Research on China's Carbon Emission Prediction and Low-Carbon Development Strategy Based on Big Data
Year: 2024
Type: article
Abstract: As the world's leading carbon emitter, China has a profound impact on global climate change. Based on data from 2000 to 2023, this paper constructs a comprehensive evaluation index system to evaluate the progress of China's "dual carbon" goal. Using entropy-weight TOPSIS method to determine the weight of each index, we found that the installed capacity of solar power generation and sales of new energy vehicles have the most significant impact on carbon emission. Since 2012, China's overall carbon emission score has continued to rise, highlighting the importance of emission reduction measures. In order to predict CO2 emission from 2024 to 2030, the GM(1,1) grey prediction model and BP neural network regression model are used in this paper. Although the relative error of the GM(1,1) model is only 1.769%, its single-variable prediction limits its persuasibility. Therefore, this paper adjusted the index system and built a BP neural network regression model, which successfully predicted the carbon dioxide emission from 2000 to 2030, with a model fitting degree of more than 0.9. The results show that the index evaluation system and prediction model can provide an effective assessment of China's carbon emission status and provide a scientific basis for the formulation of emission reduction policies.
DOI: 10.54097/9d9a3921
Authors: Huixin Man, Yanxiang Liu, Ying Cao
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4403986310