## 6 Conclusions and Future Research Direction  
In this study, SBM DEA was used to assess the eco-efficiency for 17 European countries in the time period 2013-2017. For this assessment, energy consumption per capita and share of renewable energy in total energy consumption were selected as energy inputs, while labor productivity and gross capital formation were selected as economic inputs. On the other hand, GDP per capita was selected as a desired output, while CO\({}_{2}\) emissions per capita was selected as an undesired output. The experimental results reveal the relative eco-efficiency of the 17 countries. In this time period, nine of the 17 countries were found to have the best eco-efficiency score of 1, including France, Hungary, Greece, Italy, the Netherlands, Poland, Portugal, Switzerland, and the United Kingdom. The other eight countries--Austria, Belgium, the Czech Republic, Finland, Germany, Norway, Spain, and Sweden--had relatively lower eco-efficiencies, with their scores ranging between 0.559 and 0.704. The 17 countries have an overall average efficiency change score of 0.995, which indicates a 0.5% minor recession. The overall average technological efficiency score (0.993) shows the 17 countries as a whole had a 0.7% regression. The overall average MPI change score (0.988) shows that the 17 countries as a whole had a 1.2% regression. Some management implications are derived. First, the eco-efficiency scores reveal the efficiency of each country and those countries with poor efficiency certainly need to improve their efficiency. However, the countries with the best efficiency still need to improve their eco-efficiency, as the eco-efficiency scores derived in this research are relative values. Second, the results show that these countries had a minor recession in efficiency change, so these countries need to improve their efficiency. Third, as the results show that these countries had a minor recession in technological efficiency, these countries therefore need to better innovate and develop technologies to improve their production. Finally, the MPI scores show that the loss of productivity mainly comes from the lack of technological efficiency, so improving technological efficiency is therefore the first priority for these countries. The following policy implications are derived: (1) As the increase of renewable energy can reduce CO\({}_{2}\) emissions, European governments should encourage the use of renewable energy. (2) Subsidy policy on the use of renewable energy can be used as an incentive. (3) As the use of fossil fuels leads to the increase of CO\({}_{2}\) emissions, a higher tax should be imposed on their use in order to prevent the continued use of this kind of energy. (4) The National Renewable Energy Action Plans should be promoted to all European countries in order to accomplish the aims of the European Environment Policies. The following research directions can be focused on in the future. First, due to the unavailability of data, this research only includes 17 countries, and therefore it cannot give a whole view on the whole European countries. Therefore, including more European countries can be one research direction. Second, in this study, we only considered one undesired output (CO\({}_{2}\)). More undesired outputs, such as PM2.5 (ton) and waste (ton), can be considered and included in future research. Third, comparing the results to those obtained from other approaches can be performed in future research. Finally, we found that the input and output variables used in this research may suffer from the problem of multicollinearity, as there is some high correlation between X1 and X 2 (0.76), X1 and X3 (0.63), and X1 and X4 (0.67), which suggests that two types of inputs might be enough. The study of the impact of multicollinearity on these results should be investigated further. Conceptualization, C.-N.W. ; methodology, C.-N.W. ; software, T.-T.N. ; validation, Y.-H.W. ; formal analysis, H.-P.H. ; investigation, Y.-H.W. ; resources, C.-N.W. ; data curation, T.-T.N.