Evidence in economic valuation of coastal zone and marine ecosystem services
Bibliographic record
Abstract
The aim of this review is identify and describe the global evidence regarding the economic valuations of ecosystem services in coastal and marine zones. The economic discipline has developed a range of methods to estimate the values of ecosystem services; however, this is still a challenge. In addition, the use of this information by practitioners is little, while the benefits in the design of public polices to face the pollution risks, conservation and climate change adaptation are open. Here we intend to address these gaps of information by conducting a systematic review. We will address the next question: What evidence exist on economic valuation of ecosystems services provide by coastal zones? The interest in this question is framing in the project “Basin sea interactions with communities – BASIC.” The BASIC Project`s is an applied research on the interactions between basins, sea and communities and focused on the generation of tools for adaptation to climate change for the integrated management of water resources in the coastal zone of Cartagena. This three-year multidisciplinary project carried out with the aid of the International Development Research Center of Canada (IDRC-IDRC). The EAFIT University, in partnership with the University of Los Andes, the University of Cartagena, the FHEO Foundation and the CARDIQUE Regional Corporation, directs the implementation .
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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 | 0.020 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".