Can national carbon emission trading policy effectively recover GDP losses? A new linear programming-based three-step estimation approach

Abstract

With the implementation of China’s national carbon trading market, concerns arose over how many gross domestic product (GDP) losses caused by carbon reduction could be recovered by trading carbon emissions. Accordingly, we proposed a new linear programming-based three-step estimation approach to systematically investigate four critical issues in carbon emissions trading. These are how to initially allocate carbon trading permits, set the carbon trading price, determine the amount of GDP loss recoveries that could be obtained from carbon trading, and how setting emission reduction targets. Our empirical results showed the following: (I) The allocation modes based on the sovereignty criterion and the ability-to-pay criterion resulted in the lowest (2.84%) and highest (24.56%) concussion impacts on GDP, respectively. (II) The optimal range of the carbon trading price was linked closely to the carbon emission reduction target. Specifically, when the reduction target was set at 10%, the estimated optimal carbon trading price was 256.2–394.8 Yuan/ton, which is higher than the price used in the carbon trading pilot market. (III) If the emission reduction target were set at more than 10%, China could recover at least 598.62 G Yuan of economic losses through the carbon trading market. (IV) From an economic perspective, the single allocation mode, the sovereignty-criterion-based allocation mode, was superior to the mixed allocation modes. We provide several suggestions for policymakers to implementing a carbon emissions trading market in China.