Miimu Airaksinen

Benchmarking Nature-Based Solution and Smart City Assessment Schemes Against the Sustainable Development Goal Indicator Framework

Increasing global urbanization yields substantial potential for enhanced sustainability through careful management of urban development and optimized resource use efficiency. Nature-based solutions (NBS) can provide a means for cities to successfully navigate the water-energy-climate relationship, thus enhancing urban resilience. Implementation of NBS can improve local or regional economic resilience underpinned by the sustainable use of natural resources. The innovative governance, institutional, business, and finance models and frameworks inherent to NBS implementation also provide a wealth of opportunity for social transformation and increased social inclusiveness in cities. The ultimate benefit of NBS implementation in cities is increased livability, which is typically measured as a function of multiple social, economic and environmental variables. Given the range of different interventions classified as NBS and the cross-sectoral character of their co-benefits, different assessment schemes can be used to evaluate NBS performance and impact. Herein, performance and impact indicators within three robust NBS- and Smart City-related assessment schemes – Mapping and Assessment of Ecosystems and their Services (MAES), Knowledge and Learning Mechanism on Biodiversity and Ecosystem Services (EKLIPSE), and Smart City Performance Measurement Framework (CITYkeys) – were critically analyzed with respect to Sustainable Development Goal (SDG) 11, ‘Make cities and human settlements inclusive, safe, resilient and sustainable’. Each selected assessment scheme was benchmarked with respect to the Inter-Agency Expert Group on SDG Indicators’ global indicator framework for the sub-objectives of SDG 11. The alignment between each of the selected NBS assessment schemes and the SDG indicator framework was mapped with particular emphasis on consistency with city-level framework indicators for each SDG 11 sub-objective. The results were illustrated as composite scores describing the alignment of the analyzed NBS and Smart city assessment schemes with the SDG 11 sub-objectives. These results facilitate NBS assessment scheme selection based on alignment between each analyzed assessment scheme and specific SDG 11 sub-objectives. Cities face multiple challenges amidst a complex hierarchy of legislative, regulatory and other stakeholder obligations. The present study showed that strategic selection of an NBS assessment scheme which closely aligns with one or more sub-objectives within SDG 11 can maximize operational efficiency by exploiting synergies between evaluation schemes.

Enhancing urban resilience via a real-time decision support system for smart cities

The emergence of in-memory database technologies may be seen as a groundbreaking development in the segment of data storage and data analytics enabling end-users using real-time applications on top of big data. In this work, we propose a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructures or their components grounding on in-memory database technologies and smart city data sources. A simulation software which utilizes a multi-agent based model for describing the landscape of a smart citys infrastructure or their components incorporating a generic framework for defining disruption scenarios, generates big data which is stored in a database applying in-memory database technologies. According to current urban status data and the type of disruptions, data including made decisions and strategies which are best in the sense of urban resilience is instantly collected from the database.