Can Financialization Save Nature? The Case of Endangered Species*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 financialization project can transform practices toward financialization, even if the financialization process is not complete.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it