Data-Driven GHG Estimation for Local Public Transport Fleets: Pescara City Case Study

The transport sector is among the highest contributors to greenhouse gas (GHG) emissions. Consequently, the European Union has established ambitious targets to accelerate the transition to zero-emission mobility, with a specific focus on public transit fleets. Under current directives, urban bus fleets must reduce GHG emissions by 90% by 2030 and achieve full conversion to zero-emission vehicles by 2035. In this context, continuous monitoring of emission trends by public transport operators is crucial to ensure alignment with decarbonization goals. This study presents a pragmatic, data-driven methodology for estimating GHG emissions from local public transport (LPT) fleets. Designed to be both cost-effective and easily replicable, the approach leverages real-world operational data to generate context-specific emission estimates across various vehicle categories. The methodology integrates traffic volume data with detailed fleet characteristics, such as dimensional vehicle class, passenger capacity, energy carrier type, and engine emission standard, sourced from transport operators and national statistical databases. GHG emissions are quantified for CO₂, CH₄, and N₂O, and aggregated into CO₂-equivalent (CO₂e) metrics. Particulate matter emissions (PM₂.₅ and PM₁₀) are also assessed independently. Beyond quantifying energy consumption and GHG emissions, the study introduces a set of unitary performance indices. These incorporate passenger loads and operational parameters, enabling more precise and detailed per-capita assessments of environmental impact. The methodology is applied to a case study in Pescara, a medium-sized city in central Italy, using high-resolution data from the local transit operator TUA S.p.A. A sensitivity analysis on vehicle occupancy rates has assessed the impact on per-capita energy use and emissions, highlighting key environmental performance drivers. Additionally, a strategic scenario analysis has been carried out to evaluate fleet decarbonization pathways toward a zero-emission target by 2035. Owing to its low implementation cost and reliance on readily available data, the proposed method serves as a valuable decision-support tool for public transport operators. It supports both environmental impact assessments and the strategic planning of emission reduction policies, with strong potential for adaptation across diverse urban contexts.