Smart City Indicators

Transcription

1 Smart City Indicators meanings of indicators in a place based understanding Rudolf Giffinger TU Wien Centre of Regional Science - SRF EERA JP Smart Cities Symposium on Key Performance Indicators for Smart Cities Session 1: Evaluation frameworks for Smart Cities

2 Objectives & Structure Why evaluating Smart City development? Efficiency and/or sustainable development Which frameworks for indicators? Methodology for measuring urban trends Ex-post or ex-ante evaluation of programs, projects or processes Understanding smart city Frameworks in a place based understanding (ex-post) Instead of KPI: from tangible to intangible factors experiences regarding energy efficient urban development innovation potentials Conclusions and perspectives Advantages - disadvantages

3 What is a Smart City? origins and basic idea originated from the information city using new ICTs innovatively implementing a network of sensors in the city believe in a wired, ICT-driven form of development stresses the integrated database for city governance» A Smart City is a city well performing in these 6 characteristics, built on the smart combination of endowments and activities of self-decisive, independent and aware citizens.» Giffinger et al. (2007) when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through a participated governance.» Caragliu, DelBoand, Nijkamp (2011) Smart City initiatives address problems of common interest with the aid of ICTs. To be classified as a Smart City a city addresses one or more of the following characteristics: Smart Governance, Smart People, Smart Living, Smart Mobility, Smart Economy and Smart Environment. DG Internal Policies (2014) Mapping Smart Cities in the EU.

4 Meaning of SC indicators & monitoring place based understanding Monitoring system Indicators related to trends targets ex-post evaluation Smart City ex-ante or process evaluation of programms or projects Evaluation of Challenges Programme impacts Project impacts Fig. 4. Urban Innovations in a place based Smart City approach Source: own elaboration Giffinger (2015) ; strongly modified from Batty, et al., 2012, p. 508.

5 European Smart Cities monitoring of intangible characteristics SC 4.0 Criteria of City sample inhabitants (due to core city definition of Urban Audit) Data availability cities in Urban Audit Data sources Eurostat (Base of 16 indicators) Urban Audit (Base of 32 indicators): Urban Audit Perception (Base of 26 indicators) Eurobarometer (Base of 11 indicators) Espon (Base of 3 indicators): MastersPortal.eu (Base of 2 indicators): 90 cities Total coverage: more than 80% Evaluation of Pre-conditions and (over time) the trends of programme impacts in the long run

6 Normative definition of From tangible to intangible characteristics aggregation of 90 indicators Smart City 6 Key fields 28 groups of domains 90 components as indicators Characteristics: Smart Economy Empirical description by respective set of indicators Calculation mean values across respective indicator set Smart City Key fields Domains Components Smart People Smart Governance Smart Mobility Smart Environment Smart Living Data Endowments: local conditions Framework for intangible characteristics Holistic understanding of urban development: Endowment & Performance & Perception No reduction of city to tangible KPIs but to intangible assets and deficits Key fields are result of qualitative discussion Activities: attractiveness Perceptions: meaning First model Giffinger, et al. (2007) European Smart Cities (2007) Fourth model SC 4.0 (2015)

7 Smart City monitoring detecting challenges - improving effectiveness SC understanding SC learning process Workshops Participatory settings Expert interviews based on quantitative information Detecting urban position and profile based on key fields Identifying assets and deficits Supporting benchmarking and place based evidence Documents based on qualitative information Discussion of most relevant key fields and domains Identifying strengths and weaknesses based on profiles Defining relevant objectives Monitoring for ex-post evaluation: Impact of programme on trends Strategy or Roadmap defining objectives and projects Monitoring for ex-ante evaluation: Impacts of process or projects

8 Framework for intangible characteristics Disadvantages / weaknesses Advantages / strengths Assumption beyond aggregation Assumption beyond aggregation No consideration of mutual influence not all are substitutive Aggregated mean value describes intangible features No KPI: city unequal to firm Intangible: increasing importance Data sources Data sources not real time information (3-years-delay) Some show deficits of validity Results Official, valid and reliable Results Holistic but easy to understand, transparent calculation Time delay not covering latest trends Clear message through profiles indicating assets and deficits City level aggregated information High political impact no short wins Trends and programme orientated ex-post evaluation

9 Framework for a programme based Monitoring of energy efficient urban development Planning for Energy Efficient Cities monitoring system Experiences in projects PLEEC Planning Energy Efficient Cities: FP 7 DG Energy with 6 European midsized cities Smart City Indicators: KLIEN Austria, with 7 Austrian smart cities Evidence based process Workshops, surveys, discussion with stakeholders in cities Classification of key fields and domains framework Framework: indicator based against cities ee-action plans General understanding Energy efficiency means the use of less energy to provide the same service considering aspects of economic, social and ecologic sustainability and the life-cycle of materials. Elaborated and agreed by PLEEC stakeholder cities

10 Monitoring of energy efficient development performance related indicator in PLEEC model Partner cities and experts defined groups of important indicators PLEEC Planning Energy Efficient Cities; EC, FP-7; DG Energy; TU Wien

