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Article(id=1241050002626244888, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717344000000, receivedDateStr=2024-06-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773818978220, onlineDateStr=2026-03-18, pubDate=1737302400000, pubDateStr=2025-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773818978220, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773818978220, creator=13701087609, updateTime=1773818978220, updator=13701087609, issue=Issue{id=1241049962679694215, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='1', pageStart='1', pageEnd='592', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773818968696, creator=13701087609, updateTime=1773819749443, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241053237428671382, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241053237428671383, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=571, endPage=582, ext={EN=ArticleExt(id=1241050003075035447, articleId=1241050002626244888, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Driving factors and decoupling effect of carbon emissions in China's transportation industry under the background of "dual carbon", columnId=1234106390126588740, journalTitle=China Environmental Science, columnName=Carbon Emission Control, runingTitle=null, highlight=null, articleAbstract=

Based on the LMDI decomposition model, the contribution of driving factors of transportation carbon emissions is quantified. The Tapio decoupling model is used to analyze the relationship between carbon emissions and economic growth, and the efforts made by each factor to achieve decoupling are quantitatively analyzed. The results show that: Economic output is the decisive factors leading to an increase on transportation carbon emissions, with a contribution rate of 115.93% to carbon emissions; Industrial structure has the most significant inhibiting effect on carbon emissions. The decoupling index of transportation carbon emissions in the national, eastern, central, and western regions is all in a downward trend, experiencing the decoupling trend of expansive negative decoupling → weak decoupling → strong decoupling. The driving factors of transportation carbon emissions in the four regions generally show the phased characteristic of "no decoupling effort → weak decoupling effort → strong decoupling effort". Industrial structure has made varying degrees of decoupling effort in 30provinces, with transportation intensity and population size becoming key factors hindering carbon decoupling in the vast majority of provinces.

, correspAuthors=Zhi-guo SHAO, 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, authorCompany=null, fund=null, authors=null, authorsList=Zhi-guo SHAO, Ke-xin LI, Meng-di LI), CN=ArticleExt(id=1241050011895657043, articleId=1241050002626244888, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=“双碳”背景下中国交通运输业碳排放驱动因素及脱钩效应, columnId=1234106391661704058, journalTitle=中国环境科学, columnName=碳排放控制, runingTitle=null, highlight=null, articleAbstract=

基于LMDI分解模型量化交通碳排放驱动因素的贡献情况，利用Tapio脱钩模型对行业碳排放与经济增长之间的关系进行分析，并定量分析各因素为达到脱钩状态所做的努力.结果表明：经济产出是导致交通碳排放增加的决定性因素，对碳排放的贡献率高达115.93%；产业结构对碳排放的抑制作用最显著.全国、东部、中部和西部地区的交通碳排放脱钩指数均处于下降态势，展现了从扩张负脱钩→弱脱钩→强脱钩的变化趋势.各地区交通碳排放驱动因素总体上均呈现“无脱钩努力→弱脱钩努力→强脱钩努力”的阶段性特点.产业结构对30个省份做出不同程度的脱钩努力，运输强度和人口规模成为阻碍绝大多数省份碳脱钩的关键因素.

, correspAuthors=邵志国, authorNote=null, correspAuthorsNote=
*责任作者，副教授，
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邵志国(1988-)，男，山东泰安人，副教授，博士，主要研究方向为低碳交通运输.发表论文20余篇..

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邵志国(1988-)，男，山东泰安人，副教授，博士，主要研究方向为低碳交通运输.发表论文20余篇..

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邵志国(1988-)，男，山东泰安人，副教授，博士，主要研究方向为低碳交通运输.发表论文20余篇..

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Energy conversion standard coal coefficient and carbon emissions coefficient

, figureFileSmall=null, figureFileBig=null, tableContent=
能源类型标准煤折算系数(tce/t)碳排放系数(t/tce)能源类型标准煤折算系数(tce/t)碳排放系数(t/tce)
原煤0.7140.756燃料油1.4290.618
洗精煤0.9000.765润滑油1.2000.812
其他洗煤0.4500.808溶剂油1.4670.585
型煤0.6000.798石油沥青1.3310.775
焦炭0.9710.855液化石油气1.7140.504
焦炉煤气0.5710.354其他石油制品1.2000.585
原油1.4290.585天然气1.2140.448
汽油1.4710.401液化天然气1.7570.571
煤油1.4710.571热力0.0340.252
柴油1.4570.592电力0.1230.290
), ArticleFig(id=1241050026093376365, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=CN, label=表1, caption=

能源折算标准煤系数和碳排放系数

, figureFileSmall=null, figureFileBig=null, tableContent=
能源类型标准煤折算系数(tce/t)碳排放系数(t/tce)能源类型标准煤折算系数(tce/t)碳排放系数(t/tce)
原煤0.7140.756燃料油1.4290.618
洗精煤0.9000.765润滑油1.2000.812
其他洗煤0.4500.808溶剂油1.4670.585
型煤0.6000.798石油沥青1.3310.775
焦炭0.9710.855液化石油气1.7140.504
焦炉煤气0.5710.354其他石油制品1.2000.585
原油1.4290.585天然气1.2140.448
汽油1.4710.401液化天然气1.7570.571
煤油1.4710.571热力0.0340.252
柴油1.4570.592电力0.1230.290
), ArticleFig(id=1241050026307285875, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=EN, label=Table 2, caption=

