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Sustainable rice production in the coastal region of Bangladesh is threatened due to various climatic impacts. The study investigated the current scenario of rice production and identified the linkage among different input variables related to rice production such as seed, urea, pesticide, etc. in three districts of Patuakhali, Khulna, and Cox’s Bazar, using a log–log model and panel data analysis. The Stochastic Model of multiple regression analysis helped to identify the most important input variables. The input factors found common in all three study areas were seed. The log–log model was used to identify how climatic factors influenced these input variables. Identifying such factors would help to ensure sustainable agriculture production in the future. Seed cost showed a negative elasticity in Patuakhali, while urea cost showed a negative elasticity in Khulna region. Time series forecasting of rice production up to 2030 in the study areas was examined using the best fitted ARIMA model. While rice production showed an increasing trend in two regions, it did not show a satisfactory upward trend in Kutubdia, which is mostly related to their mismanagement of fertilizer use and irrigation water scarcity. The analysis from the research indicated that an area-specific adaptation plan is required to sustain the agricultural production, as the input variables for rice production were not found similar for three different study districts where a specific attention needs to be given. Moreover, to address the nonexistence of quality panel data for rice production, measures have been introduced which includes an online data collection system using a mobile application.

The livelihood of the people in the dynamic coastal regions of Bangladesh is highly dependent on nature and thus is vulnerable to climatic events. The research study developed the Fishermen Livelihood Vulnerability Index (LVI) to measure the vulnerability of the fishing communities living in the coastal regions of Bangladesh. The weighted average method was used to measure the FLVI and the binary logistic regression was used to find out the determinants of vulnerability. The variables to measure vulnerability such as exposure, sensitivity, and adaptive capacity were taken from the IPCC climate vulnerability framework. The results, based on the findings of a survey of 164 households from 3 coastal districts (Patuakhali, Khulna & Cox’s Bazar) indicated that the coastal fishing communities are more vulnerable (FLVI = 0.48) compare to the people living in other parts of Bangladesh. The study also revealed that the livelihood activities of the coastal fishermen are mainly affected by the impacts of natural disasters. Spatial maps were prepared to show the distribution of the vulnerable areas with indicating the houses according to their level of vulnerability. The study also identified the most significant factors of vulnerability. As part of the analysis, it was found that the variables that had the most significant impact on fishermen livelihood vulnerability were natural disasters, diseases due to climate change, NGO and microcredit, income, and piracy problem. The study also identified that vulnerability factors are highly influenced by spatial characteristics and thereby identification of location specific factors is crucial in assessing and calculating vulnerability score. The study concluded that if location specific fishermen vulnerability factors are identified after certain interval with the active engagement of local communities can generate valuable information for developing appropriate strategy to secure their livelihood in a sustainable way.

Pedestrians are the most vulnerable road users and their risky behaviours make the situation worse. This study aimed to assess the crash risk associated with various pedestrian groups and their road crossing behaviours at intersections in Dhaka, Bangladesh. A two-stage, multi-criteria decision-making approach—analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)—was used for risk assessment. In the first stage, five risk assessment criteria: nearest vehicle speed, nearest vehicle type, vehicle flow, interrupted by vehicle, and minimum gap were identified from the literature, and these criteria were weighted by the AHP. In the second stage, risk associated with various pedestrian groups and their road-crossing behaviours were quantified and ranked by the TOPSIS. The results indicate that male and young pedestrians had higher risk among pedestrian groups. Furthermore, an oblique crossing type, a running crossing pattern, mobile phone usage while crossing, carrying medium-weight baggage, a rolling gap crossing type, high crossing speed, and cross by using conflict zone were found as the riskiest behaviours. Besides, using crosswalk while crossing, single-stage and two-stage crossing types, perpendicular crossing type, and group crossing were found to be the safest behaviours.

