Mobilising Finance for Biodiversity: Insights From Across the Island of Ireland
Bibliographic record
Abstract
This paper investigates how sustainable finance mechanisms can be designed and mobilised to support biodiversity objectives in Ireland. We employ a qualitative, participatory research design, drawing on interdisciplinary workshops held across the island of Ireland in 2023. Using Appreciative Inquiry (AI) and thematic analysis, we identify seven core themes shaping biodiversity finance, including the growing awareness of nature loss, the climate–biodiversity nexus, the role of financial innovation, and the importance of community and agricultural co-design. Our findings offer grounded insights into how finance can align with biodiversity policy ambitions such as the Kunming–Montreal Global Biodiversity Framework and the EU Nature Restoration Law. The study contributes to the nascent literature on biodiversity finance by highlighting the need for localised, socially embedded financial solutions, and by outlining practical pathways for bridging the biodiversity finance gap in a national context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".