Costs of land degradation and benefits of land restoration: a review of valuation methods and suggested frameworks for inclusion into policy-making.
Bibliographic record
Abstract
Abstract Land degradation has become a growing concern with the current increase in demand for arable land. Sustainable land management and land restoration practices are required in order to meet the demands to provide food and other services. Adoption of improved practices has, however, not been widespread partly because of a lack of clarity on the true economic value and setting of proper financial incentives. This review focuses on the economic costs of land degradation as a prelude to two ongoing initiatives involving the United Nations Convention to Combat Desertification (UNCCD). We review how ecosystem services derived from land have been economically valued to date. Economic valuation has mostly focused on the use value of provisioning services and cultural services, with limited valuation of non-use value of cultural services. Also, no unique valuation method has been applied following methodological developments, varying study objectives and data availability constraints. These factors impair coherent and consistent estimation of the total economic value of land degradation across countries. We identify a need to develop harmonized valuation methods to estimate total economic value under strong data and capacity constraints. We propose two alternative frameworks for harmonized total economic valuation of land degradation at country level to guide further research in making environmental valuation more relevant and practical under strong data and capacity constraints.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".