Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
The paper examines how women's political empowerment affects renewable energy demand, considering factors like energy costs, green technologies, and gross domestic product (GDP) growth in the panel dataset of 36 Organisation for Economic Cooperation and Development (OECD) economies from 1990 to 2022. The Least Absolute Shrinkage and Selection Operators (LASSOs) algorithms select the critical drivers of renewable energy demand. Then, the paper applies Bayesian Model Averaging (BMA), Partialing-out Linear Regression (POLR), Double Selection Linear Regression (DSLR), and Cross-fit Partialing-out Linear Regression (Cross-fit POLR) LASSO techniques to check the robustness of the LASSOs findings. It is found that gender inequality and green technologies have significant positive effects on renewable energy demand. Conversely, GDP growth exhibits a significant negative influence, while the effect of energy costs is found to be statistically insignificant. Potential policy implications are also discussed.