Energy Use and Emissions Impacts from
Car Technologies Market Scenarios:
A Multi-C ountry System Dynamics Model
Jonathan Gomez Vilchez, Patrick J ochem, Wolf Fichtner
Institute for Industrial Production and Graduate School of Energy Scenarios
Karlsruhe- Stuttgart, Karlsruhe Institute of Technology (KIT)
Hertzstr. 16, 76187 Karlsruhe, Germany
+49 721 608-44571
jonathan.gomez@ partner.kit.edu
ABSTRACT
In the context of high energy use and greenhouse gas emissions from road passenger
transport, the prospects of market diffusion of new car technologies is at present time
uncertain. For instance, the impact of current oil prices on the market uptake of electric
vehicles is yet to be seen. Systems thinking and scenario analysis are useful to explore
possible future outcomes. This paper focuses on car technologies scenarios for the
Chinese, German and US markets until 2030. The technologies investigated are:
gasoline, diesel, flexi-fuel, liquefied petroleum gas, natural gas, hybrid, plug-in hybrid,
battery electric and fuel cell vehicles. Based on the System Dynamics approach, a
model integrating discrete choice and accounting frameworks is developed. The
developed System Dynamics model is applied to examine alternative policies and to
estimate energy use and emissions in each of the markets under various scenarios. The
model results illustrate the importance of taking indirect emissions into account. In
conclusion, simulated policies sensibly alter car technology uptake and have an impact
on the environment. Finally, the ideas of feedback process and expansion of model
boundaries are considered to be crucial in modeling such a complex and uncertain
system.
Keywords: electric vehicles, System Dynamics, market scenarios, environmental
impacts
33rd International Conference of the System Dynamics Society
Cambridge, Massachusetts, USA, 2015
1. INTRODUCTION
Problem C ontext
According to the fifth assessment report by the Intergovernmental Panel on Climate
Change (IPCC), transport generated directly 7.0 gigatons of COveq in 2010 (IPCC,
2015). This results from transport activities that involve fuel combustion. Transport-
related energy use and emissions are expected to increase if current projections of
global vehicle stock growth (Gomez et al., 2013) materialize. Goals have been set by
national governments to reduce energy use and greenhouse gas (GHG) emissions from
the transport sector (EVI, 2013). With regard to passenger travel by car, technological
progress is expected to contribute toward these goals. In particular, technological
improvements in internal combustion engine vehicles (ICEVs) and technology
substitution of conventional for advanced technologies such as electric vehicles (EVs)
are being internationally promoted. In 2014, there were over 665,000 EVs worldwide
(EVI, 2015). Despite these plans, the successful market penetration of these
technologies is highly uncertain to date. Sustained relatively low oil prices! do not favor
the market penetration of new, cleaner car technologies. Policy analysis is required to
better understand the implications of differing development pathways for alternative car
technologies.
Objectives, Scope and Structure
The main objective of this paper is to explore possible future energy and emissions
impacts corresponding to different configurations of the car stock” in a specific market.
For this, estimation of levels of car ownership and investigations of policies that may
affect car technology choices are required. With this goal in mind, we generate market
scenarios by means of a System Dynamics (SD) model that incorporates feedback
processes. The purpose of the model is to enable the model user (ideally, policy-makers)
to experiment with the consequences of policy measures implemented in the model.
The following 9 car technologies? are included in the model: Gasoline (G), Diesel (D),
Flexi-Fuel or Biofuel (FF), Liquefied Petroleum Gas (LPG), Natural Gas (NG)‘, Hybrid
(HEV), Plug-in Hybrid (PHEV), Battery Electric Vehicles (BEV) and Fuel Cell or
Hydrogen (FC). The model simulates from the year 2000 until 2030. A calibration
period from 2000 until approximately 2010, depending on data availability, is
considered. In its current version, the model represents (using subscripts) the following
3 key car markets: China, Germany and the US. These countries share the common
' At the time of writing (2 March 2015), crude oil prices are at $62.58 per barrel of Brent and $49.76 for
the West Texas Intermediate (Oil-price.net, 2015).
? We use the term ‘car stock” throughout to refer to the number of cars operating in a given country in a
particular year. Other terms are often used: see e.g. footnote 1 on (Struben and Sterman, 2008).
Throughout this paper, the term “technology” refers to car powertrain technology.
* Represented by Compressed Natural Gas (CNG) cars.
criteria of having a high level of car stock and having declared interest in the market
uptake of EVs.
The remainder of the paper is structured as follows: section 2 contains an overview of
the literature and introduces the research approach. In section 3, the model is described.
Section 4 presents the model results. In Section 5, conclusions are drawn and further
research needs are sketched.
