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Moving toward sustainable development: Assessing the impacts of taxation and banking development on renewable energy in the UAE

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  • Samour, Ahmed
  • Shahzad, Umer
  • Mentel, Grzegorz

Abstract

Renewable energy sources have become economically attractive in the UAE. With environmental and financial benefits factored in the transition to renewables, the country targets to reach 44% of the installed capacity with clean energy resources in 2050. These policies will help achieve the country's economic and sustainable development goals. In this context, this research aims to contribute to the prevailing literature by assessing the impact of taxation and banking development on renewable energy in the case of the UAE. To the best of our research observation, the research is the first in the energy literature that assessed the impact of taxation and banking development on renewable energy in the UAE. The study used advanced methods to confirm the co-integration among investigated variables: the bootstrap Autoregressive Distributed Lag (ARDL) co-integration model and the Bayar and Hanck of co-integration test. The empirical findings reveal that banking development and taxation stimulate renewable energy. However, renewable energy is an efficient, reliable, and green energy source and, as a result, achieves the mitigation of environmental degradation. Hence, the present study confirms that financial incentives can lead to sustainable development goals in the UAE. In this context, the current research suggests that the UAE must start to use the carbon taxes policy, which is the most efficient tool to promote renewable energy and achieve sustainable development goals. In addition, the country must encourage the growth of banking development to achieve sustainable development goals, particularly clean energy.

Suggested Citation

  • Samour, Ahmed & Shahzad, Umer & Mentel, Grzegorz, 2022. "Moving toward sustainable development: Assessing the impacts of taxation and banking development on renewable energy in the UAE," Renewable Energy, Elsevier, vol. 200(C), pages 706-713.
    • Handle: RePEc:eee:renene:v:200:y:2022:i:c:p:706-713
    DOI: 10.1016/j.renene.2022.10.020
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    References listed on IDEAS

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    Cited by:

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    2. Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.

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