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https://hdl.handle.net/2440/120224
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dc.contributor.author | Liu, X. | - |
dc.contributor.author | Du, H. | - |
dc.contributor.author | Brown, M. | - |
dc.contributor.author | Zuo, J. | - |
dc.contributor.author | Zhang, N. | - |
dc.contributor.author | Rong, Q. | - |
dc.contributor.author | Mao, G. | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Energy Policy, 2018; 116:344-356 | - |
dc.identifier.issn | 0301-4215 | - |
dc.identifier.issn | 1873-6777 | - |
dc.identifier.uri | http://hdl.handle.net/2440/120224 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Xi Liu, Huibin Du, Marilyn A. Brown, Jian Zuo, Ning Zhang, Qian Rong, Guozhu Mao | - |
dc.language.iso | en | - |
dc.publisher | Elsevier | - |
dc.rights | © 2018 Elsevier Ltd. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.enpol.2018.02.001 | - |
dc.subject | Regional power system; TIMES model; technological learning; policy mix | - |
dc.title | Low-carbon technology diffusion in the decarbonization of the power sector: policy implications | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.enpol.2018.02.001 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Zuo, J. [0000-0002-8279-9666] | - |
Appears in Collections: | Architecture publications Aurora harvest 4 |
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