Mass support for conserving 30% of the Earth by 2030: Experimental evidence from five continents
Bibliographic record
Abstract
Rapid global expansion of protected areas is critical for safeguarding biodiversity but depends on political action for successful implementation. Following widespread ratification of the Kunming-Montreal Global Biodiversity Framework, an unprecedented increase in area-based conservation is required to reach its target of conserving 30% of land, waters, and seas by 2030. These expansions prompt difficult trade-offs between conservation, social, and economic interests. A key factor in securing legitimacy and practical feasibility for expansion regimes is understanding what factors determine public support for them. Using survey and experimental data, we show that in eight countries across five continents, public opinion is 1) strongly in favor of the "30-by-30"-target and 2) highly consistent regarding policy priorities for the design of international- and domestic-level expansion regimes. We find that for international-level policy regimes, support increases with protection responsibilities equally split between countries, rich countries bearing higher costs, more countries actively cooperating, and placement trade not allowed. For domestic-level policy regimes, support generally increases when nature values are prioritized over social or economic values and, in many countries, decreases when costs are borne by a general tax increase, parks are managed by private companies, and when access to parks is restricted. Together, these results demonstrate how protected area expansion policies can be shaped to facilitate reaching 30% protected areas by 2030.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".