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This study applied the IPCC methodology to estimate carbon emissions in Shanxi Province from 2010 to 2022. A Kaya-LMDI model was employed to identify the key drivers influencing carbon emissions in the region. Using 2022 as the baseline year, the LEAP-Shanxi model was developed to forecast carbon emissions from 2023 to 2060. Economic development acted as the primary driver of carbon emission growth, whereas energy intensity emerged as the dominant restraining factor; By 2060, under four specific scenarios—industrial energy efficiency improvement, low-carbon transportation transition, accelerated new energy adoption, and optimized power structure—energy conservation and carbon reduction effects would become particularly pronounced, these measures could reduce sectoral emissions by 42.16, 3.21, 1.38 and 31.73Mt respectively; Both baseline and integrated scenarios indicate that Shanxi Province could reach its carbon peak by 2030, with projected emissions of 764.33 and 742.34Mt respectively; The comprehensive scenario demonstrates a potential reduction of 107.42Mt in carbon emissions compared to the baseline projections for 2060. These findings can offer a robust theoretical foundation for Shanxi Province to develop targeted low-carbon strategies and implement effective emission reduction measures.

, authors=Shan-shan LI, Xiang FEI*, Hong-guang DONG, Yu-qing MA, Pei JIANG, authorsList=Shan-shan LI, Xiang FEI, Hong-guang DONG, Yu-qing MA, Pei JIANG, authorCompany=null, correspAuthors=Xiang FEI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314917609343116188, articleId=1314917608105796505, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于LMDI和LEAP模型的山西省碳排放影响因素及预测, columnId=1234106391661704058, journalTitle=中国环境科学, columnName=碳排放控制, runingTitle=null, highlight=null, articleAbstract=

基于IPCC方法测算了山西省2010~2022年的碳排放量,通过构建Kaya-LMDI模型识别出影响山西省碳排放的主要因素,以2022年为基准年建立了LEAP-Shanxi模型,对山西省2023~2060年的碳排放量进行预测.结果表明:经济发展水平效应是山西省碳排放增长的关键促进因素,而能源强度效应是关键抑制因素;至2060年,在工业节能增效情景、交通低碳转型情景、新能源需求加速情景和电力结构优化情景下节能降碳效果更为显著,分别使对应部门碳排放量降低42.16,3.21,1.38和31.73Mt;在基准情景和综合情景下,山西省均能在2030年达到碳达峰,碳排放量分别为764.33和742.34Mt.而综合情景与基准情景在2060年相比,减排107.42Mt的碳排放量.研究结果可为山西省提供有效的低碳发展理论依据以制定相关降碳减排措施.

, authors=李姗姗, 费翔*, 董洪光, 马雨晴, 姜佩, authorsList=李姗姗, 费翔, 董洪光, 马雨晴, 姜佩, authorCompany=null, correspAuthors=费翔, authorNote=

李姗姗(1992-),女,河北保定人,副教授,博士,主要从事环境治理理论与方法、“双碳”管理与政策.发表论文20余篇..

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* 责任作者,硕士,
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李姗姗(1992-),女,河北保定人,副教授,博士,主要从事环境治理理论与方法、“双碳”管理与政策.发表论文20余篇..

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李姗姗(1992-),女,河北保定人,副教授,博士,主要从事环境治理理论与方法、“双碳”管理与政策.发表论文20余篇..

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基于LMDI和LEAP模型的山西省碳排放影响因素及预测
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李姗姗 , 费翔 * , 董洪光 , 马雨晴 , 姜佩
中国环境科学 | 碳排放控制 2025,45(7): 4052-4063
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中国环境科学 |碳排放控制 2025 , 45 (7) : 4052 -4063
基于LMDI和LEAP模型的山西省碳排放影响因素及预测
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李姗姗(1992-),女,河北保定人,副教授,博士,主要从事环境治理理论与方法、“双碳”管理与政策.发表论文20余篇..

