[1] 全国碳市场发展报告(2024). 生态环境部. 2024.[2]ZHANG Hongyu, ZHANG Da, GUO Siyue, et al. Impact of benchmark tightening design under output-based ETS on China's power sector. Energy, vol. 288, 2024, pp. 129832.[3]ZHANG Hongyu, ZHANG Da, ZHANG Xiliang. The role of output-based emission trading system in the decarbonization of China's power sector.Renewable and Sustainable Energy Reviews, vol. 173, 2023, pp. 113080.[4] 孙洋洋. 燃煤电厂多污染物排放清单及不确定性研究[D]. 浙江大学, 2015. [5]Janssens-Maenhout, G. et al. HTAP_v2. 2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15, 11411-11432.[6]Dou, X., Hong, J., Ciais, P. et al. Near-real-time global gridded daily CO2 emissions 2021. Sci Data 10, 69 (2023).[7]Berriel, Rodrigo F., et al. Monthly energy consumption forecast: A deep learning approach. 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017.[8] 王英伟,马树才.基于ARIMA和LSTM混合模型的时间序列预测[J].计算机应用与软件,2021,38(02):291-298.[9]Kamalov, Firuz, et al. Powering Electricity Forecasting with Transfer Learning. Energies 17.3 (2024): 626.[10]Yang, Zhongsen, et al. Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model. Energy 278 (2023): 127824.