Shadow economy and energy efficiency: utilising goal programming for sustainability assessment
本文结合运筹学中的目标规划与无监督机器学习，分析了全球131个国家2017年的能源效率，发现影子经济规模对能源效率有微小但负面的影响，并建议政府关注影子经济、增加教育投资和加强制度建设。
Abstract 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.