Leveraging Greenhouse Gas Emissions Traceability in the Groundnut Supply Chain: Blockchain-Enabled Off-Chain Machine Learning as a Driver of Sustainability

IF 6.9 3区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Systems Frontiers Pub Date : 2024-07-30 DOI:10.1007/s10796-024-10514-w

Zakaria El Hathat, V. G. Venkatesh, V. Raja Sreedharan, Tarik Zouadi, Arunmozhi Manimuthu, Yangyan Shi, S. Srivatsa Srinivas

{"title":"Leveraging Greenhouse Gas Emissions Traceability in the Groundnut Supply Chain: Blockchain-Enabled Off-Chain Machine Learning as a Driver of Sustainability","authors":"Zakaria El Hathat, V. G. Venkatesh, V. Raja Sreedharan, Tarik Zouadi, Arunmozhi Manimuthu, Yangyan Shi, S. Srivatsa Srinivas","doi":"10.1007/s10796-024-10514-w","DOIUrl":null,"url":null,"abstract":"<p>As emphasized in multiple United Nations (UN) reports, sustainable agriculture, a key goal in the UN Sustainable Development Goals (SDGs), calls for dedicated efforts and innovative solutions. In this study, greenhouse gas (GHG) emissions in the groundnut supply chain from the region of Diourbel & Niakhar, Senegal, to the port of Dakar are investigated. The groundnut supply chain is divided into three steps: cultivation, harvesting, and processing/shipping. This work adheres to UN guidelines, addressing the imperative for sustainable agriculture by applying machine learning-based predictive modeling (MLPMs) utilizing the FAOSTAT and EDGAR databases. Additionally, it provides a novel approach using blockchain-enabled off-chain machine learning through smart contracts built on Hyperledger Fabric to secure GHG emissions storage and machine learning’s predictive analytics from fraud and enhance transparency and data security. This study also develops a decision-making dashboard to provide actionable insights for GHG emissions reduction strategies across the groundnut supply chain.</p>","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":"18 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems Frontiers","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10796-024-10514-w","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

As emphasized in multiple United Nations (UN) reports, sustainable agriculture, a key goal in the UN Sustainable Development Goals (SDGs), calls for dedicated efforts and innovative solutions. In this study, greenhouse gas (GHG) emissions in the groundnut supply chain from the region of Diourbel & Niakhar, Senegal, to the port of Dakar are investigated. The groundnut supply chain is divided into three steps: cultivation, harvesting, and processing/shipping. This work adheres to UN guidelines, addressing the imperative for sustainable agriculture by applying machine learning-based predictive modeling (MLPMs) utilizing the FAOSTAT and EDGAR databases. Additionally, it provides a novel approach using blockchain-enabled off-chain machine learning through smart contracts built on Hyperledger Fabric to secure GHG emissions storage and machine learning’s predictive analytics from fraud and enhance transparency and data security. This study also develops a decision-making dashboard to provide actionable insights for GHG emissions reduction strategies across the groundnut supply chain.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用落花生供应链中的温室气体排放可追溯性：区块链支持的链外机器学习推动可持续性发展

正如多份联合国（UN）报告所强调的，可持续农业作为联合国可持续发展目标（SDGs）中的一个关键目标，需要各方的不懈努力和创新解决方案。本研究调查了从塞内加尔 Diourbel & Niakhar 地区到达喀尔港口的落花生供应链中的温室气体排放情况。花生供应链分为三个步骤：种植、收获和加工/运输。这项工作符合联合国的指导方针，利用 FAOSTAT 和 EDGAR 数据库，采用基于机器学习的预测建模 (MLPM)，解决了可持续农业的当务之急。此外，它还提供了一种新颖的方法，通过在 Hyperledger Fabric 上构建的智能合约，使用区块链支持的链外机器学习，以确保温室气体排放存储和机器学习预测分析免受欺诈，并提高透明度和数据安全性。本研究还开发了一个决策仪表板，为整个花生供应链的温室气体减排战略提供可操作的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Information Systems Frontiers 工程技术-计算机：理论方法

CiteScore

13.30

自引率

18.60%

发文量

127

审稿时长

9 months

期刊介绍： The interdisciplinary interfaces of Information Systems (IS) are fast emerging as defining areas of research and development in IS. These developments are largely due to the transformation of Information Technology (IT) towards networked worlds and its effects on global communications and economies. While these developments are shaping the way information is used in all forms of human enterprise, they are also setting the tone and pace of information systems of the future. The major advances in IT such as client/server systems, the Internet and the desktop/multimedia computing revolution, for example, have led to numerous important vistas of research and development with considerable practical impact and academic significance. While the industry seeks to develop high performance IS/IT solutions to a variety of contemporary information support needs, academia looks to extend the reach of IS technology into new application domains. Information Systems Frontiers (ISF) aims to provide a common forum of dissemination of frontline industrial developments of substantial academic value and pioneering academic research of significant practical impact.