Background and Aims: Regulations response to COVID‐19 has increased internet addiction (IA), depression, and pornography addiction (PA) among adolescents worldwide. The objective of this nationwide study was to assess the current prevalence rate of IA, depression, and PA after the post‐COVID‐19 period among school‐going adolescents in Bangladesh.Methods: A total of 8832 male and female adolescents participated in this research. The cross‐sectional study was conducted online using a simple random sampling method. Including the sociodemographic variables, Young's IA Test (IAT‐20) Scale, Patient Health Questionnaire (PHQ‐9), and Pornography Craving Questionnaire (PCQ‐12) were used to measure IA, depression, and PA. By SPSS version 25.0, the prevalence and correlation between IA, depression, and PA were analyzed using the Chi‐square test, binary logistic regression, and a bivariate co‐relation matrix.Results: Sixty‐three percent, 76.6%, and 62.9% of the students were suffering from IA, depression, and PA respectively. Depressive and anxious symptoms were significantly associated with IA. Female students were more depressed than males. Males were more addicted to pornography than females. Students who utilized social media but didn't exercise had greater depression and PA. IA, depression, and PA were correlated.Conclusion: The research emphasizes the need for comprehensive mental health treatments, digital literacy programs, and family and teacher participation to reduce IA, depression, and PA among adolescents post‐COVID‐19. Promotion of physical exercise and supporting policies to build safer online settings for adolescents are also encouraged.

Elevated CO2 emissions are a primary cause of the sustainability challenges, including rising sea levels and extreme weather patterns, faced by Bangladesh and the world. This study examines the intricate relationship between CO2 emissions and various economic and industrial factors in Bangladesh, using the autoregressive distributed lag (ARDL) bound test. By analyzing data from 1971 to 2020, the research identifies both short-run and long-run dynamics influencing CO2 emissions. The findings reveal that industrial production and non-renewable energy consumption have a significant positive impact on CO2 emissions, while agricultural activities and fertilizer consumption exhibit a negative effect. The study underscores the need for Bangladesh to transition towards renewable energy sources and improve agricultural practices to mitigate CO2 emissions. Advanced econometric techniques, including the ARDL Bound Test, CUSUM, and CUSUMSQ, are employed to ensure the robustness of the results. The ARDL framework yields key metrics: RMSE = 0.034, MSE = 0.001, AIC = -160.002, BIC = -139.651, R-squared = 0.801, and adjusted R-squared = 0.753, to explore the CO2 emissions nexus in Bangladesh. The study concludes that, while industrial and energy factors significantly contribute to CO2 emissions, enhancing renewable energy use and adopting climate-smart agricultural practices are essential for sustainable environmental management. Policy recommendations include promoting renewable energy adoption, implementing carbon capture technologies, and revising carbon tax policies to achieve long-term sustainability and environmental conservation.

Background: Due to the increasing consumption of the internet, several outcomes are often seen among adolescents, including an increase in internet addiction (IA) and social media addiction (SMA). It could also have been associated with high body mass index (BMI) and eating disorders (EDs). This study aimed to assess the relationship between BMI, EDs, IA, and SMA among adolescents in Bangladesh and to determine the associated factors. Methodology: Using a cross‐sectional design, this study gathered data from adolescents aged 13–19 years old using an online questionnaire consisting of sociodemographic variables, BMI, eating disorder test scale (EAT‐26), Young's Internet addiction test scale (IAT‐20), and Bergen's social media addiction scale (BSMAS). Apart from the descriptive statistics and Pearson chi‐square test, we employed two sets of binary logistic regression and bivariate co‐relation matrix to analyze the data and to find out the relationship by SPSS 26.0. Results: A total of 2147 adolescents with a mean age of 15.6 years old participated in the study; among them, 70.70% were female. Our study found that 23.2% of participants had EDs, 30.8% of students were addicted to the Internet, and 59.9% of participants were addicted to social media. Concerning BMI, we found that 6.6% of participants were underweight, 1.9% were overweight, 24% were obese, and the remaining 67.5% had a normal BMI. Gender, the purpose of the internet, daily internet use, physical exercise, the study of novels/stories, EDs, and BMI had a significant effect on increasing IA and SMA among adolescents. IA, SMA, and EDs were co‐related. Conclusion: The stakeholders should inspire and take necessary steps to engage the adolescents in physical exercise and literature habits to control the IA and SMA. And regarding BMI and EDs, the parents should be aware of this.

This study examines how air pollution impacts mental health outcomes- such as depression, anxiety, and stress- in two Bangladeshi cities: Dhaka, a highly polluted urban center, and Rajshahi, a city with lower pollution levels. Using a cross-sectional approach, 2717 participants were assessed, including 1,431 from Dhaka and 1286 from Rajshahi. Data collection involved secondary sources and surveys evaluating environmental pollutants (PM2.5, PM10, NO2, CO, SO2, Ozone), self-reported exposure, and mental health status using the DASS-21 scale. The analysis used descriptive statistics, multiple linear regression, multilevel analysis, and structural equation modeling (SEM) to explore the links between air pollution and mental health, adjusting for factors such as age, income, and residence. Results reveal significantly higher rates of mental health issues in Dhaka compared to Rajshahi: 57.3% of Dhaka residents reported depression and anxiety, and 48.3% reported stress, whereas rates in Rajshahi were lower at 37.4%, 38.3%, and 32.1%, respectively. Higher exposure to pollutants- such as PM2.5, PM10, NO2, CO, SO2, and O3- was strongly tied to increased depression, anxiety, and stress. For example, high PM2.5 exposure was linked to 57.3% experiencing moderate to severe mental health issues versus 37.4% in lower exposure groups. Similar trends were seen with other pollutants. Self-reported exposure and perceived air quality were also significant predictors, with Dhaka residents experiencing more severe distress. These findings highlight the widespread mental health problems connected to air pollution, emphasizing the urgent need for public health measures in highly polluted areas to reduce psychological stress and improve urban well-being.

Background: Foreign direct investment (FDI) is a steadfast contributor to capital flows and plays an indispensable role in driving economic advancement and emerging as a pivotal avenue for financing growth in Bangladesh. Therefore, this study identifies the factors that influence FDI inflows in Bangladesh. Moreover, the authors explored the more appropriate model for predicting FDI by comparing the efficacy of other models’ predictions. Methods: This study is based on secondary data over the period 1973 to 2021 and collected from the publicly accessible website of the World Bank. A generalized additive model (GAM) was implemented for describing the proper splines. The model’s performance was assessed using the modified R ‐squared, the Bayesian information criterion (BIC), and the Akaike information criterion (AIC). Results: Findings depict a significant nonlinear relationship between Bangladesh’s FDI and key economic indicators, including GDP, trade openness, external debt, gross capital formation, gross national income (GNI) and government rates of exchange, total reserves, and total natural resource rent. It is also observed that the GAM ( R ² = 0.987, A I C = 608.03, and B I C = 658.28) outperforms multiple linear regressions and polynomial regression in predicting FDI, emphasizing the superiority of GAM in capturing complex relationships and improving predictive accuracy. Conclusion: A nonlinear relationship is observed between FDI along with the covariates considered in this study. The authors believed that this study’s findings would assist in taking efficient initiatives for FDI management and proactive economic indicator optimization to empower Bangladesh’s economic resilience and foster sustainable growth. The analysis revealed that FDI and its related risk factors follow a nonlinear pattern. The study recommends using the GAM regression as a reliable method for predicting FDI in Bangladesh. The authors suggest that the findings can guide policymakers in developing strategies to increase FDI inflows, stimulate economic growth, and ensure sustainable economic development in Bangladesh.