Testing the effect of ecosystem service and land classification on global values of forested watershed ecosystem services
Bibliographic record
Abstract
Forested watersheds provide a variety of ecosystem services. Their economic valuation has increased significantly over the past decades, but the literature is fragmented and heterogenous and little has been done to systematically analyse estimated values. This paper presents a global meta-analysis of the economic values of forested watershed services (FWS). We address two key methodological issues in the literature: the impact of FWS classification on value estimates and sensitivity to scale based on the stock of FWS. The latter is measured as the forested watershed area size compared to common practices to measure overall area size including other land cover and use. In the former case, we compare the detailed Common International Classification of Ecosystem Services (CICES) with more simple and informal classifications found in the literature. We show that both the explanatory and predictive power of the estimated meta-regression models increase as we include more details about the valued FWS and use more accurate estimations of the stock of FWS. Findings are cross-validated with the existing forest hydrology literature. The study highlights the economic significance of maintaining forest cover in watershed areas and the need for more harmonised and accurate reporting of the flow and stock of FWS in the non-market valuation literature. • Meta-analysis explores variation in global values of forested watershed services. • Detailed ecosystem service classification improves model explanatory and predictive power. • Forest area size better explains value variation than total site area. • Hydropower-related FWS yield the highest economic value estimates. • PES schemes yield lower forested watershed service values than valuation approaches.
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 | 0.053 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.020 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".