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PROJECT CEER-TCB18
Pan-European cost-efficiency benchmark for gas transmission system operators APPENDIX
2019-06-19 X0.9
Project TCB18Individual Benchmarking ReportGTS - 209
GAS TSO2019-07-25
CONFIDENTIAL
Document typeVersion V1.0 - NRA release only
This version is available athttps://sumicsid.worksmart.net
Citation detailsSUMICSID-CEER (2019) Transmission System Cost Efficiency Benchmarking, Final Report.
Terms of useStrictly confidential.
https://sumicsid.worksmart.net
Contents
Contents 1
1 Results 3
2 Data 52.1 Capex-break . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Capex-old . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Model input and output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Regression analysis 8
4 Sensitivity analysis 94.1 Scale efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.2 Partial Opex-capex efficiency analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.3 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.4 Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.5 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.6 Cost analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5 Second-stage analysis 33
6 Cost development 35
7 Parameters and index 50
1
Acronyms
Table 0.1: Acronyms in the report.
Acronym Definition
AE Allocatively EfficientCAPEX CAPital EXpenditureCRS Constant Returns to ScaleDEA Data Envelopment Analysisfte full time equivalentsI Indirect support services (activity)IRS Increasing Returns to ScaleL LNG terminal services (activity)M Maintenance services (activity)NDRS Non-Decreasing Returns to ScaleO Other (out-of-scope) services (activity)OPEX OPerating EXpenditureP Planning services (activity)S System operations (activity)SC Staff intensity (scaled)SE Scale EfficiencySF Energy storage services (activity)SI Staff intensity per NormGrid unitT Transport services (activity)TCB18 (CEER) Transmission Cost Benchmarking project 2018TO Offshore transport services (activity)TOTEX TOTal EXpenditureTSO Transmission System OperatorUC Unit cost (cost per NormGrid unit)VRS Variable Returns to ScaleX Market facilitation services (activity)
2
Chapter 1
Results
The following material is a summary of results, descriptive data and sensitivity analyses for GTS with codenumber 209 in the TCB18 benchmarking based on data processed 15.04.2019. This release is exclusivelymade to the authorized NRA and the information contained in this release is not reproduced as such in anyother project report for TCB18. All underlying information in this release is subject to the confidentialityagreement of TCB18. This report with associated data files is part of the final deliverables for the TCB18project. The contents of this report are strictly confidential.
The benchmarking model of the TCB18 project uses a total expenditure measure as input and the costsdrivers listed in Table 2.6 below. In addition, it is a Data Envelopment Analysis (DEA) model which meansthat it determines the best practice among the TSOs and uses this as the standard for evaluating each of thefirms in the sample.
DEA constructs a best practice frontier by departing from the actual observations and by imposing aminimal set of additional assumptions.
One assumption is that of free disposability which means that one can always provide the same servicesand use more costs and that one can always provide less services at given cost levels. In the base model, thisis an entirely safe assumption, but it does allow us to identify more comparators for any given TSO.
Another assumption is that of convexity. It basically means that one can make weighted averages of theperformance profiles of two or more TSOs. This is a more technical assumption widely used in economics.
The third assumption is that of non-decreasing returns to scale or as it is sometimes called, (weakly)increasing returns to scale. It means that if we increase the costs of any given TSO with some percent, weshould also be able to increase the service output, the costs drivers, with at least the same percent. We canalso formulate this as an assumption that it can be a disadvantage to be small, but not to be large. It isimportant that this assumption is not just imposed ex ante. The statistical analysis of alternative returns toscale models suggests that it actually is a reasonable assumption to make in the sample of gas transmissionoperators in this study.
The best practice DEA model and the theory behind it are further explained in the main report and itsaccompanying appendices.
Using the base model, we have estimated the efficiency level of GTS to be
73.2 %
The interpretation is that using best practice, the benchmarking model estimates that GTS is able toprovide the same services, i.e. keep the present levels of the cost drivers, at 73.2 % of the present totalexpenditure level. In other words, the model suggests a saving potential of 26.8 % or in absolute terms, asavings in total comparable expenditure of
159 MEUR
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Figure 1.1: Final DEA cost efficiency results for gas TSO in TCB18 .
The model considers both investment efficiency and operating efficiency under a given set of environmentalconditions. The material in this report may provide elements to explore other differences than those explicitlyincluded in the model, to understand the scores and the operating practice of the gas transmission operatorsin Europe in 2017.
To evaluate the estimated efficiency of GTS, it is always relevant to compare to the efficiencies of theother TSOs in the TCB18 project, see Figure 1.1. Structural comparability is assured by stringent activitydecomposition, standardization of cost and asset reporting, harmonized capital costs and depreciations,elimination of country-specific costs related to taxes, land, buildings, and out-of-scope activities, correction forsalary cost differences and national inflation as well as currency differences.
Table 1.1: Efficiency scores year 2017
Mean eff #outliersAll TSO 0.793 6GTS 0.732 0
CONFIDENTIAL - FINAL
Chapter 2
Data
The data collected in the TCB18 project is extremely rich and cannot be fully represented in a short summary.Hence, the reporting for each individual operator includes the following documents in addition to this report:
1. Asset sheet with Normgrid values.
2. Cost data sheet (Capex and Opex).
Below in Table 2.1, we provide an overview of the model data used and some descriptive statistics for theunits.
