Planning operational costs and CO2 emissions of municipal solid waste collection: A case study in Italy

Abstract

Waste collection represents the major component of municipal solid waste management costs and a direct source of greenhouse gas emissions. Environmental assessments often rely on generic transport emission factors that do not adequately reflect the real operating conditions of collection vehicles. To address this gap, a field monitoring campaign was carried out in central Tuscany (Italy), involving 233 collection routes operated under different collection systems, vehicle types and fuels. Measured fuel consumption data were used to estimate collection-phase CO₂ emissions and to develop a modelling framework able to predict operational costs and CO₂ emissions as a function of linear population density (LPD) and per capita waste generation.Monitoring results showed that energy consumption and CO₂ emissions during collection were systematically higher than values reported in standard transport inventories, particularly for heavy-duty vehicles. Modelling results revealed an inverse relationship between LPD and both costs and CO₂ emissions per tonne of collected waste, highlighting the importance of local context in collection planning.From an environmental perspective, the street-bin system became more carbon-efficient above 75 inhabitants·km⁻¹, although differences compared with the door-to-door system remained relatively limited. From an economic perspective, the street-bin system was the most cost-effective option across most density ranges, while the door-to-door system was competitive only in very low-density contexts.The findings demonstrate the importance of using empirically derived emission factors and accounting for territorial characteristics when evaluating and planning waste collection services. The proposed framework provides a transferable tool to support context-sensitive planning of municipal solid waste collection systems.

The authors' abstract, as published at the source. Waste Management, 2026 · DOI ↗