This study uniquely explores the impact of militarization on carbon emissions in North Atlantic Treaty Organization (NATO) countries from 1985 to 2019 using panel econometric techniques. NATO countries, characterized by substantial defense budgets, advanced technologies, high industrialization, and significant energy consumption, offer a unique context for examining these factors. Employing the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) and FMOLS models, the research analyzes the long-term and short-term dynamics across three groups: traditional NATO members (Group 1), new NATO members (Group 2), and a combined group (Group 3). Relevant variables used in the estimation are industrialization, technological innovation, energy consumption, and economic growth. Findings reveal that in Group 1, military expenditure and energy consumption significantly increase carbon emissions, while industrialization and technological innovation reduce them. In Group 2, increased military spending and industrialization reduce emissions, but energy consumption and technological innovation increase them. For Group 3, economic growth significantly drives emissions, whereas industrial advancements and selective technological innovations mitigate them. The study underscores the need for tailored environmental policies and technological advancements to reduce carbon emissions, contributing to sustainable development within military alliances. These insights are crucial for policymakers aiming to balance defense needs with environmental sustainability in NATO countries.

Bangladesh has experienced substantial improvements in life expectancy over recent decades, yet persistent structural health risks continue to limit further gains in population longevity. This study examines the combined effects of malnutrition, air pollution, suicide mortality, non-communicable diseases (NCDs), maternal mortality, and road traffic injury mortality on life expectancy in Bangladesh using annual time-series data from 1990 to 2024. The analysis employs the Autoregressive Distributed Lag (ARDL) approach to assess both long-run equilibrium relationships and short-run dynamics, with robustness checks conducted using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) estimators. The findings confirm a stable long-run relationship between life expectancy and the selected health risk factors. In the long run, malnutrition, NCD prevalence, maternal mortality, and road traffic injury mortality significantly reduce life expectancy, highlighting the enduring influence of biological, systemic, and injury-related health constraints. Short-run results indicate a rapid adjustment toward long-run equilibrium following health shocks, suggesting that temporary interventions affect longevity without altering underlying structural outcomes. By integrating environmental, biological, and psychological dimensions within a unified empirical framework, this study contributes to the longevity literature in developing-country contexts and offers policy-relevant insights aligned with Sustainable Development Goal 3 on good health and well-being.