Table 2.1: Detailed asset summary (usage share included) 2017
Code Units 2017 Units
SUMICSID-CEER/TCB18 - GTS 6(54)
Table 2.4: Detailed asset summary (usage share included) 2014
Code Units 2014 Units
SUMICSID-CEER/TCB18 - GTS 7(54)
2.1 Capex-break
In the gas benchmarking, one operator was subject to the capex-break method described in the main report.However, the application was not made to prevent an infeasible target, but to avoid an absurd datapoint. Inthe particular case, using the official inflation metric for the entire investment stream would lead to a Capexvalue that exceeds the sum of all Capex in the sector, or 10,000 times higher than the actual regulatory assetbase (RAB) for the operator! Obviously, the early inflation values in this country do not correspond to arealistic assessment of the network capital valuation. By using capex-break, a new value relatively close to theactual comparable value was calculated.
In the electricity benchmarking, no operator was subject to capex-break.
2.2 Capex-old
The assets prior to 1973 still operating at the reference year provide output in terms of NormGrid, but theinvestment stream is not reported. To compensate for this, the CapexBreak methodology above has beenapplied to calculate a corrective term with equal unit cost to the period 1973-2017. This means that theadded Capex does not change the investment efficiency for the evaluated operator, it merely assures equalconsideration of prior investments for operators with longer or shorter investment streams.
In the case of GTS the CapexOld value is calculated to 228,198,967 EUR. The correction is capped to119,438,707 EUR corresponding to the reported pre-1973 investment.
2.3 Model input and output
The single input (Totex) and the relevant outputs for the benchmarking model for GTS are listed in Table 2.6below. The exact calculation of the inputs and outputs is documented in the separate confidential spreadsheetsprovided for each TSO on the project platform.
Table 2.6: Model data year 2017
Type Name Value Mean TSO/meanInput dTotex.cb.hicpog plici 592,292,922 141,190,318 4.19Output yNG zSlope 361,893,618 99,144,917 3.65Output yCompressors.power tot 798,688 200,308 3.99Output yConnectionpoints tot 1,243 258 4.82Output yPipes Landhumidity 12,752 2,710 4.71
CONFIDENTIAL - FINAL
Chapter 3
Regression analysis
The robust regression results for the final model are presented below. The dependent variable is as beforedTotex.cb.hicpog plici. Regression results for alternative models and variants were presented at projectworkshops W4 and W5.
Table 3.1:
Dependent variable:
refmod[[rfm]]
yNG zSlope 1.841∗∗∗
(0.093)
yCompressors.power tot −178.510∗∗∗(19.783)
yConnectionpoints tot 207,935.700∗∗∗
(15,257.100)
yPipes Landhumidity −14,286.860∗∗∗(1,741.543)
Observations 70R2 0.997Adjusted R2 0.997Residual Std. Error 12,471,493.000 (df = 66)
Note: ∗p
Chapter 4
Sensitivity analysis
4.1 Scale efficiency
The productive efficiency depends on a multitude of factors, including the scale of operations. In DEA, themodel can easily calculate these effects through the concept of different assumptions of returns to scale. InFigure 4.1 a reference set of four points is analyzed. Using constant returns to scale (CRS), only operator Bis deemed cost efficient, located at the most productive scale (MPS). Thus DEACRS(B) = 1. The smalleroperator A has a lower cost-efficiency than B, operating at an inefficient scale, DEACRS(A) < 1. However, asdiscussed above, a smaller scale may be imposed by a national border and/or a concession area, beyond thecontrol of the operator. Thus, the frontier assumption of increasing returns to scale (IRS) or non-decreasingreturns to scale (NDRS) illustrated by the red curve in 4.1 renders A fully efficient; DEAIRS(A) = 1. Finally,an operator such as C that is CRS-inefficient but above optimal scale is also inefficient under IRS, but efficientunder variable returns to scale (VRS), i.e. DEACRS(C) = DEAIRS(C) < 1 and DEAV RS(C) = 1. VRS isthe weakest assumption available, it assumes both diseconomies of scale for small and large units. In networkoperations the diseconomies of size (e.g. congestion), are not considered relevant. However, the results allowthe calculation of the economy of scale effect through the formula:
DEASE(k) =DEACRS(k)DEAV RS(k)
(4.1)
The actual scale efficiency results for the gas transmission system operators in TCB18 are given in Table4.1 and in Figure 4.2 below.
Table 4.1: Scale efficiency DEA(SE)
Mean eff #scale-efficientAll TSO 0.863 9GTS 0.732 0
9
SUMICSID-CEER/TCB18 - GTS 10(54)
Totex
Output
yk
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Increasing, IRS
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Figure 4.1: DEA frontiers CRS, IRS and VRS and scale efficiency (SE).
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Figure 4.2: Scale efficiency, DEASE(k).
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SUMICSID-CEER/TCB18 - GTS 12(54)
On Partial Efficiency
1. Introduction
In regulatory benchmarking, it is common to focus on Totex efficiency. The questionis if TSOs can produce the same services with less Totex. To evaluate this, one needs amodel with one input, Totex, and the usual cost drivers as outputs.
Now, Totex is the sum of Opex and Capex,
Totex = Opex+Capex
and one may therefore ask how much the TSOs could save on Opex (with fixed Capex)or on Capex (with fixed Opex). This is what we call Opex and Capex efficiency. Toevaluate this one needs a model with two inputs (Opex and Capex) and the usual costdrivers.
Figure 1 illustrate the idea of Opex Efficiency.
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Figure 1: Opex efficiency EOpex with fixed Capex
Capex efficiency is similar except that we project the observed Opex-Capex combi-nation x = (Opex,Capex) in the vertical direction.
