Bangladesh is one of the countries which are at the most from climate change, requiring more targeted efforts to adapt properly. This study is set to find and define "climate change adaptation hotspots" in Bangladesh for districts that face serious hazards but don’t have the ability to adapt well. Secondary data from Kaggle was used here, that shows historical information from 1990 to 2025 covering all 54 districts. This study combined different indicators into composite keys to assess Hazard Index (flood impact, drought severity, and cyclone frequency) and Adaptation Index (renewable energy adoption, forest cover, and agricultural yield. K-means clustering was used along with spatial patterns from Moran's I to explore the vulnerability-adaptation trends in each district which shows that Sunamganj, Faridpur, Shariatpur, Comilla and total 12 districts with high hazards and low adaptation capabilities need urgent action. In these districts, renewable energy usage and forest cover is very low respectively less than 10% and 16%. Also, their agricultural yields are also lower than the national average. The cluster analysis shows that most districts fall into one of two groups named "Urban Emission Hotspots," (high hazard, low adaptation). Moran's I = -0.010, p = 0.454 show there is not enough concentration to prove that any hot spots have clustered patterns. So, each hot spot needs its own specific adaptation policy instead of applying the same regional solution everywhere. So, this study provides an evidence-based context for targeting adaptation investments, designing district-specific applications.
Meela et al. (Thu,) studied this question.