Low Carbon Transition in the Food System through Machine Learning-Enhanced Energy Efficiency

Abstract

Reaching global sustainability targets requires a shift in food systems toward reduced carbon emissions and increased energy efficiency. To influence the future of food systems, this study offers a novel theoretical framework that combines energy efficiency tactics with machine learning (ML) techniques. It identifies critical factors and components required for optimizing energy efficiency throughout multiple phases of food production, distribution, and consumption based on a thorough analysis of existing literature and worldwide sustainable initiatives in agriculture. Acknowledging the interdependence of elements in food systems and taking into account their overall influence on sustainability, the framework adopts a systems thinking style. The use of ML-based energy efficiency treatments has both opportunities and challenges that are examined. Finally, the goal of this research is to provide stakeholders in the food and energy sectors with insights to promote the adoption of energy-efficient methods and speed the transition to sustainable food systems through advanced technologies. © 2026 selection and editorial matter, Jen-Tsung Chen; individual chapters, the contributors.