CITYkeys: Key Performance Indicators. Peter Bosch Vera Rovers

Transcription

1 CITYkeys: Key Performance Indicators Peter Bosch Vera Rovers

2 Indicator based results How successful are smart city projects?

3 Indicator based results Do smart city projects make a difference?

4 The goal of CITYkeys is to provide a validated, holistic performance measurement framework for monitoring and comparing the implementation of Smart City solutions. Smart city projects Smart cities Smart city projects Smart city projects Smart city projects Smart city projects Smart city projects

5 Smart city definition A smart city is a city that efficiently mobilizes and uses available resources ( ) for efficiently improving the quality of life of its inhabitants, commuting workers and students, and other visitors [people] significantly improving its resource efficiency, decreasing its pressure on the environment and increasing resiliency [planet] building an innovation-driven and green economy, and, [prosperity] fostering a well-developed local democracy [governance]. 30/09/2015 5

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7 Themes of the framework PEOPLE improving the quality of life of its inhabitants, commuting workers and students, and other visitors PLANET significantly improving its resource efficiency, decreasing its pressure on the environment and increasing resiliency PROSPERITY building an innovation-driven and green economy GOVERNANCE fostering a well-developed local democracy PROPAGATION Improving the replicability and scalability of smart city project solutions at wider city scale.

8 Framework structure Themes, Subthemes and # of project indicators People Planet Prosperity Governance Propagation Health (3) Safety (4) Access to (other) services (7) Education (3) Diversity & social cohesion (3) Energy & mitigation (7) Materials, water and land (10) Climate resilience (1) Pollution & waste (4) Employment (2) Equity (2) Green economy (3) Economic performance (5) Innovation (5) Organisation (6) Community involvement (5) Multi-level governance (2) Scalability (10) Replicability (8) Quality of housing and the built environment (6) Ecosystem (2) Attractiveness & competitiveness (1)

9 Example indicators (5 out of 92) access to public transport annual final energy consumption use of local workforce stakeholder involvement solution to development needs

10 From project to city CO2 emissions renewable energy red Reduction: 135t CO 2 /yr final energy consumption /09/

11 Main sources for information collection Project level: Site visit and interviews Supporting literature and documents Automated monitoring systems Additionally for city level: Municipal statistics

12 Data processing Raw data Normalisation Aggregation Indicator x Indicator y kwh/m2 % Score 1-10 Score 1-10 One score for Indicator z Likert Score 1-10 Visualisation

13 Learning An example: generalised conclusions of a report evaluating two smart building projects Improving performance View ATES as a system component The importance of adapting to the local circumstances TECHNOLOGY Involve various expertises in an early stage Establish clear goals and agreements Communication with end users ORGANISATION Prevent split incentives FINANCIAL- ECONOMIC 13

14 CITYkeys indicators for ESPRESSO Basis for the ESPRESSO Smart City information framework (impact focused) Tested impact indicators for ESPRESSO indicator platform 30/09/

15 CITYkeys Smart city performance measurement framework Daniel Sarasa Ana Jiménez Zaragoza City Council

16 Project and city KPIs

17 The smart city is an organizational challenge

18 About the nature of data for decision making

19 Identifying gaps and opportunitie s in co-creation workshops

20 Lessons learned & Open questions Uniqueness and data automation is a myth Open Data policy mirrors the organization Cities are not fully responsible or even aware of all urban data The % youth unemployed paradigm The observer effect Are cities comparable? City learning cycle: Measure learn?? change???? What do we build from here Data sharing between urban KEY players

21 Contact: Web: Twitter: Supported by

22 CITYkeys: Feedback from testing with cities Aapo Huovila, VTT

23 Objectives of testing Validate CITYkeys KPI framework through real case studies in cities Feasibility of KPIs, data availability, resources needed Improvements to KPIs Assess the applicability of the framework in different contexts Ensure cities involvement in co-design of KPI tool 2

24 Participants Comprehensive testing in CITYkeys partner cities: Rotterdam, Tampere, Vienna, Zagreb, Zaragoza Scales covered: project & city & project-to-city + manual & automatic input/reading Voluntary cities, projects and companies outside CITYkeys consortium Smaller set of KPIs 15 cities / (lighthouse) projects / companies involved Many others still in process / planning use of the KPIs & tool 3

25 KPI tool concept 4

26 KPI Tool 5

27 City management Target end-users of the framework/tool Mayor's office, Smart city department, Metropolitan observatory Strategic level, Operative level, Policy decision making (city level, environmental planners and politicians) Deciding on new projects, steering existing ones and assessing the performance of past ones Setting targets for city and monitoring progress Project management E.g. urban planners, civil engineers Before, during and after the project Individual projects and project portfolio 6

28 Dataset analysis In total the smart city KPIs need 116 raw data sets On average 72% of them seem available in partner cities Most of them can be retrieved from statistical or external sources The boundaries of SC project data sets usually depend on the project Needed data is typically not readily available Typical sources include project documentation and interviews with project manager The share of open data providing the needed data sets varies from 1% to 25%, and is 15% on average Cities have up to 300 open data sets in their portals but only some of them are relevant for CITYkeys smart city KPIs Today, a smart city performance measurement system cannot yet rely only on open data 7

29 Data availability 8

30 Results CITYkeys has shown that cities involvement smart city KPI & tool development is crucial Most of the KPIs were tested without problems in case studies Some differences in calculation methods or units between cities Minor amendments to KPI descriptions Impact based approach well-received and useful by cities in testing, some actors prefer more technical and specific indicators 9

31 Lessons learnt Differences between cities Size and population density Climatic conditions Economic level Technology maturity Coordination of smart city activities in cities & Data management systems and practices Different aims and priorities Self-benchmarking or comparison with others? Project scale allows comparison and scoring, at city scale not possible without city specific target values Balance between quantitative vs. qualitative KPIs Flexibility for selecting and using KPIs allowed 10

32 Next steps Continue collaboration with lighthouse projects and other cities/projects Promote KPI tool RESTful APIs for KPI input Look for possible funding opportunities for further developing certain tool properties 11

33 Thank you! Tool testing account: demo/demo. Please contact for private account Web: Supported by