{"title":"Machine learning for predicting urban greenhouse gas emissions: A systematic literature review","authors":"Yukai Jin , Ayyoob Sharifi","doi":"10.1016/j.rser.2025.115625","DOIUrl":null,"url":null,"abstract":"<div><div>Greenhouse gases play a crucial role in shaping urban climate patterns and dynamics. Using machine learning methods offers opportunities for predicting greenhouse gas emissions in cities, both now and in the future. Here, we review 75 papers from 2003 to 2023 that utilized machine learning to forecast urban greenhouse gas emissions. We focus on two aspects: the models used and the driving factors of emissions. Across all models, R<sup>2</sup> range from 0.5231 to 0.9989, MAPE range from 0.3017 % to 26.3 %.Hybrid and neural network models emerged as the most popular choices. The most common combinations were spatial hybrid models, primarily blending spatial models with machine learning predictions. Time series hybrid models mostly featured optimized models and machine learning prediction models. Hybrid models outperform single models in both R<sup>2</sup> and MAPE. We propose three key recommendations to enhance the accuracy and reliability of future machine learning models: 1) Establish criteria for evaluating influential factors and model selection, 2) Enhance spatial prediction in machine learning by optimization models, and 3) Explore and compare how greenhouse gas prediction models perform across diverse urban settings.</div></div>","PeriodicalId":418,"journal":{"name":"Renewable and Sustainable Energy Reviews","volume":"215 ","pages":"Article 115625"},"PeriodicalIF":16.3000,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable and Sustainable Energy Reviews","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364032125002989","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Greenhouse gases play a crucial role in shaping urban climate patterns and dynamics. Using machine learning methods offers opportunities for predicting greenhouse gas emissions in cities, both now and in the future. Here, we review 75 papers from 2003 to 2023 that utilized machine learning to forecast urban greenhouse gas emissions. We focus on two aspects: the models used and the driving factors of emissions. Across all models, R2 range from 0.5231 to 0.9989, MAPE range from 0.3017 % to 26.3 %.Hybrid and neural network models emerged as the most popular choices. The most common combinations were spatial hybrid models, primarily blending spatial models with machine learning predictions. Time series hybrid models mostly featured optimized models and machine learning prediction models. Hybrid models outperform single models in both R2 and MAPE. We propose three key recommendations to enhance the accuracy and reliability of future machine learning models: 1) Establish criteria for evaluating influential factors and model selection, 2) Enhance spatial prediction in machine learning by optimization models, and 3) Explore and compare how greenhouse gas prediction models perform across diverse urban settings.
期刊介绍:
The mission of Renewable and Sustainable Energy Reviews is to disseminate the most compelling and pertinent critical insights in renewable and sustainable energy, fostering collaboration among the research community, private sector, and policy and decision makers. The journal aims to exchange challenges, solutions, innovative concepts, and technologies, contributing to sustainable development, the transition to a low-carbon future, and the attainment of emissions targets outlined by the United Nations Framework Convention on Climate Change.
Renewable and Sustainable Energy Reviews publishes a diverse range of content, including review papers, original research, case studies, and analyses of new technologies, all featuring a substantial review component such as critique, comparison, or analysis. Introducing a distinctive paper type, Expert Insights, the journal presents commissioned mini-reviews authored by field leaders, addressing topics of significant interest. Case studies undergo consideration only if they showcase the work's applicability to other regions or contribute valuable insights to the broader field of renewable and sustainable energy. Notably, a bibliographic or literature review lacking critical analysis is deemed unsuitable for publication.