Shifting foundations: emerging changes shaping area-based conservation for climate adaptation and mitigation
Bibliographic record
Abstract
Protected and conserved areas are already helping society – and the planet – to both mitigate and adapt to anthropogenic climate change. These important roles were highlighted to governments at the 2015 Paris meeting of the UN Framework Convention on Climate Change, as they considered the ever increasing set of challenges posed by the rapidly changing climate. But much has changed over the past decade, in ways that both strengthen and undermine the role of protected and conserved areas. We describe four recent developments and their implications: (i) the emergence of several global agreements that directly support, or could support, the use of area-based conservation in climate response strategies (e.g. the Kunming-Montreal Global Biodiversity Framework, the United Nations Convention to Combat Desertification (UNCCD) Land Degradation Neutrality target, the UNFCCC Nationally Determined Contributions and the UN Sustainable Development Goals); (ii) new tools for area-based approaches, including particularly Other Effective Area-based Conservation Measures (OECMs) and Nature-based Solutions (NbS); (iii) conversely, widespread evidence of terrestrial and marine ecosystems flipping from being sinks to sources of greenhouse gases due to mismanagement and degradation; and, finally (iv) the emergence of serious and mounting denial that human-induced climate change is occurring, by powerful players in governments and industry. The practical implications of these changes for conservation policy and practice are discussed.
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.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".