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

The data set of this article is related to an original research article entitled “Benchmarking the sustainability of urban energy, water and environment systems and envisioning a cross-sectoral scenario for the future” Kılkış, 2019. The data article provides data compilations in the context of benchmarking studies based on the composite indicator of the Sustainable Development of Energy, Water and Environment Systems City Index. Data tables for the seven dimensions of the index are provided for 35 main indicators and related sub-indicators for the newly benchmarked cities while those for other cities are monitored. In addition to periodic updates in the common data sources, some cities released updated reports for the Sustainable Energy and/or Climate Action Plans and/or relevant local statistics since the initial benchmarking. Normalized and aggregated values per dimension of the index for 120 cities are provided as an appendix for groups of 30 cities that are characterized as the pioneering, transitioning, solution-seeking, and challenged cities of the sample. The data compilation for the sources of residual energy from the industry, thermal power generation, the wastewater sector and urban biowaste are further provided for 60 cities as the basis of a scenario to encourage the integration of cross-sectoral measures in urban systems to improve benchmarked performances. The data that is contained in this data article thus enables the original application of the index to 120 cities and the analysis of a scenario in which cities reduce primary energy spending and carbon dioxide emissions.

of the index to 120 cities and the analysis of a scenario in which cities reduce primary energy spending and carbon dioxide emissions. © 2019 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

Data
This article contains data compilations for the 35 main indicators in the seven dimensions of the Sustainable Development of Energy, Water and Environment Systems (SDEWES) City Index for 18 newly benchmarked cities. 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 [2e6] 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 Specifications Table   Subject area Energy More specific subject area Renewable Energy, Sustainability and the Environment Type of data Tables and Figure  How data was acquired Compiled based on a comprehensive horizon-scanning of data sources that are processed for a composite indicator and scenario.

Experimental factors
Cities are selected for benchmarking based on criteria for data availability, geographical diversity and researcher representation in the scientific platform in which the index results are shared.

Experimental features
Compiled and formatted data compilations are utilized for data processing and analyses in the context of the research work.

Data source location
Cities around the world, including Aalborg, Birmingham, Bologna, Cape Town, Christchurch, Constant¸a, Dublin, Funchal, Gdynia, Glasgow, Hamburg, Johannesburg, Murcia, Reykjavík, Riga, Sfax, Sydney and Tallinn among a total of 120 cities. Data on residual energy for 60 cities are compiled for a cross-sectoral scenario.

Data accessibility
The data article contains Tables 1e10 and Fig. 1. Supplementary data sets that are associated with this data article based on Appendix A for Tables A1-A10 and Appendix B for Tables B1-B4 can be found in the online version, which is available at https://doi.org/10.1016/j.dib.2019.103856 Appendix C that contains additional references is similarly accessible. The atlas depicted in Fig. 1  Value of the data The data tables are of value to the scientific community based on the provision of original data compilations for cities, which can be used within and beyond the context of the Sustainable Development of Energy, Water and Environment Systems City Index. The data can be used to facilitate comparisons with future results and measure progress towards more sustainable urban energy, water and environment systems across time.
The data can be used to devise additional scenarios for cities beyond the scenario that is considered in the related research article for utilizing residual heat and urban biowaste. The data provides a basis for new research undertakings that are directed to improving the existing performance of cities and city collaboration pairs while serving as a benchmark to compare, envision and realize more sustainable urban systems in the future.
biowaste based on city level data compilations using the Pan-European Thermal Atlas (Peta) [7] and related local maps of the STRATEGO project [8].
In the context of processed and analyzed data, Fig. 1 in this article represents the layers of an atlas of the index results and Appendix B includes the data set for the normalized and aggregated values of 120 benchmarked cities per each dimension of the index. The data set of Appendix B is organized into four tables according to the top 30 cities that are characterized as the pioneering cities, the cities that are ranked 31e60 as the transitioning cities and the cities that are ranked 61e120, which contain two groups for the solution-seeking and challenged cities, respectively. These tables also correspond to the organization of the layers in the atlas that is represented in Fig. 1.
Overall, 10 tables are provided in the data article and 14 tables are provided in Appendices A and B for a total of 24 tables, which provide the basis of the research work in reference [1] for a comprehensive benchmarking of 120 cities. Additional references are provided in Appendix C. Beyond the present context, the data is relevant for Sustainable Development Goals 6, 7 and 11 on clean energy, water and sustainable cities and communities among others [9], the Global Covenant of Mayors for Climate & Energy Initiative [10] and a comprehensive assessment of urban progress for various studies, including the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [11].

