Economic Valuation of Ecosystem Services by Using the Analytic Hierarchy Process and the Analytic Network Process. Comparative Analysis Between Both Methods in the Albufera Natural Park of València (Spain)
Bibliographic record
Abstract
The Analytic Multicriteria Valuation Method (AMUVAM) was designed to value environmental assets.This method and its software allow one to incorporate all the experts' decisions in a global evaluation matrix and assign a degree of importance (weight) to the criteria.This multicriteria decision aid methodology has traditionally been based on the Analytic Hierarchy Process (AHP) and the Discounted Cash Flow.However, it can be substituted by the Analytic Network Process (ANP).Although it involves a higher complexity, it considers all the current relationships between the different alternatives and criteria so it should be more accurate.Therefore, the aim of the present work is to value the ecosystem services of an environmental area by using both methods and compare the results.A real application has been presented; therefore, this work has been applied to the valuation of the Albufera Natural Park in Valè ncia (Spain).This area is considered one of the most important Mediterranean wetlands of the Mediterranean countries.Having obtained the results, the method can be carried out using either of the two processes when the aim of the assessment is to get the Total Economic Value.In this case, the AHP can be used as a less time-consuming and cheaper method.However, if the goal is to value the ecosystem services, there are significant differences between both methods.Some of the services are overvalued or underestimated when the AHP has been used.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".