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Econometric Assessment of Institutional Quality in Mitigating Global Climate-Change Risk

Author

Listed:
  • Anam Javaid

    (Department of Economics and Statistics, University of Management and Technology, Lahore 54770, Pakistan)

  • Noman Arshed

    (Department of Economics and Business Administration, Division of Administrative and Management Science, Faisalabad Campus, University of Education, Faisalabad 38004, Pakistan)

  • Mubbasher Munir

    (Department of Economics and Statistics, University of Management and Technology, Lahore 54770, Pakistan
    Faculty of Informatics and Computing, Universiti of Sultan Zainal Abidin, Kuala Terengganu 21300, Malaysia)

  • Zahrahtul Amani Zakaria

    (Faculty of Informatics and Computing, Universiti of Sultan Zainal Abidin, Kuala Terengganu 21300, Malaysia)

  • Faten S. Alamri

    (Mathematical Sciences Department, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Hamiden Abd El-Wahed Khalifa

    (Department of Mathematics, College of Science and Arts, Al-Badaya, Qassim University, Buraydah 51951, Saudi Arabia
    Department of Operations Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt)

  • Uzma Hanif

    (Department of Economics, Forman Christian College, University Lahore, Lahore 54600, Pakistan)

Abstract

Background: Environmental deterioration is the alarming situation that results from rapid urbanization and development. The rising temperature and climate volatility are accounted for by the massive carbon dioxide (CO 2 ) emissions. The research on climate-change mitigation is trying to curtail the situations before they become irreversible and unmanageable. This study explores the role of institutions in mitigating climate change by moderating the impact of environmental quality on climate change risk. Methodology: Global data sets have been collected from world big data depositories like the World Economic Forum (WEF), the World Development Indicators (WDI), and the International Country Risk Guide (ICRG). Countries that are listed in WEF were used as the sample of the study. An analysis was based on 114 countries that are based on the availability of data. For estimation, descriptive statistics, correlation analysis, change effects, and a Panel Feasible Generalized Least Squares (FGLS) model were used for estimating the results. Results: The global assessment indicates that CO 2 emissions increase the climate risk, but its impact can be reduced by increasing the quality of institutions. Additionally, an increase in renewable energy consumption and economic growth reduces the climate risk. Implications: It is an instrumental study that empirically investigated the role of institutions in reducing climate risk by moderating CO 2 emissions. The results of this study will help policymakers to formulate policies regarding environmental protection.

Suggested Citation

  • Anam Javaid & Noman Arshed & Mubbasher Munir & Zahrahtul Amani Zakaria & Faten S. Alamri & Hamiden Abd El-Wahed Khalifa & Uzma Hanif, 2022. "Econometric Assessment of Institutional Quality in Mitigating Global Climate-Change Risk," Sustainability, MDPI, vol. 14(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:669-:d:720118
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    Citations

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    Cited by:

    1. Muhammad Shahzad Sardar & Nabila Asghar & Mubbasher Munir & Reda Alhajj & Hafeez ur Rehman, 2022. "Moderation of Services’ EKC through Transportation Competitiveness: PQR Model in Global Prospective," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
    2. Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "A New Framework for Assessment of Offshore Wind Farm Location," Energies, MDPI, vol. 15(18), pages 1-17, September.
    3. Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods," Energies, MDPI, vol. 15(16), pages 1-15, August.
    4. Arévalo, Paul & Cano, Antonio & Jurado, Francisco, 2024. "Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study," Energy, Elsevier, vol. 286(C).
    5. Hui Dai & Jamal Mamkhezri & Noman Arshed & Anam Javaid & Sultan Salem & Yousaf Ali Khan, 2022. "Role of Energy Mix in Determining Climate Change Vulnerability in G7 Countries," Sustainability, MDPI, vol. 14(4), pages 1-15, February.

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