Value of the Storm-Protection Function of Sundarban Mangroves in Bangladesh
Bibliographic record
Abstract
Globally mangrove forests are among the most severely threatened ecosystems. The protection value of mangrove forests is important for policy makers as a means of increasing forestation in coastal areas. Only a few economic studies have estimated the protective value of mangrove ecosystems. None have estimated the value of this service in the Sundarban of Bangladesh. In this study, we estimated the economic value of storm-protection services of the Sundarban Reserve Forest during cyclone Sidr in 2007 by valuing and comparing the economic damage and losses of households at two sites (i.e., near the Sundarban and far from the Sundarban). In total, 1,525 households from 9 upazillas (sub-districts) were sampled, all located within 1 km distance of the embankment. Applying the Damage-Cost-Avoided (DCA) method, the storm-protection value of the Sundarban is estimated at USD 543.30 million. The estimated value of the damage cost avoided per household (as of 2015 consumer price) also implies that the installation of a one-km width of intact mangrove forest can save USD 396 to each household during cyclones and storm surges. Conservation and restoration of the ecological status of Sundarban is, therefore, urgently needed for the continued existence and sustainable use of Sundarban’s ecosystem services in the long term.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".