Multi-Period Carbon Credit Trading Scheme Generation Using Graph-Theoretic Model: Managing Budget and Risk of Carbon Leakage

Abstract

Carbon trading is an important economic instrument for industrial emissions reduction. Carbon credits can be generated by performance discrepancies in cap-and-trade regimes or through carbon dioxide removal (CDR) in the context of deep decarbonization towards net-zero targets. The sale of carbon credits by vendors to buyers can be optimized with models just like that of any other commodity in a supply chain. Pinch analysis and process graph (P-graph) techniques have recently been developed for optimizing multi-period carbon credit trading networks considering temporal constraints on the flow of credits; however, these methods did not account for costs and carbon leakage risks. To address this gap, this work develops a P-graph optimization model that synthesizes multi-period carbon credit allocation trading networks with the consideration of budget constraints and cross-boundary credit trading. Two case studies are used to illustrate the effectiveness of the proposed methodology. The first case study focuses on carbon credit trading within a single region, while the second expands its scale to cover multiple regions. The result of the first case study demonstrates the capability of the model to generate all feasible trading schemes, including a set of optimal and near-optimal solutions (i.e. 720 out of 12,240 trading schemes found can offer the lowest penalty and credit waste). The second case study demonstrates how cross-boundary carbon credit trading can be discouraged to restrict the potential risk of carbon leakage. Results show that a credit discount rate of 80% can be imposed to prevent cross-boundary trading which, therefore, mitigates the potential risk of carbon leakage.

To read the full paper, please click: https://doi.org/10.1007/s41660-025-00563-2