Variable definition

, figureFileSmall=null, figureFileBig=null, tableContent=
变量定义单位
E能源消耗总量万tce
T折算后的运输周转量亿t·km
GDP地区生产总值万元
P人口规模，即年末常住人口万人
ECI能源碳排放强度，即单位能源消耗所释放的碳排放量t/tce
EE能源效率，即单位周转量能耗万tce/(亿t·km)
TI运输强度，即单位交通运输业增加值的周转量亿t·km/万元
IS产业结构，即交通运输业增加值占地区生产总值的比重%
EO经济产出，即人均地区生产总值元/人
), ArticleFig(id=1241050027972424568, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=CN, label=表2, caption=

变量定义

, figureFileSmall=null, figureFileBig=null, tableContent=
变量定义单位
E能源消耗总量万tce
T折算后的运输周转量亿t·km
GDP地区生产总值万元
P人口规模，即年末常住人口万人
ECI能源碳排放强度，即单位能源消耗所释放的碳排放量t/tce
EE能源效率，即单位周转量能耗万tce/(亿t·km)
TI运输强度，即单位交通运输业增加值的周转量亿t·km/万元
IS产业结构，即交通运输业增加值占地区生产总值的比重%
EO经济产出，即人均地区生产总值元/人
), ArticleFig(id=1241050028106642301, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=EN, label=Table 3, caption=

Division of three regions in 30 provinces

, figureFileSmall=null, figureFileBig=null, tableContent=
区域省/直辖市/自治区
东部北京、天津、河北、辽宁、上海、江苏、浙江、福建、山东、广东、海南
中部山西、吉林、黑龙江、安徽、江西、河南、湖北、湖南
西部内蒙古、广西、重庆、四川、贵州、云南、陕西、甘肃、青海、宁夏、新疆
), ArticleFig(id=1241050028299580291, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=CN, label=表3, caption=

30个省份三大地区划分

, figureFileSmall=null, figureFileBig=null, tableContent=
区域省/直辖市/自治区
东部北京、天津、河北、辽宁、上海、江苏、浙江、福建、山东、广东、海南
中部山西、吉林、黑龙江、安徽、江西、河南、湖北、湖南
西部内蒙古、广西、重庆、四川、贵州、云南、陕西、甘肃、青海、宁夏、新疆
), ArticleFig(id=1241050028534461321, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=EN, label=Table 4, caption=

Decomposition results of driving factors of transport carbon emissions in each region

, figureFileSmall=null, figureFileBig=null, tableContent=
时期地区△ECI△EE△TI△IS△EO△P△TC
2001~2005年全国24.301915.42975.91-1158.412713.72208.804679.73
东部18.79982.05648.11-581.371453.41187.252708.23
中部1.04451.08183.74-320.66611.340.67927.21
西部4.47482.30144.06-256.38648.9720.881044.29
2006~2010年全国220.45-2382.533401.13-2265.335081.08613.354668.17
东部59.24-783.741062.96-1023.822343.83544.852203.33
中部100.24-894.291161.28-570.691231.3039.851067.69
西部60.97-704.511176.89-670.821505.9628.661397.14
2011~2015年全国-161.45181.93-3495.76-583.845310.71446.411698.00
东部2.52-510.11-1112.71-651.362281.21378.25387.81
中部-105.11661.44-1212.69134.591465.98-27.68916.53
西部-58.8630.59-1170.37-67.071563.5295.84393.66
2016~2020年全国-515.87-1124.70-1001.92-2068.043898.45222.40-589.68
东部-109.95-1556.56306.54-912.531728.52197.05-346.93
中部-213.97357.43-719.90-612.401136.10-72.30-125.05
西部-191.9574.43-588.56-543.101033.8497.65-117.69
2001~2020年全国-174.73-327.842327.28-5453.5115880.581446.4013698.18
东部-36.75-1172.781777.65-2598.527188.061249.236406.88
中部-50.37539.60422.43-1520.924384.93-11.103764.56
西部-87.61305.35127.20-1334.074307.60208.273526.74
), ArticleFig(id=1241050028681261966, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=CN, label=表4, caption=

各区域交通碳排放驱动因素分解结果

, figureFileSmall=null, figureFileBig=null, tableContent=
时期地区△ECI△EE△TI△IS△EO△P△TC
2001~2005年全国24.301915.42975.91-1158.412713.72208.804679.73
东部18.79982.05648.11-581.371453.41187.252708.23
中部1.04451.08183.74-320.66611.340.67927.21
西部4.47482.30144.06-256.38648.9720.881044.29
2006~2010年全国220.45-2382.533401.13-2265.335081.08613.354668.17
东部59.24-783.741062.96-1023.822343.83544.852203.33
中部100.24-894.291161.28-570.691231.3039.851067.69
西部60.97-704.511176.89-670.821505.9628.661397.14
2011~2015年全国-161.45181.93-3495.76-583.845310.71446.411698.00
东部2.52-510.11-1112.71-651.362281.21378.25387.81
中部-105.11661.44-1212.69134.591465.98-27.68916.53
西部-58.8630.59-1170.37-67.071563.5295.84393.66
2016~2020年全国-515.87-1124.70-1001.92-2068.043898.45222.40-589.68
东部-109.95-1556.56306.54-912.531728.52197.05-346.93
中部-213.97357.43-719.90-612.401136.10-72.30-125.05
西部-191.9574.43-588.56-543.101033.8497.65-117.69
2001~2020年全国-174.73-327.842327.28-5453.5115880.581446.4013698.18
东部-36.75-1172.781777.65-2598.527188.061249.236406.88
中部-50.37539.60422.43-1520.924384.93-11.103764.56
西部-87.61305.35127.20-1334.074307.60208.273526.74
), ArticleFig(id=1241050028911948695, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=EN, label=Table 5, caption=

