Consistent Allocation of Emission Responsibility in Fossil Fuel Supply Chains

Since 2016, Canada’s federal government has pledged to factor in upstream emissions during the environmental impact assessment of fossil fuel energy projects. The upstream emissions attributable to a proposed project could be compared against a rejection threshold—a maximum permissible level of emissions—or the firm could be mandated to offset the attributed emissions. We adopt a cooperative game-theoretic model and propose the nucleolus mechanism to apportion upstream emission responsibilities in a fossil fuel supply chain, represented by a directed tree, wherein the nodes correspond to various entities in the supply chain such as extractors, distributors, refineries, and end consumers. The nucleolus allocation avoids the distortionary effects of double counting and exhibits a certain consistency property that is especially important in a regulatory context wherein fossil fuel supply chains span multiple legal jurisdictions. We develop a polynomial-time algorithm to compute the nucleolus and further prove that it arises as the unique subgame perfect equilibrium allocation of a noncooperative game induced by two easily stated and verifiable policies, thereby providing an implementation framework. We then demonstrate the strong Nash stability of the nucleolus mechanism subject to the two policies, study its sensitivity to parameter changes, and characterize it on the basis of fairness considerations. Furthermore, under the common assumption that the emissions allocated to a firm and the resulting financial penalties do not impact the revenues from the firm’s core operations, we also provide lower-bound guarantees on the welfare gains it delivers to firms in the fossil fuel supply chain and on the incentives it offers such firms to adopt emission abatement technologies. Finally, we contextualize our discussion with a case study on a proposed expansion of the Trans Mountain pipeline in Western Canada. This paper was accepted by Chung Piaw Teo, optimization.