*4.2. Results of DEA Method*

In this part of the paper, based on the objective of the study, the results of the application of the non-radial DEA model are presented. At this point, the potential factors of *EEE* are only mentioned, without any statistical or other analysis.

All DEA results were calculated by Excel Solver. The calculation was conducted for each transport sector separately and for each year. The availability of data was the best in terms of road transport sector, followed by air and rail.

The results for the **road transport sector** (Tables 5 and 6) indicated that the best *EEEI* for countries was in the green cells each year, meaning that these countries were relatively energy-environment efficient. The countries in red cells with the least *EEEI* were: Cyprus (CY) in 2006, 2008, 2010, 2012, 2014–2016, and Austria (AT) in 2007. Please note that *EEEI* was improved for both countries in 2012 and 2014 as compared to previous years, but after 2014 it slightly deteriorated. Regarding Cyprus (CY) it can be seen (see supplementary material) that in these years, all values of data for desirable variables

were lower, while those for undesirable were higher in comparison with other DMUs. However, Austria (AT) was ine fficient, probably due to a higher number of undesirable variables in comparison to other DMUs (see raw data in supplementary material). The improvement of *EEE* in the road sector could be a result of stricter policy measures through prioritization in de-carbonization with primary introduction of CO2 emission standards for new passenger cars and heavy vehicles [1,50], highlighted the use of bioenergy and renewable energy [62], new technologies for vehicles and tra ffic managemen<sup>t</sup> [1], as well as improved conditions of cabotage. The best value of *EEEI* for each year of the evaluation was for Lithuania (LT) and Luxemburg (LU), almost each year for Slovakia (SK) and Slovenia (SI) and thus they could be considered the countries with the best practices. The main reason lies in the fact that these countries had the lowest values for undesirable variables in comparison with other DMUs, while desirable variables were higher and comparable with other DMUs (see supplementary material). It could be also seen that most of the countries improved their *EEEI* in the period of 2014–2016, while Ireland (IE) worsened drastically the values of *EEEI* after 2012.


**Table 5.** Results of e fficiency of non-radial DEA model and rank of TOPSIS method for the road sector (2006–2012).

Green color: the best *EEEI*. Red color: the least *EEEI*.


**Table 6.** Results of efficiency of non-radial DEA model and rank of TOPSIS method for the road sector (2014–2016).

> Green color: the best *EEEI*. Red color: the least *EEEI*.

As far as the **rail transport sector** was concerned, the number of DMUs was smaller due to data unavailability (Tables 7 and 8). It could be noticed that the number of units with the highest value of *EEEI* was in 2006. The most efficient countries were represented in green cells per year. The least value of *EEE* Index in 2006, 2007, 2008, and period 2014–2016 was in a red cell for Greece (EL), due to lack of data, the second one for 2010 and 2012 were the United Kingdom (UK) and Romania (RO). However, similar to the case with road transport, inefficiency of these countries can be related to higher values of undesirable variables while desirable variables were lower in comparison with other DMUs (see supplementary material). It would be interesting to note that Latvia (LV), Italy (IT) and Sweden (SE) (data available only for 2006–2008, 2010, and 2012) had a constant best value of *EEEI* and

represented the best practices. Based on the supplementary material, i.e., raw data, it can be seen that these countries had lower values for *energy and GHG emissions* while values for *volume of passenger and freight transport* were high in comparison with other DMUs. In terms of countries considered per each year, it could be concluded that scores of efficiency were not homogeneous. In 2012 half of DMUs were improved, while the other half of DMUs deteriorated. In the period 2014–2016 it could be noted drastically improvement of *EEEI* for Germany (DE), Austria (AT), and Poland (PL). In terms of the rail sector, the value of efficiency scores declined and a decline in efficiency for some countries could be attributed to insufficient market opening and modernization of rail sectors, incomplete implementation of modern traffic managemen<sup>t</sup> systems such as ERTMS for European railway, insufficient European high speed rail network and interoperability, lack of modal shift in each country—i.e., involvement in the transport market [1,3]-as well as incomplete electrification of railway networks.


