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

Zakaria El Hathat, V. G. Venkatesh, V. Raja Sreedharan, Tarik Zouadi, Arunmozhi Manimuthu, Yangyan Shi and S. Srivatsa Srinivas
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V. G. Venkatesh: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
V. Raja Sreedharan: Cardiff Metropolitan University
Tarik Zouadi: UIR - Université Internationale de Rabat
Arunmozhi Manimuthu: Aston Business School - Aston University [Birmingham]
Yangyan Shi: Macquarie University [Sydney]
S. Srivatsa Srinivas: IIT Jodhpur - Indian Institute of Technology Jodhpur

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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.

Keywords: contracts Machine learning Senegal; Groundnut supply chain Sustainable agriculture Greenhouse gas (GHG) emissions Blockchain smart (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

Published in Information Systems Frontiers, 2024, ⟨10.1007/s10796-024-10514-w⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04888405

DOI: 10.1007/s10796-024-10514-w

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