China's future energy mix and emissions reduction potential: A scenario analysis incorporating technological learning curves

Hongyang Zou, Huibin Du, David Clive Broadstock, Junpeng Guo, Yuqin Gong, Guozhu Mao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

52 Citations (Scopus)

Abstract

This paper examines the impacts of CO2emission reduction targets and carbon taxes on the structure of power generation in China. A model is developed to minimize the total electricity generation cost and select the optimal energy technology and resource mix for China. The model contributes to existing work by utilizing the learning curve concept (which manifests as diminishing costs of production), and includes constraints for minimum energy generation and also an emissions cap. The result shows that the introduction of the CO2emission reduction targets and carbon taxes both shift energy production technologies away from high carbon content fossil-fuels towards low carbon content fossil-based and renewable energies. CO2emission reduction targets turn out to be more effective in the early years, while carbon taxes become more effective in the later periods. Perhaps unsurprisingly, all options result in a net increase in total production costs. In addition, some scenario analyses are conducted to consider the possible roles of shale gas and improved carbon capture and storage technologies, showing the general conclusions to be robust.
Original languageEnglish
Pages (from-to)1475-1485
Number of pages11
JournalJournal of Cleaner Production
Volume112
DOIs
Publication statusPublished - 20 Jan 2016
Externally publishedYes

Keywords

  • Carbon tax
  • CO emission reduction 2
  • Learning curve
  • Structure of power generation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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