**Table 7.** Results of efficiency of non-radial DEA model and rank of TOPSIS method for the rail sector (2006–2012).

> Green color: the best *EEEI*. Red color: the least *EEEI*.



Green color: the best *EEEI*. Red color: the least *EEEI*.

For the **air transport sector**, the availability of data was better than in the rail sector (Tables 9 and 10), and the *EEEI* was also better compared to rail. The highest values of *EEEI* were for countries Cyprus (CY) and Luxembourg (LU). They had the best scores of efficiency throughout the entire evaluation period. Belgium (BE) and the Netherlands (NL) had the best value until 2012, after that their *EEE* indices drastically decreased. The lowest value of *EEEI* was in red cells for the United Kingdom (UK) in 2006, followed by Finland (FI) in 2007, the United Kingdom (UK) in 2008 and 2010, Portugal (PT) in 2012, Ireland (IE) in 2014 and 2015, and France (FR) in 2016. Similar to previous modes of transport, DMUs with higher values of desirable variables and lower values of undesirable variables (see supplementary material) in comparison with other DMUs have better values of *EEEI*. Surprisingly, the United Kingdom (UK) with three red values until 2012, had the best values for all three last years of evaluation period. The ine fficiency of DMUs could be attributed to old aircraft, waiting for improvement of their aircraft's fuel e fficiency, or switching to green fuels [36].


**Table 9.** Results of e fficiency of non-radial DEA model and rank of TOPSIS method for the air sector (2006–2012).

> Green color: the best *EEEI*. Red color: the least *EEEI*.

Observing the highest values of *EEEI* for all transport sectors, Luxembourg (LU) was most frequently present in road and air transport sector, while data for the Luxembourg rail transport sector were missing. United Kingdom (UK) showed the lowest values of *EEEI* for rail and air transport sector.

### *4.3. Results of the TOPSIS Method*

As with any other method, DEA also has its drawbacks. Regardless of its orientation, the DEA method has a tendency to assign maximum or minimum values to input and output, regardless of their initial values, by assigning the best value for *EEEI*. To eliminate this problem, weights of TOPSIS were used for considering the initial values of input and output variables. Furthermore, non-radial DEA shows discriminating power but does not indicate the di fference between DMUs with e fficiency results of 1. Consequently, a defect in the DEA analysis is the existence of multiple e fficient units. In the

literature, di fferent DEA ranking methods exist for ranking DMUs that attempt to consider DMUs from input or output oriented aspects.


**Table 10.** Results of e fficiency of non-radial DEA model and rank of TOPSIS method for the air sector (2014–2016).

> Green color: the best *EEEI*. Red color: the least *EEEI*.

Therefore, the TOPSIS method with both viewpoints—i.e., pessimistic and optimistic-was used in order to evaluate and rank DMUs. Moreover, TOPSIS was employed with the aim of checking the results of the non-radial DEA model. Based on all these considerations, in order to verify di fferences between these two methods a research hypothesis was formed. The results of the TOPSIS method were calculated using Excel environment.

In terms of the **road sector** one country ranked first in three years, Lithuania (LT) in 2006, 2012 and 2016, while Slovakia (SK) ranked first in two years, 2008 and 2010. In 2007 the best ranked was Estonia (EE), in 2014 Slovenia (SI), and in 2015 United Kingdom (UK). In all cases, the *EEEI* was 1 (see Tables 5 and 6).

In the **rail sector**, Sweden (SE) received a rank of 1 in 2006, 2007 and 2008, and Austria (AT) in 2014, 2015, and 2016. Germany (DE) and Hungary (HU) were ranked first in 2010 and 2012 (see Tables 7 and 8). In all cases, except in the case of Germany (DE), the *IEEE* was 1.

