How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning. (15th April 2023)

• Record Type: Journal Article
• Authors: Shi, Changfeng; Zhi, Jiaqi; Yao, Xiao; Zhang, Hong; Yu, Yue; Zeng, Qingshun; Li, Luji; Zhang, Yuxi
• Is Part Of: Energy. Volume 269(2023)
• Publication Date: 2023-04-15
• Subjects: Carbon peak -- Influencing factors -- Genetic Algorithm(GA) -- LSTM neural network -- Scenario analysis; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2023.126776
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: The cross-analysis of the low carbon economy and deep learning is used for research. A GA-LSTM model was built to predict the carbon peak in China. Scenario analysis is applied to explore China's scientific emission reduction path. China's 2030 carbon peak target is likely to be met.