Machine learning for predicting urban greenhouse gas emissions: A systematic literature review

IF 16.3 1区 工程技术 Q1 ENERGY & FUELS Renewable and Sustainable Energy Reviews Pub Date : 2025-03-16 DOI:10.1016/j.rser.2025.115625
Yukai Jin , Ayyoob Sharifi
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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.
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温室气体在塑造城市气候模式和动态方面发挥着至关重要的作用。使用机器学习方法为预测城市现在和未来的温室气体排放提供了机会。在此,我们回顾了从 2003 年到 2023 年利用机器学习预测城市温室气体排放的 75 篇论文。我们重点关注两个方面:所使用的模型和排放的驱动因素。在所有模型中,R2 从 0.5231 到 0.9989 不等,MAPE 从 0.3017 % 到 26.3 % 不等。最常见的组合是空间混合模型,主要是将空间模型与机器学习预测相结合。时间序列混合模型大多采用优化模型和机器学习预测模型。混合模型的 R2 和 MAPE 均优于单一模型。我们提出了三项关键建议,以提高未来机器学习模型的准确性和可靠性:1)建立评估影响因素和模型选择的标准;2)通过优化模型加强机器学习中的空间预测;3)探索和比较温室气体预测模型在不同城市环境中的表现。
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来源期刊
Renewable and Sustainable Energy Reviews
Renewable and Sustainable Energy Reviews 工程技术-能源与燃料
CiteScore
31.20
自引率
5.70%
发文量
1055
审稿时长
62 days
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.
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