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3.6.2 Benchmarking of the SuperGreen Corridors

A methodology aiming at benchmarking a corridor in its entirety was suggested by SuperGreen (Ilves et al., 2010, 2011). It was built around the concept of:

• decomposing the corridor into transport chains,

• benchmarking these chains using a set of KPIs, and then

• aggregating the chain-level KPIs to corridor-level ones using proper weights for the averaging.

Initially, the methodology included the following steps:

Step 1. Select one of the nine SuperGreen corridors to be used as pilot case for testing the methodology. The corridor with the best coverage in terms of data availability should be selected.

Step 2. Identify the 'critical' segment of the corridor involving a major link that cannot be bypassed due to geographical constraints. Examples are the Brenner passage of the Brenner corridor (link between Munich and Verona), the channel crossing of the Cloverleaf corridor (link between Calais and Dover) or the Pyrenees crossing of the Finis Terrae corridor (link between Valladolid and Irun). The rationale was that these segments are usually better studied than others improving the probability of securing the necessary data.

Step 3. Analyse cargo flows along the critical segment in terms of:

• origin/destination,

• types of cargoes moved,

• modes used,

• routes taken,

• trade imbalances (empties), etc.

Step 4. Select 4–5 typical cargoes being transported along the critical segment of the corridor. Unitized (containerized) cargoes should be given emphasis due to the importance of co-modality in green corridor projects. Part load break bulk is also suggested due to the special logistics requirements imposed by this type of cargo. Dry bulk and liquid bulk commodities can be selected due to their high volume and different supply chain organization. In general, the selection should be based on the relevant importance of each type of cargo and the special requirements that it imposes on the transportation means and the supply chains.

Step 5. Select 1–2 typical transport chains for each selected type of cargo. The origin/destination of the cargo could be any pair of nodes within or outside the corridor, provided that the routes/modes used are among those defined for the corridor. At this point the analysis moves away from the critical segment to cover the entire corridor. All branches of the corridor and all modes involved should be covered. Transport chains involving more than one mode are highly desirable. For sea-based corridors, transport chains should be selected based on:

• typical cargoes using each port in the corridor (use of port statistics)

• existing connections between ports in the corridor

• relative importance of connections in terms of volumes of cargo

• connections to land-based corridor segments

• types of vessels used

• 'best practice' cases identified in literature.

The output of Step 5 is a set of 10–15 transport chains that need to be analysed in terms of the selected KPIs.

Step 6. Locate the proper data sources for estimating the KPI values. Take into consideration that KPI estimation requires detailed information on the types of vehicles used, the technologies applied and other operational characteristics of the chains under examination.

Step 7. Estimate one set of KPIs for each chain selected under Step 5. Due to the length of the SuperGreen corridors, it is probable to have segments with different “green” qualities along a single corridor. It is thus preferable to do the analysis in segments to the extent possible.

Step 8. Identify obstacles in KPI estimation. A KPI re-engineering process might be needed for obstacles that can be addressed. KPIs running into unsolvable obstacles should be dropped. It is conceivable at this stage that segments of the corridor for which sufficient data is not available need to be dropped from further examination.

Step 9. Transform the KPI values estimated at the chain level to a single set of KPI values at the corridor level. Most probably weighted averages would have to be employed, using appropriate weights like cargo volumes, transport work, number of shipments, etc. It is, thus, important to come up with reliable information enabling calculation of the respective weights.

Step 10. Transform the set of KPI values derived under Step 9 to a single corridor rating. Relative weights should be assigned to each KPI. It is expected that different stakeholders would propose different weights for this calculation. A flexible approach of user specified weights should be considered as an alternative.

Step 11. Once the methodology suggested above has passed the applicability test

successfully, it can be applied for the remaining SuperGreen corridors.

In applying this methodology, the Brenner corridor, extending from Malm€o (SE) to Palermo (IT) with branches from Salzburg (AT) to Trieste (IT) through the Tauern axis, and from Bologna (IT) to Athens/Thessaloniki (GR) through the Italian and Greek Adriatic ports, was selected to be examined as a pilot case. The following steps were followed:

• the Brenner pass (Munich—Verona) was selected as the corridor's critical segment;

• the cargo flows along this critical segment were identified in literature;

• a small number (15) of typical transport chains concerning typical cargoes were selected;

• detailed information concerning these transport chains (type of vehicles used, load factors, etc.) was collected from studies and interviews with transport service providers; and

• the selected KPIs were evaluated for each one of these transport chains (emis-

sions were estimated through the EcoTransIT World web based tool).

The chains examined for the Brenner corridor and the corresponding KPI values are presented in Table 3.6. It is noted that the KPIs on ICT tools, cargo security, cargo safety, NOx and PM10 emissions were later on dropped from the analysis.

It soon became evident that the aggregation of Step 9, i.e. from chain-level KPIs

to corridor-level ones for each and every segment of the corridor, would be problematic due to limited reliability on the grounds that:

Table 3.6 The Brenner corridor chains

Table 3.7 KPI values for the Brenner corridor

KPIs

Intermodal

Road

Rail

SSS

Cost (€/tkm)

0.03–0.09

0.05–0.07

0.05–0.80

0.04

Av. speed (km/h)

9–41

19–40

44–98

23

Reliability (%)

95–99

50–99

50–100

100

Frequency (no/year)

26–624

104–2,600

208–572

52

CO2 (g/tkm)

10.62–42.11

46.51–71.86

9.49–17.61

16.99

SOx (g/tkm)

0.02–0.14

0.05–0.08

0.04–0.09

0.12

Source: Ilves et al. (2011)

• the sample was very thin (for some segments there was only one observation) and the resulting figure would have limited statistical value, if any;

• not all of the chains reflected the entire door-to-door transport as needed to ensure comparability; some of them covered only terminal-to-terminal operations; and

• most data was collected through interviews and reflected personal assessments without strict validation.

