An Integrated optimization-blockchain framework for transparent and fair Eco-industrial park design under carbon constraints

Eco-industrial parks (EIPs) offer significant potential for improving industrial resource efficiency and reducing emissions, yet practical deployment requires transparent monitoring of resource exchanges and verifiable emissions accounting among participating plants. This study presents a framework that enables verifiable emissions and resource tracking in EIPs by coupling a mixed-integer linear programming (MILP) superstructure optimization model with a private permissioned blockchain. The MILP model determines optimal process selection, production capacities, and mass-balanced flows of energy, water, materials, and COQ while accounting for Scope 1 and Scope 2 emissions. A binding COQ cap is imposed, and alternative compliance pathways are evaluated including capture, utilization, storage, carbon taxation, and credit purchases. Free allowances and credit eligibility are allocated proportionally to baseline emissions, and coalition surplus is distributed using Shapley values. Optimization outputs are subsequently operationalized on a private permissioned blockchain that registers entities, records resource exchanges and emissions reports, and enforces policy rules and financial settlements through smart contracts, thereby providing an auditable link between design decisions and operational outcomes. A case study EIP consisting of seven plants and multiple carbon capture routes demonstrates the framework. Across emission caps of 10 %, 62 %, and 95 % below baseline, optimal profits are 277, 272, and 243 million USD yr-1, with captured COQ of 270, 1660, and 1946 kt yr-1, respectively. Under very stringent caps, the system relies on COQ credits and carbon taxation to close the remaining compliance gap. The proposed framework generates complete ledgers of resource exchanges and net emissions while maintaining MRV-aligned records suitable for verification, auditing, and settlement. Blockchain energy demand remained negligible for daily to hourly logging and increased materially only under real-time reporting frequencies. The results highlight how explicit policy representation and cooperative profit allocation influence technology selection, trading patterns, and financial outcomes within low-carbon industrial ecosystems.