Research on Optimization Method and Decision Support Model of Allowance Allocation Based on Genetic Algorithm under New Carbon Emission Trading Mechanisms

Under the background of "dual-carbon" target, carbon trading mechanism, as the core carrier of marketbased emission reduction tools, the scientificity and flexibility of its quota allocation directly affects the efficiency of emission reduction and the fairness of the industry.This study proposes a dynamic allocation framework integrating entropy weight method and multi-objective genetic algorithm (MOGA), aiming at the synergistic optimization of fairness, efficiency and operability of carbon emission quotas.By constructing a two-layer allocation model for carbon trading mechanism, the quota object is firstly divided into two types of equipment: power generation and heat production, and the initial quota is dynamically allocated based on the benchmark value of carbon emission per unit of power.In view of the limitations of the baseline method, the entropy weight method is introduced to construct a three-level index system of "emission reduction responsibility-capacity-potential", quantify the weights of each link in the coal power supply chain, and solve the complex multi-objective optimization problem by combining with the improved MOGA algorithm.Simulation results show that the improved MOGA converges to the Pareto frontier in the IEEE 30-node system in only 53 iterations, and the optimization efficiency is improved by 36.05%, and the running time is shortened to 34.17 seconds, which is significantly better than that of the traditional genetic algorithm (47.33 seconds) and the ant colony algorithm (53.43 seconds).In the case of industrial carbon emission allocation in province A, the efficiency value of each region is improved to 1.00 after optimization, and the direction of quota adjustment is linked to the potential of emission reduction, e.g., the quota of area G is increased by 1,294,800 tons, and the quota of area K is decreased by 1,435,100 tons, which verifies the model's dynamic synergistic ability in terms of fairness and efficiency.