It was, thus, decided to express corridor benchmarks as ranges of values that resulted from the transport chain data, i.e. minimum and maximum values of all chain-level KPIs. Table 3.7 summarizes the KPI values of the Brenner corridor presented by transportation mode.

The most important conclusion of this exercise is the width of the fluctuation range of some KPIs. Even after taking into consideration the drawbacks mentioned above, one would expect more concise estimates.

Furthermore, the aggregation of Step 10 of the initial methodology involving the transformation of all KPIs into a single corridor rating proved overoptimistic. The rationale for such a rating was to cope with interactions between different KPI groups, as is for example the case where measures introduced to improve performance in relation to one area might have adverse effects on another. However, this approach was later considered as an unnecessary complication given that:

• the weights needed for such calculation very much depend on the user (different users will propose different weights),

• it is a political issue best left for policy makers to decide,

• weights, if assigned, might lead to wrong interpretations,

• weights change over time (e.g. social issues might become more significant in the future), and

• weights would not reflect country specific characteristics of transportation operations.

The issue was discussed extensively in a SuperGreen workshop organized in Napoli, Italy and a decision was reached to exclude such attempt from the methodology. The decision was later confirmed by the project's Advisory Committee.

Table 3.8 Benchmarking results (all corridors)

Corridor

Mode

Cost (€/tkm)

Av. speed (km/h)

Reliability (%)

Frequency (no/year)

CO2 (g/tkm)

SOx (g/tkm)

Brenner

Intermodal

0.03–0.09

9–41

95–99

26–624

10.62–42.11

0.02–0.14

Road

0.05–0.07

19–40

50–99

104–2,600

46.51–71.86

0.05–0.08

Rail

0.05–0.80

44–98

50–100

208–572

9.49–17.61

0.04–0.09

SSS

0.04

23

100

52

16.99

0.12

Cloverleaf

Road

0.06

40–60

80–90

4,680

68.81

0.09

Rail

0.05–0.09

45–65

90–98

156–364

13.14–18.46

0.01–0.02

Nureyev

Intermodal

0.10–0.18

13–42

80–90

156–360

13.43–33.36

0.03–0.15

SSS

0.05–0.06

15–28

90–99

52–360

5.65–15.60

0.07–0.14

Strauss

IWT

0.02–0.44

9.86–22.80

0.01–0.03

Mare Nostrum

SSS

0.003–0.20

17

90–95

52–116

6.44–27.26

0.09–0.40

DSS

15.22

0.22

Silk Way

Rail

0.05

26

41.00

DSS

0.004

20–23

12.50

Source: Ilves et al. (2011)

The methodology, as it resulted from the pilot exercise, was applied for benchmarking five other corridors (Cloverleaf, Nureyev, Strauss, Mare Nostrum and Silk Way). Lack of data combined with time and resource restrictions did not permit the examination of the remaining three corridors (Finis Terrae, Two Seas and Edelweiss). The results are summarized in Table 3.8.

It is important to note that the emission KPIs of Table 3.7 were produced by the

EcoTransIt World web emission calculator (EcoTransIt, 2014), while the remaining indicators are based on self-reported figures from interviewees and literature review. As such, they are only indicative. Using other tools and methods might have led to different results. The accuracy problem identified in the Brenner corridor was confirmed.

Table 3.8 leads to the following observations:

• The comparison of rail transportation attributes across corridors shows very high variance of cost and reliability for the Brenner corridor, which requires further investigation.

• The very low speed and high emissions of the trans-Siberian service is also noticeable, albeit expected due to the diesel traction and the gauge incompatibility problem along this route.

• The wide fluctuation of intermodal transportation attributes is also impressive and can be explained by the different nature of schemes examined in each case.

The more general conclusions stemming from the SuperGreen benchmarking work are summarized below:

• Corridor benchmarking is possible but we need to standardize both the process and the KPIs, if we want to make it operational.

• Even then, comparisons across corridors are problematic due to the fact that no consideration is given to corridor specific characteristics. It is certain that the attributes of the logistical solutions employed in crossing the Baltic Sea are much different than those used for crossing the Alps. This type of risk is eliminated when comparing a time series of KPI values for the same corridor.

• The construction of sample chains on the basis of the 'critical segment' flows

proved difficult in some cases, and in any event the characteristics of the critical segment might be totally irrelevant for other remote segments of the same corridor. Another solution should be conceived.

• Data collection proves to be a serious problem. Relevant obligations imposed by the corridor management might be a solution. The formation of corridor specific stakeholder groups can be helpful in this regard. Automated ICT applications, able to provide cargo flow data without causing physical disruptions of the vehicle flows or other administrative bottlenecks, can also be of particular importance.

• Aggregating chain-level KPIs to a single set of corridoror segment-level ones is possible provided that an adequate sample of transport chains is examined under the same conditions. Otherwise, the use of value ranges is suggested.

• Aggregating corridor-level KPIs to an overall corridor rating should be omitted because there are problems associated with the weights needed for such calculation and the issue is a political one best left for policy makers to decide.

 
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