Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/120224
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dc.contributor.authorLiu, X.-
dc.contributor.authorDu, H.-
dc.contributor.authorBrown, M.-
dc.contributor.authorZuo, J.-
dc.contributor.authorZhang, N.-
dc.contributor.authorRong, Q.-
dc.contributor.authorMao, G.-
dc.date.issued2018-
dc.identifier.citationEnergy Policy, 2018; 116:344-356-
dc.identifier.issn0301-4215-
dc.identifier.issn1873-6777-
dc.identifier.urihttp://hdl.handle.net/2440/120224-
dc.description.abstractThe 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.-
dc.description.statementofresponsibilityXi Liu, Huibin Du, Marilyn A. Brown, Jian Zuo, Ning Zhang, Qian Rong, Guozhu Mao-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2018 Elsevier Ltd. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.enpol.2018.02.001-
dc.subjectRegional power system; TIMES model; technological learning; policy mix-
dc.titleLow-carbon technology diffusion in the decarbonization of the power sector: policy implications-
dc.typeJournal article-
dc.identifier.doi10.1016/j.enpol.2018.02.001-
pubs.publication-statusPublished-
dc.identifier.orcidZuo, J. [0000-0002-8279-9666]-
Appears in Collections:Architecture publications
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