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Date
April 08, 2011
Authors
Charis Kouridis
Ioannis Kioutsioukis
Thomas Papageorgiou
Stephen Mills
Les White
Leonidas Ntziachristos
Client
European Commission
Climate Action DG
Directorate A: International & Climate Strategy, Unit A4: Strategy & Economic Assessment
1049 Brussels, BELGIUM
Final Report
EMISIA SA Report
No: 11.RE.01.V3
Uncertainty/Sensitivity analysis of the transport model TREMOVE
EMISIA SA ANTONI TRITSI 15-17
SERVICE POST 2 GR 57001 THESSALONIKI GREECE
tel: +30 2310 473352 fax: + 30 2310 473374 http://www.emisia.com
Project Title
Uncertainty/Sensitivity analysis of the transport model TREMOVE
Call for Tender No:
ENV.C.5/SER/2009/0017
Report Title
Final Report
Contract No:
07.0307/2009/538771/C5
Project Manager
Giorgos Mellios
Author(s)
Charis Kouridis, Ioannis Kioutsioukis, Thomas Papageorgiou, Stephen
Mills, Les White, Leonidas Ntziachristos
Coordinator: EMISIA SA
Sub-contractor LWA Ltd
Summary
This is the final report for the Climate Action DG of the European Commission project designed to estimate the uncertainty of the TREMOVE output and its sensitivity to the input variables. This report includes a summary of the methods used, the results of the uncertainty and sensitivity analysis for the baseline and three scenarios, and a list of conclusions and recommendations. The report is accompanied by a DVD with a summary of the output in aggregated form and a full dataset of all modelled output. For the analysis, United Kingdom was examined as a test case and TREMOVE v3.3.1 was used. The study identified 14 variables that were found to be most important for the uncertainty of the model output. It also identified linear associations between output and input variables in several instances. Elasticities between intermodal shifts and other choices (vehicle types, fuels, etc.) appeared limited. Choices to decrease model uncertainty include better estimates for key input variables and simplification of the model structure.
Keywords
Uncertainty, Sensitivity, Monte Carlo, TREMOVE, Statistics, Emissions
Internet reference
http://www.emisia.com/gui/unc.php Version / Date
Final Version / 08 April 2011
Classification statement
PUBLIC No of Pages
233
No of Figures
24
No of Tables
23
No of References
19 Approved by:
Emisia is an ISO 9001 certified company
Contents
Executive Summary ........................................................................................ 7
1 Introduction ................................................................................... 17 1.1 Background ............................................................................... 17 1.2 Objectives of the study................................................................ 19 1.3 Structure of this report................................................................ 19
2 Uncertainty ranges of input variables and modelling parameters ............ 20 2.1 General..................................................................................... 20 2.2 Input variables description........................................................... 21 2.3 Emission factor modelling ............................................................ 47 2.4 Input variables and parameters not varied ..................................... 53 2.5 Changes over interim report......................................................... 54
3 Modelling theory / approach ............................................................. 58 3.1 General..................................................................................... 58 3.2 Methods.................................................................................... 58 3.3 Parameterisations of input data .................................................... 62
4 TREMOVE software modification and update........................................ 74 4.1 General..................................................................................... 74 4.2 Software code modification .......................................................... 74 4.3 Software code added .................................................................. 75 4.4 New features ............................................................................. 77 4.5 Guidance to use the software ....................................................... 78 4.6 Differences between the two steps................................................ 78
5 Variance of the baseline output ......................................................... 80 5.1 General..................................................................................... 80 5.2 Screening uncertainty and sensitivity analysis ................................ 81 5.3 Variance-based uncertainty and sensitivity analysis ......................... 96 5.4 Discussion................................................................................109
6 Uncertainty and sensitivity analysis of scenarios .................................112 6.1 General....................................................................................112 6.2 Methodology.............................................................................112 6.3 Ownership tax increase ..............................................................112 6.4 Effect of fuel cost ......................................................................118 6.5 HDV Euro VI .............................................................................123
7 Conclusions and recommendations ...................................................129
References .................................................................................................135
ANNEX I: uncertainty estimates of the baseline per vehicle category ...................137
ANNEX II : uncertainty estimates of the scenarios per vehicle category ...............161 Scenario 1.........................................................................................162 Scenario 2.........................................................................................185 Scenario 3.........................................................................................208
ANNEX III: Description of the DVD contents ....................................................231
ANNEX IV: Description of the full dataset ........................................................233
7
Executive Summary
This is the final report of the study entitled “Uncertainty/Sensitivity analysis of the transport
model TREMOVE”, funded by the European Commission. TREMOVE has been the main model
used in Europe for impact assessments of road transport related policies. Recent applications of
the model include impact assessments related to the Eurovignette directive, the Euro VI
emission standards for heavy duty vehicles, CO2 regulations for passenger cars, etc. Aim of this
study was to characterize the uncertainty in the output of the TREMOVE model (v3.3.1 – June
2010), i.e. to estimate the variance in the activity and emission results and to identify the
factors which are most important for this variability. Specifically, the study aimed at:
- Identifying the variance of the input data to TREMOVE.
- Determining the uncertainty range of the basecase.
- Determining the uncertainty range of three indicative scenarios.
- Conducting a sensitivity analysis to identify the most important factors in terms of
uncertainty.
TREMOVE covers all transport modes (road, rail, aviation, inland waterways, maritime) for all
EU27 member states plus Switzerland, Croation, Norway, and Turkey. The uncertainty in the
calculations should in principle depend on the country considered, as different sets of
parameters are important in each case. However, in this study we addressed uncertainty only
in the case of UK as an example. Characterising the uncertainty for all countries would have
been impossible due to time and cost constraints. UK was selected of all countries, as we could
obtain access to estimates of primary data variance. Uncertainty estimates are derived for the
transport activity, vehicle population, fuel consumption, pollutant emission factors (PM, CO,
VOC, NOx), and cost components.
This report outlines the main findings of this study and makes recommendations on how the
quality of the output may be improved. It follows up on an inception report clarifying the
targets and the approach implemented and on an interim report discussing the selection of the
statistical method used.
Approach
TREMOVE aims at assessing the environmental impact of different policy options. Each new
policy is associated with a marginal cost which drives the demand. This is simulated in the
demand module. The new demand also leads to variations in vehicle choices, which are
estimated in the stock module. Then, an emissions and fuel consumption module calculates the
environmental impact of the vehicle stock operation. The calculation is done on an iterative
process, as the costs initially assumed and finally calculated should match. Following the
iterations, a welfare and a well-to-wheel module calculate the total cost-benefit (including
external costs) and upstream fuel production emissions respectively.
8
The model is built around a basecase which is exogenous. This exogenous basecase offers
transport quantities and costs which are used to calibrate the price elasticities of the model,
based also on assumptions regarding the elasticities of substitution between different transport
options. The elasticities of substitution themselves are also either estimates receiving a value
while calibrating the model, or constant empirical values. In general, several elasticities of
substitution are selected during calibration so that the derived price elasticities receive values
that are close to the literature values. After calibration, the price elasticities may be used to
simulate alternative scenarios. This scheme offers intrinsic difficulties in characterizing the
uncertainty of the demand, when using any TREMOVE version:
1. The model uses detailed exogenous data (from SCENES, PRIMES, TRANS-TOOLs, etc.)
to define its baseline. As a result, the uncertainty of the basecase is largely defined by
the uncertainty in the output of such higher-order models.
2. All demand functions are calibrated to this exogenous basecase. During simulations,
the model uses these calibrated values. Individually changing any of the price
elasticities of the model to assess its impact on output uncertainty will bring the model
off-balance.
3. Tremove uses elasticities of substitution which are constant with respect to the income
while, in scenarios, the income is assumed constant relative to the basecase. In
principle this means that the demand module can be used to calculate changes in
demand only for small changes in the (generalized) cost. The definition of ‘small’ in
this last sentence is arbitrary. The expected error in the calculation of the demand
using the Tremove demand tree structure increases the more distant are the scenario
costs from the basecase costs. This introduces an uncertainty which is exogenous to
the model and cannot be estimated. However, it largely defines the total uncertainty of
the calculation.
Individually characterising the uncertainty of the TREMOVE demand module would mean to
quantify the output variance, using realistic variability indicators for the input data. For the
reasons outlined above, it was decided that a realistic uncertainty analysis of the demand
module is not possible. Neither the input data can be independently varied (since each model
version is calibrated around a fixed basecase), nor the output variance produced would be
realistic (due to the constant elasticities of substitution effect). The only useful approach that
can be recommended to assess the demand module uncertainty is to compare its output using
the projections of alternative models as input. This can be considered to encapsulate all
uncertainties, ranging from uncertainties in input data, modelling approach, assumptions for
parameters, etc. However, this was clearly not the target of this study.
The analysis therefore focused on the characterization of the uncertainty produced by the
vehicle stock and the emission modules. The parameters of the two modules are perturbed
across their range and the output variance is observed. This is done for the basecase and for
three alternative scenarios built to reflect options related to taxation policy, fuel costs, and
introduction of a new emission standard. The statistical method chosen also results to a
detailed sensitivity analysis, i.e. demonstrates which individual model variables or variable
combinations are important (i.e. having large impact on model output) or not.
9
The approach followed is considered to calculate not a theoretical uncertainty, but the actual
uncertainty of the model output when applying it to examine the impact of a new policy.
Therefore, our approach is considered to reliably address the following points:
1. The uncertainty induced by the model to the exogenously defined baseline.
2. The confidence by which activity and emission differences can be distinguished
between different scenarios and the baseline.
3. The key variables/parameters which can be better estimated to reduce the uncertainty
of the output.
Statistical methods
Various methods are available to evaluate the model output uncertainty and quantify the
importance of the input factors. The selection of the appropriate method is a function of the
system’s uncertainty and the stakes involved. For the case of models with a direct policy
orientation like TREMOVE, global sensitivity analysis methods are preferable. The global
sensitivity analysis methods involve multiple evaluations of the model using Monte-Carlo
simulations, where values for the input variables are selected according to specific sampling
strategies. Here, we have adopted variance-based techniques, and in particular the extended-
FAST, that display a number of attractive features like the exploration of the whole range of
variation of the input factors and the consideration of interaction effects. This approach allowed
us to gain useful insight in the TREMOVE processes in order to assess the dependencies of the
individual variables as well as the quality of its estimates, with the view to better defend policy
messages.
The high computational load of TREMOVE as well as its large number of uncertain input
variables is tackled through a screening sensitivity analysis experiment prior to the extended-
FAST setting. The relative importance of the uncertain input factors is explored with a design
based on quasi-random LPτ sequences. The estimated sensitivity coefficients, calculated in the
nth-D space defined by the input factors, identified the non-influential input factors that were
fixed in their nominal values in the subsequent variance-decomposition analysis.
Software implementation
The TREMOVE model code was modified in order to be able to execute the thousands of Monte
Carlo simulations that were necessary. The code was modified in several ways with regard to
its execution but it was not affected with respect to its operation. In particular, it has been
made possible to decouple scenario from basecase execution that has greatly reduced
processing time. New pieces of code have been added to induce perturbations to the input
variables across their variance range. Depending on the variable considered and the exact
formulation, this was made possible either by feeding externally alternatives, or by introducing
an error factor in the calculation of the variable, or by replacing the variable value after its
calculation by the code (e.g. emission factors). A new graphical user interface was also
developed to allow the Monte Carlo execution of TREMOVE.
10
Uncertainty of the input variables
All the input factors were examined in order to understand their role in the structure of the
model. Out of the more than 100 input variables, those who received fixed values were
excluded in the analysis. Also variables related to the demand, welfare, well-to-wheel and non
road modules were removed from the analysis. This filtering procedure led to 33 input variables
for which uncertainty ranges were estimated.
The estimation of the uncertainty ranges for the input variables was done for the case of UK
using different available sources, including public authorities, clubs and association information,
vehicle manufacturers, research institutes, the public domain, and expert judgment in some
cases where published information was not available. Uncertainty ranges were collected for
vehicle related information (costs, specifications, scrappage rates, existence of air-conditioning,
etc), vehicle operation (mileage, speed, trip distance), fuel parameters (composition, heating
value, specifications), cost related issues (purchase, maintenance, ownership, labour, etc.),
environmental factors (temperatures), and infrastructure (availability of CNG stations).
With regard to internal model parameters, only the emission factor uncertainty has been
characterized, based on the available experimental data used to derive them. For hot emission
factors and fuel consumption, log-normal probability distributions were developed around the
factors for fourteen different speed classes. In the absence of robust experimental data for cold
start, the standard deviation over mean of the hot emission factors has been also used, also
assuming log-normal probability functions.
Uncertainty and sensitivity of the basecase
Based on the rationale outlined in the ‘approach’ section, uncertainty characterisation of the
basecase means to quantify the variance induced by the model variables uncertainty to the
fixed exogenous projection. It does not mean to quantify the uncertainty of the projection per
se, as this would require determining the uncertainty in the demographic and macroeconomic
data assumed to develop it. This is not relevant for TREMOVE but for the models used to derive
this projection. Also, the uncertainty of the basecase is limited to the model variables. For
example, TREMOVE includes no hybrid nor electric vehicles that are expected to become quite
popular up to 2030. Inclusion of such technologies would have increased the uncertainty of the
baseline but this was not possible to quantify, as these are not part of the model formulation.
The uncertainty of the basecase was assessed by formulating ‘baseline’ scenarios around the
fixed basecase. The scenarios were built by perturbing the values of the input variables along
their range collected from literature data. The mean value of the uncertainty range is very
close to the value assumed in the TREMOVE basecase for several variables. However, in other
cases (more predominantly for the purchase cost and ownership tax of two-wheelers) the
TREMOVE basecase value was much lower than the literature values. In these cases, literature
values have been used for baseline uncertainty characterisation. This led to some divergence
between the TREMOVE basecase and the mean output of the baseline scenarios, in particular
for two wheelers. This is recognised but it has no impact on the conclusions related to
uncertainty and sensitivity of TREMOVE.
In order to assess the uncertainty, a screening test (512 runs) was first performed to identify
the most influential variables. The screening procedure identified 14 input variables which
11
explain most of the uncertainty of the model output. Using the uncertainty of this limited set of
variables, TREMOVE was executed for 5950 runs in a Monte Carlo analysis to calculate the total
uncertainty of the model and to quantify the contribution of each variable to the output
uncertainty.
The uncertainty of the baseline in the years 2010, 2020, and 2030 expressed as a coefficient of
variance (cov) is shown in Table ES1. The main conclusions drawn from the uncertainty
analysis are:
Table ES1: Median value and coefficient of variation (cov) of the baseline output in TREMOVE
shown in a descending order according to the cov2030 ranks.
Output Variable Units Median 2010 Median 2020 Median 2030 cov 2010 cov 2020 cov 2030
CO Ton 252,000 119,402 119,439 69% 51% 51%
PM Ton 10,750 3,400 3,421 25% 27% 27%
VOC Ton 46,927 30,279 31,559 37% 26% 24%
TAXrest M€ -8,454 -8,768 -9,365 21% 22% 22%
NOx Ton 331,319 170,500 158,458 19% 17% 17%
COSTinsurance M€ 24,922 38,291 44,671 6% 13% 14%
TAXinsurance M€ 1,260 1,934 2,255 6% 13% 14%
VATfuel M€ 8,041 9,418 11,412 13% 13% 13%
TAXfuel M€ 33,301 41,490 48,064 12% 12% 12%
COSTfuel M€ 30,727 32,106 40,202 11% 11% 12%
TAXownership M€ 6,166 10,810 12,070 8% 11% 12%
FC Ton 46,618,917 55,591,702 60,043,602 11% 11% 11%
COSTrepair M€ 59,798 73,706 86,483 3% 10% 10%
VATrepair M€ 7,293 9,090 10,691 3% 10% 10%
COSTlabour M€ 10,869 14,896 16,607 9% 9% 9%
COSTlabourtax M€ 11,634 15,944 17,773 9% 9% 9%
VATpurchase M€ 10,841 11,566 13,107 5% 9% 9%
COSTpurchase M€ 84,178 99,323 114,467 4% 8% 9%
TAXregistration M€ 22.5 24.2 27.4 7% 8% 8%
VATrest M€ 1,252 1,293 1,377 5% 5% 5%
Costs M€ 323,333 396,036 458,228 2% 4% 4%
Vehicles # 33,652,081 37,918,723 40,997,888 2% 3% 3%
Vehkms ×106 km 585,653 665,914 720,553 2% 3% 3%
COSTrest M€ 41,193 44,395 47,695 2% 2% 2%
- The uncertainty is large for the emission of pollutants, mostly due to the uncertainty in
the emission factors. Cov’s are in the order of 20-30% but can reach up to 50% in the
case of CO.
- Fuel dependent variables (fuel costs and consumption) are second with regard to
output uncertainty with cov values in the order of 10-15%.
- Total cost figures exhibit uncertainty ranges in the order of 4-10%, i.e. they are rather
little dependent on the variance of the input data.
- Finally, population and activity data are found to be associated with very small
uncertainty, in the order of 2-3%. The uncertainty per vehicle category is of the same
magnitude. This means that large fluctuations in the input data (i.e. costs and other
variables) will result in relatively small changes in the activity and population data.
12
The contribution of each individual input variable to the uncertainty is summarized in Table
ES2. The first order dependencies (ΣSI’s) show how much of the output variance is explained
by the variance of each single input variable. A value of 1 would mean that the total output
uncertainty is explained only by first-order dependencies to the input variables.
Interdependencies of input variables become less and less important in explaining the output
variance the closer the value moves to 1. The table also shows the one or two most influential
input variables per model output. The following conclusions may be drawn based on the
sensitivity analysis conducted:
Table ES2: Summary of first-order (ΣSI) dependencies of output variance to input variance, in
a decreasing order according to the dependencies in 2030.
Output Variable Most Important Input Variable ΣSI2010 ΣSI
2020 ΣSI2030
COSTrepair eRREPMAINTCFRACTION, eRPCSBASE
0.97 0.98 0.99
VATrepair eRREPMAINTCFRACTION, eRPCSBASE
0.98 0.99 0.99
FC eEFfc 0.96 0.97 0.98
COSTfuel eEFfc 0.96 0.97 0.97
TAXfuel eEFfc 0.96 0.97 0.97
VATfuel eEFfc 0.96 0.97 0.97
COSTlabour RLABOURC 0.96 0.96 0.96
COSTlabourtax RLABOURTX 0.96 0.96 0.96
TAXrest PUBLICCOSTCOV 0.96 0.96 0.96
VATrest PUBLICCOSTCOV 0.96 0.96 0.96
PM eEF 0.97 0.96 0.96
COSTpurchase eRPCSBASE 0.97 0.95 0.95
TAXownership ROWNTX 0.96 0.95 0.95
COSTinsurance RINSCFRACTION 0.94 0.94 0.95
TAXinsurance RINSCFRACTION 0.94 0.94 0.95
COSTrest PUBLICCOSTCOV 0.97 0.96 0.95
NOx eEF 0.96 0.95 0.95
VATpurchase eRPCSBASE 0.97 0.94 0.94
TAXregistration uparaBT 0.89 0.92 0.92
CO eEF 0.91 0.92 0.91
VOC eEF 0.93 0.91 0.91
Costs eRPCSBASE, eEFfc 0.88 0.88 0.88
Vehicles eRPCSBASE, eEFfc 0.89 0.88 0.88
Vehkms eRPCSBASE, eEFfc 0.89 0.88 0.88
- The hot emission factors (eEF) influence most the variance of the emissions (VOC,
NOX, PM, CO) while the basic road vehicle purchase resource cost (eRCPSBASE)
controls the variability of the stock and activity variables (vehicles and vehicle-kms).
On the other hand, many input factors are responsible for the variability of the cost
related output.
- All model outputs exhibit high linearity to input variables. The least amount of
explained-by-single-contributions variance estimated was 88% and corresponds to the
output variables Costs, Vehicles, and Vehkms. Only the remaining fraction depends on
higher order interdependencies between the input variables.
13
- The linearity of the output variables is generally constant in time. On the other hand,
the total effects are either constant or decreasing in the future. This implies that the
reduction of the variance of single input variables will be always effective in decreasing
the uncertainty of the estimates.
- Most of the interaction effects were observed for TAXregistration and these are
attributed to interdependencies of all input variables. Least dependencies on second
and higher order terms have been observed for COSTpurchase and VATpurchase. This
gives the opportunity to work only on the uncertainty of the RPCSBASE in the future,
in order to reduce the variability of the purchase cost related items.
Scenario uncertainty
The uncertainty of three alternative scenarios was quantified in order to understand the
uncertainty and sensitivity of the model in simulated applications of policy impact analysis. The
three scenarios were formulated in such way as to potentially activate different paths of
uncertainty of the model. The three scenarios were:
1. Increase the ownership tax of passenger cars to demonstrate mostly shifts to other
modes of transport within the road sector.
2. Increase the road fuel price to demonstrate general drop in road transport activity.
3. Introduce a new emission standard (Euro VI heavy duty vehicles) to demonstrate drop
in total emissions.
Those simulations led to the following observations:
In Scenario 1, providing a much higher ownership tax for passenger cars (three times higher
than the basecase in 2030) affects fifteen out of the 24 output variables, mainly the cost-
related ones. The substantial increase in ownership costs increases total road transport costs
by 2.4% and this leads to an almost equal decrease in vehicle number and vehicle kilometres
of passenger cars. Despite the high increase of car operation costs, no substantial intermodal
shifts between the road vehicles or between road transport and other modes were observed.
The main impact of the cost increase was a proportional drop in the activity of passenger cars.
In Scenario 2, the base cost of road fuel was assumed to range within ±30% of its mean value
from 2010 onwards. The mean fuel price did not change in the scenario compared to the
basecase. This variation only affected the confidence interval of the fuel cost output variable in
a statistically significant manner. The fuel tax is independent of fuel cost. Interestingly, the
VAT of fuel was not seen to vary significantly between the two cases. This was because the
VAT is applied on the (fuel cost + fuel tax) value and the constant fuel tax range attenuated
the impact of the larger fuel cost variability. The confidence intervals for all other variables
were only marginally affected. Due to the small relative effect, the sensitivity analysis produces
identical results between the basecase and the scenario, i.e. the output depends on the
uncertainty of the input variables in the same fashion as in the baseline.
14
Estimated emission reductions in PM and NOx together with increased purchase, operation
costs and – marginally – increased fuel consumption values were considered for the
introduction of a heavy duty Euro VI emission standard in Scenario 3. The effect of the
introduction was only shown for PM and NOx and for no other output variable. The contribution
of input variables to the uncertainty of the scenario is identical to the basecase as the
coefficient of variance of the Euro VI emission factors has been assumed the same as Euro V.
Major conclusions and recommendations
The analysis conducted in this study demonstrated that a Monte Carlo analysis of TREMOVE is
a useful tool to characterise its uncertainty and sensitivity. Based on this analysis, a number of
conclusions may be drawn:
1. A limited number of input variables (14) seems to drive the total model uncertainty. In
addition, several output variables can be approximated as linear combinations of input
variables with a small loss in precision. This is probably due to the limited elasticity in
shifts between different modes of transport and vehicle types offered by the demand
module. If this limited flexibility is validated (see point 6 in this list), then it can be
suggested that several model operations can be simplified with a beneficial effects on
model transparency and processing time.
2. The fact that a limited number of variables is important for most of the model output
uncertainty means that better quality / more precision in the estimates of these
specific variables will reduce the uncertainty of the output. Of particular importance
appear to be the emission factors, the purchase cost of vehicles, the parameters
defining the scrappage probability, the parameters used to estimate the residual cost
when a vehicle is scrapped and cost-related parameters (maintenance, insurance,
ownership, labour).
3. This study was limited to one country only (UK). Given the linear behaviour of the
model and the limited sensitivity of the demand to the input variables uncertainty,
extending the analysis to other countries does not seem to offer new insights. This
might affect the numerical values of the uncertainty indicators produced but would not
change the conclusions of the study.To improve the model output priority should
rather be given in improving the quality of the major input variables identified in this
study.
However:
4. The total uncertainty of the projection, taking into account macroeconomic and
demographic factors may be realistically assessed only by introducing alternative
basecase projections in the model. This can be a useful future activity.
5. Our analysis only took into account the variables and parameters inclusive in the
model. Expanding the model to cover additional vehicle types, such as alternative fuel
vehicles, hybrids, plug-in hybrids, and electric vehicles may increase the uncertainty of
the estimates but is deemed necessary to cover future applications of the model.
Finally the following recommendations spur from the analysis carried out:
15
6. The currently (2010-2011) changing environment in Europe due to the financial and
credit crisis offers several opportunities for validation of key model elasticities. The
model could be applied to simulate effects of increasing fuel taxation, ownership
taxation, scrappage activities, etc., that take place today in several countries, and
compare with real-world trends.
7. A follow up activity could be to derive the linear functions between output data and
input variables and compare how much they deviate from TREMOVE output. This could
serve three purposes: (i) Quantify how much TREMOVE output deviates from linear
behaviour, (ii) have a simplified TREMOVE model to easily perform scenarios for which
maximum accuracy is not necessary, (iii) identify areas where TREMOVE structure
could be simplified without loss of precision.
17
1 Introduction
The European Commission awarded the contract for estimating the uncertainty of the
TREMOVE model output with respect to changes in model input data to EMISIA SA. This final
report summarizes all activities that was conducted in the framework of the project, presents
the results of the calculations and offers recommendations for further improvement of
TREMOVE in terms of uncertainty calculations.
1.1 Background
Road-transport is a significant source of air pollution and greenhouse gas emissions. According
to European Environment Agency data, road transport is responsible for 22.4%, 39.8%, 42.7%
and 16.2% of total CO2, NOx, CO and PM10 emitted in the EEA32 territory. Since many years
the EC has been defining and implementing policies which aim at organising and controlling
transport in such a manner that it serves its purpose with minimum impacts.
In parallel, the European Commission and the Council have set forward a Directive which sets
National Emission Ceilings (NECD – 2001/81/EC) to regulate the total amount of pollutants that
can be produced annually in each country, which targeted year 2010. The Commission is also
working on the revision of this directive, which would target year 2020 and the inclusion of
PM2.5 ceilings.
Key regulations in the European Commission need to be accompanied by detailed impact
assessments (http://ec.europa.eu/governance/impact/index_en.htm), i.e. studies which
provide an informative assessment of the impacts and the costs that the different policy
options are associated with. In the transport sector, the TREMOVE model [1] has been used to
facilitate impact assessments. TREMOVE is a policy assessment model developed by Transport
and Mobility Leuven (TML). The initial sources of TREMOVE are the models Trenen, Foremove
and COPERT. It covers EU27+CH, HR, NO, TR for the period 1995-2030.
TREMOVE is a model consisting of three main modules: a demand, a stock, and an emissions
module. These are accompanied by two additional modules, the well-to-tank and the welfare
modules. These two are add-ons on the main structure of the model, aimed at estimating the
upstream (fuel production) costs of transport and the benefit (in monetary terms) of emission
reduction to the society, respectively. Well-to-tank and the welfare modules are post-
calculations on the TREMOVE main output (activity, costs, emissions and consumption).
In TREMOVE, a baseline projection is the first development. This is inherent to the model
version and includes a number of exogenous parameters (demand and GDP projections, fuel
and operation costs, vehicle stock mix, etc.). Once the baseline has been built, the model is
calibrated around this baseline. This means that all model functions are calibrated so that the
model produces the desired output (baseline demand and emissions) when fed with the
assumed costs (baseline costs). After the baseline has been agreed and the model is
calibrated, it is then ready for scenario execution.
18
The basic idea in running a scenario is to feed in the marginal costs and the technological
impacts of each policy scenario considered. Costs may be associated with fuel price, purchase
expenses, taxation, operation and maintenance expenses, etc. Higher costs lead to a lower
demand than the baseline and vice-versa. Demand then drives vehicle sales and this gears the
vehicle stock replacement. After the technology mix has been determined, the emission module
will calculate emissions. The complete calculation is not executed in one loop. The new stock
and the, presumably, new fuel consumption calculated, after changing the cost assumptions,
also lead to differentiated total cost calculation. The initial cost assumptions and the costs
calculated by the model need therefore to be equilibrated by performing some internal software
loops.
A large number of modelling assumptions, parameters, and variables are required to simulate
the whole chain of events, from costs to demand, distribution of demand to vehicle classes and
then estimate of emissions and consumption of all individual vehicle classes. Currently, all
model outputs are produced in a deterministic manner, i.e. only one result is possible with a
given input. However, one may expect that the reality is more complicated than that. Due to
uncertainties in the estimation of input data and, naturally, all modelling parameters, the
model output is also bound to an uncertainty range. That is, there is a range of variation in the
modelling output induced by the uncertainty in the estimation of the input and modelling
approach. The first target of this study was to characterize the uncertainty of the main model
outputs (costs, activity, emissions and consumption). Such a characterization is important in
order to understand what level of variation is examined in a modelling output, i.e. how much
could reality deviate from the values calculated by the model. This information may then be
used to judge whether two different scenarios lead to statistically different results and/or if a
scenario (policy decision) will lead to a statistically different value than the baseline.
Once the uncertainty in the output has been established, one will wonder how this can be
reduced. This question can be answered by running a sensitivity analysis which identifies the
terms which induce most of the variance in the modelling output. A large variance of the
output may be induced either by a term which is largely unknown and hence associated with a
large uncertainty range or by a term which has a large impact on the modelling output. In the
latter case, even a small perturbation in its value is largely magnified in the modelling output
(non linear term). Finally, uncertainty may be induced by interrelations between the different
model terms, which designate a second-order effect. The identification of the terms which are
most important in the modelling output and better estimates of their values would then greatly
benefit the reliability of the model output. Such a sensitivity analysis was the second target of
this study.
Uncertainty and sensitivity analysis of the TREMOVE model is a complicated procedure.
TREMOVE is written in GAMS code and uses a number of different files for the execution of a
run. The main code is included in GAMS (.gms) files. All the calculations performed by the
model can be found in these files. The structure of the files is based on a “subroutine” format,
where the code in each file is read/called from another “parent” file. Input variables and
parameters can be found in ‘include’ (.inc) files. They are actually text files, using a different
file extension. To transfer data between the different modules GAMS uses .gdx files which can
be created and later read by the GAMS code more efficiently compared to MSAccess or MSExcel
files. Calculated data are then exported to MSAccess and MSExcel files since users are more
familiar with such data structure. Additional files, such as compressed (.zip) files and batch
19
(.bat) files are also used by the model to initialise the run and complete the calculations. For
the purposes of this project only the .gms and .inc files were studied and modified.
1.2 Objectives of the study
The main objectives of the study were to:
� Characterize the uncertainty associated with input data to the model and the modelling
terms. This required identification of the main input variables and key model
parameters and collection of literature data on the variance associated with them.
� Characterize the uncertainty of the model output, in terms of the activity, costs,
emissions and fuel consumption calculated.
� Examine the uncertainty of the baseline projection and three separate scenarios. The
scenarios should cover a large range of situations in order to examine what is the
uncertainty of the model in a range of situations.
� Run a sensitivity analysis to identify which are the main modelling terms and input
data that can be considered responsible for the uncertainty in the output. The analysis
should identify both linear effects, non-linear effects, and second-order effects.
1.3 Structure of this report
This is the final report of the project summarizing all activities and presenting the results of the
uncertainty and sensitivity analyses conducted on TREMOVE v 3.3.1 (June 2010). Further to
this introductory chapter, the report is structured as follows:
- Chapter 2 discusses the uncertainty range of the input variables and emission factors.
- Chapter 3 outlines the modelling theory and the data parameterisations.
- Chapter 4 summarizes the software modifications required to facilitate the calculations.
- Chapter 5 presents the results of the baseline uncertainty and sensitivity analyses.
- Chapter 6 describes the uncertainty and sensitivity analysis for three scenarios.