Decoupling state of transportation carbon emissions in 30 provinces

, figureFileSmall=null, figureFileBig=null, tableContent=
地区省份2001~2005年2006~2010年2011~2015年2016~2020年
脱钩指数脱钩状态脱钩指数脱钩状态脱钩指数脱钩状态脱钩指数脱钩状态
东部北京1.887END1.789END2.338END10.180RD
天津1.596END0.451WD-0.811SD-0.279SD
河北7.164END0.605WD-0.092SD-1.210SD
辽宁5.922END1.958END0.220WD1.314RD
上海1.775END1.364END0.192WD-0.355SD
江苏1.838END0.895EC2.988END1.032EC
浙江1.144EC0.736WD0.669WD-0.177SD
福建5.051END2.475END0.746WD0.926EC
山东30.499END1.045EC-1.264SD-0.393SD
广东1.999END1.403END0.310WD-0.471SD
海南49.830END4.455END-0.168SD0.285WD
整体3.411END1.062EC0.187WD-0.277SD
中部山西21.239END7.241END0.360WD-1.182SD
吉林5.254END2.892END0.916EC-4.974SD
黑龙江2.016END0.127WD0.741WD-3.648SD
安徽2.003END1.928END1.604END0.255WD
江西2.109END2.833END0.915EC1.355END
河南4.773END5.696END0.382WD1.067EC
湖北3.298END0.824EC-0.232SD0.877RC
湖南12.956END0.876EC1.302END1.371END
整体3.950END1.773END0.498WD-0.227SD
西部内蒙古5.941END1.364END-0.407SD-0.748SD
广西2.596END0.602WD0.212WD-1.100SD
重庆2.623END1.035EC1.779END-1.035SD
四川1.976END-54.822SND-0.216SD0.618WD
贵州1.651END1.651END1.293END0.029WD
云南4.500END2.830END0.173WD0.614WD
陕西3.956END2.771END-0.387SD-0.272SD
甘肃-0.158SD1.550END2.025END-0.434SD
青海0.796WD3.023END0.951EC20.485END
宁夏11.776END0.220WD1.108EC1.145RC
新疆-4.117SND0.679WD0.631WD-1.156SD
整体2.760END1.678END0.234WD-0.199SD
全国-3.393END1.341END0.312WD-0.245SD
), ArticleFig(id=1241050029046166428, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050002626244888, language=CN, label=表5, caption=

30个省份交通碳排放脱钩状态

, figureFileSmall=null, figureFileBig=null, tableContent=
地区省份2001~2005年2006~2010年2011~2015年2016~2020年
脱钩指数脱钩状态脱钩指数脱钩状态脱钩指数脱钩状态脱钩指数脱钩状态
东部北京1.887END1.789END2.338END10.180RD
天津1.596END0.451WD-0.811SD-0.279SD
河北7.164END0.605WD-0.092SD-1.210SD
辽宁5.922END1.958END0.220WD1.314RD
上海1.775END1.364END0.192WD-0.355SD
江苏1.838END0.895EC2.988END1.032EC
浙江1.144EC0.736WD0.669WD-0.177SD
福建5.051END2.475END0.746WD0.926EC
山东30.499END1.045EC-1.264SD-0.393SD
广东1.999END1.403END0.310WD-0.471SD
海南49.830END4.455END-0.168SD0.285WD
整体3.411END1.062EC0.187WD-0.277SD
中部山西21.239END7.241END0.360WD-1.182SD
吉林5.254END2.892END0.916EC-4.974SD
黑龙江2.016END0.127WD0.741WD-3.648SD
安徽2.003END1.928END1.604END0.255WD
江西2.109END2.833END0.915EC1.355END
河南4.773END5.696END0.382WD1.067EC
湖北3.298END0.824EC-0.232SD0.877RC
湖南12.956END0.876EC1.302END1.371END
整体3.950END1.773END0.498WD-0.227SD
西部内蒙古5.941END1.364END-0.407SD-0.748SD
广西2.596END0.602WD0.212WD-1.100SD
重庆2.623END1.035EC1.779END-1.035SD
四川1.976END-54.822SND-0.216SD0.618WD
贵州1.651END1.651END1.293END0.029WD
云南4.500END2.830END0.173WD0.614WD
陕西3.956END2.771END-0.387SD-0.272SD
甘肃-0.158SD1.550END2.025END-0.434SD
青海0.796WD3.023END0.951EC20.485END
宁夏11.776END0.220WD1.108EC1.145RC
新疆-4.117SND0.679WD0.631WD-1.156SD
整体2.760END1.678END0.234WD-0.199SD
全国-3.393END1.341END0.312WD-0.245SD
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“双碳”背景下中国交通运输业碳排放驱动因素及脱钩效应
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邵志国 1, * , 李可心 1 , 李梦笛 2
中国环境科学 | 碳排放控制 2025,45(1): 571-582
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中国环境科学 | 碳排放控制 2025, 45(1): 571-582
“双碳”背景下中国交通运输业碳排放驱动因素及脱钩效应
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邵志国1, * , 李可心1, 李梦笛2
作者信息
  • 1.青岛理工大学管理工程学院，山东 青岛 266525
  • 2.同济大学经济与管理学院，上海 200092

    • 邵志国(1988-)，男，山东泰安人，副教授，博士，主要研究方向为低碳交通运输.发表论文20余篇..

