title: Life cycle sustainability assessment of staple food processing: A double and dynamic materiality approach
Creators: name: Mwape, Mwewa Chikonkolo
name: Parmar, Aditya
name: Roman, Franz
name: Emmambux, Naushad Mohammad
name: Azouma, Yaovi Ouézou
name: Hensel, Oliver
Publisher: name: KOBRA Universität Kassel
Year: 2025
Subjects: subject: LCSA
subject: staple foods
subject: double materiality
subject: dynamic materiality
subject: MEFA
subject: global warming potential (GWP)
subject: Python modeling
subject: ESG
classificationCode: 300
subject: 300
classificationCode: 500
subject: 500
classificationCode: Lebenszyklus <Wirtschaft>
subject: Lebenszyklus
classificationCode: Nachhaltigkeit
subject: Nachhaltigkeit
classificationCode: Grundnahrungsmittel
subject: Grundnahrungsmittel
classificationCode: Materialität
subject: Materialität
classificationCode: Erwärmung <Meteorologie>
subject: Erwärmung
classificationCode: Python <Programmiersprache>
subject: Python
classificationCode: Environmental, Social and Governance
subject: Environmental, Social and Governance

description: <p>Globally, 70 % of people are fed through peasant food systems that are responsible for growing 50 % of the world's food calories on 30 % of the land. In the global south, particularly in Sub-Saharan Africa, small-scale farming serves as a crucial lifeline for the food and income needs of local populations. Yet, it remains underfunded and under-researched in the context of sustainable development. Even if the traditional Life Cycle Sustainability Assessment offers a holistic approach to evaluating the impacts of staple food processing across environmental, economic, and social dimensions, its inability to track dynamic materiality limits its application in evaluating future impacts. Therefore, this study aimed to provide a comprehensive Life Cycle Sustainability Assessment framework for staple food processing, using cassava to produce gari, a staple food for more than 300 million West Africans, as a case study. This framework integrates Material and Energy Flow Analysis techniques to trace resource use and emissions. The research incorporated Environmental, Social and Governance pillars; double materiality, evaluating both the direct and indirect impacts of processing activities, alongside dynamic materiality to capture evolving environmental, financial, and social factors through scenarios. Python computational modeling was used to perform these complex analyses, ensuring accuracy and adaptability. The findings highlight significant energy inefficiencies (6.67 kWh kg-1) coupled with a high Global Warming Potential (GWP) of 9.02 kgCO2eq kg-1 and production costs of $0.56 kg-1. The most significant opportunities for improvement were identified in optimizing energy consumption and transforming waste into biogas. The dynamic model revealed that integrating renewable energy sources could substantially reduce environmental impacts and increase the Net Profit Margin from 34.43 to 52.52 %, as proposed in the energy transition from woodfuel and gasoline to a Hybrid Solar and Biogas energy system. This study contributes to the growing body of literature on Life Cycle Sustainability Assessment by applying a comprehensive framework to staple food processing. The findings offer valuable insights into the environmental, social, and economic trade-offs in food processing systems, providing practical recommendations for improving sustainability throughout the food supply chain. Extended studies using these methods on other staples are highly recommended.</p>