Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method

Jinjie Zhao, Lei Kou, Haitao Wang, Xiaoyu He, Zhihui Xiong, Chaoqiang Liu and Hao Cui
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Jinjie Zhao: School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Lei Kou: School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Haitao Wang: School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Xiaoyu He: Key Laboratory of Building Structure of Anhui Higher Education Institutes, Anhui Xinhua University, Hefei 230088, China
Zhihui Xiong: School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Chaoqiang Liu: School of Computer, Northeast Electric Power University, Jilin 132012, China
Hao Cui: School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450001, China

Sustainability, 2022, vol. 14, issue 10, 1-17

Abstract: Excessive carbon emissions seriously threaten the sustainable development of society and the environment and have attracted the attention of the international community. The Yellow River Basin is an important ecological barrier and economic development zone in China. Studying the influencing factors of carbon emissions in the Yellow River Basin is of great significance to help China achieve carbon peaking. In this study, quadratic assignment procedure regression analysis was used to analyze the factors influencing carbon emissions in the Yellow River Basin from the perspective of regional differences. Accurate carbon emission prediction models can guide the formulation of emission reduction policies. We propose a machine learning prediction model, namely, the long short-term memory network optimized by the sparrow search algorithm, and apply it to carbon emission prediction in the Yellow River Basin. The results show an increasing trend in carbon emissions in the Yellow River Basin, with significant inter-provincial differences. The carbon emission intensity of the Yellow River Basin decreased from 5.187 t/10,000 RMB in 2000 to 1.672 t/10,000 RMB in 2019, showing a gradually decreasing trend. The carbon emissions of Qinghai are less than one-tenth of those in Shandong, the highest carbon emitter. The main factor contributing to carbon emissions in the Yellow River Basin from 2000 to 2010 was GDP per capita; after 2010, the main factor was population. Compared to the single long short-term memory network, the mean absolute percentage error of the proposed model is reduced by 44.38%.

Keywords: carbon emissions; influencing factors; machine learning; QAP model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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