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Exploring influential factors of CO2 emissions in China’s cities using machine learning techniques

  • Climate Change, Agricultural Production, Urbanization, and Carbonization: Potential Solutions for Environmental Sustainability
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Abstract

The precise and exhaustive discernment of factors influencing CO2 emissions underpins the advancement toward sustainable, low-carbon development. Although numerous studies have probed the correlation between predetermined proxy variables and carbon emissions, methodological constraints have often led to an inability to effectively discern carbon emission determinants among numerous potential variables or unravel complex, non-linear relationships, and interaction effects. To redress these research gaps, this research utilized machine learning models to correlate urban CO2 emissions with socioeconomic indicators. The model outputs were then visualized and interpreted using explainable methods. The findings indicated that the model successfully identified a comprehensive array of dominant influences on urban CO2 emissions, principally associated with local fiscal policies, land use, energy consumption, industrial development, and urban transportation. The findings further revealed a complex non-linear association between these factors and urban CO2 emissions; however, the majority of these variables displayed a prevalent propensity to intensify carbon emissions in correspondence with an increase in sample value. Additionally, these factors exhibited a complex interactive influence on urban CO2 emissions, with distinct pairings producing a suppressive effect exclusively at specific combination of sample values. Consequently, this research posited that a robust correlation between urban socioeconomic development and CO2 emissions in China remains to be established. Given the varied impacts of these influencing factors across different cities, a differentiated approach to development should be adopted when charting low-carbon trajectories.

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Data availability

The datasets used and/or analyzed during the current study/Supplementary Materials are available from the corresponding author upon reasonable request.

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Funding

This research is supported by Social science funding from the Hubei education department (No. 19Q038).

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Authors and Affiliations

  1. Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, 32816, USA

    Kun Xiang, Haofei Yu & Md Hasibul Hasan

  2. Research Center of Machine Learning and Environment Science, China Three Gorges University, Yichang, 443002, China

    Kun Xiang, Hao Du, Siyi Wei & Xiangyun Xiang

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  1. Kun Xiang
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  2. Haofei Yu
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Contributions

Kun Xiang: writing the original draft, software coding, and data analyses, revising the manuscript critically for important content, and English writing. Haofei Yu: resources, revising the manuscript critically for important content. Hao Du: revising the manuscript critically for important content and English writing. Md Hasibul Hasan: software coding and data analyses. Siyi Wei: revising the manuscript critically for important content. Xiangyun Xiang: writing review and editing.

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Correspondence to Kun Xiang.

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Xiang, K., Yu, H., Du, H. et al. Exploring influential factors of CO2 emissions in China’s cities using machine learning techniques. Environ Sci Pollut Res (2023). https://doi.org/10.1007/s11356-023-28285-3

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