Accounting for protected areas: Approaches and applications
Bibliographic record
Abstract
The System of Environmental-Economic Accounts Ecosystem Accounting (SEEA EA) provides a statistical framework for measuring ecosystems and the services they supply, complementing the System of National Accounts (SNA). Although accounting for protected areas (PAs) is proposed in the SEEA EA and would provide consistent and useful information on PAs, it has not yet been widely implemented. This article examines different possibilities of applying the SEEA EA to PAs by reviewing existing work in that field, including case studies for South Africa, Uganda and Andalusia. We show that accounting for PAs using the SEEA EA would benefit PA planning, management and investment decisions, by i) bringing statistical rigour and consistent data over time and space, ii) compiling disparate data together and making them coherent, and iii) revealing the relationships between PAs, the economy and social well-being, enabling their integration into development planning and decision making. This information can help inform better decision making by allowing synergies and trade-offs between environmental, economic and social outcomes linked to PAs and their management to be explored, fostering a more integrated development approach. This will be essential if the flagship target of the Kunming-Montreal Global Biodiversity Framework to conserve 30% of the world’s surface by 2030 is to be achieved in an ecologically meaningful, economically sustainable and socially inclusive manner.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.033 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".