2. SURVEY OF STUDIES AND RESEARCH APPROACH
Survey of Studies
Due to the wealth of available studies on the subject, this survey is selective and we
restrict ourselves to research questions involving: (i) car ownership forecasting, (ii)
choice of the type (e.g. technology) of car, and (iii) estimation of energy and emissions
impacts.
Given the topic of this paper, two main bodies of literature were identified: global
simulation models and national SD models. The former group of models includes three
large-scale models that provide relevant scenarios or roadmaps: IEA Mobility Model
(MoMo), ICCT Energy Roadmap and UNECE ForFITS. Table 1 shows their main
features.
Table 1 — Overview of global simulation models
, Time Vehicle Key Model
Model Editor Country Horizon | Technologi Ouput
Mobility Global (29 GIDILPG? | | varket shares
CNG / HEV /
Model IEA world 2050 e Energy use
F PHEV / BEV we
(MoMo) regions) LEC e Emissions
7 world G/D/FF/ |e Energy use
Energy ICCT regions & 9 2050 LPG/CNG/ |e Emissions
Roadmap individual HEV / PHEV (GHG & local
countries / BEV / FC pollutants)
e Transport
ForFITS |UNECE] Global | 2040 aI ay
powertrains |e Energy use
e CO, emissions
Source: own representation based on (IEA, 2009), (ICCT, 2012) and (UNECE, 2015)
Strictly speaking, none of these models can qualify as an SD model if feedback
processes” are not explicitly incorporated, which seems to be the case at present time. In
our view, ForFITS has the potential to become a truly SD model in a future version, as
it has already been implemented in the Vensim® platform.
The second group contains models that are more consistent with the SD philosophy. For
the choice of technology, most of the available studies make use of some logit
framework. Discrete choice modeling is a common method to estimate the market
penetration of new vehicle technologies (Al-Alawi and Bradley, 2013). We distinguish
between “estimation” and “application” studies. By “estimation” studies we mean those
that are the result of designing and conducting a survey® and statistically estimating
discrete choice model parameters. The resulting output of primary interest is a set of
(utility) coefficients. Within this group, we highlight the papers listed in Table 2.
Table 2: Selected “estimation” studies
Vehicl Model
Author(s) Country T ‘. i - Type***
eehnologies [# Attributes]
(Bunch et al., . _ ‘ NMNL
1993) US (CA*) | Gasoline / Alternative** / Electric [5]
(Brownstone and US(CA) Gasoline / CNG / Methanol /| Mixed MNL
Train, 1998) Electric [10-12]
(McFadden and US(CA) Gasoline / CNG / Methanol /| Mixed MNL
Train, 2000) Electric [10]
(Brownstone et US (CA) Gasoline / CNG / Methanol / | MNL/ Mixed
al., 2000) Electric logit [>10]
; Gasoline / Diesel / Hybrid / Standard /
(Achtnicht, Germany | LPG/CNG / Biofuel / Hydrogen /| Mixed logit
2011) i
Electric [6]
' Gasoline / Diesel / Hybrid / Gas / MNP
(Ziegler, 2012) Germany Biofuel / Hydrogen / Electric [5]
Gasoline / Diesel / CNG / LPG / MNL/
(Hackbarth and) Germany | HEV | PHEV / BEV | Biofuel /| Mixed logit
Madlener, 2013)
Hydrogen [8]
*CA = State of California. **Methanol, ethanol, CNG (see page 6). ***MNL = Multinomial Logit /
NMNL = Nested-MNL / MNP = Multinomial Probit.
Source: own representation based on the original references
5 Feedback loops can be seen as the result of “the endogenous point of view” (Richardson, 2011).
§ Usually based on stated preferences (SP). Fortunately, revealed preference (RP) data is becoming
increasingly available (cf. e.g. (Schithle, 2014)). See (Brownstone et al., 2000) for some critical issues
related to SP-RP data.
By “application” studies we mean here those that develop a discrete choice modeling
framework capable of deriving market shares based on selected information from
“estimation” studies. In our view, “application” studies represent a pragmatic
application of the results derived from “estimation” studies. A selection’ of
“application” studies based on SD modeling is shown in Table 3.
Table 3 Selected “application” studies
Main Time Vehicle Applied
Author Purpose* Country Horizon | Technologi Logit Values
(Ford, 1995) *K
(BenDorand | PP | us(ca)| 2020 |° ae nee l ai
Ford, 2006)
G/D/FF/LPG/
ene PP EU 2050 | CNG/HEV/
BEV / FC
(Meyer, A Japan / 2035 G/D/HEV/ (BenDor and
2009) Germany BEV / FC Ford, 2006)
(Brownstone
(Walther et G/D/HEV/ .
al., 2010) AI/PP | US(CA) | 2021 PHEV / BEV or
(Weikl, 2010)| At | Germany | 2030 at nde Tie
(Wansart ICE /HEV-G/ (Brownstone
2012) " AI/PP | US(CA) 2030 HEV-D / PHEV / and Train,
BEV / FC 1998)
(Keith, 2012) | al/PP | US 2050 | o/ a - HEV Coot
*Main purpose: Public Policy (PP) and/or Automotive Industry (AI). **AL = Alcohol.
