Open AccessFrom Assessment to Implementation: Design Considerations for Scalable Decision-Support Solutions in Sustainable Urban Development
Author(s) -
Viktor Bukovszki,
Diana Apró,
Ahmed Khoja,
Natalie Eßig,
András Reith
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/290/1/012112
Subject(s) - scalability , performance indicator , computer science , process management , maturity (psychological) , sustainable development , sustainability , smart city , decision support system , knowledge management , business , database , computer security , marketing , psychology , ecology , developmental psychology , artificial intelligence , political science , law , biology , internet of things
Cities have to face the challenges of steady population growth, the related increase in energy and resource demands, intensifying climate change impacts and rapid technological development. To handle these complex challenges and promote sustainable development, the smart city approach – data-driven planning based on emergent ICT technologies – has been gaining prevalence. However, the lack of shared standards, frameworks, and evidence-based decision-support tools limit the collaboration among smart city actors and the utility of the mainly business-driven technical solutions. This study explores the scalability of indicator systems into a shared framework for smart and sustainable cities by practice-based research during the development of the SmartCEPS project. SmartCEPS is an assessment system and maturity model based on key performance indicators (KPIs) for small- and medium-size European cities. In its architecture, indicators are organized in a causal network capable of capturing synergies, co-benefits and payoffs of decisions; structural metadata provides the means for a gradual customisation of the system; and finally, the indicator pool is scalable by complexity, ensuring different levels of detail in assessments. The study concludes that gradual customisation, network organisation, and open-ended scalability are the proxies for developing decision-support instruments from KPIs.