- Chapter 7 offers the conclusions and some recommendations derived from this work.
Finally, four Annexes present results in more detail.
20
2 Uncertainty ranges of input variables and modelling parameters
2.1 General
Following initial scoping and analysis work, Emisia supplied LWA with a selection of variables
for which “real world” values were required. Wherever possible, these values were to be
provided with accompanying upper and lower range limits to allow probability distribution
functions to be estimated. In keeping with LWA’s local knowledge it was agreed to supply data
for the UK.
Input variables are not single dimensional but multi dimensional vectors, since their value
depends on the type of vehicle, and year and emission factors also depend on the pollutant
considered. A thorough examination was made for all dimensions of the 33 variables studied in
this exercise.
Some discussions took place regarding the definition of the selected variables and their internal
relationships to the TREMOVE model. GAMS code was accessed regularly to help clarify the
variables’ specification.
Appropriate data sources were identified for as many variables as possible using the general
principle of official Government data as a preference, followed by trade and industry data,
followed by other public domain information sources.
Specific reference was made to data and publications from:
o UK Government Department for Transport,
o UK Government Office of National Statistics,
o UK Government Revenue and Customs,
o UK Driver and Vehicle Licensing Agency,
o UK Vehicle Certification Agency,
o UK Petrol Industry Association (2009 data)
o Society of Motor Manufacturers and Traders, (2009 data)
o Road Haulage Association, (2009 data)
o Automobile Association (AA) (2009 Data)
o Royal Automobile Club (2008 data)
o European Union Directorates
o European Central Bank
o Selected vehicle manufacturers
o Relevant research organisations
o Other public domain information sources such as press articles and expert internet forums
Where necessary the collected data was recalibrated and formatted to be compatible with the
units used by the TREMOVE variables. In cases where no external data source could be
identified for a particular variable then, where possible, expert judgment on likely ranges and
21
modelling approaches was made. In a small number of cases neither data nor expert judgment
was possible and an arbitrary variance range was selected to examine the sensitivity of the
model output to the variable variance.
Finally TREMOVE generated data for the UK was used for comparison with the LWA identified
data. In some cases where alternative data sources were not available TREMOVE UK data was
used with upper and lower limits based on experience with “real world” data.
2.2 Input variables description
Table 1 presents the input variables for which the uncertainty was studied. There are 4
categories of information for each parameter, the name used in the model, a short description
of the parameter, information on the way the parameter was varied (standard deviation used,
the assumption behind this decision or other relevant information) and if this parameter was
finally modified or not. Some of these variables either did not have an uncertainty range
associated with them or it was selected to modify them during scenario executions.
Table 1: Input variables studied to calculate the TREMOVE output uncertainty.
Name Description Justification Varied
FUEL_ENERGY_DENSITY Fuel energy density - GJ per kg
Y
FUELSPEC Fuel specification history Y
LTRIP Average estimated trip length – km
Y
paraB
b - parameter in TRENDS detailed report 1 : Road transport module page 15 - ie characteristic service life
Y
paraT
T - parameter in TRENDS detailed report 1 : Road transport module page 15 - ie failure steepness
Y
PUBLICCOSTCOV Public transport fare cost coverage
Y
RFACTORUNCONV
Ratio fuel consumption unconventional vs equivalent conventional vehicle - [(kg/km) / (kg/km)]
Y
RFC_REDUC_RESISTANCE
Real world fuel consumption reduction from utilisation of technologies to reduce vehicle and engine resistance factors – percentage
Y
RFCairco
Extra fuel consumption from use of air-conditioning equipment - litre per km
Y
RFUEL_COMPOSITION Average share of components in blended fuels - % in weight
Y
RHC Ratio of hydrogen to carbon atoms in fuels
Y
RINSCFRACTION
Insurance cost as percentage of vehicle purchase resource cost - %
Y
22
Name Description Justification Varied
RLABOURC Labour cost - net wage - for truck drivers - EURO per hour
Y
RLABOURTX
Labour tax - bruto wage minus netto wage - for truck drivers - EURO per hour :
Y
RLOADCAP Average maximum loading capacity big truck - tonne
Y
RLOGITCNGAVAIL
Relative availability of CNG in fuel stations [# fuel stations with CNG / # cars in fleet]
Y
RLOGITPACC
Acceleration for big and medium car logit - seconds to 100 km per hour
Y
RLPG_FIT_COST Resource cost to retrofit LPG installation - EURO per vehicle
Y
RMILage Relative annual mileage as a function of vehicle age - %
Y
RMILnew
Average annual mileage of new cars in each year - exogenous estimate - vehicle kilometres per year
Y
ROWNTX Annual Ownership tax road vehicles - EURO 2005
Y
RPCS_BASE Road vehicle basic purchase resource cost - EURO 2005
Y
RPCS_INCREASE_2009 % Vehicle purchase cost increase to reach the 140g car CO2 target in 2009
Y
RPCS_INCREASE_2012
% Vehicle purchase cost increase to reach the car CO2 target in 2012 - on top of 140g costs
Y
RREPMAINTC_INCREASE_RTECH_RES
Increase in yearly maintenance cost for using technologies to reduce vehicle and engine resistance factors - EURO 2000
Y
RREPMAINTCFRACTION
Repair and Maintenance Cost excl. taxes as % of purchase resource cost (ex tax)
Y
RSHairco Share of new sold vehicles fitted with air-conditioning - %
Y
RSTNBY
Base year stock of road vehicles per road vehicle type and age - in thousands vehicles
Y
RVP Gasoline volatility (Reid Vapour Pressure) - kPa
Y
SRESIDUALparaA factor in the determination function for residual value
Y
SRESIDUALparaB factor in the determination function for residual value
Y
TMAX Maximum temperature per month - Celsius degrees
Y
TMIN Minimum temperature per month - Celsius degrees
Y
N
23
Name Description Justification Varied
PUBLICVAT Public transport VAT rate - %
0 in UK
r Annuity interest rate No uncertainty addressed N
Rairco_maintenancefreq Interval between airco maintenance services - years
Expected to have minimal impact on COST calculation and cost is assumed to be implemented in total maintenance cost uncertainty.
N
REDUC_NEW_TECH
Emission reduction percentage for future emission standards relative to latest existing standard
Will be changed in scenario N
RFACTORACEA
COPERT III factor to include historic and projected decrease in car fuel cons following ACEA voluntary 140g agreement - 1.00 for 2002
Reduction factors calculated based on actual historic data (fixed values)
N
RFACTORDIE
COPERT III diesel car fuel consumption factor from ACEA agreement monitoring dB - litre per 100 km
Reduction factors calculated based on actual historic data (fixed values)
N
RFACTORREAL
COPERT III factor to convert COPERT car fuel cons to ACEA monitoring dB value plus real world factor
Reduction factors calculated based on actual historic data (fixed values)
N
RFC_ACEA_2002
2002 measured fuel consumption in ACEA agreement monitoring dB - l/100 km - dm³ for CNG
Reduction factors calculated based on actual historic data (fixed values)
N
RFC_REDUC_GSI
Real world fuel consumption reduction from utilisation of Gear Shift Indicator - %
Can be changed in scenario N
RFCairco_REDUC_SCENARIO
Reduction in real-world airco fuel consumption for policy scenario - %
Can be changed in scenario N
RFCOST_COMP
Road fuel component resource cost - euro 2000 per litre - except CNG in euro 2000 per m³
Can be changed in scenario N
RFTAX_COMP
Road fuel component excise tax - euro 2000 per litre - except CNG in euro 2000 per m³
Can be changed in scenario N
RFVAT Road fuel VAT rate - % No uncertainty addressed N
RINSTXfix Fix annual tax on insurance
0 in UK N
RINSTXrate % tax rate on insurance No uncertainty addressed N
RLOGITPGDP GDP per inhabitant - EURO 1995
GDP is fixed for historic years. GDP can have a big impact for projection years. However TREMOVE is known not to be able to model accurately changes in macro economic indicators. As a result this has been kept fixed and TREMOVE sensitivity in GDP values can be run as a scenario.
N
RMILinc Annual increase of mileage per year for road vehicles - %
0 in UK N
N
24
Name Description Justification Varied
RPCS_INCREASE_AIRCO_SCENARIO
Absolute vehicle purchase cost increase for airco policy scenario - EURO - 0 in basecase
Will be addressed through the RPCS variable
RPCS_INCREASE_GSI Vehicle purchase cost increase for gear shift indicator - euro 2000
Will be addressed through the RPCS variable
N
RPCS_INCREASE_TPMS
Vehicle purchase cost increase for tyre pressure monitoring system - euro 2000
Will be addressed through the RPCS variable
N
RRegTX Registration Tax new purchased road vehicles - EURO 2005
No uncertainty addressed N
RTECH_GSI_SHARE Percentage of new sold road vehicles equipped with Gear Shift Indicator
Can be changed in scenario (minimal impact expected on uncertainty)
N
RTECH_RESISTANCE_MX
% Vehicles equipped with technologies to reduce vehicle and engine resistance
Can be changed in scenario (minimal impact expected on uncertainty)
N
RVAT VAT percentages per road vehicle type in 2000 - %
No uncertainty addressed N
TECHMX Technology distribution matrix - share of new cars fitted with technology (%)
Will be changed in scenario N
A special reference should be made to the cost input variables of the model. TREMOVE
exchanges cost data between the vehicle stock and the demand module. To facilitate the
exchange of the information a model parameter was introduced; the COSTROAD parameter.
This parameter summirises the cost information calculated in the vehicle stock module. In this
study all input data resulting in the population of the COSTROAD parameter as well as all
components of this parameter were investigated. This includes the following variables: vehicle
purchase resource cost, vehicle purchase VAT, vehicle registration tax, vehicle ownership tax,
vehicle insurance cost, vehicle insurance tax, vehicle repair/maintenance cost, vehicle
repair/maintenance tax, fuel resource cost, fuel tax, fuel VAT, driver labour cost, driver labour
tax.
In order to collect all data related to the input variables it was decided to follow a predefined
format to include the necessary information for the uncertainty estimate. This would facilitate
the collection of information, by providing a complete overview of the available data. For this
reason a table (Table 2) was created which contained the following information:
• Name: the name of the variable
• Type: the type of the variable (input or parameter)
• Units: the units of the variable
• Description: a short description of the variable
• Sources: sources used for the uncertainty of the variable
• Comments: general comments on the variable
• Quantification of variability: the actual values used for the uncertainty characterisation
where possible, or alternatively, a short description of it. All actual values used can be
found in Annex III.
• Type of distribution: the shape of the distribution selected for the probability density
functions
25
• Reasoning: information taken into account in order to decide on the type of
distribution
Table 2: Template used to display variable related information.
UK Name: -
Type: - units: -
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:-
-
-
-
-
-
The following tables contain the information on the input variables studied for their uncertainty.
FUEL_ENERGY_DENSITY
UK 5 Name: FUEL_ENERGY_DENSITYType: - units: GJ/kg
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Equal propability within a small range
Uniform
Fuel energy density
Small variation from IEA 2004 report for Europe [18]
3s=0.03×µ
-
FUELSPEC
UK 6 Name: FUELSPECType: - units: -Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Equal propability within a small range
Uniform
fuel specification history
Specifications are mandated by regulations. Small variability reflects typical refinery output range.
3s=0.02×µ
-
26
LTRIP
UK 7 Name: LTRIPType: - units: km
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: By definition
L-Normal
The average distance travelled by a trip of passenger cars in a country.
Duboudin C., Crozat C., T F., 2002. Analyse de la méthodologie COPERT III. Analyse d’incertitude et de sensibilité, Rapport d’activité remis à l’ADEME par la Société de Calcul Mathématique, SA. En application du contrat n° 01 03 021, Paris, France, p.262.
s = 0.2×µ, according to French COPERT Uncertainty report
In the absence of more detailed data for UK the French uncertainty range has been utilised.
The probability of vehicles to remain in the stock, as a function of their age, can be approached
by a Weibull distribution. In fact, the Weibull distribution provides the survival probability for
each vehicle category with age ϕi(age), and this can be used to calculate the age distribution of
the fleet. This probability is given by the following equation:
+−=
iparaB
i
ii paraT
paraBageage exp)(ϕ where φ(0) = 1 (Eq: 1)
The determination of the probability requires a pair two parameters, paraB and paraT. The two
parameters do not have an exact physical meaning. However, it can be considered that they
approximate the useful life of the vehicle (paraT) and a characteristic (paraB) of the rate by
which the probability decreases. By taking an initial age distribution at a historical year (in our
case: 1995) and by introducing the new registrations per year (vehicles of age 0) and the
Weibull scrappage probability, TREMOVE calculates the age distribution of the vehicles at any
given year.
A more detailed description of the methodology used can be found in the Detailed Report 1 for
the Road Transport Module of the Project “Development of a Database System for the
Calculation of Indicators of Environmental Pressure Caused by Transport” (Giannouli et al. [2])
and does not need to be repeated here.
At a second step, the technology split for each country is calculated by applying the technology
implementation matrix of the particular country to the age distribution. The technology
implementation matrix contains the distribution of new registrations of different years to the
various technologies. The central estimate for the age distribution of vehicles of UK was based
on the FLEETS data and the paraB and paraT parameters were calculated on this basis. Then,
an artificial uncertainty range was assigned to the probability function of UK. This artificial
uncertainty is schematically shown in Figure 1. It was in principle assumed that the survival
probability for vehicles with age of five and fifteen years ranges between +/- 10 and +/- 15
percentage units respectively from the central value. Figure 1 shows the original Weibull
27
distribution function for gasoline passenger cars <1.4 l, the range assumed for the uncertainty
of the survival probability, and three alternative curves which fulfil the selected uncertainty
range.
Gasoline PC <1,4 l
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
0 5 10 15 20age
φ(a
ge
)G<1,4Alt1Alt2Alt3
Figure 1: Weibull distribution function of probability as a function of age. Three alternative solutions
that fulfill the artificial uncertainty introduced (example: Gasoline cars <1.4 l).
By using the above methodology a number of paraB and paraT pairs were calculated for each
vehicle category, that fulfilled the uncertainty range introduced. From these pairs, 100 were
finally selected by sampling percentiles from the joint probability distribution function of paraB
and paraT. They served as data pool providing each time the required couple of values used for
the calculations.
paraB
8 Name: paraB- units: Coefficient
See text
Uniform
b - parameter in TRENDS detailed report 1 : Road transport module page 15 -
i.e. characteristic service life
Neither data nor commentary on suggested values was possible.
It was in principle assumed that the survival probability for vehicles with age of
five and fifteen years ranges between +/- 10 and +/- 15 percentage units
respectively from the central value.
-
28
paraT
9 Name: paraT- units: Coefficient
See text
Uniform
T - parameter in TRENDS detailed report 1 : Road transport module page 15 -
i.e. failure steepness
Neither data nor commentary on suggested values was possible.
It was in principle assumed that the survival probability for vehicles with age of
five and fifteen years ranges between +/- 10 and +/- 15 percentage units
respectively from the central value.
-
PUBLICCOSTCOV
UK 10 Name: PUBLICCOSTCOVType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Cost coverage of public transport fares
Department of Transport UK - Annual Bus Statistics 2009/10 Department of Transport - options for reform March 2008.
3s=0.2×µ
-
RFACTORUNCONV
UK 18 Name: RFACTORUNCONVType: - units: (kg/km) / (kg/km)
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Ratio fuel consumption unconventional vs equivalent conventional vehicle
-
For Buses the value will be between 0.75 and 0.9, for Passenger cars between 0.81 and 0.85
-
29
RFC_REDUC_RESISTANCE
UK 21 Name: RFC_REDUC_RESISTANCEType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Equal propability within a small range
Uniform
Real world fuel consumption reduction from utilisation of technologies to reduce
vehicle and engine resistance factors
-
Range will be between µ+-0.01
-
RFCairco
UK 22 Name: RFCaircoType: - units: l/kmDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:
This will depend on combination of parameters (type of vehicle, ambient conditions, driving pattern etc) so normal distribution seems appropriate
Normal
Extra fuel consumption from use of airconditioning equipment
Large range reflecting the uncertainty in additional fuel consumption from A/C use, due to vehicle type, ambient conditions, driving conditions.
3s=0.5×µ
-
RFUEL_COMPOSITION
UK 26 Name: RFUEL_COMPOSITIONType: - units: % in weight
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:Composition limits defined by regulations. Normal distribution expresses typical
refinery output effect.
Normal
Average share of components in blended fuels
UKPIA (UK Petrol Industry Association - 2009) data supplied
Six "real world" values quoted for UK for the years 2008-9 through 2013-14.
There may be future changes dependent on the standards, suggested range
3s=0.3×µ. In total pure unblended diesel and pure unblended petrol will be
calculated as the remaining fuel in use.
The oil industry is adding biofuels to road fuels under the Renewable Transport
Fuel Obligation (RTFO), of 2.5% by volume in 2008/9, 3.25% in 2009/10, 3.5%
in 2010/11, 4% in 2011/12, 4.5% in 2012/13 and 5% in 2013/14
30
RHC
UK 28 Name: RHCType: - units: -
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:
Hydrocarbon species in fuel can vary within small range. Normal distribution expresses variation in chemical composition.
Normal
The ratio of atoms of hydrogen over carbon in the fuel molecule
Estimated range based on country submissions through COPERT inventories.
Similar uncertainty for both Gasoline and Diesel. The ratio is expected to vary from 1.8 to 2.1, therefore, 3s = 0.15
Typically 1.8-2.1
RINSCFRACTION
UK 29 Name: RINSCFRACTIONType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Insurance cost as percentage of vehicle purchase resource cost
Automobile Association and Road Hauliers Association
The HDV data was from the RHA. It was possible to prepare a selection of samples for passenger cars.
Notes: Assumptions are in the UK AA report that motorists will benefit from an average 60% discount on the full price of insurance. It is worth noting a considerable variation is possible with different underwriters/providers. One example in 2010 gave a range of between £470 and £750 with a mean of £636. This gave a range of ratio of between 0.016 - 0.0214. There are a considerable number of variables influencing the price of vehicle insurance: age and experience of driver, male/female, age and value of vehicle, model of vehicle (currently 20 categories), security of vehicle, address where it is kept, cost of parts (?imports).
RLABOURC
UK 32 Name: RLABOURCType: - units: Euro/hourDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Labour cost - net wage - for truck drivers
Road Hauliers Association and Her Majesty's Revenue & Customs (HMRC)
3s=0.3×µ
The nett wage for truck drivers appears significantly less than shown in the model results.
31
RLABOURTX
UK 33 Name: RLABOURTXType: - units: Euro/hourDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Labour tax - bruto wage minus netto wage - for truck drivers
Road Hauliers Association and Her Majesty's Revenue & Customs (HMRC)
3s=0.3×µ
This delta in the UK is much smaller than already shown in the mode results.
RLOADCAP
UK 34 Name: RLOADCAPType: - units: tonneDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:Average values of loading capacity are to be expected, normal distribution expresses typical uncertainty of standard value.
Normal
Average maximum loading capacity big truck
Typical uncertainty range for average payload of trucks in Europe.
3s=0.2×µ
-
RLOGITCNGAVAIL
UK 35 Name: RLOGITCNGAVAILType: - units: Coefficient
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Relative availability of CNG in fuel stations [# fuel stations with CNG / # cars in fleet]
Note the distinction between CNG and LPG and the variable usage and supply situation across Europe. Neither data nor commentary on suggested values was possible.
3s=0.2×µ
-
32
RLOGITPACC
UK 36 Name: RLOGITPACCType: - units: seconds
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:Acceleration within vehicle class varies within a small normally distributed range.
Normal
Acceleration for big and medium car logit - seconds to 100km per hour
Data sourced from What Car publication July 2010.
UK "real world" values for parameters were provided, with accompanying upper and lower range limits to allow probability distribution functions to be estimated. Where "real world" values were not possible a pragmatic decision was made to utilise the existing TREMOVE output for the UK and apply upper and lower limits based on experience with "real world" data.
Representative vehicle data was combined and statistics applied according to TREMOVE categories small, medium and large diesel, and small medium and large gasoline. No data available for CNG vehicles, gasoline values used.
RLPG_FIT_COST
UK 38 Name: RLPG_FIT_COSTType: - units: EuroDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:
Expected minimun effect on calculations. Uniform distribution better expresses variation between vehicle types and retrofitting stations prices.
Uniform
Resource cost to retrofit LPG installation
Market information
Costs vary between 1800 and 2500 Euro.
-
RMILage
UK 39 Name: RMILageType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: See text
Uniform
The decrease of the annual mileage relative to vehicle age
Annual mileage per vehicle type has been found from national statistics on
mobility.
Data aquired from the FLEETS project
RMILage for a brand new vehicle (age=0) equals 1. For a vehicle of 40 years of
age this value could be as low as below 0.1.
33
RMILnew
UK 41 Name: RMILnewType: - units: km/year
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: See text
-
Average annual mileage of new cars in each year - exogeneous estimate
-
-
The effect of mileage on total uncertainty will be addressed through the
uncertainty of the RMILage variable.
The calculation of the average annual mileage driven per vehicle (RMIL) in a particular vehicle
technology is a function of the annual mileage of a new vehicle (RMILnew) and a correction
function for the effect of vehicle age (RMILage). The decrease of annual mileage with age has
been approached by a Weibull function. This reflects the fact that new cars are driven more
than old ones. The shape of the curve is considered to be a good approximation of the actual
shape of the mileage reduction with age. An example of actual mileage degradation with age,
which is based on recordings of Inspection and Maintenance data from the Italian passenger
car fleet is shown in Caserini et al. [16]. It is evident that the curves flat out after some years.
The equation of the Weibull function used is given in (Eq: 2). The modelling parameters (bm,
Tm) and RMILnew are specific to country and vehicle subsector considered.
Figure 2: Annual mileage as a function of vehicle age for the Italian passenger car fleet.
Source: (Caserini et al. [16]).
34
bm
Tm
bmageRMILage
+−= exp
(Eq: 2)
RMILnewRMILageRMIL ⋅= (Eq: 3)
The uncertainty in the calculation of the RMIL parameter originates from the uncertainty in bm,
Tm and RMILnew. This uncertainty will be addressed through the uncertainty of the dependency
of the mileage to the vehicle age (RMILage), thus the mileage of a new vehicle (RMILnew) will
not be explicitly modelled.
The RMILage is assumed to range between a minimum and a maximum. These boundaries are
defined from the extents of the functions of all countries that submitted such detailed data in
the framework of the FLEETS project (Ntziachristos et al. [3]). These extents, for the example
of gasoline passenger cars of <1.4 l are shown in Figure 3. It was therefore assumed in our
case, that RMILage can receive any value within these two boundaries. We then calculated all
(bm, Tm) pairs that satisfied this limitation. With this procedure, a large number of bm and Tm
couples were derived, different for each vehicle category. From these pairs, 100 were finally
selected by sampling percentiles from the joint probability distribution function of bm and Tm.
They served as data pool providing each time the required couple of bm and Tm used for the
calculations.
PC Gasoline <1,4l
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
0 10 20 30 40age
φ (
age
)
minmaxAlt1Alt2Alt3
Figure 3: Range for the RMILage variable (example of passenger cars <1.4l) and three examples of
bm and Tm functions (Alt1 through 3) fulfilling the selected criteria (min and max).
35
ROWNTX
UK 42 Name: ROWNTXType: - units: Euro 2005Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:Value differs normally according to vehicle and driver characteristics.
Normal
Annual Ownership tax road vehicles
Driver and Vehicle Licensing Authority (DVLA)
"Real world" data supplied for light duty trucks, motorcycles and mopeds for the year 2010.
The UK system for annual licensing (road tax) has operated a scale based on CO2 emissions and not engine size, making the correlation for passenger cars very difficult. For HGV's , the taxation system again is based on number of axles and weight, again not easy correlation for this exercise.
RPCS_BASE
UK 43 Name: RPCS_BASEType: - units: €Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Road vehicle basic purchase resource cost
Car data sourced from What Car - edition July 2010. HDT data from the Road Haulage Association and Motorcycle data from the Royal Automobile C lub - 2008
UK "real world" values for parameters were provided, with accompanying upper and lower range limits to allow probability distribution functions to be estimated. Where "real world" values were not possible a pragmatic decision was made to utilise the existing TREMOVE output for the UK and apply upper and lower limits based on experience with "real world" data.
The passenger cars were categorised as per TREMOVE and average values obtained. In the absence of data for Buses, LTD's and VAN's, the TREMOVE UK output was quoted using the spread of values as for the 32t HDT. For motorcycles, (MC2-4), data was extracted from the RAC publication for 2010. For Mopeds and MC1, again TREMOVE Uk data was suggested using the same spread as for the MC2-4 group.
RPCS_INCREASE_2009
UK 44 Name: RPCS_INCREASE_2009Type: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Vehicle purchase cost increase to reach the 140g car CO2 target in 2009
Typical range assumed to account for manufacturer to manufacturer variability.
3s=0.2×µ
-
36
RPCS_INCREASE_2012
UK 45 Name: RPCS_INCREASE_2012Type: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Vehicle purchase cost increase to reach the car CO2 target in 2012 - on top of 140g costs
Typical range assumed to account for manufacturer to manufacturer variability.
3s=0.2×µ
-
RREPMAINTC_INCREASE_RTECH_RES
UK 50 Name:RREPMAINTC_INCREASE_RTECH_RES
Type: - units: Euro 2000
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Increase in yearly maintenance cost for using technologies to reduce vehicle and engine resistance factors
Assuming additional cost of low resistance tires of 50-100 euros per 4 tire set, and a lifetime of 3.5 years. Assuming 15 euros per liter of oil, consumption of 6.5 liters per year and additional cost of low friction oil of 10-30%.
For the LRRT the valua will range from 15-30 euros and for the LV from 10-30.
-
RREPMAINTCFRACTION
UK 51 Name: RREPMAINTCFRACTIONType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Repair and Maintenance Cost excl. taxes as % of purchase resource cost (ex tax)
Value is a fraction of purchase price, therefore takes into account general increase of maintenance cost with retail price. The range assumes typical variation of maintenance cost per manufacturer.
3s=0.3×µ
-
37
RSHairco
UK 52 Name: RSHaircoType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Typical uncertainty distribution
Normal
Share of new sold vehicles fitted with air-conditioning
Data was extracted from ABOUT publishing "Global Market for Automotive Aircon 2004".
UK "real world" values for parameters were provided, with accompanying upper and lower range limits to allow probability distribution functions to be estimated. Where "real world" values were not possible a pragmatic decision was made to utilise the existing TREMOVE output for the UK and apply upper and lower limits based on experience with "real world" data.
Data from the ABOUT report was analysed to give values and ranges for vehicles in each TREMOVE sub category. The car groupings were not identical to TREMOVE and sensible assumptions were made to the groupings.
RSTNBY
UK 53 Name: RSTNBYType: - units: no vehicles
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Base year stock of road vehicles per road vehicle type and age
-
Uncertainty estimated through the uncertainty of the paraB and paraT variables.
Depending on the paraB and paraT value a different scrappage rate is calculated
and as a result the distribution of vehicles according to age is varied in order to
meet the total demand.
-
RVP
UK 57 Name: RVPType: - units: kPa
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning:RVP limits defined by regulations. Normal distribution expresses typical refinery
output effect.
Normal
The vapour pressure of gasoline (defined by a test at 38 oC). The vapour
pressure is a measure of the fuel volatility. The higher the vapour pressure, the
easier the fuel evaporates at a given temperature. The vapour pressure is
important to calculate NMVOC emissions due to evaporation losses. These are
only relevant for gasoline, due to the low volatility of the diesel fuel.
The maximum RVP is defined by the regulations. Some detailed data on RVP for
different countries and relevant information and sources may be found in Hill N.
[19].
Limited uncertainty expected, as fuels are centrally produced and the refineries
need to follow the regulations. Assumption 3s = 0,05×µ
The typical range is 70 kPa (summer grade) to 110 kPa (winter grade).
38
SRESIDUALparaA
UK 58 Name: SRESIDUALparaAType: - units: Coefficient
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: See text
Uniform
estimation residual value function as a percentage of purchase cost
Neither data nor commentary on suggested values was possible.
It was in principle assumed that the residual value for vehicles with age of five and fifteen years ranges between +/- 10 and +/- 15 percentage units respectively from the central value.
-
SRESIDUALparaB
UK 59 Name: SRESIDUALparaBType: - units: Coefficient
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: See text
Uniform
estimation residual value function as a percentage of purchase cost
Neither data nor commentary on suggested values was possible.
It was in principle assumed that the residual value for vehicles with age of five and fifteen years ranges between +/- 10 and +/- 15 percentage units respectively from the central value.
-
The calculation of the residual value (SRESIDUAL) for a particular vehicle is a function of the
purchase value of a new vehicle (RPCS_base) and a correction function for the effect of vehicle
age. The decrease of the vehicle value with age has been approached by an exponential
function. This reflects the fact that cars lose value as they are getting older.
This function is given by the following equation:
( )[ ]1exp)( −⋅⋅= agearaBSRESIDUALparaASRESIDUALpageRES i (Eq: 4)
The function uses two parameters, (SRESIDUALparaA and SRESIDUALparaB). The two
parameters do not have an exact physical meaning.
As a result the residual value (SRESIDUAL) can be calculated by the following equation:
baseRPCSageRESSRESIDUAL i _)( ⋅= (Eq: 5)
To calculate these two parameters (SRESIDUALparaA and SRESIDUALparaB) the central
estimate for the residual value of vehicles of UK was in TREMOVE was used. Then, an artificial
uncertainty range was assigned to the probability function of UK. This artificial uncertainty is
schematically shown in Figure 4. It was in principle assumed that the residual value for vehicles
with age of five and fifteen years ranges between ±10 and ±15 percentage units respectively
from the central value. Figure 4 shows the original function for gasoline passenger cars <1.4 l,
39
the range assumed for the uncertainty of the residual value, and three alternative curves which
fulfil the selected uncertainty range.
Residual value
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 5 10 15 20 25 30 35 40
Vehicle age
%
Tremove values
Alt1
Alt2
Alt3
Figure 4: Residual value as a function of age. Three alternative solutions that fulfill the artificial
uncertainty introduced (example: Gasoline cars <1.4 l).
By using the above methodology a number of SRESIDUALparaA and SRESIDUALparaB pairs
were calculated for each vehicle category that fulfilled the uncertainty range introduced. From
these couples, 100 were finally selected by sampling. They served as data pool providing each
time the required couple of values used for the calculations.
TMAX
UK 61 Name: TMAXType: - units: oC
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Effect of nature
Normal
The average of the maxima in daily temperature for a duration of a month. This maximum temperature is required as input to both evaporation and cold-start calculations. For countries with significant temperature differences over their area (e.g. south and north), the temperature should correspond to the average (possibly weighted average) of areas where most of the traffic is located.
National meteorological insitutes and internet databases (i.e. www.weatherbase.com).
An uncertainty range required to cover national differences between north and south. 3s=3oC
Month specific. Average min temperature ranges between 8 to +22.
40
TMIN
UK 62 Name: TMINType: - units: oC
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Effect of nature
Normal
The average of the minima in daily temperature for a duration of a month. This minimum temperature is required as input to both evaporation and cold-start calculations. For countries with significant temperature differences over their area (e.g. south and north), the temperature should correspond to the average (possibly weighted average) of areas where most of the traffic is located.
National meteorological insitutes and internet databases (i.e. www.weatherbase.com).
An uncertainty range required to cover national differences between north and south. 3s=3oC
Month specific. Average min temperature ranges between 2 to +11.
PUBLICVAT
UK 11 Name: PUBLICVATType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Public Transport VAT rate
http://ec.europa.eu/taxation_customs
No uncertainty was estimated.
In UK there is no VAT on public transport in UK. There is considerable variation across the EU with some MS (e.g. Hungary) charging 25% VAT on Public transport.
r
UK 12 Name: rType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Annuity interest rate
-
No uncertainty was estimated.
