Multi-pressure based environmental vulnerability assessment in a coastal area of Bangladesh: A case study on Cox’s Bazar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bangladesh ranks among the top 10 countries globally in terms of climate change impacts and faces numerous anthropogenic and natural pressures. Cox’s Bazar, its primary tourist district, is experiencing severe degradation of its physical and ecological environments due to anthropogenic disturbances and climate change. To improve its environmental quality and preserve its ecological resources effectively, it is essential to develop a spatial decision support instrument addressing multi-pressures and cumulative environmental vulnerability (EV). This study presents an expert opinion-independent, scalable, and customizable spatial methodological framework, integrating multi-sourced geospatial data with GIS-based Fuzzy Logic to assess spatial distributions of five pressure groups and their resulting EV in Cox’s Bazar. 18 criteria were chosen based on a structured literature review to evaluate the five pressure groups. Results revealed that 17% to 27% of the study area is exposed to high to very high hydro-meteorological, topographic, land resource, anthropogenic, and natural hazard pressures. The EV results indicated that one-third of the study area, majorly covering Kutubdia, Pekua, Cox’s Bazar Sadar, Teknaf, and Ukhia upazilas, is highly environmentally vulnerable. For enhanced environmental protection, this study improved the existing method of environmental protection zoning by introducing a novel zoning approach that integrates in-situ biodiversity data with EV data. This new zoning method delineated 24% (555 sq. km.) as strict, 45% (1047 sq. km.) as medium, and 31% (725 sq. km.) as soft protection zones in the study area. The sensitivity analysis identified land resource pressure as the most influential component of EV. With a correlation coefficient of 0.91, the accuracy assessment confirms a high level of reliability in the EV results. This study provides valuable insights into environmental pressures and vulnerability in Cox’s Bazar, which are crucial for informing policies at various levels, including international and national frameworks, emphasizing terrestrial ecosystem protection, coastal vulnerability mitigation, climate change impact reduction, biodiversity preservation, and sustainable land resource management. • The methodology used is expert opinion independent, customizable, and scalable. • Integration of in-situ biodiversity data improved environmental protection zoning. • Kutubdia, Pekua, and Ukhia are the most environmentally vulnerable areas. • EV hotspots are in Kutubdia, Pekua, Cox’s Bazar City, Maheshkhali, and Ukhia. • 555 km 2 of Cox’s Bazar needs strict protection to preserve ecological resources.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it