It follows from these definitions that all points on the input isoquant will be fullyefficient from a partial Opex as well as a partial Capex perspective. This does not mean
Preprint submitted to Springer Volume July 10, 2019
Figure 4.3: Opex efficiency EOpex with fixed Capex.
4.2 Partial Opex-capex efficiency analyses
In regulatory benchmarking, it is common to focus on Totex efficiency. The question is whether TSOs canprovide the same level of services with less Totex. To evaluate this, one needs a model with one input, Totex,and the usual cost drivers as outputs.
Now, Totex is the sum of Opex and Capex,
Totex = Opex+ Capex
and one may therefore ask how much the TSOs could save on Opex (with fixed Capex) or on Capex (withfixed Opex). This is what we call Opex and Capex efficiency. To evaluate this, we need a model with twoinputs (Opex and Capex) and the usual cost drivers.
Figure 4.3 illustrates the idea of Opex Efficiency where we project horizontally (on Opex) for a fixed levelof Capex (vertical axis).
Capex efficiency is similar except that we project the observed Opex-Capex combination x = (Opex,Capex)in the vertical direction for a fixed Opex level.
It follows from these definitions that all points on the input isoquant will be fully efficient from a partialOpex as well as a partial Capex perspective. This does not mean that all the points are fully Totex efficienthowever. In the illustration, the sum of Opex and Capex is only minimal at one point on the isoquant, namelyxAE .
In our analysis, we do not know the location of the isoquant. Instead we estimate the location using DataEnvelopment Analysis. This means that the isoquant becomes piecewise linear like in Figure 4.4 below withcorresponding values in Table 4.2.
It also means that there will typically be quite a large number of TSOs on the estimated frontier and inconsequence a large number of TSOs that cannot save Opex given Capex and vice versa. However, this doesnot necessarily mean that they are all Totex efficient. Note in the numerical example that only TSO C isTotex efficient, as can easily be seen also from the table. Notwithstanding, TSOs A, B, C, and D are all fullyOpex and Capex efficient.
To sum up, TSOs that are Opex- and Capex-efficient cannot save Opex for fixed Capex, nor Capex forfixed Opex. However, this does not imply that they cannot save on Totex. The reason is that the mix betweenOpex and Capex may not be optimal. A TSO like D in the numerical example can save a lot of Opex, but itrequires a small increase in Capex.
Note that in Fig. 4.5-4.7 a single point in the graph may represent multiple operators with the same value,the graphs contain all participating operators.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 13(54)
that all the points are fully Totex efficient however. In the illustration, the sum of Opexand Capex is only minimal on at one point on the isoquant, namely xAE .
In our analysis, we do not know the location of the isoquant. Instead we estimatethe location using Data Envelopment Analysis. This means that the isoquant becomespiecewise linear like in Figure 2 below with corresponding values in Table 1.
It also means that there will typically be quite a large number of TSOs on the esti-mated frontier and therefore quite a large number of TSOs that cannot save Opex givenCapex and vise versa. This does not mean however that they are necessarily Totex effi-cient. In the numerical example only TSO C is Totex efficient as can easily be seen alsofrom the table. TSOs A, B, C, and D are all fully Opex and Capex efficient however.
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Figure 2: Numerical example
TSO Opex Capex Output TotexA 2 12 1 14B 2 9 1 11C 5 5 1 10D 10 4 1 14E 10 6 1 16F 3 12 1 15
Table 1: Numerical example
To sum up, TSOs that are Opex and Capex efficient cannot save Opex for fixedCapex nor Capex for fixed Opex. This does not mean however that they cannot saveTotex. The reason is that the balance may not be optimal between Opex and Capex.A TSO like D in the numerical example can save a lot of Opex, but it requires a smallincrease in Capex.
3
Figure 4.4: Partial Opex- and Capex-efficiency: numerical example.
Table 4.2: Partial opex-capex efficiency: numerical example.
TSO Opex Capex Output TotexA 2 12 1 14B 2 9 1 11C 5 5 1 10D 10 4 1 14E 10 6 1 16F 3 12 1 15
Table 4.3: Partial DEA scores year 2017
DEA(Opex) DEA(Capex)All TSO 0.816 0.761GTS 0.718 0.628
CONFIDENTIAL - FINAL
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Figure 4.6: Partial OPEX vs TOTEX efficiency in TCB18 (red dashed line=mean).
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Figure 4.8: Unit cost UC(Opex) vs UC(Capex).
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SUMICSID-CEER/TCB18 - GTS 18(54)
4.3 Sensitivity analysis
The calculated cost functions are proportional to a number of parameters, e.g. the NormGrid weights. However,since a frontier benchmarking is an investigation into relative, not absolute, changes, the scales of the inputsand outputs are not important. The relevant evaluation in this context is whether a change in a technicalparameter would lead to changes in the relative ranking or level of the benchmarked units. To investigate thisaspect, the following model parameters have been varied and the resulting changes in the efficiency score forGTS are illustrated in the following graphs
Tested parameters
1. Interest rate, Fig. 4.9
2. Normgrid weights: calibration between Opex and Capex parts, Fig. 4.10
3. Normgrid weights: calibration for transport assets, Fig. 4.11
4. Normgrid weights: calibration for compressor/transformer assets, Fig. 4.12
5. Age assumptions for standardized life time, Fig. 4.13
6. Salary corrections for capitalized labor in investments, Fig. 4.14
For the analyses 1-4, a specific parameter w is varied using a factor k from 20% (-80%) to 200% (+100%)multiplied with the base value for the parameter, w0. All other parameters remain at their base value, usedfor the final run. The graph then shows the efficiency score DEA(kw0) and the mean efficiency in the dataset.
