Optimization of food-energy-water-waste nexus in a sustainable food supply chain under the COVID-19 pandemic: a case study in Iran

Seif, Marziye; Yaghoubi, Saeed; Khodoomi, Mohammad Reza

doi:10.1007/s10668-023-03004-7

Cited by 6 publications

(1 citation statement)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Expanding on this theme, [22] develop a fuzzy group decision-making model to measure resilience in food supply chains, applied to Andalusia's agricultural sector, identifying natural disasters and water system failures as significant risk factors. Similarly, [23] delve into optimizing the food-energy-water-waste nexus in Iran's food supply chain during the COVID-19 pandemic, revealing the interconnectedness of health crises with other supply chain aspects. Their findings show the pandemic's impact on consumer behavior and the importance of adapting supply chains to changing circumstances, highlighting the evolving nature of supply chain management where resilience and environmental considerations are increasingly critical.…”

Section: Introductionmentioning

confidence: 99%

Forecasting fluidity: A green predictive approach to water risk management in supply chains

Lumintu

2024

E3S Web of Conf.

View full text Add to dashboard Cite

This research utilizes the 2016 Carbon Disclosure Project (CDP) dataset and predictive analytics to explore water risk management in global supply chains within the landscape of ecological imperatives intersecting with economic realities. Employing a Random Forest (RF) model, the study investigates water risks from local to global scales, emphasizing the strategic importance of understanding and managing these risks. The findings reveal the RF model’s efficacy in predicting the financial impacts of water risks, highlighting the necessity for proactive risk management strategies in supply chains. This research not only demonstrates the application of machine learning in green supply chain management but also paves the way for future studies on comprehensive and adaptive environmental risk mitigation approaches.

show abstract

Section: Introductionmentioning

confidence: 99%