Meeting climate change challenges in coastal Bangladesh: A study of technology-based adaptations in water use in Satkhira District
Bibliographic record
Abstract
Climate-change-induced stress impacting water availability is a major threat to agriculture and livelihoods in low- and middle-income countries (LMIC), including Bangladesh. While technology-based adaptation measures can mitigate the effects of such stresses, and help to build community resilience, evidence-based research on this topic is scant. In consideration of this gap, using the southwestern coastal communities of Bangladesh as case study, the present study investigates empirically the dynamics of technology-based adaptations that affect the availability of water for drinking, domestic, and agricultural purposes. To this end, the efficacy of various technologies, adoption processes, accessibility, and societal resource distribution disparities is examined. Field-level primary data were collected in Kaliganj Upazila of Satkhira District—one of most vulnerable areas in Bangladesh—chiefly using three Participatory Rural Appraisal tools: a household survey (n = 300 households), Key Informant Interviews (n = 15) and Focus Group Discussions (n = 6). The findings of our investigation revealed that shallow tube wells (23.7%), deep tube wells (59.0%), rainwater harvesting (37.3%), pond sand filters (6.3%), reverse osmosis (37.3%), low-lifting pumps (38.0%), and deep submersible pumps (8.0%) were the technologies most often employed to address water-related needs; these measures significantly reduced climate-induced water stress. Significant variation in water source-dependency between two study Unions was found (P < 0.05). Community-based organizations, neighboring community members, and electronic media played a critical role in the diffusion of technology, mainly through their ability to raise awareness of these adaptation options, while affordability was identified as being vital to the ability to use technology to access water. This research underscores that advancing technology and deploying it in climate-vulnerable areas is not sufficient for achieving the desired outcomes of technology-based adaptations; also, it is necessary to ensure equitable access by various socioeconomic groups to water usage for attaining climate change adaptation goals in LMICs, like Bangladesh.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".