Can Financialization Save Nature? The Case of Endangered Species*
Bibliographic record
Abstract
ABSTRACT The current biodiversity loss is dramatic. Over the past 50 years, more than 68% of the mammals, birds, amphibians, reptiles, and fish on Earth have disappeared, putting the planet's survival and its inhabitants—including human beings—at risk. Financialization, or the transformation of nature into financial assets, is increasingly proposed as a solution to the biodiversity crisis. Proponents of financialization believe that assigning a monetary value to nature will incentivize human beings to protect habitats and their species. This article offers a four‐mechanism model of nature's financialization, explaining why it is virtually impossible to financialize nature. We collected data through a unique two‐stage data collection process, including a single case study and additional interviews with conservationists and conservation finance specialists. We analyzed the development of a calculative device, the “Index,” designed to assess the impact of conservation efforts on the survival of endangered species. Conservationists hoped to use the Index to calculate the financial return of a conservation impact bond, a financial instrument designed to finance conservation projects. However, they did not achieve their goal. We discuss the implications for the financialization and conservation literature and the role of accounting therein. We notably question previous accounts of financialization, including the need for financial numbers or financial actors. We ultimately show that a financializationprojectcan transform practices toward financialization, even if the financializationprocessis not complete.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".