Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends

Abstract

In order to promote the achievement of the dual-carbon goal, this paper proposes an extended STIRPAT model and a PSO-BP neural network prediction model to analyze and predict the factors influencing carbon emissions and future carbon emissions. To address the multicollinearity problem, the STIRPAT model was validated using ridge regression, and the BP neural network was optimized using the particle swarm algorithm (PSO) to improve the prediction accuracy of the model. Taking the metal smelting industry in China as the research object, the results show that the influencing factors of carbon emission in the metal smelting industry are, in descending order, population size, energy structure, urbanization rate, intensity of energy consumption, added value of the secondary industry, and per capita GDP. In the future, the carbon emission of the metal smelting industry in China will keep the downward trend of the industry year by year, and the adjustment of the energy structure is the key to the achievement of carbon emission reduction in this industry. Finally, a series of countermeasures are proposed to reduce carbon emissions in the metal smelting industry with regard to the influencing factors and trends of carbon emissions.

Analysis of Influencing Factors and Prediction of the Peak Value of Industrial Carbon Emission in the Sichuan-Chongqing Region

“…The STIRPAT model has been used by many scholars in the field of the decomposition and prediction of carbon emission-influencing factors. Tang et al used the extended STIRPAT model to analyze countermeasures and suggestions for carbon emissions in China's metal smelting industry, identifying population, coal consumption, urbanization rate, total metal output, carbon intensity, proportion of secondary industries, and per capita GDP as important factors affecting carbon emissions [10]. Cai et al adopted the novel STIRPAT model to explore ways to reduce carbon emissions from the perspective of household consumption and assessed the impact of major factors such as carbon emission intensity, consumption structure, per capita consumption, and population on indirect household carbon emissions, finding that the energy intensity of economic industries is an important factor affecting carbon emission intensity [11].…”

“…APer capita income Regional GDP per capita (ten thousand yuan)Rapid economic development changes people's consumption structure and affects carbon emission level and carbon intensity[10] …”