A Review of the Research Methods of Carbon Emission Driving Factors Analysis and Carbon Peak Prediction

Yixue Sun; Kai Zhang; Yang He; Ran Xu; Yimin Zhao; Yujia Zhao; Qingsong Qin; Baozhu Xu; Shi Li

doi:10.62051/ijnres.v8n1.05

Authors

Yixue Sun
Kai Zhang
Yang He
Ran Xu
Yimin Zhao
Yujia Zhao
Qingsong Qin
Baozhu Xu
Shi Li

DOI:

https://doi.org/10.62051/ijnres.v8n1.05

Keywords:

Carbon emission; Carbon peak; Model building; Factor decomposition.

Abstract

With the growing severity of global climate change, accelerating low-carbon transition and emission reduction has become urgent. A key research focus is how to identify the main mechanisms driving carbon emissions and to accurately predict the timing and pathway of future carbon peaking. This paper systematically reviews and synthesizes two core methodological strands, and examines their respective strengths and limitations. The results indicate that, in decomposition of emission drivers, combined models help overcome the constraints of single methods, while in carbon peaking prediction, machine learning models can improve forecasting accuracy. Nevertheless, both types of approaches still have considerable room for improvement. Building on the summary of existing methods and current applications, this study proposes suggestions and directions for future research.

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