通讯作者:

*责任作者，副教授，
Driving factors and decoupling effect of carbon emissions in China's transportation industry under the background of "dual carbon"
Zhi-guo SHAO1, * , Ke-xin LI1, Meng-di LI2
Affiliations
  • 1.School of Management Engineering, Qingdao University of Technology, Qingdao 266525, China
  • 2.School of Economics and Management, Tongji University, Shanghai 200092, China
出版时间: 2025-01-20
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摘要
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基于LMDI分解模型量化交通碳排放驱动因素的贡献情况，利用Tapio脱钩模型对行业碳排放与经济增长之间的关系进行分析，并定量分析各因素为达到脱钩状态所做的努力.结果表明：经济产出是导致交通碳排放增加的决定性因素，对碳排放的贡献率高达115.93%；产业结构对碳排放的抑制作用最显著.全国、东部、中部和西部地区的交通碳排放脱钩指数均处于下降态势，展现了从扩张负脱钩→弱脱钩→强脱钩的变化趋势.各地区交通碳排放驱动因素总体上均呈现“无脱钩努力→弱脱钩努力→强脱钩努力”的阶段性特点.产业结构对30个省份做出不同程度的脱钩努力，运输强度和人口规模成为阻碍绝大多数省份碳脱钩的关键因素.

交通运输业  /  碳排放  /  驱动因素  /  Tapio模型  /  脱钩努力

Based on the LMDI decomposition model, the contribution of driving factors of transportation carbon emissions is quantified. The Tapio decoupling model is used to analyze the relationship between carbon emissions and economic growth, and the efforts made by each factor to achieve decoupling are quantitatively analyzed. The results show that: Economic output is the decisive factors leading to an increase on transportation carbon emissions, with a contribution rate of 115.93% to carbon emissions; Industrial structure has the most significant inhibiting effect on carbon emissions. The decoupling index of transportation carbon emissions in the national, eastern, central, and western regions is all in a downward trend, experiencing the decoupling trend of expansive negative decoupling → weak decoupling → strong decoupling. The driving factors of transportation carbon emissions in the four regions generally show the phased characteristic of "no decoupling effort → weak decoupling effort → strong decoupling effort". Industrial structure has made varying degrees of decoupling effort in 30provinces, with transportation intensity and population size becoming key factors hindering carbon decoupling in the vast majority of provinces.