Experimental design, materials and methods
According to the scope of the data article, this section provides the means of acquiring data for seven dimensions of the index D 1 to D 7 to perform analyses for 120 cities.  [55]. As indicated in the specifications table, the benchmarked cities are selected based on criteria for data availability (M 1 ), geographical diversity (M 2 ), and representation in the scientific platform in which the index results are shared (M 3 ). Cities from a particular country are prioritized according to population in descending order. This is necessary given that CoM signatories can have populations less than 10,000 inhabitants. The process of acquiring data is facilitated by the involvement of cities in climate initiatives. One data source is updates from CoM signatories based on the progress of completed and ongoing actions at least every 2 years and monitoring inventories every 4 years [56]. Data sources for local statistics may be available annually if reported in the same scope. In addition to the cities in Table 1, a dedicated questionnaire 2 was sent to the contact points of the 102 benchmarked cities to confirm the availability of any local reporting in addition to any updated monitoring reports on the CoM website [57]. The contact points were managers for urban energy and environment issues and those responsible for the monitoring of Sustainable Energy Action Plans (SEAP), Sustainable Energy and Climate Action Plans (SECAP) and/or other equivalent local plans and strategies. Such aspects addressed related data challenges in the process of pursuing data inputs.
Updated data sources for 25 cities based on [58e94] were identified, including new monitoring reports as retrieved from the CoM database [57]. 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 [2e6]. 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 mm in diameter (PM 10 ) based on urban monitoring station readings. All other data sources, including the Urban Waste Water Treatment Directive (UWWTD) database [96, 97], were comprehensively re-assessed and reviewed to determine any changes for the 120 cities. a Excluding the cities benchmarked in [6] for which updated monitoring data is found to be already integrated. b Also includes references that were shared by the city energy managers through the index questionnaire. c In some cities, such as Zadar, the recent monitoring report was only for actions rather than data [94].
The data article proceeds with a dimension by dimension description of the original data acquisition process for 18 newly benchmarked cities while the original data compilation for 120 cities are represented based on the average (mean) value in the sample that is marked for C AV . The normalized and aggregated values per dimension for 120 cities in the sample are appended. Table 3 represents the process of acquiring data for the first dimension on "Energy Usage and Climate" (D 1 ) based on data inputs for the urban energy system. The main data sources are sufficient to attain data inputs on energy usage in the building sector for residential, tertiary, and municipal buildings and the transport sector based on the energy usage of private, public, and municipal vehicle fleets. The total energy usage of buildings, transport, industry (non-ETS) and public lighting is evaluated on a per capita basis and calculated for each city when necessary. Other aspects of energy usage include climate and the overall efficiency with which primary energy spending is used, including energy production, transmission, and distribution. In this respect, a total degree days factor and the final to primary energy ratio are obtained for the data compilation.

