## 2 Materials and Methods
### Methods
Figure 1 shows graphically the interpretation of DEA results. As it is shown best practice DMUs are A, B, C, D and F. Peer groups for DMU E are input minimization (A, B) and output maximization (C, D). The efficiency of DMU E in input minimization is Xp/Xo, and in the output maximization is Yo/Yp. There is sufficient evidence in the literature that the number of DMUs must be greater than the number of inputs and outputs, although there is no fixed rule as to the ratio [75] pointed out that there are situations where a significant number of DMUs are in fact efficient, and in some cases, the population size is small and does not permit one to add actual DMUs beyond a certain point. In addition, as it is graphically represented, targets for DMU E are the two projections onto the efficient frontier. This research implemented the classical DEA model incorporating weight restrictions to overcome two model problems. The first problem has to do with the number of inputs and outputs considered in the case study. The number of inputs and outputs considered in a DEA influences the number of efficient DMUs, in this case types of engines or bus alternatives. The higher the number of inputs and outputs, the higher the number of efficient DMUs. The second problem has to do with the variability of the weights obtained in the DEA. The original DEA proposal provides for complete freedom of variation in the weights, which puts the DMU being evaluated in the best possible light. Without the possibility to introduce weight constraints, a total flexibility of the weights is allowed. This can lead to the identification of a DMU with unrealistically efficient weighting behavior. Problems arising from full weight flexibility in the DEA model are usually addressed by incorporating weight restrictions. Hence, to solve the two problems mentioned above, restrictions on the weights have been introduced. In particular, Assurance Regions (AR), which impose restrictions on the ratios between the weights of inputs and/or outputs, will be imposed on the inputs. The DEA-CCR-efficiency module of Diviz software has been used to compute efficiency scores for the given DMUs (vehicle types) using the proposed CCR data envelopment analysis model. The number of inputs and outputs considered in the DEA model influences the number of efficient alternatives, in this case types of engines or bus alternatives. The higher the number of inputs and outputs, the higher the number of efficient alternatives. In order to avoid unrealistic weights appearing that allow some DMU to seem as efficient, a panel of twenty experts has been consulted to determine the model criteria weights. For that purpose, the commonly used Delphi method [71; 76] has been applied to determine the weights of the criteria. The Delphi method produces a consensus on the average value attributed to each criterion. The selected group of experts were professionals or scholars of the transport industry (45%) or energy in transport (35%) or both (20%), with at least three years' experience. Two rounds of individual online consultation were needed for achieving agreement on the importance of the weights of the criteria. In the second consultation the mean values of first round answers were communicated to experts before their second response. \[E_{0}=Max\frac{\sum_{j=1}^{s}u_{j}y_{j0}}{\sum_{i=1}^{m}v_{i}x_{i0}} \tag{1}\]0 A graphically representation of the interpretation of DEA results. Table 3 shows the mean weights determined by the group of experts to each criterion, as well as the standard deviation, the first-round coefficient of variation, the second-round coefficient of variation, and the absolute difference between the coefficients of variation of the two rounds.