Policy drivers for peatland conservation
Bibliographic record
Abstract
Introduction Peatlands have long been recognised as a high priority for protection under international and national wildlife laws and agreements. Over the last half century this protection has essentially been reactionary in the face of more widespread land management policy and market forces, which have encouraged damage to peatlands. This damage has been mainly to support the delivery of provisioning services, such as food, timber and pulp, or the widespread extraction of peat and oil. Across the world, peatlands of different types face a variety of pressures from land use and land-use change as well as pollution (e.g. atmospheric pollution on British blanket bogs), making them more susceptible to impacts of climate change. Within the general framework of international agreements on peatland conservation, each country has developed its own approach to tackling the threats with varying degrees of success. While established wildlife conservation policy has helped limit the extent of damage to peatlands in some countries, there is a need and opportunity for a stronger and more urgent public policy response to address the significant ongoing losses of peatland biodiversity and ecosystem services. The recognition of the multiple benefits that peatlands provide has presented new avenues to support sustainably managed peatlands, in addition to reducing peatland loss through active restoration (e.g. Bain et al. 2011; Joosten, Tapio-Biström and Tol 2012). This chapter presents an overview of the principal international and national policy drivers, with examples from selected countries across the world to highlight how new resources could be directed at wise use and conservation of peatlands. Global overview of policy drivers for peatland conservation While peatlands have been regarded as wastelands, and areas to be ‘improved’ for agriculture and forestry since the late eighteenth century (Chapter 2), they are now recognised for their wildlife and increasingly for their ecosystem services. Peatlands, therefore, feature in some of the world's highest-level environmental policies. One of the earliest global agreements to recognise the importance of peatlands for protection was the Ramsar Convention (1971) that promoted the establishment and management of a network of protected wetlands. In 1996, it was reported that though peatlands represented 50% of the world's freshwater and terrestrial wetlands, less than 10% of the designated Ramsar sites had peatland as their dominant habitat (Chapter 15). Given continuing peatland loss and degradation, Contracting Parties set out guidelines to improve peatland protection (Ramsar 2003).
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 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".