{"title":"The impact of women's political empowerment on renewable energy demand: Evidence from OECD countries","authors":"Giray Gozgor, Jing Li, Irfan Saleem, Riazullah Shinwari","doi":"10.1016/j.eneco.2024.108081","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"17 1","pages":""},"PeriodicalIF":13.6000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1016/j.eneco.2024.108081","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.
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