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Article(id=1208362383010021383, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208362378329174732, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2024.07.00789, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1720022400000, receivedDateStr=2024-07-04, revisedDate=1724601600000, revisedDateStr=2024-08-26, acceptedDate=1758124800000, acceptedDateStr=2025-09-18, onlineDate=1766025642397, onlineDateStr=2025-12-18, pubDate=1760284800000, pubDateStr=2025-10-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1729094400000, onlineIssueDateStr=2024-10-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=1729094400000, onlineFirstDateStr=2024-10-17, sourceXml=null, magXml=null, createTime=1766025642397, creator=13701087609, updateTime=1774079218797, updator=sys-migrate, issue=Issue{id=1208362378329174732, tenantId=1146029695717560320, journalId=1146031591421210625, year='2025', volume='43', issue='19', pageStart='1', pageEnd='136', issueExtLink='null', onlineDate='null', pubDate='1760284800000', pubDateStr='2025-10-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766025641281, creator='13701087609', updateTime=1778551765812, updator='13041195026', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1260900752743064342, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208362378329174732, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1260900752743064343, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208362378329174732, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=125, endPage=128, ext={EN=ArticleExt(id=1208362383295234062, articleId=1208362383010021383, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Distribution characteristics and spatial correlation effects of transportation carbon emissions in Western China based on spatiotemporal evolution, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=

Taking western China as the research object, based on provincial panel data from 2013 to 2022, this study employs the "top−down" method to estimate transportation carbon emissions. Using the natural breaks method, standard deviational ellipse, and global Moran’s index, the evolution characteristics of transportation carbon emissions in western China are systematically analyzed from three dimensions: spatiotemporal distribution, center−of−gravity migration, and spatial correlation. The results show that transportation carbon emissions in western China generally exhibit a slow growth trend, with a spatially uneven distribution pattern characterized by "high in the north and south, low in the middle". The center of carbon emissions has shifted from the northwest to the southwest, demonstrating a clear centripetal aggregation trend. The findings provide a scientific basis for formulating differentiated and coordinated transportation carbon reduction policies in western China.

, authors=null, authorsList=Haixiu GONG, Wei FU, authorCompany=null, correspAuthors=Wei FU, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., 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=1208362384037625910, articleId=1208362383010021383, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于时空演变的西部地区交通碳排放分布特征及空间关联效应研究, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

以中国西部地区为研究对象，基于2013—2022年省级面板数据，采用“自上而下”法测算交通碳排放量，运用标准差椭圆和空间莫兰指数方法，从时空分布、重心迁移和空间关联3个维度系统分析西部交通碳排放的演变特征。结果表明：西部地区交通碳排放总体呈缓慢增长趋势，空间上呈现“南北高、中间低”的非均衡分布格局；碳排放重心由西北向西南方向迁移，且存在明显的向心聚集特征，研究结论可为西部地区制定差异化、协同化的交通碳减排政策提供科学依据。

, authors=

巩海秀，硕士研究生，研究方向为农林经济管理，电子信箱：

, authorsList=巩海秀, 付伟, authorCompany=null, correspAuthors=付伟, authorNote=null, correspAuthorsNote=
付伟（通信作者），副教授，研究方向为生态经与可持续发展，电子信箱：
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巩海秀，硕士研究生，研究方向为农林经济管理，电子信箱：

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巩海秀，硕士研究生，研究方向为农林经济管理，电子信箱：

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注：本图审图号为GS（2024）0650号，底图无修改

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年份 莫兰指数 P Z 年份 莫兰指数 P Z
2013 −0.068 0.396 0.263 2018 0.276 0.002 2.952
2014 −0.045 0.327 0.449 2019 0.286 0.001 3.033
2015 0.033 0.145 1.057 2020 0.302 0.001 3.169
2016 0.063 0.098 1.293 2021 0.335 0.000 3.458
2017 0.310 0.001 3.251 2022 0.350 0.000 3.557
), ArticleFig(id=1242141550445142437, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1208362383010021383, language=CN, label=表1, caption=

