**5. Conclusions**

Over the last decade, the main and intensive topics of research among scholars were energy consumption and environmental impacts caused by transport systems. One of the major contributors to energy consumption and endangerment of the environment in Europe has been the overall transport sector. Among all modes of transport, the road sector was recognized as the main energy consumer and environmental pollutant. Notwithstanding the importance of this fact, there was not any research on *energy-environment e*ffi*ciency* of European transport sectors.

In this paper, therefore *energy-environment e*ffi*ciency* (*EEE*) of European road, rail and air transport sectors were evaluated using a modified non-radial DEA model under the joint production framework proposed by Wu et al. [6]. The evaluation was conducted for European countries in terms of road, rail and air transport sectors for the period 2006 to 2008, 2010, 2012, 2014, 2015, and 2016. The first reason for the adoption of non-radial DEA model was simultaneous minimization of energy inputs and undesirable outputs for the given level of inputs and outputs, and this was a primary motivation for our paper. This non-radial DEA model has benefits in terms of the ability to use different non-proportional

adjustments and weighting for energy inputs and undesirable outputs. In the paper, non-energy inputs, named several assets (see Table 3), were defined and used in the evaluation of transport *EEE* for the first time.

Furthermore, the concept of transport *EEE* was introduced in this study through the reflection of the relationship among transport energy, non-energy inputs, and transport desirable and undesirable outputs. Following the aims of the paper, all used variables were described and their changes were presented only through figures-without any statistical analysis, while factors of *EEE* were only mentioned.

An additional contribution provided in the paper was the introduction of the TOPSIS method as a tool in the evaluation of transport *EEE* through the ranking of DMUs. With this evaluation of *EEE* for European road, rail, and air transport sectors, the stakeholders from each member state may find the best practices toward the most e fficient means of improving overall e fficiency.

Based on the results of the DEA approach, we found that the lowest number of DMUs with the best value of *EEEI* for the road sector was in 2008. In terms of rail transport, the highest DMUs had the best *EEEI* in 2006, and after a decrease in 2007, has since remained fairly unchanged. As far as air transport was concerned, the best value of *EEEI* was attributed to the least number of DMUs in 2007 and 2012.

Rail and air transport had much more room for *EEE* improvement than the road transport sector, which was relatively e fficient in many European countries. Accordingly, it could be concluded that periodical documents of EU policies for sustainable transport contributed to the improvement of *EEE* in road transport sector. However, a modal shift as one of the policies and advanced technologies was not fully completed for rail transport. Therefore, the potential of the rail transport sector was not totally realized, which resulted in ine fficiency within the rail transport sector. Ramanathan's [11] findings confirmed this, stating that rail transport could capture around 50% of the expected tra ffic, which would result in saving of about 37% in energy consumption and associated CO2 emissions that would result if the existing patterns of modal split did not change. Additionally, Song et al. [22] stated that a higher rate of railway concentration was associated with higher environment e fficiency. In terms of air transport, the measures for *EEE* improvement implied newer and more fuel-e fficient aircraft through new technology and larger planes [36].

The main conclusions could be drawn through the application of the TOPSIS method. All DMU with *EEEI* result 1 had the first rank. However, in some cases DMUs with an *EEEI* score 1 and lower had a rather wildly varying ranks. This is because the non-radial DEA model minimizes desirable and undesirable inputs for a given level of the desirable outputs. Then, the non-radial model benchmarks one DMU in comparison with others DMUs. However, based on the changes of raw data (see supplementary material) with the non-radial DEA model some ine fficient DMUs can become efficient and vice versa. Then, the consequence could be a result of the TOPSIS method considering all inputs and outputs with the possibility of minimization and maximization during the process of analysis–they strove for clear values. Furthermore, the weights used in the TOPSIS method were assigned to each input and output.

The authors proposed using the TOPSIS method for finding the best practice in accordance with the challenges of European transport. The main European challenge is the demand for transport, which has significantly increased since 2000 and is expected to continue growing. On the other hand, the European transport sector is heavily dependent on oil. It releases GHGs and air pollutants into the atmosphere and contributes to climate change, but also makes the European economy more vulnerable regarding fluctuations in global energy supplies and prices [69]. The overall improvement of transport *EEE* in Europe could be achieved through progress in terms of *EEE* for each member state for each transport sector.

Bearing that in mind, finding the best practice which realizes the highest volume of freight and passenger transport with minimal energy consumption and environmental impact could be found through the TOPSIS method rather than any DEA approach.

*Energies* **2019**, *12*, 2907

Consequently, the authors highlight the importance of including of the TOPSIS method in future evaluation of transport *EEE*. Some proposals for the development of the European transport sector in terms of *energy-environment e*ffi*ciency* are:


As for future work, the focus should be on research in terms of changes in results of non-radial DEA models with weights assigned to all inputs and outputs, as compared to the TOPSIS method. Additionally, TOPSIS could be used with other DEA models for checking results during the evaluation of transport *EEE*. Moreover, attention should be drawn to research into the impacts of technological innovation for improving transport *EEE*, primarily in the rail transport sector.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1996-1073/12/15/2907/s1, Table S1: Real data.

**Author Contributions:** Conceptualization, B.D. and E.K.; methodology, B.D. and E.K.; validation and formal analysis, B.D.; investigation, B.D. and E.K.; resources, and data curation, B.D.; writing, review and editing, E.K.; visualization, supervision, and funding acquisition, E.K. Both authors have read and approved the final manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
