Demand Hidden by Stockouts: Censored Demand Recovery and Green Waste-Reduction Forecasting for Sustainable Fresh-Food Consumption Under Emerging-Market Urbanization

Fresh-food retail demand is only partially observed when products stock out: point-of-sale records report sales, whereas the unmet part of demand is censored. This study develops a probabilistic decision-support framework, termed CADRE, that treats stockout-flagged observations as lower bounds, separates recurring diurnal structure from censoring effects, and shapes the replenishment decision with an asymmetric shortage–spoilage loss. The evaluation uses FreshRetailNet-50K, an hourly Chinese fresh-retail benchmark with observed stockout labels, and a complementary daily robustness experiment on the Ecuadorian Favorita panel with a constructed high-demand censoring mask. On FreshRetailNet-50K, CADRE reduces weighted absolute percentage error (WAPE) from 39.42% for the TimeXer backbone to 36.71% and reduces re-censored demand bias from −8.1% to −1.3%. In a controlled replenishment simulation on known re-censored demand, modelled waste decreases from 9.8% under a censored-sales policy to 6.4% under CADRE while service level increases from 92.9% to 94.7%. These results indicate potential improvements in replenishment decision support under the evaluated assumptions, rather than measured operational food-waste or emissions reductions. Operational food-waste and emission effects remain to be validated through field deployment.