Optimal and secure peer-to-peer carbon emission trading: A game theory informed framework on blockchain

Abstract

We address two key challenges towards an effective and accountable emission trading system (ETS). The first is to determine the individual policies while ensuring a fair allocation of costs between the stakeholders to meet an overall environmental target for an industrial ecosystem. The second is to ensure privacy and traceability of shared data when participating in an ETS. Specifically, we propose a mixed-integer nonlinear program (MINLP) for peer-to-peer carbon trading in ETS under fixed carbon tax or cap-and-trade systems. To ensure data privacy and traceability, the MINLP model is integrated with smart contracts on the Ethereum blockchain. The model determines how much the participants buy/sell emission allocations, how much they invest in carbon capture, which capture technologies they select, and how they respond to changes in carbon prices. The planning requires simultaneous decision-making for all participants, which can be obtained as a Generalized Nash Equilibrium (GNE).