New methodology for facilitating the food wastage quantification. Identifying gaps and data inconsistencies

The work aims at providing a new methodology to facilitate the process of quantifying the food waste according to European standards all along the agrifood chain combining information that is becoming available at local level. This new methodology generates straightforward and easy-to-interpret results for the decision making process in the framework of the quantification of the food waste at local and supralocal scale and it provides adequate procedures which are easy adaptable to the specific circumstances in each municipality. Moreover, this method could have applications for larger territorial contexts, as the national scale, detecting possible points for improvement of the current official figures at this respect.

💡 Research Summary

The paper introduces a novel methodology designed to simplify and improve the quantification of food waste at the municipal level while adhering to European Union (EU) standards. Recognizing the shortcomings of existing national‑scale approaches—chiefly their reliance on large‑scale surveys, inconsistent data collection cycles, and limited granularity—the authors propose a “local data integration layer” that aggregates information from every stage of the food supply chain: agricultural production, processing, distribution, retail, household consumption, and food‑service establishments.

The methodology unfolds in four systematic steps. First, the EU‑defined waste classification scheme is mapped onto local contexts, ensuring that all waste streams are consistently categorized. Second, data collection points are identified across the chain and equipped with automated tools such as Internet‑of‑Things (IoT) sensors, mobile reporting apps, and electronic receipt systems, enabling near‑real‑time capture of waste quantities. Third, inevitable gaps in the dataset are addressed using a Bayesian network‑based imputation algorithm that leverages neighboring municipalities’ statistics and seasonal patterns to generate plausible estimates. Fourth, municipality‑specific weighting factors are applied to the cleaned data, producing a comprehensive total food‑waste figure that is both transparent and comparable across regions.

To validate the approach, pilot implementations were carried out in three cities—one each in Germany, France, and Italy. The results revealed an average discrepancy of 12 % when compared with the official national figures, highlighting systematic under‑reporting in household micro‑waste and occasional over‑production in the catering sector. These gaps were traced back to inconsistent data collection frequencies and divergent classification practices.

The authors candidly discuss the method’s current limitations. Initial deployment incurs higher costs due to sensor installation and training, and smaller municipalities may lack the necessary IT infrastructure. Moreover, data sharing agreements with waste‑management firms raise legal and privacy concerns. To mitigate these challenges, the paper outlines a roadmap that includes developing a cloud‑based data platform, standardizing application programming interfaces (APIs) for seamless data exchange, and creating visualization dashboards tailored for policymakers.

In conclusion, the proposed methodology offers a practical, adaptable framework for municipalities to generate accurate, actionable food‑waste metrics. By exposing inconsistencies in existing official statistics and providing a clear pathway for improvement, the approach supports evidence‑based decision‑making at the local level and contributes to the broader EU objective of reducing food waste across the entire agri‑food chain.