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李姗姗 , 费翔* , 董洪光, 马雨晴, 姜佩
作者信息
  • 安徽理工大学经济与管理学院,安徽 淮南 232001
通讯作者:
* 责任作者,硕士,
作者简介:

李姗姗(1992-),女,河北保定人,副教授,博士,主要从事环境治理理论与方法、“双碳”管理与政策.发表论文20余篇..

Analysis of carbon emission influencing factors and forecast in Shanxi Province based on LMDI and LEAP model
Shan-shan LI , Xiang FEI* , Hong-guang DONG, Yu-qing MA, Pei JIANG
Affiliations
  • School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China
出版时间: 2025-07-20
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基于IPCC方法测算了山西省2010~2022年的碳排放量,通过构建Kaya-LMDI模型识别出影响山西省碳排放的主要因素,以2022年为基准年建立了LEAP-Shanxi模型,对山西省2023~2060年的碳排放量进行预测.结果表明:经济发展水平效应是山西省碳排放增长的关键促进因素,而能源强度效应是关键抑制因素;至2060年,在工业节能增效情景、交通低碳转型情景、新能源需求加速情景和电力结构优化情景下节能降碳效果更为显著,分别使对应部门碳排放量降低42.16,3.21,1.38和31.73Mt;在基准情景和综合情景下,山西省均能在2030年达到碳达峰,碳排放量分别为764.33和742.34Mt.而综合情景与基准情景在2060年相比,减排107.42Mt的碳排放量.研究结果可为山西省提供有效的低碳发展理论依据以制定相关降碳减排措施.

碳排放  /  LMDI模型  /  LEAP模型  /  情景预测

This study applied the IPCC methodology to estimate carbon emissions in Shanxi Province from 2010 to 2022. A Kaya-LMDI model was employed to identify the key drivers influencing carbon emissions in the region. Using 2022 as the baseline year, the LEAP-Shanxi model was developed to forecast carbon emissions from 2023 to 2060. Economic development acted as the primary driver of carbon emission growth, whereas energy intensity emerged as the dominant restraining factor; By 2060, under four specific scenarios—industrial energy efficiency improvement, low-carbon transportation transition, accelerated new energy adoption, and optimized power structure—energy conservation and carbon reduction effects would become particularly pronounced, these measures could reduce sectoral emissions by 42.16, 3.21, 1.38 and 31.73Mt respectively; Both baseline and integrated scenarios indicate that Shanxi Province could reach its carbon peak by 2030, with projected emissions of 764.33 and 742.34Mt respectively; The comprehensive scenario demonstrates a potential reduction of 107.42Mt in carbon emissions compared to the baseline projections for 2060. These findings can offer a robust theoretical foundation for Shanxi Province to develop targeted low-carbon strategies and implement effective emission reduction measures.

carbon emissions  /  LMDI model  /  LEAP model  /  scenario prediction
李姗姗, 费翔, 董洪光, 马雨晴, 姜佩. 基于LMDI和LEAP模型的山西省碳排放影响因素及预测. 中国环境科学, 2025 , 45 (7) : 4052 -4063 .
Shan-shan LI, Xiang FEI, Hong-guang DONG, Yu-qing MA, Pei JIANG. Analysis of carbon emission influencing factors and forecast in Shanxi Province based on LMDI and LEAP model[J]. China Environmental Science, 2025 , 45 (7) : 4052 -4063 .
  • 安徽省高校优秀科研创新团队项目(2022AH010054)
  • 国家自然科学基金资助项目(52304195)
  • 煤炭安全精准开采国家地方联合工程研究中心项目(EC2023004)
  • 国家重点研发计划项目(2023YFC3009002)
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  • 接收时间:2024-12-03
  • 首发时间:2026-10-08
  • 出版时间:2025-07-20
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  • 收稿日期:2024-12-03
基金
安徽省高校优秀科研创新团队项目(2022AH010054)
国家自然科学基金资助项目(52304195)
煤炭安全精准开采国家地方联合工程研究中心项目(EC2023004)
国家重点研发计划项目(2023YFC3009002)
作者信息
    安徽理工大学经济与管理学院,安徽 淮南 232001

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* 责任作者,硕士,
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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