China launched its national carbon emissions trading scheme (ETS) in 2017. The choice of allowance allocation methods can strongly influence the political acceptance of an ETS by enterprises/sectors that are covered by it. This article builds a computable general equilibrium model to conduct a quantitative analysis of the effects of nine common allowance allocation methods on both the macro-economy and the industries covered by the ETS. The results of the model show that national gross domestic product (GDP) decreases by 0.37–0.44% during the 13th Five-Year Plan period against a backdrop of a 2% annual reduction in carbon emissions from the sectors covered by the ETS compared with the business-as-usual scenario. China's total emissions drop by 1.71–1.76%. When auctioning and allocation approaches without ex-post adjustment are used, the allowance price is 40–45 yuan/tCO2. When the dynamic allocation methods are used, the allowance price increases to 70–75 yuan/tCO2. Auctioning and allocation approaches without ex-post adjustment exert the same influence on macroscopic indicators (such as GDP and total emissions) and industry indicators (such as output and price). The dynamic allocation methods have a subsidy effect, which can significantly reduce the effect of the ETS on GDP and industry output while significantly increasing the allowance price and decreasing the economic efficiency of the ETS. The cement and steel industries are the most sensitive to the output subsidy effect of the dynamic allocation methods. This article suggests a limit on the use of dynamic allocation approaches to avoid excessively high allowance prices and excessive subsidies for overcapacity industries. Key policy insightsAuctioning and one-off allocation purely based on historical data are most economically efficient; dynamic allocation based on updated or actual output data could reduce the impact of the ETS on enterprises’ output, but will increase the allowance price and thus reduce the economic efficiency of the ETS.Implementing a national ETS will have limited impact on China's GDP, but could promote emissions abatement of the whole economy in an efficient way.Different allocation methods have almost the same impact on GDP, but the impacts on different sectors are significantly different. Auctioning and one-off allocation purely based on historical data are most economically efficient; dynamic allocation based on updated or actual output data could reduce the impact of the ETS on enterprises’ output, but will increase the allowance price and thus reduce the economic efficiency of the ETS. Implementing a national ETS will have limited impact on China's GDP, but could promote emissions abatement of the whole economy in an efficient way. Different allocation methods have almost the same impact on GDP, but the impacts on different sectors are significantly different.