@article {1399003,
	title = {The general equilibrium impacts of carbon tax policy in China: a multi-model assessment},
	journal = {Energy Economics},
	volume = {99},
	year = {2021},
	pages = {105284},
	abstract = {We conduct a multi-model comparison of a carbon tax policy in China to examine how different models simulate the impacts in both near-term 2020, medium-term 2030, and distant future 2050. Though Top-down computable\&nbsp;<a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/general-equilibrium" title="Learn more about general equilibrium from ScienceDirect{\textquoteright}s AI-generated Topic Pages">general equilibrium</a>(CGE) models have been applied frequently on climate or other environmental/energy policies to assess emission reduction, energy use and economy-wide general equilibrium outcomes in China, the results often vary greatly across models, making it challenging to derive policies. We compare 8 China CGE models with different characteristics to examine how they estimate the effects of a plausible range of carbon tax scenarios {\textendash} low, medium and high carbon taxes.. To make them comparable we impose the same population growth, the same GDP growth path and world energy price shocks. We find that the 2030 NDC target for China are easily met in all models, but the 2060 carbon neutrality goal cannot be achieved even with our highest carbon tax rates. Through this carbon tax comparison, we find all 8 CGE models differ substantially in terms of impacts on the macroeconomy, aggregate prices, energy use and carbon reductions, as well as industry level output and price effects. We discuss the reasons for the divergent simulation results including differences in model structure, substitution parameters, baseline renewable penetration and methods of revenue recycling.},
	url = {https://www.sciencedirect.com/science/article/pii/S0140988321001894},
	author = {Jing, Cao and Hancheng Dai and Shantong Li and Chaoyi Guo and Ho, Mun and Wenjia Cai and Jianwu He and Hai Huang and Jifeng Li and Yu Liu and Haoqi Qian and Can Wang and Libo Wu and Xiliang Zhang}
}
