InSmart – A methodology for combining modelling with stakeholder input towards EU cities decarbonisation

Abstract In a world where an increasing share of the population lives in cities, its energy transition is gaining more relevance. The decision-making process in urban planning is frequently fragmented across departments considering different criteria. Integrated city planning approaches are not commonly employed, especially for the promotion of sustainable energy. This paper presents an innovative approach to promote holistic decision-support combining complex integrated city energy system models (ESM) with effective stakeholder input using a Multi-Criteria Decision Analysis (MCDA). A holistic optimization city-ESM, based on the TIMES model generator, was developed and implemented in four European cities (Evora in Portugal, Cesena in Italy, Nottingham in UK and Trikala in Greece). Each city defined a range of future scenarios for sustainable energy promotion up to 2030 which were modelled in the city-ESM. The scenarios considered both individual measures, such as expansion of bicycle lanes, and combinations of measures. Going one step further from typical approaches in urban energy modelling, the modelling work was carried out in close cooperation with city stakeholders whom participated not only in the definition of the modelled scenarios, but also on the critical review of the modelling results. This review was conducted through a MCDA exercise which considered both quantitative and qualitative criteria in the selection of candidate scenarios to be implemented. It was found that there are substantial differences in most desirable scenarios if the MCDA is performed as well or if only the city-ESM model results are considered. The critical review of quantitative modelling results and their ranking via the MCDA led to improved communication of model results to non-modellers, which in turn made possible a better scrutiny and improvement of the modelling. It is concluded that at an urban level is important to combine qualitative analysis with quantitative modelling to identify the optimum mix of measures for a sustainable urban energy future.