Robust data envelopment analysis-based energy efficiency analysis of China’s regional economies

Energy efficiency, or energy saving, is the criterion according to which energy consumption is managed and controlled. Since energy consumption accounts for a substantial proportion of CO 2 emissions, energy efficiency assessment can be considered one of the most straightforward and cost-effective solutions at hand. Uncertainty, however, represents a significant challenge for evaluating both CO 2 emissions and regional energy because numerous random errors arise in the process of CO 2 emission measurement. Thus, this study calculates the ranks and energy efficiency scores of decision-making units (DMUs) by utilizing a robust data envelopment analysis (DEA) model that can handle undesirable outputs under varying degrees of conservatism and data uncertainty. Additionally, a cross-efficiency examination of China’s regional economies reveals how uncertainty might impact energy efficiency scores and rankings. As a result, energy-efficient regions are identified. In fact, the discriminatory power of the employed robust approach is greater than that of traditional deterministic DEA models when uncertain indexes are present. In other words, the applied robust DEA model can not only find extremely inefficient regions—which traditional deterministic approaches fail to identify—but also assign them a unique rank. We find that Tianjin, Haikou, Shanghai and Guangzhou have the best robust energy efficiency and Lanzhou, Yinchuan, Guiyang and Xining have the worst robust efficiency. Also the analysis shows that East coast Area and South Coast Area have the most energy efficiency in comparison with other areas.