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.

中文翻译：

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妇女政治赋权对可再生能源需求的影响：来自经合组织国家的证据


本文研究了女性的政治赋权如何影响可再生能源需求，考虑了 1990 年至 2022 年经济合作与发展组织 （OECD） 的 36 个经济体的面板数据集中的能源成本、绿色技术和国内生产总值 （GDP） 增长等因素。最小绝对收缩和选择运算符 （LASSO） 算法选择可再生能源需求的关键驱动因素。然后，本文应用贝叶斯模型平均 （BMA）、部分线性回归 （POLR）、双重选择线性回归 （DSLR） 和交叉拟合部分线性回归 （Cross-fit POLR） LASSO 技术来检查 LASSO 发现的稳健性。研究发现，性别不平等和绿色技术对可再生能源需求具有显著的积极影响。相反，GDP 增长表现出显着的负面影响，而能源成本的影响在统计上不显著。还讨论了潜在的政策影响。