Dataset in municipal strategies for food waste collection in Portugal

Published: 24 November 2025| Version 2 | DOI: 10.17632/sv66hbm297.2
Contributors:
Diego del Oro Alcalde, Diogo Bugarim, Telmo Coelho, Emilia Almeida, Catarina Silva,
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Description

The dataset provides an updated overview of the separate collection of organic kitchen waste, primarily food waste, across Portugal’s 308 municipalities. Data was gathered via a structured questionnaire sent to all municipalities and through a systematic search of secondary official sources, mainly municipal websites and reports. The collected data includes (i) the waste collection methods used by municipalities, (ii) the sectors targeted by separate collection, (iii) the type of collection in single-family, multi-family, or mixed areas, along with access to waste containers, whether free or controlled, (iv) the date when separate collection was introduced, (v) any changes to regular collection caused by separate collection, (vi) capture rates, (vii) operational and investment costs, (viii) the presence of economic incentive mechanisms, and (ix) the number of residents and establishments served by the separate collection.

Files

Steps to reproduce

1. Questionnaire Design: Develop a structured questionnaire using Microsoft Forms to gather details on various municipal biowaste collection models- such as nearby bring points, door-to-door, and co-collection. The questions focus on specifics like service coverage area or inhabitants, quantities collected, and financial data. 2. Distribution to Municipalities: Send the questionnaire via email to respective municipal waste treatment services. 3. Primary Data Collection: Gather and compile the responses, totalling 93 submissions. 4. Secondary Data Collection: Perform a systematic search of publicly available official sources to supplement or fill gaps for municipalities that did not respond or had incomplete data. These sources include municipal websites, local or national news outlets, and waste management reports. 5. Data Validation and Integration: Cross-check, validate, and enrich the primary data with secondary sources to create a final, well-refined dataset.

Institutions

  • Universidade Aberta Departamento de Ciencias e Tecnologia

Categories

Municipal Waste, Waste Collection, Recycling Performance Indicator, Food Waste, Kitchen Waste

Funders

Licence