11 PLEEC 3 level procedure for indicator based monitoring system first level indicators: mandatory for all cities GDP Number of inhabitants Settled area Basic data Number of households Number of dwellings Number of residential buildings Basic energy data Final energy consumption Green buildings and land-use Share of annual thermal renovations Share of public low- (zero-) energy buildings Population density Mobility and transport Transport performance in public transport CO2 emissions in public transport Key fields Cost of a monthly ticket for public transport Transport performance in motorized private transport CO2 emissions in motorized private transport Level of motorization Transport performance in bicycle transport Technical infrastructure Waste generation Recycling of waste Waste collection fee Production and consumption Energy demand in industry CO2 emissions in industry Energy demand in private households CO2 emissions in private households Energy supply Electricity tariff - traditional mix Electricity tariff - renewables mix Second level indicators A target based energy efficiency indicators Second level indicators B perception indicators

12 Assessing Smart City Initiatives for the Mediterranean Region - ASCIMER Objectives framework for ex-post evaluation A Smart City project should not follow the same strategies in one or another urban area because the challenges, starting conditions, available resources and citizens willingness are completely different in every case. Introduction 2 nd ASCIMER Workshop Knowledge exchange North-South Best practices but no copy paste Importance of City FORUMS for learning and decision finding

13 Framework for indicator based monitoring focussing on ee-action plans Disadvantages / weaknesses Advantages / strengths Key fields and domains Key fields and domains Domains may vary across cities due to differentiated importance of key field Data sources and availability Classification well accepted Involvement of stakeholders their perceptions, assessments and knowledge Information base very heterogeneous across cities Specific frameworks existing Problems of cities Data sources and availability Actual information reflecting city specific challenges, discussions and attitudes Missing capacities and willingness/no benchmarking Very problematic because of missing political commitment and strategic barriers Utility for cities Showing impacts Of programmes in long run PLEEC Of projects in short / medium run ASCIMER Impacts as arguments for experts

14 City evidence / perception based profile of innovation potential»how would you judge the current contribution of the domain for energy efficiency in your city today?how would you judge the innovation potential for energy efficiency in the domain in your city in the near future?»(1 very low, 2 low, 3 fair, 4 high, 5 very high)«profile of Tartu, Estonia p. Private households n. Private and public services t. Renewable energy r. Fossil and nuclear energy a. Renovation and refurbishment 5,00 4,50 4,00 3,50 3,00 2,50 2,00 1,50 1,00 0,50 0,00 b. Innovative building technologies c. Spatial structures and landuse d. Public transport e. Motorised private transport m. Industry and commerce f. Pedestrian traffic and cycling k. Public lighting g. Transport of goods j. (District) heating and h. Waste, water and sewage cooling grids management i. Electric power grids innovation potential current contribution PLEEC Planning Energy Efficient Cities; EC, FP-7; DG Energy; TU Wien

15 Framework for perception based monitoring Disadvantages / weaknesses Advantages / strengths Key fields and domains Domains may vary across cities due to differentiated importance of key field Data sources and availability Hardly comparable data in cities Problems of cities Key fields and domains Classification well accepted Involvement of stakeholders their perceptions, assessments and knowledge Data sources and availability Actual information reflecting city specific challenges, discussions and attitudes Very sensitive against group composition. Stakeholder based Evidence Consideration of different expertise & interests Integration into decision finding process

16 Perspectives of monitoring Why Smart city unequal Sim city: interaction of socio-cultural, economic, environmental systems not driven by technical innovations but by different local effective initiatives no KPI possible Learning process: interaction of indicator based evidence and a social decision finding process monitoring of effective outcomes in terms of efficiency and/or sustainability What and How Assessing trends and programmes: impacts in long run sustainability Assessing distinct projects: impacts in short/medium run efficiency (& sust.) Pre-condition for successful implementation Smart initiatives need impulses from above based on political commitment and strategic expertise of main objectives and financial subsidies Basically cities are sceptical against monitoring utility of monitoring system

17 Literature ASCIMER, Batty, M. et al. (2012): "Smart Cities of the Future". European Journal of Physics Special Topics 214: Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), p DG Internal Policies (2014) Mapping Smart Cities in the EU. Giffinger, R. et al. (2007): "Smart cities Ranking of European medium-sized cities". accessed on 24th of January, 2015 Giffinger, R. et al. (2014): PLEEC report: WP2 Deliverable 2.4 Methodology for monitoring pdf accessed on 24th of January, 2015 Rudolf Giffinger (2015) Smart City concepts: chances and risks of energy efficient urban development; presented at Smartgreens Conference 2015 Lisbon, 4th International Conference on Smart Cities and green ICT systems; will be published in Springer-Verlag Nam, T; Pardo, T. (2011) "Conceptualizing Smart City with Dimensions of Technology, People, and Institutions." The Proceedings of the 12th Annual International Conference on Digital Government Research, June PLEEC Planning for Energy Efficient Cities (2013) EC, FP-7; DG Energy

18 Many Thanks for Your Attention Univ.Prof. Dr. Rudolf Giffinger Centre of Regional Science - SRF Department of Spatial Planning TU Wien rudolf.giffinger@tuwien.ac.at