P-Graph Approach for Optimal Carbon Trading with Cost and Time Constraints

Abstract

Interest in industrial decarbonization is growing steadily in response to climate change concerns. National net-zero commitments have led to the proliferation of corporate carbon neutrality goals, as firms seek to secure an advantage in an increasingly carbon-constrained business environment. These pledges are anchored on decarbonization measures based on various clean technologies. Carbon trading has also emerged as an important economic instrument to enable corporations to outsource emissions reductions, either under cap-and-trade policies or in voluntary carbon markets. In a cap-and-trade system, carbon credits can be generated by overperforming firms with surplus unused emissions quotas; alternatively, credits can also be produced through dedicated carbon dioxide removal using a variety of emerging negative emissions technologies. In either case, the carbon credits can be treated like any other operational input whose purchase cost and timing need to be considered. In this work, a graph-theoretic model is developed for optimizing carbon trading. Using the P-graph framework, the model coordinates the sale of carbon credits from multiple vendors to multiple buyers, while considering the cost and time constraints of all parties. The system-level perspective ensures that global benefit is maximized. The methodology is demonstrated with two illustrative case studies that are representative of the typical industry scenario. The first case study results in a minimal cost of US$ 12.1 million, requiring an additional 20 kt of external carbon credits. Examining alternative solutions demonstrates that 10 kt of external credits can be delayed in the planning horizon. The second case study results in a minimal cost of US$ 13.5 million, requiring 5 kt of external credits. However, if higher-quality carbon credits are to be prioritized, a 14.8% increase in cost is observed.

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