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The knowledge transfer potential of online data pools on nature-based solutions
Barbara Schröter1, Aude Zingraff-Hamed2, Edward Ott1
1Leibniz Centre for Agricultural Landscape Research, Working Group "Governance of Ecosystem Services", Eberswalder Str. 84, 15374 Müncheberg, Germany.
The Science of the Total Environment
|November 2, 2020
Summary
Online data pools for Nature-based Solutions (NBS) show varied information quality, hindering knowledge transfer. Improving these platforms is crucial for learning from NBS successes and failures to support wider adoption.
Area of Science:
- Environmental Science
- Conservation Biology
- Urban Planning
Background:
- Nature-based Solutions (NBS) adoption requires learning from implemented cases to identify drivers, barriers, and lessons learned.
- Online data pools can facilitate knowledge creation for scientists and practitioners regarding NBS case studies.
Purpose of the Study:
- To assess the knowledge transfer potential of online data pools for implementing and upscaling Nature-based Solutions (NBS).
- To compare 21 online data pools reporting on NBS case studies regarding information content, availability, and quality.
Main Methods:
- Systematic review and comparison of 21 online data pools documenting Nature-based Solutions (NBS) case studies.
- Analysis of information topics, quantity, type, and quality related to NBS implementation and outcomes.
Main Results:
- High variability in the quantity, type, and quality of documented information across NBS data pools was observed.
- Commonly reported information included actions, outcomes, site descriptions, challenges, and responsible entities.
- Crucial information on ecosystem processes, services, governance, and financing was frequently omitted, limiting comparative research.
Conclusions:
- The variability and omissions in current online data pools significantly hinder knowledge transfer and the upscaling of Nature-based Solutions (NBS).
- Recommendations are provided for developing more efficient data pool categories and for future research directions in NBS.
- Improved data standardization and content are essential for overcoming implementation gaps and advancing NBS adoption.
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