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    Considering the alarming rate of environmental degradation, all countries are looking for solutions to close their environmental gaps in order to ensure long-term sustainability. In order to achieve green ecosystems, economies seeking clean energy are motivated to embrace environmentally friendly practices that can support resource efficiency and sustainability. In this sense, the current paper addresses measuring the link between CO2 emissions, economic growth (GDP), renewable and non-renewable energy (RE), tourism, financial development, foreign direct investment, and urbanization in the United Arab Emirates (UAE). The aim of this paper is to empirically evaluate the link between CO2 emissions and macroeconomic factors in the UAE. The UAE was chosen as the country for the case study as the UAE is one of the world's richest oil-based economies and one of the countries with the highest per capita income, adopts sustainable technologies, and has signed the Paris agreement supporting the transition to clean energy. To verify the environmental Kuznets curve (EKC) for UAE, the timespans for the years 1990-2021 have been chosen according to data availability. The long-run coefficients supported the EKC hypothesis of an inverted U shape for income and CO2 emissions, according to the findings. Notably, urbanization and financial development both reduce pollution while foreign direct investment increases environmental pollution. The study recommended the creation of more environmental policies to promote sustainable business operations and nationwide green awareness, increase the use of clean energy technology, reduce energy intensity, and achieve a net zero carbon target. © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

    Citation

    Nurcan Kilinc-Ata, Mohamed Alshami. Analysis of how environmental degradation affects clean energy transition: evidence from the UAE. Environmental science and pollution research international. 2023 Jun;30(28):72756-72768

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    PMID: 37178283

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