## 2 Literature Review
### Relevant Researches  
DEA-based approaches have been widely used for performance assessment, including eco-efficiency. [PERSON] et al. [30] used SBM DEA to assess the CO\({}_{2}\) emission of some regions in China. The results showed that the east region had preferred CO\({}_{2}\) emanation efficiency, though the west region had the least CO\({}_{2}\) discharge proficiency. The results can help the Chinese government to characterize its natural resource strategies. [PERSON] et al. [31] employed SBM-DEA to survey the natural efficiency of port urban communities. In that study, work populace was used as one input variable. On the other hand, gross territorial local item and holder throughput were used as the desirable outputs, while nitrogen oxide (NOx), sulfur oxide (SO\({}_{2}\)), and carbon dioxide (CO\({}_{2}\)) outflows were used as undesired outputs. The results showed that Singapore, Busan, Rotterdam, Kaohsiung, Antwerp, and New York had the best efficiency, while Tianjin had the worst ecologically productivity. [PERSON] et al. [32] used SBM DEA to investigate vitality efficiency of BRICs countries (Brazil, Russia, India, China), South Africa, and G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). In that study workforce, net fixed capital development and energy utilization were used as inputs, while CO\({}_{2}\) and GDP were used as outputs. The results showed that the BRICs countries have different vitality substances, which will likewise result in various vitality arrangements. In contrast, the G7 countries are more homogeneous. Reference [33] proposed a hybrid approach combining the Analytical Hierarchical Process (AHP) and the Data Envelopment Analysis (DEA) methodologies to deal with the decision-making problem of choosing the best of several alternatives. The comparison among them is based on both qualitative and quantitative criteria. This method is capable of dealing with a situation where more than one alternative turns out to be efficient. Other studies also contribute to eco-efficiency assessment. [PERSON] et al. [34] developed several DEA models to assess efficiency of resource and environment for some DMUs. However, due to different results obtained from these models, the selection of the right model for application becomes difficult. To resolve this difficulty, the authors proposed an extended Shannon-DEA procedure to find a suitable model. Then, the data collected from 30 provinces in China were used for an empirical study. [PERSON] et al. [35] applied the DEA model to evaluate the efficiency of resources and the environment for some European countries in the two time periods--2000-2004 and 2005-2011. In that study, capital, labor, fossil fuels, and renewable energy consumption were used as input variables, while GDP and GHG were used as output variables. The results showed that these European countries had improved eco-efficiency in the time period 2005-2011. [PERSON] et al. [36] used a hybrid approach combining the DEA model with the quantum regression technique to assess the economic and environmental efficiency for European countries in 2001-2012. First, DEA was used to measure the performance of the countries, and then the quantum regression technique was used to identify different efficiency points among the countries. In that study, labor productivity, capital productivity, weight of fossil energy, and share of renewable energy in GDP were used as input variables, while GDP and GHGs were used as output variables. The results showed that GHG emissions is significantly affected by the shares of renewable energy and non-renewable energy used. Taxation can also affect the rankings of technical eco-efficiency. [PERSON] et al. [37] used a DEA model combined with a Tapio decoupling model to assess the eco-efficiency of Latin American nations. First, the DEA was used to assess the efficiency changes of eco-efficiency for these countries, and then the Tapio decoupling model was used to show the decoupling index elasticity.