Data compilation on penetration of energy and carbon dioxide saving measures
The means of acquiring data for dimension D 2 on "Penetration of Energy and CO 2 Saving Measures" necessitates an evaluation of the strategic actions of the city for climate mitigation. Table 4 represents the data inputs for the main indicators of D 2 while those for the sub-indicators are provided in Appendix A. In this appendix, Table A1 represents the data acquisition process for evaluating the energy system characteristics considering combined heat and power (CHP) based on district heating and/ or cooling (DH/C) networks, the use of renewable energy sources, including geothermal energy, and Table 3 Data inputs to the energy usage and climate dimension (D 1 ). progress towards a climate neutral heating sector in 18 cities. Table A2 represents the data acquisition process for evaluating the implementation status of nearly net-zero energy buildings and/or districts. Other aspects of data acquisition for D 2 require a comprehensive evaluation for public transport, including bus, trolleybus, and/or urban rail options for the transport sector (Table A3). These data inputs include urban rail density and daily ridership as well as the use of such decentralized options as bicycle sharing in support of the public transport network. Numerous local sources are used to acquire such data inputs in D 2 for each city, including those for cities that have higher levels of penetration in solid-state lighting and solar energy based armatures. Table 5 represents the data acquisition process for data inputs into the main indicators of the dimension on "Renewable Energy Potential and Utilization" (D 3 ). The data inputs that are compiled are necessary to evaluate the prevalence of renewable energy potential in cities while requiring cities to utilize higher shares of renewable energy for replacing the combustion of high exergy fossil fuels, especially in the electricity and transport sectors. The data acquisition process requires data inputs on the annual mean solar energy potential based on solar insolation on an optimally inclined plane, the average wind speed at 50 m height, and the mean heat-flow density for geothermal energy. The data acquisition process also extends to the renewable energy share in electricity generation to distinguish cities that have progressed towards or reached 100% renewable electricity grids with or without progress for decarbonizing the transport sector. The latter aspect requires data compilations for the share of green energy in transport, including biofuels and/or electricity.  a The minimum is zero based on the samples with partial points for monitoring without an action plan. b Top points received by DH/C based on CHP with >75% penetration and renewable energy, see Table A1. c Scored based on sub-indicators for nearly net-zero energy buildings or districts implementation, see Table A2. d Based on urban rail density, daily usership, and decentralized options with bicycle sharing (see Table A3). e Penetration of LED armatures using solar energy and/or best practices obtain an extra point.

Data compilation on water usage and environmental quality
Data acquisition for the dimension on "Water Usage and Environmental Quality" (D 4 ) requires data inputs that are related to the use and quality of water resources and cleaner air as well as any ecological surplus or deficit, which can have an impact on maintaining or harming environmental integrity. Table 6 represents the data acquisition process for D 4 that includes water usage per capita based on the water footprint of domestic blue water consumption and the level of water quality that is given out of a score of 100. The main data source for the annual mean PM 10 concentration was sufficient for all cities in Table 6 except Cape Town and Funchal that required additional data sources [105,106]. Similarly, data acquisition for ecological footprint and biocapacity per capita were obtained from the main data sources and compared with additional studies, e.g. ecological footprints of other Australian cities or housing and food shares in urban ecological footprints [107].

Data compilation on carbon dioxide emissions and industrial profile
The data acquisition process for the dimension on "CO 2 Emissions and Industrial Profile" (D 5 ) as represented in Table 7 involves data inputs to assess the impacts of the urban system on carbon dioxide (CO 2 ) emissions. The main data sources are sufficient to attain data inputs on the CO 2 emissions of residential, tertiary, and municipal buildings as well as those of private vehicles, public transport, and the municipal vehicle fleet. The CO 2 intensity is calculated to determine the level of decarbonization in urban sectors and the level of decoupling between energy usage and CO 2 emissions. Data inputs extend to components of the urban system that are otherwise not required in regular emissions reporting. The data compilation for the presence of any energy-intense industries in urban and related port areas, including iron and steel, basic chemicals and chemical products are represented in Table A4. The implementation of measures for on-site energy generation from renewable energy sources in airports towards carbon neutrality is evaluated from the annual reports of airports that service each city also considering the Airport Carbon Accreditation (ACA) levels.

Data compilation on urban planning and social welfare
The process of acquiring data to evaluate the provision of liveable areas with high levels of social welfare is based on the data sources of the indicators for the dimension on "Urban Planning and Social Welfare" (D 6 ). The data inputs in Table A5 are used to evaluate aspects of the waste hierarchy based on waste generation per capita and the share of waste that is recycled, reused or composted. Table A6 provides data inputs for any share of discharge without treatment and compliance with biochemical (BOD) and chemical oxygen demand (COD) as well as total suspended solids (TSS) in the wastewater treatment infrastructure primarily based on the thresholds of UWWTD [118]. In addition to urban services for waste and wastewater management, Table A7 provides data inputs based on the share of the population that lives in core urban areas, the sprawl index, the share of green urban areas, and protected green areas in the vicinity. The multiple data inputs compare urban compactness with the presence of green areas in support of ecological services [119] and climate adaptation [120]. Economic and educational opportunities are evaluated based on other data inputs as represented in Table 8, namely gross domestic product (GDP) per capita if distributed equally in society, inequality-adjusted well-being depending on survey results for daily experience satisfaction, including employment, and the tertiary education rate.  Table A4. c Scores greater than 3 require renewable energy best practices on the land side, air side and/or ground side.