transportation industry  /  carbon emissions  /  driving factors  /  Tapio model  /  decoupling effort
邵志国, 李可心, 李梦笛. “双碳”背景下中国交通运输业碳排放驱动因素及脱钩效应. 中国环境科学, 2025 , 45 (1) : 571 -582 .
Zhi-guo SHAO, Ke-xin LI, Meng-di LI. Driving factors and decoupling effect of carbon emissions in China's transportation industry under the background of "dual carbon"[J]. China Environmental Science, 2025 , 45 (1) : 571 -582 .
正文
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“双碳”背景下，中国各行业均面临着巨大的减排压力.交通运输业是国民经济中具有基础性、先导性和战略性的产业，然而，从碳排放占总量比例来看，中国交通运输业已成为仅次于工业和建筑业的第三大碳排放行业[1].因此，在经济发展不以牺牲资源环境为代价的共识下，促进区域交通运输业经济增长和碳排放协调发展，推动交通运输业绿色低碳转型，对促进行业高质量发展及助力“双碳”目标的实现具有重要的现实意义.
目前，国内外学者针对交通运输业碳排放（以下简称“交通碳排放”）的研究主要集中于行业碳排放测算、碳排放影响因素、碳排放与经济增长的关系等方面.首先，已有关于交通碳排放测算方法的研究中，“自上而下”法和“自下而上”法是目前被广泛应用的两种碳排放测算方法.“自上而下”法主要基于交通运输业的能源消耗量及能源碳排放因子进行碳排放量的估算[2-4]；“自下而上”法主要基于不同交通运输工具车型、保有量、行驶里程、单位行驶里程燃料消耗等数据进行能源消耗量的估算，进而测算碳排放量[5-7].近年来，逐渐有学者将两种测度方法结合应用[8-9].其次，根据研究方法的不同，将有关交通碳排放影响因素的研究分为以下两类.第一，采用计量经济学方法主要包括空间面板数据模型[10]、STIRPAT模型[11]和岭回归[12]研究交通碳排放的影响因素.第二，因素分解分析通过将碳排放量表示为几个因素的乘积，根据不同的确定权重的算法进行分解，以量化各因素对碳排放的影响程度[13].其可以分为3种：结构分解分析（SDA）[14-15]、指数分解分析（IDA）[16]和生产理论分解分析（PDA）[17-18].最后，明晰交通碳排放与经济增长之间的关系是促进二者和谐发展的必要条件.已有文献主要涉及到行业碳排放总量的环境库兹涅茨曲线（EKC）形态判断[19-20]、碳排放与经济增长之间的动态作用机理研究[21-23]及两者之间的脱钩状态分析[24-27]等.
已有关于中国交通碳排放的研究成果为交通运输业碳减排提供了一定的参考与借鉴，但目前仍存在未解决的问题：第一，大部分文献对交通运输能源消耗的核算仅局限于原煤、汽油、煤油和柴油等几种能源，但由于各省份每年的交通运输能源消费品种存在差异，便导致以往研究的核算结果准确性较低；第二，现有关于交通碳排放的研究多从国家宏观层面或针对具体某个省份展开，鲜有文献将中国所有省份进行整体性对比，即在交通碳排放空间维度的研究较为匮乏；第三，在有关交通碳排放影响因素的研究中，传统的计量回归模型通常将因素视为单一指标，忽略了它们之间的相互作用.因素分解分析能够改善这一问题，但已有研究大多选取能源结构、经济产出、人口规模等指标，仅依赖这些指标无法全面准确地揭示交通碳排放的变化程度，需要对因素进行深入分解；第四，大多数关于行业碳排放与经济增长脱钩关系的研究都以每年为研究间隔，但碳排放与经济发展均存在时间上的滞后性，以每年为时间尺度无法准确反映脱钩关系.此外，多数研究在使用Tapio脱钩模型判断脱钩状态时未能深入分析各驱动因素在碳脱钩进程中所发挥的作用，缺少对实现脱钩所做努力有效性的评估.
鉴于此，本文使用能源平衡表在对中国30个省份（不含西藏、香港、澳门和台湾）2001~2020年交通碳排放量进行测算的基础上，根据国家五年规划将研究期划分为4个阶段（2001~2005年、2006~2010年、2011~2015年、2016~2020年），在对数平均迪氏指数（LMDI）分解模型中引入交通运输能源效率、交通运输强度、产业结构等重要变量的影响，分析各驱动因素在不同阶段对区域及省份碳排放影响的差异，并运用Tapio脱钩模型考察行业碳排放与经济增长之间相对关系的变化情况.最后，建立脱钩努力模型以反映各因素在碳脱钩过程中发挥的作用.基于此，提出促进交通运输业经济与环境协调发展的政策建议.研究成果可为统筹推进交通运输节能减排和环境保护工作，加快推动行业绿色低碳转型提供科学指导，助力实现国家“碳达峰、碳中和”目标.
1 方法与数据
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1.1 交通碳排放测算
“自上而下”法只需收集交通运输领域终端能源消耗量数据，数据获取难度相对较低.因此，本文从终端能源消费角度，采用“自上而下”法测算中国交通碳排放量，公式如下.
式中：TC为交通碳排放量；Ei为第i类交通运输能源消耗实物量；Ki为第i类能源折算标准煤系数；Fi为第i类能源碳排放系数.各种能源折算标准煤系数和碳排放系数，见表1.
1.2 碳排放驱动因素分解模型
IDA模型下有多种方法，其中，LMDI分解模型具有全分解、无残差等优点[28-30].因此，本文采用LMDI加法形式，根据交通碳排放的特征，在已有研究基础上[31]，将影响交通碳排放的因素分解为能源碳排放强度、能源效率、运输强度、产业结构、经济产出和人口规模，模型公式如下.
分解模型中相关变量的定义，见表2.从基期到t期交通碳排放差值称为总效应，总效应及各分解因素对碳排放贡献值的公式如下.
式中：ΔECI、ΔEE、ΔTI、ΔIS、ΔEO、ΔP分别为交通能源碳排放强度、能源效率、运输强度、产业结构、经济产出、人口规模引起的碳排放变化量.若某因素对碳排放的贡献值大于0，说明该因素会造成交通碳排放的增加；反之，说明该因素会抑制交通碳排放的增长.