Analysis 5 in Fig. 4.13 looks at the impact on the score of the assumptions regarding the standardized lifetime per asset. For simplicity, we have reduced the simulation to two alternative cases, Agelow and Agehigh,respectively with correspondingly about 10 years shorter and longer lifetimes. The exact parameters arereproduced in Table 4.4 below.
Table 4.4: Standard age variants (years)
Age-Low Base case Age-HighPipelines 50 60 70Regulators 20 30 40Compressors 20 30 40Connection points 20 30 40Metering stations 20 30 40Control centers 20 20 30
Analysis 6 in Fig. 4.14 concerns the possible adjustment for local labor costs in the investment stream.Here, we simulate a part a of the total gross investment stream to be constituted of labor costs corrected usingthe PLICI index used in the study. The labor part ranges from 0% (base case) to 25% of the full investmentvalue.
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Figure 4.9: Average and operator-specific DEA-score as function of interest rate.
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_ope
x−ca
pex
DEA(SA)
0.79
20.
792
0.79
20.
792
0.79
20.
793
0.79
30.
793
0.79
40.
794
21.
81.
61.
41.
21
0.8
0.6
0.4
0.2
●TC
B18
mea
n sc
ore
GTS
0.73
20.
732
0.73
20.
732
0.73
20.
732
0.73
20.
732
0.73
20.
732
Figure 4.10: Average and operator-specific DEA-score as function of calibration NormGrid opex vs capex(-80pct, + 100pct)
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 21(54)
●●
●●
●●
●●
●●
0.00.20.40.60.81.0
ng_p
ipel
ines
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1493
0/ng
_pip
elin
es
DEA(SA)
0.79
40.
793
0.79
30.
793
0.79
30.
793
0.79
20.
793
0.79
30.
793
21.
81.
61.
41.
21
0.8
0.6
0.4
0.2
●TC
B18
mea
n sc
ore
GTS
0.73
20.
732
0.73
20.
732
0.73
20.
732
0.73
20.
732
0.73
20.
732
Figure 4.11: Average and operator-specific DEA-score as function of calibration NormGrid for pipelines(-80pct, + 100pct)
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 22(54)
●●
●●
●●
●●
●●
0.00.20.40.60.81.0
ng_c
ompr
esso
rs
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1493
0/ng
_com
pres
sors
DEA(SA)
0.79
30.
792
0.79
20.
792
0.79
20.
793
0.79
30.
794
0.79
50.
797
21.
81.
61.
41.
21
0.8
0.6
0.4
0.2
●TC
B18
mea
n sc
ore
GTS
0.73
20.
732
0.73
20.
732
0.73
20.
732
0.73
20.
732
0.73
20.
732
Figure 4.12: Average and operator-specific DEA-score as function of calibration NormGrid for compressors(-80pct, + 100pct)
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 23(54)
●●
●
0.00.20.40.60.81.0
age
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1493
1/ag
e
DEA(SA)
0.78
80.
793
0.79
age−
low
age−
base
age−
high
●A
vera
ge s
core
GTS
0.72
50.
732
0.72
4
Figure 4.13: Average and operator-specific DEA-score as function of standard lifetimes (age-low = shorterlives, age-base = base case, age-high = longer lives)
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 24(54)
●●
●●
●●
●●
●●
●
0.00.20.40.60.81.0
capl
abor
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1493
1/ca
plab
or
DEA(SA)
0.79
10.
791
0.79
20.
792
0.79
20.
792
0.79
20.
792
0.79
20.
792
0.79
3
0.25
0.22
50.
20.
175
0.15
0.12
50.
10.
075
0.05
0.02
50
●TC
B18
mea
n sc
ore
GTS
0.73
70.
737
0.73
60.
735
0.73
50.
734
0.73
40.
733
0.73
30.
732
0.73
2
Figure 4.14: Average and operator-specific DEA-score as function of share of investments adjusted for locallabor costs (0pct = base case to 25pct).
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 25(54)
4.4 Profile
The specific profile of GTS compared to the other operators in TCB18 is illustrated in Figures 4.15 and 4.16:
• The relative gridsize in Fig. 4.15 depicts the NormGrid sizes of the reference set, scaled such that themean is set to 100. This analysis gives an impression of the scale differences in the benchmarking.
• The output profile in Fig. 4.16 gives a graphical image of the magnitude of the inputs and outputs forGTS in red compared to the range of those in TCB18. A value of 100 here corresponds to the highest inthe sample, a value of 0 is the smallest, respectively. The median values are indicated in blue.
4.5 Age
The age profile of the European operators in comparison to GTS is illustrated in the Figures 4.17 and 4.18 below.
In Figure 4.17 the ages for all assets in the gas dataset have been processed as a confidence interval, theyellow box marks the mean in bold black, the box edges are 25% and 75% quartiles and the outer whiskers arelimits for one standard deviation up or down, respectively. The mean ages for the assets per type for GTS areindicated with a red triangle and a (rounded) number. A circle to the left or right of the confidence intervalbox indicates an outlier.
In Figure 4.18 we investigate the prevalence of very old (pre-1973) assets that are still used in 2017.The average share of capital for different asset types (symbols) is graphed on the horizontal axis. Theshare of capital for pre-1973 assets is given on the vertical axis. The respective asset ages for GTSaredepicted using red symbols, the blue symbols depict the mean age and shares, respectively, in the TCB18project. If the red symbols are located north-east on the corresponding blue symbol, it means that your assetsare both relatively older and also that the asset type represents a higher importance than for the mean operator.
