{"title":"中国沿海地区旅游运输碳排放时空变化及驱动机制","authors":"Jingyi Zhang, Dehao Cao, Huaqiong Liu","doi":"10.1016/j.clrc.2025.100374","DOIUrl":null,"url":null,"abstract":"<div><div>This paper employs a bottom-up methodology to estimate tourism-related transportation carbon emissions in China's coastal regions from 2006 to 2023, analyzing their spatiotemporal evolution characteristics across multiple scales. It further extends the LMDI model from both intra- and inter-sectoral perspectives and constructs an M-R spatial decomposition model to comparatively analyze factor-driven contributions and the drivers of spatial disparities. Key findings show: (1) the centroid of emissions in coastal areas has shifted southeastward, with greater changes in the south; (2) Per capita wealth effect (<span><math><mrow><mo>Δ</mo></mrow></math></span> Cl) is the main driver of emission growth, while transportation intensity is the key restraining factor; (3) eight provinces, including Guangdong and Shandong, exceed the average emission level, with Jiangsu showing the largest deviation.</div></div>","PeriodicalId":34617,"journal":{"name":"Cleaner and Responsible Consumption","volume":"21 ","pages":"Article 100374"},"PeriodicalIF":6.1000,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal variations & driving mechanisms of tourism transport carbon emissions in coastal China\",\"authors\":\"Jingyi Zhang, Dehao Cao, Huaqiong Liu\",\"doi\":\"10.1016/j.clrc.2025.100374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper employs a bottom-up methodology to estimate tourism-related transportation carbon emissions in China's coastal regions from 2006 to 2023, analyzing their spatiotemporal evolution characteristics across multiple scales. It further extends the LMDI model from both intra- and inter-sectoral perspectives and constructs an M-R spatial decomposition model to comparatively analyze factor-driven contributions and the drivers of spatial disparities. Key findings show: (1) the centroid of emissions in coastal areas has shifted southeastward, with greater changes in the south; (2) Per capita wealth effect (<span><math><mrow><mo>Δ</mo></mrow></math></span> Cl) is the main driver of emission growth, while transportation intensity is the key restraining factor; (3) eight provinces, including Guangdong and Shandong, exceed the average emission level, with Jiangsu showing the largest deviation.</div></div>\",\"PeriodicalId\":34617,\"journal\":{\"name\":\"Cleaner and Responsible Consumption\",\"volume\":\"21 \",\"pages\":\"Article 100374\"},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2026-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cleaner and Responsible Consumption\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666784325001251\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/12/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cleaner and Responsible Consumption","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666784325001251","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/12/24 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Spatiotemporal variations & driving mechanisms of tourism transport carbon emissions in coastal China
This paper employs a bottom-up methodology to estimate tourism-related transportation carbon emissions in China's coastal regions from 2006 to 2023, analyzing their spatiotemporal evolution characteristics across multiple scales. It further extends the LMDI model from both intra- and inter-sectoral perspectives and constructs an M-R spatial decomposition model to comparatively analyze factor-driven contributions and the drivers of spatial disparities. Key findings show: (1) the centroid of emissions in coastal areas has shifted southeastward, with greater changes in the south; (2) Per capita wealth effect ( Cl) is the main driver of emission growth, while transportation intensity is the key restraining factor; (3) eight provinces, including Guangdong and Shandong, exceed the average emission level, with Jiangsu showing the largest deviation.