How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning

Changfeng Shi, Jiaqi Zhi, Xiao Yao, Hong Zhang, Yue Yu, Qingshun Zeng, Luji Li and Yuxi Zhang

Energy, 2023, vol. 269, issue C

Abstract: This paper studied the carbon peak through the cross-analysis of low-carbon economics and deep learning. The STIRPAT model and ridge regression was used to distinguish and rank the importance of influencing factors to carbon emissions. In addition, an innovative GA-LSTM model was constructed for prediction. It combined the scenario analysis to explore the path of China's low-carbon development. The results showed that China's carbon emissions have been showing a growing trend, and among the many influencing factors, only the technological level had an inhibitory effect on carbon emissions. China's carbon peak will be reached around 2030 under all three scenarios of benchmark, steady growth, and green development, with peak values of 11.82, 11.94, and 11.64 billion tons, respectively. Meanwhile, there was a big difference in the rate of change in China's carbon emissions before and after the carbon peak. The rising-rate was faster before the carbon emission peak, while the decline rate was slower after the peak. This paper argued that China should start from the energy consumption structure and industrial structure to promote the development of emission reduction work and, at the same time, vigorously promote the effect of the technological level of emission reduction.

Keywords: Carbon peak; Influencing factors; Genetic Algorithm(GA); LSTM neural network; Scenario analysis (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001706

DOI: 10.1016/j.energy.2023.126776

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