Water resources’ use efficiency is an important issue under China’s rapid economic growth. This is because some provinces’ economic development may be delayed due to lack of adequate water resources. Whereas, high economically developed provinces may overuse water resources in order to achieve their economic goals; while also creating a large amount of pollutants. To assess water resources’ use efficiency from the resampling super data envelopment analysis (DEA) approach, our research comprehensively utilizes the following as inputs and outputs: (1) water resources: supply of water (SW), per capita water consumption (PCWC), and total water resources (TWR); (2) economic development: gross domestic product (GDP); (3) environmental issues: governance wastewater investment (GWI), wastewater discharge (WD), chemical oxygen demand (COD), and other major pollutants (OMP). The results show that Tibet, Beijing, Guangdong, Qinghai, Shandong, Sichuan, Yunnan, Tianjin, Jiangsu, and Henan have relatively good water resources’ use efficiency with efficiency values larger than 1. The best efficiency is in 2015, while the worst is in 2017. Water resources’ use efficiency shows significant regional differences in 2013–2017, with the best average efficiency value in southwest China (1.4355) and the worst in north China (0.2987). The results of the Wilcoxon test present that PCWC, GDP, COD, and OMP exhibit very significant differences, PN and WD have significant differences, and SW and TWR have no significant influence. These results imply that China’s regional governments must formulate a better water resource strategy based on the water resource distribution of each region. Lastly, the emissions of environmental pollutants must be strictly monitored.

China’s economic development status continues to grow, but its environmental degradation issue is also becoming a global concern. This study uses dynamic network data envelopment analysis (DN-DEA) to evaluate the energy using efficiency and environmental efficiency in China over the period 2014–2017. The result shows that the former is greater than the latter for all years. This study utilizes a policy-oriented matrix to find the relationship between energy efficiency and Malmquist productivity index (MPI), showing that Chongqing and 13 other provinces have relatively poor energy efficiency, and they therefore must formulate a more effective energy policy to improve undesirable gas emissions. Shanghai and 6 other provinces exhibit relatively good energy efficiency, but are not progressing in MPI, and hence they must develop a stable energy strategy to avoid different efficiencies of catch-up and frontier-shift across time periods. High energy-consuming industries must also choose a low-carbon energy strategy so that they can promote economic development, while taking into account environmental protection in China’s provincial level.

This study comprehensively considers any input and output that has a certain physical dimension, utilizes the super slacks-based measure directional distance function data envelopment analysis (DDF-DEA) model to measure global energy performance in the period 2010–2016, and compares regional differences in Americas, Europe and Asia. We employ contained directional, non-directional, and undesirable inputs and outputs, which include population number, fossil fuels energy consumption, gross capital formation, gross domestic product, renewable energy consumption, and carbon dioxide emission. From the full energy efficiency and ranking of the DDF-DEA approach herein, the empirical results show that Trinidad and Tobago exhibits the best efficiency (2.8194) and Uzbekistan has the worst efficiency (0.5734). The best regional energy performance is Americas, and the worst is Asia for 2010–2016, showing that regional energy policies have a significant impact. The Environmental Performance Index is an important sustainable environment index, and most Environmental Performance Index levels are quite consistent with the trend of energy efficiency and ranking with DDF-DEA in this study. The energy efficiencies of the higher Environmental Performance Index group and higher renewable energy consumption group are significantly larger than the lower Environmental Performance Index group and better than the lower renewable energy consumption group, respectively. Therefore, we suggest that all countries should adjust their future energy using a strategy based on annual Environmental Performance Index. Their goals can be to reduce fossil fuels energy consumption, increase renewable energy use, and reduce undesirable output of carbon dioxide. Doing so will help them to develop their economies while taking into account a sustainable environment, thus achieving sustainable economic development.

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This study evaluates the performance of the financial industry with meta-frontier (MF) dynamic network data envelopment analysis (DN-DEA) from 2009 to 2015. We divide the sample into two groups, Financial Holding group (FHG) and Insurance & Securities group (ISG), for all decision making units (DMUs) of the financial industry. Our goal is to study the effects of operating performance across divisions and across periods and to compare the differences between these two different groups within Taiwan’s bank industry. We find the best business performance was during 2013, where the average value was 0.5485. The best average value of FHG was 0.7192 in 2012, and ISG was 0.7099 in 2010. FHG has best average overall efficiency (OE) value in 2009–2015 periods. However, the average technical efficiency gap ratio (TGR) value of ISG’s (0.7490) is larger than FHG (0.6959), indicating that business performance is affected by group and meta-frontier. FHG has a larger scale than ISG, and so, those firms can input a relatively large proportion of investments and resources to produce better performance. Finally, many DMUs have excess inputs of labor costs and operating expenses, resulting in an average TGR value that is lower than ISG in 2009–2015.