4.6 Cost analysis
In this section we analyze the staff profile, the functional costs and the overhead allocation share for GTScompared to the gas operators in TCB18. The cost analysis is purely informative and does not intervene assuch in the benchmarking. In Fig.4.19 the mean staff intensity SIf for all operators is presented using theNormGrid per activity f :
SIf = meank{Stafffk
NormGridk} (4.2)
where Stafffk is the staff count (fte) for activity f for operator k and NormGridk is the sum of the NormGridfor operator k in the corresponding year. This intensity is then used to obtain a size-adjusted comparator forthe mean staff in the sample, SCfk, scaled to the size of GTS, i.e. k = 209 here:
SCf,209 = SIfNormGrid209 (4.3)
In Fig 4.20 the allocation key for indirect expenditure (I) is based on total expenditure per activityexcluding energy and depreciation, i.e. the graph can also be interpreted as the relative shares of expenditureby function. In Fig 4.21 we graph the actual allocation of indirect expenditure to the benchmarked activitiesT,M,P per operator, along with the mean allocation in the sample.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 26(54)
TSO
sor
ted
Mean NormGrid = 100
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
2728
29
0100200300400
3.2
4.6
7.6
14.5
16.2
16.6
21.1
22.8
25.9
26.5
27.9
32
32.6
42.7
42.7
46.8
47.3
67.2
73.4
93.1
105.7
110.7
128.9
150.8
156.3
321.3
390.7
410
461
TCB
18 m
ean
Rel
ativ
e gr
id s
ize
(Nor
mG
rid)
for
gas
: GTS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1493
9/
Figure 4.15: Relative gridsize in TCB18, (100=mean level in 2017).
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 27(54)
Range of value
dTot
ex.c
byN
G_z
Slo
peyC
ompr
esso
rs.p
ower
_tot
yCon
nect
ionp
oint
s_to
tyP
ipes
_Lan
dhum
idity
00.20.40.60.81R
ange
TS
O 2
09R
ange
med
ian
TCB
18
Out
put p
rofil
e G
TS 2
017
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1493
8/
0.85
0.8
0.61
11
0.85
0.8
0.61
11
0.07
0.09
0.02
0.07
0.09
Figure 4.16: Inputs and outputs compared to median range in TCB18 (0.0 = minimum, 1.0 = maximum).
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 28(54)
●
010
2030
4050
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1494
9/A
vera
ge a
ge (t
runc
., ye
ars)
Pip
elin
es
Reg
ulat
ors
Com
pres
sors
Con
nect
ions
Met
ers
Con
trolro
om
48
43
35
41
27
Ass
et a
ge p
er a
sset
gro
up T
CB
18 v
s G
TS
Figure 4.17: Asset ages (confidence interval) for all TCB18 and mean age for a specific operator.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 29(54)
●
0.0
0.2
0.4
0.6
0.8
1.0
0.00.20.40.60.81.0
Sha
re o
f NG
cap
ex fo
r pre
−197
3 as
set
Share of NG capex per asset category
0.15
0.08
0.06
0.05
0.13
●0.
52
0.56
0.33
0.22
0.15
●P
ipel
ines
Reg
ulat
ors
Com
pres
sors
Con
nect
ions
Met
ers
Con
trolro
om
● ●
GTS
Mea
n TC
B18
Figure 4.18: Share of total capital and share for old assets per asset category.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 30(54)
Year
Staff (fte)
2013
2014
2015
2016
2017
0500100015002000S
taff
GTS
Med
ian
staf
f (si
ze−a
dj)
2052
1930
1918
1960
2054
1507
1405
1461
1469
1478
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1493
5/
Figure 4.19: Actual staff (fte) compared to size-adjusted level for a median operator in TCB18.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 31(54)
Mean allocation of I (%)
TM
PS
XTO
O
00.10.20.30.40.50.6
Allo
catio
n of
indi
rect
sup
port
by
activ
ity G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1495
0/
0.22
0.27
0.12
0.14
0.02
0.05
0.18
0.57
0.28
0.02
0.03
0.02
0
0.07
GTS
Mea
n al
loca
tion
TCB
18
Figure 4.20: Allocation of overhead by function, mean and by operator, 2017.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 32(54)
●
●
●
●
●
●
●
●
●●
●
●●
●●
●●
●●
●●
●●
●●
●●
●●
05
1015
2025
30
0.20.40.60.81.0
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1495
0/
TSO
sor
ted
Allocation of I to TMP (%)
Mea
n al
loca
tion
= 0
.88
209
0.60
Figure 4.21: Overhead allocation (per cent) to TMP activities in TCB18.
CONFIDENTIAL - FINAL
Chapter 5
Second-stage analysis
In order to investigate whether some potentially relevant variables have been omitted in the final modelspecification, a so-called second stage analysis has been performed. The idea of the second stage analysis isto investigate if some of the remaining variation in performance can be explained by any of the unused costdrivers. This is routinely done by regressing the efficiency scores on these variables in turn. The second-stageregression is concretely regressing an omitted factor, ψ against the DEA-score, i.e.
DEANDRS = β0 + β1ψ + � (5.1)
The result of such an exercise is given in Table 5.1 below. A small value of the p-statistics or equivalent ahigh t-value would indicate that the parameter ψ is interesting. maxImpact indicates the coefficient value β1multiplied with the maximum range for the variable concerned, max(ψ)−min(ψ).
As seen from Table 5.1, no parameter is significant at the 5% or 1% levels, indicating that the dimensionsherein are considered in the model and do not merit specific post-run corrections.
