Carbon mitigation by quota allocation

Abstract

To avoid the calamitous consequences of an even warmer world, the efforts are focused on overarching and immediate solutions to reduce the greenhouse gases. The emissions trading scheme is deemed worldwide as an ecological and robust management mechanism to curb carbon emissions. The challenge is how to design such a scheme to attain the basic purpose of installing a uniform system and equilibrium efficiency achievement. For the first time, existing idea of group method of data handling (GMDH) type neural network (NN) is developed to predict capital stock, labor force, CO₂ emission, energy consumption, and gross domestic product (GDP) based on past information for the top-26 emitting countries. Then, this study deals with the allocation of emission quotas by proposing a two-step optimization mechanism that takes full advantages of the context-dependent data envelopment analysis (DEA) and equilibrium efficient frontier DEA (EEFDEA) models. In the first step, under the premise of constant total carbon emissions, a pragmatic efficiency-oriented carbon quota trading system is established to attain equilibrium state. In the second step, under the measured total emission mitigation target, the carbon quotas allocation mechanism is formulated to translate the top emitters' mitigation target into national purposes from two features of efficiency and fairness as well as to specify the comprehensive targets for the top emitters to maintain their equilibrium state. Two of the main findings are: 1) The top emitters should decrease the total CO2 emission by at least 37% by 2023. 2) In light of the CO2 emission mitigation allocation, the countries with larger potentials are China, Japan, and the US yet to receive the larger portions of 20%, 9% and 22%, respectively. Finally, the allocation method that takes regional heterogeneity into account is more logical since it alleviates pressure on the emitters to decrease carbon emissions and establishes a baseline for distributing CO₂ emission quotas across the emitters to enhance adaptation to nations' present circumstances.

Keywords: Data envelopment analysis (DEA); Emission-mitigation target allocation; Group method of data handling type neural network; International emission quotas trading; Prediction.