Matching of Carbon Allowance Trading and Green Credit Powered by Intelligent Algorithms

Against the backdrop of the coordinated development of carbon markets and green finance, carbon quota trading and green credit allocation suffer from information asymmetry and resource misallocation, necessitating the introduction of intelligent algorithms to improve their matching efficiency and emission reduction incentives. This paper first constructs a joint indicator system of carbon behavior and financing characteristics based on multi-dimensional data such as corporate carbon emission intensity, carbon quota trading behavior, compliance status, and financing costs to characterize the heterogeneity of corporate emission reduction. Building upon this, a machine learning model is used to predict corporate emission reduction potential, and the prediction results are embedded into the matching rules of carbon quota trading and green credit, achieving dynamic linkage between credit resources and carbon market performance. Furthermore, a reinforcement learning mechanism is introduced to characterize the feedback process of credit allocation on corporate emission reduction behavior and carbon trading decisions, thereby achieving synergistic optimization of the carbon market and green credit. After algorithm intervention, the emission reduction efficiency per unit of credit increased from 0.15 to 0.28 tons/10,000 yuan (an increase of 86.7%), and the matching degree (ρ) between credit and emission reduction targets improved from 0.32 to 0.71. Simultaneously, the proportion of credit obtained by the top 30% of highly efficient enterprises increased from 41.5% to 63.2%. This confirms that the algorithm can effectively solve the problem of resource misallocation and strengthen the emission reduction incentives of green credit.