2013—2022年西部交通碳排放全局莫兰指数统计

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年份 莫兰指数 P Z 年份 莫兰指数 P Z
2013 −0.068 0.396 0.263 2018 0.276 0.002 2.952
2014 −0.045 0.327 0.449 2019 0.286 0.001 3.033
2015 0.033 0.145 1.057 2020 0.302 0.001 3.169
2016 0.063 0.098 1.293 2021 0.335 0.000 3.458
2017 0.310 0.001 3.251 2022 0.350 0.000 3.557
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基于时空演变的西部地区交通碳排放分布特征及空间关联效应研究
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巩海秀 , 付伟 *
科技导报 | 研究论文 2025,43(19): 125-128
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科技导报 |研究论文 2025 , 43 (19) : 125 -128
基于时空演变的西部地区交通碳排放分布特征及空间关联效应研究
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巩海秀，硕士研究生，研究方向为农林经济管理，电子信箱：

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巩海秀，硕士研究生，研究方向为农林经济管理，电子信箱：

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巩海秀 , 付伟*
作者信息
  • 西南林业大学经济管理学院，昆明 650000
通讯作者:
付伟（通信作者），副教授，研究方向为生态经与可持续发展，电子信箱：
Distribution characteristics and spatial correlation effects of transportation carbon emissions in Western China based on spatiotemporal evolution
Haixiu GONG , Wei FU*
Affiliations
  • School of Economics and Management, Southwest Forestry University, Kunming 650000, China
出版时间: 2025-10-13 doi: 10.3981/j.issn.1000-7857.2024.07.00789
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摘要
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以中国西部地区为研究对象，基于2013—2022年省级面板数据，采用“自上而下”法测算交通碳排放量，运用标准差椭圆和空间莫兰指数方法，从时空分布、重心迁移和空间关联3个维度系统分析西部交通碳排放的演变特征。结果表明：西部地区交通碳排放总体呈缓慢增长趋势，空间上呈现“南北高、中间低”的非均衡分布格局；碳排放重心由西北向西南方向迁移，且存在明显的向心聚集特征，研究结论可为西部地区制定差异化、协同化的交通碳减排政策提供科学依据。

西部交通碳排放  /  区域非均衡  /  重心转移  /  空间自相关

Taking western China as the research object, based on provincial panel data from 2013 to 2022, this study employs the "top−down" method to estimate transportation carbon emissions. Using the natural breaks method, standard deviational ellipse, and global Moran’s index, the evolution characteristics of transportation carbon emissions in western China are systematically analyzed from three dimensions: spatiotemporal distribution, center−of−gravity migration, and spatial correlation. The results show that transportation carbon emissions in western China generally exhibit a slow growth trend, with a spatially uneven distribution pattern characterized by "high in the north and south, low in the middle". The center of carbon emissions has shifted from the northwest to the southwest, demonstrating a clear centripetal aggregation trend. The findings provide a scientific basis for formulating differentiated and coordinated transportation carbon reduction policies in western China.

