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Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options

Publiceringsår: 2010
Språk: Engelska
Sidor: 3921-3931
Publikation/Tidskrift/Serie: Energy
Volym: 35
Nummer: 8
Dokumenttyp: Artikel i tidskrift
Förlag: Elsevier


Abstract in Undetermined

In Copenhagen climate conference China government promised that China would cut down carbon intensity 40–45% from 2005 by 2020. CET (carbon emissions trading) is an effective tool to reduce emissions. But because CET is not fully implemented in China up to now, how to design it and its potential impact are unknown to us. This paper studies the potential impact of introduction of CET on China’s power sector and discusses the impact of different allocation options of allowances. Agent-based modeling is one appealing new methodology that has the potential to overcome some shortcomings of traditional methods. We establish an agent-based model, CETICEM (CET Introduced China Electricity Market), of introduction of CET to China. In CETICEM, six types of agents and two markets are modeled. We find that: (1) CET internalizes environment cost; increases the average electricity price by 12%; and transfers carbon price volatility to the electricity market, increasing electricity price volatility by 4%. (2) CET influences the relative cost of different power generation technologies through the carbon price, significantly increasing the proportion of environmentally friendly technologies; expensive solar power generation in particular develops significantly, with final proportion increasing by 14%. (3) Emission-based allocation brings about both higher electricity and carbon prices than by output-based allocation which encourages producers to be environmentally friendly. Therefore, output-based allocation would be more conducive to reducing emissions in the Chinese power sector.


  • Earth and Related Environmental Sciences
  • Carbon emissions trading
  • Emission-based allocation
  • Output-based allocation
  • Agent-based model


  • ISSN: 1873-6785

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