From traffic data to GHG emissions: A novel bottom-up methodology and its application to Valencia city

• Published in: Sustainable cities and society Vol. 66; p. 102643
• Main Authors: Mateo Pla, Miguel A., Lorenzo-Sáez, Edgar, Luzuriaga, Jorge E., Mira Prats, Santiago, Moreno-Pérez, Juan A., Urchueguía, Javier F., Oliver-Villanueva, Jose-Vicente, Lemus, Lenin G.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2021
• Subjects: Climate change; Decision making tool; GHG; Real time emission monitoring; Urban road transport; Real time emission monitoring; Urban road transport; Climate change; Decision making tool; GHG
• ISSN: 2210-6707; 2210-6715
• DOI: 10.1016/j.scs.2020.102643

•New bottom-up methodology for estimating urban transport CO2 emissions from real data.•CO2 emissions estimations based on information coming from traffic control system.•The traffic control system data used allows high temporal and geographical resolution.•A full functional pilot system has been developed in the city of Valencia (Spain).•It could contribute to better plan and monitor urban GHG mitigation strategies. Sustainable cities will only be possible with effective local measures tackling Greenhouse Gas (GHG) emissions. Transport and mobility represent the main sources of these emissions, particularly in urban settings. National and local public administrations need accurate and more responsive tools to quantify GHG emissions. Digitisation and ICTs are key elements in the development of such tools, which, additionally, have to be based on robust methodologies validated by the scientific community. This research presents a bottom-up methodology for the quantification of road traffic's GHG emissions with higher levels of immediacy and spatial resolution when compared to other already existing methods. The methodology uses data from the urban traffic control and monitoring systems as a baseline to calculate emissions. A pilot test has been conducted in Valencia city (Spain). Its results show a highly detailed picture of GHG emission in the city with high temporal (hour) and space (street) resolutions. The emission patterns reflect the dynamics of the city and its citizenship mobility. Since the tools developed for the pilot test can be adapted to other cities, public decision-makers could benefit from a precise diagnosis system based on traffic data to offer and evaluate solutions to reduce road transport GHG emissions.