Predicting carbon peak at the provincial level using deep learning - Xiaoyan Tang; Kunsheng Fang (Environmental Research Communications)
Assessing carbon peak status is crucial in designing mitigation strategies for provinces to mitigate CO _2 emissions while maintaining economic development. This study proposes a comprehensive research framework to evaluate carbon peak status at the provincial level. The framework involves identifying the critical emission sources and their primary contribution sectors using the LASSO regression method, predicting the CO _2 emissions of critical sources using recurrent neural networks, and exploring mitigation schemes using scenario analysis. This study uses Guizhou Province as an example and analyzes the CO _2 emissions of 17 energy consumption types and one production process in 47 socio-economic sectors and discovers that: ① Between 1997 and 2005, the most critical sources of CO _2 emissions were the consumption of Raw Coal; ② Between 2006 and 2021, the critical sources of emissions were the consumption of Raw Coal, Other Washed Coal (OWC), and Diesel Oil, and one industry process (Cement Production Process, CPP); ③ Between 2006 and 2021, the primary contribution sector for Raw Coal and OWC emissions is the Production and Supply of Electric Power, Steam, and Hot Water (PSEPSHW) sector. The main contributors to Diesel Oil emissions are the Transportation, Storage, Post and Telecommunication Services (TSPTS), and Other Services (OS) sectors. This study projects that the rising trend in total CO _2 emissions will continue from 2022 to 2040 and that emissions will not yet reach their peak by 2030. Furthermore, CO _2 emissions from Raw Coal consumption and CO _2 emissions from Diesel Oil consumption will continue to increase. These are crucial when designing mitigation schemes for total CO _2 emissions. The scenario analysis presents three mitigation schemes for Raw Coal and Diesel Oil emissions that have the potential to reverse the upward trend in CO _2 emissions.