transportation carbon emissions in Western China  /  regional imbalance  /  center−of−gravity shift  /  spatial autocorrelation
巩海秀, 付伟. 基于时空演变的西部地区交通碳排放分布特征及空间关联效应研究. 科技导报, 2025 , 43 (19) : 125 -128 . DOI: 10.3981/j.issn.1000-7857.2024.07.00789
Haixiu GONG, Wei FU. Distribution characteristics and spatial correlation effects of transportation carbon emissions in Western China based on spatiotemporal evolution[J]. Science & Technology Review, 2025 , 43 (19) : 125 -128 . DOI: 10.3981/j.issn.1000-7857.2024.07.00789
正文
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交通运输业作为温室气体排放的主要行业，产生的碳排放量约占全球排放总量的25%，严重制约了人类社会可持续发展。据统计，2035年中国交通运输业将成为第2大排放源，未来产生的碳排放量会持续增长[1−3]。西部地区处于国际贸易的关键区位，交通运输业驶入“快车道”，增添了环境压力。为促进西部地区高质量发展，亟须促进运输方式绿色转型。
交通碳排放作为低碳研究的热点话题，受到学者们的广泛关注，并围绕着测算方式、时空分布等展开研究。关于测算方式，多是基于生产和消费视角选用“自下而上”法或“自上而下”法[4]，关于时空分布，主要是利用空间计量模型探究宏观层面的演变规律[5−7]。综上，虽然目前积累了较多成果，但仍存在不足之处，在研究对象方面，多聚焦于交通发展较好区域，对经济欠发达的西部地区关注较少；在研究视角方面，对于空间依赖性的揭示略显不足。本研究视角聚焦于西部地区，并延伸至空间层面的研究，从碳排放量分布、转移情况、关联效应3个维度揭示西部交通碳排放分布特征，并提出减排建议。
1 研究区概况、研究方法和数据来源
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1.1 研究区概况
西部地区占中国国土面积的72%，与周边13个国家接壤，处于中国关键区位（图1）。一是内外联动的关键位置，对内能加强区域联系，促进经济协调发展；对外能推进内外联动，形成高效的物流网络。二是发展潜力大，2013—2022年中国由交通大国迈入交通强国，实现跨越式转变，西部地区在经济和交通发展双驱动下，交通运输行业上升空间大。三是环境建设需要，西部地区多生态基地，对于环境水平要求较高，交通能源利用效率低阻碍了可持续发展，因此降低交通碳排放量，增强民生福祉是研究的重点。
1.2 研究方法
自上而下法：测算研究范围内交通能源消耗量，适用于西部交通碳排放水平的研究，如式（1）：
$ \begin{split}C= & \; C_1+C_2 \\ C_1= & \sum_{i=1}^9E_i\times F_i = \sum_{i=1}^9E_i\times CV_i\times CE_i\times CF_i\times 44/12 \\ C_2= & \; E\mathrm{_d}\times F\mathrm{_d}\end{split} $
式中：C、C1、C2为交通碳排放总量、化石燃料以及电力消耗产生的碳排放量（万t）；Ei、Fi为第$ i $种能源消耗及对应的碳排放系数，CVi、CEi、CFi为第$ i $种化石能源的平均地位发热值（kJ·kg−1）、单位含碳量及氧化率；Ed、Fd为交通运输消耗的电力及对应的碳排放系数（kgCO2·kWh−1）。
标准差椭圆法：能够通过重心位置、迁移轨迹和长短轴进行可视化分析，重心位置反映聚集程度，迁移轨迹反映重心空间移动方位，长短轴反映离散程度，差距越大，移动特征越显著。
空间莫兰指数法：选用全局莫兰指数衡量地理临近区域的空间相关性。
1.3 数据来源
本文选取2013—2022年西部11个省份作为研究对象，研究中所涉及的能源消耗数据参考各省（市）《统计年鉴》和《能源统计年鉴》，存在个别数据缺失情况，参照Mi等[8]方法进行修正。
2 结果与分析
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2.1 西部地区交通碳排放量时间演变特征
由西部地区交通碳排放总量（图2）可知，2013—2022年西部交通碳排放量由2013年15790.93万t增长至2022年16462.38万t，整体呈波动上升趋势，但增速较缓。从整体角度看，中国高度重视西部经济，并颁布相应政策促发展，如坚持推进西部大开发战略使基础设施逐步完善，“五纵七横”西部路段相继开通，不仅加强西部与中东部联系还提高了居民生活水平，因此整体呈增长状态。从阶段角度看，2013年将生态文明作为全面深化改革的重点领域、西部大开发“十三五”规划开通交通要道加强内外开放格局，以及2022年提倡加快推进交通基础设施建设等，在经济发展和生态建设相互制约下增长趋势较缓。