Source: own representation based on the original references
The application of a logit framework to derive market shares for each vehicle
technology allows the calculation of sales by type of technology. Relying only on this
method, disregarding the importance of feedback loops and path dependency (Sterman,
2000), is however a severe limitation (Gomez et al., 2014).
Invariably, the studies mentioned in Table 3 need to make assumptions concerning car
ownership levels and the resulting total number of cars operating in the area of analysis.
In mature markets, the assumption of a constant car stock is usually adopted.
7 Other models of interest are (Keles et al., 2008), (Struben and Sterman, 2008), (Krail, 2009), (Armenia
et al., 2010), (Park et al., 2011), (Kithn and Gloser, 2012), (Shepherd et al., 2012) and (Kieckhafer, 2013).
Research Method
In dealing with complex social systems, Meadows identified four common research
methods: optimization, input-output, System Dynamics and econometrics (Meadows in
(Randers, 1980)).
In order to successfully deal with the uncertainties of an inherently complex system, an
adequately holistic perspective is required. The benefits of systems thinking have been
highlighted by, among others, (Senge, 2006) and (Meadows and Wright, 2008). In cases
of policy-making in a context of high uncertainty, the scenarios method is suitable for
exploring alternative options (Grunwald in (Most et al., 2009)) (Dieckhoff et al., 2011)
(Dieckhoff et al., 2014).
Furthermore, the use of computer-based numerical simulation models can contribute to
an increase in understanding on the quantitative impacts of different policy options,
thereby improving the effectiveness through which they act.
Consistent with the ideas of systems thinking, scenarios analysis, and feedback thought
and policy analysis, we choose to develop an SD model in an attempt to meet the
research objective stated in section 1. Note that some of the studies listed on Table 3
have a main focus on the automotive industry and some on public policy. This can be
understood as a reflection of the fact that SD, although initially conceptualized for
industrial and corporate problems, later found successful applications in a wide range of
areas dealing with public policy. In any case, our main interest is in studying problems
relevant for public policy. In addition to the models we have mentioned in this section,
the SD approach has been applied to many other transport problems®.
Pioneered by (Forrester, 1958) (Forrester, 1961) (Forrester, 1968), SD stands today as
“a computer-aided approach to policy analysis and design”, applicable to dynamic
problems that require feedback thinking (SDS, 2014). (Richardson, 1991) traces the
origins of SD to the thread of “engineering - servomechanism” research in the social
sciences.
8 A special issue was devoted to transport on the SD Review (Shepherd and Emberger, 2010) and a more
recent review of SD applications on transport is given by (Shepherd, 2014).
3. THE DEVELOPED MODEL
Following (Bossel, 2007), the modularization approach is adopted and the model is
conveniently split into 9 views’, each of them representing a particular module. The
linkages among the different model modules are represented schematically in Figure 1.
goo
POPULATION CAR STOCK
a CAR ATTRIBUTES TECHNOLOGY
ENERGY ~~~“ OWNERSHIP& —___» CHOICEBY
wy DRIVING COSTS CONSUMER
TRAVEL
DEMAND _| Poucy
Figure 1 — Representation of the module linkages
Source: own work using Vensim®
Since Figure 1 represents modules and not individual variables, no link polarity is
shown for some of the arrows connecting modules, as these in fact entail various
linkages (from which an ambiguous relationship between modules arises). For those
arrows with a single polarity, the sign of the polarity is shown. It has to be
acknowledged that representing the sign of the feedback loop in this type of graph is not
straightforward. Further details about the specific relationships and feedback loops
(including polarity sign when relevant) are shown for each of the model modules in the
following sections. The description of each of the modules below is rather concise: the
documentation of the values of assumptions and equations can be found at the end of
the paper (see A ppendix).
*Figure 1 shows 8 modules, because the “Car Attributes” and the “Ownership and Driving Costs”
modules are merged in that figure. In the model, the “Car Attributes” module also contains an
“Infrastructure” component. The dotted arrows indicate feedback assumptions that are implicit in the
current version of the model.
Population - GDP Module
Key socio-economic assumptions drive the model. These include population and gross
domestic product (GDP). Concerning population, although the model can be
exogenously fed by available data (UN, 2012), it was deemed more insightful to use
that data to approximately determine the reference values of the fractional birth rate. In
this way, the model user can still easily vary the population assumptions. A more
elaborate population model using cohorts, although feasible to implement, is not
developed in this version of the model. With regard to GDP, growth is assumed in all
the countries, partially based on (WB, 2014). In the case of China, the rate of growth
decreases as the year 2030 is approached.
