Allocation of carbon emission allowance based on DLA-GA model: a case study in China.

To achieve China's determined contributions by 2030 and establish nationwide carbon emission trading system (ETS) which main participants are sectors, appropriated carbon emission allowance (CEA) allocation among sectors is crucial. In CEA distribution, fairness is primary; and sectoral efficiency is another significant factor. Nevertheless, considering fairness and efficiency while covering various sectors is a challengeable issue. Hence, combined with a new tow-objective data envelopment analysis (DEA) model and genetic algorithm (GA), a novel allocation framework is proposed, i.e., dual level allocation scheme incorporated with GA (DLA-GA). On the basis of evaluating the CO emission performance of various sectors in China, the corresponding allocation steps are put forward. Through the value convergence and value repetition tests, the stability and feasibility of DLA-GA are justified. Then, the results of the DLA-GA and grandfathering principle are compared. The research shows that: (1) under the same constraint conditions, the emission right of CO is allocated with DLA-GA, which leads to lower cost and higher overall sectoral performance; (2) through utilizing the En-Lorenz and En-Gini coefficients, it has found that higher allocation equity among sectors emerges via DLA-GA;(3) the key reduction sectors have been revealed through emission value estimation. This work may contribute to enrich the methodologies in CEA allocation at different dimensions, and provide some references for policymaker regarding the achievement of 2030 carbon reduction target.