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Government Effectiveness, Solar Energy, and CO2 Emissions in OECD Countries: A Panel Quantile Regression Approach

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  • Taner Güney

    (Karamanoglu Mehmetbey University)

  • Ersin Nail Sağdıç

    (Kutahya Dumlupinar University)

Abstract

A capable government with stability can ensure the strict implementation of envisioned environmental policies. Solar power has a great potential. Therefore, this study examines the relationships between solar energy, government effectiveness, and carbon dioxide (CO2) emissions. For this purpose, we used the data of 37 The Organization for Economic Co-operation and Development (OECD) countries from 1996-to 2020. We considered slope homogeneity and cross-section dependence and in panel data analyses. According to the estimates, there is a cointegration relationship between CO2 emissions, government efficiency, solar energy, fossil energy, and GDP. This study mainly featured panel quantile regression (PQR) estimations. According to PQR estimates, government effectiveness harms CO2 emissions. The negative effect of government effectiveness on CO2 emissions continues in all quantiles. In other words, government effectiveness in reducing CO2 emissions in countries with low or high CO2 emissions levels is rather stable and significant. PQR estimates showed that solar energy harmed CO2 emissions in all quantiles. Moreover, this negative effect increases almost uninterruptedly, from the 10th to the 90th quantile. In other words, as the CO2 emissions level rises, the CO2 emissions reduction level of solar energy also increases. These estimation results show how valuable and indispensable government effectiveness is to the success of the Paris Agreement and the 2030 Sustainable Development Goals. As a result, this study recommends that governments provide bureaucratic facilities that will facilitate private-sector solar energy investments.

Suggested Citation

  • Taner Güney & Ersin Nail Sağdıç, 2025. "Government Effectiveness, Solar Energy, and CO2 Emissions in OECD Countries: A Panel Quantile Regression Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(3), pages 11836-11855, September.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:3:d:10.1007_s13132-024-02370-5
    DOI: 10.1007/s13132-024-02370-5
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