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

IF 5.4 Q1 ENVIRONMENTAL SCIENCES Environmental and Sustainability Indicators Pub Date : 2024-08-08 DOI:10.1016/j.indic.2024.100455
{"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":"","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":null,"pages":null},"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\":\"\",\"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\":null,\"pages\":null},\"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}
引用次数: 0

摘要

减少二氧化碳排放是一项全球性挑战,而交通部门的排放量在总排放量中占相当大的比重,不同经济体之间存在明显差异。从宏观角度看,世界各国和各地区可以有不同的分类方法。然而,依靠单一或少数几个指标往往无法满足碳减排和可持续发展的分类要求。在本研究中,我们利用机器学习,以 10 个经济指标为指导,将 188 个国家/地区划分为 6 个可识别的群组。随后,我们运用比率分析和随机森林方法,进行了基于矩阵的分析,阐明了每个交通部门的独特排放特征。特征重要性分析表明,对于高度发达经济体,总人口的贡献很大,尤其是在国内和国际航空领域,分别占排放量的 50%和 25%。相比之下,对于中低收入国家和地区,虽然人口总数仍然起着关键作用,但其影响在铁路运输中最为明显,达到 67%。对于快速发展的经济体,国内航空排放的影响达到顶峰,占 61%。这些见解强调了根据经济实体的独特属性制定战略的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Environmental and Sustainability Indicators
Environmental and Sustainability Indicators Environmental Science-Environmental Science (miscellaneous)
CiteScore
7.80
自引率
2.30%
发文量
49
审稿时长
57 days
期刊最新文献
Decoding the mystery of Bangladesh's jute decline: A climate crisis or plastic predicament Perceived accessibility matters: Unveiling key urban parameters through traditional and technology-driven participation methods Water provision benefits from karst ecosystems: An example for Watuputih groundwater basin, North Kendeng Mountain, Indonesia Towards net-zero carbon emissions: A systematic review of carbon sustainability reporting based on GHG protocol framework Ecosystem services driving factors and ecological conservation pattern construction, Qinghai-Tibet Plateau
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1