Predicting Energy-Based CO 2 Emissions in the United States Using Machine Learning: A Path Toward Mitigating Climate Change

Longfei Tian, Zhen Zhang, Zhiru He, Chen Yuan, Yinghui Xie, Kun Zhang and Ran Jing ()
Additional contact information
Longfei Tian: College of Natural Resources and Environment, Joint Institute for Environmental Research & Education, South China Agricultural University, Guangzhou 510642, China
Zhen Zhang: College of Natural Resources and Environment, Joint Institute for Environmental Research & Education, South China Agricultural University, Guangzhou 510642, China
Zhiru He: School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA
Chen Yuan: Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA
Yinghui Xie: College of Natural Resources and Environment, Joint Institute for Environmental Research & Education, South China Agricultural University, Guangzhou 510642, China
Kun Zhang: Institute of Agricultural Resources and Environment, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Ran Jing: Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA

Sustainability, 2025, vol. 17, issue 7, 1-18

Abstract: Climate change is one of the most pressing global challenges that could potentially threaten ecosystems, human populations, and weather patterns over time. Impacts including rising sea levels and soil salinization are caused by climate change, primarily driven by human activities such as fossil fuel combustion for energy production. The resulting greenhouse gas (GHG) emissions, particularly carbon dioxide (CO 2 ) emissions, amplify the greenhouse effect and accelerate global warming, underscoring the urgent need for effective mitigation strategies. This study investigates the performance and outcomes of various machine learning regression models for predicting CO 2 emissions. A comprehensive overview of performance metrics, including R 2 , mean absolute error, mean squared error, and root-mean-squared error, and cross-validation scores for decision tree, random forest, multiple linear regression, k-nearest neighbors, gradient boosting, and support vector regression models was conducted. The biggest source of CO 2 emissions was coal (46.11%), followed by natural gas (25.49%) and electricity (26.70%). Random forest and gradient boosting both performed well, but multiple linear regression had the highest prediction accuracy among machine learning models (R 2 = 0.98 training, 0.99 testing). Support vector regression (SVR) and k-nearest neighbors (KNN) demonstrated lower accuracies, whereas decision tree displayed overfitting. The decision tree, random forest, multiple linear regression, and gradient boosting models were found to be extremely sensitive to coal, natural gas, and petroleum (transportation sector) based on sensitivity analysis. Random forest and gradient boosting demonstrated the most sensitivity to coal usage, whereas KNN and SVR maintained excellent R 2 scores (0.94–0.98) but were less susceptible to changes in the variables. This analysis provides insights into the agreement and discrepancies between predicted and actual CO 2 emissions, highlighting the models’ effectiveness and potential limitations.

Keywords: climate change; carbon dioxide emissions; life cycle assessment; decision tree; random forest; multiple linear regression; K-nearest neighbors; gradient boosting; support vector regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/7/2843/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/7/2843/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:7:p:2843-:d:1618419

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().