The current study investigates the association of various economic, non-economic, governance, and environmental indicators on human health for seven emerging economies. Covering the period from 2000Q1 to 2018Q1, this study uses various panel data approaches for empirical estimations. The data is found first-order stationary. Besides, the panel slope is heterogeneous and cross-sectional dependence is present. Further, the cointegration association is found valid among the variables. Therefore, panel quantile regression is used to determine the long-run impact of each explanatory variable on human health at four quantiles (Q25, Q50, Q75, and Q90). The estimated results asserted that economic growth, government health expenditure, and human capital significantly reduce human health disasters like malaria incidences and cases. At the same time, greenhouse gas emissions and regulatory quality are significantly and positively correlated to human health issues in emerging economies. Moreover, mixed (unidirectional and bidirectional) causal associations exist between the variables. This study also provides relevant policy implications based on the empirical results, providing a path for regulating various economic, environmental, and governance sectors. Effective policy implementation and preventive measures can reduce the spread of diseases and mortality rates due to Malaria.
In the current times, the global economies and international organizations declared that pollution is one of the prominent causes of declined human health. Still, most literature is biased toward economic sustainability and ignores such vital issues. The current study tends to identify the factors affecting public health in the Group of Seven economies except for Italy (G6). Specifically, this study aims to investigate the influence of household waste (HHW), bureaucratic quality (BQ), democratic accountability (DA), urbanization growth (URP), GDP per capita, and renewable energy use (EPR) on public health, throughout 1996-2020. This study uses advanced panel data approaches and finds the heterogeneity of slope coefficients, the dependence of cross-sections, and the persistence of cointegration between the variables. The asymmetric distribution of data leads to employing the novel method of moment quantile regression. The estimated results reveal that URP, GDPPC, and EPR significantly increase domestic general government health expenditures, improving public health. However, HHW and BQ adversely affect public health by reducing health expenditures. The robustness of the results is tested via utilizing the panel quantile regression. Based on the empirical findings, this study suggests policies regarding the improvement in public health expenditure, R&D investment, spending in renewable energy sector, and strengthening of the institutional quality.
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