Linear regression models, especially the extended STIRPAT model, are routinely-applied for analyzing carbon emissions data. However, since the relationship between carbon emissions and the influencing factors is complex, fitting a simple parametric model may not be an ideal solution. This paper investigated various nonparametric approaches in statistics and machine learning (ML) for modeling carbon emissions data, including kernel regression, random forest and neural network. We selected data from ten Chinese cities from 2005 to 2019 for modeling studies. We found that neural network had the best performance in both fitting and prediction accuracy, which implies its capability of expressing the complex relationships between carbon emissions and the influencing factors. This study provides a new means for quantitative modeling of carbon emissions research that helps to understand how to characterize urban carbon emissions and to propose policy recommendations for "carbon reduction". In addition, we used the carbon emissions data of Wuhu city as an example to illustrate how to use this new approach.

翻译：线性回归模型，特别是扩展的STIRPAT模型，是常规用于分析碳排放数据的方法。然而，由于碳排放与影响因素之间的关系复杂，拟合简单的参数化模型可能并非理想解决方案。本文研究了统计学和机器学习中的多种非参数方法，包括核回归、随机森林和神经网络，用于对碳排放数据建模。我们选取了2005年至2019年中国十个城市的数据进行建模研究。结果表明，神经网络在拟合和预测精度方面均表现最佳，这表明其能够表达碳排放与影响因素之间的复杂关系。本研究为碳排放的定量建模提供了一种新手段，有助于理解如何刻画城市碳排放特征，并为“碳减排”提出政策建议。此外，我们以芜湖市碳排放数据为例，阐述了如何应用这种新方法。