Forecasting the total carbon allowance cap under emission-reduction targets using a hybrid path analysis and supervised machine learning framework

Carbon allowances constitute a foundational component of national carbon emission control frameworks, as they govern the equitable distribution of subsequent allocations and directly shape the overall effectiveness of greenhouse gas mitigation strategies. However, the temporal evolution of carbon allowances is inherently complex, high-dimensional, and nonlinear, thereby posing substantial challenges to the rigorous prediction of the aggregate allowance cap. Although artificial intelligence techn