33
SUMICSID-CEER/TCB18 - GTS 34(54)
Table 5.1: Second-stage analysis, final model gas
Parameter t-value p-value maxImpact Sign-5% Sign-1%yNG -0.805 0.428 -0.124yNG yArea -0.890 0.381 -0.134yNG zLandhumidity -0.801 0.430 -0.123yNG zGravel -0.828 0.415 -0.130yNG yAreaShare.forest lmrob corr -0.827 0.415 -0.126yNG yShare.area.wetland.tot lmrob corr -0.919 0.366 -0.132yNG yShare.area.urban.tot lmrob corr -0.929 0.361 -0.141yNG yShare.area.infrastructure.tot lmrob corr -0.775 0.445 -0.124yNG yShare.area.cropland.tot lmrob corr -0.803 0.429 -0.123yNG yShare.area.woodland.tot lmrob corr -0.819 0.420 -0.126yNG yShare.area.grassland.tot lmrob corr -0.803 0.429 -0.124yNG yShare.area.shrubland.tot lmrob corr -0.819 0.420 -0.127yNG yShare.area.wasteland.tot lmrob corr -0.765 0.451 -0.117yNG zHumidity.wwpi lmrob corr -0.809 0.426 -0.125yNG zRugged lmrob corr -0.788 0.437 -0.121yNG zGravel S mean lmrob corr -0.836 0.411 -0.131yNG zGravel T mean lmrob corr -0.803 0.429 -0.124yNG yClimate.icing lmrob corr -0.703 0.488 -0.112yNG yClimate.heat lmrob corr -0.805 0.428 -0.124yNG zDensity.railways lmrob corr -1.038 0.308 -0.152yPipes tot -0.697 0.492 -0.116yInjection.tot.vol -1.228 0.230 -0.227yPipes Slope -0.697 0.492 -0.104yPipes Area -0.762 0.453 -0.125yPipes Gravel -0.731 0.471 -0.121age1y -0.254 0.801 -0.059age meany -0.384 0.704 -0.087dist coast -0.343 0.734 -0.052near coast 0.194 0.848 0.028
CONFIDENTIAL - FINAL
Chapter 6
Cost development
In this chapter the dynamic cost development for GTS compared to that for the gas operators in TCB18 isanalyzed, first by activity, then by cost type for the benchmarked activities T,M,P. The graph for the generaldevelopment, both in terms of grid growth (NormGrid) and in terms of expenditure, are drawn with dashedlines. The line for GTS is drawn as a solid line if the costs are reported for several years, otherwise the graphsare only providing mean information.In the activity cost graphs, a solid green line is indicating the base line of one (no change in expenditure). Allcost data are adjusted for inflation using 2017 as base year, the analysis thus concerns real cost development.
This information is useful to consider specific sources of efficiency and in-efficiency compared to thecomparators, considering the earlier analyses for profile, age and sensitivity.
35
SUMICSID-CEER/TCB18 - GTS 36(54)
1.001.051.101.151.201.251.30
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●●
1.03
1.02
11.
01
●●
●
●
1.11
51.
120
0.97
6
0.99
3
Dev
elop
men
t of T
otex
for
GTS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
9/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eTo
tex
Figure 6.1: Totex development (TMP)
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 37(54)
1.01.21.41.6
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●●
●
●
1.01
0.99
80.
973
0.99
2
●
●
●
●
1.36
1
1.44
0
0.87
2
0.92
3
Dev
elop
men
t of O
pex
for
GTS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
9/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pex
Figure 6.2: Opex development (TMP)
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 38(54)
1.001.051.101.151.20
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●●
1.04
1.03
1.02
1.02
●
●●
●
1.06
1.02
1.02
1.02
Dev
elop
men
t of C
apex
for
GTS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
9/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eC
apex
Figure 6.3: Capex development
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 39(54)
1.01.52.02.53.0
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●●
0.96
3
1.23
10.
976
●
●
●●
0.94
3
3.20
4
0.76
50.
753
Dev
elop
men
t of c
ost f
or T
rans
port
(T) f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.4: Cost development transport (T) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 40(54)
0.951.001.051.10
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●
●
0.94
9
1
0.95
4
0.98
6
●
●
●
●
1.05
0
1.10
1
0.93
7
0.97
8
Dev
elop
men
t of c
ost f
or M
aint
enan
ce (M
) for
GTS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.5: Cost development maintenance (M) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 41(54)
0.900.951.001.05
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●
●
0.98
5
0.90
5
0.92
9
0.97
1
●
●
●
●
1.04
1.05
1.01
1.09
Dev
elop
men
t of c
ost f
or P
lann
ing
(P) f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.6: Cost development planning (P) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 42(54)
0.81.01.21.4
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●
●
1.21
1.12
1.09
0.84
5
●
●
●
●
1.53
6
1.23
0
1.31
7
0.72
6
Dev
elop
men
t of c
ost f
or S
yste
m O
pera
tions
(S) f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.7: Cost development system operations (S) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 43(54)
0.81.01.21.41.6
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●
●
1.63
1.49
0.83
1
1.05
●
●
●
●
1.06
1.10
1.35
1.01
Dev
elop
men
t of c
ost f
or M
arke
t Fac
ilita
tion
(X) f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.8: Cost development market facilitation (X) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 44(54)
1.01.21.41.61.8
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●
●
1.8
0.86
8
1.12
1.04
●
●
●
●
1.66
0
1.34
5
0.87
9
1.04
9
Dev
elop
men
t of c
ost f
or T
rans
port
Off
shor
e (T
O) f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.9: Cost development offshore transport (TO) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 45(54)
051015
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●●
●●
1.29
1.18
0.90
91.
