Economic Benefit of Coastal ‘Blue Carbon’ Stocks in Moroccan Lagoon Ecosystem: A Case Study From Moulay Bousselham lagoon
Bibliographic record
Abstract
Abstract Land degradation is a problem that increasingly affects large areas of territories and affects various ecosystem services provided by coastal wetlands. These marine ecosystems provide valuable benefits to the environment and to humans, including services such as coastal blue carbon sequestration (CBCS) the economic value of which is still poorly understood. This paper investigated land use/cover (LULC) changes in Moulay Bousselham lagoon (MBL) from 1971 to 2020 and their effects on CBCS variation. The transformation of LULC and their cumulative conversions in coastal wetlands were studied during the 1971-2010 and 2010-2020 periods based on LULC data. Then the InVEST model was used to quantify the carbon storage provided by coastal ecosystems in response to LULC changes. The results show that the overall area of strictly wetland habitats in the MBL has decreased by 8.83% since 1971. There were 94 types of LULC transformation over 1971-2020, with significant wetland losses marked by the conversion of wet lawn and juncus meadow to cropland. Using recent estimates of social cost of carbon (SCC) and CO2 European Emission Allowances (EUA), the monetary value of CBCS service was calculated over the entire lagoon during the study period to reach gains between 371,053 and 3,803,295US$/y and losses between -10,127 and -103,806US$/y. If current trends of habitat loss continue, the capacity of coastal habitats to sequester and store CO2 will be significantly reduced. The study shows that revenues from CBCS service can accelerate the implementation of wetland rehabilitation strategies that have a positive impact on climate regulation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".