Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis. (1st August 2021)

• Record Type: Journal Article
• Authors: Xu, Haitao; Pan, Xiongfeng; Guo, Shucen; Lu, Yuduo
• Is Part Of: Energy. Volume 228(2021)
• Publication Date: 2021-08-01
• Subjects: CO2 emissions forecasting -- Consumption preference -- Emissions peak -- Knowledge capital -- NL-MIOM model; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120514
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The whole community have paid a lot of attention to whether China can achieve its emission target. The paper adds to the existing literature on emission forecast by considering consumption preference, knowledge capital and technological innovation mechanism with in a non-linear multi-agent intertemporal optimization model (NL-MIOM) which can further improve the accuracy of CO2 emission prediction. The historical fitting test shows that the MAPE value of NL-MIOM model is 1.81%, which is lower than the GM, NGM, ARIMA, OGM, SVR and BR-AGM models. By using this model, we forecast the CO2 emissions and energy consumption structure in China under different scenarios from 2018 to 2035. We find that China's CO2 emissions will peak around 2032, 2029 or 2027 with 12.34, 11.59 or 11.17 billion tons CO2 emissions under the benchmark scenario, Preference A (American consumption preference) scenario and Preference B (Japanese consumption preference) scenario. Based on the methodology of LMDI decomposition, we identify the main factors affecting China's CO2 emissions. The results show that the technical progress is the main reason for the reduction of CO2 emissions in the historical stage, pre-peak stage and post-peak stage. Moreover, we also forecast the energy use of 14 different industries in China under different scenarios. Graphical abstract: Image 1 Highlights: Establishing a non-linear multi-agent intertemporal optimization model to forecast emissions. We link the energy consumption of different industries in China with a set of energy consumption drivers. We add the knowledge accumulation into the model. Comparing and analyze the emission peak of different industries under different scenarios.