[Machine Learning-based Factor Analysis and Carbon Peaking Scenario Simulation for Resource-based Cities from Shanxi...

As a national resource-based economy transformation comprehensive supporting reform pilot area， predicting the carbon emissions of resource-based cities in Shanxi Province is of great significance for the orderly promotion of the dual-carbon target. The study takes the carbon emissions of 11 cities in Shanxi Province from 2000 to 2023 provided by the Emissions Database for Global Atmospheric Research （EDGAR） as a sample， identifies the key influencing factors of carbon emissions based on the random forest algorithm， and constructs a PSO-LSTM-XGBoost hybrid model to predict the carbon peak trend of resource-based cities in Shanxi Province. The results showed that： ① From 2000 to 2023， the carbon emission in Shanxi Province demonstrated a fluctuating upward trend in general， except for Linfen City， which had a downward trend in recent years， and the remaining cities had a fluctuating upward trend. ② Total energy consumption， urbanization level， secondary industry GDP， total GDP， highway passenger traffic， R&D internal expenditure， primary industry structure， and primary industry GDP were the key influencing factors of carbon emission in Shanxi Province. ③ The PSO-LSTM-XGBoost model had the lowest prediction error and the highest goodness of fit among multiple optimization models， providing a relatively accurate tool for the subsequent prediction of carbon emission scenarios in Shanxi Province. ④ Under the baseline scenario， Shanxi Province reaches a peak of 705 million tons in 2031， and the low-carbon scenario achieves peak carbon three years earlier than in the baseline scenario and reduces the peak value by 7.94%， while under the high-carbon scenario， the peak value is delayed until 2034， and the peak value is nearly 800 million tons. Based on this， the study proposes city-specific， optimized capacity structure and cross-region synergistic governance for low-carbon development， which provides a reference for city-specific policymaking and precise carbon reduction.