Carbon peak prediction in China based on Bagging-integrated GA-BiLSTM model under provincial perspective

Yue Yu, Qiyong Chen, Jiaqi Zhi, Xiao Yao, Luji Li and Changfeng Shi

Energy, 2024, vol. 313, issue C

Abstract: Many studies have revealed potential carbon peaking pathways in China. However, to minimize the uncertainty inherent in forecasting studies, more accurate forecasting models as well as more segmented forecasting subjects are always necessary. Based on panel data of 30 Chinese provinces from 2000 to 2020, this study applies the STIRPAT model and ridge regression to examine the key influencing factors of carbon emissions in each province in China, further constructs an innovative Bagging-integrated GA-BiLSTM model for forecasting, and finally explores China's peak carbon pathways in conjunction with scenario analysis. The results show that: (1) increasing urbanization and population growth are the main reasons driving the increase of carbon emissions in most provinces; (2) there is significant variability in the carbon peaking dynamics of China's regions, which can be classified into three categories: autonomous earlier peaking, policy-guided peaking, and difficulty in peaking; (3) For most provinces, the green scenario is more effective than the development scenario in reducing the peak carbon level rather than advancing the peak time; (4) under the development and the green scenario, China's overall carbon peak will be reached in 2032 and 2028, with the peak carbon levels of 11.88 billion tons and 11.42 billion tons, respectively.

Keywords: Carbon peak; Influencing factors; Genetic algorithm; LSTM neural network; Ensemble learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s036054422403295x

DOI: 10.1016/j.energy.2024.133519

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