Strategic Benchmarking of Renewable Energy Transitions Across OECD Countries: An Explainable Machine-Learning Framework and a Strategic Gap Priority Index for Türkiye

Effective renewable energy policy requires benchmarks that reflect countries’ structural energy characteristics rather than generic performance rankings. This study develops an explainable machine-learning framework that identifies structurally comparable peers and derives peer-relative priorities, using annual World Bank data for 37 Organisation for Economic Co-operation and Development (OECD) countries over 2010–2021. Thirteen energy, economic and demographic indicators are standardized and analysed with principal component analysis, Ward hierarchical clustering and cosine similarity, supported by bootstrap, density-based, temporal, scaling and leave-one-country-out robustness checks. Random Forest with Gini, permutation and SHAP importance is used descriptively to characterize the clusters, and directionally filtered standardized gaps between Türkiye and its closest peer form a Strategic Gap Priority Index (SGPI). Three weakly separated regimes emerge: renewable-intensive economies, larger urbanized fossil-based economies, and less urbanized lower-income economies. Under historical averages, Türkiye and the United Kingdom are mutually closest peers, and coal-based electricity generation and net energy import dependence are the two leading SGPI priorities under all weighting schemes tested. These results are conditional on the profile definition: adding annual trends makes Hungary the closest peer, and excluding hydropower makes non-hydro renewable generation the second priority.