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This study aims to analyze the impact of environmental degradation on health using the Autoregressive Distributed Lag (ARDL) method with data from 1976 to 2020. The primary focus is on the influence of CO2 emissions on life expectancy in Bangladesh, evaluating the short-term and long-term relationships among key variables such as CO2 emissions, urban population growth (UPG), money supply (M2), and renewable energy consumption (REC). The study employs unit root tests, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, to determine the stationarity of the time-series data. Results indicate mixed integration orders, necessitating the use of the ARDL bounds testing approach to confirm cointegration. The findings reveal that CO2 emissions have a significant negative effect on life expectancy, while M2 and REC exhibit varying impacts. The ARDL model confirms a long-term relationship between the variables, with a significant speed of adjustment towards equilibrium. Diagnostic tests validate the model’s robustness, ensuring no issues with heteroskedasticity, autocorrelation, or model misspecification. The study underscores the importance of effective governance in mitigating the adverse effects of environmental degradation on health, advocating for stricter environmental regulations and improved public health policies. These findings have significant implications for policymakers aiming to enhance public health outcomes amidst growing environmental challenges..
Akter et al. (Sun,) studied this question.