An intersection of machine learning and advanced econometric techniques to analyse sustainable development in Europe through resource efficiency, institutional quality, and ICT

Abstract Sustainable development is a critical challenge as well as an opportunity for modern economies, especially for European countries, where technological and institutional frameworks are increasingly evolving and reshaping. This prospective study investigates the role of resource efficiency, institutional quality and information and communication technologies (ICT) to promote sustainable development in 26 European countries for 2000–2023. The study uses a comprehensive methodology, including econometric methodologies such as the Three-Stage Least Squares (3SLS), Generalized Method of Moments (GMM), and Full Information Maximum Likelihood (FIML), along with machine learning models, so that the results are accurate and have predictive depth. Based on the results, there are significant factors that promote sustainability in Europe countries such as better resource utilization, improved institutional quality and advanced ICT infrastructure. All these effects are positive and statistically significant, with institutional quality (coefficient = 0.023–0.030), ICT (0.011–0.025) and resource efficiency (≈ 0.008–0.016) being the most influential drivers, while CO₂ emissions have a significant negative effect. High percentage of the variation in the Sustainable Development Index is explained by the econometric models (3SLS R² = 0.96) and machine learning models have high out-of-sample accuracy (Gradient Boosted Trees R² = 0.948).