Can be changed in scenario
41
Rairco_maintenancefreq
UK 13 Name: Rairco_maintenancefreqType: - units: YearsDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Interval between airco maintenance services - years
-
-
Expected to have minimal imapact on COST calculation and cost is assumed to be implemented in total maintenance cost uncertainty.
REDUC_NEW_TECH
UK 14 Name: REDUC_NEW_TECHType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Emission reduction percentage for future emission standards relative to latest existing standard
-
No uncertainty was estimated.
Can be changed in scenario
RFACTORACEA
UK 15 Name: RFACTORACEAType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
COPERT III factor to include historic and projected decrease in car fuel cons following ACEA voluntary 140g agreement - 1.00 for 2002
-
-
Reduction factors calculated based on actual historic data (fixed values)
42
RFACTORDIE
UK 16 Name: RFACTORDIEType: - units: l/100 km
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
COPERT III diesel car fuel consumption factor from ACEA agreement monitoring dB
-
-
Reduction factors calculated based on actual historic data (fixed values)
RFACTORREAL
UK 17 Name: RFACTORREALType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
COPERT III factor to convert copert car fuel cons to acea monitoring dB value plus real world factor
-
-
Reduction factors calculated based on actual historic data (fixed values)
RFC_ACEA_2002
UK 19 Name: RFC_ACEA_2002Type: - units: l/100 km
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
2002 measured fuel consumption in ACEA agreement monitoring dB
-
-
Reduction factors calculated based on actual historic data (fixed values)
RFC_REDUC_GSI
UK 20 Name: RFC_REDUC_GSIType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Real world fuel consumption reduction from utilisation of Gear Shift Indicator
-
No uncertainty was estimated.
Can be changed in scenario
43
RFCairco_REDUC_SCENARIO
UK 23 Name: RFCairco_REDUC_SCENARIOType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Reduction in real-world airco fuel consumption for policy scenario
-
No uncertainty was estimated.
Can be changed in scenario
RFCOST_COMP
UK 24 Name: RFCOST_COMPType: - units: Euro/litleDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Road fuel component resource cost
-
No uncertainty was estimated.
Can be changed in scenario
RFTAX_COMP
UK 25 Name: RFTAX_COMPType: - units: Euro/litleDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Road fuel component excise tax
-
No uncertainty was estimated.
Can be changed in scenario
RFVAT
UK 27 Name: RFVATType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Road fuel VAT
Her Majesty's Revenue & Customs (HMRC)
Fixed factor, uncertainty is 0.
VAT has been constant at 17.5% to 2008. It was reduced to 15% in 2009 and
rose to 17.5% in 2010 and is now 20% in 2011.
44
RINSTXfix
UK 30 Name: RINSTXfixType: - units: EuroDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Fix annual tax on insurance
-
-
0 in UK
RINSTXrate
UK 31 Name: RINSTXrateType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Tax rate on insurance
Her Majesty's Revenue & Customs (HMRC)
No uncertainty was estimated.
Taxation on insurance premiums commenced in 1995 and has shown increases over the years to 5% in 2009/10
RLOGITPGDP
UK 37 Name: RLOGITPGDPType: - units: EuroDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
GDP per inhabitant
-
-
GDP is fixed for historic years. GDP can have a big impact for projection years. However TREMOVE is known not to be able to model accurately changes in macro economic indicators. As a result this has been kept fixed and TREMOVE sensitivity in GDP values can be run as a scenario.
RMILinc
UK 40 Name: RMILincType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Annual increase of mileage per year for road vehicles
-
-
0 in UK
45
RPCS_INCREASE_AIRCO_SCENARIO
UK 46 Name:RPCS_INCREASE_AIRCO_SCENARIO
Type: - units: Euro
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Absolute vehicle purchase cost increase for airco policy scenario
-
-
Can be changed in scenario
RPCS_INCREASE_GSI
UK 47 Name: RPCS_INCREASE_GSIType: - units: EuroDescription:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Vehicle purchase cost increase for gear shift indicator
-
-
Can be changed in scenario (minimal impact expected on uncertainty)
RPCS_INCREASE_TPMS
UK 48 Name: RPCS_INCREASE_TPMSType: - units: Euro
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Vehicle purchase cost increase for tyre pressure monitoring system
-
-
Can be changed in scenario (minimal impact expected on uncertainty)
RRegTX
UK 49 Name: RRegTXType: - units: €Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Registration tax new vehicles
Driver and Vehicle Licensing Authority (DVLA)
No uncertainty was estimated.
There is one registration fee for all vehicles of £55 - based on the average € rate for 2009 of 1.1224, equates to €61.73
46
RTECH_GSI_SHARE
UK 54 Name: RTECH_GSI_SHAREType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
New sold road vehicles equipped with Gear Shift Indicator
-
-
Can be changed in scenario (minimal impact expected on uncertainty)
RTECH_RESISTANCE_MX
UK 55 Name: RTECH_RESISTANCE_MXType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Vehicles equipped with technologies to reduce vehicle and engine resistance
-
-
Can be changed in scenario (minimal impact expected on uncertainty)
RVAT
UK 56 Name: RVATType: - units: %Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
VAT percentages per road vehicle type in 2000
-
-
Fixed values
TECHMX
UK 60 Name: TECHMXType: - units: %
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: -
-
Technology distribution matrix - share of new cars fitted with technology
-
-
Can be changed in scenario
47
2.3 Emission factor modelling
The uncertainty of emission factors is a major part of the uncertainty in all transport emission
models, as they constitute the core of the emission calculation. The uncertainty of the emission
factors originates from the variability of the underlying experimental data, i.e. the variability in
the emission level of each individual vehicle which has been included in the sample of vehicles
used to derive the emission factors. A typical range of the variability of individual
measurements for emission factors is shown in Figure 5 for gasoline passenger cars of Euro 3
technology. In TREMOVE, there are two sets of emission factors, the hot ones and the cold-
start ones. The hot emission factors originate from individual measurements of
vehicles/engines mainly conducted in the Artemis project. Some older measurements were
based in previous projects, such as CORINAIR89, COST319, MEET, etc. The uncertainty of old
emission factors was taken from a previous Monte Carlo exercise (Kioutsioukis et al. [5])
conducted in COPERT III. However, emission factors for Euro 1 and later technologies are
solely based on the FP5 Artemis project. The uncertainty of cold-start emission factors was
more difficult to assess, as the values used in TREMOVE and have been transferred from
COPERT 4 are a hybrid of the Artemis and the older CORINAIR methodologies. In the absence
of detailed data and in order not to neglect the contribution of cold start variability, we
assumed that the ratio of standard deviation over mean for the cold emission factors is equal
to the hot ones. This is an approximation which was introduced in the absence of more detailed
data.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 10 20 30 40 50 60 70 80 90 100 110 120 130
Speed [km/h]
NO
x E
F[g
r/km
]
MeasurementsBest Fit Curve
Figure 5: Example of variability of individual measurements for the derivation of emission factors.
Gasoline Euro 3 passenger cars. Source: ARTEMIS database.
Table 3 lists the parameters (parameter name and a short description) that are used to
calculate the emission factors and fuel consumption factors in TREMOVE. However these were
not directly modified in the uncertainty calculations. Instead the emission factor and fuel
consumption values were first calculated by TREMOVE and then they were varied at a post-
48
processing procedure by means of an error correction. This modification was included in the
TREMOVE code. A detailed description of the methodology can be found in chapter 3.
Table 3: Emission and fuel consumption input parameters used in TREMOVE.
Name Description Appearance
A1 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
A2 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
A3 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
A4 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
A5 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
A6 COPERT IV coefficient for hot emission and fuel consumption factors
Emissions
A7 COPERT IV coefficient for hot emission and fuel consumption factors
Emissions
A8 COPERT IV coefficient for hot emission and fuel consumption factors
Emissions
AA0 COPERT IV coefficient for hot emission and fuel consumption factors for hdv
Emissions
AA1 COPERT IV coefficient for hot emission and fuel consumption factors for hdv
Emissions
AA2 COPERT IV coefficient for hot emission and fuel consumption factors for hdv
Emissions
AA3 COPERT IV coefficient for hot emission and fuel consumption factors for hdv
Emissions
AA4 COPERT IV coefficient for hot emission and fuel consumption factors for hdv
Emissions
AMCEUDC COPERT III coefficient for mileage corrections (from LAT/Thessaloniki) - slope scaled by 100 000
Emissions
AMCUDC COPERT III coefficient for mileage corrections (from LAT/Thessaloniki) - slope scaled by 100 000
Emissions
AV_TEMP Average ambient temperature per month - Celsius decrees
Emissions
B0 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
B1 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
B2 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
B3 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
BETAEST1 Cold mileage percentage parameter 1 Emissions
BETAEST2 Cold mileage percentage parameter 2 Emissions
BETAEST3 Cold mileage percentage parameter 3 Emissions
BETAEST4 Cold mileage percentage parameter 4 Emissions
BETAREV COPERT III cold start quicker light-off revision for cold mileage percentage for gasoline EURO II-IV vehicles - 1 for other vehs and techs
Emissions
BMCEUDC COPERT III coefficient for mileage corrections (from LAT/Thessaloniki)
Emissions
BMCUDC COPERT III coefficient for mileage corrections (from LAT/Thessaloniki)
Emissions
C0 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
C1 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
C2 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
CCID COPERT III coefficient for hot emission and fuel consumption factors
Emissions
CMCEUDC COPERT III coefficient for mileage corrections (from Emissions
49
Name Description Appearance
LAT/Thessaloniki)
CMCUDC COPERT III coefficient for mileage corrections (from LAT/Thessaloniki)
Emissions
D1 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
D2 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
D3 COPERT III coefficient for hot emission and fuel consumption factors
Emissions
E0 COPERT III speed limit for hot emission and fuel consumption factors
Emissions
E0c COPERT IV coefficient for hot emission and fuel consumption factors cold E.F. equivalent to E0
Emissions
EFNXPM Non-exhaust PM emission factor for road vehicles Emissions
F0 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
F0c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
F1 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
F1c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
F2 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
F2c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
FF0 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
FF0c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
FF1 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
FF1c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
FF2 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
FF2c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
FFF0 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
FFF0c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
FFF1 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
FFF1c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
FFF2 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
FFF2c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
LC_EMI_FACTOR Non-road fuel and electricity production emission factor - tonne pollutant per tonne fuel or kWh electricity
Emissions
LC_EMI_FACTOR_RFUEL_COMP
Road fuel component production emission factor - tonnes pollutant per tonne fuel produced
Emissions
LIM1 COPERT III speed limit for hot emission and fuel consumption factors
Emissions
LIM2 COPERT III speed limit for hot emission and fuel consumption factors
Emissions
LIM3 COPERT III speed limit for hot emission and fuel consumption factors
Emissions
LIM4 COPERT III speed limit for hot emission and fuel consumption factors
Emissions
LIMTRCH COPERT III temperature limit for cold hot ratio parameters - °C
Emissions
LIMVRCH COPERT III speed limit for cold hot ratio parameters - km per h
Emissions
50
Name Description Appearance
M0 COPERT III coefficient for evaporative VOC emissions Emissions
M1 Driving mode share on evaporation Emissions
M2 COPERT III coefficient - fraction of benzene in NMVOC fraction of exhaust emissions - % weight
Emissions
M3 COPERT III coefficient - fraction of benzene in NMVOC fraction of evaporative emissions - % weight
Emissions
NVFUNC COPERT III coefficient - 1 if not speed-dependent function for hot emission and fuel consumption factors
Emissions
R0COLDFAST COPERT III coefficient for cold to hot ratio Emissions
R0COLDSLOW COPERT III coefficient for cold to hot ratio Emissions
R0WARMFAST COPERT III coefficient for cold to hot ratio Emissions
R0WARMSLOW COPERT III coefficient for cold to hot ratio Emissions
R1COLDFAST COPERT III coefficient for cold to hot ratio Emissions
R1COLDSLOW COPERT III coefficient for cold to hot ratio Emissions
R1WARMFAST COPERT III coefficient for cold to hot ratio Emissions
R1WARMSLOW COPERT III coefficient for cold to hot ratio Emissions
R2COLDFAST COPERT III coefficient for cold to hot ratio Emissions
R2COLDSLOW COPERT III coefficient for cold to hot ratio Emissions
R2WARMFAST COPERT III coefficient for cold to hot ratio Emissions
R2WARMSLOW COPERT III coefficient for cold to hot ratio Emissions
REDUC COPERT III coefficient - emission reduction percentage for future technology
Emissions
REDUC_UNCONV Emission reduction percentage for unconventional vehicle types
Emissions
RHFC134a_IRREGairco Irregular leakage of HFC134a - grammes per airconditioned vehicle year
Emissions
RHFC134a_REGairco Regular leakage of HFC134a - grammes per airconditioned vehicle-year
Emissions
RHFC134a_SALEairco HFC134a emissions of airco installation - grammes per new sold airconditioned vehicle
Emissions
RHFC134a_SCRAPairco HFC134a emissions of airco scrappage - grammes per airconditioned vehicle scrappage
Emissions
RHFC134a_SERVICEairco HFC134a emissions of airco maintenance - grammes per maintenance service
Emissions
SHC Fraction of vehicle categories equipped with emission control
Emissions
SHFI Fraction of vehicle categories equipped with fuel injection
Emissions
SULP_LIM1 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
SULP_LIM1c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
SULP_LIM2 COPERT IV coefficient for hot emission factors FOR N2O
Emissions
SULP_LIM2c COPERT IV coefficient for cold emission factors FOR N2O
Emissions
As described above, four TREMOVE intermediate parameters were used to model the emissions
factors calculation: the hot emission factor, the cold emission factor, the hot fuel consumption
factor and the cold fuel consumption factor. The following tables describe their sources of
uncertainty used to model the emissions module.
51
eEF
UK 1 Name: Hot emission factorType: - units: g/km
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Distribution of experimental data
L-Normal
The emission rate of vehicles of a specific technology in g/km, under thermally stabilised engine operation. In COPERT the emission factors are expressed as a function of mean travelling speed. In cases with limited information, emission factors are expressed as a function of the drving mode (urban, rural, highway).
Hot emission factors have been derived from measurements conducted in several research programmes. The most important ones include COST319, FP4 MEET, and FP6 ARTEMIS. Vehicles are driven over specific driving cycles, considered representative of actual driving conditions and the emission level is associated with the mean travelling speed over the cycle. A function is then drawn using regression analysis to associate emission level with travelling speed.
For all pollutants, the uncertainty range has been expressed as standard deviation of the experimental data per 10 km/h speed class intervals.
There is no typical range, as this depends on the uncertainty of the experimental data used to develop the emission factor.
eEF_FC
UK 2 Name: Hot fuel consumption factorType: - units: g/km
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Distribution of experimental data
L-Normal
The fuel consumption rate of vehicles of a specific technology in g/km, under thermally stabilised engine operation. In COPERT the fuel consumption factor is expressed as a function of mean travelling speed. In cases with limited information, fuel consumption factor is expressed as a function of the drving mode (urban, rural, highway).
Fuel consumption factors have been derived from measurements conducted in several research programmes. The most important ones include COST319, FP4 MEET, and FP6 ARTEMIS. Vehicles are driven over specific driving cycles, considered representative of actual driving conditions and the fuel consumption is associated with the mean travelling speed over the cycle. A function is then drawn using regression analysis to associate emission level with travelling speed.
For the fuel consumption, the uncertainty range has been expressed as standard deviation of the experimental data per 10 km/h speed class intervals.
There is no typical range, as this depends on the uncertainty of the experimental data used to develop the fuel consumption factor.
52
eEFratio
UK 3 Name: Cold-start emission factorType: - units: -
Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Distribution of experimental data
L-Normal
The ratio expressing cold-start over hot emission. Cold-start emissions lead to higher emissions as both the engine and the emission control system have not reached their normal operation temperature.
The over-emission ratio in COPERT has been derived as computed value out of a detailed cold-start study conducted in the framework of FP4 MEET and further elaborated in FP6 ARTEMIS (Andre and Joumard, INRETS report LTE 0509). Since these are computed values, it is difficult to obtain independent (literature) sources to quantify it.
Cold emission factors in COPERT have been produced as a hybrid of the COPERT II, MEET and Artemis methodologies, using approximations to convert the MEET approach (as corrected in Artemis) to older CORINAIR cold-start approach. Cold-start modelling is one of least elaborate items of COPERT 4. As it was not possible to estimate the uncertainty of the emission factors from the uncertainty in the experimental data, we have assumed that the standsrd deviation over mean ecold/ehot is the same with the standard deviation over mean of the hot emission factor. In this way, the contribution of cold-start to uncertainty is estimated in a realistic way.
There is no typical range, as this depends on the uncertainty of the experimental data used to develop the emission factor.
eEFratio_FC
UK 4 Name: Cold-start fuel consumtpion factor
Type: - units: -Description:
Sources:
Comments:
Quantification of
variability (UK):
Type of distribution:
Reasoning: Distribution of experimental data
L-Normal
The ratio expressing cold-start over hot fuel consumption.
The over-emission ratio in COPERT has been derived as computed value out of a detailed cold-start study conducted in the framework of FP4 MEET and further elaborated in FP6 ARTEMIS (Andre and Joumard, INRETS report LTE 0509). Since these are computed values, it is difficult to obtain independent (literature) sources to quantify it.
Cold emission factors in COPERT have been produced as a hybrid of the COPERT II, MEET and Artemis methodologies, using approximations to convert the MEET approach (as corrected in Artemis) to older CORINAIR cold-start approach. Cold-start modelling is one of least elaborate items of COPERT 4. As it was not possible to estimate the uncertainty of the emission factors from the uncertainty in the experimental data, we have assumed that the standsrd deviation over mean ecold/ehot is the same with the standard deviation over mean of the hot emission factor. In this way, the contribution of cold-start to uncertainty is estimated in a realistic way.
There is no typical range, as this depends on the uncertainty of the experimental data used to develop the emission factor.
53
2.4 Input variables and parameters not varied
The inception and the interim reports of this study explain that the uncertainty will be studied
only for the stock and emission and consumption modules of the model. In summary, the
justification is as follows: The demand module is calibrated on the basis of an exogenous
baseline, therefore objective uncertainty characterisation is not possible without characterising
the uncertainty of this exogenous baseline. In addition, it is structured using calibrated price
elasticities derived from empirical elasticities of substitution. It is again not possible to
independently vary these price elasticities, as this would bring the model off-equilibrium.
Therefore, factors in the demand model were not modified.
In addition, no values of the welfare and life-cycle modules were modified. First, these modules
seem outdated and are not much used today which makes uncertainty estimates obsolete.
Second, they are used as post-processors, i.e. they do not take part in the equilibration model
loops. Therefore, one can characterise their uncertainty independently, without needing to run
the model. Third, these are modules utilising variables for which there is still large scientific
discussion related to their uncertainty. For example, the external cost of a tonne of pollutant is
a highly uncertain value, much beyond the range of the ‘more conservative’ values used in the
main TREMOVE module. Characterising the uncertainty of such values goes much beyond the
aims of this study. In principle, quantifying the uncertainty of the welfare module boils down in
quantifying the uncertainty of the external cost of pollution.
We also did not modify the values in the aviation, inland waterways, and rail modules. The first
reason was that the approach in these modules is much more simplified than the road
transport one. In aviation, much more elaborate models exist and have been used for aviation
scenarios, such as the Eurocontrol AEM model. Tremove is not a very elaborate model for
realistic policy development in aviation emissions. Hence characterising the uncertainty of the
aviation module would serve no real purpose. Inland waterways and rail modules on the other
hand have at least an order of magnitude less significance in total emissions than road
transport. For UK in particular inland waterways play an insignificant role (0,6% of total freight
activity in UK). In addition, they also use simplified approaches compared to the elaborate road
transport module. It was therefore decided that the uncertainty in the calculation of emissions
and activity in these modules will be only induced by the uncertainty of the road transport
module and no additional modifications were introduced.
Table 4: Input variables not related to Road Transport.
Name Description Appearance
AIR_DETOUR % detour-deviation from straight OD distance Air
AIRCONSFfuelD Fuel consumption factor for aircrafts by distance class - g fuel per pkm
Air
AIRCONSFfuelDsplit_alt LTO and cruise in total aircraft fuel consumption Air
AIREMIFD Emission factor for aircrafts by distance class - g pollutant per pkm
Air
AIREMIFDsplit_alt LTO and cruise in total aircraft emissions Air
AIRPOLLCOSTUNIT External cost per unit of emitted pollutant for air - differentiated over distance classes in EURO per tonne
Air
PLANEVAT Plane transport VAT rate - % Air
NETWORKTAX Road tolls by category road type and period - EURO per vkm This is part of the COSTROAD parameter in the vehicle stock module but is actually calculated in the demand module
Demand
IWCONFIG Indicates for each configuration if and when it becomes available for each vessel type separately
IWW
54
Name Description Appearance
IWCONSFfuel Inland Waterway Fuel consumption factor - g per vkm IWW
IWEMIF Inland Waterway Emission factor - g per vkm IWW
IWENGCOST Additional cost of the engine type compared to the basecase configuration - EURO per vkm
IWW
IWEQCOST Additional cost of the engine type compared to the basecase configuration - EURO per vkm
IWW
IWFCOST Inland waterway fuel resource cost - in EURO per litre IWW
IWFREDUC Reduction in fuel consumption of the configuration compared to the basecase configuration - %
IWW
IWFTAX Inland waterway fuel tax - in EURO per litre IWW
IWFUELdens Density of inland waterway fuels - grammes per litre IWW
Ktoe_per_Pjoule Converson factor from PJ to ktoe Report
Pjoule_per_GWh Converson factor from GWh to Pjoule Report
METRAMCONSFelec Metro-Tram electricity consumption factor - kWh per vkm Rail
SULPHUR_RDIESEL Sulphur content of diesel train fuel for years for years for which it is specified - ppm
Rail
TACTTREXalleng TRENDS/EX-TREMIS activity by train type - in million pkm or tonkm per year
Rail
TCONSFelec Train electricity consumption factor - kWh per vkm Rail
TEMIF Train direct emission factor - g per vkm Rail
TLOADfDif UIC % difference between load factor for diesel and electric - 0 for electric
Rail
TSTBY EX-TREMIS stock of train vehicles (1980-2030) per train vehicle type - in thousands vehicles
Rail
TSTNBY EX-TREMIS stock of train vehicles per train vehicle type and age 1995-2005 - in thousands vehicles
Rail
TVKMEXTREMIS EX-TREMIS vehicle-kilometres by train type : data and projections - million vkm
Rail
ACCIDENTCOST Average accident cost for non-road modes - EURO per 1000 pkm or 1000 tkm
Welfare
ACCIDENTMARGCOST Marginal external accident cost for road modes - EURO per vkm - by road type
Welfare
L Marginal cost of public funds Welfare
LC_POLLCOSTUNIT External cost per unit of emitted pollutant in EURO per tonne Welfare
NOISECOST Average noise cost - EURO per 1000 pkm or 1000 tkm Welfare
POLLCOSTUNIT External cost per unit of emitted pollutant for road in EURO per tonne
Welfare
WEARMARGCOST Marginal wear and tear cost - EURO per vkm Welfare
2.5 Changes over interim report
TREMOVE is a policy assessment model, designed to study the effects of different transport and
environment policies on the emissions of the transport sector. The model consists of three
main modules: a demand, a stock, and an emissions module. These are accompanied by two
additional modules, the well-to-tank and the welfare modules. The model structure also
includes a number of input variables, model variables and model parameters. The input
variables contain all the information coming from different sources necessary to calculate the
basecase. Model parameters contain the information necessary to perform the calculations
internally in the model. They are mainly equation parameters used to calculate eg emission
factors. Model variables store all intermediate information exchanged between the different
subroutines and modules. The sensitivity analysis aimed at quantifying the uncertainty of the
input variables. Due to the large number of input variables it was initially decided to model a
subset of the input variables and for the remaining to influence them via model parameters.
For this reason, and in order to avoid inconsistencies a comprehensive study of the model was
55
conducted. Key variables and parameters were identified and their interaction and relationship
was examined.
In the interim report, it was decided that one of the key model parameters that was going to
be independently varied was the COSTROAD parameter. This parameter summarizes the cost
components originating from the vehicle stock module for all non-bus road modes, in euro per
vkm. There were two main reasons why this parameter was initially selected to be varied
although this was not one of the input variable. First, to save precious calculation time, instead
of varying each individual variable that contributes to the cost items of the COSTROAD
variable. Second, this parameter summarised all the information related to costs in an orderly
fashion (€/vkm). Literature values on cost could be directly found in these units per cost item
of the COSTROAD variable and this transparently facilitated the modelling procedure as well as
the software modifications.
Following this logic the first test runs were performed and results were collected. After
examining the results it was apparent that they were not consistent with expected results. The
correlation between the modification of the input and the resulting output data could not be
explicitly explained. A more detailed examination showed the causes of this error. First of all
the level of aggregation of the input data and the model parameter COSTROAD was different.
Input data were aggregated according to the vehicle type (eg vehicle size) or even vehicle
technology, while the COSTROAD parameter was aggregated according to vehicle category.
Vehicle categories in the TREMOVE model are cars, buses, mopeds, motorcycles, light duty
trucks and vans, heavy duty vehicles in 4 weight categories. This lead to the second and most
important error inherent in the model calculations. The COSTROAD parameter exchanges
information between the vehicle stock and the demand module. Calculations in the demand
module are performed at the aggregation level of vehicle category. On the other hand
calculations in the vehicle stock module are performed at a vehicle type or even technology
level. The flow of information exchange can be seen in Figure 6.
Figure 6: Data flow between vehicle stock and demand module in TREMOVE.
The demand module determines the total vehicle kilometres per vehicle category for a specific
year. This data is loaded to the vehicle stock module where it is split into the different vehicle
types and technologies. Total cost in TREMOVE is then calculated based on this data and the
cost information given to the model as input data. This information follows the aggregation
56
level of the vehicle stock module, so in order for this to be sent back to the demand module,
data must be aggregated back to vehicle category level.
In the first approach the COSTROAD parameter was altered according to the specific scenario
right before being inserted back to the demand module. However by modifying the COSTROAD
parameter at this point, the vehicle split that was underlying to the total cost was not taken
into account, since it was not affected by this modification. For this approach to be consistent
with the model calculations one should modify the vehicle split accordingly, something that
would not be possible. In any case this would create a vicious circle where the aggregated cost
would be calculated based on the vehicle split that would be affected by the same total cost.
For these reasons, it was decided, in spite the extra calculation time that would be needed, to
vary independently only the t variables in order to ensure model calculation consistency.
The second modification to the interim report is that the final calculations do not include an
uncertainty variance of the logit module parameters. In the interim report, it was proposed
that surrogate values could be used for the logit parameter variance. These surrogates were
basically literature values of elasticity of demand equivalent to logit-functions relevant
parameters. That is, it was suggested that literature uncertainty ranges for elasticity of
demand of ownership costs, fuel costs, and running costs could be used as proxies for the
uncertainty range of the parameters RLOGITP_ACC, RLOGITP_FCOSTS, and RLOGITP_OCOSTS
respectively. However, in the meeting where the interim report was presented, the Commission
experts suggested that this is not a reasonable approach as the elasticity of demand is
potentially an equivalent of the logit function output and not the logit functions parameters.
Therefore, in the absence of any other alternative to characterise the variance of the
parameters, it was decided not to vary them independently. Therefore, the variance of the logit
functions output is only determined by the variance of the input variables to the logit functions
(section 2.2). Several other parameters in the logit functions are dummy parameters, i.e. they
have obtained fixed values so that the output of the logit functions agrees historical data on
vehicle choices. Naturally, no uncertainty range could be defined since these are fixed to
historical data.
Table 5: Input variables & parameters used in TREMOVE logit module.
Name Description Appearance
RLDVskale Scale parameter in LDV logit model Logit
RMOCskale Scale parameter in MOC logit model Logit
RREPMAINTC_LINK_RPCS Switch to relate vehicle repair and maintenance cost linearly to purchase cost - 1 is linear - 0 is no link
Logit
Rrepmaintcevol % change in proportion of rep&maint cost RVEH vs PCDM for cars over time - ie vintage - 0 to 1
Logit
RINC_LFC_UNSOLD Enables setting LIFEC high for vehicles that are not sold in year T
Logit
RLDVdummy Dummy parameter in LDV logit model Logit
RLOGITCBUS Coefficient in the bus logit model Logit
RLOGITDUM_B Car logit model - dummy coefficient for big cars Logit
RLOGITDUM_DB Car logit model - dummy coefficient for big diesel cars
Logit
RLOGITDUM_DM Car logit model - dummy coefficient for medium diesel cars
Logit
RLOGITDUM_DS Car logit model - dummy coefficient for small diesel cars
Logit
RLOGITDUM_NO Car logit model - dummy for Norway Logit
57
Name Description Appearance
RLOGITDUM_S Car logit model - dummy coefficient for small cars Logit
RLOGITDUM_UK Car logit model - dummy for United Kingdom Logit
RLOGITP_ACC Car logit model - acceleration coefficient Logit
RLOGITP_ACCNO Car logit model - additional acceleration coefficient for Norway
Logit
RLOGITP_ACCUK Car logit model - additional acceleration coefficient for United Kingdom
Logit
RLOGITP_DUMMYDB Car logit model - big diesel car dummy coefficient Logit
RLOGITP_FCOSTS Car logit model - fuel cost coefficient Logit
RLOGITP_INCLARGE Car logit model - parameter for income in case of big car choice
Logit
RLOGITP_INCSMALL Car logit model - parameter for income in case of small car choice
Logit
RLOGITP_IVLARGE Car logit model - inclusive value scaling parameter for big cars
Logit
RLOGITP_IVMEDIUM Car logit model - inclusive value scaling parameter for medium cars
Logit
RLOGITP_IVSMALL Car logit model - inclusive value scaling parameter for small cars
Logit
RLOGITP_OCOSTS Car logit model - other cost coefficient Logit
RMOCdummy Dummy parameter in motorcycle logit model Logit
SLOGITCONST car scrap logt: constant term Logit
SLOGITDBIG car scrap logt: dummy for big cars Logit
SLOGITDCOUNTRY car scrap logt: dummy for countries Logit
SLOGITDSMA car scrap logt: dummy for small cars Logit
SLOGITDUMMY car scrap logt: dummies Logit
SLOGITPROPACC car scrap logt: probability of scrap due to accident Logit
SLOGITPURCHASE car scrap logt: purchase cost coefficient Logit
SLOGITREPAIR car scrap logt: repair cost coefficient Logit
SLOGITREPxRES car scrap logt: repair x residual value coefficient Logit
SLOGITRESIDUAL car scrap logt: residual value coefficient Logit
58
3 Modelling theory / approach
3.1 General
Uncertainty analysis is the study of the variation in model output resulting from the collective
variation in the model inputs. The objective of uncertainty analysis is to:
1. quantify the uncertainty in the model output, given the uncertainty in the inputs,
2. develop confidence intervals about the mean or distribution function of the model
output.
Sensitivity analysis quantifies the relative contribution of the input factors in forming the
uncertainty in the model output. The output uncertainty is mapped back to the input factors to
identify the ones that are mainly responsible for that output uncertainty. The objectives of the
sensitivity analysis are:
1. identify input variables that have a large influence on the output uncertainty
(for subsequent calibration / optimisation tasks, or prioritisation of research);
2. identify non relevant variables (for model reduction purposes);
3. improve the understanding of the model structure (highlighting interactions among
variables, combinations of variables that result in high / low values for the model
output);
4. model verification and corroboration (to check whether the model behaviour is in line
with scientist expectations);
5. model quality assessment (to check whether the model output uncertainty depends on
hard science, eg lack of knowledge in data, or on soft-science, eg subjective
preferences and assumptions), etc.
