# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
###### Abstract  
Formulating a scientifically sound and efficient approach to allocating carbon quota aligned with the carbon peaking goal is a fundamental theoretical and practical challenge within the context of climate-oriented trading in the power sector. Given the highly irrational allocation of carbon allowances in China's power sector, as well as the expanding role of renewable energy, it is essential to rationalize the use of green energy in the development of carbon reduction in the power sector. This study addresses the risk of \"carbon transfer\" within the power industry and develops a predictive model for CO\({}_{2}\) emission based on multiple influential factors, thereby proposing a carbon quota distribution scheme adapted to green energy growth. The proposed model employs a hybrid of the gray forecasting model-particle swarm optimization-enhanced back-propagation neural network (GM-PSO-BPNN) for forecasting and allocating the total carbon quota. Assuming consistent total volume control through 2030, carbon quota is distributed to regional power grids in proportion to actual production allocation. Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. With the total national carbon emissions remaining unchanged in 2030, in the end, the regional grids received the following quota values: 873.29 million tons in North China, 522.69 million tons in Northwest China, 194.15 million tons in Northeast China, 1283.16 million tons in East China, 1556.40 million tons in Central China, and 1085.37 million tons in the Southern Power Grid. The power sector can refer to this carbon allowance allocation standard to control carbon emissions in order to meet the industry's emission reduction standards. renewable energy; carbon quota; power sector; PSO-BP neural network; GM(1,1); carbon emission projection +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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sector has entered a golden period of development, especially wind and solar energy, which will hold a leading position in the future energy generation market [1; 2]. In December 2017, the comprehensive CO\({}_{2}\) emissions trading system in China was officially launched, with the power segment as the first and only industry to be included. The allocated CO\({}_{2}\) emission allowances in China are crucial for the establishment of a national carbon market trading mechanism and the realization of China's 2030 carbon peaking target. The CO\({}_{2}\) emissions trading system is an effective vehicle to reduce carbon emissions through market mechanisms [3; 4]. The primary starting points for existing researchers' research on quota allocation are efficiency and equity. The issue of equity is considered extremely critical in international climate negotiations [5]. There is no single definition of equity that is universally accepted, and the principle of equity refers to a more general concept of distributive justice or fairness [6]. Based on the principle of equity, [PERSON] et al. [7] applied it to the allocation of global CO\({}_{2}\) quotas based on three dimensions: responsibility, capacity, and equity. The carbon sink attribute of agriculture is considered and characterized as a safeguard-type equity indicator in light of the somewhat increased theoretical bounds of carbon emissions [8]. Cost-effectiveness and input-output effectiveness are the two metrics typically employed to assess the efficiency principle. As far as efficiency is concerned, cost-effectiveness refers to the minimization of the gross abatement cost. [PERSON] et al. [9] allocated CO\({}_{2}\) allowances from a cost-negligible perspective. Among the input-output criteria, DEA is the most frequently used approach to evaluate the efficiency of CO\({}_{2}\) emissions.