A scenario framework to explore migration and adaptation in deltas: A multi-scale and participatory approach
Bibliographic record
Abstract
Coastal deltas are home for over 500 million people globally, and they have been identified as one of the most vulnerable coastal environments during the 21st century. They are susceptible to multiple climatic (e.g., sea-level rise, storm surges, change in temperature and precipitation) and socio-economic (e.g., human-induced subsidence, population and urbanisation changes, GDP growth) drivers of change. These drivers also operate at multiple scales, ranging from local to global and short- to long-term. This highlights the complex challenges deltas face in terms of both their long-term sustainability as well as the well-being of their residents and the health of ecosystems that support the livelihood of large (often very poor) population under uncertain changing conditions. A holistic understanding of these challenges and the potential impacts of future climate and socio-economic changes is central for devising robust adaptation policies. Scenario analysis has long been identified as a strategic management tool to explore future climate change and its impacts for supporting robust decision-making under uncertainty. This research presents the overall scenario framework, methodology, and processes adopted for the development of scenarios in the DECCMA1 project. DECCMA is analysing the future of three deltas in South Asia and West Africa: (i) the Ganges-Brahmaputra-Meghna (GBM) delta (Bangladesh/India), (ii) the Mahanadi delta (India), and (iii) the Volta delta (Ghana). This includes comparisons between these three deltas. Hence, the scenario framework comprises a multi-scale hybrid approach, with six levels of scenario considerations: (i) global (climate change, e.g., sea-level rise, temperature change; and socio-economic assumptions, e.g., population and urbanisation changes, GDP growth); (ii) regional catchments (e.g., change in river flow), (iii) regional seas (e.g., fisheries), (iv) regional politics (e.g., transboundary issues), (v) national (e.g., socio-economic factors), and (vi) delta-scale (e.g., future adaptation and migration policies) scenarios. The framework includes and combines expert-based and participatory approaches and provides improved specification of the role of scenarios to analyse the future state of adaptation and migration across the three deltas. It facilitates the development of appropriate and consistent endogenous and exogenous scenario futures: (i) at the delta-scale, (ii) across all deltas, and (iii) with wider climate change, environmental change, and adaptation & migration research.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".