A number of sensitivity analysis methods can be used to accomplish such task and,
consequently, many techniques have been proposed (e.g. linear regression or correlation
analysis, measures of importance, sensitivity indices, screening, etc.). A thorough description
of such techniques can be found in Saltelli et al. [7]. Here we will focus on two of them, which
have been extensively used in this project: the screening approach with quasi-random LpTau
sampling (Sobol et al. [9]) and the variance-based methods, these latter being implemented
via the Sobol method (Sobol [10]).
3.2 Methods
The TREMOVE model is a complex model that involves a large number of input factors. The
choice of a well-designed experiment is essential in order to identify the most important factors
among a large number and quantify their importance.
59
Global sensitivity analysis using variance-based methods considers the full range of variation of
the input parameters along their joint distribution. Variance-based methods seek to decompose
the total output variance into its contributions from each input factor. The importance of a
given input factor can be measured via the so-called sensitivity index, which is defined as the
fractional contribution to the model output variance due to the uncertainty in the input factor.
These methods involve Monte-Carlo (MC) sampling of the input factors according to specific
sampling strategies. Thus they reflect the full range of variation of the input factors. Because
the factors are varied simultaneously, this involves a multidimensional averaging. The apparent
drawback of variance based methods is the so-called curse of dimensionality, which is palpable
when the number of factors becomes large: the number of terms in the decomposition of the
output variance grows exponentially with the number of factors. In cases where the model
contains a large number of factors or/and it is computationally too expensive, the application of
variance based methods like FAST or Sobol’ is not possible.
Screening designs are a convenient choice when the objective is to identify the subset of input
factors that can be fixed at any given value over their range of uncertainty without reducing
significantly the output variance (i.e. identify non-influential factors). The screening methods
provide a list of factors ranked in order of decreasing importance allowing the modeller to
identify the subset of less influential ones. Screening designs like the Morris method are
computationally cheap and model free. As a drawback, these methods tend to provide
qualitative sensitivity measures, i.e. they rank the input factors in order of importance, but do
not quantify how much a given factor is more important than another. Nevertheless, it does
not supply the variance decomposition obtained with the variance-based measures.
For these reasons, the analysis has been performed in two steps. First, a screening analysis
based on quasi-random LpTau sequences identified the most influential input parameters.
Then, a variance based sensitivity analysis technique (Sobol) quantified the uncertainty of the
road transport emissions.
3.2.1 Variance-Based Methods
Let Y be an output value of the model and Xi the input factors or modelling terms this depends
on. In variance-based methods the output variance V(Y) can be decomposed in the sum of a
top marginal variance and a bottom marginal variance. Specifically,
( ) ( )[ ] ( )[ ]UYVEUYEVYV += (Eq: 6)
where U is a group of one or more Xi terms. The top marginal variance from U is the expected
reduction of the variance of Y in case U becomes fully known and is fixed at nominal values,
whereas other inputs remain variable as before. The bottom marginal variance from U is defined
as the expected value of the variance of Y in case all inputs but U become fully known, U
remaining as variable as before.
The main effect or “first order” sensitivity index Si, representing the sensitivity of Y to the factor
Xi, is defined as the top marginal variance divided by the total variance, where the subset U
reduces to the single factor Xi:
60
)Y(V
)]xXY(E[VS
*ii
i
==
(Eq: 7)
and represents the average output variance reduction that can be achieved when Xi becomes
fully known and is fixed. Estimation procedures for Si are the Fourier Amplitude Sensitivity Test,
FAST, the method of Sobol’ and others. Higher order sensitivity indices, which quantify the
sensitivity of the model output to interactions among subsets of factors, can be estimated
using similar formula. For instance, the second order sensitivity index Sij, representing the
sensitivity of Y to the interaction between Xi and Xj, is:
)Y(V
)]xXY(E[V)]xXY(E[V)]xX,xXY(E[VS
*jj
*ii
*jj
*ii
ij
=−=−===
(Eq: 8)
From the definitions in equations (Eq: 7) and (Eq: 8), a complete series development of the
output variance can be achieved:
k...12mji
ijmi ji
iji S...SSS1 ++++= ∑∑ ∑<<<
(Eq: 9)
where higher order terms are defined in a similar way to (Eq: 8).
Given that the estimation of each sensitivity index, be it Si, Sij or higher order, might require a
significant number of model executions, the analysis is rarely carried further after the
computation of second order indices (their number is k(k-1) where k is the number of input
variables), as the related computational load might be impracticable.
The investigation of higher order effects is computationally cheaper if total sensitivity indices
are employed. The total sensitivity index STi for the factor Xi collects in one single term all the
interactions involving Xi. It is defined as the average output variance that would remain as long
as Xi stays unknown (i.e. the bottom marginal variance with U grouping all factors but Xi):
)Y(V
)]xXY(V[ES
*ii
Ti
−− ==
(Eq: 10)
The term X-i indicates all the factors but Xi. The usefulness of the STi for each term is in that
they can be computed without necessarily evaluating the single indices Sijm…, thus making the
analysis affordable from a computational point of view.
Estimating the pair (Si, STi) is important to appreciate the difference between the impact on Y
of the factor Xi alone (the Si) and the overall impact on Y of factor Xi through interactions with
the others (the STi). Such property is particularly interesting in a calibration framework, where
high order interactions are usually encountered. Efficient estimators of the pair (Si, STi) are
provided by variance-based techniques such as the extension of the Fourier Amplitude
Sensitivity Test (xFAST) and the Sobol' method.
The extended FAST (Saltelli et al. [8]) and the Sobol methods (Sobol [9]) yields estimates of
the total sensitivity indices, STi defined as the sum of all the indices (Si and higher orders)
where the variable Xi is included. The STi concentrates in one single term all the effects of Xi on
Y. For additive models (no interactions), Si = STi for all the Xi. The estimation of the total
61
sensitivity indices STi makes the analysis affordable from a computational point of view, as only k
total indices are needed to account completely for the total output variance V. Furthermore, those
methods allow the simultaneous evaluation of the first and total effect indices. The estimation of
the pair (Si ,STi) is important to appreciate the difference between the impact of Xi alone on Y
(i.e. Si) and the overall impact of factor Xi through interactions with the other input variables
on Y (i.e. STi). Clearly the Si1, i2, …,is add up to one; this is not true for the STi’s.
3.2.2 Screening Methods
Screening methods are useful in the modelling practice to investigate which factors - among
the many potentially important factors - are really important. This could help in coming up with
a short list of influential factors.
Screening methods deal with models containing hundreds of input variables, or/and with very
computationally expensive models, such as TREMOVE. They are economical from a
computational point of view, but as a drawback, they provide qualitative sensitivity measures
(i.e. they rank the input variables in order of importance, but do not quantify how much a
given variable is more important than another). There is clearly a trade-off between
computational cost and information. Several approaches to the problem of screening have been
proposed in the literature.
Quasi-Random LpTau sampling generates quasi-random sequences. These are uniformly
distributed sets of points in the N-dimensional unit cube. Each point generated is then used as
input to the transformation that calculates the inverse cumulative function of each element of
the factor set. The sensitivity of model predictions to individual input variables can be then
determined by means of techniques based on regression analysis (PEAR and PCC, SRC) and
their rank transformation (SPEA, PRCC and SRRC). A brief description of the indices is given
hereafter.
Assume that after the Monte Carlo study is performed, the propagation of the sample through
the model creates a mapping of the form
[ ] m,...1i,x,...,x,x,y ik2i1ii = (Eq: 11)
where k is the number of independent variables and m is the sample size.
- PEAR: Pearson product moment correlation coefficient is the usual linear correlation
coefficient computed between yi and xij (i = 1, …, m). It provides a measure of the
linear relationship between Xj and Y.
- SPEA: Spearman coefficient is essentially the same as PEAR, but using the order ranks
instead of the raw values of both Y and Xj. The rank transformation is a simple
procedure which involves replacing the data with their corresponding ranks, i.e. assign
rank 1 to the smallest observation and continue to rank N for the largest observation.
For non-linear models, SPEA is preferred as a measure of correlation.
- PCC: Partial correlation coefficient between Y and Xj is defined as the correlation
coefficient between YY − and jj XX − . The partial correlation coefficients provide a
measure of the strength of the linear relationship between two variables after a
62
correction has been made for the linear effects of other variables in the analysis. In
other words, PCC gives the strength of the correlation between Y and a given input Xj
cleaned of any effect due to any correlation between Xj and any of the Xi,i≠j. In
particular PCC’s provide a measure of variable importance that tends to exclude the
effects of other variables.
- PRCC: Partial rank correlation coefficient is PCC computed on the ranks. For non-linear
models, PRCC is preferred as a measure of partial correlation.
- SRC: Standardised Regression Coefficients quantify the effect of varying each input
variable away from its mean by a fixed fraction of its variance while maintaining all
other variables at their expected values. They are computed through a regression
model, hence it is important to consider the model coefficient of determination (CMD)
obtained from the regression model. CMD provides a measure of how well the linear
regression model based on SRC’s can reproduce the actual output y. It represents the
fraction of the variance of the output explained by the regression. The closer CMD is to
unit, the better is the model performance. The validity of the SRC’s as a measure of
sensitivity is conditional on the degree to which the regression model fits the data, i.e.
to CMD.
- SRRC: Standardised Rank Regression Coefficients are SRCs computed on the ranks.
The difference between the CMDs computed on the raw values and on the ranks, is a
useful indicator of the non-linearity of the model.
3.3 Parameterisations of input data
TREMOVE estimates transport demand, modal shifts and vehicle stock renewal as well as
emissions of air pollutants and costs for policies such as road pricing, public transport pricing,
emission standards, subsidies for cleaner cars, and others. The model consists of three main
modules: a demand, a stock, and an emissions module. These are accompanied by two
additional modules, the well-to-tank and the welfare modules. These two are add-ons on the
main structure of the model, aiming at estimating the upstream (fuel production) costs of
transport and the benefit (in monetary terms) of emission reduction to the society,
respectively, in addition to the TREMOVE main output (cost, emissions, consumption).
Emission and fuel consumption estimates follow the COPERT4 methodology and are generally
distinguished in three sources: emissions produced during thermally stabilised engine operation
(hot emissions), emissions occurring during engine start from ambient temperature (cold-start
and warming-up effects) and NMVOC emissions due to fuel evaporation. The total emissions
are calculated as a product of activity data provided by the user and speed-dependent emission
factors calculated by the software. According to the recent COPERT4 uncertainty study
(Kouridis et al. [15]), the most important inputs to the emissions module are the emission
factors, the traffic data (like vehicle mileage) and the model parameters (like vehicle speed,
average trip length).
In addition, several uncertain variables are included in the demand and stock modules like the
acceleration, the vehicle registration tax, the vehicle repair cost and others. Many input
parameters are usually multi-dimensional arrays. For example, the emission factor is a 5-
dimensional variable, depending on the Vehicle Category (PC, LDV, etc), Technology (Euro-1,
Euro 2, etc), Engine Size (<1.4lt, >2.0lt, etc), pollutant and speed range considered.
63
The "total error", totalε , of the TREMOVE estimates results from an entire "chain of errors". This
total error consists of three error contributions:
(a) STOCKε denotes the error which comes along with the estimation of the total
amount of stock;
(b) COSTε represents the uncertainty in the parameters related to the cost
module;
(c) EMISSIONSε denotes the uncertainty associated with the road transport
emissions.
In the Monte Carlo version of TREMOVE, we wish to acknowledge the uncertainty in all these
inputs. The process of considering uncertainty in scalar (0-D) variables is straightforward
through their statistical distribution (although perhaps not easy to quantify), based on the
literature values collected in the previous chapter. The process is however not easy for multi-
dimensional input variables, for which we need to identify, via a statistical model, a suitably
small set of parameters that describe well the multi-dimensional system. By associating a
proper uncertainty to these model parameters, we can then represent and characterize the
uncertainty in the multi-dimensional system.
The parameterization of the contribution of all sources of uncertainty in TREMOVE resulted in a
significant reduction of the total uncertainty inputs to 33. Specifically:
(a) 6 parameters corresponding to STOCKε : uparaBT, erlogitpACC, eRSHairco,
rlogitCNGAVAIL, uBTmileage, RFACTORUNCONV.
(b) 12 parameters corresponding to COSTε : eRPCS_BASE, usresidualparaAB,
RLPG_FIT_COST, eRPCS_INCREASE_2009, eRPCS_INCREASE_2012, RINSCFRACTION,
RLABOURC, RLABOURTX, ROWNTX, RREPMAINTC_INCREASE_RTECH_RES,
eRREPMAINTCFRACTION, PUBLICCOSTCOV.
(c) 15 parameters corresponding to EMISSε : eEF, eEF_fc, eEFratio, eEFratio_fc, RHC,
eFUEL_ENERGY_DENSITY, eFUELSPEC, eRFC_REDUC_RESISTANCE, eRFCairco,
eRLOADCAP, RFUEL_COMPOSITION, RVP, TMAX, TMIN, ltrip.
The details of this process are given in the next section.
3.3.1 Parameterisation of STOCKε
The uncertainty of the log-normally distributed stock parameters rlogitpACC and RSHairco was
appointed to the stochastic variables erlogitpACC and eRSHairco and was quantified as
described hereafter.
(a) We have generated the statistical distributions based on the variable
uncertainty provided by expert opinion (LWA) as mean zijexpert
and standard deviation
sijexpert
for the year 2005 as well as the TREMOVE data for the period 1991-2030. This
yields zij(t)=(zijexpert
/ zijTREMOVE(2005))*zij
TREMOVE(t) for the variable mean and
sij(t)=(std(zijexpert)/mean(zij
expert))*zijTREMOVE(t) for the variable standard deviation.
64
(b) We fit a time dependent log-normal distribution to the TREMOVE data with
mean and standard deviation calculated in the previous step. The stochastic input Z (e.g.,
RSHairco) for the year t is based on the formula:
(Eq: 12)
(Eq: 13)
(Eq: 14)
e ~ N(0,1) (Eq: 15)
The parameterization of RFACTRUNCONV was based on the mean and standard deviation
provided by the expert group. A log-normal non-time dependent distribution was then fit to the
data utilizing the stochastic factor eRFACTRUNCONV.
Further, we evaluated the combination of values for the model parameters bm and Tm
corresponding to the characteristic service life that do not violate the constraints imposed
towards acceptable model response. This generated the 2-dimensional fitting function surface
in the parameter space. A stochastic variable (uparaBT) was employed to sample values from
the generated response surface, i.e. the permissible values of the joint probability distribution
function of bm and Tm. The same approach was also adopted for the average annual mileage of
new cars in each year (uBTmileage).
Finally, the variable rlogitCNGAVAIL was parameterized as N(0.29, 0.02) and was read directly
from the generated sample.
Table 6: List of the uncertain input variables, belonging to the stock error category, with their
statistical distributions
Normal distr µ σ
Uniform distr No Error Cat Input Variable Description Units Distribution
min max
1 eSTOCK uparaBT (B,T) - parameter in TRENDS: characteristic service life & failure steepness
- Uniform 0 1
2 eSTOCK eRLOGITPACC Acceleration for big and medium car logit
sec Normal 0 1
3 eSTOCK eRSHairco Share of new sold vehicles fitted with air-conditioning
% Normal 0 1
4 eSTOCK rlogitCNGAVAIL Relative availability of CNG in fuel stations
% Normal 0.29 0.02
5 eSTOCK uBTmileage Average annual mileage of new cars in each year - exogenous estimate - vehicle kilometres per year
# Uniform 0 1
6 eSTOCK eRFACTORUNCON
V
Ratio fuel consumption unconventional vs equivalent conventional vehicle - [(kg/km) / (kg/km)]
- Normal 0 1
65
The following figures display, for all related input variables, the probability density functions
(PDFs) that result from the modification of the basecase TREMOVE value, which was actually
used for the uncertainty calculations. The figure shows as an example the PDFs for correspond
to Passenger car Gasoline 1,4-2l for the year 2010. The values for the rest of the distributions
can be found in ANNEX III.
0
20
40
60
80
100
120
Fre
quency
seconds
RLOGITPACC
0
20
40
60
80
100
120
Fre
quency
-
RSHairco
0
20
40
60
80
100
120
Fre
quency
Coefficient
RLOGITCNGAVAIL
0
20
40
60
80
100
120
Fre
quency
(kg/km) / (kg/km)
RFACTORUNCONV
Figure 7: Probability density functions of the input variables used in TREMOVE (example gasoline PC
1.4-2.0 l).
3.3.2 Parameterisation of COSTε
The uncertainty of the log-normally distributed stock parameter RCPS_BASE was appointed to
the stochastic variable eRCPS_BASE and was quantified according to the equations (Eq: 12) to
(Eq: 15).
The parameterization of RREPMAINTC_INCREASE_RTECH_RES was based on the mean and
standard deviation provided by the expert group. A log-normal non-time dependent distribution
was then fit to the data utilizing the stochastic factor eRREPMAINTC_INCREASE_RTECH_RES.
The uncertainty for RREPMAINTCFRACTION, RPCS_INCREASE_2009, RPCS_INCREASE_2012,
RLABOURC, RLABOURTX and PUBLICCOSTCOV is given as a perturbation to the nominal values
in TREMOVE:
66
( ) ( ) )e*r1(*tZtZ TREMOVE += (Eq: 16)
The factor r has the following values (originating from the input data collected in the previous
chapter, i.e. CV of 20% or 30% respectively):
r = 0.1 RLABOURC, RLABOURTX, RREPMAINTCFRACTION
r = 0.07 PUBLICCOSTCOV, RPCS_INCREASE_2009, RPCS_INCREASE_2012
The uncertainty for RINSCFRACTION and ROWNTX is given as a perturbation to the statistical
distribution proposed by the expert group (LWA-EMISIA). The distribution for RINSCFRACTION
is non-time dependent while for ROWNTX two different distributions were provided
(before/after 2010):
( ) ( ) )t(sigma*etmeantZ += (Eq: 17)
For the remaining two variables, usresidualparaAB is modelled in a similar manner to uparaBT (but in a time-dependent mode) while RLPG_FIT_COST is read directly from the Monte Carlo sample.
Table 7: List of the uncertain input variables, belonging to the cost error category, with their statistical distributions
Normal distr µ σ
Uniform distr No Error Cat Input Variable Description Units Distribution
min max
2 eSTOCK eRPCS_BASE Road vehicle basic purchase resource cost
Euro Normal 0 1
3 eCOST RLPG_FIT_COST Resource cost to retrofit LPG installation
Euro Uniform 1800 2500
4 eCOST eRREPMAINTC_INCREASE
_RTECH_RES
Increase in yearly maintenance cost for using technologies to reduce vehicle and engine resistance factors
Euro Normal 0 1
5 eCOST eRREPMAINTCFRACTION Repair and Maintenance Cost excl. taxes as % of purchase resource cost (ex tax)
% Normal 0 1
6 eCOST eRPCS_INCREASE_2009 Vehicle purchase cost increase to reach the 140g car CO2 target in 2009
% Normal 0 1
7 eCOST eRPCS_INCREASE_2012 Vehicle purchase cost increase to reach the car CO2 target in 2012 - on top of 140g
% Normal 0 1
8 eCOST eRINSCFRACTION Insurance cost as percentage of vehicle purchase resource cost
% Normal 0 1
9 eCOST eRLABOURC Labour cost - net wage - for truck drivers
Euro Normal 0 1
10 eCOST eRLABOURTX Labour tax - bruto wage minus netto wage - for truck drivers
Euro/h Normal 0 1
11 eCOST eROWNTX Annual Ownership tax road vehicles
Euro Normal 0 1
12 eCOST ePUBLICCOSTCOV Public transport fare cost coverage
% Normal 0 1
The following two figures display, for cost relevant input variables, the distribution that results
from the modification of the basecase TREMOVE value, which was actually used for the
uncertainty calculations. Figure 8 presents examples of the distributions for gasoline passenger
cars 1.4-2l for year 2010; for all other vehicle categories and years data can be found in
ANNEX III.
67
0
20
40
60
80
100
120
Fre
quency
€
RPCS_BASE
0
20
40
60
80
100
120
Fre
quency
Euro 2005
ROWNTX
0
20
40
60
80
100
120
Fre
quency
Euro 2000
RREPMAINTC_INCREASE_RTECH_RES
0
20
40
60
80
100
120
Fre
quency
-
RREPMAINTCFRACTION
0
20
40
60
80
100
120
Fre
quency
%
RPCS_INCREASE_2009
0
20
40
60
80
100
120
Fre
quency
%
RPCS_INCREASE_2012
0
20
40
60
80
100
120
Fre
quency
%
RINSCFRACTION
Figure 8: PDFs of the cost-related input variables in TREMOVE (examples for gasoline passenger
cars 1.4-2l for year 2010).
68
0
20
40
60
80
100
120
Fre
quency
Euro/hour
RLABOURTX
0
20
40
60
80
100
120
Fre
quency
Euro/hour
RLABOURC
0
20
40
60
80
100
120
Fre
quency
%
PUBLICCOSTCOV
Figure 9: PDFs of additional cost-related input variables in TREMOVE (examples for the year 2010).
3.3.3 Parameterisation of EMISSε
The uncertainty of the emission and consumption relevant parameters RHC, RVP, TMIN and
TMAX was quantified on the basis of the mean and standard deviation provided by the expert
group. A log-normal non-time dependent distribution was then fit to the data utilizing the
stochastic factors eRHC, eRVP, eTMAX and eTMIN.
The uncertainty for eFUEL_ENERGY_DENSITY, eFUELSPEC, eRFC_REDUC_RESISTANCE,
eRFCairco, eRLOADCAP and RFUEL_COMPOSITION was simulated according to equation (Eq:
16 (r=1). The perturbation size proposed by the expert group was 2% for FUELSPEC, 3% for
FUEL_ENERGY_DENSITY, 20% for RLOADCAP, 30% for RFC_REDUC_RESISTANCE and
RFUEL_COMPOSITION and 50% for RFCairco. The average road trip length (ltrip) is read
directly from the MC sample.
The data collected from laboratory measurements are usually processed by regression analysis
to provide a set of regression coefficients that are meant to explain the underlying
phenomenon through a polynomial curve that fits the observed data. Such regression
coefficients are subsequently stored in tables and employed during the execution of the
emission module (resp. COPERT 4). In order to estimate the uncertainty of the emission
factors, raw measured data were analyzed, as described by Kouridis et al. [15]. On the basis of
the statistical analysis of the vehicle measurements available, experimental errors for the
69
coefficients were estimated. Such experimental errors are in the form of stochastic variables
that, coupled with the polynomial regression curves (efCOPERTi), reproduce the experimental
pattern. The probability distribution functions for the stochastic emission factors (ef) are set up
utilising the following procedure:
HOT EMISSION FACTORS AND FUEL CONSUMPTION FACTORS
(a) The laboratory measurements have been clustered to 14 equally sized
velocity classes (1: 0-10 km/h, 2: 10-20 km/h, …, 14: 130-140 km/h); for each velocity
class (v1, v2,…, vK), k=1,2,…,14, we calculate its standard deviation (s1, s2,…, sK).
(b) We fit a speed dependent log-normal distribution to the laboratory
measurements with mean equal to the polynomial regression curve (efHOTCOPERT
i) and
standard deviation calculated in the previous step (s1, s2,…, sK). The hot emission factor
(efHOT) for the sampled velocity Vj is based on the formula:
( ) eEF*jHOT
jjeVefσ+µ
= (Eq: 18)
( )( ) ( )
+−=µ
2
jCOPERTHOT
ij
COPERTHOTj
Vef
s1ln5.0Vefln
(Eq: 19)
( )
+=σ
2
jCOPERTHOT
ij
Vef
s1ln
(Eq: 20)
eEF ~ N(0,1) (Eq: 21)
This procedure, which reproduces the experimental pattern of the hot emission factors, has
been repeated for all Vehicle Categories (PC, LDV, etc), Technologies (Euro-1, Euro 2, etc),
Engine Sizes (<1.4lt, >2.0lt, etc) and pollutants.
COLD EMISSION FACTORS AND FUEL CONSUMPTION FACTORS
(a) The cold emission factors have been split in fourteen speed classes, similar to hot ones (1:
0-10 km/h, 2: 10-20 km/h, …, 14: 130-140 km/h); for each velocity class (v1, v2,…, vK),
k=1,2,…,14, we calculate the standard deviation (s1, s2,…, sK) of (efCOLD/efHOT-
1)*efHOTCOPERT, assuming that the ratio of standard deviation over mean of the hot emission
factors is equal to the standard deviation over mean for the cold emission factor. The
highest speed classes are not relevant for the cold-start emission factor as the COPERT 4
cold-start functions are valid only up to 45 km/h and cold-start is allocated to urban
conditions only.
(b) No uncertainty of cold-start emission factors to temperature has been assumed, as no data
were available. Therefore, the uncertainty of cold-start on ambient conditions originates
only from the uncertainty in the temperature ranges, described earlier in this section.
(c) We fit a speed dependent log-normal distribution to the approximated variance of cold-
70
start emission factors with mean equal to the calculated value for the particular speed
(efCOPERTi) and standard deviation calculated in the previous step (s1, s2,…, sK). The cold
emission factor (efCOLD) for the sampled velocity Vj is based on the formula:
( ) ( )jCOPERTHOTjRATIOjCOLD Vef*)1)V(ef(Vef −= (Eq: 22)
( ) eEFratio*jRATIO
jje1Vefσ+µ
+= (Eq: 23)
( )( ) ( )
−+−−=µ
2
jCOPERTRATIO
ij
COPERTRATIOj
1Vef
s1ln5.01Vefln
(Eq: 24)
−+=σ
2
COPERTRATIO
ij
1ef
s1ln
(Eq: 25)
eEFratio~N(0,1) (Eq: 26)
The above procedure, which reproduces the experimental pattern of the cold-start emission
factors, has been repeated for all Vehicle Categories (PC, LDV, etc), Technologies (Euro-I, Euro
II, etc), Engine Sizes (<1.4lt, >2.0lt, etc) and pollutants.
71
Table 8: List of the uncertain input variables, belonging to the emissions error category, with
their statistical distributions.
Normal distr µ σ
Uniform distr No Error Cat Input Variable Description Units Distribution
min max
1 eEMISS eEF amplitude HOT Emission Factor
gr/km L-Normal 0 1
2 eEMISS eEFratio Cold-start emission factor - L-Normal 0 1
3 eEMISS eEFfc amplitude HOT Fuel consumption Factor
gr/km Normal 0 1
4 eEMISS eEFfcratio Cold-start emission factor - Normal 0 1
5 eEMISS ltrip Mean trip length km L-Normal 2.5 0.2
6 eEMISS eFUEL_ENERGY_DENSITY Fuel energy density GJ per
kg Uniform -0.03 0.03
7 eEMISS eFUELSPEC Fuel specification history - Uniform -0.02 0.02
8 eEMISS eRFC_REDUC_RESISTANCE
Real world fuel consumption reduction from utilisation of technologies to reduce vehicle and engine resistance factors
% Uniform -0.3 0.3
9 eEMISS eRFCairco Extra fuel consumption from use of air-conditioning equipment
l/km Normal 0 0.16
10 eEMISS RHC Ratio of hydrogen to carbon atoms in fuels
- Normal 0 1
11 eEMISS RVP Gasoline volatility (Reid Vapour Pressure)
kPa Normal 0 1
12 eEMISS TMAX Maximum temperature per month
oC Normal 0 1
13 eEMISS TMIN Minimum temperature per month
oC Normal 0 1
14 eEMISS eRLOADCAP Average maximum loading capacity big truck
t Normal 0 0.07
15 eEMISS RFUEL_COMPOSITION Average share of components in blended fuels
% Normal 0 0.1
The following figures display, for all related input variables, the distribution that results from
the modification of the basecase TREMOVE value, which was actually used for the uncertainty
calculations.
72
0
20
40
60
80
100
120
140Fre
quency
km
LTRIP
0
10
20
30
40
50
60
Fre
quency
GJ/kg
FUEL_ENERGY_DENSITY
0
10
20
30
40
50
60
Fre
quency
-
FUELSPEC
0
10
20
30
40
50
60
Fre
quency
%
RFC_REDUC_RESISTANCE
0
20
40
60
80
100
120
Fre
quency
l/km
RFCairco
0
20
40
60
80
100
120
Fre
quency
-
RHC
0
20
40
60
80
100
120
Fre
quency
kPa
RVP
0
20
40
60
80
100
120
140
Fre
quency
oC
TMAX
73
0
20
40
60
80
100
120
140
160
180
Fre
quency
oC
TMIN
0
20
40
60
80
100
120
Fre
quency
tonne
RLOADCAP
0
20
40
60
80
100
120
Fre
quency
% in weight
RFUEL_COMPOSITION
Figure 10: PDFs of the emission and consumption related input variables in TREMOVE (examples).
74
4 TREMOVE software modification and update
4.1 General
To determine the uncertainty of the model a number of modifications needed to be made to the
model code, to the input files, to the way the model is being executed and to the way the
model performs the calculations. As mentioned earlier, these modifications did not affect the
TREMOVE basecase output but were only introduced to facilitate the execution of uncertainty
runs.
4.2 Software code modification
In order for the runs to be performed, the original TREMOVE values needed to be altered. Two
ways were used to facilitate this task. The first one was the direct replacement of the value of
a parameter (e.g. paraB, paraT). The second one was the implement in the code the different
equations used to calculate the PDFs . It was very important to make sure that the updates
were applied to the proper part of the code so that the correct values were used throughout
the calculation of the model. For this reason parts of the original model code were rendered
inactive and a new set of files were included in the code. This allowed the study team to have a
better overview of the modifications since all the additional code could be found in those files.
All of the modifications were included in the Vehicle Stock and emission and consumption
modules of TREMOVE, since no changes were made to the Demand module. The original files
modified in the code are the following:
o Vehicle Stock Module\Calculate_Transport_Demand.gms
o Vehicle Stock Module\Calculate_Transport_Demand_BY.gms
o Vehicle Stock Module\Calibrate_Base_Case.gms
o Vehicle Stock Module\Calibrate_CES_Tree.GMS
o Vehicle Stock Module\Calibrate_CES_Tree_BY.gms
o Vehicle Stock Module\Define_Parameters.gms
o Vehicle Stock Module\Define_Parameters_Emissions.gms
o Vehicle Stock Module\Define_Parameters_Road.gms
o Vehicle Stock Module\Define_Sets.gms
o Vehicle Stock Module\Define_Sets_Road.gms
o Vehicle Stock Module\Degradation_Mileage.gms
o Vehicle Stock Module\Emissions_Road.gms
o Vehicle Stock Module\External_Cost_Module.gms
75
o Vehicle Stock Module\External_Costs_Accidents.gms
o Vehicle Stock Module\Fuel_Consumption_Road.gms
o Vehicle Stock Module\Logit_Life_Time_Costs.gms
o Vehicle Stock Module\main.gms
o Vehicle Stock Module\Money_Costs_Road.gms
o Vehicle Stock Module\Money_Costs_Road_Private.gms
o Vehicle Stock Module\Purchase_Cost_Road.gms
o Vehicle Stock Module\Purchase_Cost_Road_residual.gms
o Vehicle Stock Module\Read_Demand_Module_Output.gms
o Vehicle Stock Module\Road_Scrap_Policy.gms
o Vehicle Stock Module\Run_Simulation.gms
o Vehicle Stock Module\Run_TREMOVE.gms
o Vehicle Stock Module\Sale_Shares_Road.gms
4.3 Software code added
Apart from the code modification a number of additional files were used during calculation.
These files include the necessary data and code fragments to be used by the model. Four
groups of files were added.