Data compilation on research, innovation and sustainability policy
The process of acquiring data for dimension D 7 on "Research and Development (R&D), Innovation and Sustainability Policy" involves at least seven data sources for cross-cutting data inputs on the alignment of R&D and innovation assets in support of sustainable energy, transport, water and environment systems ( Table 9). The sub-indicators are based on R&D and innovation policy orientation (Table A8) and national patents in clean technologies based on Y02 and Y04 coded patents (Table A9). Both sub-indicators also require additional data acquisition, including patents in building technologies, energy generation, transport, smart grid and carbon capture and storage to determine technological competences [135]. Other data inputs are based on the presence of higher education and research institutions in the city (Table A10), the knowledge production capacity based on the h-index, and the emissions mitigation target as a major target for sustainability policy. Targets beyond the year 2020 towards carbon neutrality are annualized to 2020 for a common basis.  (Tables A5-A6). b Based on compact urban form including sprawl index and green spaces sub-indicators (Table A7).

Data analyses based on the compiled data inputs
As indicated in the specifications table, formatted data that takes place in Tables 1e10 and  Tables A1-A10 in Appendix A require processing and analysis. The analysis of the compiled data inputs for 120 cities are undertaken as described in the research article [1] based on the determination of the presence of any outlier values according to higher order moments, normalization based on the Min-Max method, uncertainty analyses based on 10,000 Monte Carlo simulations and sensitivity analyses based on various schemes using linear aggregation and/or aggregation based on the geometric mean at the dimension and index levels. These experimental factors led to the use of winsorized values for minimum or maximum values in at least one indicator (i x.y ) in D 1 (i 1.1 -i 1.3 ), D 3 (i 3.3 , i 3.5 ), D 4 (i 4.3 , i 4.5 ), D 5 (i 5.1 , i 5.2 ), and D 7 (i 7.3 , i 7.5 ) as shared in this data article. In particular, these indicators include those on energy usage per capita (Washington D.C. from the benchmarking in [4]), PM 10 (e.g. Cape Town and Johannesburg from the newly benchmarked cities and Bangalore from [6]), and biocapacity per capita (Helsinki and Espoo from [6,4]). The winsorized values in the scope of these indicators have been included in the values for C AV .
Data acquisition through data compilation, data processing, and the process of performing data analysis accumulates into the composite indicator. The normalized values with linear aggregation at the dimension level are provided in Appendix B. The relevant values are provided for the top 30 cities as the pioneering cities in Table B1, the top 31e60 cities as the transitioning cities in Table B2, the lower 61e90 cities as the solution-seeking cities in Table B3 and the lower 91e120 cities as the challenged cities in Table B4. Moreover, the compiled and analyzed data sets for 120 cities are processed to create a SDEWES Index Atlas. In this atlas, a 3-D maps feature is used to establish layers that are juxtaposed on Google Maps by dimensions and the index value per city for each group of 30 cities as represented in Fig. 1. The atlas supports another means in which the index results are used for city comparisons, including pairs as given in the research article [1]. a Based on the approach for thematic priorities and R&D expenditure as a share of GDP (Table A8). b Patents are limited to clean energy technology coded patents, e.g. Y02B for buildings etc. (Table A9). c Sum of universities located in the city. Those in the Scimago list receive double points (Table A10). d Sustainable development is a multidisciplinary field with inputs from multiple fields (fields not restricted).

Data compilation on residual energy in the urban vicinity
As indicated in the specifications table, data acquisition was further required for an original scenario application to cities. In the context of this cross-sectoral scenario as put forth in the research article [1], Table 10 represents the process of data acquisition that took place separately for 60 cities based on the values of the theoretically available residual heat from the industry and thermal power generation in about a 15 km radius based on spatial data in local maps [7,8]. Data related to wastewater and urban biowaste also take place in Table 10. According to the method of the companion research article [1], scenario multipliers are applied to these theoretical amounts to obtain scenario values after which the index is re-calculated for comparative analysis.