INITIAL SEES INITIAL GDP
POPULATION
Population
birth rate death rate
+ +
FRACTIONAL GDP
GROWTH RATE
PROJECTED
GDP CHINA .
2030
GDP per capita
FRACTIONAL
BIRTH RATE
+
Figure 2 — Structure of the module “Population - GDP”
Source: own work using Vensim®
The output of this module is “GDP per capita”, which enters the “Car Stock” model as
an input.
GDP per cap
2000 ©2004 += 2008» «2012-2016 «= 2020-2024 += 2028
Time (¥ ear)
(GDP per captalChial » Cureat
Cum
GDP per citlGeraay)
Exogenous GDP par
Figure 3 — Behavior of “GDP per capita”: historical and simulated
Source: own work using Vensim®
Travel Demand by Car Module
The main assumption in this module’” is average annual car mileage. For simplicity and
scenario comparability issues, we assume a constant “annual VKT by car” (vehicle-km
traveled) of 13,000 km for all the countries. Although satisfactory data for these
variables is available for Germany and the US, in the case of China no access to reliable
data could be gained.
The key outputs of this module are: (i) VKT, used as an input by the “Energy” module;
and (ii) “PKM by car” (passenger-km), which is affected by VKT and can be influenced
by policies targeted at average car occupancy rates.
There is a potential of making this module more sophisticated and realistic by linking
travel demand by car to income (e.g. using elasticity values).
Car Stock Module
This module contains two sub-sections: the projection of the aggregate car stock and the
simulation of the car stock disaggregated by technology. The latter contains a set of
subscripts with 9 car technologies.
With regard to the projection of the aggregate car stock, a nonlinear growth model
formulation has been chosen. Although different functional forms are available in the
literature, we adopt a Gompertz function following (Dargay et al., 2007) and fit
coefficients using the calibration optimization tool provided by Vensim®. Key
parameters affecting the car ownership ratio’! are GDP per capita and the level of car
saturation.
(Sterman, 2000) warns against over relying on curve fitting exercises. In the second
sub-section, we create a stock-and-flow formulation with two levels representing the
stock of new cars (<1 year) and the stock of older cars (>1 year), disaggregated into 9
possible car technologies. The sales rate is the result of “the demand for replacement”
and “the demand for first purchase”, a distinction recognized long time ago by (Wolff,
1938). It is initially assumed that 50% of the scrappaged cars turn into replacement sales
for the same technology, creating a reinforcing feedback loop. For this, a constant’
named “share of technology switching” has been created. Concerning “the demand for
first purchase”, the simulated choice of technology is determined by the output of a
discrete choice modeling framework (see the “Technology Choice” module).
*° Since this basic module does not contain feedback processes, its structure is not shown here. Refer to
the Appendix for further details.
" Other common terms are “car ownership rate” or, more generally, “motorization rate” (usually
measured as the number of cars per thousand people). In order to avoid the use of the word “rate”, which
is in this module reserved for the inflows and outflows from the car stock, we choose to use “ratio”
instead.
12 Tn the model, constants are written using capital letters.
<GDP per nto
capita>
<Population>
coef GDP per cap
BETA COER——__ wy
car ownership
ar ‘io fw
&
projected aggregate
GAMMA COEF total car stock
ADJUSTMENT
CAR SATURATION a
LEVEL divergence between projected
and simulated aggregate total
car stock SHARE OF
+ TECHNOLOGY
replacement Sales —f SWITCHING
AVERAGE
RS LIFETIME
INITIAL NEW INITIAL CAR
CAR
[Owercar| :
Stock | scrappage rate
AVERAGE
AGEING TIME
totalcarstock by aggregate older
tech car stock
simulated aggregate
total carstock
Figure 4 — Structure of the module “Car Stock”
Source: own work using V ensim®
projected car stock vs. total car stock
200 M
150M
& 100M
50M
0
2000 «©2004 «= 2008392012) 2016 = 2020'S 2024 = 2028
Time (Y ear)
projected 3}: Curent,
total ‘Current
projected Curent
total Curent
projected car sock(US] : Cument
total carstocUS] : Curent
Figure 5 Behavior of “aggregate car stock”: projected and simulated
Source: own work using V ensim®
10
The link between both sub-sections is provided by the dummy variable “divergence
between projected and simulated aggregate total car stock”. The resulting simulation
behavior is a rough approximation of the projection trend, as can be seen in Figure 5.
Car Attributes and Infrastructure Module
This module is divided into two sub-sections: car technical attributes and infrastructure
availability. The former contains the representation of car fuel efficiency improvements.
