Reducing Food Loss and Associated Greenhouse Gas Emissions Using a Dynamic Shelf Life Approach

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 CO₂-eq yr^-1 (≈10.9% of China's food-chain emissions), despite introducing 7.7 Mt CO₂-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.