Blockchain-Enabled Adaptive Monitoring for Sustainable Food Supply Chains: A Green Innovation Framework for Waste Reduction and Traceability
持続可能な食品サプライチェーンのためのブロックチェーン対応適応型モニタリング：廃棄物削減とトレーサビリティのためのグリーンイノベーションフレームワーク (AI 翻訳)
Xiao Xiao, Miaomiao Zhang, Yueyi Wei, Mengqiu Zhang
🤖 gxceed AI 要約
日本語
本研究は、食品サプライチェーンの廃棄物削減と脱炭素化を目的に、許可型ブロックチェーンと適応型モニタリングを組み合わせたグリーンイノベーションフレームワークを提案する。中国の乳製品サプライチェーンでの12ヶ月のパイロット実証により、データ転送量を90.2%、エッジノードのエネルギー消費を85.4%削減し、CO2排出量を34%、食品廃棄物を42%削減した。さらに、リコール対応時間を6.2時間から4.1秒に短縮し、投資回収期間は1.8年、5年NPVは234万米ドルと試算された。
English
This study proposes a green innovation framework combining permissioned blockchain with adaptive monitoring to reduce waste and decarbonize food supply chains. A 12-month pilot in a Chinese dairy supply chain reduced data transmission by 90.2%, edge-node energy consumption by 85.4%, CO2 emissions by 34%, and food waste by 42%. Recall response time dropped from 6.2 hours to 4.1 seconds, with a payback period of 1.8 years and a five-year NPV of US$2.34 million.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、食品ロス削減とサプライチェーン全体の脱炭素化が重要な課題であり、本フレームワークはトレーサビリティとエネルギー効率を両立する実装例として参考になる。SSBJ開示やScope 3排出量算定において、一次データ収集の効率化に寄与する可能性がある。
In the global GX context
Globally, this framework addresses the need for verifiable provenance and reduced environmental impact in agri-food supply chains, aligning with ISSB and CSRD disclosure requirements. It demonstrates how blockchain and adaptive IoT can enhance traceability and energy efficiency, offering a scalable model for perishable goods chains.
👥 読者別の含意
🔬研究者:Provides empirical evidence on the integration of blockchain and adaptive monitoring for sustainable supply chains, with detailed performance metrics.
🏢実務担当者:Offers a deployable blueprint for reducing energy consumption and waste in perishable food logistics, with clear cost-benefit analysis.
🏛政策担当者:Highlights the potential of digital technologies to achieve SDG targets related to responsible consumption and climate action.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://inatgi.net/index.php/jbgi/article/download/jbgi_20250723/jbgi_20250723.pdffirst seen 2026-07-29 05:32:34 · last seen 2026-08-02 06:11:41
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