Impacts of China's emissions trading schemes on deployment of power generation with carbon capture and storage

Academic Article


  • The establishment of an emissions trading scheme (ETS) in China creates the potential for a “least cost” solution for achieving the greenhouse gas (GHG) emissions reductions required for China to meet its Paris Agreement pledges. China has pledged to reduce CO2 intensity by 60–65% in 2030 relative to 2005 and to stop the increase in absolute CO2 emissions around 2030. In this series of studies, we enhance the MIT Economic Projection and Policy Analysis (EPPA) model to include the latest assessments of the costs of power generation technologies in China to evaluate the impacts of different potential ETS pathways on deployment of carbon capture and storage (CCS) technology. This paper reports the results from baseline scenarios where power generation prices are assumed to be homogeneous across the country for a given mode of generation. We find that there are different pathways where CCS might play an important role in reducing the emission intensity in China's electricity sector, especially for low carbon intensity targets consistent with the ultimate goals of the Paris Agreement. Uncertainty about the exact technology mix suggests that decision makers should be wary of picking winning technologies, and should instead seek to provide incentives for emission reductions. While it will be challenging to meet the CO2 intensity target of 550 g/kWh for the electric power sector by 2020, multiple pathways exist for achieving lower targets over a longer timeframe. Our initial analysis shows that carbon prices of 35–40$/tCO2 make CCS technologies on coal-based generation cost-competitive against other modes of generation and that carbon prices higher than 100$/tCO2 favor a major expansion of CCS. The next step is to confirm these initial results with more detailed modeling that takes into account granularity across China's energy sector at the provincial level.
  • Authors

    Published In

  • Energy Economics  Journal
  • Digital Object Identifier (doi)

    Pubmed Id

  • 22562532
  • Author List

  • Morris J; Paltsev S; Ku AY
  • Start Page

  • 848
  • End Page

  • 858
  • Volume

  • 81