A systematic review of markets for forest ecosystem services at an international level
Bibliographic record
Abstract
Markets for ecosystem services (MES) can play a key role in the protection of natural capital and the remuneration of sustainable management practices. This study aims to present the state of the art on forestry MES at the international level through a systematic review. The main objectives are (i) to analyse the distribution of actual or potential markets for forest ecosystem services (FES) that exist internationally today, (ii) to identify the spatial scale at which market-based instruments (MBIs) are applied and the respective measures of economic value used to assess FES, and (iii) to identify the actors and their involvement in the implementation of forestry MES. The study collected 304 peer-reviewed publications using the Scopus and Web of Science databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) protocol was used to guide the systematic process and select the 52 articles analysed in the review. The results show that Europe is the most representative continent in terms of geographical areas involved ( n = 8) by forestry MES, followed by America ( n = 6), Asia ( n = 5), and Africa ( n = 1). The main scale of application of MBIs for forestry MES is local, i.e., at the level of forest stand, municipality, or province ( n = 31), followed by subnational ( n = 10), national ( n = 9), and international ( n = 2). The main pattern of social composition in forestry MES is buyers, sellers, and intermediaries ( n = 25), followed by buyers and sellers only ( n = 12), buyers, sellers, intermediaries, and knowledge providers ( n = 5), and buyers, sellers, and knowledge providers ( n = 3). In terms of the measure of economic value, most studies use willingness to accept ( n = 30), as opposed to willingness to pay ( n = 17), and only 5 studies used both. Future research on forestry MES should be directed towards a better understanding of the process leading to their creation, implementation, effectiveness, governance, and level of satisfaction in economic terms of the actors involved.
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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 | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".