{"title":"不同经济体交通部门二氧化碳排放量的全球差异:跨越 188 个国家/地区的宏观探索","authors":"Bailing Zhang , Jing Kang , Tao Feng","doi":"10.1016/j.indic.2024.100455","DOIUrl":null,"url":null,"abstract":"<div><p>Reducing CO<sub>2</sub> emissions represents a global challenge, and the mobility sectors account for a considerable portion of total emissions, with marked disparities across diverse economies. Viewed from a macroscopic perspective, countries and regions around the world can be categorized in various ways. However, relying on a single or a few indicators often proves inadequate to meet the classification requirements for carbon reduction and sustainable development. In this study, employing machine learning and guided by 10 economic indicators, we classified 188 countries/regions into 6 identifiable clusters. Subsequently, by applying ratio analysis and random forest methodologies, we conducted a matrix-based analysis that elucidates the distinct emission characteristics of each mobility sector. Feature importance analysis revealed that for highly developed economies, the total population's contribution was significant, especially in domestic and international aviation, accounting for 50% and 25% of emissions, respectively. In contrast, for lower-middle-income countries and regions, while the total population still played a pivotal role, its influence was most pronounced in railway transportation, reaching 67%. For rapidly developing economies, domestic aviation emissions reached a peak influence of 61%. These insights emphasize the necessity for strategies tailored to the unique attributes of economic entities.</p></div>","PeriodicalId":36171,"journal":{"name":"Environmental and Sustainability Indicators","volume":"23 ","pages":"Article 100455"},"PeriodicalIF":5.4000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665972724001235/pdfft?md5=694f390934a2cbe3220e5364089b174c&pid=1-s2.0-S2665972724001235-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Global disparities in CO2 emissions from mobility sectors of diverse economies: A macroscopic exploration across 188 countries/regions\",\"authors\":\"Bailing Zhang , Jing Kang , Tao Feng\",\"doi\":\"10.1016/j.indic.2024.100455\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Reducing CO<sub>2</sub> emissions represents a global challenge, and the mobility sectors account for a considerable portion of total emissions, with marked disparities across diverse economies. Viewed from a macroscopic perspective, countries and regions around the world can be categorized in various ways. However, relying on a single or a few indicators often proves inadequate to meet the classification requirements for carbon reduction and sustainable development. In this study, employing machine learning and guided by 10 economic indicators, we classified 188 countries/regions into 6 identifiable clusters. Subsequently, by applying ratio analysis and random forest methodologies, we conducted a matrix-based analysis that elucidates the distinct emission characteristics of each mobility sector. Feature importance analysis revealed that for highly developed economies, the total population's contribution was significant, especially in domestic and international aviation, accounting for 50% and 25% of emissions, respectively. In contrast, for lower-middle-income countries and regions, while the total population still played a pivotal role, its influence was most pronounced in railway transportation, reaching 67%. For rapidly developing economies, domestic aviation emissions reached a peak influence of 61%. These insights emphasize the necessity for strategies tailored to the unique attributes of economic entities.</p></div>\",\"PeriodicalId\":36171,\"journal\":{\"name\":\"Environmental and Sustainability Indicators\",\"volume\":\"23 \",\"pages\":\"Article 100455\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2665972724001235/pdfft?md5=694f390934a2cbe3220e5364089b174c&pid=1-s2.0-S2665972724001235-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental and Sustainability Indicators\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665972724001235\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental and Sustainability Indicators","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665972724001235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Global disparities in CO2 emissions from mobility sectors of diverse economies: A macroscopic exploration across 188 countries/regions
Reducing CO2 emissions represents a global challenge, and the mobility sectors account for a considerable portion of total emissions, with marked disparities across diverse economies. Viewed from a macroscopic perspective, countries and regions around the world can be categorized in various ways. However, relying on a single or a few indicators often proves inadequate to meet the classification requirements for carbon reduction and sustainable development. In this study, employing machine learning and guided by 10 economic indicators, we classified 188 countries/regions into 6 identifiable clusters. Subsequently, by applying ratio analysis and random forest methodologies, we conducted a matrix-based analysis that elucidates the distinct emission characteristics of each mobility sector. Feature importance analysis revealed that for highly developed economies, the total population's contribution was significant, especially in domestic and international aviation, accounting for 50% and 25% of emissions, respectively. In contrast, for lower-middle-income countries and regions, while the total population still played a pivotal role, its influence was most pronounced in railway transportation, reaching 67%. For rapidly developing economies, domestic aviation emissions reached a peak influence of 61%. These insights emphasize the necessity for strategies tailored to the unique attributes of economic entities.