Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

Xiwen Cui, Shaojun E, Dongxiao Niu, Bosong Chen and Jiaqi Feng
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Xiwen Cui: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Shaojun E: School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Bosong Chen: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Jiaqi Feng: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2021, vol. 13, issue 21, 1-18

Abstract: As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

Keywords: gradient lifting tree; whale optimization algorithm; carbon emissions; carbon peak (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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