Carbon Emission Trend Prediction for Regional Cities in Jiangsu Province Based on the Random Forest Model

Wanru Yang, Long Chen, Tong Ke, Huan He, Dehu Li, Kai Liu and Huiming Li ()
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Wanru Yang: School of Environment, Nanjing Normal University, Nanjing 210023, China
Long Chen: School of Environment, Nanjing Normal University, Nanjing 210023, China
Tong Ke: School of Environment, Nanjing Normal University, Nanjing 210023, China
Huan He: School of Environment, Nanjing Normal University, Nanjing 210023, China
Dehu Li: School of Environment, Nanjing Normal University, Nanjing 210023, China
Kai Liu: School of Environment, Nanjing Normal University, Nanjing 210023, China
Huiming Li: School of Environment, Nanjing Normal University, Nanjing 210023, China

Sustainability, 2024, vol. 16, issue 23, 1-17

Abstract: This study accounted for and analyzed the carbon emissions of 13 cities in Jiangsu Province from 1999 to 2021. We compared the simulation effects of four models—STIRPAT, random forest, extreme gradient boosting, and support vector regression—on carbon emissions and performed model optimization. The random forest model demonstrated the best simulation performance. Using this model, we predicted the carbon emission paths for the 13 cities in Jiangsu Province under various scenarios from 2022 to 2040. The results show that Xuzhou has already achieved its peak carbon target. Under the high-speed development scenario, half of the cities can achieve their peak carbon target, while the remaining cities face significant challenges in reaching their peak carbon target. To further understand the factors influencing carbon emissions, we used the machine learning interpretation method SHAP and the features importance ranking method. Our analysis indicates that electricity consumption, population size, and energy intensity have a greater influence on overall carbon emissions, with electricity consumption being the most influential variable, although the importance of the factors varies considerably across different regions. Results suggest the need to tailor carbon reduction measures to the differences between cities and develop more accurate forecasting models.

Keywords: carbon emission; regional variations; machine learning; SHAP analysis; scenario simulation; trend forecasts (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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