Haoran Fu , Huahui Li , Angran Fu , Xuzhang Wang , Qi Wang
{"title":"Transportation emissions monitoring and policy research: Integrating machine learning and satellite imaging","authors":"Haoran Fu , Huahui Li , Angran Fu , Xuzhang Wang , Qi Wang","doi":"10.1016/j.trd.2024.104421","DOIUrl":null,"url":null,"abstract":"<div><div>Determining and monitoring greenhouse gas (GHG) emissions remains complex as the global community endeavours to achieve emissions reduction goals. The road transportation sector poses a notable challenge in accurately calculating and monitoring global emissions due to its significant contribution to emissions worldwide. This research proposes integration of machine learning, satellite imaging, and localized emissions data to establish a precise and universally applicable system for monitoring GHG emissions in road transportation. This approach is known for its high level of accuracy, global scalability, and adaptability to diverse needs. The results indicate that integrating machine learning algorithms with satellite imagery is very effective method for monitoring GHG emissions in the transportation industry. The study’s conclusions are important for policymakers, transport authorities, and worldwide organizations working to reduce GHG emissions.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part D-transport and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S136192092400378X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
Determining and monitoring greenhouse gas (GHG) emissions remains complex as the global community endeavours to achieve emissions reduction goals. The road transportation sector poses a notable challenge in accurately calculating and monitoring global emissions due to its significant contribution to emissions worldwide. This research proposes integration of machine learning, satellite imaging, and localized emissions data to establish a precise and universally applicable system for monitoring GHG emissions in road transportation. This approach is known for its high level of accuracy, global scalability, and adaptability to diverse needs. The results indicate that integrating machine learning algorithms with satellite imagery is very effective method for monitoring GHG emissions in the transportation industry. The study’s conclusions are important for policymakers, transport authorities, and worldwide organizations working to reduce GHG emissions.
期刊介绍:
Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution.
We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.