China's future energy mix and emissions reduction potential: a scenario analysis incorporating technological learning curves. (20th January 2016)

• Record Type: Journal Article
• Authors: Zou, Hongyang; Du, Huibin; Broadstock, David C.; Guo, Junpeng; Gong, Yuqin; Mao, Guozhu
• Is Part Of: Journal of cleaner production. Volume 112:Part 2(2016:Jan.)
• Page Start: 1475
• Page End: 1485
• Publication Date: 2016-01-20
• Subjects: CO2 emission reduction -- Carbon tax -- Structure of power generation -- Learning curve; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2015.08.012
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: This paper examines the impacts of CO2 emission 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 CO2 emission 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. CO2 emission 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. Highlights: Future costs of electricity generation in China are obtained using 'learning curve' concepts. The energy mix is modeled up to 2040 by minimizing the total power generation cost. CO2 emission reduction potential is obtained under various emissions reduction and carbon tax scenarios. The effect of learning curve considers the implications of shale gas and carbon capture and storage. Policy implications are that a mix of CO2 emissions reduction targets and carbon taxes may be desirable.