2.2 西部地区交通碳排放量空间演变特征
从整体角度看，西部交通碳排放呈现“南部递增、北部递减”的分布趋势。从阶段角度看，2013年，内蒙古自治区处于高值聚集区，青海、甘肃和宁夏回族自治区处于低值聚集区，2016年，部分省份开始跨级演变，向下一级转移。2022年，演变趋势放缓，四川成为高值聚集区“新成员”。究其原因是西北地区如宁夏回族自治区和青海人口稀少，经济发展水平较低，处于低值聚集区；内蒙古自治区前期依靠“倚能倚重”的发展方式和“四通八达”的交通区位优势，处于高值聚集区，后期随着低碳转型，交通碳排放量有减少趋势。西南地区如四川、云南属于西部经济水平较高区域，尤其是四川处于交通枢纽，与其他省份联系广泛，物流运量多，而云南凭借丰富的旅游资源，交通碳排放量增多。
2.3 西部交通碳排放重心转移特征
研究期间，西部交通碳排放重心分布在“104.83°E~107.1°E，32.02°N~34.95°N”，整体呈现“东北—西南”的转移趋势。从主轴方向看，长轴由2013年的472655.15 km增加至2022年的526143.63 km，短轴由2013年的1608389.50 km缩短至1329735.06 km，说明西部交通碳排放存在发散趋势和向心聚集的情况。从阶段角度来看，2013—2016年西部交通碳排放的重心聚焦于西北地区，以甘肃省和陕西省为主。2017—2022年，随着整体向西南转移，交通碳排放的重心落在西南地区，以四川省为主。究其原因是西北地区能源丰富，依靠能源产业带动发展，交通运输强度高于西南地区，因此前期重心集中于西北地区，后期随着生态文明提出，西北地区能源优势被削弱，而西南地区在陆海交通新通道、长江经济带等政策推动下交通运输业蓬勃发展，重心逐渐向西南地区倾斜。
2.4 西部交通碳排放空间聚类特征
由全局空间自相关性（表1）可知，2017—2022年，莫兰指数围绕0.310~0.350上下波动，整体呈上升趋势，其中Z值得分均超过1.96且通过P=0.05的显著性检验，表明西部地区各省交通碳排放存在显著空间相关效应，并不是孤立的，在区域经济一体化和综合交通网日益完善的驱动下有增强趋势。
3 结论与建议
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基于上述研究分析，得出以下结论并对西部地区交通碳减排放提出相应建议。
1） 西部地区交通碳排放量整体呈现增长趋势，但增长幅度缓慢，应持续推进绿色低碳交通技术和观念的传播，养成低碳出行习惯，从源头削减交通碳排放量。
2） 西部交通碳排放非均衡分布特征显著，呈现“南北高中间低”的分布趋势。对于高值聚集区，在满足居民基本需求的同时推进低碳交通产业发展；对于低值聚集区需以发展内部经济完善交通基础设施为主导，并不断提高交通利用效率。
3） 西部交通碳排放重心呈现“东北—西南”迁移趋势，前期重心聚焦于西北地区，后期被西南地区“顶替”。应兼顾“省情”制定交通碳减排方案，如陕西省需同时兼顾产业转型和绿色交通技术开发；四川省应着重提高交通能源效率，实现“以点带面”促进交通运输业发展。
4） 西部交通碳排放在2017—2022年期间存在显著关联效应，全局莫兰指数从0.31增长至0.35，关联效应在经济、政策推动下逐渐增强。应借助区域关联效应实现跨区域协同治理，强化区域互惠性关联。
基金
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  • 国家自然科学基金项目（72264035）；云南省兴滇英才青年人才专项
文献
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参考文献 引证文献
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2025年第43卷第19期
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doi: 10.3981/j.issn.1000-7857.2024.07.00789
  • 接收时间：2024-07-04
  • 首发时间：2025-12-18
  • 出版时间：2025-10-13
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  • 收稿日期：2024-07-04
  • 修回日期：2024-08-26
  • 录用日期：2025-09-18
基金
国家自然科学基金项目（72264035）；云南省兴滇英才青年人才专项
作者信息
    西南林业大学经济管理学院，昆明 650000

通讯作者:

付伟（通信作者），副教授，研究方向为生态经与可持续发展，电子信箱：
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