Cities are facing big challenges related with urbanism, transport, air pollution and clean energy. While local policymakers need to steer developments in order to fulfil national and international agreements in this regard, policy measures often fall short due to a lack of feedback and / or knowledge about their actual effects. In parallel, open data is becoming more and more prominent and huge amounts of data are already provided by cities’ data portals. These would allow a detailed tracking of the effects of past policy measures, but due to the huge amounts and difficult handling of the data this potential is often not used.
Based primarily on data from municipal open data portals, we develop a GHG emission model that estimates the emissions arising from motor transport as a function of actual daily and hourly traffic measurement data (vehicle flow, average velocity) from over 4000 measuring points distributed around the city. This allows evaluating the development of index variables (i.e., contaminant concentrations, traffic, GHG emissions) for the city of Madrid on both micro and macro-scale in order to detect statistically significant deviations from the historical trends by means of a continuous econometric regression analysis. Events with significant positive or negative impact on the target variables can be identified, and possibly the underlying policy measure. This allows understanding and tailoring urban policy by identifying singular or sudden events that eventually triggered a statistically significant alteration of historical trends and corresponding impacts on air pollution or GHG emissions.
By combining the assessment with life cycle assessment data, also the impact on the material footprint and thus the circularity of the municipal economy can be assessed, providing a comprehensive picture of potential policy impacts in terms of sustainability.