Benchmarking Nature-Based Solution and Smart City Assessment Schemes Against the Sustainable Development Goal Indicator Framework - Wendling, Laura A.; Huovila, Aapo; zu Castell-Rüdenhausen, Malin; Hukkalainen, Mari; Airaksinen, Miimu (Frontiers in Environmental Science, 2018)
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.