Enhancing nature-based solutions acceptance through stakeholders' engagement in co-benefits identification and trade-offs analysis
R.Giordano, I.Pluchinotta, A.Pagano, A.Scrieciu, F.Nanu
Science of The Total Environment, 2020

Abstract

Nature-based solutions (NBS) are increasingly recognized as a valid alternative to grey infrastructures - i.e. hard, human-engineered structures – as measures for reducing climate-related risks. Increasing evidences demonstrated that NBS can reduce risks to people and property as effectively as traditional grey infrastructures, but potentially offering many additional benefits, e.g. improving the natural habitat for wildlife, enhancing water and air quality, improving socio-cultural conditions of communities. The growing attention on the NBS, triggered an increasing interest in developing integrated and multi-disciplinary frameworks for assessing NBS effectiveness accounting for the co-benefits production. Starting from the analysis of the existing frameworks, this work claims for a more direct engagement of stakeholders – i.e. co-benefits beneficiaries – in developing NBS assessment framework. This work aims at demonstrating that differences in co-benefits perception and valuation might lead to trade-offs and, thus, to potential conflicts. An innovative methodology using a quasi-dynamic Fuzzy Cognitive Map approach based on multiple-time-steps was developed in order to assess NBS effectiveness, and to detect and analyze trade-offs among stakeholders due to differences in co-benefits perception. The developed methodology was implemented in the Lower Danube case study. The trade-off analysis among stakeholders shows that they are quite low in the short term. Most of the potential conflicts can be detected in the long term, involving mainly the stakeholders that assigned a high value to the agricultural productivity variable. The results demonstrated that accounting for the different stakeholders' perception of the co-benefits is key for reducing trade-offs and enhance NBS acceptability

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