1.3 Tapio脱钩模型
经济合作与发展组织（OECD）最早将脱钩模型引入环境经济领域，并构建了OECD脱钩指数[32-33].OECD脱钩模型虽然易于应用，但其缺点也很明显.第一，它对基准年的选择高度敏感，导致计算结果的稳定性较差[34].第二，模型只能大致揭示两种状态：脱钩和不脱钩，无法解释微小的变化[35].为解决上述缺点，Tapio[36]在2005年提出脱钩弹性概念并细化了脱钩指数分类0，将脱钩状态分为3个大类，包括8个子类，见图1.Tapio脱钩模型公式如下.
式中：e为交通碳排放与经济增长的脱钩指数；%ΔTC和%ΔTGDP分别为交通碳排放量和交通运输业增加值的变化率；t和0分别为研究期期末和期初.
由图1可知，脱钩分为强脱钩、弱脱钩和衰退脱钩3种状态，是碳排放污染开始好转的状态；连接分为增长连接和衰退连接两种状态，是碳排放和经济发展同时增加或减少且增速和降速相当的状态；负脱钩分为强负脱钩、弱负脱钩和扩张负脱钩3种状态，是碳排放污染相对严重的状态.
1.4 脱钩努力模型
在不影响经济发展的前提下，剔除由经济产出效应引起的碳排放，可以进一步评估脱钩努力的有效性[37-38].在Tapio脱钩模型和LMDI分解模型的基础上，构建脱钩努力模型.当经济产出的增长效应为正时，各驱动因素做出的脱钩努力程度计算公式如下.
式中：D为剔除经济产出因素后的总脱钩努力程度，DECI、DEE、DTI、DIS、DP分别为5个驱动因素各自做出的脱钩努力.D≥1，为强脱钩努力，表明驱动因素对碳排放与经济增长之间的脱钩具有促进作用，且能够完全抵消经济产出带来的碳排放贡献；D≤0，为无脱钩努力，表明驱动因素不会促进碳排放与经济增长之间的脱钩，反而促进了碳排放的增长；0<D<1，为弱脱钩努力，表明驱动因素对碳排放与经济增长之间脱钩具有促进作用，但无法完全抵消经济产出带来的碳排放贡献.
1.5 数据来源及区域划分
根据中国《国民经济行业分类》（GB/T4754-2017），本文所指的交通运输业泛指交通运输、仓储和邮政业.考虑到仓储和邮政业在整体能源消费中所占比例较小，且大部分能源消费用于支持交通运输活动[39].因此涉及的交通运输业统计年鉴中的指标数据，均近似取值于对应的交通运输、仓储和邮政业数据[40-41].选用2001~2020年中国30个省份的相关变量数据，交通运输能源消耗量和能源折算标准煤系数来源于《中国能源统计年鉴2022》、中国碳核算数据库及各省份能源统计年鉴.能源碳排放系数来源于《中国能源统计年鉴2022》及《IPCC国家温室气体清单指南》.交通运输业增加值、各地区生产总值、旅客周转量、货物周转量及人口数量来源于《中国统计年鉴》及各省份统计年鉴.交通运输业增加值和地区生产总值均以2001年不变价为基期进行平减折算调整.铁路、公路、水路的旅客周转量转换为货物周转量的系数分别取1、0.1、0.33[42].
鉴于中国东部、中部和西部地区在经济发展水平、地理和交通条件及资源配置和产业结构等方面的差异，根据国家统计局三大地带分类标准[43]，将30个省份划分为东部、中部和西部3个地区，见表3.
2 结果与分析
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2.1 交通碳排放驱动因素分解
有学者发现中国的“五年规划”政策对交通碳排放具有一定的影响[44-45].因此，根据国家五年规划将研究期划分为4个阶段（2001~2005年、2006~2010年、2011~2015年、2016~2020年），基于LMDI分解模型分别对各驱动因素在中国各区域及30个省份的交通碳排放的贡献值进行量化.
2.1.1 区域层面分析
各区域交通碳排放驱动因素分解结果，见表4.总体来看，2001~2020年运输强度、经济产出和人口规模均促进了交通碳排放的增加.其中，经济产出是主导因素，累计增加碳排放15880.58万t，对交通碳排放的贡献率高达115.93%.人口规模对交通碳排放产生的正效应较小，主要因为中国常住人口的增幅较小.从促降因素来看，产业结构是抑制碳排放增加的最主要贡献因素，累计减少碳排放5453.51万t.
能源碳排放强度、能源效率和运输强度对交通碳排放的影响存在阶段性差异，对交通碳排放既表现出促进作用，也表现出抑制效果.能源碳排放强度在2001~2010年促进了交通碳排放的增加，而在2011~2020年对交通碳排放表现为抑制作用，说明交通能源消耗结构改善，清洁能源产业建设力度正逐步加大.无论是促进作用还是抑制作用，能源碳排放强度对交通碳排放的贡献值均较小，2001~2020年累计贡献率仅为1.3%.
能源效率在2001~2005年和2011~2015年对交通碳排放表现为促进作用，而在2006~2010年和2016~2020年表现为抑制作用.这是因为“十五”时期国家加大了能源、交通运输等基础设施建设的投资，而“十一五”时期，交通运输业加大科技投入，不断强化创新能力建设，“十三五”则着手优化交通运输结构，力促节能减排.虽然“十二五”时期能源效率对交通碳排放在全国仍表现为促进作用，但其贡献率很小，且东部地区能源效率对碳排放表现为抑制作用，是因为东部地区作为中国的较发达地区，能源结构调整力度大，交通运输结构合理，交通基础设施完善.
运输强度在2001~2010年对交通碳排放表现为促进作用，在2011~2020年对交通碳排放表现为抑制作用，与能源碳排放强度的作用趋势类似，但运输强度对碳排放的贡献率更高.在“十五”和“十一五”期间，运输强度导致全国交通碳排放分别增加975.91和3401.13万t，“十二五”和“十三五”期间，运输强度已成为抑制交通碳排放增加的核心因素.
2.1.2 省份层面分析
各省份交通碳排放驱动因素分解结果，见图2.可以看出，不同省份交通碳排放驱动因素的影响差异较大.经济产出在各阶段均是导致各省份交通碳排放增加的决定性因素.2001~2020年，除河北、山东、河南和青海4省外，其他省份经济产出对交通碳排放的贡献率均大于100%.人口规模在绝大多数省份对交通碳排放总体上均存在较小的促进作用，但4个阶段都有个别中西部省份的人口规模对交通碳排放表现出抑制效果，如安徽、广西、贵州等地.在“十三五”期间，人口规模对交通碳排放呈现抑制作用的省份数量增多，其中包括北京、天津等经济发达省份，这主要与近几年年轻人生育意愿降低，人口数量小规模下降有关.产业结构是抑制各省份交通碳排放增长的主导因素，但也存在个别省份的产业结构产生了碳增排效应.如图2（c）可以看出，部分省份的产业结构效应促进交通碳排放的增长，如辽宁、河南等地.这主要与GDP和交通运输业增加值增速的相对大小有关[46].当GDP增速大于交通运输业增加值增速时，产业结构对交通碳排放的效应值为负，反之，效应值为正.2011~2015年，辽宁省GDP增速为37.88%，小于交通运输业增加值的增速39.85%；河南省GDP增速为46.37%，小于交通运输业增加值的增速78.85%.
