Generative AI for Sustainable Food Consumption: A Pilot Study on Reducing Household Waste

Food waste in urban households is a critical barrier to sustainable development, often driven by inefficient inventory management and consumer forgetfulness. While institutional interventions exist, effective tools for the domestic pre-consumption stage remain scarce. This paper presents the design, development, and pilot validation of “ZeroWasteAI,” a novel mobile application developed by the authors that integrates Generative AI (Gemini 1.5 Flash) to automate food tracking and expiration monitoring. To evaluate its technical feasibility and impact on household waste, a four-week longitudinal pilot study was conducted with a sample of 11 households in Lima, Peru, employing a quasi-experimental pre-post design. The methodology combined quantitative waste tracking (kg) with qualitative assessments using the uMARS scale. Results validated the primary hypothesis (H1), achieving a 26.5% reduction in household food waste (from 31.3% to 23.0% waste rate). Furthermore, the study revealed a significant behavioral gap between purchasing and consumption, highlighting “overbuying” as a key target for future AI interventions. High usability scores confirm that integrating GenAI reduces the cognitive load of manual tracking, offering a scalable, software-based solution for sustainable consumption in developing economies.