Low-carbon technology diffusion in the decarbonization of the power sector: Policy implications
Marilyn A. Brown,
Qian Rong and
Energy Policy, 2018, vol. 116, issue C, 344-356
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 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:116:y:2018:i:c:p:344-356
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