04
●
●●
●
0.3
45
1.0
16 1
.132
16.3
52
Dev
elop
men
t of c
ost f
or E
nerg
y S
tora
ge (S
F) fo
r G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.10: Cost development energy storage (SF) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 46(54)
0.981.001.021.041.061.08
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●
●
0.96
7
0.98
2
1.08
0.99
6
Dev
elop
men
t of c
ost f
or L
NG
Ter
min
als
(L) f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.11: Cost development LNG terminals (L) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 47(54)
−4−2024
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●
●●
0.97
2
1.6
1.26
1.17
●
●
●
●
−0.2
71
4.8
92
−0.5
03
−5.2
10
Dev
elop
men
t of c
ost f
or O
ther
act
iviti
es (O
) for
GTS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.12: Cost development out-of-scope (O) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 48(54)
1.01.52.02.53.03.54.04.5
Year
s
Index
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
1.01
1.00
1.00
1.00
●
●●
●
1.3
0.97
20.
985
1.01
●
●
●●
4.43
2
1.11
5
0.97
20.
972
Dev
elop
men
t of c
ost f
or In
dire
ct E
xpen
ses
(I) f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1543
5/
GTS
TCB
18
●
Nor
mal
ized
grid
siz
eO
pera
ting
cost
s
Figure 6.13: Cost development indirect support (I) vs grid growth.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 49(54)
1.001.051.101.151.20
Year
s
Index (inflation adj expenditure)
1.01
1.00
1.00
1.00
●
●
●
●
1.05
1.07
1.05
1.02
Dev
elop
men
t of D
irec
t man
pow
er c
ost f
or G
TS
SU
MIC
SID
/Agr
ell&
Bog
etof
t/TC
B18
/CO
NFI
DE
NTI
AL/
1907
25_1
1544
4/
GTS
TCB
18
●
Nor
mG
ridO
pera
ting
expe
nditu
re
2013
/201
420
14/2
015
2015
/201
620
16/2
017
1.01
1.02
11
●
●
●
●
0.97
40.
983
0.99
1
1.05
Figure 6.14: Cost development personnel expenditure (TMP)
CONFIDENTIAL - FINAL
Chapter 7
Parameters and index
The technical parameters in Table 7.1 and the indexes in Figures 7.1 and 7.2 are used in the calculations forthe efficiency. The choice of these parameters is discussed further in the final report.
Table 7.1: Key parameters.
parameter.names parameter.valuesTemplate version May 2018Real interest rate 0.03Exchange rate EUR 2017 1Inflation index name: hicpog cpiwLabor cost index name: pliciLabor cost index 2017 1.07Labor cost index 2016 1.086Labor cost index 2015 1.03Labor cost index 2014 1.057Labor cost index 2013 0.961
Overhead allocation T 0.216Overhead allocation M 0.272Overhead allocation P 0.115Overhead allocation S 0.14Overhead allocation X 0.024Overhead allocation TO 0.051Overhead allocation SF 0Overhead allocation L 0Overhead allocation O 0.181
Investment life pipes 60Investment life regulators 30Investment life compressors 30Investment life cp 30Investment life ms 30Investment life cc 20Investment life equip 10
50
SUMICSID-CEER/TCB18 - GTS 51(54)
Table 7.2: Environmental variables.
parameter datafiledist coast tcb18 env rugged 10.csvnear coast tcb18 env rugged 10.csvrugged tcb18 env rugged 10.csvrugged lsd tcb18 env rugged 10.csvrugged pc tcb18 env rugged 10.csvrugged popw tcb18 env rugged 10.csvrugged slope tcb18 env rugged 10.csvwSubRegion tcb18 env area3 10.csvyArea.arable tcb18 env area 10.csvyArea.artifical tcb18 env area2 10.csvyArea.bareland tcb18 env area2 10.csvyArea.builtup tcb18 env area2 10.csvyArea.coastalwetlands tcb18 env area2 10.csvyArea.cropland tcb18 env area2 10.csvyArea.forest tcb18 env area 10.csvyArea.grassland tcb18 env area2 10.csvyArea.greenhouses tcb18 env area2 10.csvyArea.inlandwetlands tcb18 env area2 10.csvyArea.land.tot tcb18 env area 10.csvyArea.meadows tcb18 env area 10.csvyArea.other tcb18 env area 10.csvyArea.service tcb18 env areaservice 10.csvyArea.shrubland tcb18 env area2 10.csvyArea.tot tcb18 env area 10.csvyArea.water tcb18 env area2 10.csvyArea.wetland tcb18 env area2 10.csvyArea.woodland tcb18 env area2 10.csvyAreaShare.arable tcb18 env area 10.csvyAreaShare.forest tcb18 env area 10.csvyAreaShare.grass tcb18 env vegetation 10.csvyAreaShare.meadows tcb18 env area 10.csvyAreaShare.other tcb18 env area 10.csvyAreaShare.shrubs tcb18 env vegetation 10.csvyAreaShare.woods tcb18 env vegetation 10.csvyClimate.heat tcb18 env climate 10.csvyClimate.icing tcb18 env climate 