Mapping the planet’s critical natural assets
Bibliographic record
Abstract
Abstract Sustaining the organisms, ecosystems, and processes that underpin human well-being is necessary to achieve sustainable development. Here we identify critical natural assets, natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature’s contributions to people (NCP). Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP mapped) comprise 30% of total global land area and 24% of national territorial waters, while 44% of land area is required for maintaining all NCP (including those that accrue at the global scale, carbon storage and moisture recycling). At least 87% of the world’s population lives in the areas benefiting from critical natural assets for local-scale NCP, while only 16% lives on the lands containing these assets. Critical natural assets also overlap substantially with areas important for biodiversity (covering area requirements for 73% of birds and 66% of mammals) and cultural diversity (representing 96% of global Indigenous and non-migrant languages). Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets for NCP could simultaneously advance development, climate, and conservation goals. Crafting policy and investment strategies that protect critical natural assets is essential for sustaining human well-being and securing Earth’s life support systems.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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 teacher head, 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".