As for **air transport**, Belgium (BE) was ranked 1 in 2006, 2007, and 2012, Luxembourg (LU) in 2008, 2010, 2014, and 2015, while Denmark (DK) received rank of 1 in 2016. In all cases the *EEEI* was 1. (see Tables 9 and 10).

All the countries with a rank of 1 for the rail and air transport sectors (except the DE in 2010 for the rail sector) at the same time had the best value of *EEEI*. However, the results of TOPSIS method were different. For instance, for the road sector Lithuania (LT) was ranked 1 by TOPSIS in 2006, 2012 and 2016 and also had the best *EEEI* for those years, as well as Slovakia (SK) in 2008 and 2010, while in 2007 Estonia (EE) whose *EEEI* was 0.622 had a rank of 1.

In addition, the results of the TOPSIS method were different from the results of the non-radial DEA model. Estonia (EE), for example, had a rank of 1 in 2007 even though the result of the *EEEI* of the DEA model was lower: 0.622. Furthermore, considering other DMUs, we note similar situations. For the road sector in 2012, DMUs with ranks from 1 to 4 obtained from TOPSIS had efficiency scores of 1 obtained by the non-radial DEA model, while DMU with rank 5 had an efficiency score of 0.807. Moreover, for the same year Belgium (BE) with a rank of 19 by TOPSIS had an efficiency score 1 by the non-radial DEA method. The situation is similar for other years; for example, Luxembourg (LU) had an efficiency score of 1 for 2008 and Ireland for 2010, while with TOPSIS Luxembourg (LU) had 9 and Ireland (IE) 12. From 2014 to 2016 Belgium (BE) has *EEEI* equal 1, but it was ranked as 22, 24, and 23 respectively. The similar is for Bulgaria (BG), Germany (DE), Italy (IT), Luxemburg (LU), Netherlands (NL), and Poland (PL).

It is significant to note that the results of the TOPSIS method for the rail and air transport sectors were different for a large number of DMUs in comparison to the non-radial DEA model. For example, for rail Sweden (SE) was ranked first in 2006, 2007 and 2008, while in 2012 the best ranked was Hungary (HU); on the other side, both DMUs had the highest efficiency scores. However, in 2010 Germany (DE) was ranked first, although by the non-radial DEA model the obtained efficiency score was 0.653537. Germany (DE), Italy (IT), Latvia (LV), and Poland (PL) had the efficiency scores for 2014, 2015, and 2016 equal 1, while they were not ranked as first by TOPSIS.

Similar to the results of the TOPSIS for road, for rail France (FR) received an efficiency score of 1 in 2006 and 2008, ye<sup>t</sup> was ranked 18; and for 2007 and 2012 it ranked 19 and 14 while having the highest efficiency score.

Regarding the air sector, the picture in terms of results given by DEA and TOPSIS is the same as with the road and rail sectors. Belgium (BE), with an efficiency score of 1 in 2006, 2007 and 2012 had a rank of 1, while in 2008 and 2010 Luxembourg (LU), with the highest efficiency score, ranked first. However, for example, Spain (ES), with an efficiency score of 1 by DEA model in 2006, 2008 and 2010 had ranks of 18, 22, and 20, while in 2007 the Netherlands (NL) ranked 18 witha1efficiency score, and in 2012 the United Kingdom (UK) ranked 22 ye<sup>t</sup> had the highest efficiency score. Furthermore, Germany (DE) with the highest efficiency scores in 2014, 2015, and 2016 ranked 26.

Therefore, it could be said that the DEA is not the most suitable benchmarking tool in the field of the evaluation of the transport *EEE*.

Consequently, based on the significant differences between the results of the non-radial DEA model and the TOPSIS method, our research hypothesis could be confirmed. The reason for differences in results should be found in the fact that DEA considered inputs for a given level of outputs, while the TOPSIS method, in order to find the best DMUs, closest to the ideal positive solution and furthest from the negative weights its criteria. Another reason for differences in results of the TOPSIS method and

the DEA method is the involvement of weights for each criterion, not only for variables in the goal function in the non-radial DEA model.