The latter shows the assumptions concerning the deployment of fuelling/charging
infrastructure. Both sections can be heavily influenced by policy inputs. In the case of
car fuel efficiency, emission standards define the rate of technological improvement for
ICEVs. Approved policy is already incorporated by default into the model (e.g. EU
emission standards for gasoline and diesel cars until 2021). Thus the model user can, in
this example, set new emission standards for the period 2022-2030.
INITIAL ICE CO2
perKM
+ —— fractional effect of ICE TORE
INITIAL NEW cee emission standards policy CAR FUEL
FUEL INTENSITY
(LITRE)
<time>
new car fuel intensity
change rate (lire)
(oF ais APPROVED ICE Ny,
EMISSION
STANDARDS
POLICY
Figure 6 — Partial view of the module “Car A ttributes and Infrastructure”
Source: own work using Vensim®
INITIAL EV
CHARGING SHARE ALTERNATIVE
FUEL AVAILABILITY
charging infras Fa
deployment rate
j Se Seaew
public EV charging
nfastruchue deploymen
i
CONVENTIONAL
oe FUEL STATIONS
INITIAL H2
FILLING
+2 fling station
deployment rate
Figure 7 — Partial view of the module “Car A ttributes and Infrastructure”
Source: own work using Vensim®
i.
The key outputs of this module, namely car fuel intensities, relative range and relative
fuelling, are used as inputs to the “Technology Choice” and “Energy” modules.
Ownership and Driving C osts Module
This module is divided into two sub-sections: “ownership costs” and “driving! costs”.
For the initial assumptions, the information shown on Table 4 has, to a large extent,
been followed.
Table 4 — Real-world information by selected car technology
Make Technol Battery capacity [kWh] Consumption Car price
(version) ogy (range [km]) (per 100 km) (US dollar)+++
Toyota Auris Gasoline 0 5.41 21,761
(Comfort) Diesel 0 4.21 24,000
HEV (gas.) : 3.61 25,741
Nis. Leaf (Visia) BEV 24 (199) 15.0 kWh 32,337
Gasoline 0 5.01 26,058
VW Golf Diesel 0 4.51 28,535
(Comfort-line) CNG (gas.) 0 3.5 kg 27,664
BEV 24,2 (130-190) 12.7 kWh 38,010
Gasoline 0 5.01 22,349
Diesel 0 4.51 24,199
aa FF 0 B31 23,981
LPG 0 7.61 25,125
BEV 23 (162) 15.4kWh 43,558
Opel Ampera EREV** 16 (40-80) 1.21 /16.9kWh 42,066
Toyota Prius HEV (gas.) - 4.01 30,448
(Comfort)* PHEV (gas.) 4.4 (23) 2.11 (combined) 39,881
Toyota Mirai* FC NA NA 57,500
* Segment D (the rest of the cars belong to segment C). ** EREV = Extended Range EV (gas.). **
Original prices in Euros (conversion at 1 EUR = 1.088583 US dollars)
Source: own work using information on the carmaker’s European website
The assumption concerning battery costs is taken exogenously from (EVI, 2013). For
gasoline, diesel and EVs, the final purchase price can be affected by national taxation
and subsidization.
The structure of this module can be seen in Figure 8. The module outputs are purchase
cost and driving cost (dollar per km) by car technology. These are primarily used as
inputs to the “Technology Choice” module.
costs (i
'S This is a proxy of total e, etc.) perceived by the car owner.
12
cost D FRACTIONAL
PURCHASE PRICE
RATE
—.
- purchase cost
pitches oat a
BEV REQUIRED
BATTERIES IN
CAR LIFETIME
total battery cost
total b st v
a a
rete bcd battery cost PHEV
per unit
INITIAL
BATTERY COST
FRACTIONAL
BATTERY COST
REDUCTION RATE
Battery ee
cost Datiety cost
(ef reduction rate
Figure 8 — Partial view of the module “Ownership and Driving Costs”
Source: own work using V ensim®
Technology Choice Module
This module is crucial to elicit new car sales by type of technology. We limit the choice
to the full set of technologies available in the market at the time the decision is made.
For this, a “commercialization year” variable is created. For instance, FCs become
available in 2015 (Germany and US) (Toyota, 2015) and 2016 (China).
As shown in section 2, discrete choice is a common modeling framework for this
purpose. There are many types of models and studies that have been applied. In this
paper, we use for five attributes (purchase cost, driving cost, emissions, range and
fuelling) the utility coefficients by (Hackbarth and Madlener, 2013) for each of the
countries. The aggregation process to estimate the market shares is based on (Ben-
Akiva et al., 1985). Figure 9 shows the structure of this module.
The key output of this module is “market share first sales” by car technology. As
expected, the sum of market shares equals one. Given the annual aggregate sales rate
and the predicted market shares by technology, the total number of cars (stock) by
13
technology can be derived. Thus the outcomes of this module are fed back to the “Car
Stock” module.
