*1.1. Background*

During recent decades, there have been increased debates concerning the gradual increase of global warming and the resulting climate change. The primary sources of global warming are increased concentrations of greenhouse gas (GHG) emissions, primarily carbon dioxide (CO2), which is a product of human activities. The transport sector is one of the inevitable and essential parts of human activities, backbone of the economy, representing advantages for society in terms of transportation of goods and people, market integration, and provision of growth and jobs. It has been estimated that transport sector within the European Union (EU) contributes for 7% of European gross value added and 7.06% of employment [1].

Yet despite benefits, transport activities include disadvantages related to responsibilities for enormous energy consumption and resulting GHG emissions. According to the European Environmental Agency [2], with 348.5 Mtoe (Million tonnes oil equivalent), the transport sector was the biggest energy consumer in 2013, followed by households (295.9 Mtoe), industry (276.6 Mtoe), services (152.5 Mtoe) and fishing, agriculture, forestry and non-specified (30.2 Mtoe). Among transport modes in 2012, road transport had the largest share in the amount of consumed energy (307.5 Mtoe), followed by air (international and domestic) transport (51.5 Mtoe), international marine bunkers (46.4 Mtoe), rail transport (7.2 Mtoe), and domestic navigation (5.7 Mtoe) [1].

To ameliorate these disadvantages, the European Commission periodically published White Papers and emphasizing where the targets of EU policies were highlighted. The strategy set by the European Commission [3] was based on targets such as:


Presently, the need for meeting the demands of transportation services and enhancing mobility is increasing, as well as the need for improving the *EEE* [1]. Awareness and concern about the energy consumption and environmental problems are becoming increasingly important worldwide. Numerous techniques have been employed to address the issues related to energy and the environment. The technique, which has received grea<sup>t</sup> attention, is the Data Envelopment Analysis (DEA) method as a non-parametric approach to e fficiency evaluation [4]. Recognizing the share that transport has in energy and environmental problems, and having in mind the potential of the DEA method in *energy-environment e*ffi*ciency* evaluation, DEA has been included in the analysis of transport *EEE*. The DEA method has been used in *EEE* analysis for di fferent sectoral levels, countries and regional levels, as well as timely levels [5]. However, *EEE* evaluation and comparison of transport sectors on a macro level for EU countries is missing. Since the countries of the EU could have di fferent strategies and measures in energy consumption and environment protection, it is of the utmost importance to identify the best practice.

### *1.2. The Aim and the Scope of the Paper*

The aim of this paper is twofold. The first is to evaluate and analyze the changes of *EEE* of European road, air, and rail transport sectors, where the methodology for evaluating *EEE* is based on a non-radial DEA model proposed by Wu et al. [6] for 2006–2008, 2010, 2012, and 2014–2016, using the available data for the European countries which represent DMUs. The second aim of the paper is the introduction of the TOPSIS method in the evaluation of *EEE*, where the TOPSIS method is used for the ranking of DMUs. The evaluation of transport *EEE* has been done under the joint production framework, using non-energy inputs (labor and transport assets) and energy input (energy consumption) to produce desirable outputs (volume of passenger and freight transport) and undesirable output (GHG emissions). Aside from other widely used non-radial DEA models such as Slack-based models, Russell measure models, and Directional distance function, in this paper, the non-radial DEA model has been chosen due to its ability to use di fferent non-proportional adjustments, with decision maker specified weights assigned to each e fficiency score, and because of its ability to proportionally decrease the amounts of energy inputs and undesirable outputs simultaneously as much as possible [5,6].

The main contributions of this study are: (i) a newly systematic literature review in the field of transport *EEE* evaluation, (ii) a new definition of transport *EEE*, (iii) the evaluation of *EEE* with an extended set of used inputs, (iv) the evaluation of *EEE* of road, air, and rail transport sectors of European countries and their changing tendencies in terms of the *EEE*, (v) use of non-radial DEA and introduction of the TOPSIS method through DMUs ranking in the evaluation of transport *EEE*, as well as the comparison of their results and the identification of the most suitable one for the evaluation of the transport *EEE*. Based on the evaluation with the non-radial DEA model all stakeholders can create a sense of tendencies in terms of *EEE* of EU transport sectors. Through the introduction of the TOPSIS method for the same purpose, the science community can consider it as a potential tool for monitoring changes regarding *EEE*.

The following section presents the review of previous papers which have used DEA or TOPSIS methods in terms of transport *EEE* evaluation. Section 3 describes the methodology and considers which DEA model is appropriate for our purpose as well as the adoption of the TOPSIS method. The data used, DMUs selection, energy input, non-energy inputs, desirable outputs and undesirable

output for EU countries are described in the second part of this section. Section 4 offers an overview of inputs and outputs for transport sectors and compares the results produced by non-radial DEA and the TOPSIS method, as well a discussion related to the obtained results. Finally, the summary of this study and some future directions in transport *EEE* evaluation are presented in Section 5.
