工业和能源领域减排的必要性。因此,实现碳中和的关键挑战在于大幅减少碳排放。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/3a1d2e86d322/13021_2025_314_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/3a1d2e86d322/13021_2025_314_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/6846984a918a/13021_2025_314_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/e05b8c1cf9c0/13021_2025_314_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/38565b809226/13021_2025_314_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/697087d023e8/13021_2025_314_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/83f31cbcfbe8/13021_2025_314_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/703e118644fb/13021_2025_314_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/7559ee16ba31/13021_2025_314_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/d7467376136d/13021_2025_314_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/766d94561b00/13021_2025_314_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/83dfc40a501c/13021_2025_314_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5618/12275343/3a1d2e86d322/13021_2025_314_Fig12_HTML.jpg

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