The Economic Benefit of Coastal Blue Carbon Stocks in a Moroccan Lagoon Ecosystem: a Case Study at Moulay Bousselham Lagoon
Bibliographic record
Abstract
Land degradation is a problem which affects large areas of land and the ecological services provided by coastal wetlands. Coastal ecosystems offer significant benefits for humans and the environment, including services like coastal blue carbon sequestration (CBCS), the economic value of which merits further study. The aim of this paper is to estimate the economic value of coastal blue carbon in Moulay Bousselham lagoon (MBL), Morocco, by analysing the changes in carbon storage that have taken place over 49 years in response to changes in land use and cover (LULC). To achieve this, high resolution orthophotos were used to map LULC changes and investigate the flow of cumulative LULC transformation in the MBL over the period 1971-2020. InVEST was then used to model the quantity and economic value of the CBCS service provided by coastal ecosystems. The results indicate that there were 94 types of LULC transformation over the period 1971-2020, most of them involving the conversion of wet lawn and juncus meadow into cultivated land and the extension of non-wetland areas, especially coastal dunes and built-up areas, at the expense of wetland habitats. These conversions have to some extent affected the capacity of coastal habitats to sequester and store CO2, which reached 1.47 Mt C of CBCS in 2020. In addition, the monetary value of CBCS was subject to gains of between US$ 371,053 and 3,803,295 per year, and losses of between US$ 10,127 and 103,806 per year, according to recent estimates by the European Emission Allowances (EUA) social cost of carbon (SCC) and CO2. This study reveals that revenues from CBCS service can accelerate the implementation of wetland rehabilitation strategies, which 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".