COEF PURCHASE COEF DRIVING SORE COEF RANGE COEF
cost cost EMISSIONS FUELLING
Car purrhas .
aS “at NN >
pr U purchase cost U driving cost Uemissions Urange U fueling
fe total
sum ofall market
denominator
Figure 9 — Structure of the module “Technology Choice”
Source: own work using Vensim®
Energy Module
There are 7 types of energy sources represented in the model. The mapping" of fuels to
the different car technologies is illustrated by Figure 10.
Given the difficulties of predicting oil prices, as exemplified by past forecasting studies
(cf. (Dahl, 2004)) and by the recent stark decrease in oil prices, we simply opt to assume
throughout this exercise that the oil price follows a long-term upward trend until
reaching 164 dollars per oil barrel (bbl) in 2030. The final (at the pump) price for
gasoline and diesel can also be influenced by taxation. The price of the rest of the fuels
(ethanol 85 (E85)!°, autogas, CNG, electricity and hydrogen (H2)) are assumed to
remain constant during the simulations.
Figure 10 — Conceptual linkages between car technologies and fuels
| D | NG | tre || FE | G | HEV || PHEV |) BEV || EC |
[= |
| diesel | CNG | autogas electric.
| E85 | gasoline
Source: own work
“ This is admittedly a model simplification, since physical processes already today enable additional
linkages between some fuels and technologies.
'S Blend of 85% bioethanol and 15% gasoline.
14
The key results of this module are: aggregate gasoline use and electricity use resulting
from the different configurations of the car stock by technology (“car-mix”).
Emissions Module
The model covers three main long-lived GHG emissions: CO, NO and CHy. The key
emission output, using Global Warming Potential (GWP)-100 year values based on
(IPCC, 2006), is expressed in grams of CO2eq.
The emissions-related accounting method'® employed, based on (IPCC, 2006) emission
factors, includes:
¢ Calculation of CO2/km for new cars by technology. This values are used as an
input in the “Technology Choice” module;
¢ Well-to-tank (WTT)!’ GHG emissions;
e Tank-to-wheel (TTW)'® GHG emissions;
e Well-to-wheel (WTW) GHG emissions (which equals WTT plus TTW);
e Manufacturing and Scrappage (M&S) emissions;
Lifecycle’ GHG emissions (which results from adding WTW and M&S).
In terms of total GHG emissions generated by the total car stock, we deliberately choose
to show the module output for two types of analysis: TTW and lifecycle.
Policy Module
In practice, the model view named “Policy-maker’s Lab” can be regarded as the
“Policy” module. It allows the model user to explore the consequences of varying
testing assumptions. (S)He can “shock” the modeled system with policy inputs. Several
policy variables specifically target conventional vehicles (CV): gasoline and diesel cars.
Furthermore, this module shows key intermediate and final model output and provides
access to more detailed country-specific charts.
The listing of the policy measures available in the current version of the model,
illustrated by three exemplary scenarios, is shown in the “Scenarios and Policy
Analysis” section.
‘© This module is basically an accounting module based on an adaptation of the A-S-I-F framework
(Schipper et al., 2000). Since it contains no feedback loops, the structure of this module is not shown
here. See the Appendix for further details.
17 \)so known as ‘upstream’ or ‘indirect’ emissions.
* Also known as ‘on-road’ or ‘direct’ emissions.
18 Tt is necessary to remark that no complete lifecycle analysis (LCA) has been undertaken as part of this
study.
15
Model Validation
Given the fact that all models are wrong (Sterman, 2002), it follows that models cannot
be verified (Sterman, 2000). System Dynamicists propose validity tests: (Barlas, 1996)
indicates three major stages of model validation: structural tests, structure-oriented
behavior tests and behavior pattern tests. (Bossel, 2007) recommends that model
validity be demonstrated according to structure, behavior, empirical validity and
application.
The proposed model is, to a large extent, validated through coherent model purpose and
output, careful investigation of causal structures, collection and observation of relevant
data and general matching of behavior patterns over the relevant time horizon. In
addition, the model is fully formulated and the dimensional analysis indicates that all
the units of the equations are consistent.
Scenarios and Policy Analysis
The model is run”’ for three slightly different scenarios. The scenarios considered in this
modeling exercise can be briefly described as:
e Scenario 1 (S1) “Reference”: Implementation of approved policies (e.g. EU
emission standards until 2021). No additional policies to promote a certain
technology.
¢ Scenario 2 (S2) “Fossil focus”: Policies mainly targeting at ICE efficiency
improvements are introduced. No strong attempt is made at improving the
carbon intensity of the electricity grid.
e Scenario 3 (S3) “EV breakthrough”: Additional policies aiming at facilitating
EV market update are promoted. The measures include EV subsidies and
investment plans for the deployment of public charging infrastructure.
