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Regional Opportunities for China to Go Low-Carbon: Results from the REEC Model

Authors: Gürkan Kumbaroglu; Lei Zhu; Hongbo Duan; Ying Fan;

Regional Opportunities for China to Go Low-Carbon: Results from the REEC Model

Abstract

The intention of this paper is to (i) introduce a multi-regional dynamic emissions trading model and (ii) examine the potential impact of an emissions trading scheme (ETS) on the long-term evolution of energy technologies from national and regional perspectives in China. The establishment of this model is a salutary attempt to Sinicize the global integrated assessment model that combines economy, energy, and environment systems. The simulation results indicate that: (1) for majority of regions, ETS is more effective in cutting CO2 emissions than a harmonized carbon tax (HCT), but this might not be true for the entire country, which means that these two options have little difference in overall carbon reduction; (2) carbon tax policy is a more cost-effective option in curbing CO2 with respect to ETS in the long run; (3) neither ETS nor pure carbon tax provide enough incentives for the breakthrough of carbon-free energy technologies, which illustrates that matching with some other support policies, such as subsidies and R&D investment, is essential to extend the niche market; and (4) In the context of ETS, the diffusion of non-fossil technologies in regions that act as sellers performs much better than this diffusion in the buyer regions.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Average
hybrid