Abstract
Carbon emission (CE) has led to increasingly severe climate problems. The key to reducing CE is to identify the dominant influencing factors and explore their influence degree. The CE data of 30 provinces from 1997 to 2020 in China were calculated by IPCC method. Based on this, the importance order of six factors included GDP, Industrial Structure (IS), Total Population (TP), Population Structure (PS), Energy Intensity (EI) and Energy Structure (ES) affecting the CE of China’s provinces were obtained by using symbolic regression, then the LMDI and the Tapio models were established to deeply explore the influence degree of different factors on CE. The results showed that the 30 provinces were divided into five categories according to the primary factor, GDP was the most important factor, followed by ES and EI, then IS, and the least TP and PS. The growth of per capita GDP promoted the increase of CE, while reduced EI inhibited the increase of CE. The increase of ES promoted CE in some provinces but inhibited in others. The increase of TP weakly promoted the increase of CE. These results can provide some references for governments to formulate relevant CE reduction policies under dual carbon goal.
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This work was supported by the Beijing Natural Science Foundation (Grant No. 3202029).
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Chunjing Liu: Writing - original draft, Conceptualization, Software, Writing - review & editing. Weiran Lyu: Material preparation, data collection and analysis. Xuanhao Zang: Writing - review & editing. Fei Zheng: Writing - review & editing. Wenchang Zhao: Writing - review & editing. Qing Xu: Writing - review & editing. Jianyi Lu: Project administration, Writing - review & editing.
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Liu, ., Lyu, W., Zang, X. et al. Exploring the factors effecting on carbon emissions in each province in China: A comprehensive study based on symbolic regression, LMDI and Tapio models. Environ Sci Pollut Res 30, 87071–87086 (2023). https://doi.org/10.1007/s11356-023-28608-4
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DOI: https://doi.org/10.1007/s11356-023-28608-4