The first group are GAMS (gms) files which include the code for the different PDF equations for
the emissions module. They were included in the Vehicle Stock module, since they are the
same for all runs performed. The prefix “ZZZ_” was added for a better visualization of the
filenames in the folder where the files are located.
o ZZZ_cold_emissions.gms
o ZZZ_cold_FC.gms
o ZZZ_hot_emissions_for_HDV_&_Buses.gms
o ZZZ_hot_emissions_for_non_HDV_buses.gms
o ZZZ_hot_FC_for_HDV_&_Buses.gms
o ZZZ_hot_FC_for_non_HDV_buses.gms
o ZZZ_set_year.gms
The second group are GAMS (gms) files which include the updated code used to replace the
original TREMOVE input variables. These refer to all input variables other then the emissions
module. They are also common for all runs and thus included in the Vehicle Stock module. The
prefix “zz_” was added for a better visualization of the filenames in the folder where the files
are located.
76
o zz_FUEL_ENERGY_DENSITY.gms o zz_LTRIP.gms o zz_paraBT.gms o zz_RFACTORUNCONV.gms o zz_RFC_REDUC_RESISTANCE.gms o zz_RFCairco.gms o zz_RHC.gms o zz_RINSCFRACTION.gms o zz_RLOADCAP.gms o zz_RLPG_FIT_COST.gms o zz_RMILage.gms o zz_RREPMAINTC_INCREASE_RTECH_RES.gms o zz_RREPMAINTCFRACTION.gms o zz_RVP.gms o zz_SRESIDUALparaA.gms o zz_SRESIDUALparaB.gms o zz_TMAX.gms o zz_TMIN.gms o zzT_FUELSPEC.gms o zzT_PUBLICCOSTCOV.gms o zzT_RFUEL_COMPOSITION.gms o zzT_RLABOURC.gms o zzT_RLABOURTX.gms o zzT_RLOGITCNGAVAIL.gms o zzT_RLOGITPACC.gms o zzT_ROWNTX.gms o zzT_RPCS_BASE.gms o zzT_RPCS_INCREASE_2009.gms o zzT_RPCS_INCREASE_2012.gms o zzT_RSHairco.gms
The third group are TREMOVE (inc) files which include the parameters used in the above files.
They are also common for all runs and thus included in the Vehicle Stock module.
o m_RLOGITPACC o m_RPCS_BASE o m_RSHairco o m_ROWNTX o m_RVP o m_RFACTORUNCONV o m_RREPMAINTC_INCREASE_RES o m_RHC o m_TMAX o m_TMIN o m_RINSCFRACTION o s_RLOGITPACC o s_RPCS_BASE o s_RSHairco o s_ROWNTX o s_RVP o s_RFACTORUNCONV o s_RREPMAINTC_INCREASE_RES o s_RHC o s_TMAX o s_TMIN o s_RINSCFRACTION o std_V_hdv_hot.inc o std_V_non_hdv_cold.inc o std_V_non_hdv_hot.inc
The fourth group are the files for the individual runs. These are GAMS (gms) files which include
the parameters used in the different scenario runs. They are included in each ‘Scenario’ folder
which corresponds to each single individual run.
77
Table 9: Files which include all scenario related data
Filename Parameter included para_B.gms ParaB para_T.gms ParaT res_A.gms ResidualparameterA res_B.gms ResidualparameterB empty_values.inc RLOGITCNGAVAIL, LTRIP, RLPG_FIT_COST,
eEF, eEFratio, eEF_FC, eEFratio_FC
standard_values.inc RREPMAINTCFRACTION, RPCS_INCREASE_2009, RPCS_INCREASE_2012, FUEL_ENERGY_DENSITY, RFC_REDUC_RESISTANCE, RFCairco, RLOADCAP, RLABOURC, RLABOURTX, RFUEL_COMPOSITION, PUBLICCOSTCOV, FUELSPEC
Distributions_values.inc RLOGITPACC, RPCS_BASE, RSHairco, ROWNTX, RVP, RFACTORUNCONV, RREPMAINTC_INCREASE_RES, RHC, TMAX, TMIN, RINSCFRACTION
4.4 New features
Two new features were added to the software to facilitate the execution of the runs. The first
one aimed at reducing calculation time and the second one at the automatic execution of the
calculations.
A full TREMOVE run for one country on an average PC (basecase and scenario) takes about 40
minutes. The modifications included in the code by the project members increased this time by
30%. Taken into account the vast number of runs that had to be be performed (order of
several thousands) it was very important to reduce this time. TREMOVE calculates each time
the basecase and then the scenario, so a logical approach was to omit the basecase
calculations since they are not affected by the updated values. To do this an in depth analysis
of the model calculation process was performed. This analysis showed that it was possible to
save the intermediate files used to transfer the calculated data from the basecase to the
scenario calculations. These files include all the necessary data used by the scenario. They are
GAMS data files (gdx) used to transfer data between the Demand module and the Vehicle
Stock module and compressed files that contain the rest of the data. They were calculated
once and then used by the model every time a new run-scenario was performed. This new
feature decreased the total calculation time by 25%.
To facilitate the use of the saved basecase data, and the execution of the multiple scenarios a
second feature was introduced. A new Graphical Users Interface (Figure 11) was designed
which had 3 main features:
o the overview of the calculations;
o the use of the basecase data, thus eliminating the need to perform the basecase
calculations more than once;
o the preparation of the model to run each time a different scenario. This includes:
o preparing the input data for the each scenario,
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o copying the results folder each time to a different location and naming it
according to the scenario number;
o executing the model.
Figure 11: Graphical User Interface of the TREMOVE uncertainty model.
4.5 Guidance to use the software
The study team prepared a new version of the TREMOVE model. This version was based on the
model version 3.3.1. The main functionalities and structure of the model were not altered. In
order for a run to be prepared the user has to provide all the necessary data for all runs. As
mentioned in the above paragraphs there are four groups of files which include the additional
code and data for the scenario execution. The first 3 groups along with the original TREMOVE
files form the new version of the model. The user will not need to modify these files. However
the fourth group, which includes the scenario data, must be created by the user. Since there is
a large amount of data required for the uncertainty calculations, an MsExcel file was created in
order for these files to be created automatically. The user has the option to fill all related data
in this file using a standard MsExcel sheet. An MsExcel Visual Basic code has been written in
order for the files to be created automatically.
As mentioned in the previous paragraph a simple graphical user interface has been designed to
facilitate the execution of the runs. A single button click will start the procedure. If the
computer uses a multi-core processor multiple instances of the software can be executed
simultaneously to reduce the required calculation time.
4.6 Differences between the two steps
The analysis has been performed in two steps. First, a screening analysis identified the most
influential input parameters (512 runs). Then, a variance based sensitivity analysis technique
quantified the uncertainty of the results (5950 runs). These runs were not identical, since the
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second step took into account only the influential parameters. The new software however was
designed in such a way that some files and custom variables used in the first step could simply
be omitted in the second step in order for the calculations to be performed. Table 10 lists these
files, and indicates in what step they were used.
Table 10: Files used in the 512 screening and the 5950 sensitivity runs
Filename 512 runs
5950 runs
ZZZ_cold_emissions.gms YES YES
ZZZ_cold_FC.gms YES YES
ZZZ_hot_emissions_for_HDV_&_Buses.gms YES YES
ZZZ_hot_emissions_for_non_HDV_buses.gms YES YES
ZZZ_hot_FC_for_HDV_&_Buses.gms YES YES
ZZZ_hot_FC_for_non_HDV_buses.gms YES YES
ZZZ_set_year.gms YES YES
zz_FUEL_ENERGY_DENSITY.gms YES
zz_LTRIP.gms YES YES
zz_paraBT.gms YES YES
zz_RFACTORUNCONV.gms YES
zz_RFC_REDUC_RESISTANCE.gms YES
zz_RFCairco.gms YES
zz_RHC.gms YES
zz_RINSCFRACTION.gms YES YES
zz_RLOADCAP.gms YES
zz_RLPG_FIT_COST.gms YES
zz_RMILage.gms YES
zz_RREPMAINTC_INCREASE_RTECH_RES.gms YES
zz_RREPMAINTCFRACTION.gms YES YES
zz_RVP.gms YES
zz_SRESIDUALparaA.gms YES YES
zz_SRESIDUALparaB.gms YES YES
zz_TMAX.gms YES
zz_TMIN.gms YES
zzT_FUELSPEC.gms YES
zzT_PUBLICCOSTCOV.gms YES YES
zzT_RFUEL_COMPOSITION.gms YES
zzT_RLABOURC.gms YES YES
zzT_RLABOURTX.gms YES YES
zzT_RLOGITCNGAVAIL.gms YES
zzT_RLOGITPACC.gms YES
zzT_ROWNTX.gms YES YES
zzT_RPCS_BASE.gms YES YES
zzT_RPCS_INCREASE_2009.gms YES
zzT_RPCS_INCREASE_2012.gms YES
zzT_RSHairco.gms YES
m_RLOGITPACC YES
m_RPCS_BASE YES YES
m_RSHairco YES
m_ROWNTX YES YES
m_RVP YES
m_RFACTORUNCONV YES
m_RREPMAINTC_INCREASE_RES YES
m_RHC YES
m_TMAX YES
m_TMIN YES
m_RINSCFRACTION YES YES
s_RLOGITPACC YES
s_RPCS_BASE YES YES
s_RSHairco YES
s_ROWNTX YES YES
s_RVP YES
s_RFACTORUNCONV YES
s_RREPMAINTC_INCREASE_RES YES
s_RHC YES
s_TMAX YES
s_TMIN YES
s_RINSCFRACTION YES YES
std_V_hdv_hot.inc YES YES
std_V_non_hdv_cold.inc YES YES
std_V_non_hdv_hot.inc YES YES
Modified code files following the TREMOVE structure can be found in ANNEX III.
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5 Variance of the baseline output
5.1 General
A realistic uncertainty calculation of the TREMOVE basecase is not possible without
characterising the uncertainty of the exogenous baseline projection used. This is because, the
exogenous baseline is the result of a forecast procedure, taking into account most probable
estimates for macroeconomic (GDP, energy costs) and demographic (population growth) data,
as well as energy and transport efficiency assumptions to estimate baseline demand. Change in
any of these values will affect the baseline demand – however in a fashion exogenous to
TREMOVE. It is obvious that the only way to realistically estimate the uncertainty of the
TREMOVE baseline is in fact to simulate alternative baselines. This can be made in two ways:
First, to compare the output of two different model versions as a measure of uncertainty, since
each individual model version is linked to a different baseline. However, this is also associated
to differences in the formulation of the different model versions (i.e. some parameterizations
will have changed between the two versions) and it seems that although this is straightforward
to conduct, it may lead to a higher uncertainty than should realistically be expected. The
second option, which is conceptually better, is to import alternative baseline estimates into
TREMOVE. For example, one could use the POLES, TRANS-TOOLS or PRIMES models to
estimate total demand based on different macroeconomic data and then introduce these into
TREMOVE. This would determine the uncertainty range of the TREMOVE baseline. However, this
is outside the scope of the study.
Instead, the target of this project was to characterize the variance in baseline output of a given
TREMOVE version, assuming that the exogenous data are fixed. This has a practical meaning:
The variance of the baseline output expresses the additional uncertainty introduced by the
model formulations, assuming that the macroeconomic and demographic data have been
agreed. This is useful in practical model applications. When Tremove is used, for example, to
assess the impact of the introduction of a new emission standard, one is not interested on the
total uncertainty including uncertainty of the macroeconomic data. Instead, one is interested in
identifying what is the uncertainty expected in the activity, emission and costs associated with
the introduction of the new standard, in comparison to the baseline. In order to identify this,
one needs to estimate the uncertainty of the baseline and the scenario induced by the model
formulations and not by external factors to the model. Therefore, this chapter quantifies the
variance of the baseline model output induced by the uncertainty in the model variables and
not by higher-order external factors to the model.
The results of the uncertainty and sensitivity analysis of the baseline are presented for the
United Kingdom. At a first stage, the rather large number of uncertain input variables (33) has
been filtered out from its non-influential inputs through a screening sensitivity analysis using
the LPτ sequence (Sobol at al. [9]). At the second stage, the set of influential inputs is
explored thoroughly by means of a quantitative sensitivity analysis (Cukier et al. [4], Saltelli et
al. [8]) to provide uncertainty and sensitivity estimates for the total cost, activity, consumption
and pollutant emissions for the years 2005-2030.
The sensitivity analysis has been performed through the following steps:
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1. Prepare the Monte Carlo sample for the screening experiment using quasi-random LPτ
sequences.
2. Execute the Monte Carlo simulations and collect the results.
3. Compute the sensitivity measures corresponding to the raw and the rank data in order
to isolate the non-influential inputs.
4. Prepare the Monte Carlo sample for the variance-based sensitivity analysis, for the
influential variables identified important in the previous step.
5. Execute the Monte Carlo simulations and collect the results
6. Quantify the importance of the uncertain inputs, taken singularly as well as their
interactions.
The sensitivity indices have been calculated for 24 output variables listed in Table 11.
5.2 Screening uncertainty and sensitivity analysis
The relative importance of the 33 uncertain input factors is initially explored with the screening
design based on quasi-random LPτ sequence. A sample of 512 simulations was generated for
this screening test. The estimated sensitivity coefficients, calculated over the 24 output
variables listed in Table 11, are displayed in the 24 individual screens of Figure 12. In each
screen, the first column of graphs displays the sensitivity coefficients calculated on raw data
(PEAR, PCC, SRC), see section 3.2.2. The second column of graphs displays the sensitivity
coefficients calculated on ranks (SPEA, PRCC, SRRC). A value close to one (minus one) means
that the output variable is positively (negatively) correlated to the individual variable.
The linearity of the regression model (SRC, SRRC) is shown in the CMD graph (third column),
which gives the percentage of data variance explained by the regression model. The closer the
CMD value is to unit, the better the output coefficient can be modelled by a linear combination
of the input variables. Two lines are given in each graph. The line “data” corresponds to the
linearity of the output variables in respect to the input variables, while the line rank
corresponds to the linearity of the output variable ranks in respect to the input variable ranks.
According to the measures, the most influential input set, for all the output variables
considered, contains 14 entries:
� the average trip length (ltrip)
� the hot and cold emission factors (eEF, eEFratio, eEFfc, eEFfcratio)
� the (B,T) - parameter: characteristic service life & faillure steepness (paraB and paraT
pairs)
� the road vehicle basic purchase resource cost - EURO 2000 (eRPCSBASE)
� the estimated residual value function as a percentage of purchase cost
(usresidualparaAB)
� the repair and maintenance cost excluding taxes as % of purchase resource cost (ex
tax) (eRREPMAINTCFRACTION)
� the insurance cost as percentage of vehicle purchase resource cost (RINSCFRACTION)
� the labour cost - net wage - for truck drivers - EURO per hour (RLABOURC)
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� the labour tax - bruto wage minus netto wage - for truck drivers - EURO per hour
(RLABOURTX)
� the annual ownership tax road vehicles - EURO 2005 (ROWNTX)
� the public transport fare cost coverage (PUBLICCOSTCOV)
It is evident, based on Figure 12 that the output variables belonging to the stock and cost
modules behave linearly in respect to the input variables uncertainty. TAXregistration,
COSTpurchase (2010 only) and VATpurchase (2010 only) are the least linear variables of those
subgroups with a minimum CMD of 0.85. The variables referring to the emission module have a
quasi-similar behaviour. The most non-linear variable is CO (CMD~0.79) followed by VOC
(CMD~0.85) while NOx and PM have CMD around 0.95.
Some first conclusions can already be obtained based on this screening test:
- The model output referring to activity and cost is mostly a linear combination of the
input variables. This means that second order effects, such as combined effects of two
or more variables or non-linear dependence of the model output to the input variables
are limited.
- A different manifestation of the previous point is that each output variable is mostly
determined by the corresponding input variable. Emissions are mostly correlated to
emission factors, costs are correlated with their individual cost input variables. Stock
and activity data are basically determined by purchase and fuel costs.
- Some interesting effects are observed, depending on the time horizon considered. For
example, the vehicle number in the short future is more correlated to the fuel price
than the purchase cost. However, fuel prices affect both new and existing vehicles,
therefore the correlation between number of vehicles and fuel costs becomes less
important into the future. On the other hand, purchase costs only affects new
registrations and not the existing stock. Therefore purchase cost becomes a much
more relevant parameter into the future. Such observations confirm both the correct
operation of the model and our screening analysis results.
- The linearity of the effects may at first seem striking for a complex model like
TREMOVE. On the other hand, one needs to take into consideration that the structure
and the values in the elasticities tree allows only limited flexibility to different choices.
- A relatively small number of input variables (14) seem to determine the model output.
The reduced set of uncertain inputs is further analyzed in the next section.
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Table 11: List of the output variables analysed and their respective units
Module Output Variable Description Units
1 EMISS FC Fuel Consumption kTon
2 EMISS PM exhaust PM emissions kTon
3 EMISS CO exhaust CO emissions kTon
4 EMISS VOC exhaust VOC emissions kTon
5 EMISS NOx exhaust NOx emissions kTon
6 COST COSTpurchase Resource cost for purchase Euro
7 COST TAXregistration TAX for registration Euro
8 COST VATpurchase VAT for purchase Euro
9 COST TAXownership TAX for ownership Euro
10 COST COSTinsurance Resource cost for insurance Euro
11 COST TAXinsurance TAX cost for insurance Euro
12 COST COSTfuel Resource cost for fuel Euro
13 COST TAXfuel TAX cost for fuel Euro
14 COST VATfuel VAT cost for fuel Euro
15 COST COSTrepair Resource cost for repair and maintanance Euro
16 COST VATrepair VAT cost for repair and maintenance Euro
17 COST COSTlabour Resource cost for labour Euro
18 COST COSTlabourtax Labourtax is a resource cost Euro
19 COST COSTrest COSTrest contains monetary resource costs that can not
be allocated to other components Euro
20 COST TAXrest TAXrest contains monetary resource TAXes that can not be allocated to other components, eg public transport
subsidies are counted here as negative Euro
21 COST VATrest VATrest contains monetary resource VAT that can not be
allocated to other components Euro
22 COST Costs Total Cost Euro
23 STOCK Vehicles Number of vehicles #
24 STOCK VehKms Number of vehicle kilometres #
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85
86
87
88
89
90
91
92
93
94
95
Figure 12: The sensitivity coefficients (PEAR, PCC, SRC, SPEA, PRCC, SRRC) calculated for each of the 24 output variables and the coefficient of model determination (CMD). The indices calculated on the raw (rank) data are shown in the first (second) column. The linearity of the regression model
(SRC, SRRC) is shown in the CMD graph, which gives the percentage of data variance explained by a linear regression model.
96
5.3 Variance-based uncertainty and sensitivity analysis
The 14 most influential parameters identified from the screening analysis were used next in a
quantitative sensitivity analysis. The target in this analysis is not only to qualitatively identify
which are the important input variables but also to quantitatively determine what is the total
uncertainty of the output and how much the uncertainty of each influential input variable
affects the variance of each output variable.
For this purpose, a sample was built by selecting 5950 design points over a particular space-
filling curve in the 14th dimensional input space so as to explore each factor with a different
frequency (Cukier et al. [4]). In modelling terms, this means that 5950 individual TREMOVE
runs were executed.
The results of this analysis, i.e. uncertainty in the calculation of the stock, activity and
emissions in the case UK is presented in Figure 13 while their descriptive statistics are given in
Table 12. The figures show the evolution of the medial of each output variable over time,
together with the 5th and the 95th probability percentiles. Such an uncertainty criterion was
selected because it can better facilitate the visualisation of the output also in case of non-
symmetrical distributions.
Observing the general trends, one observes that all output variables corresponding to the
demand or stock groups exhibit increase through time (with the exception of TAXrest), similar
to fuel consumption, while the opposite is true for the emission pollutants but not fuel
consumption. From a macroscopic point of view (i.e. real life experience) one may confirm that
the output of the 5950 runs is consistent to what one would expect.
The uncertainty of each output variable depends on the uncertainty of each individual input
variable (SI) individually but also combined. The first order sensitivity index shows how much
of the output variable uncertainty can be explained individually by the uncertainty of the
specific input variable. This is shown in Figure 14. A value of 1 would mean that the output
variance is fully explained by the variance of a single input variable. For several variables, the
output uncertainty is mostly determined by a single variable. For example, more than 80% of
the cost purchase is explained by the eRPCSBASE in the future.
The actual values of the sensitivity analysis are quoted in Table 13. The first order index (SI)
corresponds to the values also shown in Figure 14. Their summation (last raw per output
variable) shows how much of the total output variable uncertainty is explained by the additive
variance of all input variables. STI also shows higher order dependencies, i.e. how much of the
output variance is explained by each specific input variable in combination to the variance of
the other input variables. The difference between the total effect (STI) and the first order index
for an input variable (SI) indicates the fraction of the output variance that is accounted for by
interactions in which the specific input variable is involved. This means that the input variable
interacts with other input parameters but it does not indicate with which parameters this
interaction occurs.
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Analytically:
Emiss Module:
FC: Fuel consumption is increasing in time but its uncertainty remains rather constant. 96% of
the FC variance in 2010 is explained by single contributions of the 14 variables, increasing
gradually to 97% in 2020 and 98% in 2030. Approximately 98% of the explained by single
contributions variance is due to only three variables: eEFfc (85% in 2010, 79% in 2020, 77%
in 2030), eEFfcratio (8% in 2010, 9% in 2020, 9% in 2030) and eRPCSBASE (1% in 2010, 7%
in 2020, 8% in 2030). The importance of eEFfc is always principal; however, it’s fraction of
explained variance exhibits a small decrease through time by the same amount that the first
order index of eRPCSBASE is increasing. Higher-order interactions are small and of the same
magnitude for all input factors. The behaviour of FC is identical with the variables belonging to
the stock module COSTfuel, TAXfuel and VATfuel.
PM: Particulate matter is decreasing through time while its uncertainty is relatively constant.
PM emissions variance explained by single contributions is due to the eEF (91% in 2010, 91%
in 2020, 93% in 2030). Other input factors identified are eEFratio and ltrip whose contribution
is less than 2% (taken singularly). Higher-order interactions are small.
CO: Carbon monoxide uncertainty is decreasing from 2010 to 2030 and so does its uncertainty.
The results are similar to those of PM. Uncertainty in the CO emissions is mostly influenced by
the hot emission factors, which taken singularly explain 87-90% of the variance. The decrease
in CO uncertainty with time is the expected gradual increase of diesel vehicles compared to
gasoline, as well as the better emission control expected from gasoline vehicles of the future.
The interaction effects of second and higher-order in the CO emissions are responsible for
about 9% of the total variance. The input factor exhibiting higher interactions is eEF, followed
by ROWNTX, RINSFRACTION and eEFratio.
VOC: The emissions and the uncertainty of the volatile organic compounds are decreasing. The
VOC uncertainty is roughly half compared to CO. 84% of the VOC emissions variance is
explained by the single contribution of the eEF in 2010 and drops to 84% in 2020-2030. Like
PM and CO, the fraction of the explained variance through interactions is decreasing in the
future, with a contribution from all variables but principally from eEF and eEFratio. The reasons
for this behaviour are similar to CO.
NOx: Nitrogen oxide emissions are decreasing with their uncertainty being relatively constant.
Like the other emissions, uncertainty in the NOx emissions is lumped principally to the
emission factors (eEF) whose self-contribution explain 94% of their variability in 2010,
dropping slightly to 91% and 92% in 2020 and 2030 respectively. Interactions are responsible
for less than 5% of the total variance.
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Figure 13: Temporal evolution of the output uncertainty for UK. The bold line represents the
median while the dotted lines correspond to the 5th and 95th percentiles. Units for each variable are
given in Table 11. Negative TAXrest values indicate subsidies delivered to public transport.
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Table 12: Descriptive statistics of the histograms presented in Figure 13 that belong to the cost,
emissions and vehicle module.
COSTpurchase [mil Euro] 2010 2020 2030
COSTrepair [mil Euro] 2010 2020 2030
Mean 84,320 99,596 114,760 mean 59,868 73,877 86,583
Median 84,178 99,323 114,467 median 59,798 73,706 86,483
st, deviation 3,628 8,375 10,187 st, deviation 1,827 7,361 8,801
TAXregistration
[mil Euro] 2010 2020 2030 VATrepair [mil Euro] 2010 2020 2030
mean 23 24 28 mean 7,302 9,110 10,699
median 23 24 27 median 7,293 9,090 10,691
st, deviation 2 2 2 st, deviation 218 896 1,073
VATpurchase
[mil Euro] 2010 2020 2030 COSTlabour [mil Euro] 2010 2020 2030
mean 10,861 11,599 13,137 mean 10,874 14,908 16,620
median 10,841 11,566 13,107 median 10,869 14,896 16,607
st, deviation 493 1,007 1,222 st, deviation 973 1,347 1,504
TAXownership
[mil Euro] 2010 2020 2030 COSTlabourtax
[mil Euro] 2010 2020 2030
mean 6,176 10,794 12,044 mean 11,629 15,941 17,771
median 6,166 10,810 12,070 median 11,634 15,944 17,773
st, deviation 494 1,161 1,403 st, deviation 1,023 1,394 1,554
COSTinsurance
[mil Euro] 2010 2020 2030 COSTrest [mil Euro] 2010 2020 2030
mean 24,974 38,372 44,742 mean 41,214 44,411 47,714
median 24,922 38,291 44,671 median 41,193 44,395 47,695
st, deviation 1,606 5,148 6,176 st, deviation 780 864 926
TAXinsurance
[mil Euro] 2010 2020 2030 TAXrest
[mil Euro] 2010 2020 2030
mean 1,263 1,938 2,259 mean -8,500 -8,818 -9,418
median 1,260 1,934 2,255 median -8,454 -8,768 -9,365
st, deviation 81 260 311 st, deviation 1,814 1,943 2,075
COSTfuel [mil Euro] 2010 2020 2030
VATrest [mil Euro] 2010 2020 2030
mean 30,963 32,309 40,455 mean 1,252 1,292 1,377
median 30,727 32,106 40,202 median 1,252 1,293 1,377
st, deviation 3,521 3,715 4,659 st, deviation 59 62 66
TAXfuel
[mil Euro] 2010 2020 2030 Costs
[mil Euro] 2010 2020 2030
mean 33,554 41,758 48,376 mean 323,890 396,704 458,755
median 33,301 41,490 48,064 median 323,333 396,036 458,228
st, deviation 3,866 4,841 5,619 st, deviation 7,930 16,826 19,578
VATfuel
[mil Euro] 2010 2020 2030
mean 8,119 9,490 11,497
median 8,041 9,418 11,412
st, deviation 1,082 1,261 1,519
FC
[ton] 2010 2020 2030 Nox
[kton] 2010 2020 2030
mean 46,962,996 55,921,530 60,405,978 mean 343,591 176,528 164,178
median 46,618,917 55,591,702 60,043,602 median 331,319 170,500 158,458
st, deviation 5,191,806 6,253,930 6,824,457 st, deviation 65,800 30,477 27,340
PM
[kton] 2010 2020 2030 VOC
[kton] 2010 2020 2030
mean 11,303 3,582 3,609 mean 52,523 32,702 33,974
median 10,750 3,400 3,421 median 46,927 30,279 31,559
st, deviation 2,881 966 964 st, deviation 19,662 8,358 8,258
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CO [kton] 2010 2020 2030
mean 327,133 141,775 142,778
median 252,000 119,402 119,439
st, deviation 227,061 71,907 72,774
Vehicles
[#] 2010 2020 2030 VehKms [x10^6] 2010 2020 2030
mean 33,626,944 37,902,637 40,996,424 mean 585,216 665,651 720,489
median 33,652,081 37,918,723 40,997,888 median 585,653 665,914 720,553
st, deviation 576,196 1,154,149 1,288,394 st, deviation 9,886 19,654 21,921
� Stock Module:
o Vehicles & Veh-kms: Both variables increase in the future but their uncertainty
increases relatively more. 89% of the stock variance in 2010 is explained by
single contributions of the 14 variables; unlike the emission variables, the
linearity of the stock module is approximately constant (temporally) but at the
same time, the magnitude of the higher-order interactions is decreasing. Five
input factors control the variability, with the most important being eRPCSBASE
and eEFfc. The factor eRPCSBASE explains roughly 65% of the variance in the
future (28% in 2010) while the factor eEFfc exhibits a substantial drop down
to 12%, although in 2010 it is the principal uncertain factor responsible for
45% of the stock variability. Other contributing factors are usresidualparaAB,
eRREPMAINFRACTION and eEFfcratio.
� Cost Module:
o COSTpurchase - VATpurchase: Both variables increase in the future while their
uncertainty becomes double relative to its value in 2010.Approximately 97%
of their variance is explained by single contributions of the 14 variables;
among them, the most important single contribution is from the eRCPSBASE
which increases from 59% in 2010 to 85% in the future. The sum of all the
iS ’s is very close to 1 indicating that the model behaves almost additively
(with respect to the input parameters). Although the higher order interactions
are responsible for less than 5% of the total variance, they exhibit a
significant decrease in their absolute magnitude from 2010 to 2020 (it is about
half that of 2010). This should be explained by the dominant importance of
eRCPSBASE in the future.
o TAXregistration: TAXregistration increases in the future while keeping the
same level of uncertainty. Uncertainty in TAXregistration is principally lumped
to uparaBT that explain ~85-86% of its variability. The second important
factor is eRCPSBASE. The sum of all the iS ’s is 0.89 in 2010 and becomes
0.92 in the future. Between all examined output variables, TAXregistration
shows the highest magnitude of interactions that, in addition, are relatively
constant and arise from all input factors.
o TAXownership: TAXownership in 2030 is double relative to 2010 but its
uncertainty is increased to a lesser extend (factor of 1.5). Approximately 95%
of the variance is explained by single contributions of the 14 variables. This
fraction is explained principally by the single contribution of two variables:
ROWNTX (89% in 2010, 56% in 2020, 48% in 2030) and eRPCSBASE (4% in
2010, 34% in 2020, 41% in 2030). In 2010, the variability is controlled by the
101
ROWNTX while in 2030 both ROWNTX and eRPCSBASE are influencing
TAXownership. Interactions are unimportant.
o COSTinsurance - TAXinsurance: Insurance variables are doubled in 2030 in
comparison to 2010 and their uncertainty is increased by the same factor.
About 94% of their variance is explained by single contributions of the 14
variables; in 2010, this is attributed to three variables: eRPCSBASE (36%),
RINSCFRACTION (47%) and eEFfc (8%). In the future, eEFfc is unimportant
while the importance of the other two variables is increased; it reaches 42%
and 51% for eRPCSBASE and RINSCFRACTION respectively. Interactions are
negligible for all input factors except for RLABOURC.
o COSTfuel – TAXfuel - VATfuel: their behaviour is identical with FC.
o COSTrepair - VATrepair: Those variables demonstrate a 50% increase in 2030
over the 2010 values but their uncertainty shows an increase by a factor
higher than 3. The explained by single contributions variance is ~ 98% and is
due to three variables: eRPCSBASE, RINSCFRACTION and eEFfc with higher
contributions from eRPCSBASE and RINSCFRACTION. Analytically, the
explained variance is ~45% for eRPCSBASE, ~41-52% for RINSCFRACTION
and ~1-7% for eEFfc. RINSCFRACTION becomes the most important factor in
the future while at the same time, eEFfc turns into unimportant.
o COSTlabour: COSTlabour increases in the future while keeping the same level
of uncertainty. Uncertainty in COSTlabour is lumped solely to RLABOURC that
explain ~94% of their variability (the sum of all the iS ’s is 0.96).
o COSTlabourtax: Like COSTlabour, COSTlabourtax increases in the future while
keeping the same level of uncertainty. Uncertainty in COSTlabourtax is lumped
solely to RLABOURTX that explain ~94% of their variability (the sum of all the
iS ’s is 0.96).
o COSTrest: COSTrest increases in the future but maintains the same level of
uncertainty. The explained by single contributions variance is ~96% and is due
principally to PUBLICCOSTCOV (90% in 2010, 81% in 2020, 80% in 2030).
