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Type: Journal article
Title: Low-carbon technology diffusion in the decarbonization of the power sector: policy implications
Author: Liu, X.
Du, H.
Brown, M.
Zuo, J.
Zhang, N.
Rong, Q.
Mao, G.
Citation: Energy Policy, 2018; 116:344-356
Publisher: Elsevier
Issue Date: 2018
ISSN: 0301-4215
Statement of
Xi Liu, Huibin Du, Marilyn A. Brown, Jian Zuo, Ning Zhang, Qian Rong, Guozhu Mao
Abstract: The Chinese power sector faces a significant challenge in attempting to mitigate its CO₂ emissions while meeting its fast-growing demand for electricity. To address this challenge, an analytical framework is proposed that incorporates technological learning curves in a technology optimization model. The framework is employed to evaluate the technology trajectories, resource utilization and economic impacts in the power sector of Tianjin in 2005–2050. Using multi-scenario analysis, this study reveals that CO₂ emissions could be significantly reduced if relevant mitigation policies are introduced. The main technologies adopted are ultra-super-critical combustion, integrated gasification combined cycle, wind power, hydropower, biomass power, solar photovoltaic power and solar thermal power. Despite uncertainties, nuclear power and CO₂ capture and storage technology could be cost competitive in the future. The CO₂ emissions cap policy has the advantage of realizing an explicit goal in the target year, while the renewable energy policy contributes to more cumulative CO₂ emissions reduction and coal savings. A carbon tax of 320 CNY/ton CO₂ would contribute to early renewable energy development and more CO₂ reduction in the short run. A sensitivity analysis is conducted to examine the impacts on the power system of learning rates, technology cost reductions and energy fuel price trajectories.
Keywords: Regional power system; TIMES model; technological learning; policy mix
Rights: © 2018 Elsevier Ltd. All rights reserved.
RMID: 0030083131
DOI: 10.1016/j.enpol.2018.02.001
Appears in Collections:Architecture publications

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