Incentives and Emission Responsibility Allocation in Supply Chains

• 成果类型: Article
• 署名作者: Gopalakrishnan, Sanjith; Granot, Daniel; Granot, Frieda; Sosic, Greys; Cui, Hailong
• 署名单位: McGill University; University of British Columbia; University of Southern California; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3724
• 发表日期: 2021
• 页码: 4172-4190
• 关键词: Supply chains; cooperative games; Shapley value; incentives; emission responsibility allocation

摘要

Because greenhouse-gas (GHG) emissions from the supply chains of just the 2,500 largest global corporations account for more than 20% of global emissions, rationalizing emissions in supply chains could make an important contribution toward meeting the global CO2 emission-reduction targets agreed upon in the 2015 Paris Climate Agreement. Accordingly, in this paper, we consider supply chains with joint production of GHG emissions, operating under either a carbon-tax regime, wherein a regulator levies a penalty on the emissions generated by the firms in the supply chain, or an internal carbon pricing scheme. Supply chain leaders, such as Walmart, are assumed to be environmentally motivated to induce their suppliers to abate their emissions. We adopt a cooperative game theory methodology to derive a footprint-balanced scheme for reapportioning the total carbon emissions amongst the firms in the supply chain. This emission responsibility allocation scheme, which is the Shapley value of an associated cooperative game, is shown to have several desirable characteristics. In particular, (i) it is transparent and easy to compute; (ii) when the abatement-cost functions of the firms are private information, it incentivizes suppliers to exert pollution-abatement efforts that, among all footprint balanced allocation schemes, minimize the maximum deviation from the socially optimal pollution level; and (iii) the Shapley value is the unique allocation mechanism satisfying certain contextually desirable properties.