The other two factors of less importance are eEFfc (constantly 4%) and
eRCPSBASE (1% in 2010, 8% in 2020, 9% in 2030).
o TAXrest - VATrest: Both variables increase (in absolute values) in the future
while preserving their uncertainty magnitude. Uncertainty in TAXrest and
VATrest is lumped solely to PUBLICCOSTCOV that explain ~96% of their
variability, i.e. the sum of all the iS ’s.
o Costs: The variable increase in the future but its uncertainty increases
relatively more. It exhibit very high and significant correlation (-0.99) with the
stock variables. Five input factors control the variability, with the most
important being eRPCSBASE and eEFfc. The factor eRPCSBASE explains
roughly 59% of the variance in the future (30% in 2010) while the factor
eEFfc exhibits a substantial drop down to 14%, although in 2010 it is the
principal uncertain factor responsible for 41% of the stock variability. Other
contributing factors are usresidualparaAB, eRREPMAINFRACTION and
eEFfcratio.
102
Figure 14: Sensitivity coefficients of the model output based on the extended-FAST method.
103
Table 13: First and Total Order Sensitivity Indices (extended-FAST) for the output variables listed in
Table 11.
COSTpurchase SI 2010 SI
2020 SI 2030 STI
2010 STI 2020 STI
2030 uparaBT 0.05 0.01 0.01 0.17 0.05 0.05 eRPCSBASE 0.59 0.85 0.85 0.73 0.91 0.91 usresidualparaAB 0.16 0.04 0.04 0.28 0.09 0.09 eEF 0.01 0.00 0.00 0.13 0.04 0.04 eEFratio 0.01 0.00 0.00 0.11 0.03 0.03 ltrip 0.01 0.00 0.00 0.12 0.04 0.04 eRREPMAINTCFRACTION 0.01 0.00 0.00 0.11 0.04 0.04 RINSCFRACTION 0.01 0.00 0.00 0.13 0.04 0.04 RLABOURC 0.01 0.00 0.00 0.10 0.03 0.03 RLABOURTX 0.01 0.00 0.00 0.12 0.04 0.04 ROWNTX 0.01 0.00 0.00 0.13 0.04 0.04 PUBLICCOSTCOV 0.01 0.00 0.00 0.11 0.04 0.04 eEFfc 0.08 0.02 0.02 0.19 0.06 0.05 eEFfcratio 0.02 0.00 0.00 0.17 0.05 0.05 SUM 0.97 0.95 0.95 TAXregistration SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.85 0.85 0.86 0.98 0.96 0.96 eRPCSBASE 0.01 0.03 0.03 0.13 0.13 0.13 usresidualparaAB 0.00 0.00 0.00 0.12 0.10 0.10 eEF 0.00 0.00 0.00 0.13 0.11 0.10 eEFratio 0.00 0.00 0.00 0.13 0.11 0.10 ltrip 0.00 0.00 0.00 0.13 0.11 0.11 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.12 0.11 0.10 RINSCFRACTION 0.00 0.00 0.00 0.12 0.10 0.09 RLABOURC 0.01 0.01 0.01 0.13 0.11 0.11 RLABOURTX 0.00 0.00 0.00 0.13 0.10 0.10 ROWNTX 0.00 0.00 0.00 0.12 0.10 0.10 PUBLICCOSTCOV 0.00 0.00 0.00 0.13 0.11 0.10 eEFfc 0.00 0.00 0.00 0.12 0.10 0.10 eEFfcratio 0.00 0.00 0.00 0.12 0.10 0.10 SUM 0.89 0.92 0.92 VATpurchase SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.01 0.00 0.00 0.15 0.04 0.04 eRPCSBASE 0.56 0.84 0.84 0.71 0.91 0.91 usresidualparaAB 0.19 0.05 0.05 0.32 0.09 0.10 eEF 0.01 0.00 0.00 0.15 0.04 0.04 eEFratio 0.01 0.00 0.00 0.12 0.04 0.04 ltrip 0.01 0.00 0.00 0.13 0.04 0.04 eRREPMAINTCFRACTION 0.01 0.00 0.00 0.12 0.04 0.04 RINSCFRACTION 0.01 0.01 0.01 0.15 0.05 0.05 RLABOURC 0.01 0.00 0.00 0.12 0.03 0.04 RLABOURTX 0.01 0.00 0.00 0.13 0.04 0.04 ROWNTX 0.01 0.00 0.00 0.14 0.04 0.04 PUBLICCOSTCOV 0.01 0.00 0.00 0.12 0.04 0.04 eEFfc 0.10 0.03 0.02 0.21 0.06 0.06 eEFfcratio 0.02 0.01 0.01 0.18 0.05 0.05 SUM 0.97 0.94 0.94 TAXownership SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.04 0.34 0.41 0.07 0.37 0.45 usresidualparaAB 0.00 0.00 0.00 0.03 0.02 0.02 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.02 0.02 0.02 ltrip 0.00 0.00 0.00 0.02 0.02 0.02 eRREPMAINTCFRACTION 0.00 0.03 0.04 0.03 0.05 0.06 RINSCFRACTION 0.00 0.00 0.00 0.02 0.02 0.02 RLABOURC 0.00 0.00 0.00 0.02 0.02 0.02 RLABOURTX 0.00 0.00 0.00 0.02 0.02 0.02 ROWNTX 0.89 0.56 0.48 0.93 0.60 0.51 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.01 eEFfc 0.02 0.01 0.01 0.05 0.03 0.03 eEFfcratio 0.00 0.00 0.00 0.03 0.02 0.02 SUM 0.96 0.95 0.95
104
COSTinsurance SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.01 0.00 0.00 eRPCSBASE 0.36 0.42 0.42 0.41 0.46 0.45 usresidualparaAB 0.02 0.00 0.00 0.04 0.01 0.01 eEF 0.00 0.00 0.00 0.02 0.01 0.01 eEFratio 0.00 0.00 0.00 0.02 0.01 0.01 ltrip 0.00 0.00 0.00 0.02 0.01 0.01 eRREPMAINTCFRACTION 0.00 0.01 0.01 0.02 0.01 0.01 RINSCFRACTION 0.47 0.51 0.51 0.51 0.53 0.54 RLABOURC 0.01 0.00 0.00 0.15 0.13 0.12 RLABOURTX 0.00 0.00 0.00 0.01 0.00 0.00 ROWNTX 0.00 0.00 0.00 0.02 0.01 0.01 PUBLICCOSTCOV 0.00 0.00 0.00 0.01 0.00 0.00 eEFfc 0.08 0.01 0.01 0.10 0.02 0.02 eEFfcratio 0.01 0.00 0.00 0.02 0.01 0.01 SUM 0.94 0.94 0.95 TAXinsurance SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.01 0.00 0.00 eRPCSBASE 0.36 0.42 0.41 0.41 0.45 0.45 usresidualparaAB 0.02 0.00 0.00 0.04 0.01 0.01 eEF 0.00 0.00 0.00 0.02 0.01 0.01 eEFratio 0.00 0.00 0.00 0.02 0.01 0.01 ltrip 0.00 0.00 0.00 0.02 0.01 0.01 eRREPMAINTCFRACTION 0.00 0.01 0.01 0.02 0.01 0.01 RINSCFRACTION 0.47 0.51 0.51 0.51 0.54 0.54 RLABOURC 0.01 0.00 0.00 0.15 0.13 0.12 RLABOURTX 0.00 0.00 0.00 0.01 0.00 0.00 ROWNTX 0.00 0.00 0.00 0.02 0.01 0.01 PUBLICCOSTCOV 0.00 0.00 0.00 0.01 0.00 0.00 eEFfc 0.07 0.01 0.01 0.10 0.02 0.02 eEFfcratio 0.01 0.00 0.00 0.02 0.01 0.01 SUM 0.94 0.94 0.95 COSTfuel SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.01 0.06 0.08 0.03 0.09 0.11 usresidualparaAB 0.00 0.00 0.00 0.02 0.02 0.02 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.02 0.02 0.02 ltrip 0.02 0.02 0.02 0.04 0.04 0.04 eRREPMAINTCFRACTION 0.00 0.01 0.01 0.02 0.03 0.03 RINSCFRACTION 0.00 0.00 0.00 0.02 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.02 0.01 0.01 RLABOURTX 0.00 0.00 0.00 0.02 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.02 eEFfc 0.85 0.79 0.77 0.88 0.82 0.80 eEFfcratio 0.08 0.09 0.09 0.09 0.10 0.10 SUM 0.96 0.97 0.97 TAXfuel SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.01 0.06 0.08 0.03 0.08 0.10 usresidualparaAB 0.00 0.00 0.00 0.02 0.02 0.02 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.02 0.02 0.02 ltrip 0.02 0.02 0.02 0.04 0.04 0.04 eRREPMAINTCFRACTION 0.00 0.01 0.01 0.02 0.03 0.03 RINSCFRACTION 0.00 0.00 0.00 0.02 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.02 0.01 0.01 RLABOURTX 0.00 0.00 0.00 0.02 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.03 0.03 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.02 eEFfc 0.85 0.79 0.77 0.88 0.82 0.80 eEFfcratio 0.08 0.09 0.09 0.09 0.10 0.10 SUM 0.96 0.97 0.97
105
VATfuel SI 2010 SI
2020 SI 2030 STI
2010 STI 2020 STI
2030 uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.01 0.06 0.09 0.03 0.09 0.11 usresidualparaAB 0.00 0.00 0.00 0.02 0.02 0.02 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.02 0.02 0.02 ltrip 0.02 0.02 0.02 0.04 0.04 0.04 eRREPMAINTCFRACTION 0.00 0.01 0.01 0.02 0.03 0.03 RINSCFRACTION 0.00 0.00 0.00 0.02 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.02 0.01 0.01 RLABOURTX 0.00 0.00 0.00 0.02 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.02 eEFfc 0.85 0.79 0.77 0.88 0.82 0.80 eEFfcratio 0.08 0.09 0.09 0.09 0.10 0.10 SUM 0.96 0.97 0.97 COSTrepair SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.01 0.00 0.00 0.03 0.02 0.02 eRPCSBASE 0.45 0.46 0.45 0.53 0.50 0.49 usresidualparaAB 0.02 0.00 0.00 0.05 0.02 0.02 eEF 0.00 0.00 0.00 0.04 0.02 0.02 eEFratio 0.00 0.00 0.00 0.07 0.04 0.04 ltrip 0.00 0.00 0.00 0.03 0.02 0.02 eRREPMAINTCFRACTION 0.41 0.51 0.52 0.45 0.54 0.55 RINSCFRACTION 0.00 0.00 0.00 0.02 0.01 0.01 RLABOURC 0.00 0.00 0.00 0.01 0.01 0.01 RLABOURTX 0.00 0.00 0.00 0.02 0.01 0.01 ROWNTX 0.00 0.00 0.00 0.02 0.01 0.01 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.01 0.01 eEFfc 0.07 0.01 0.01 0.09 0.01 0.02 eEFfcratio 0.01 0.00 0.00 0.02 0.01 0.01 SUM 0.97 0.98 0.99 VATrepair SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.03 0.02 0.02 eRPCSBASE 0.46 0.47 0.46 0.55 0.52 0.51 usresidualparaAB 0.02 0.00 0.00 0.05 0.02 0.02 eEF 0.00 0.00 0.00 0.05 0.02 0.02 eEFratio 0.00 0.00 0.00 0.08 0.04 0.04 ltrip 0.00 0.00 0.00 0.03 0.02 0.02 eRREPMAINTCFRACTION 0.38 0.50 0.51 0.43 0.53 0.54 RINSCFRACTION 0.00 0.00 0.00 0.03 0.01 0.01 RLABOURC 0.00 0.00 0.00 0.02 0.01 0.01 RLABOURTX 0.00 0.00 0.00 0.02 0.01 0.01 ROWNTX 0.00 0.00 0.00 0.02 0.01 0.01 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.01 0.01 eEFfc 0.10 0.01 0.01 0.11 0.02 0.02 eEFfcratio 0.01 0.00 0.00 0.03 0.01 0.01 SUM 0.98 0.99 0.99 COSTlabour SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.01 0.01 0.01 eRPCSBASE 0.00 0.01 0.01 0.01 0.02 0.02 usresidualparaAB 0.00 0.00 0.00 0.01 0.01 0.01 eEF 0.00 0.00 0.00 0.01 0.01 0.01 eEFratio 0.00 0.00 0.00 0.01 0.01 0.01 ltrip 0.00 0.00 0.00 0.01 0.01 0.01 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.01 0.01 0.01 RINSCFRACTION 0.00 0.00 0.00 0.01 0.01 0.01 RLABOURC 0.94 0.93 0.93 0.98 0.97 0.97 RLABOURTX 0.02 0.02 0.02 0.04 0.04 0.04 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.02 eEFfc 0.00 0.00 0.00 0.02 0.02 0.02 eEFfcratio 0.00 0.00 0.00 0.02 0.02 0.02 SUM 0.96 0.96 0.96
106
COSTlabourtax SI 2010 SI
2020 SI 2030 STI
2010 STI 2020 STI
2030 uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.00 0.01 0.01 0.02 0.03 0.03 usresidualparaAB 0.00 0.00 0.00 0.02 0.02 0.02 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.02 0.02 0.02 ltrip 0.00 0.00 0.00 0.02 0.02 0.02 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.02 0.02 0.02 RINSCFRACTION 0.00 0.00 0.00 0.02 0.02 0.02 RLABOURC 0.02 0.02 0.02 0.03 0.04 0.04 RLABOURTX 0.94 0.92 0.92 0.98 0.96 0.96 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.02 eEFfc 0.00 0.00 0.00 0.03 0.02 0.02 eEFfcratio 0.00 0.00 0.00 0.02 0.02 0.02 SUM 0.96 0.96 0.96 COSTrest SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.01 0.08 0.09 0.04 0.11 0.12 usresidualparaAB 0.00 0.00 0.00 0.04 0.04 0.04 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.03 0.03 0.03 ltrip 0.00 0.00 0.00 0.02 0.02 0.02 eRREPMAINTCFRACTION 0.00 0.01 0.01 0.03 0.03 0.03 RINSCFRACTION 0.00 0.00 0.00 0.03 0.02 0.02 RLABOURC 0.00 0.00 0.00 0.04 0.04 0.04 RLABOURTX 0.00 0.00 0.00 0.03 0.03 0.03 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.90 0.81 0.80 0.94 0.85 0.84 eEFfc 0.04 0.04 0.04 0.08 0.08 0.08 eEFfcratio 0.01 0.01 0.01 0.04 0.04 0.04 SUM 0.97 0.96 0.95 TAXrest SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.00 0.00 0.00 0.03 0.03 0.03 usresidualparaAB 0.00 0.00 0.00 0.03 0.03 0.03 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.03 0.03 0.03 ltrip 0.00 0.00 0.00 0.02 0.02 0.02 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.02 0.02 0.02 RINSCFRACTION 0.00 0.00 0.00 0.02 0.02 0.02 RLABOURC 0.00 0.00 0.00 0.03 0.03 0.03 RLABOURTX 0.00 0.00 0.00 0.03 0.03 0.03 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.96 0.96 0.96 1.00 1.00 1.00 eEFfc 0.00 0.00 0.00 0.04 0.04 0.04 eEFfcratio 0.00 0.00 0.00 0.04 0.04 0.04 SUM 0.96 0.96 0.96 VATrest SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.00 0.01 0.01 0.03 0.04 0.04 usresidualparaAB 0.00 0.00 0.00 0.03 0.03 0.03 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.03 0.03 0.03 ltrip 0.00 0.00 0.00 0.02 0.02 0.02 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.03 0.03 0.03 RINSCFRACTION 0.00 0.00 0.00 0.03 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.03 0.03 0.03 RLABOURTX 0.00 0.00 0.00 0.03 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.95 0.95 0.95 0.99 0.99 0.99 eEFfc 0.00 0.00 0.00 0.04 0.04 0.04 eEFfcratio 0.00 0.00 0.00 0.04 0.04 0.04 SUM 0.96 0.96 0.96
107
Costs SI 2010 SI
2020 SI 2030 STI
2010 STI 2020 STI
2030 uparaBT 0.00 0.00 0.00 0.03 0.02 0.02 eRPCSBASE 0.30 0.59 0.59 0.33 0.63 0.62 usresidualparaAB 0.03 0.01 0.01 0.07 0.04 0.04 eEF 0.00 0.00 0.00 0.03 0.02 0.02 eEFratio 0.00 0.00 0.00 0.04 0.03 0.03 ltrip 0.01 0.00 0.00 0.04 0.02 0.02 eRREPMAINTCFRACTION 0.05 0.08 0.08 0.10 0.11 0.11 RINSCFRACTION 0.01 0.02 0.02 0.04 0.03 0.03 RLABOURC 0.01 0.01 0.01 0.06 0.03 0.03 RLABOURTX 0.01 0.00 0.00 0.03 0.02 0.02 ROWNTX 0.01 0.00 0.00 0.05 0.02 0.02 PUBLICCOSTCOV 0.02 0.01 0.00 0.05 0.02 0.02 eEFfc 0.41 0.14 0.14 0.45 0.16 0.16 eEFfcratio 0.03 0.01 0.01 0.05 0.02 0.02 SUM 0.88 0.88 0.88 FC SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.02 0.02 0.02 eRPCSBASE 0.01 0.07 0.08 0.03 0.09 0.11 usresidualparaAB 0.00 0.00 0.00 0.02 0.02 0.02 eEF 0.00 0.00 0.00 0.02 0.02 0.02 eEFratio 0.00 0.00 0.00 0.02 0.02 0.02 ltrip 0.02 0.02 0.02 0.04 0.04 0.04 eRREPMAINTCFRACTION 0.00 0.01 0.01 0.02 0.03 0.03 RINSCFRACTION 0.00 0.00 0.00 0.02 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.02 0.01 0.01 RLABOURTX 0.00 0.00 0.00 0.02 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.02 eEFfc 0.85 0.79 0.77 0.88 0.82 0.80 eEFfcratio 0.08 0.09 0.09 0.09 0.10 0.10 SUM 0.96 0.97 0.98 PM SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.01 0.01 0.00 0.03 0.02 0.01 eRPCSBASE 0.00 0.01 0.01 0.02 0.03 0.02 usresidualparaAB 0.00 0.00 0.00 0.01 0.01 0.01 eEF 0.91 0.91 0.93 0.95 0.95 0.96 eEFratio 0.02 0.01 0.01 0.05 0.04 0.03 ltrip 0.02 0.01 0.01 0.05 0.03 0.03 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.02 0.02 0.02 RINSCFRACTION 0.00 0.00 0.00 0.02 0.02 0.02 RLABOURC 0.00 0.00 0.00 0.02 0.02 0.02 RLABOURTX 0.00 0.00 0.00 0.02 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.02 0.02 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.02 0.02 eEFfc 0.00 0.00 0.00 0.02 0.02 0.02 eEFfcratio 0.00 0.00 0.00 0.02 0.02 0.02 SUM 0.97 0.96 0.96 CO SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.04 0.03 0.03 eRPCSBASE 0.00 0.00 0.00 0.04 0.03 0.03 usresidualparaAB 0.01 0.00 0.00 0.04 0.02 0.02 eEF 0.87 0.90 0.90 0.97 0.98 0.98 eEFratio 0.02 0.01 0.01 0.08 0.07 0.06 ltrip 0.01 0.00 0.00 0.06 0.05 0.05 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.05 0.04 0.04 RINSCFRACTION 0.00 0.00 0.00 0.06 0.05 0.05 RLABOURC 0.00 0.00 0.00 0.05 0.04 0.04 RLABOURTX 0.00 0.00 0.00 0.04 0.04 0.04 ROWNTX 0.00 0.00 0.00 0.06 0.06 0.06 PUBLICCOSTCOV 0.00 0.00 0.00 0.05 0.04 0.04 eEFfc 0.00 0.00 0.00 0.05 0.04 0.04 eEFfcratio 0.00 0.00 0.00 0.05 0.04 0.04 SUM 0.91 0.92 0.91
108
VOC SI 2010 SI
2020 SI 2030 STI
2010 STI 2020 STI
2030 uparaBT 0.01 0.00 0.00 0.04 0.02 0.02 eRPCSBASE 0.00 0.02 0.02 0.03 0.05 0.04 usresidualparaAB 0.01 0.00 0.00 0.03 0.02 0.02 eEF 0.88 0.84 0.84 0.95 0.92 0.91 eEFratio 0.02 0.03 0.04 0.07 0.09 0.09 ltrip 0.01 0.01 0.01 0.06 0.05 0.05 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.04 0.04 0.03 RINSCFRACTION 0.00 0.00 0.00 0.05 0.04 0.04 RLABOURC 0.00 0.00 0.00 0.04 0.04 0.03 RLABOURTX 0.00 0.00 0.00 0.04 0.03 0.03 ROWNTX 0.00 0.00 0.00 0.05 0.05 0.05 PUBLICCOSTCOV 0.00 0.00 0.00 0.04 0.03 0.03 eEFfc 0.00 0.00 0.00 0.04 0.04 0.03 eEFfcratio 0.00 0.00 0.00 0.04 0.04 0.03 SUM 0.93 0.91 0.91 NOx SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.01 0.00 0.02 0.02 0.02 eRPCSBASE 0.00 0.02 0.01 0.02 0.04 0.03 usresidualparaAB 0.00 0.00 0.00 0.02 0.02 0.02 eEF 0.94 0.91 0.92 0.98 0.96 0.97 eEFratio 0.01 0.01 0.01 0.04 0.04 0.03 ltrip 0.00 0.00 0.00 0.02 0.03 0.03 eRREPMAINTCFRACTION 0.00 0.00 0.00 0.02 0.03 0.03 RINSCFRACTION 0.00 0.00 0.00 0.03 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.02 0.03 0.03 RLABOURTX 0.00 0.00 0.00 0.02 0.02 0.03 ROWNTX 0.00 0.00 0.00 0.03 0.03 0.03 PUBLICCOSTCOV 0.00 0.00 0.00 0.02 0.03 0.03 eEFfc 0.00 0.00 0.00 0.02 0.03 0.03 eEFfcratio 0.00 0.00 0.00 0.02 0.03 0.03 SUM 0.96 0.95 0.95 Vehicles SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.01 0.00 0.00 0.04 0.02 0.02 eRPCSBASE 0.28 0.65 0.64 0.33 0.69 0.68 usresidualparaAB 0.05 0.02 0.02 0.11 0.04 0.04 eEF 0.00 0.00 0.00 0.03 0.02 0.02 eEFratio 0.00 0.00 0.00 0.05 0.02 0.02 ltrip 0.01 0.00 0.00 0.05 0.02 0.02 eRREPMAINTCFRACTION 0.04 0.07 0.07 0.10 0.09 0.09 RINSCFRACTION 0.01 0.01 0.01 0.04 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.07 0.03 0.03 RLABOURTX 0.00 0.00 0.00 0.04 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.05 0.03 0.03 PUBLICCOSTCOV 0.00 0.00 0.00 0.05 0.02 0.02 eEFfc 0.45 0.12 0.12 0.50 0.14 0.14 eEFfcratio 0.03 0.01 0.01 0.06 0.02 0.03 SUM 0.89 0.88 0.88 VehKms SI
2010 SI 2020 SI
2030 STI 2010 STI
2020 STI 2030
uparaBT 0.00 0.00 0.00 0.04 0.02 0.02 eRPCSBASE 0.28 0.64 0.64 0.33 0.68 0.67 usresidualparaAB 0.05 0.02 0.02 0.10 0.04 0.04 eEF 0.00 0.00 0.00 0.03 0.02 0.02 eEFratio 0.00 0.00 0.00 0.05 0.02 0.03 ltrip 0.01 0.00 0.00 0.05 0.02 0.02 eRREPMAINTCFRACTION 0.04 0.07 0.07 0.10 0.10 0.10 RINSCFRACTION 0.01 0.01 0.01 0.04 0.03 0.03 RLABOURC 0.00 0.00 0.00 0.07 0.03 0.03 RLABOURTX 0.00 0.00 0.00 0.04 0.02 0.02 ROWNTX 0.00 0.00 0.00 0.05 0.03 0.02 PUBLICCOSTCOV 0.00 0.00 0.00 0.05 0.02 0.02 eEFfc 0.45 0.12 0.13 0.50 0.14 0.15 eEFfcratio 0.03 0.01 0.01 0.06 0.02 0.03 SUM 0.89 0.88 0.88
A more detailed representation of the temporal evolution of the output uncertainty, for all
vehicle types, compared against the TREMOVE basecase can be found in the ANNEX.
109
5.4 Discussion
A summary of the uncertainty analysis of TREMOVE application in UK is shown in Table 14. The
median value calculated together with the coefficient of variance (cov=standard deviation over
mean) is shown per output variable. The output variables are in a decreasing order of cov in the
year 2030. The results are shown in summary for all vehicle categories. The results of the
uncertainty analysis per vehicle category are shown in the Annex.
Table 14: Summary of Uncertainty Analysis results for UK
Output Variable Units Median 2010 Median 2020 Median 2030 cov 2010 cov 2020 cov 2030
CO Ton 252,000 119,402 119,439 69% 51% 51%
PM Ton 10,750 3,400 3,421 25% 27% 27%
VOC Ton 46,927 30,279 31,559 37% 26% 24%
TAXrest M€ -8,454 -8,768 -9,365 21% 22% 22%
NOx Ton 331,319 170,500 158,458 19% 17% 17%
COSTinsurance M€ 24,922 38,291 44,671 6% 13% 14%
TAXinsurance M€ 1,260 1,934 2,255 6% 13% 14%
VATfuel M€ 8,041 9,418 11,412 13% 13% 13%
TAXfuel M€ 33,301 41,490 48,064 12% 12% 12%
COSTfuel M€ 30,727 32,106 40,202 11% 11% 12%
TAXownership M€ 6,166 10,810 12,070 8% 11% 12%
FC Ton 46,618,917 55,591,702 60,043,602 11% 11% 11%
COSTrepair M€ 59,798 73,706 86,483 3% 10% 10%
VATrepair M€ 7,293 9,090 10,691 3% 10% 10%
COSTlabour M€ 10,869 14,896 16,607 9% 9% 9%
COSTlabourtax M€ 11,634 15,944 17,773 9% 9% 9%
VATpurchase M€ 10,841 11,566 13,107 5% 9% 9%
COSTpurchase M€ 84,178 99,323 114,467 4% 8% 9%
TAXregistration M€ 22.5 24.2 27.4 7% 8% 8%
VATrest M€ 1,252 1,293 1,377 5% 5% 5%
Costs M€ 323,333 396,036 458,228 2% 4% 4%
Vehicles # 33,652,081 37,918,723 40,997,888 2% 3% 3%
Vehkms ×106 km 585,653 665,914 720,553 2% 3% 3%
COSTrest M€ 41,193 44,395 47,695 2% 2% 2%
The most important conclusions drawn from the uncertainty analysis include:
� The uncertainty is large for the emission of pollutants, mostly due to the uncertainty in the
emission factors. This is in the order of 20-30% but can reach up to 50% in the case of CO.
� Fuel dependent variables are second with regard to output uncertainty with cov values in
the order of 10-15%.
� Total cost figures exhibit uncertainty ranges in the order of 4-10%, i.e. they are rather
small depended on the variance of input data.
� Finally, population and activity data exhibit very little uncertainty, in the order of 2-3%. The
uncertainty per vehicle category (see Annex) is of the same magnitude. This shows that the
activity and population is little elastic to changes in costs and other variables in the model.
This is potentially very important. This means that changes in the activity and population
data are very limited despite the much wider uncertainty ranges assumed for the input
variables.
110
� The levels of all output variables corresponding to the demand or stock group increase
through time (with the exception of TAXrest). For the emissions group the opposite is true
except for the fuel consumption. This is expected assuming that road transport activity will
continue to increase in the future while emission standards will continue to effectively
control emissions. The increase in fuel consumption remains to be seen – it is reminded
that the TREMOVE version examined does not include hybrid vehicles, or electric vehicles.
� The variables corresponding to the demand or stock group that demonstrate an increase in
their uncertainty in the future are: COSTpurchase, VATpurchase, TAXownership,
COSTinsurance, TAXinsurance, COSTrepair, VATrepair, Costs, Vehicles and Vehkms. The
highest increase has been seen for COSTrepair and VATrepair. The variables corresponding
to the emissions group that demonstrate a decrease in their uncertainty in the future are
VOC and CO. The largest decrease has been seen for CO, however its uncertainty is always
the highest among the 24 considered output variables. All the other variables that are not
mentioned exhibit a very small variability in their temporal uncertainty. The large but
decreasing uncertainty of CO and VOC is caused by their large dependence on the emission
factors of gasoline cars. As gasoline cars are both expected to become less important in the
future (compared to diesel ones) but as also Euro 5 and 6 emission standards are expected
to lead to even more strict control over the lifetime of the vehicles, the observed trend is
explainable.
With regard to the sensitivity of the output to the input variables Table 15 shows a summary of
the first order and higher order contribution of each input variable to the output variables
uncertainty. The values have been ranked in a descending order according to the SI2030
value. Based on the sensitivity analysis, the following conclusions may be drawn:
Table 15: Summary of Sensitivity Analysis results for UK
Output Variable
Most Important Input Variable ΣSI
2010 ΣSI2020 ΣSI
2030 ΣSTI2010 ΣSTI
2020 ΣSTI2030
COSTrepair eRREPMAINTCFRACTION, eRPCSBASE 0.97 0.98 0.99 1.41 1.22 1.23
VATrepair eRREPMAINTCFRACTION, eRPCSBASE 0.98 0.99 0.99 1.48 1.23 1.24
FC eEFfc 0.96 0.97 0.98 1.26 1.27 1.27
COSTfuel eEFfc 0.96 0.97 0.97 1.26 1.27 1.27
TAXfuel eEFfc 0.96 0.97 0.97 1.26 1.27 1.27
VATfuel eEFfc 0.96 0.97 0.97 1.26 1.27 1.27
COSTlabour RLABOURC 0.96 0.96 0.96 1.16 1.16 1.16
COSTlabourtax RLABOURTX 0.96 0.96 0.96 1.25 1.24 1.24
TAXrest PUBLICCOSTCOV 0.96 0.96 0.96 1.36 1.36 1.36
VATrest PUBLICCOSTCOV 0.96 0.96 0.96 1.36 1.36 1.36
PM eEF 0.97 0.96 0.96 1.26 1.24 1.21
COSTpurchase eRPCSBASE 0.97 0.95 0.95 2.62 1.49 1.48
TAXownership ROWNTX 0.96 0.95 0.95 1.32 1.25 1.23
COSTinsurance RINSCFRACTION 0.94 0.94 0.95 1.36 1.21 1.20
TAXinsurance RINSCFRACTION 0.94 0.94 0.95 1.36 1.21 1.20
COSTrest PUBLICCOSTCOV 0.97 0.96 0.95 1.38 1.35 1.35
NOx eEF 0.96 0.95 0.95 1.27 1.31 1.31
VATpurchase eRPCSBASE 0.97 0.94 0.94 2.76 1.50 1.53
TAXregistration uparaBT 0.89 0.92 0.92 2.61 2.35 2.30
111
CO eEF 0.91 0.92 0.91 1.61 1.52 1.54
VOC eEF 0.93 0.91 0.91 1.50 1.45 1.42
Costs eRPCSBASE, eEFfc 0.88 0.88 0.88 1.37 1.16 1.16
Vehicles eRPCSBASE, eEFfc 0.89 0.88 0.88 1.51 1.19 1.19
Vehkms eRPCSBASE, eEFfc 0.89 0.88 0.88 1.51 1.18 1.18
� Ten out of the fourteen uncertain inputs are of principal importance for one or more output
variables.
� The hot emission factors (eEF) influences most the variability of the emissions (VOC, NOX,
PM, CO) while the basic road vehicle purchase resource cost (eRCPSBASE) controls the
variability of the stock and activity variables (vehicles and vehicle-kms). On the other hand,
many input factors are responsible for the variability of the cost related output.
