Blockchain-Enabled Adaptive Monitoring for Sustainable Food Supply Chains: A Green Innovation Framework for Waste Reduction and Traceability
Main article
Abstract
Reducing food loss and decarbonising agri-food logistics are central to the United Nations Sustainable Development Goals, yet conventional monitoring infrastructures remain energy-intensive, generate fragmented audit trails, and struggle to deliver verifiable provenance across multi-actor supply chains. This study develops a green innovation framework that couples permissioned blockchain with adaptive, context-aware monitoring for sustainable food supply chains. The framework integrates four architectural layers — physical operations, IoT-enabled sensing with adaptive sampling, edge-level filtering, and a Hyperledger Fabric permissioned ledger with hash-anchored off-chain storage — and embeds smart contracts that automate compliance, custody, and exception handling. We instantiate and evaluate the framework using a twelve-month pilot of a Chinese dairy supply chain encompassing 8 farms, 3 processing plants, 12 logistics nodes, and 147 retail points-of-sale. Across 9.4 million sensor observations and 11,236 ledger transactions, the adaptive scheme reduces transmitted data volume by 90.2% and edge-node energy consumption by 85.4% relative to fixed 1 Hz sampling, while maintaining critical-event detection accuracy at 96.3%, well above the 90% compliance threshold. Pilot-month CO₂ emissions and chilled-product food waste decline by 34% and 42% respectively, and traceability response time for a recall query falls from 6.2 hours to 4.1 seconds. Cost-benefit analysis indicates a payback period of 1.8 years and a five-year net present value of US$2.34 million. Theoretically, the work re-frames adaptive monitoring as a green innovation enabler that operationalises decentralised trust at the data-acquisition boundary. Practically, it offers a deployable blueprint for perishable-goods chains pursuing SDG 7, SDG 9, SDG 12, and SDG 13.
