1Department of Business Administration, College of Business and Economics, Qassim University, PO Box 6640, Buraidah 51452, Saudi Arabia
2Department of Finance and Insurance, College of Business Administration, Northern Border University, PO Box, 1321 Arar 91431, Saudi Arabia
BibTex Citation Data :
@article{IJRED60683, author = {Wided Ragmoun and Ousama Ben-Salha}, title = {The contribution of green technological innovation, clean energy, and oil rents in improving the load capacity factor and achieving SDG13 in Saudi Arabia}, journal = {International Journal of Renewable Energy Development}, volume = {13}, number = {6}, year = {2024}, keywords = {Load capacity factor; sustainable development; renewable energy; technological innovation; Saudi Arabia; Quantile regression.}, abstract = { This research aims to assess the effects of green technological innovation, renewable energy sources, and oil rents on the load capacity factor in Saudi Arabia from 1988 to 2021. The primary conclusions can be outlined as follows. The combined cointegration and Saikkonen-Lütkepohl cointegration tests reveal long-run relationships between the load capacity factor and the explanatory variables at the 1% significance level. In comparison, the Phillips-Ouliaris test shows evidence of cointegration only at 10%. Moreover, the quantile regression indicates that oil rents adversely impact environmental quality; however, they remain contingent upon environmental conditions. A 1% increase in oil rents results in a decline in environmental quality by 0.025% under poor conditions, 0.036% under moderate/normal conditions, and 0.108% under good conditions. On the contrary, renewable energy consumption and green technological innovation improve environmental quality, irrespective of the prevailing environmental conditions. However, the environmental impacts of renewable energy consumption exceed those of green technological innovation. Results show that a 1% increase in renewable energy consumption leads to a 0.052-0.253% improvement in environmental quality, whereas a 1% increase in green technological innovation results only in a 0.017-0.047% improvement. Finally, population and GDP per capita exert negative and positive implications on the load capacity factor, respectively, while energy intensity has no significant environmental effects. The research findings provide significant insights and suggest policy recommendations to address climate change and meet the targets set out in SDG13. }, pages = {1125--1135} doi = {10.61435/ijred.2024.60683}, url = {https://ijred.cbiore.id/index.php/ijred/article/view/60683} }
Refworks Citation Data :
This research aims to assess the effects of green technological innovation, renewable energy sources, and oil rents on the load capacity factor in Saudi Arabia from 1988 to 2021. The primary conclusions can be outlined as follows. The combined cointegration and Saikkonen-Lütkepohl cointegration tests reveal long-run relationships between the load capacity factor and the explanatory variables at the 1% significance level. In comparison, the Phillips-Ouliaris test shows evidence of cointegration only at 10%. Moreover, the quantile regression indicates that oil rents adversely impact environmental quality; however, they remain contingent upon environmental conditions. A 1% increase in oil rents results in a decline in environmental quality by 0.025% under poor conditions, 0.036% under moderate/normal conditions, and 0.108% under good conditions. On the contrary, renewable energy consumption and green technological innovation improve environmental quality, irrespective of the prevailing environmental conditions. However, the environmental impacts of renewable energy consumption exceed those of green technological innovation. Results show that a 1% increase in renewable energy consumption leads to a 0.052-0.253% improvement in environmental quality, whereas a 1% increase in green technological innovation results only in a 0.017-0.047% improvement. Finally, population and GDP per capita exert negative and positive implications on the load capacity factor, respectively, while energy intensity has no significant environmental effects. The research findings provide significant insights and suggest policy recommendations to address climate change and meet the targets set out in SDG13.
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