Abstract
This two-dimensional study makes significant incursions into the health-environment literature by interrogating whether non-renewable energy moderates the impact of environmental degradation on mortality rates. It further aligns with the 2030 United Nations Sustainable Development Goals and 11, which aim to ensure healthy lives and promote well-being for all at all ages and make cities and human settlements inclusive, safe, resilient and sustainable. It contributes to the health-environment literature by investigating the intrinsic relationships among mortality rates, carbon emissions (environmental degradation), and non-renewable energy consumption. The study uses an unbalanced sample of 42 Asia and Pacific countries to determine (1) whether carbon emissions exaggerate the incidence of mortality rates and (2) if the interaction of non-renewable energy with carbon emissions enhances or alters the impact of carbon emissions on mortality rates. Consistent findings from the panel spatial correlation consistent least-squares dummy variables (PSCC-LSDV) and two-step system generalized method of moments (GMM) techniques reveal that (i) carbon emissions exacerbate mortality rates; (ii) non-renewable energy consumption exhibit mortality-reducing properties; (iii) non-renewable energy attenuates the impact of carbon emissions on mortality rates, (iv) persistency in mortalities occurs; and (v) the health-environment-energy dynamics differ across income groups. The paper’s conjecture is expected to channel a new line of discourse on how non-renewable energy influences the environment and health outcomes.
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Notes
For simplicity, mortality rate refers to both infant and under-5 mortality rate except where either is specifically mentioned.
No low-income classification.
Weighted least squares.
We use income groups rather than country dummies because the latter will require 42 dummy variables which will significantly weaken the efficiency of the estimator. Hence, 3 income group dummies are created which align with the objectives of this paper. The dummy for high income countries is the base income group dummy variable.
The number of countries in each income group: high income (11), lower-middle income (20), and upper-middle income (11). Since the time span is only 11 years, only the lower-middle income countries meet the criteria for performing sys-GMM and because this will make a comparative analysis impossible, this approach is dropped. Hence, only the PSCC-LSDV approach is used for comparative analysis.
Total impact of carbon emissions on infant mortality rate is calculated as: \({~}^{\partial MINF}\!\left/ \!{~}_{\partial CO2 PC}\right.=0.4586-0.0427 ENUPC\); where ENUPC can be evaluated at the mean, lowest, or highest values.
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Barua contributed to the introduction, literature review, conclusion, policy implications, and prepared the overall manuscript for submission as a lead author. Adeleye conceptualized the project, wrote the abstract, crafted the model, analyzed the data, and contributed to the interpretation of the results; Ogunrinola contributed to the modeling; Akam contributed to results interpretation; and Shafiq contributed to conclusion and policy recommendation.
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Barua, S., Adeleye, B.N., Akam, D. et al. Modeling mortality rates and environmental degradation in Asia and the Pacific: does income group matter?. Environ Sci Pollut Res 29, 30548–30567 (2022). https://doi.org/10.1007/s11356-021-17686-x
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DOI: https://doi.org/10.1007/s11356-021-17686-x