Factor Analysis and Prediction of Carbon Emission in Guangdong Province

• Published in: 2023 2nd International Conference on Clean Energy Storage and Power Engineering (CESPE) pp. 99 - 107
• Main Authors: Wang, Shangjiu, Du, Xueyan, Cheng, Liang, Li, Keqiang, Zhang, Peiling, Xu, Jiana
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.12.2023
• Subjects: Analytical models; Carbon dioxide; carbon emission; emission peak; Green products; Linear regression; multi-model fusion; Predictive models; scenario forecasting; Sociology; STIRPAt model; Urban areas
• DOI: 10.1109/CESPE60923.2023.00029

Basing on the national conditions and actual situation, we study whether Guangdong Province can reach its emission peak before 2030, and provide suggestions for Guangdong Province's CO2 emission reduction strategy. This article takes Guangdong Province as the research object and predicts carbon emissions under baseline, green, and technology scenarios from 2022 to 2035 based on multi model fusion. It also deduces whether emissions under different scenarios can reach their peak before 2030. Constructing the STIRPAT model to analyze the factors affecting carbon emissions, it was found that per capita GDP, urbanization rate, energy intensity, energy structure, industrial structure, and technological progress promoted carbon emissions, while the total population size and carbon intensity of energy consumption suppressed carbon emissions. ARIMA model is used to predict the values of carbon emission influencing factors from 2022 to 2035, and the average annual rate of change is calculated for the three scenarios. Using a multiple linear regression model combined with ELM and GA-ELM models, carbon emission forecasts for Guangdong Province from 2022 to 2035 are obtained under three scenarios. It is found that only under the green scenario can Guangdong Province achieve carbon peak before 2030.