Members’ Publication

Climate change disrupts the ecosystem and decimates nature-dependent populations. The dried fish business in Cox's Bazar relies on raw fish. The community in Cox's Bazar district largely depends on these industries, which have contributed to poverty alleviation and local economic growth. However, they also have mixed ecological impacts, including positive outcomes and challenges such as waste generation. This paper assesses livelihood vulnerability in the coastal areas of Cox’s Bazar and Kutubdia through the LVI, LVI-IPCC, and various statistical tests. Data collection involved a survey of 150 households at each site, two focus group discussions (FGDs) at each site, and one key informant interview (KII) in each area. The results indicate that Kutubdia Island is more vulnerable than Cox’s Bazar, with an LVI value of 0.437. Initiatives for eco-friendliness, such as the adoption of Smart Solar Sun-Dryers, are being implemented. At the same time, the waste from this industry, which constitutes 14% of the waste in Cox's Bazar, is increasingly being reused for fish feed. These findings underscore the dual impact of this industry on the environment and its crucial role in the livelihoods of local communities. This will help policymakers formulate effective strategies for resource allocation, development planning, and policy formulation.

Strong and flexible Monitoring and Evaluation (M&E) systems must be established immediately due to the Pacific Island Countries' (PICs') dynamic development landscape. Geographical dispersion, acute climate change vulnerability, and a variety of socioeconomic contexts present special challenges for international development organizations, especially UN agencies, which make it difficult to ensure accountability, promote learning, and support evidence-based decisionmaking. In order to improve program outcomes, build resilience, and promote community-focused sustainable development, this article contends that bolstering M&E systems throughout the Pacific is not just an operational improvement but also a strategic necessity. Existing frameworks, contextual difficulties, technological opportunities, and the vital significance of national ownership and inclusive approaches including the use of Utilization-Focused Evaluation (UFE) are all examined.

The major rivers and lakes of Bangladesh are increasingly threatened by pollution resulting from rapid urbanization and the effects of climate change. This study assessed the water quality of the Karnaphuli River using remote sensing techniques, focusing on three key parameters—chlorophyll-a, turbidity, and surface temperature—analyzed through Landsat imagery for the years 2000, 2007, and 2015. These parameters were measured across three representative months (March, July, and November), spaced at four-month intervals, and observed at 15 georeferenced stations. The results revealed a notable decline in chlorophyll-a concentrations between 2007 and 2015 and a steady increase in turbidity levels, indicating a deterioration in water quality from moderate to severe conditions. While remote sensing proved valuable for capturing large-scale temporal and spatial patterns, its inherent limitation in accurately detecting chemical properties such as salinity, alkalinity, and pathogens highlights the importance of integrating ground-based laboratory measurements. A hybrid approach—combining remote sensing with in-situ data collection—can enhance the accuracy of regression models and allow for the calibration and validation of satellite-derived estimates. Field surveys revealed that many river users have already reduced or ceased their use of river water and are inclined to shift to alternative sources if available, underscoring growing public concern over water safety. If current pollution trends persist, the Karnaphuli River may become unsuitable for domestic use within the next 15 years. These findings emphasize the need for continuous, integrated water quality monitoring and management strategies to safeguard river systems and support long-term water resource sustainability in the region.

Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. Among those models, the Random Forest model achieved the highest overall accuracy (82.5%) and F1-score (82.8%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.90, demonstrating its robustness and reliability for real-time incident detection. Gradient Boosting, with an AUC-ROC of 0.90 and Area Under the Precision–Recall Curve (AUC-PR) of 0.90, also performed strongly, particularly excelling at predicting true alerts. The analysis further emphasized the importance of key predictors such as time of day, report ratings, and road type. These findings provide actionable insights for enhancing the accuracy of incident detection and improving the reliability of crowdsourced traffic alerts, supporting more effective traffic management and emergency response systems.

The Readymade Garments Sector contributes significantly to Bangladesh's national economy. Gazipur district is the hub of this industrial agglomeration, contributing about 50% of the sectoral contribution to Gross domestic product. However, this sectoral infrastructure was not pre-planned, and there is an acute housing shortage for low-income people. The industrial workers live in informal houses with substandard living conditions. Community development is the integrated approach consisting of civic amenities, schools, playgrounds, shopping, and recreational facilities for low-income people to enrich their living standards. This study was conducted to assess the affordability of low-income people for providing housing and promoting community facilities, green space creation, and facilitating joyful urban life for the low-income people of Gazipur. The study was conducted on 300 randomly selected ready-made garment workers from different industrial and economic agglomerations to know about the affordability and payment potentials for the repayment of the monthly installments. The study reveals that about 66.6% of the respondents like to purchase between 801–1200 sq. ft. About 51.3 % of the workers are not interested in purchasing a house other than renting a house. Rather they are interested in constructing houses in their own districts. The intention rate of purchasing flats for the workers is 44.3%. The increasing rate of house construction is one of the major threats to agricultural land depletion. People are not satisfied with the amenities and facilities of urban life. The affordability of low-income people should be assessed through comprehensive research to resolve the acute housing problems of the high-density industrial area of Gazipur.