Each of the three scenarios is applied to the three countries examined in this study. An
overview of the set of policies considered is given in Table 5.
Table 5 — Policy inputs under different scenarios
Policy S1 S2 $3
Measures [units] cry G,[u|;/c;]Gl[u/c ]G {vu
to aan 0% 3% | 1% | 2% | 3% | 1% | 2%
= Oh) eee 0% 3% | 1% | 2% | 3% | 1% | 2%
ee i 0% 0.5% 3%
® Vensim® supports Euler and Runge- Kutta ion for solving the
me Runge-Kutta (fourth order) is “probably the most reliable workhorse of numerical integration”
(Bossel, 2007) (p. 81), Euler is adequate for our purpose (Sterman, 2000) (Bossel, 2007) and hence it is
the one we use.
16
Car occupancy rate 12. 1.2 12
i]
CV purchase tax [dollar] 1,000 1,000 3,000
EV purchase subsidy
Tala 0 0 2,000
Eeono- [Gasoline tax [dollarliter] [0.2 [0.6 | 0.2 | 02 [0.6 [02/05 [07105
Diesel tax [dollar/liter] 0.3 | 0.6 | 0.2 | 0.3 | 0.6 | 0.2 | 0.5 | 0.7 | 05
Target electricity price
[dollar/kWh] 0.2 0.2 0.2
Public EV charging
Invest. _| infrastructure deployment 10 | 10 | 10 | 10 | 10 | 10 800
mers [station/year]
men! Public H) filling station 1 1 1 1 1 1 50
deployment [station/year]
* C =China/G = Germany / U =US. Note that the policies for EV subsidy and infrastructure investment
have a temporary validity and are written as step functions.
Source: own illustration of possible scenarios.
4, DISCUSSION OF RESULTS
Key Results
An important intermediate result is provided by the simulated variable “total car stock
by tech”, which includes new and older cars disaggregated by technology. An
illustrative example for the US is shown in Figure 11.
US total car stock by tech (standard) US total car stock by tech (advanced)
100M 100M
| |
75M = SSSeee | 7M |
— 50M 2 & som
25M —- = = | 25M |
t) 0 _—_——
2000 2008 2008 20122016 002022008 20002008 2008-2012 2016 2020-2024 2028
Time (Year) Tine Year
total car stock by tek{US,HEV] : Curent
(US| -Cunent total car stock by tec{US,PHEV] Curent
cette total ear stock by teck{US BEV] : Curent
total car stock by teck{US,FC] : Curent
{USNol caret
Figure 11 — Behavior of “total car stock by technology”
Source: own work using Vensim ®
In addition, the two main results of interest shown in this section are: aggregate gasoline
use and GHG emissions. Whereas the former is shown in Figure 12 for each country
under the three constructed scenarios; Figure 13 illustrates, using the results of Scenario
1, two different ways of representing corresponding GHG impacts.
As can be seen, although additional policies supporting alternative car technologies
contribute to reducing gasoline use, the differences between S2 and S3 are rather small.
17
The decrease in the demand for oil-based fuels results in an increase in the demand for
electricity. Suitable models need to be developed to assess the practical consequences of
massive EV charging for the local grid.
Conceming GHG emissions, as the example of S1 illustrates, accounting for TTW
emissions only (neglecting WTT and car manufacturing & scrappage emissions) distorts
the overall picture about the environmental impacts of car travel. With regard to
lifecycle emissions, the potential to dramatically reduce GHGs from car travel remain,
for the three markets and under the scenarios examined, untapped.
Aggregate Gasoline Use
18E+11
16E+11 |S
14E+11 ——
1,2E+11
E 1EH1 —
g SEH0 —
6E+10 —
4E+10 :
2E+10 —— a
0
Ym oO oe WS AD A AO WS AD YX no ab nO
FLL LL HK MHP MM MM MP Ww”
$1 - China S1 - Germany S1-US
— — §2-China = =— S2-Gemany= — S2-US
seeeee S3-China «eeere $3 - Germany ++++++ $3-US
Figure 12 — Energy impacts: “aggregate gasoline use”
Source: own work using Vensim® and Excel®
total TTW CO2eq total lifecycle CO2eq
eid Bes014 PS
Gex1d 6e1014 StS
: : 2
seid ae e104
8 “Prt S| 8 ee
eid it pew Prey
a Q
2000 2004 -2008—<2012—«2016-=«2020:«2024 «2028 2000 2004 2008 2012-2016 +2020 «2024 +~—-2028
Time (Year) ‘Time (Y ear)
Ba
NY] :Bal) }:BaU
Bau 2 u
*Note the different scale of the Y -axis.