The intensity and formation of urban heat island (UHI) phenomena are closely related to land use/land cover (LULC) and land surface temperature (LST) change. The effect of UHI can be described quantitatively by urban thermal field variance index (UTFVI). For measuring urban health and ensuring sustainable development, the analysis of LST and UTFVI are receiving boosted attention. This study predicted LULC, seasonal (summer & winter) LST, and UTFVI variations using machine learning algorithms (MLAs) in Cumilla City Corporation (CCC), Bangladesh. Landsat 4–5 TM and Landsat 8 OLI satellite images were used for 1999, 2009, and 2019 to predict future scenarios for 2029 and 2039. MLAs such as Cellular Automata (CA) and Artificial Neural Network (ANN) methods were used to predict the future change in LULC, LST, and UTFVI. The result suggests that, in the year 2029 and 2039, the urban area will likely to be increased by around 8 % and 11 %, where significant decrease will be taken place in green cover by 9 % and 14 %. If the rapid urban growth continues, more than 30 % of the CCC area will likely to be experienced more than 33 ◦C temperature and strongest UTFVI effect in the year 2029 and 2039. In addition, an average 4 ◦C higher LST was recorded in the urban area compared with vegetation cover. In urban construction practice, avoiding concentrated impermeable layers (built-up areas) and increasing green covers, are effective ways of mitigating the effect of UTFVI. This study will contribute in achieving sustainable development and provide useful insights to understand the complex relationship among different elements of urban environments and promotion of city competence.

According to the statement of Intergovernmental Panel on Climate Change (IPCC), since the late 19th century, the global mean land surface temperature (LST) has increased 20C due to the increase in greenhouse gas emissions and rapid land use/land cover change (LULCC) from human activities. The dramatic increase in LST leads to sever urban heat island (UHI) effect. In the last few decades, massive LULCC transition leads to higher LST in Bangladesh's different city space. The dramatic decrease in green land and water bodies with a massive increase in the urban builtup area influence LST in Chattogram city. This study estimates the existing and future dynamic of LST using remote sensing techniques. A series of Landsat TM/OLI images collected in the summer season were used to explore LST variation from 1999 to 2019. Also, the correlation between LST and normalized difference water index (NDWI), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference bare soil index (NDBSI) is estimated. Finally using artificial neural network predict the future LST for the year 2029 and 2039 to identify the impact of future LST increase in climate change. The study results suggest that an overall increase in LST is 2.50C for the last 20 years. Correlation results indicate that LST shows a strong positing correlation with NDBI and a negative correlation with NDWI and NDVI. Simulation results indicate that LST will increase 20C (2029) and 30C (2039) compare with 2019. A significant increase in LST will create environmental degradation and ecological imbalance in Chattogram city. Higher LST hotspot areas were found in urban built-up areas, which are the most vulnerable to the UHI effect. The findings will help the city authorities to prepare suitable strategies to reduce the UHI effect and improve the city's climate change scenario.

In the last few decades, rapid urban development has a significant impact on land use/landcover (LULC) parameters, i.e., water bodies, built-up area, agricultural and bare land. According to the yearly report of the Rajshahi district, almost 10% of the total agricultural land has been lost in the last 20 years due to the tremendous development pressure and massive LULC change. Monitoring and prediction of urban growth and the LULC change scenario is the essential element in current strategies for conserving agricultural land and natural resources for ensuring sustainable urbanization and food security in the future. This study aims to identify the loss of agricultural land using maximum likelihood supervised image classification algorithm and LULC indexes such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference bare soil index (NDBSI) for the year 1999, 2009 and 2019 using Landsat 4-5 TM and Landsat 8 OLI satellite images in Rajshahi district. The study also simulates the urban growth and loss of agricultural land for the year 2029 using machine learning-based cellular automata (CA) approach. The result suggests that almost 12% of the built-up area is increased in the last 20 years, and based on the simulation, the built-up area will be increased to 15% in 2029 in the Rajshahi district. 13% of agricultural land already converted to built-up areas, and in 2029 the percentage will increase by almost 21%. The loss in agricultural land will become unstable the biodiversity, ecosystem services and create unemployment as well as food insecurity in the district. District authority needs to implement effective strategies with the help of urban planners, environmental engineers, and policymakers to conserve district agricultural wealth and natural resources, which will ensure sustainable, planned, and inclusive urban development in the Rajshahi district.

