Evidence map›Paper›PMID 31760615›Full record

ArticleEnvironmental science and pollution research international2020

How quota allocation affects the unified ETS of China: a simulation with dynamic CGE model.

Qunli Wu, Chunxiang Li

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Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

2 authors.

Qunli WuBeijing Key Laboratory of New Energy and Low-carbon Development, North China Electric Power University, Beijing, 102206, China.
Chunxiang LiDepartment of Economics and Management, North China Electric Power University, Baoding, 071003, China. 18330108823@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The national unified carbon trading market has been officially launched at the end of 2017. The carbon emission quotas should be primary concern, which can be allocated in the form of free and paid ways. However, few literatures studied the economic and environmental impacts of quotas allocation. Thus, this paper constructs 7 scenarios and employs a dynamic, recursive computable general equilibrium (CGE) model to simulate carbon trading market, to probe the relationship between quota allocation and carbon price, and the economic and environmental impact of carbon trading scheme (ETS). Empirical results indicate (1) carbon price has an upward trend with time, which reflects a corresponding increase in emission reduction pressure. Specifically, carbon price increases from 12.44-90.57 CNY/t in 2017 to 65.20-523.44 CNY/t in 2030. In addition, whether under carbon intensity criterion (CIC) or carbon emission criterion (CEC), there is a positive relationship between carbon price and free allocation ratio due to the change of the relationship between supply and demand of quota. With a given free allocation ratio, the price formed with CIC grows faster than that with CEC. (2) Compared with the benchmark scenario, the GDP of China decreases in all scenarios. However, a high level of free allocation ratio combined with CIC may prevent GDP dropping too fast. (3) As for industrial output, covered industries in ETS undertake the largest output losses with an average decline by 4.03-13.60%. Similar to GDP variation, a high free allocation ratio combined with CIC is helpful for sustainable development of industry. (4) Carbon trading has a remarkable effect on emission reductions both in covered and uncovered industries of ETS. Free allocation will reduce market efficiency, which implies it should be cut down gradually at the later stages.

Indexed as

CommerceIndustrySustainable DevelopmentCarbonChinaCarbonAllocation criterionCarbon quota allocationCarbon trading scheme (ETS)Dynamic recursive CGE modelFree allocation ratio

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