Literature DB >> 34036502

Environmental pollution and energy research and development: an Environmental Kuznets Curve model through quantile simulation approach.

Faik Bilgili1, Solomon Prince Nathaniel2,3, Sevda Kuşkaya4, Yacouba Kassouri5.   

Abstract

Energy research and development (R&D) and environmental sustainability is often referred to as two interrelated trends, especially in the current context of the 4th industrial revolution. As a primary input of energy innovations, R&D in the energy sector constitutes a vital tool in addressing global environmental and energy challenges. In this frame, we observe the effects of disaggregated energy R&D on environmental pollution within the Environmental Kuznets Curve (EKC) framework in thirteen developed countries over the period 2003-2018. By employing the panel quantile regression technique, we find an inverted U-shaped nexus between economic growth and carbon emissions only in higher carbon-emitting countries, thus, confirming the EKC hypothesis. However, the U-shaped nexus is more predominant in lower carbon-emitting countries. As such, we demonstrate that there is not any single dynamic in the relationship between economic growth and pollution as reported in previous studies. Contrary to expectations, we find that energy efficiency research and development is more effective in curbing carbon emissions compared to fossil fuels and renewable energy research and development. The empirical results indicate also that only energy efficiency R&D mitigates significantly the CO2 emissions from the 50th quantile up to 90th quantile, although the magnitude of the negative sign is more pronounced (in absolute term) at the highest quantile (90th). In this light, our findings would guide policymakers in the establishment of sustainable energy research and development schemes that will allow the preservation of equilibrium for the environment while also promoting energy innovations.

Entities:  

Keywords:  Carbon emissions; Energy efficiency research and development; Energy research and development; Quantile regression; Renewable energy research and development

Year:  2021        PMID: 34036502     DOI: 10.1007/s11356-021-14506-0

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


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