Biophysical and Socioeconomic State and Links of Deltaic Areas Vulnerable to Climate Change: Volta (Ghana), Mahanadi (India) and Ganges-Brahmaputra-Meghna (India and Bangladesh)
Bibliographic record
Abstract
We examine the similarities and differences of specific deltaic areas in parallel, under the project DEltas, vulnerability and Climate Change: Migration and Adaptation (DECCMA). The main reason for studying Deltas is their potential vulnerability to climate change and sea level rise, which generates important challenges for livelihoods. We provide insights into the current socioeconomic and biophysical states of the Volta Delta (Ghana), Mahanadi Delta (India) and Ganges-Brahmaputra-Meghna (India and Bangladesh). Hybrid methods of input-output (IO) construction are used to develop environmentally extended IO models for comparing the economic characteristics of these delta regions with the rest of the country. The main sources of data for regionalization were country level census data, statistics and economic surveys and data on consumption, trade, agricultural production and fishing harvests. The Leontief demand-driven model is used to analyze land use in the agricultural sector of the Delta and to track the links with final demand. In addition, the Hypothetical Extraction Method is used to evaluate the importance of the hypothetical disappearance of a sector (e.g., agriculture). The results show that, in the case of the Indian deltas, more than 60% of the cropland and pasture land is devoted to satisfying demands from regions outside the delta. While in the case of the Bangladeshi and Ghanaian deltas, close to 70% of the area harvested is linked to internal demand. The results also indicate that the services, trade and transportation sectors represent 50% of the GDP in the deltas. Still, agriculture, an activity directly exposed to climate change, plays a relevant role in the deltas’ economies—we have estimated that the complete disappearance of this activity would entail GDP losses ranging from 18 to 32%.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".