In this paper, we compare the properties of the traditional additive-based data envelopment analysis (hereafter, referred to as DEA) models and propose two generalized DEA models, i.e.
, the big M additive-based DEA (hereafter, referred to as BMA) model and the big M additive-based super-efficiency DEA (hereafter, referred to as BMAS) model, to evaluate the performance of the biomass power plants in China in 2012. The virtues of the new models are two-fold: one is that they inherited the properties of the traditional additive-based DEA models and derived more new additive-based DEA forms; the other is that they can rank the efficient decision making units (hereafter, referred to as DMUs). Therefore, the new models have great potential to be applied in sustainable energy project evaluation. Then, we applied the two new DEA models to evaluate the performance of the biomass power plants in China and find that the efficiency of biomass power plants in the northern part of China is higher than that in the southern part of China. The only three efficient biomass power plants are all in the northern part of China. Furthermore, based on the results of the Wilcoxon-Mann-Whitney rank-sum test and the Kolmogorov-Smirnov test, there is a great technology gap between the biomass power plants in the northern part of China and those in the southern part of China.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited