Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis

Haitao Xu, Xiongfeng Pan, Shucen Guo and Yuduo Lu

Energy, 2021, vol. 228, issue C

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.

Keywords: CO2 emissions forecasting; Consumption preference; Emissions peak; Knowledge capital; NL-MIOM model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221007635
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:228:y:2021:i:c:s0360544221007635

DOI: 10.1016/j.energy.2021.120514

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().