Shadow economy and energy efficiency: utilising goal programming for sustainability assessment - Alharbi SS; Boubaker S; Ngo T; Yuen MK (Springer Science+Business Media, LLC, 2025)
This paper combined different methods of operations research, goal programming, and unsupervised machine learning into a single framework to examine energy efficiency across the globe. Using the latest data from 131 countries in 2017, our empirical findings reveal different patterns of energy efficiency among countries and country groups under both the meta-frontier and group-frontiers. We found an inequality in production technology for many countries, which made it difficult for them to improve their energy efficiency. Importantly, our analysis also reveals that the size of the shadow economy has a small but negative impact on energy efficiency. Consequently, we suggest that governments should (i) pay more attention to the shadow economy, (ii) increase investments in education and human capital, and (iii) strengthen their institutions.
Keywords: Meta-frontier; Data envelopment analysis (DEA); Goal programming (GP); Euclidean common set of weights (ECSW); Sustainability; Machine learning (ML)