Some notes on the economic assessment of land degradation
Bibliographic record
Abstract
Abstract Economic factors are an important direct and indirect driver of desertification and land degradation, associated with market failures and the lack of appropriate economic policies to address these failures. Hence, economic and political instruments and mechanisms are required to modify the market in such a way that it encourages land owners to invest in sustainable land management (SLM) options and thereby help to combat land degradation. This article synthesizes the economic aspects of land degradation, first in a rather general way. It then discusses existing valuation methods used to assign economic values to land degradation including the resulting problems which in turn hamper cost–benefit analyses. Finally, based on these points a brief review is given of potential financial mechanisms to combat land degradation and promote SLM. The paper argues that valuation of the economic costs of land degradation and desertification would increase awareness of the extent of the land degradation phenomenon and its impacts on rural development and agriculture. This could also be a useful tool for decision‐making on sectoral orientations for development assistance targeted at desertification, land degradation and drought. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".