Reducing Food Loss and Associated Greenhouse Gas Emissions Using a Dynamic Shelf Life Approach
Junzhang Wu, Yifeng Zou, Gang Liu, Xue Li, Zhimin Shi, Andrea Fedele, Alessandro Manzardo
Abstract
High Resolution Image Download MS PowerPoint Slide The integration of IoT sensors with dynamic shelf life (DSL) systems unlocks real-time visibility into perishable goods, yet the full life-cycle trade-offs of such technologies remain underexplored. This study develops a process-based life cycle framework, incorporating a kinetic quality-degradation model and Monte Carlo simulations, to evaluate both avoided food loss and waste and sensor-embedded climate burdens across China’s fresh food chains. Results show this IoT-DSL regime extends average shelf life by 8.1–13.8% in fruits, dairy, and vegetables, although gains fall two- to 5-fold for animal and aquatic products at lower quality thresholds, while nontechnical interventions deliver only 3.2–6.5% waste reductions. Large-scale IoT-DSL deployment could avert 17.32 ± 3.65 Mt yr –1 of waste and achieve a net cut of 51.00 ± 10.38 Mt CO 2 -eq yr –1 (≈10.9% of China’s food-chain emissions), despite introducing 7.7 Mt CO 2 -eq yr –1 from sensors. Upstream, sensor fabrication dominates impacts, underscoring the need for eco-designed materials and robust e-waste recovery. Sensitivity analysis identifies production emission intensity, inherent shelf life, and logistics crate capacity as critical drivers. Projected improvements in the input–output efficiency indicator─from 17.5 in 2020 to 18.9 by 2030─and future scenarios incorporating food-tech innovations and plant-based dietary shifts underscore further mitigation potential.