能源碳排放强度、能源效率和运输强度对交通碳排放的影响具有异质性.首先，就能源碳排放强度而言，研究期内，不同省份能源碳排放强度对交通碳排放既呈现正向驱动效应，又存在负向抑制作用，但作用效果均较小，且在4个阶段的作用趋势变化较小.这主要因为在中国的能源消费结构中，消费比例最大的能源是煤炭，煤炭在能源体系中的主体地位短期不会改变[45,47].其次，就能源效率而言，能源效率在2001~2005年间对交通碳排放的影响在24个省份表现为促进作用，在山东的增排效果达到了666.74万t；2006~2010年，能源效率对交通碳排放的影响在23个省份表现为抑制作用，共减少碳排放2726.37万t，是该阶段抑制交通碳排放增加的主导因素，其中在辽宁的减排效果达到了399.75万t，贡献率高达-197.52%；2011~2015年，绝大多数省份能源效率对交通碳排放的影响又重新表现为促进作用；2016~2020年，绝大多数省份能源效率对交通碳排放的影响又表现为抑制作用.最后，就运输强度而言，绝大多数省份运输强度对交通碳排放的影响在2001~2010年表现为促进作用，主要是因为中国交通运输业在这一时期经历了快速增长阶段，但相对缺乏环保技术和清洁能源的应用，中国政府在这一时期主要关注经济发展和基础设施建设，对环保要求相对较低，交通运输业的能源政策也相对宽松.2011~2015年和2016~2020年，运输强度对交通碳排放表现为抑制作用，累计减排量分别为3495.76万t和1001.92万t，是交通碳减排的重要因素，这与中国的交通运输业能源政策逐渐加强有关.
此外，还可以发现同一地区内不同省份的影响因素差异较大.由图2（d）可知，2016~2020年，同属东部地区的辽宁和上海两地的能源效率和运输强度对交通碳排放的作用效果相反，辽宁的能源效率和运输强度对交通碳排放分别呈现促进和抑制作用，上海则相反.这主要因为两地在经济发展模式、交通运输网络等方面的差异.辽宁有中国最大的重工业基地，工业发展相对较为集中，尤其是重化工业和能源行业，这导致辽宁的交通运输活动所需的能源消耗较大，单位周转量能耗相对较高，从而促进了交通碳排放的增加.而作为中国的经济中心之一，上海具有完善的城市交通网络，包括高速公路、城际铁路、轨道交通等多种交通方式，交通网络的发达使得上海地区运输强度相对较大，从而伴随着交通碳排放的增加.因此，有必要根据地区的交通碳排放量、经济发展水平和交通基础设施完善程度等因素，制定差异化交通碳减排路径方案.
2.2 交通碳排放与经济增长脱钩分析
利用Tapio脱钩模型分别对中国各区域及30个省份在4个阶段的交通碳排放与经济增长之间的脱钩状态进行研究，结果分别见图3和表5.从区域角度来看，4个阶段中，全国、东部、中部和西部地区的交通碳排放脱钩指数均处于下降态势，均经历了从“扩张负脱钩→弱脱钩→强脱钩”的脱钩变化趋势，脱钩状态好转.结合图3和表5中数据可以看出，除了2001~2005年，东部地区的脱钩指数在其余3个阶段均小于全国整体脱钩指数；中部地区的脱钩指数在4个阶段均大于全国整体脱钩指数；西部地区脱钩指数相对于全国整体脱钩指数的大小趋势不稳定，2001~2005年和2011~2015年的脱钩指数小于全国整体脱钩指数，2006~2010年和2016~2020年的脱钩指数大于全国整体脱钩指数.
从省份角度来看，30个省份的脱钩状态主要表现为扩张负脱钩、增长连接、弱脱钩和强脱钩4种.这是因为交通运输业增加值总体上一直保持增长态势.2001~2005年，绝大多数省份均表现为扩张负脱钩状态，表明此期间刚性交通需求增加的同时，各地区尚未形成有效的交通减排措施，从而导致交通碳排放加剧.山东、海南、山西、湖南、宁夏等地的脱钩指数较高，意味着这些省份交通运输业经济水平增长速度远远小于碳排放量增长速度.2006~2010年，处于扩张负脱钩状态的省份数量由26个减至17个，且脱钩指数明显降低，处于弱脱钩和增长连接状态的省份数量增多，脱钩状态有所好转.2011~2015年，处于弱脱钩和强脱钩状态的省份数量增多，这与许多省份的交通碳排放增长率下降密切相关.2016~2020年，处于强脱钩状态的省份数量已增至15个，脱钩状态优化明显.在这4个阶段，虽然多数省份的脱钩状态逐步向强脱钩靠拢，脱钩水平向好发展，但仍存在一些省份的脱钩状态反弹和恶化，如北京、江苏、江西、湖南等地，表明实现各省份交通碳排放与经济增长的绝对脱钩还需要做出更多努力.
2.3 交通碳排放驱动因素脱钩努力分析
运用LMDI分解模型评价各驱动因素对碳排放的贡献，并分析了碳排放与经济增长之间的脱钩水平.然而，上述研究并不能定量地评估驱动因素对脱钩状态的影响，而脱钩努力模型可以将碳排放驱动因素分解模型和脱钩理论结合，定量分析各因素为达到脱钩状态所做的努力.交通碳排放驱动因素的脱钩努力情况，见图4.可知，剔除经济产出因素后，全国、东部、中部和西部总体呈现出“无脱钩努力→弱脱钩努力→强脱钩努力”的阶段性特点.“十五”期间表现为无脱钩努力效应，“十一五”和“十二五”期间表现为弱脱钩努力效应，“十三五”期间表现为强脱钩努力效应.
从脱钩努力驱动因素来看，全国层面上，2001~2005年，产业结构因素脱钩努力值为0.427，做出了弱脱钩努力，其余因素均未做出脱钩努力；2006~2010年，能源效率和产业结构因素脱钩努力值分别为0.469和0.446，均做出了弱脱钩努力；2011~2015年，能源碳排放强度、运输强度和产业结构因素脱钩努力值分别0.030、0.658和0.110，能源效率和人口规模因素未做出脱钩努力；2016~2020年，除人口规模外，其余因素均做出了弱脱钩努力.总体而言，为实现交通碳排放与经济增长脱钩而做出努力的驱动因素数量逐步增加，产业结构因素在4个阶段的脱钩努力效应均为正值，人口规模因素在4个阶段的脱钩努力效应均为负值.