� All model outputs exhibit high linearity. The least amount of explained-by-single
contributions variance estimated was 88% and corresponds to the output variables Costs,
Vehicles and Vehkms. The remaining fraction depends on higher order interdependencies
between the input variables.
� The linearity of the output variables is generally constant in time. On the other hand, the
total effects are either constant or decreasing in the future. This implies that the control of
the singular perturbations will be always effective.
� The highest amount of interaction effects has been seen for TAXregistration but it is
attributed to all input factors. The highest decrease in the effect of the second and higher
order terms has been observed for COSTpurchase and VATpurchase. This gives the
opportunity to work on the uncertainty of the RPCSBASE only in the future in order to
reduce the variability of COSTpurchase and VATpurchase.
It should be made clear that the conclusions reached from this analysis are specific to UK only.
Selecting a different country to perform the analysis would have an impact on both the total
uncertainty ranges estimated (Table 14) and the impact of each input variable uncertainty to the
output (Table 15). For example, repeating the analysis in the case of Greece would lead to
different cost and pollutants uncertainty as the Greek fleet does not include diesel passenger
cars except of taxis. This would have an impact on the contribution of uncertainty from gasoline
vs diesel passenger car emission factors. Similarly, uncertainty ranges for several values in
some of the late Member States (e.g. Bulgaria, Romania) would have been relatively wider than
in UK, mostly due to relatively poorer statistics available in these countries. The particular
numerical values that would have been produced would be interest in a national environment
where one needs to assess the uncertainty of the national emission calculations. However, they
would offer limited additional insights in our work. This is mainly because the uncertainty ranges
between the different output categories are clearly distinct, with activity data, cost items, and
total emissions to be on completely different levels (CV of ~2% for activity, ~10% for costs,
>20% for emissions). This is due to the structure of the TREMOVE model, i.e. its rather linear
behaviour of output to input values and the limited impact to total activity. Using a different
uncertainty range for some of the input variables would have a rather limited impact on activity
and a proportional impact to the affected cost category and a limited impact on emissions. Such
an effect does not change the main conclusions reached in this study concerning the treatment
of uncertainty and ways of improving it in future applications of Tremove.
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6 Uncertainty and sensitivity analysis of scenarios
6.1 General
TREMOVE is by definition used to calculate the impact of different policy options, i.e. executing
scenarios to identify what is the impact of different policies on activity data, costs, and finally
emissions. It is therefore important to analyze the uncertainty of a number of different
scenarios in comparison to the baseline. In order to demonstrate this, we decided to run three
alternative scenarios that may activate different paths of uncertainty of the model. The three
scenarios considered were the following:
1. Increase the ownership tax of passenger cars to demonstrate shifts to other modes of
transport within the road sector.
2. Increase the fuel price to demonstrate general drop in transport activity
3. Introduce a new emission standard (Euro VI heavy duty vehicles) to demonstrate drop
in total emissions.
6.2 Methodology
Uncertainty and sensitivity analysis is applied in order to study the effect of selected
perturbations in the TREMOVE model. For each of the scenarios, two different sets of Monte
Carlo runs are computed: one corresponding to the control case and one to the perturbed case.
Each setting is related with a set of 512 simulations, based on quasi-random LPτ sequences.
The original set of 33 uncertain input factors was kept as the minimum input factor basis. A
Kolmogorov-Smirnov test is then applied on the results of the two different settings in order to
identify the variables where their output distribution has changed between the scenario and the
baseline. For those variables, sensitivity analysis investigates the changes in the output
distribution in terms of the changes in the input factors importance versus the new
parameterization changes.
6.3 Ownership tax increase
6.3.1 Description
The first scenario is a policy scenario which assumes that the ownership tax of passenger cars
is set to increase in the future. Such a policy could be followed for example as a means to
decrease passenger car use and promote other means of transport. Other reasons would be to
introduce a ‘green’ ownership tax, i.e. increase revenues that can be then used to fund
environment protection activities. By increasing the ownership tax of cars one would expect to
shift traffic to busses and power two wheelers, and also some traffic to aviation and railways.
In order to demonstrate the TREMOVE uncertainty behaviour to such a policy, it was decided to
increase the ownership tax of all passenger car categories. We assumed a rather aggressive
113
policy scenario, i.e. assuming that the ownership tax will linearly triple up to 2030. An example
of the values used for gasoline passenger cars in the category 1,4-2,0 l is shown in Figure 15.
It was considered to keep the uncertainty in the ownership tax of the scenario proportional to
the uncertainty in the baseline. Hence, the standard deviation used for the input uncertainty in
the scenario would be three times higher than the one used for the baseline uncertainty. The
mean and standard deviation values used in the Years 2011 and 2030 are summarized in Table
16.
Car 1.4-2.0l - Petrol
0
100
200
300
400
500
600
700
800
2000 2010 2020 2030
Year
Euro
/Year
ROWNTX - Scenario
ROWNTX - Baseline
Figure 15: Evolution of the ownership tax in the basecase of the TREMOVE model and the scenario
designed (Example gasoline cars 1.4 – 2.0 l).
Table 16: Ownership tax (€/year) in Scenario 1 for the boundary years 2011 and 2030. Values
have been linearly varied in the intermediate years.
Mean Stdev Vehicle Type
2011 2030 2011 2030
Car <1.4l - Petrol 126.0 378.0 0.0 0.0
Car 1.4-2.0l - Petrol 239.0 717.0 23.4 70.3
Car >2.0l - Petrol 769.0 2,307.0 66.2 198.7
Car <1.4l - Diesel 126.0 378.0 0.0 0.0
Car 1.4-2.0l - Diesel 239.0 717.0 23.4 70.3
Car >2.0l - Diesel 769.0 2,307.0 66.2 198.7
Car <1.4l - CNG 126.0 378.0 0.0 0.0
Car 1.4-2.0l - CNG 239.0 717.0 23.4 70.3
Car >2.0l - CNG 769.0 2,307.0 66.2 198.7
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6.3.2 Results
A Kolmogorov-Smirnov test identified that different output distributions exist for fifteen of the
twenty-four variables. For all other output variables, the scenario and the baseline result to
almost identical output distributions. The variables for which statistically significant differences
in the output values exist are (in chronological order):
- TAXownership: all years after 2012
- Costs, Vehicles, VehKms: all years after 2015
- COSTrest: all years after 2022
- COSTpurchase, VATpurchase, COSTrepair, VATrepair: all years after 2023
- COSTinsurance, VATfuel: all years after 2027
- TAXinsurance, TAXfuel, FC: all years after 2028
- COSTfuel: all years after 2029
Figure 16 shows the uncertainty evolution for the abovementioned fifteen output variables in
the baseline (red) and the scenario (blue) run for UK. ‘TAXownership’ variable that, in terms of
its median, takes statistically significant higher values in the scenario run after 2012. This
would be expected given the large variation introduced in the ownership tax values.
In addition to the observed changes in ‘TAXownership’, increasing the ownership tax also shifts
overall costs higher and decreases the total number of vehicles, vehkms, fuel consumption and
the other cost related variables. Table 17 shows the difference in the medial values for the
output variables which are mostly affected. The differences are up to 3%.
115
Figure 16: Temporal evolution of the output uncertainty for UK, for the output variables that
demonstrate changes in their distribution between the scenario (blue) and the baseline (red) case.
The bold line represents the median while the dotted lines correspond to the 5th and 95th percentiles.
116
Table 17: Median values of the scenario 1 and the baseline in the years 2020 and 2030 and
percentage difference between the two.
Vehicle km [x10^6]
2020 2030
Vehicles [#]
2020 2030
Baseline 657,732 712,662 Baseline 37,434,661 40,543,655 Scenario 1 651,764 701,648 Scenario 1 37,091,918 39,884,879 % diff -0.9 -1.5 % diff -0.9 -1.6
NOx [Ton]
2020 2030
PM [Ton]
2020 2030
Baseline 204,279 179,722 Baseline 4,128 4,068 Scenario 1 203,329 178,311 Scenario 1 4,102 4,017 % diff -0.5 -0.8 % diff -0.6 -1.3
Costs [M€]
2020 2030
TAXownership [M€]
2020 2030
Baseline 375,041 434,464 Baseline 11,345 12,513 Scenario 1 380,952 446,045 Scenario 1 20,776 33,746 % diff 1.6 2.7 % diff 83.1 169.7
In order to further elaborate on the differences, Figure 17 shows examples of the scenario
effect in individual vehicle categories for the total activity and the vehicle population. Even at
this higher magnification level differences are only marginal. Total car activity in 2030 drops by
1% while power two wheeler activity (but also bus and light commercial vehicle activities) is
identical between the scenario and the baseline. Increase in the car ownership tax decreased
total road transport activity but does not seem to shift passenger transport to any other
modes.
400,000
450,000
500,000
550,000
600,000
650,000
700,000
2005 2010 2015 2020 2025 2030
VK
M [x1
0^
6]
Year
CAR
5,000
5,200
5,400
5,600
5,800
6,000
6,200
6,400
6,600
6,800
7,000
2005 2010 2015 2020 2025 2030
VK
M [x1
0^
6]
Year
2W
25,000,000
27,000,000
29,000,000
31,000,000
33,000,000
35,000,000
37,000,000
39,000,000
2005 2010 2015 2020 2025 2030
PO
P [#
]
Year
CAR
900,000
950,000
1,000,000
1,050,000
1,100,000
1,150,000
1,200,000
1,250,000
1,300,000
1,350,000
2005 2010 2015 2020 2025 2030
PO
P [#
]
Year
2W
Figure 17: Examples of ownership tax cost difference for selected output variables (vkm and population and selected vehicle categories). Continuous lines are baseline and dashed lines are
scenarios.
117
Sensitivity analysis applied on those variables is shown as pie charts (fraction of explained
variance calculated from standardized regression coefficients) in Figure 18. Although the
distribution of ‘TAXownership’ is statistically different between the baseline and the scenario,
rather small changes (<3%) occur in its input factors importance and their fraction of
explained variance. The sensitivity of the other fourteen variables remains unaltered in the
scenario compared to the baseline and for this reasons the pie charts are excluded.
Figure 18: Sensitivity analysis results: variation in input factor importance between the baseline
(left column) and the scenario (right column) run for UK for the output variable “Taxownership”.
Sensitivity for all other variables is identical between the scenario and the baseline.
6.3.3 Discussion
In Scenario 1, providing a much higher ownership tax estimate for passenger cars that reaches
three times higher than the baseline in 2030 only marginally affects all the output variables (of
course with the exception of the ownership cost). Median values between the baseline and the
scenario differ maximum by 1.5% in 2030 in the case of total road transport costs. All other
activity and emission data differ by less than 1%. The output variance is not significantly
affected compared to its baseline range. In view of the small changes, the sensitivity analysis
results to identical effect of each input variable uncertainty to the output variables. This
scenario shows that changes in the ownership tax have practically very limited effect on the
median and the uncertainty ranges of the TREMOVE activity and emission output.
Activity shifts within the road sector, i.e. shift of the passenger transport to either busses or
power two wheelers could not be observed. Overall, increasing the cost of passenger car
ownership leads only to a marginal (1%) decrease of activity in the road transport sector which
comes solely from an identical decrease in the passenger car activity.
118
6.4 Effect of fuel cost
6.4.1 Description
One major cost component in TREMOVE is the price of the different fuel categories. This cost is
directly connected to the fuel consumption and the total vehicle kilometres driven by each
vehicle. Detailed historical data is available for the fuel price. This means that no uncertainty
exists for past years, and the fuel price has no impact on the uncertainty of the model.
However the estimation of the future price can be difficult; taking also into consideration that
energy consumption affects the economy of the entire world. An example of this uncertainty is
the rise of the crude oil price in 2008 over 146$/barrel [13]. Another point is that even within a
small country like Greece, the price can vary between short periods of time. The pre-tax price
of the unleaded gasoline in Greece varied between 0.3 to 0.5 Euros per litre in a period of less
than 8 months (Oct 2009 to May 2010) [14]. It is evident that high uncertainty exists for the
estimation of the fuel price. The uncertainty does not come from some technical variance in the
input data; this is a very volatile value with its uncertainty depending on macroeconomic
factors and world events. This increased uncertainty could not be included in the baseline runs
due the fact that such large variance in the fuel price when combined with the rest of the input
data variability could have unpredictable effect in the final results. For this reason the second
scenario was designed to study the effect of the highly uncertain fuel price. Of course, it should
be repeated that the TREMOVE structure is not appropriate to quantify large differences in the
cost of fuel, such as fundamental differences in the projection of the price of the oil barrel. This
was first observed in the scientific review of TREMOVE by Annema et al. [19]. In their report,
they state: “The assumptions in TREMOVE restrict the range of policy assessments for which
the model is suitable to only those that are expected to have a limited impact on incomes or
production. TREMOVE assumes that incomes and production are unaffected by policy changes
and that income elasticity of any transport demand is equal to 1. Accordingly, TREMOVE is not
designed for the assessment of policies that are expected to have a significant impact on
incomes or production.” Obviously, fuel prices are one of the policies, or better, developments
that largely affect both income and production. Hence, TREMOVE is not well suited to reflect
the impacts of different fuel prices on demand and activity. However, it is still interesting to
observe how TREMOVE reacts to differences in the price of petroleum products used in road
transport, as behaviour of the model, even if this is not representative of realworld changes.
It was decided to use the variability of the unleaded Gasoline in Greece as a starting point and
calculate the fuel price based on that uncertainty. A price range from 0.3 to 0.5 Euro/litre gives
an uncertainty range of ±30%. This uncertainty was introduced in the model from year 2010
and onwards. The sensitivity analysis was performed for all output data. This means that the
median value of the scenario is identical to the baseline but the scenario introduced an
uncertainty range not existing in the baseline.
119
UK prices
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1995 2005 2015 2025Year
Euro
/Litre
Gasoline
+3s Gas
-3s Gas
UK prices
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1995 2005 2015 2025Year
Euro
/Litre
Diesel
+3s Dies
-3s Dies
UK prices
0.0
0.1
0.1
0.2
0.2
0.3
1995 2005 2015 2025Year
Euro
/Litre
LPG
+3s LPG
-3s LPG
UK prices
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1995 2005 2015 2025Year
Euro
/Litre
CNG
+3s CNG
-3s CNG
Figure 19: Scenario data introduced in the model.
6.4.2 Results
The second scenario deals with a different set-up of the FCOST parameter. FCOST in the
perturbed run imposes a 30% variability over the control run, such that the range in the
perturbed run is [0.7*FCOST(control), 1.3*FCOST(control)].
A Kolmogorov-Smirnov test identified that different distributions exist only for COSTfuel,
between the perturbed and the control case, for all years after 2010.
Figure 20 shows the temporal evolution of the output uncertainty for COSTfuel. The different
distributions produced by the scenario setting have the same median with the control case but
with both tails of the distribution expanded (i.e. small and large percentiles become more
extreme). This is particularly evident, as expected, for COSTfuel (and VATfuel and TAXfuel)
whose coefficient of variation (in 2030) jumps from 12% to 20%.
120
Figure 20: Temporal evolution of the output uncertainty for UK, for the output variables that
demonstrate changes in their distribution between the scenario (blue) and the baseline (red) case.
The bold line represents the median while the dotted lines correspond to the 5th and 95th percentiles.
Sensitivity analysis applied on COSTfuel as pie charts (fraction of explained variance calculated
from standardized regression coefficients) is shown in Figure 21. The sensitivity does not
change for any other of the output variables. Specifically, FCOST becomes the principal
important factor for COSTfuel explaining more than 64% of its variance while the second
variable, eEFfc (the previously ranked one in importance), explains less than half of the
variance fraction attributed to FCOST (29-30%).
121
Figure 21: Sensitivity analysis results for the COSTfuel variable: variation in input factor
importance between the baseline (left column) and the scenario (right column) run for UK.
In this case, it is interesting to observe the change in the confidence interval of the output
variables as medians have not changed between the scenario and the baseline. This is shown
in Table 18 for selected output variables. The output is practically only wider for the COSTfuel
variable and it is only little varied for the other output variables (total cost being the second
important one). The tax fuel is not affected as its value is independent of the fuel cost.
Interestingly, the fuel VAT confidence interval is also much less affected that the fuel cost. This
is because the VAT is applied on the sum (fuel cost + fuel tax) and the constant fuel tax value
attenuates large variations in its output confidence interval. If one looks on effects on a per
vehicle category level (Annex 2), one can see that confidence intervals of VAT fuel for light
duty vehicles and two-wheelers increase somehow more as a combined effect of direct fuel
increase cost and cross-effects of the high variations of costs in the passenger car sector.
However, the induced increase in the confidence intervals for these vehicle categories is too
small to be shown in the final result, which is dominated by passenger cars.
122
Table 18: 95% confidence intervals for selected output variables between scenario 2 and the
baseline in the years 2020 and 2030.
Vehicle km [x10^6]
2020 2030
Vehicles [#]
2020 2030
Baseline 61,184 68,251 Baseline 3,581,258 3,989,701
Scenario 2 62,117 69,647 Scenario 2 3,642,697 4,077,402
NOx [Ton]
2020 2030
PM [Ton]
2020 2030
Baseline 120,352 93,729 Baseline 4,265 4,175
Scenario 2 118,574 94,837 Scenario 2 4,260 4,176
Costs [M€]
2020 2030
COSTfuel [M€]
2020 2030
Baseline 53,492 61,795 Baseline 12,603 15,890
Scenario 2 55,881 65,939 Scenario 2 21,330 26,647
TAXfuel [M€]
2020 2030
VATfuel [M€]
2020 2030
Baseline 15,716 18,256 Baseline 4,201 5,109
Scenario 2 15,822 18,441 Scenario 2 4,559 5,516
It is interesting to examine in this case how the change in the uncertainty of the fuel cost in
road transport affects the activity in other modes and in particular in aviation and railways. It
would be expected that increasing the road fuel price would shift some activity to non road
modes and vice versa. Therefore, by modifying the range of costs of road transport, one should
expect to observe an inversely proportional shift of non road transport demand. The change in
total demand for the aviation and railway sectors as an effect of the modified road transport
fuel prices is shown in Figure 21. Despite expectations, the fuel cost does not seem to affect
the median or the uncertainty of the activity in passenger transport of other modes, as the
median, the 5th percentile, and the 95th percentile are identical between the basecase and the
scenario.. This is an indication of very small elasticity between different passenger transport
options. It mostly shows that changing the road fuel price will get more people on or off the
road, depending of whether the fuel price has been reduced or increased but will not shift more
people to other modes. This result should be validated with real-world examples.
AIR
100 000
105 000
110 000
115 000
120 000
125 000
130 000
135 000
140 000
2005 2010 2015 2020 2025 2030Year
Million p
km
MED_BC
PERC _05
PERC _95
MED_S2
PERC _05
PERC _95
TRAIN
60 000
65 000
70 000
75 000
80 000
85 000
90 000
2005 2010 2015 2020 2025 2030Year
Million p
km
Figure 22: Effect of the road fuel prices uncertainty on the uncertainty of pkm conducted by
aviation and railways. The MED_PC and MED_S2 lines correspond to the medial values of the
baseline and the scenario, while the corresponding 05 and 95 lines correspond to the 5th and 95th
percentiles.
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6.4.3 Discussion
In this scenario, the base price of fuels was varied within ±30% of its mean value in the
scenario while no uncertainty was considered in the baseline. This variation only affected the
relevant cost output variable. Confidence intervals for the output significantly increase only for
the fuel components and for total cost. The confidence intervals for all other variables were
only marginally affected. Due to the small relative effect, the sensitivity analysis produces
identical results between the baseline and the scenario, i.e. the output depends on the
uncertainty of the input variables in the same fashion as in the baseline.
Interestingly, introducing such a large uncertainty for the road fuel cost, does not seem to
drive more activity to other modes of transport. The variance in total pkm in air and rail
transport remains practically identical between the baseline and the scenarios. The total
variance of pkm increased by no more than 1.5% in both aviation and rail transport. This
dictates limited intermodal shifts.
6.5 HDV Euro VI
6.5.1 Description
As a third scenario for demonstrating TREMOVE’s application we chose one of the cases where
one of the TREMOVE versions (originally v2.43b) has also been used in the past: the heavy-
duty Euro VI emission standard introduction. Our aim is to demonstrate the expected
uncertainty in TREMOVE calculations when a new emission standard is simulated. It should be
made clear that the TREMOVE response depends on the vehicle type on which the new
emission standard is introduced. For example, the uncertainty would probably be different if a
new emission standard was introduced for passenger cars, just because of the different
modelling approach of passenger cars compared to heavy duty vehicles. However, we decided
to demonstrate the uncertainty in a scenario were TREMOVE has already been applied on and
for which realistic information of input data uncertainty can be found.
Euro VI vehicles will be introduced in 2013 in the fleet. The emission limits values for Euro VI
heavy duty trucks have been set by Regulation 595/2009. Table 19 presents the limit values
for Euro VI and Euro V as well as the percentage reduction of Euro VI over Euro V. The
emission factors of Euro VI in TREMOVE were simulated by introducing the relative emission
limit reduction of Table 19 directly on the Euro V emission factor. One may question whether
there is no uncertainty in this reduction, i.e. will the Euro VI emission factors perfectly comply
with the emission reductions over Euro V imposed by the regulations? The answer is probably
no because history has shown that real-world emissions usually exceed the emission limit
requirements. This is for various reasons, including the engine calibration and the
aftertreatment efficiency. However, as in several instances in this report, our intention is not to
make a prediction of what the real-world will bring but what is the inherent uncertainty in the
modelling approach of TREMOVE. The real-world vs. emission limit disparity is actually a
compromise of the emission regulation and not an effect that has to be simulated with
TREMOVE. When TREMOVE was used to simulate the Euro VI standards, fixed values for the
emission reductions were used per scenario. One would therefore need to calculate what is the
124
uncertainty associated with these fixed values and not to introduce yet another variance to
these values. To calculate the uncertainty in the emission factor, the cov of Euro V and Euro VI
emission factors has been assumed the same.
Table 19: Euro V and Euro VI emission limit values for heavy duty vehicles (g/kWh).
CO HC NOx PM
Euro V 1.5 0.46 2 0.02
Euro VI 1.5 0.13 0.4 0.01
% Reduction 0 72 80 50
The uncertainty associated with the introduction of a new emission standard has mainly to do
with the additional costs that this new technology is associated with. Costs are difficult to
estimate, let alone to predict beforehand. Therefore, an uncertainty range for the assumed
costs has to be introduced. Input data for the uncertainty in the cost calculation were obtained
by the report of Gense et al. [11]. The Euro VI emission standards finally decided, correspond
to Scenario 5 of that report (with a small variation concerning THC). Therefore, the cost values
linked to Scenario 5 will be used as an input. The report of Gense et al. [11] came up with two
sets of cost figures: One assuming that all cost of new technology was attributable to the need
to meet the Euro VI limits (100% allocation) and the second set assuming that 50% of the cost
of some new components would reflect new technology that would have been introduced in any
case to improve vehicle performance. With these assumptions, only 50% of the total cost of
some components should be attributed to Euro VI. These values are shown in Table 20.
Table 20: Costs (€/veh.) allocated to Euro VI technology in the report of Gense et al. [11].
100% allocation 50% allocation Engine Size [l]
Min Max Min Max
6 3,355 3,553 2,855 3,053
9 4,318 4,615 3,718 4,015
13 5,351 5,780 4,651 5,080
In order to allocate the cost figures from that report onto the four weight classes of heavy duty
trucks and one class of diesel busses in TREMOVE, a number of assumptions will have to be
made, which are largely based on the analysis of Zierock et al. (2007):
1. It seems reasonable to assume that since the exact percentage of cost allocation to
Euro VI cannot be predicted, a uniform distribution between the 50% and the 100%
allocation is the best approach to simulate uncertainty.
2. The report of Gense et al. [11] came up with cost figures per 3 engine capacity class
and not the 4 gross vehicle weight class (plus one for busses), as TREMOVE uses.
Zierock et al. [12] used market information to link the two size figures. However, there
is an uncertainty in the estimation because different GVW classes may use engines of
different capacity. In order to simulate this uncertainty, we have used the following
assumptions: Vehicles <7.5 t use only engines of 6l, Vehicles of >32 t use engines of
13 l, Vehicles of 7.5-16 t, use a mix of 6 l and 9 l engines, and vehicles 16t-32 t use a
mix of 9 l and 13 l engines. Busses use 9l-13 l engines. A 50-50 allocation has been
assumed for these intermediate classes Therefore the min-max range for the
intermediate GVW and bus classes are wider than the individual engine classes. Based
on these assumptions, Table 21 shows the final cost allocation per TREMOVE vehicle
category.
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Table 21: Costs (€/veh.) allocated to Euro VI vehicle classes in TREMOVE.
Vehicle class Min Max
<7,5 t 2,855 3,553
7,5 – 16 t 3,287 4,084
16 – 32 t 4,185 5,198
>32 t 4,651 5,780
Diesel busses 4,185 5,198
The last component of cost calculation is attributed to the cost of urea. The report of Zierock et
al. [12] provides a range of values for the consumption rate (3-6% of diesel fuel) and cost
(0.27-0.67 Euro/lt) of urea. This can be converted to a range of Euro/km values (to be added
in the maintenance cost in TREMOVE) by considering a mean fuel consumption rate per vehicle
class. We have assumed that the mean fuel consumption of Euro V trucks also holds true for
Euro VI as well. This is not entirely true as we will later demonstrate but it is a very good
approximation for the needs of the uncertainty calculation we are attempting here. Finally,
Zierock et al. [12] assumed that only 30% of the urea cost should be allocated to Euro VI,
assuming that 70% of the Euro V vehicles already consume urea. In fact, we have updated
market figures which show that 74% to 76% of the Euro V market are SCR equipped vehicles.
For consistency with the earlier report we have therefore assumed that only 30% of the total
urea costs should be allocated to Euro VI. Based on this approach, the range of costs for urea
are presented in Table 22.
Finally, we have assumed that the fuel consumption of Euro VI vehicles will be higher than
Euro 5 uniformly by 0.5-2%. This is due to the much lower NOx limits which lead to lower
efficiency compared to Euro V.
Table 22: Calculation of urea cost per Euro VI kilometre (only 30% of urea cost allocated to Euro VI
as incremental difference over Euro V).
Vehicle class Fuel
Consumption (g/km)
Rate of urea cons. vs fuel consumption
(l/g fuel)
Cost of Urea (€/lt)
Min Cost of Urea (€/km)
Max Cost of Urea (€/km
<7,5 t 104 0.0003 0.0016
7,5 – 16 t 206 0.0006 0.0031
16 – 32 t 329 0.0010 0.0050
>32 t 387 0.0012 0.0058
Diesel busses 260
0.0000375- 0.000075
0.27-0.67
0.0008 0.0039
6.5.2 Results
The third scenario deals with a different set-up of three variables controlling the “extra car
cost”, the “extra fuel cost” and the “consumption increase”. A uniform distribution is attributed
to all of them.
A Kolmogorov-Smirnov test identified that different distributions exist for PM and NOx after
2016, between the perturbed and the control case. Interestingly, no cost related variables were
affected, despite the change in the cost items describing the Euro VI technology.
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Figure 23 shows the uncertainty evolution for the abovementioned output variables in the
baseline (red) and the scenario (blue) cases for UK. Both variables demonstrate a decrease in
the future. NOx demonstrates a shift of the whole distribution towards lower values while PM is
characterized by a shrink that keeps the same lower tail. The change occurs after 2016. This is
expected as Euro VI introduced decreased emission limits compared to the baseline. Euro VI
was assumed to have been introduced in 2013. After about 2016, the NOx median of the
scenario becomes lower than the 5th percentile of the baseline.
The individual differences in the median values of the baseline and the scenario for selected
output variables are shown in Table 22. The table confirms that the only differences are
observed for NOx and PM and for no other output variable.
Sensitivity analysis applied on those variables is shown as pie charts (fraction of explained
variance calculated from standardized regression coefficients) in Figure 24. It is apparent that
actually there exist no changes in the input factors importance. In the perturbed setting, eEF
continue to drive the PM and NOx uncertainty, with a small reduction (~1-4%) in the fraction
of the explained variance. This is because the cov in the Euro V and Euro VI remained the
same, which means that the induced uncertainty is less in the Euro VI case, as a result of a
drop in the mean emission factor. The drop in the uncertainty is realistic as the aftertreatment
used at a Euro VI level (SCR and DPF) is expected to effectively control emissions within their
limits.
Figure 23: Temporal evolution of the output uncertainty for UK, for the output variables that
demonstrate changes in their distribution between the perturbed (blue) and the control (red) case.
The bold line represents the median while the dotted lines correspond to the 5th and 95th percentiles.
127
Table 23: Median values of the scenario 3 and the baseline in the years 2020 and 2030 and
percentage difference between the two for selected output variables.
Vehicle km [x10^6]
2020 2030
Vehicles [#]
2020 2030
Baseline 657,732 712,662 Baseline 37,434,661 40,543,655
Scenario 3 657,673 712,541 Scenario 3 37,433,657 40,541,438
% diff 0.0 0.0 % diff 0.0 0.0
NOx [Ton]
2020 2030
PM [Ton]
2020 2030
Baseline 204,279 179,722 Baseline 4,128 4,068
Scenario 3 144,814 104,497 Scenario 3 3,594 3,364
% diff -29.1 -41.9 % diff -12.9 -17.3
Fuel cons. [Ton]
2020 2030
VOC [Ton]
2020 2030
Baseline 52,179 56,216 Baseline 29,874 31,501
Scenario 3 52,221 56,228 Scenario 3 29,687 31,369
% diff 0.1 0.0 % diff -0.6 -0.4
Costs [milEuro]
2020 2030
COSTpurchase [milEuro]
2020 2030
Baseline 375,041 434,464 Baseline 105,590 121,983
Scenario 3 375,216 434,913 Scenario 3 105,722 122,287
% diff 0.0 0.1 % diff 0.1 0.2
COSTrepair [milEuro]
2020 2030
Baseline 77,359 90,137
Scenario 3 77,449 90,317
% diff 0.1 0.2
6.5.3 Discussion
A Euro VI scenario was introduced in this case for HD vehicles, including reductions in emission
limits and increased purchase and operation costs. The effect of the introduction was only
shown for PM and NOx and for no other output variable. The contribution of input variables to
the uncertainty of the scenario closely matches to the baseline as the relative variance of the
Euro VI emission factors has been assumed the same as Euro V.
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(a)
(b)
Figure 24: Sensitivity analysis results: variation in input factor importance between the control (left
column) and the perturbed (right column) run for UK, for the output variables (a) PM and (b) NOx
identified from the Kolmogorov-Smirnov test.
129
7 Conclusions and recommendations
Objective of this study was the uncertainty and sensitivity analysis of TREMOVE both with
regard to the baseline and three indicative scenarios. This was made possible by first
identifying a variance range for the input variables of the model. Out of the several variables
33 were the ones for which an uncertainty range was identified. These variables are contained
in the stock and emission and fuel consumption modules. For reasons that are explained
thoroughly in this final report of the study variables in the demand, welfare, well to tank and
non road modules were not varied. For the variables varied uncertainty ranges were
characterized on the basis of literature data or experimental data or in few cases based on
empirical assumptions. The output of TREMOVE was observed with a selection of 24 variables
including cost, activity data, stock, fuel consumption and pollutant variables.