10.csvyLanduse.agriculture tcb18 env landuse 10.csvyLanduse.industry tcb18 env landuse 10.csvyLanduse.nonproductive tcb18 env landuse 10.csvyLanduse.urban tcb18 env landuse 10.csvyShare.area.agriculture 1 tcb18 env area3 10.csvyShare.area.agriculture 2 tcb18 env area3 10.csvyShare.area.agriculture 3 tcb18 env area3 10.csvyShare.area.agriculture 4 tcb18 env area3 10.csvyShare.area.cropland.tot tcb18 env area3 10.csvyShare.area.forest 1 tcb18 env area3 10.csvyShare.area.forest 2 tcb18 env area3 10.csvyShare.area.forest 3 tcb18 env area3 10.csvyShare.area.grassland 1 tcb18 env area3 10.csvyShare.area.grassland 2 tcb18 env area3 10.csvyShare.area.grassland 3 tcb18 env area3 10.csvyShare.area.grassland.tot tcb18 env area3 10.csvyShare.area.infrastructure airport tcb18 env area3 10.csvyShare.area.infrastructure port tcb18 env area3 10.csvyShare.area.infrastructure roadrail tcb18 env area3 10.csv
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 52(54)
yShare.area.infrastructure.tot tcb18 env area3 10.csvyShare.area.noaccess 1 tcb18 env area3 10.csvyShare.area.noaccess 2 tcb18 env area3 10.csvyShare.area.otherw.tot tcb18 env area3 10.csvyShare.area.shrubland.tot tcb18 env area3 10.csvyShare.area.urban 1 tcb18 env area3 10.csvyShare.area.urban 2 tcb18 env area3 10.csvyShare.area.urban ind tcb18 env area3 10.csvyShare.area.urban.tot tcb18 env area3 10.csvyShare.area.wasteland 1 tcb18 env area3 10.csvyShare.area.wasteland 2 tcb18 env area3 10.csvyShare.area.wasteland 3 tcb18 env area3 10.csvyShare.area.wasteland.tot tcb18 env area3 10.csvyShare.area.water 1 tcb18 env area3 10.csvyShare.area.water 2 tcb18 env area3 10.csvyShare.area.water 3 tcb18 env area3 10.csvyShare.area.water 4 tcb18 env area3 10.csvyShare.area.water 5 tcb18 env area3 10.csvyShare.area.wetland 1 tcb18 env area3 10.csvyShare.area.wetland 2 tcb18 env area3 10.csvyShare.area.wetland 3 tcb18 env area3 10.csvyShare.area.wetland 4 tcb18 env area3 10.csvyShare.area.wetland 5 tcb18 env area3 10.csvyShare.area.wetland.tot tcb18 env area3 10.csvyShare.area.woodland.tot tcb18 env area3 10.csvyShare.motorways tcb18 env roads 10.csvyShare.other tcb18 env area3 10.csvyShare.urbanroads tcb18 env roads 10.csvzDensity.railways tcb18 env roads 10.csvzDensity.roads tcb18 env roads 10.csvzGravel S mean tcb18 env subsoil 10.csvzGravel S00 tcb18 env subsoil 10.csvzGravel S05 tcb18 env subsoil 10.csvzGravel S15 tcb18 env subsoil 10.csvzGravel S40 tcb18 env subsoil 10.csvzGravel S41 tcb18 env subsoil 10.csvzGravel T mean tcb18 env subsoil 10.csvzGravel T00 tcb18 env subsoil 10.csvzGravel T05 tcb18 env subsoil 10.csvzGravel T15 tcb18 env subsoil 10.csvzGravel T40 tcb18 env subsoil 10.csvzGravel T41 tcb18 env subsoil 10.csvzHumidity.wwpi tcb18 env wetness 10.csvzLandhumidity.dry tcb18 env wetness 10.csvzLandhumidity.water.perm tcb18 env wetness 10.csvzLandhumidity.water.temp tcb18 env wetness 10.csvzLandhumidity.wet.perm tcb18 env wetness 10.csvzLandhumidity.wet.temp tcb18 env wetness 10.csvzSlope.flat tcb18 env slope 10.csvzSlope.hilly tcb18 env slope 10.csvzSlope.mountain tcb18 env slope 10.csvzSlope.undulating tcb18 env slope 10.csvzSoil.dr D tcb18 env subsoil 10.csvzSoil.dr M tcb18 env subsoil 10.csvzSoil.dr S tcb18 env subsoil 10.csvzSoil.dr V tcb18 env subsoil 10.csv
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 53(54)
Country
Inde
x E
U=1
00
PTLV
GRES
EELT
SIBE
NLUK
ATDE
DKSE
NOFI
050
100
150
58.4
71.8
73.7
79.5
86.3
86.5
93.1
103.
8
107
107
114.
8
132.
2
136.
3
149.
1
150.
8
165.
4
EU27 average
Index: PI civil works (EU) year 2017
Figure 7.1: Labour cost index PLICI (EU civil engineering) by country 2017.
CONFIDENTIAL - FINAL
SUMICSID-CEER/TCB18 - GTS 54(54)
Country
Inde
x E
U=1
00
LTLV
EEPT
GRSI
ESUK
FIDE
ATNL
SEBE
DKNO
050
100
150
29.9
30.2
43.7
52.6
54.1
63.4
79.1
95.9
122
127.
2
127.
2
129.
9
142.
9
147.
8
158.
6
190.
3
EU27 average
Index: LCI (EU) year 2017
Figure 7.2: Labour cost index LCIS (EU general) by country 2017.
CONFIDENTIAL - FINAL
ContentsResultsDataCapex-breakCapex-oldModel input and output
Regression analysisSensitivity analysisScale efficiencyPartial Opex-capex efficiency analysesSensitivity analysisProfileAgeCost analysis
Second-stage analysisCost developmentParameters and index