从脱钩努力的省份分布来看，“十五”期间，中国仅有4个省份脱钩努力值为正，分别为天津、江西、甘肃和青海.其中，甘肃的脱钩努力值大于1，说明其为实现交通碳排放与经济增长脱钩做出了强脱钩努力，碳减排努力引致的碳排放减量完全抵消了经济发展引致的碳排放增量.“十一五”期间，脱钩努力值为正的省份数量增加至20个，均做出了弱脱钩努力.“十二五”期间，做出强脱钩努力的省份数量增至8个，安徽和新疆成为仅有的未做出脱钩努力的省份.“十三五”期间，做出强脱钩努力的省份数量增至19个，青海在该阶段的脱钩努力值为负，其他省份的脱钩努力值都保持在正向的中等水平.总体而言，大部分省份的交通碳排放与经济增长之间的脱钩关系在“十一五”期间就已有所改善，并在“十二五”和“十三五”时期进一步得到优化.
结合省份特征和驱动因素来看，2001~2020年，能源碳排放强度因素的作用效果最不明显，30个省份脱钩努力绝对值最大仅为0.144，在19个省份做出一定的脱钩努力；能源效率因素对15个省份做出脱钩努力，在海南呈现强脱钩努力状态，而在天津、山东和湖南等地阻碍了碳脱钩的实现；运输强度因素在天津有明显的强脱钩努力效果，在山东、黑龙江、湖南、贵州和云南呈现弱脱钩努力状态，在其余24个省份呈现无脱钩努力状态；产业结构因素对30个省份均做出脱钩努力，展现出对加快脱钩进程的积极影响，意味着产业结构升级做出的碳减排努力引致的碳排放减量可以不同程度地抵消经济发展引致的部分碳排放增量；人口规模因素在26个省份未做出脱钩努力，尤其是在北京、天津、上海等人口密集区.总体而言，2001~2020年，除河北、山东、河南和青海4省外，其余省份均为碳脱钩做出了努力.其中，甘肃做出的脱钩努力最大，脱钩努力值达到了0.661.
2.4 政策建议
基于上述研究结论，结合《“十四五”现代综合交通运输体系发展规划》的要求，提出如下促进交通运输业经济与环境协调发展的建议.
经济产出和人口规模是导致交通碳排放增加的主导因素，但通过限制行业规模产出的方式来减少交通碳排放，明显不符合经济发展现实.政府应在确保经济增长不受影响的前提下，加速推动产业结构的优化升级和高能耗行业的改革，重点发展具有战略意义的新兴产业和高科技产业，以实现经济、环境和社会效益的三方面平衡.与此同时，注重人口密集地区的交通规划与管理，提升城市公共交通的覆盖率和服务质量，优化公共交通运行效率，减少拥堵和能源浪费.积极推广绿色低碳出行理念，鼓励和推广电动车、混合动力车辆等低碳交通工具的使用，在满足交通需求的基础上，尽可能地减少碳排放.
产业结构的有效调整可以加快碳脱钩进程.这就需要对高能耗产业进行优化改革，以降低碳排放为前提，同时保持其高附加值，最大化碳排放空间的经济效益，实现产业发展与经济增长之间相对平衡的状态.清洁能源的广泛应用大大抑制了化石燃料需求的增长，为加速向非化石能源转型提供了机遇.IEA发布的《2023年全球碳排放报告》中指出，如果没有清洁能源技术，过去五年全球二氧化碳排放量将增加3倍.因此，应鼓励交通运输企业大力发展清洁化运输装备，使用清洁能源驱动技术.此外，由于不同省份的经济结构、产业布局、区域发展水平和交通需求等方面存在差异，导致各驱动因素在各省份的脱钩努力程度不同.只有因地制宜制定碳减排政策，才能更好地实现交通运输业经济与环境的协调发展.
虽然中国碳排放脱钩指数总体呈下降态势，脱钩水平向好发展，但存在一些省份的脱钩状态反弹和恶化，碳排放脱钩状态不稳定.因此，建立跨区域合作机制，以促进各省份间的技术互通、政策协作和经验共享，共同探讨低碳交通解决方案，推动低碳交通技术在不同地区的普及应用.脱钩水平落后的省份应借鉴先进省份的治理经验，完善碳减排政策，因地制宜，分类施策，以实现交通碳排放稳定脱钩为目标，促进交通运输业经济高质量发展.
3 结论
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3.1 运输强度、经济产出和人口规模总体上均促进了交通碳排放的增加.经济产出对交通碳排放的累计贡献率高达115.93%.产业结构是抑制交通碳排放增长的最主要贡献因素，累计减少碳排放5453.51万t.能源碳排放强度、能源效率和运输强度对交通碳排放的影响存在阶段性差异，能源碳排放强度对交通碳排放的累计贡献率仅为1.3%.
3.2 30个省份交通碳排放的脱钩主要表现为扩张负脱钩、增长连接、弱脱钩和强脱钩4种状态.“十三五”时期，处于强脱钩的省份数量已增至15个，但仍存在一些省份的脱钩状态出现反弹和恶化.
3.3 为实现交通碳排放与经济增长脱钩而做出努力的驱动因素数量正逐步增加.产业结构在4个阶段的脱钩努力效应均为正，而人口规模因素的脱钩努力效应均为负.
基金
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  • 国家自然科学基金资助项目(72304161)
  • 国家自然科学基金资助项目(71874123)
  • 山东省自然科学基金资助项目(ZR2022QG029)
文献
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2025年第45卷第1期
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  • 接收时间：2024-06-03
  • 首发时间：2026-03-18
  • 出版时间：2025-01-20
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  • 收稿日期：2024-06-03
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国家自然科学基金资助项目(72304161)
国家自然科学基金资助项目(71874123)
山东省自然科学基金资助项目(ZR2022QG029)
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    1.青岛理工大学管理工程学院，山东 青岛 266525
    2.同济大学经济与管理学院，上海 200092

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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