Benchmarking South East European Cities with the Sustainable Development of Energy, Water and Environment Systems Index

Abstract

Tools that can benchmark cities, including cities in South East Europe, are necessary to enable the comparison and diffusion of more sustainable practices for urban systems. The "Sustainable Development of Energy, Water and Environment Systems Index" provides a composite indicator for benchmarking city performance based on 7 dimensions and 35 main indicators. In this research work, the Index is applied to a new sample of 18 cities in South East Europe for which data is collected, normalized, and aggregated. Klagenfurt (3.08), Velenje (3.06) and Pécs (3.01) are found to be the top three cities in the sample while an average city receives an index score of 2.85. The results are further compared to reference averages and evaluated based on the mean simulated values of 10,000 Monte Carlo experiments. The results are interpreted in quartiles for pioneering, transitioning, solution-seeking, and challenged cities. The results are then applied within a benchmarking tool of the Index that supports policy learning to trigger collaboration between cities and further used to match cities according to a search algorithm based on index performance. In addition, the results are compared to urban hierarchy as well as development contexts and mapped onto the spatial dimension as an initial step for enabling a "Sustainable Development of Energy, Water and Environment Systems Future City Network". The paper concludes with a set of four proposed steps to enable decision-makers and urban planners in using the Sustainable Development of Energy, Water and Environment Systems Index in support of more sustainable urban systems.

Data on cities that are benchmarked with the sustainable development of energy, water and environment systems index and related cross-sectoral scenario

“…The energy and sustainability managers of cities also provided additional resources for local statistics, including those for Espoo in a Finnish emissions database at the local level [75]. Table 2 identifies the cities with updated references for climate mitigation related plans and statistics since the initial benchmarking of a city in references [2], [3], [4], [5], [6]. Other updates in the data sources include those based on a newer version of the World Health Organization (WHO) Global Urban Ambient Air Pollution Database [95] that provides a basis to compile data inputs for the annual mean particulate matter concentrations less than 10 μm in diameter (PM 10 ) based on urban monitoring station readings.…”

“…These data compilations are provided in data tables for the main indicators of each dimension in this article while data inputs for the sub-indicators are provided in data tables in Appendix A. As the companion data article of an original research article [1] that provides a benchmarking study for 120 sampled cities and the application of a cross-sectoral scenario, other tables contain updated data sources for 25 cities in comparison to references [2], [3], [4], [5], [6] and data on the theoretically available sources of residual energy in the urban vicinity for 60 cities. The latter includes data on the residual heat from industry, thermal power generation, the wastewater sector as well as urban biowaste based on city level data compilations using the Pan-European Thermal Atlas (Peta) [7] and related local maps of the STRATEGO project [8].…”

Tools for Measuring Energy Sustainability: A Comparative Review

“…These best practices are focused on action-oriented measures that can be implemented by other cities to improve the performance of dimensions. The actions range from buildings based on cogeneration with combined heat and energy cycle, zero energy buildings, sustainable water management, large urban forests, urban planning, and incentive policies for research, development, and innovation [123][124][125][126][127].…”

Multiple Criteria Decision Analysis Theory and Tools for the SDEWES Index

“…This process consists in finding pairs of cities with similar behaviour (either under or below average) on all dimensions. Results of city pairing are reported also in Kılkış [12], where a further sample of 18 SEE cities is considered. In Kılkış [13], a further sample of 26 world cities is evaluated, and a "normative scenario" (see discussion below) for Rio de Janeiro is analysed.…”