Understanding the major driving forces of CO₂ emissions has become indispensable as the challenges of climate change and adaptation become more widely recognized. This systematic review presents a comprehensive overview of the econometric models applied to disentangling the relationships among carbon emissions, energy consumption, and economic growth. The electronic databases Web of Science, SCOPUS, and EconPapers were used to identify 776 citation records to discuss this issue. Resultantly, 182 peer-reviewed journal articles met the pre-defined eligibility criteria and were retained for discussion. We found varied modelling approaches, but artificial neural networks, STIRPAT model, and Granger causality were the primary models used by authors in recent years. Overall, the analyzed methods could be considered efficient in investigating the relationships between the analyzed variables, promoting discussion about the urgency of investment in renewable energy sources, and implementing appropriate CO₂ emissions mitigation policies.

了解CO ₂的主要驱动力随着气候变化和适应挑战越来越广泛地认识到，排放已成为必不可少的。这篇系统的综述全面介绍了计量经济学模型，该模型用于解开碳排放，能源消耗和经济增长之间的关系。电子数据库Web of Science，SCOPUS和EconPapers用于识别776个引用记录以讨论此问题。结果，有182篇经过同行评审的期刊文章符合预定的资格标准，并保留进行讨论。我们发现了各种建模方法，但是人工神经网络，STIRPAT模型和Granger因果关系是近年来作者使用的主要模型。总体而言，在研究分析变量之间的关系时，可以认为分析方法是有效的，₂减排政策。