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

A prospective study of 26 European countries covering 2000–2023 found that resource efficiency, institutional quality and information and communication technology (ICT) were positively and statistically significantly associated with the Sustainable Development Index in the model estimates reported in the article’s abstract. Carbon dioxide emissions had a significant negative association, while institutional quality had the largest estimated coefficient range among the three positive factors.

The study combined three econometric approaches—Three-Stage Least Squares, Generalized Method of Moments and Full Information Maximum Likelihood—with machine-learning models. According to the abstract, the 3SLS model explained 96% of the variation in the Sustainable Development Index. The machine-learning component also showed strong predictive performance: Gradient Boosted Trees achieved an out-of-sample R² of 0.948.

The reported coefficient ranges were 0.023–0.030 for institutional quality, 0.011–0.025 for ICT and approximately 0.008–0.016 for resource efficiency. The authors described better resource utilization, improved institutional quality and more advanced ICT infrastructure as significant factors associated with sustainability outcomes in European countries. These are model-based associations, and the abstract does not establish that changing any of the measured factors would cause the reported changes in sustainable development.

The available source is an abstract for an open-access research article published online on 11 October 2026 in Discover Sustainability. It identifies the study population, period, methods, estimated coefficients and reported model performance, but does not provide enough methodological detail to assess the underlying full analysis, robustness checks or uncertainty around the estimates. The publisher’s early-access notice also states that the version may undergo further edits before replacement by the final Version of Record.

Subject of Research: Econometric and machine-learning analysis of sustainable development in 26 European countries from 2000 to 2023

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

Article References: Waqas, H., Ashfaq, M., Gill, A. R., Dadwal, S., Rauf, A., & Ahmad, U. (2026). An intersection of machine learning and advanced econometric techniques to analyse sustainable development in Europe through resource efficiency, institutional quality, and ICT. Discover Sustainability. https://doi.org/10.1007/s43621-026-04864-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04864-5

Keywords: sustainable development, European countries, resource efficiency, institutional quality, information and communication technology, econometrics, machine learning, carbon dioxide emissions, Gradient Boosted Trees, intersection, machine, learning