Carbon Dioxide Emission Forecast: A Review of Existing Models and Future Challenges

Yaxin Tian, Xiang Ren, Keke Li and Xiangqian Li ()
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Yaxin Tian: School of Finance, Capital University of Economics and Business, Beijing 100070, China
Xiang Ren: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Keke Li: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Xiangqian Li: School of Statistics, Capital University of Economics and Business, Beijing 100070, China

Sustainability, 2025, vol. 17, issue 4, 1-29

Abstract: In the face of global climate change, accurately predicting carbon dioxide emissions has become an urgent requirement for environmental science and policy-making. This article provides a systematic review of the literature on carbon dioxide emission forecasting, categorizing existing research into four key aspects. Firstly, regarding model input variables, a thorough discussion is conducted on the pros and cons of univariate models versus multivariable models, balancing operational simplicity with high accuracy. Secondly, concerning model types, a detailed comparison is made between statistical methods and machine learning methods, with a particular emphasis on the outstanding performance of deep learning models in capturing complex relationships in carbon emissions. Thirdly, regarding model data, the discussion explores annual emissions and daily emissions, highlighting the practicality of annual predictions in policy-making and the importance of daily predictions in providing real-time support for environmental policies. Finally, regarding model quantity, the differences between single models and ensemble models are examined, emphasizing the potential advantages of considering multiple models in model selection. Based on the existing literature, future research will focus on the integration of multiscale data, optimizing the application of deep learning models, in-depth analysis of factors influencing carbon emissions, and real-time prediction, providing scientific support for a more comprehensive, real-time, and adaptive response to the challenges of climate change. This comprehensive research outlook aims to provide scientists and policymakers with reliable information on carbon emissions, promoting the achievement of environmental protection and sustainable development goals.

Keywords: carbon emissions; time series prediction; machine learning; model; climate change; sustainable (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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