This study examines the effects of health expenditure, energy consumption, CO2 emissions, population size, and income on health outcomes in 46 Asian nations between 1997 and 2019. Cross-sectional dependence (CSD) and slope heterogeneity (SH) tests are utilized due to the close linkages between Asian nations as a result of commerce, tourism, religion, and international agreements. The research uses unit root and cointegration tests of the second generation after validating CSD and SH issues. Due to the results of the CSD and SH tests, it is clear that conventional methods of estimation are inappropriate, so a new panel method, the inter autoregressive distributive lag (CS-ARDL) model, is used instead. In addition to CS-ARDL, the study’s results were checked with a common correlated effects mean group (CCEMG) method and an augmented mean group (AMG) method. According to the CS-ARDL study, higher rates of energy use and healthcare spending lead to better health outcomes for Asian countries over the long run. CO2 emissions are shown to be harmful to human health, according to the study. The influence of a population’s size on health outcomes is shown to be negative in the CS-ARDL and CCEMG, but favorable in the AMG. Only the AMG coefficient is significant. In most instances, the results of the AMG and CCEMG corroborate the results of the CS-ARDL. Among all the factors influencing life expectancy in Asian countries, healthcare spending is the most influential. Hence, to improve health outcomes, Asian countries need to take the required actions to boost health spending, energy consumption, and long-term economic growth. To achieve the best possible health outcomes, Asian countries should also reduce their CO2 emissions.

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As the sustainability of the environment is a very much concerning issue for developed countries, the drive of the paper is to reveal the effects of nuclear, environment-friendly, and non-friendly energy, population, and GDP on CO2 emission for Italy, a developed country. Using the extended Stochastic Regression on Population, Affluence, and Technology (STIRPAT) framework, the yearly data from 1972 to 2021 are analyzed in this paper through an Autoregressive Distributed Lag (ARDL) framework. The reliability of the study is also examined by employing Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegration Regression (CCR) estimators and also the Granger causality method which is used to see the directional relationship among the indicators. The investigation confirms the findings of previous studies by showing that in the longer period, rising Italian GDP and non-green energy by 1% can lead to higher CO2 emissions by 8.08% and 1.505%, respectively, while rising alternative and nuclear energy by 1% can lead to falling in CO2 emission by 0.624%. Although population and green energy adversely influence the upsurge of CO2, they seem insignificant. Robustness tests confirm these longer-period impacts. This analysis may be helpful in planning and developing strategies for future financial funding in the energy sector in Italy, which is essential if the country is to achieve its goals of sustainable development.