Rapid urbanization creates a significant impact on urban land use land cover (LULC), increasing the land surface temperature (LST). Remote Sensing is one of the most proficient techniques for identifying the pattern of urban growth and LULC change in a particular region and its impact on urban LST variations. A significant increase in urban LST influenced by rapid LULC change substantially impacts the ecosystem, climate change, and accelerated global warming. Haphazard urban growth and lack of proper planning negatively impact natural resources (vegetation and water bodies) and result in a replacement with non-evaporating surfaces (urban area). This study used multi-temporal Landsat OLI/TM satellite data sets to analyze the impact of LULC changes on LST increase for two decades (1999, 2009, 2019) in Mymensingh city, Bangladesh. After becoming the divisional center, the result suggests that rapid urban growth takes place with a remarkable decrease in vegetation cover and water bodies in the study area. The urban growth almost doubled in the last 20 years, significantly increasing the LST by almost 8˚C. The LST distribution showed a higher concentration in urban areas, followed by bare land, vegetation cover, and water bodies. The study reveals that the decrease in urban green cover/wetlands and expansion in urban areas significantly increased the study area's surface temperature for the last two decades. This study will help urban planners, and environmental engineers quantify LULC change impacts on LST and proposed appropriate policy measures to control it

Water bodies are one of the essential components of the urban community. The pollution of surface and groundwater bodies will significantly hamper the ecosystem and reduce environmental sustainability. Rajshahi city stands beside the river of Padma and one of the primary sources of freshwater in Bangladesh's northwest region. Rapid urban growth of Rajshahi city in the last few decades causes several environmental problems, pollution of surface water, and groundwater. The study examines and maps the ground and surface water quality of Rajshahi city based on community perception using Geographic Information System (GIS). Finally, considering the viewpoints given by the community people and city officials, the study proposed useful strategies to control water pollution and ensure an integrated water resource management (IWRM) plan for Rajshahi city. Based on a structural questionnaire, 300 samples were obtained from 30 wards of with a 95% confidence level and 5% confidence intervals. Along with, two focus group discussions (FGD) and 20 key informants, interviews (KII) were conducted to control water pollution and develop strategies for IWRM plan. The result indicates that groundwater is iron contaminated, and more than 80% of the respondents ensure this water creates skin and hair diseases. Moreover, the supply water treated from the Padma River, which is regularly contaminated with residential and commercial waste materials, household sanitation waste and so on. Proper monitoring systems and awareness in the community level will significantly help to minimize this problem. The city authority, policymakers, and urban planners need to ensure water quality management awareness through the campaign, workshops, education, and regular monitoring. The research will help policymakers to identify effective strategies for ensuring sustainable IWRM plan for this growing city

Pedestrians are the most vulnerable road users and their risky behaviours make the situation worse. This study aimed to assess the crash risk associated with various pedestrian groups and their road crossing behaviours at intersections in Dhaka, Bangladesh. A two-stage, multi-criteria decision-making approach—analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)—was used for risk assessment. In the first stage, five risk assessment criteria: nearest vehicle speed, nearest vehicle type, vehicle flow, interrupted by vehicle, and minimum gap were identified from the literature, and these criteria were weighted by the AHP. In the second stage, risk associated with various pedestrian groups and their road-crossing behaviours were quantified and ranked by the TOPSIS. The results indicate that male and young pedestrians had higher risk among pedestrian groups. Furthermore, an oblique crossing type, a running crossing pattern, mobile phone usage while crossing, carrying medium-weight baggage, a rolling gap crossing type, high crossing speed, and cross by using conflict zone were found as the riskiest behaviours. Besides, using crosswalk while crossing, single-stage and two-stage crossing types, perpendicular crossing type, and group crossing were found to be the safest behaviours.