Going beyond market-based mechanisms to finance nature-based solutions and foster sustainable futures
Bibliographic record
Abstract
Failure to address the climate and biodiversity crises is undermining human well-being and increasing global inequality. Given their potential for addressing these societal challenges, there is growing attention on scaling-up nature-based solutions (NbS). However, there are concerns that in its use, the NbS concept is dissociated with the social and economic drivers of these societal challenges, including the pervasive focus on market-based mechanisms and the economic growth imperative, promoting the risk of greenwashing. In this perspective, we draw on recent research on the effectiveness, governance, and practice of NbS to highlight key limitations and pitfalls of a narrow focus on natural capital markets to finance their scaling up. We discuss the need for a simultaneous push for complementary funding mechanisms and examine how financial instruments and market-based mechanisms, while important to bridge the biodiversity funding gap and reduce reliance on public funding, are not a panacea for scaling NbS. Moreover, market-based mechanisms present significant governance challenges, and risk further entrenching power asymmetries. We propose four key recommendations to ensure finance mechanisms for biodiversity and NbS foster more just, equitable, and environmentally sustainable pathways in support of the CBD’s (Convention on Biological Diversity) 2050 vision of “living in harmony with nature”. We stress that NbS must not be used to distract attention away from reducing emissions associated with fossil fuel use or to promote an agenda for perpetual economic growth and call on government policy makers to decenter GDP growth as a core economic and political target, refocusing instead on human and ecological well-being.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".