Important variables
A screening test was first conducted which identified the influential input variables. 14 variables
were deemed important. These were:
- the average trip length (ltrip)
- the hot and cold emission factors (eEF, eEFratio, eEFfc, eEFfcratio)
- the (B,T) - parameter: characteristic service life & faillure steepness (paraB, paraT
pair)
- the road vehicle basic purchase resource cost - EURO 2000 (eRPCSBASE)
- the estimated residual value function as a percentage of purchase cost
(usresidualparaAB)
- the repair and maintenance cost excluding taxes as % of purchase resource cost (ex
tax) (eRREPMAINTCFRACTION)
- the insurance cost as percentage of vehicle purchase resource cost (RINSCFRACTION)
- the labour cost - net wage - for truck drivers - EURO per hour (RLABOURC)
- the labour tax - bruto wage minus netto wage - for truck drivers - EURO per hour
(RLABOURTX)
- the annual ownership tax road vehicles - EURO 2005 (ROWNTX)
- the public transport fare cost coverage (PUBLICCOSTCOV)
Further to the identification of the important variables the screening test also leads to the
following conclusions:
- the model output referring to activity and cost is mostly a linear combination of the
input variables
- differently expressed, the previous point means that each output variable is mostly
determined by its corresponding input variable. For example the total purchase cost is
determined by the uncertainty in the purchase cost of single vehicles
- the linear behaviour is mostly an effect of the structure and the values in the elasticity
trees of the model
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Total uncertainty baseline
The uncertainty of the baseline expresses the variance which is induced to the basecase by the
model variables. The total baseline uncertainty is determined by exogenous factors to
TREMOVE such as macroeconomic and demographic data which can be assumed when
producing this basecase by higher level models (eg TRANSTOOLS, PRIMES, Poles).
Characterizing this overall uncertainty is beyond the objective of this study. The uncertainty of
the baseline for each of the years 2010, 2020, 2030 is given as the coefficient of variance
(cov=standard deviation over mean) for each output variable in the following table. The output
variables in the table are presented in a descending order according to the cov for the year
2030. Based on the values in the table the following conclusions can be drawn.
Output Variable
Units Median 2010 Median 2020 Median 2030 cov 2010
cov 2020
cov 2030
CO Ton 252,000 119,402 119,439 69% 51% 51%
PM Ton 10,750 3,400 3,421 25% 27% 27%
VOC Ton 46,927 30,279 31,559 37% 26% 24%
TAXrest M€ -8,454 -8,768 -9,365 21% 22% 22%
NOx Ton 331,319 170,500 158,458 19% 17% 17%
COSTinsurance M€ 24,922 38,291 44,671 6% 13% 14%
TAXinsurance M€ 1,260 1,934 2,255 6% 13% 14%
VATfuel M€ 8,041 9,418 11,412 13% 13% 13%
TAXfuel M€ 33,301 41,490 48,064 12% 12% 12%
COSTfuel M€ 30,727 32,106 40,202 11% 11% 12%
TAXownership M€ 6,166 10,810 12,070 8% 11% 12%
FC Ton 46,618,917 55,591,702 60,043,602 11% 11% 11%
COSTrepair M€ 59,798 73,706 86,483 3% 10% 10%
VATrepair M€ 7,293 9,090 10,691 3% 10% 10%
COSTlabour M€ 10,869 14,896 16,607 9% 9% 9%
COSTlabourtax M€ 11,634 15,944 17,773 9% 9% 9%
VATpurchase M€ 10,841 11,566 13,107 5% 9% 9%
COSTpurchase M€ 84,178 99,323 114,467 4% 8% 9%
TAXregistration M€ 22.5 24.2 27.4 7% 8% 8%
VATrest M€ 1,252 1,293 1,377 5% 5% 5%
Costs M€ 323,333 396,036 458,228 2% 4% 4%
Vehicles # 33,652,081 37,918,723 40,997,888 2% 3% 3%
Vehkms ×106 585,653 665,914 720,553 2% 3% 3%
COSTrest M€ 41,193 44,395 47,695 2% 2% 2%
- The uncertainty is large for the emission of pollutants, mostly due to the uncertainty in
the emission factors. This is in the order of 20-30% but can reach up to 50% in the
case of CO.
- Fuel dependent variables are second with regard to output uncertainty with cov values
in the order of 10-15%.
- Total cost figures exhibit uncertainty ranges in the order of 4-10%, i.e. they are rather
small depended on the variance of input data.
- Finally, population and activity data exhibit very little uncertainty, in the order of 2-
3%. The uncertainty per vehicle category (see Annex) is of the same magnitude. This
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shows that the activity and population is little elastic to changes in costs and other
variables in the model. This is potentially very important. This means that changes in
the activity and population data are very limited despite the much wider uncertainty
ranges assumed for the input variables.
- The levels of all output variables corresponding to the demand or stock group increase
through time (with the exception of TAXrest). For the emissions group the opposite is
true except for the fuel consumption. This is expected assuming that road transport
activity will continue to increase in the future while emission standards will continue to
effectively control emissions. The increase in fuel consumption remains to be seen – it
is reminded that the TREMOVE version examined does not include hybrid vehicles, or
electric vehicles.
- The variables corresponding to the demand or stock group that demonstrate an
increase in their uncertainty in the future are: COSTpurchase, VATpurchase,
TAXownership, COSTinsurance, TAXinsurance, COSTrepair, VATrepair, Costs, Vehicles
and Vehkms. The highest increase has been seen for COSTrepair and VATrepair. The
variables corresponding to the emissions group that demonstrate a decrease in their
uncertainty in the future are VOC and CO. The largest decrease has been seen for CO,
however its uncertainty is always the highest among the 24 considered output
variables. All the other variables that are not mentioned exhibit a very small variability
in their temporal uncertainty. The large but decreasing uncertainty of CO and VOC is
caused by their large dependence on the emission factors of gasoline cars. As gasoline
cars are both expected to become less important in the future (compared to diesel
ones) but as also Euro 5 and 6 emission standards are expected to lead to even more
strict control over the lifetime of the vehicles, the observed trend is explainable.
Sensitivity analysis
The sensitivity analysis quantifies how much the uncertainty of each input variable contributes
to the uncertainty if the output variables. The sensitivity analysis conducted identified both first
order dependencies and higher order ones. The following table summarizes the first order
interactions between input and output variables in a descending order of interdependency for
the year 2030. The table shows that first order interactions explain from 99% (COSTrepair) to,
worst case, 88% (vehkms) of the output uncertainty.
- Ten out of the fourteen uncertain inputs are of principal importance for one or more
output variables.
- The hot emission factors (eEF) influences most the variability of the emissions (VOC,
NOX, PM, CO) while the basic road vehicle purchase resource cost (eRCPSBASE)
controls the variability of the stock and activity variables (vehicles and vehicle-kms).
On the other hand, many input factors are responsible for the variability of the cost
related output.
- All model outputs exhibit high linearity. The least amount of explained-by-single
contributions variance estimated was 88% and corresponds to the output variables
Costs, Vehicles and Vehkms. The remaining fraction depends on higher order
interdependencies between the input variables.
132
- The linearity of the output variables is generally constant in time. On the other hand,
the total effects are either constant or decreasing in the future. This implies that the
control of the singular perturbations will be always effective.
- The highest amount of interaction effects has been seen for TAXregistration but it is
attributed to all input factors. The highest decrease in the effect of the second and
higher order terms has been observed for COSTpurchase and VATpurchase. This gives
the opportunity to work on the uncertainty of the RPCSBASE only in the future in order
to reduce the variability of COSTpurchase and VATpurchase.
Output Variable ΣSI
2010 ΣSI2020 ΣSI
2030
COSTrepair 0.97 0.98 0.99
VATrepair 0.98 0.99 0.99
FC 0.96 0.97 0.98
COSTfuel 0.96 0.97 0.97
TAXfuel 0.96 0.97 0.97
VATfuel 0.96 0.97 0.97
COSTlabour 0.96 0.96 0.96
COSTlabourtax 0.96 0.96 0.96
TAXrest 0.96 0.96 0.96
VATrest 0.96 0.96 0.96
PM 0.97 0.96 0.96
COSTpurchase 0.97 0.95 0.95
TAXownership 0.96 0.95 0.95
COSTinsurance 0.94 0.94 0.95
TAXinsurance 0.94 0.94 0.95
COSTrest 0.97 0.96 0.95
NOx 0.96 0.95 0.95
VATpurchase 0.97 0.94 0.94
TAXregistration 0.89 0.92 0.92
CO 0.91 0.92 0.91
VOC 0.93 0.91 0.91
Costs 0.88 0.88 0.88
Vehicles 0.89 0.88 0.88
Vehkms 0.89 0.88 0.88
Scenarios
Three scenarios were executed to quantify the uncertainty of TREMOVE when used for policy
impact assessment. The scenarios were selected in a way as to examine different instances of
the model operation. Specifically the three scenarios were simulating:
1. Scenario 1: increase in the ownership tax of passenger cars, ie a policy aiming at
transferring activity from the passenger car sector to other transport means.
133
2. Scenario 2: introduction of uncertainty range for the cost of road fuels. Despite that
TREMOVE is generally considered not appropriate to simulate large effects of fuel
prices, such a scenario may stimulate some shift of activity to non road modes.
3. Scenario 3: introduction of a new emission standard. EURO VI heavy duty was
selected as this has been real scenario simulated by a previous version of TREMOVE.
In scenario 1 the ownership cost for passenger cars was increased linearly from 2011 to 2030
to reach in the scenario three times as high as the baseline in 2030. This variation was
statistically observed to affect fifteen out of the 24 output variables, in principle the ones
related to the cost. Despite this tremendous assumed increase in ownership costs, the number
of vehicles and vehicle-km is only marginally (~1.5%) lower than the baseline, while total fuel
consumption is affected by 2.2%. Increase in the ownership cost of passenger cars led to an
almost proportional drop in their activity but did not lead to substantial modal shifts to other
road vehicles or non-road modes. Also, due to the small difference the impact of input variable
uncertainty to output variance (sensitivity) was identical between the scenario and the
baseline.
In scenario 2, a ±30% variance over the mean value was introduced for all road fuel
components. This is expected not to affect the median of the output variables but only their
confidence intervals. Despite the relatively large variance induced the only output variables
significantly affected are cost figures and in particular fuel cost figures. The confidence interval
of stock, activity and emission output was not affected by more than 2%. The activity of non
road modes was not at all influenced.
Finally, in scenario 3, emission factors (and their associated uncertainty) for heavy duty trucks
were reduced according to the EURO VI over EURO V limit values while additional costs were
introduced to account for the cost of deNOx aftertreatment and the operation cost increase due
to NOx reduction agent. Fuel consumption was also marginally increased. The scenario leads to
significantly different values only for PM and NOx, as expected. All other activity, stock and
cost figures remained practically unchanged.
Recommendations
The analysis conducted in this study made possible to derive some recommendations on how
TREMOVE behaves and how its estimates can be potentially improved:
1. The total uncertainty of the projection, taking into account macroeconomic and
demographic factors may be realistically assessed only by introducing alternative
basecase projections in the model. This can be a useful future activity.
2. A limited number of input variables (14) seems to drive the total model uncertainty. In
addition, several output variables can be approximated as linear combinations of input
variables with a small loss in precision. These effects are induced by the rather limited
elasticity in shifts between different modes of transport and vehicle types offered by
the demand module. If this limited flexibility is validated (see point 6 in this list), then
it can be suggested that several model operations can be simplified with a beneficial
effects on model transparency and processing time.
134
3. Our analysis only took into account the variables and parameters inclusive in the
model. Expanding the model to cover additional vehicle types, such as alternative fuel
vehicles, hybrids, plug-in hybrids, and electric vehicles may increase the uncertainty of
the estimates but is deemed necessary to cover future applications of the model.
4. The fact that a limited number of variables is important for most of the model output
uncertainty means that better quality / more precision in the estimates of these
specific variables will reduce the uncertainty of the output. Of particular importance
appear to be the emission factors, the purchase cost of vehicles, the parameters
defining the scrappage probability, the parameters used to estimate the residual cost
when a vehicle is scrapped and cost-related parameters (maintenance, insurance,
ownership, labour).
5. This study was limited to one country only (UK). Given the linear behaviour of the
model and the limited sensitivity of the demand to the input variables uncertainty,
extending the analysis to other countries does not seem to offer new insights. This
might affect the numerical values of the uncertainty indicators produced but would not
change the conclusions of the study. To improve the model output priority should
rather be given in improving the quality of the major input variables identified in this
study and in exploring how much the model elasticities reflect reality.
6. Validation of key model elasticities would be an interesting exercise, in the light of the
limited elasticity identified. The currently (2010-2011) changing environment in Europe
due to the financial and credit crisis offers several opportunities for validation. The
model could be applied to simulate effects of increasing fuel taxation, ownership
taxation, scrappage activities, etc., that take place today in several countries, and
compare with real-world trends.
7. A follow up activity could be to derive the linear functions between output data and
input variables and compare how much they deviate from TREMOVE output. This could
serve three purposes: (i) Quantify how much TREMOVE output deviates from linear
behaviour, (ii) Have a simplified TREMOVE model to easily perform scenarios for which
maximum accuracy is not necessary, (iii) Identify areas where TREMOVE structure
could be simplified without loss of precision.
135
References
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137
ANNEX I: uncertainty estimates of the baseline per vehicle category
138
COSTfuel
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
2005 2010 2015 2020 2025 2030
CO
STfu
el (
Mil E
uro
)
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
CO
STfu
el (
Mil E
uro
)
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
CO
STfu
el (
Mil E
uro
)
Year
LDV
0
20
40
60
80
100
120
140
2005 2010 2015 2020 2025 2030
CO
STfu
el (
Mil E
uro
)
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
CO
STfu
el (
Mil E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
139
COSTinsurance
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030CO
STin
sura
nce
(M
il E
uro
)
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030CO
STin
sura
nce
(M
il E
uro
)
Year
HDV
0
500
1,000
1,500
2,000
2,500
2005 2010 2015 2020 2025 2030CO
STin
sura
nce
(M
il E
uro
)
Year
LDV
0
50
100
150
200
250
300
350
400
450
2005 2010 2015 2020 2025 2030CO
STin
sura
nce
(M
il E
uro
)
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030CO
STin
sura
nce
(M
il E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
140
COSTlabour
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STla
bour (M
il Euro
)
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
CO
STla
bour (M
il Euro
)
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STla
bour (M
il Euro
)
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STla
bour (M
il Euro
)
Year
2W
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
CO
STla
bour (M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
141
COSTlabourtax
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STla
bourt
ax
(Mil E
uro
)
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
CO
STla
bourt
ax
(Mil E
uro
)
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STla
bourt
ax
(Mil E
uro
)
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STla
bourt
ax
(Mil E
uro
)
Year
2W
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
CO
STla
bourt
ax
(Mil E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
142
COSTpurchase
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
CO
STpurc
hase
(M
il Euro
)
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2005 2010 2015 2020 2025 2030
CO
STpurc
hase
(M
il Euro
)
Year
HDV
0
2,000
4,000
6,000
8,000
10,000
12,000
2005 2010 2015 2020 2025 2030
CO
STpurc
hase
(M
il Euro
)
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
1,600
2005 2010 2015 2020 2025 2030
CO
STpurc
hase
(M
il Euro
)
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
2005 2010 2015 2020 2025 2030
CO
STpurc
hase
(M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
143
COSTrepair
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
2005 2010 2015 2020 2025 2030
CO
STre
pair (M
il Euro
)
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2005 2010 2015 2020 2025 2030
CO
STre
pair (M
il Euro
)
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
CO
STre
pair (M
il Euro
)
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
2005 2010 2015 2020 2025 2030
CO
STre
pair (M
il Euro
)
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
CO
STre
pair (M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
144
COSTrest
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STre
st (M
il Euro
)
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
CO
STre
st (M
il Euro
)
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STre
st (M
il Euro
)
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
CO
STre
st (M
il Euro
)
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
CO
STre
st (M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
145
TAXfuel
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
TAXfu
el (
Mil E
uro
)
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
TAXfu
el (
Mil E
uro
)
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
TAXfu
el (
Mil E
uro
)
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
TAXfu
el (
Mil E
uro
)
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
2005 2010 2015 2020 2025 2030
TAXfu
el (
Mil E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
146
TAXinsurance
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
TAXin
sura
nce
(M
il Euro
)
Year
CAR
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
TAXin
sura
nce
(M
il Euro
)
Year
HDV
0
20
40
60
80
100
120
140
2005 2010 2015 2020 2025 2030
TAXin
sura
nce
(M
il Euro
)
Year
LDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
TAXin
sura
nce
(M
il Euro
)
Year
2W
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
TAXin
sura
nce
(M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
147
TAXownership
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
TA
Xow
ners
hip
(M
il Euro
)
Year
CAR
0
100
200
300
400
500
600
2005 2010 2015 2020 2025 2030
TA
Xow
ners
hip
(M
il Euro
)
Year
HDV
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
TA
Xow
ners
hip
(M
il Euro
)
Year
LDV
0
10
20
30
40
50
60
70
2005 2010 2015 2020 2025 2030
TA
Xow
ners
hip
(M
il Euro
)
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
TA
Xow
ners
hip
(M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
148
TAXregistration
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030TAXre
gis
trat
ion (M
il E
uro
)
Year
CAR
0
1
2
3
4
5
6
2005 2010 2015 2020 2025 2030TAXre
gis
trat
ion (M
il E
uro
)
Year
HDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030TAXre
gis
trat
ion (M
il E
uro
)
Year
LDV
0
1
2
3
4
5
6
7
8
2005 2010 2015 2020 2025 2030TAXre
gis
trat
ion (M
il E
uro
)
Year
2W
0
5
10
15
20
25
30
35
2005 2010 2015 2020 2025 2030TAXre
gis
trat
ion (M
il E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
149
VATfuel
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
VATfu
el (
Mil E
uro
)
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
VATfu
el (
Mil E
uro
)
Year
HDV
0
50
100
150
200
250
300
350
400
450
2005 2010 2015 2020 2025 2030
VATfu
el (
Mil E
uro
)
Year
LDV
0
5
10
15
20
25
30
35
40
45
50
2005 2010 2015 2020 2025 2030
VATfu
el (
Mil E
uro
)
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
VATfu
el (
Mil E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
150
VATpurchase
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
VA
Tp
urc
hase
(M
il Euro
)
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
VA
Tp
urc
hase
(M
il Euro
)
Year
HDV
0
50
100
150
200
250
300
2005 2010 2015 2020 2025 2030
VA
Tp
urc
hase
(M
il Euro
)
Year
LDV
0
50
100
150
200
250
2005 2010 2015 2020 2025 2030
VA
Tp
urc
hase
(M
il Euro
)
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
VA
Tp
urc
hase
(M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
151
VATrepair
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
VA
Tre
pair (M
il E
uro
)
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
VA
Tre
pair (M
il E
uro
)
Year
HDV
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030
VA
Tre
pair (M
il E
uro
)
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
VA
Tre
pair (M
il E
uro
)
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
VA
Tre
pair (M
il E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
152
VATrest
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
VATre
st (M
il Euro
)
Year
CAR
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
VATre
st (M
il Euro
)
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
VATre
st (M
il Euro
)
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
VATre
st (M
il Euro
)
Year
2W
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
VATre
st (M
il Euro
)
Year
ALL
BC
MED
PERC_05
PERC_95
153
SumCosts
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
2005 2010 2015 2020 2025 2030
Sum
Co
sts
(M
il E
uro
)
Year
CAR
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
Sum
Co
sts
(M
il E
uro
)
Year
HDV
0
5,000
10,000
15,000
20,000
25,000
30,000
2005 2010 2015 2020 2025 2030
Sum
Co
sts
(M
il E
uro
)
Year
LDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2005 2010 2015 2020 2025 2030
Sum
Co
sts
(M
il E
uro
)
Year
2W
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
2005 2010 2015 2020 2025 2030
Sum
Co
sts
(M
il E
uro
)
Year
ALL
BC
MED
PERC_05
PERC_95
154
FC
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
2005 2010 2015 2020 2025 2030
FC (Thousand T
onnes
)
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2005 2010 2015 2020 2025 2030
FC (Thousand T
onnes
)
Year
HDV
0
1,000
2,000
3,000
4,000
5,000
6,000
2005 2010 2015 2020 2025 2030
FC (Thousand T
onnes
)
Year
LDV
0
20
40
60
80
100
120
140
160
180
200
2005 2010 2015 2020 2025 2030
FC (Thousand T
onnes
)
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
FC (Thousand T
onnes
)
Year
ALL
BC
MED
PERC_05
PERC_95
155
NOx
0
50
100
150
200
250
300
350
400
2005 2010 2015 2020 2025 2030
NO
x (T
housand T
onnes
)
Year
CAR
0
50
100
150
200
250
2005 2010 2015 2020 2025 2030
NO
x (T
housand T
onnes
)
Year
HDV
0
10
20
30
40
50
60
70
2005 2010 2015 2020 2025 2030
NO
x (T
housand T
onnes
)
Year
LDV
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2005 2010 2015 2020 2025 2030
NO
x (T
housand T
onnes
)
Year
2W
0
100
200
300
400
500
600
700
2005 2010 2015 2020 2025 2030
NO
x (T
housand T
onnes
)
Year
ALL
BC
MED
PERC_05
PERC_95
156
CO
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2005 2010 2015 2020 2025 2030
CO
(Thousand T
onnes
)
Year
CAR
0
10
20
30
40
50
60
70
80
2005 2010 2015 2020 2025 2030
CO
(Thousand T
onnes
)
Year
HDV
0
10
20
30
40
50
60
2005 2010 2015 2020 2025 2030
CO
(Thousand T
onnes
)
Year
LDV
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030
CO
(Thousand T
onnes
)
Year
2W
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2005 2010 2015 2020 2025 2030
CO
(Thousand T
onnes
)
Year
ALL
BC
MED
PERC_05
PERC_95
157
PM
0
2
4
6
8
10
12
14
2005 2010 2015 2020 2025 2030
PM
(Thousand T
onnes
)
Year
CAR
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2005 2010 2015 2020 2025 2030
PM
(Thousand T
onnes
)
Year
HDV
0.0
1.0
2.0
3.0
4.0
5.0
6.0
2005 2010 2015 2020 2025 2030
PM
(Thousand T
onnes
)
Year
LDV
0.0
0.1
0.1
0.2
0.2
0.3
2005 2010 2015 2020 2025 2030
PM
(Thousand T
onnes
)
Year
2W
0
5
10
15
20
25
30
2005 2010 2015 2020 2025 2030
PM
(Thousand T
onnes
)
Year
ALL
BC
MED
PERC_05
PERC_95
158
VOC
0
20
40
60
80
100
120
140
160
2005 2010 2015 2020 2025 2030
VO
C (Thousand T
onnes
)
Year
CAR
0
2
4
6
8
10
12
14
16
18
2005 2010 2015 2020 2025 2030
VO
C (Thousand T
onnes
)
Year
HDV
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
2005 2010 2015 2020 2025 2030
VO
C (Thousand T
onnes
)
Year
LDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
VO
C (Thousand T
onnes
)
Year
2W
0
20
40
60
80
100
120
140
160
180
200
2005 2010 2015 2020 2025 2030
VO
C (Thousand T
onnes
)
Year
ALL
BC
MED
PERC_05
PERC_95
159
POP
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
2005 2010 2015 2020 2025 2030
PO
P (Thousand T
onnes
)
Year
CAR
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
PO
P (Thousand T
onnes
)
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2005 2010 2015 2020 2025 2030
PO
P (Thousand T
onnes
)
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
2005 2010 2015 2020 2025 2030
PO
P (Thousand T
onnes
)
Year
2W
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
2005 2010 2015 2020 2025 2030
PO
P (Thousand T
onnes
)
Year
ALL
BC
MED
PERC_05
PERC_95
160
VKM
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
2005 2010 2015 2020 2025 2030
VKM
(M
illio
n)
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
2005 2010 2015 2020 2025 2030
VKM
(M
illio
n)
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
VKM
(M
illio
n)
Year
LDV
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2005 2010 2015 2020 2025 2030
VKM
(M
illio
n)
Year
2W
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
2005 2010 2015 2020 2025 2030
VKM
(M
illio
n)
Year
ALL
BC
MED
PERC_05
PERC_95
161
ANNEX II : uncertainty estimates of the scenarios per vehicle category
162
Scenario 1
VKM
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
2005 2010 2015 2020 2025 2030[x
10^
6]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
LDV
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
2W
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
163
POP
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
2005 2010 2015 2020 2025 2030
[#]
Year
CAR
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
2005 2010 2015 2020 2025 2030
[#]
Year
HDV
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
2005 2010 2015 2020 2025 2030
[#]
Year
LDV
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
2005 2010 2015 2020 2025 2030
[#]
Year
2W
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
45,000,000
2005 2010 2015 2020 2025 2030
[#]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
164
FC
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
165
NOx
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
50,000
100,000
150,000
200,000
250,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
166
PM
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
1,000
2,000
3,000
4,000
5,000
6,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
20
40
60
80
100
120
140
160
180
200
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
30,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
167
CO
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
168
VOC
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
2,000
4,000
6,000
8,000
10,000
12,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
50,000
100,000
150,000
200,000
250,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
169
SumCosts
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
5,000
10,000
15,000
20,000
25,000
30,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
170
COSTfuel
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
171
COSTinsurance
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
50
100
150
200
250
300
350
400
450
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
172
COSTlabour
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
173
COSTlabourtax
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
174
COSTpurchase
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
2,000
4,000
6,000
8,000
10,000
12,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
1,600
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
175
COSTrepair
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
176
COSTrest
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
177
TAXfuel
05,000
10,00015,00020,00025,00030,00035,00040,00045,00050,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
178
TAXinsurance
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
179
TAXownership
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
100
200
300
400
500
600
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
10
20
30
40
50
60
70
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
180
TAXregistration
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
1
2
3
4
5
6
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
1
2
3
4
5
6
7
8
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5
10
15
20
25
30
35
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
181
VATfuel
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
50
100
150
200
250
300
350
400
450
500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
5
10
15
20
25
30
35
40
45
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
182
VATpurchase
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
50
100
150
200
250
300
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
200
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
183
VATrepair
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
184
VATrest
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN1
P05_SCEN1
P95_SCEN1
185
Scenario 2
VKM
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
2005 2010 2015 2020 2025 2030[x
10^
6]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
LDV
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
2W
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
186
POP
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
2005 2010 2015 2020 2025 2030
[#]
Year
CAR
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
2005 2010 2015 2020 2025 2030
[#]
Year
HDV
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
2005 2010 2015 2020 2025 2030
[#]
Year
LDV
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
2005 2010 2015 2020 2025 2030
[#]
Year
2W
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
45,000,000
2005 2010 2015 2020 2025 2030
[#]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
187
CO
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
188
FC
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
189
NOx
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
50,000
100,000
150,000
200,000
250,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
190
PM
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
1,000
2,000
3,000
4,000
5,000
6,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
20
40
60
80
100
120
140
160
180
200
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
30,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
191
VOC
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
2,000
4,000
6,000
8,000
10,000
12,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
50,000
100,000
150,000
200,000
250,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
192
SumCosts
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
5,000
10,000
15,000
20,000
25,000
30,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
193
COSTfuel
05,000
10,00015,00020,00025,00030,00035,00040,00045,00050,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
1,000
2,000
3,000
4,000
5,000
6,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
194
COSTinsurance
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
50
100
150
200
250
300
350
400
450
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
195
COSTlabour
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
196
COSTlabourtax
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
197
COSTpurchase
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
2,000
4,000
6,000
8,000
10,000
12,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
1,600
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
198
COSTrepair
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
199
COSTrest
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
200
TAXfuel
05,000
10,00015,00020,00025,00030,00035,00040,00045,00050,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
201
TAXinsurance
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
20
40
60
80
100
120
140
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
202
TAXownership
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
100
200
300
400
500
600
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
10
20
30
40
50
60
70
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
203
TAXregistration
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
1
2
3
4
5
6
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
1
2
3
4
5
6
7
8
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5
10
15
20
25
30
35
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
204
VATfuel
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
50
100
150
200
250
300
350
400
450
500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
5
10
15
20
25
30
35
40
45
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
205
VATpurchase
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
50
100
150
200
250
300
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
50
100
150
200
250
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
206
VATrepair
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
207
VATrest
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN2
P05_SCEN2
P95_SCEN2
208
Scenario 3
VKM
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
2005 2010 2015 2020 2025 2030[x
10^
6]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
LDV
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
2W
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
2005 2010 2015 2020 2025 2030
[x10^
6]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
209
POP
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
2005 2010 2015 2020 2025 2030
[#]
Year
CAR
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
2005 2010 2015 2020 2025 2030
[#]
Year
HDV
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
2005 2010 2015 2020 2025 2030
[#]
Year
LDV
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
2005 2010 2015 2020 2025 2030
[#]
Year
2W
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
45,000,000
2005 2010 2015 2020 2025 2030
[#]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
210
CO
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
211
FC
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
212
NOx
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
50,000
100,000
150,000
200,000
250,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
213
PM
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
1,000
2,000
3,000
4,000
5,000
6,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
20
40
60
80
100
120
140
160
180
200
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
30,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
214
VOC
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[ton]
Year
HDV
0
2,000
4,000
6,000
8,000
10,000
12,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
LDV
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
2W
0
50,000
100,000
150,000
200,000
250,000
2005 2010 2015 2020 2025 2030
[to
n]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
215
SumCosts
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
5,000
10,000
15,000
20,000
25,000
30,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
216
COSTfuel
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
217
COSTinsurance
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
50
100
150
200
250
300
350
400
450
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
218
COSTlabour
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
219
COSTlabourtax
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5,000
10,000
15,000
20,000
25,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
220
COSTpurchase
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
2,000
4,000
6,000
8,000
10,000
12,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
1,600
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
221
COSTrepair
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
200
400
600
800
1,000
1,200
1,400
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
20,000
40,000
60,000
80,000
100,000
120,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
222
COSTrest
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
223
TAXfuel
05,000
10,00015,00020,00025,00030,00035,00040,00045,00050,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
10,000
20,000
30,000
40,000
50,000
60,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
224
TAXinsurance
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
500
1,000
1,500
2,000
2,500
3,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
225
TAXownership
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
100
200
300
400
500
600
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
10
20
30
40
50
60
70
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
226
TAXregistration
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
1
2
3
4
5
6
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
5
10
15
20
25
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
1
2
3
4
5
6
7
8
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
5
10
15
20
25
30
35
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
227
VATfuel
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
50
100
150
200
250
300
350
400
450
500
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
5
10
15
20
25
30
35
40
45
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
228
VATpurchase
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
50
100
150
200
250
300
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
200
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
229
VATrepair
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
20
40
60
80
100
120
140
160
180
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
230
VATrest
00000111111
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
CAR
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
HDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
LDV
0
0
0
0
0
1
1
1
1
1
1
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
2W
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
[mil E
uro
]
Year
ALL
MED_BC
P05_BC
P95_BC
MED_SCEN3
P05_SCEN3
P95_SCEN3
231
ANNEX III: Description of the DVD contents
Related information has been included in an accompanying DVD. In detail this DVD contains:
• The presentation of the final meeting
• The results in an aggregated format
• The modified model code
• The setup files for the GUI
• Distribution parameters used for the uncertainty estimate
A copy of the DVD can be downloaded from the following address:
http://www.emisia.com/gui/unc.php
The copy of this DVD can be also obtained from the European Commission:
European Commission – DG Climate Action
Marek Sturc (unit CLIMA.A.4)
1049 Bruxelles
Belgium
email: [email protected]
233
ANNEX IV: Description of the full dataset
The full set of the output data as well as the scenario files used for the calculations have been
included in an accompanying HDD. Due to the large number of files and the total size of the
data more than 40 DVDs would be required to include this information. It was decided to use
the external drive instead. In detail this HDD contains 5 folders:
5950 – the 5950 runs executed to perform the uncertainty analysis
BASELINE – the 512 runs executed to perform the sensitivity analysis
SCEN_1 – the 512 runs executed to perform the ownership tax increase uncertainty-sensitivity
analysis
SCEN_2 – the 512 runs executed to perform the fuel cost estimation uncertainty-sensitivity
analysis
SCEN_3 – the 512 runs executed to perform the HDV Euro VI uncertainty-sensitivity analysis
DVD – the contents of the DVD