Protecting 30 percent of the planet: Costs, benefits and economic implications
Bibliographic record
Abstract
The World Economic Forum (WEF) and Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) have both identified biodiversity loss as one of the main threats to global economic prosperity. In particular, this high-level warning specifies that any further loss of natural habitats and biodiversity will cause extensive and costly flooding, climate change, disease emergence and ill health, clean water shortages, loss of crop pollination, decline in productivity, and numerous other risks. All of these negative outcomes are the consequence of degrading the natural infrastructure that supports human economic activity and wellbeing. One of the main policy instruments to slow the loss of biodiversity and the degradation of nature is the creation of protected or conservation areas (simple examples being a Nature Reserve or Marine Reserve). Currently, ~16% of the land and 7.4% of the ocean is in areas designated or proposed for protection (although only 2.5% of the ocean is in highly/fully protected areas). This level of protection is widely acknowledged as being inadequate to achieve biodiversity protection goals. One of the headline proposals for the 15th meeting of the Conference of Parties to the CBD, and Action Target 2 of the draft post-2020 Global Biodiversity Framework, is to increase the area covered by protected areas (PAs) and other effective area-based conservation measures (OECMs) to 30% of the planet by 2030, including both land and water protection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".