Abstract

ABSTRACTChina's national emissions trading scheme (ETS) is expected to be operational in 2017. Effectively addressing regional disparities at the provincial level in allowance allocation will greatly affect the acceptance of the allocation approach and thus deserves careful consideration. This article aims to explore possible approaches for addressing regional disparities, by introducing regional adjustment factors (RAF) in free allowance allocation. Based on the principle of ‘national unified rules + stricter adjustment by provincial authorities’, four single factorial and three multi-factorial methods are proposed to calculate the RAFs, through a normalization process. These methods are associated with the most acknowledged factors dealing with regional disparities, including per-capita GDP; per-capita CO2 emissions; industrial sector contribution to GDP; economy-wide emissions control targets and CO2 emissions per unit GDP, per unit power and heat output and per unit industrial added value. A comparative analysis is made for the seven methods, in regard to value distribution and level of matching regional political demand.Key policy insights‘Allowing stricter regional adjustment’ represents a dominant feature for China's national ETS, which aims to address regional disparities and government demands.How the adjustment plan is designed will have a major influence on the operation of the national ETS and regional business competitiveness. Provincial governments need to consider the trade-off between auction revenue and local business competitiveness.Applying the different methods leads to more scattered results for some regions, for whom the choice of adjustment approach will therefore have a greater impact.Based on the analysis, four adjustment methods that generate similar results – the per-capita GDP-based method, the intensity reduction target-based method, the 12th FYP target-based method and intensity-based grandfathering – are recommended for most provincial-level regions, with some exceptions.

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