Accounting for protected areas: Approaches and applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it