4.7 Article

Synergistic effect of carbon ETS and carbon tax under China's peak emission target: A dynamic CGE analysis

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 825, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2022.154076

关键词

Carbon emission peak; The dynamic CGE model; Hybrid policy; Carbon tax and carbon ETS

资金

  1. Zhejiang Provincial Natural Science Foundation of China

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This study examines the impact of different carbon emission reduction policies on achieving carbon emissions peak by using a dynamic computable general equilibrium model. The results show that a hybrid system of carbon tax and carbon ETS can help achieve the peak at a lower economic cost and promote optimization of energy consumption structures.
Global warming resulting from greenhouse gas emissions poses threats to humankind and has become a worldwide issue. As the top CO2 emitter in the world, China has committed to achieving its carbon emission peak by no later than 2030; in this context, how to best use and apply carbon emission reduction policy is particularly critical. By constructing a dynamic computable general equilibrium (CGE) model, we first examine a pure ETS included only the electricity sector in 2021, and the eight sectors starting in 2022, considering a declining carbon intensity rate of 4.5% and a higher rate of 4.8%. With the carbon intensity rates of 4.3% and 4.5%,we further evaluate two-hybrid systems of the carbon tax and carbon ETS, where the carbon tax of 10 yuan per ton is the starting levied rate in 2022 and increases at 4 yuan per ton year by year. The results proved that hybrid emission reduction policy can help reach a carbon emissions peak before 2030 and do so at a lower economic cost compared to the effect of pure carbon ETS. Besides, the coordinated use of a carbon tax and a carbon ETS can promote optimization of energy consumption structures and accelerate the decline of energy intensity and carbon intensity; this can contribute to curbing the growth of total energy consumption and total carbon emissions.

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