Capturing the Value and Making the Business Case for Nature-Based Solutions - A step-by-step Guide
Bibliographic record
Abstract
Nature-based solutions (NBS) have attracted increasing interest globally as effective approaches to addressing urban challenges while promoting environmental sustainability and community well-being. However, decision-making around NBS funding and implementation tends to be centralized within public authorities and government bodies, often overlooking input from local communities, NGOs, and small to medium enterprises, which are key stakeholders impacted by urban infrastructure decisions. This limited engagement, coupled with a narrow focus on economic returns and a lack of awareness about their broader benefits, often hampers the realization of NBS full potential. In response to these challenges, this guide aims to equip urban stakeholders with the necessary knowledge to build compelling business cases for NBS implementation. By adopting a comprehensive valorization approach, the guide seeks to broaden traditional economic perspectives and leverage NBS values across environmental, economic, social, and health dimensions. Drawing upon research findings and experiences from the Horizon Europe CONEXUS project, the guide provides practical tools and methodologies for approaching the entire NBS valorization process: from identifying urban challenges, to choosing the right NBS, understanding and capitalizing on its value, and pitching a robust business case.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".