Figure 13 — GHG impacts: “TTW” and “lifecycle” emissions (S1)
Source: own work using V ensim®
18
Sensitivity Analysis and Discussion
In order to investigate the critical assumption reflected by the variable “share of
technology switching”, a simple sensitivity analysis was undertaken. For this purpose, a
Monte-Carlo simulation using Vensim® sensitivity setup was conducted. The critical
parameter was represented using a random uniform distribution [0,1] and, as an
example, the chosen output variable was the stock of gasoline cars in China. The
resulting confidence bounds are shown in Figure 14.
Curent
50% 75% I 95% | 100%
total car stock by tech{China,G]
40M
4000 2008 2015 2023 2030
Time (Y ear)
Figure 14 — Sensitivity of “car stock (G)” to “share of technology switching”
Source: own work using Vensim ®
Only three scenarios out of a potentially long list of plausible scenarios have been
constructed as part of the modeling exercise presented here. Much work remains to be
done concerning the construction of altemative scenarios, policy analysis and sensitivity
analysis. Nevertheless, the benefits of designing and conducting experiments on such a
simulation model can be, at this point, highlighted.
5. CONCLUSIONS AND FURTHER RESEARCH
Summary and Conclusions
For this study, a simulation model based on the SD approach has been developed. The
SD model is capable of generating scenarios for the market penetration of different car
powertrain technologies at the national level until 2030. Furthermore, the model enables
the user to explore a set of 11 policy options. In this paper, the application of the model
to three key car markets (China, Germany and the US) has been illustrated by means of
scenario building.
Based on the modeling exercise and SD simulation results, the authors conclude that the
market scenarios outcomes are highly sensitive to the different assumed input policies.
The simulation output also confirms a reasonable initial hypothesis: given the larger
19
distance from car saturation in the Chinese market, the prospects of a more rapid
penetration of non-conventional cars is more promising than in the mature German and
US markets. This, however, depends greatly on the assumption concerning the lock-in
of mature technologies, represented by the proxy variable “share of technology
switching”.
Perhaps the most insightful result is the one arising from comparing total gasoline use
and lifecycle GHG emissions, in particular for China and the US which have a similar
level of car stock around 2030. This, at first counterintuitive, result can be explained
upon a second thought by three key aspects: (i) emissions are higher for manufacturing
than for scrappage and China’s projected number of sales is unmatched by the other two
mature markets; (ii) manufacturing emissions (but not scrappage) are higher for BEV
than for conventional cars and the former penetrate the Chinese market more rapidly
than in Germany and the US; (iii) the larger number of cars operating in China and the
assumed slow de-carbonization of the electricity grid. This example highlights the need
to strive for the expansion of model boundaries. By “trespassing” the narrow frontier of
on-road transport emissions on those commonly located in the energy system (i.e.
moving from TTW to WTT and overall WTW emissions analysis), we gained valuable
insights into the far-reaching environmental impacts of a specific market scenario.
Finally, the modeling exercise illustrates the suitability of the SD approach to
investigate the dynamic problems inherent in this area of research. With minor
adaptations, the same model structure could be used to represent systems from different
countries, from which a variety of behavior patterns can arise.
Limitations and Further Research
In our view, this study contains four main limitations. The first one is related to the
arbitrary definition of the system (model) boundary. Secondly, the critical issue of
modeling replacement sales by technology. The third one is the need to refine key
model assumptions and to collect the most recently available data, particularly for
China. Lastly, the hypothesis that EV deployment worldwide is expected to lead to
beneficial economies of scale and battery cost reductions is not explicitly covered in the
current version of the model.
Given the aforementioned limitations, we expect to devote additional research effort on
four main areas: (i) expansion of model boundaries to take into account potential
feedback processes (e.g. rebound effects); (ii) rethinking the causal structure for the
demand for car replacement, probably adding a Bass sub-model; (iii) update of the
model assumptions related to technology choices in view of new available knowledge
(e.g. data from revealed preference surveys and new discrete choice models); (iv) model
extension to include other relevant markets (in particular, France, India and Japan)
leading to the explicit consideration of technological leaps in the global automotive
market.
20
ACKNOWLEDGMENT
The authors gratefully acknowledge the support provided by the Helmholtz Association
and the Graduate School of Energy Scenarios Karlsruhe- Stuttgart.
REFERENCES
Achtnicht, M., 2011. German car buyers’ willingness to pay to reduce CO2 emissions.
Clim. Change 113, 679-697. doi:10.1007/s10584-011-0362-8
Al-Alawi, B.M., Bradley, T.H., 2013. Review of hybrid, plug-in hybrid, and electric
vehicle market modeling Studies. Renew. Sustain. Energy Rev. 21, 190-203.
doi:10.1016/j.rser.2012.12.048
Armenia, S., Baldoni, F., Falsini, D., Taibi, E., 2010. A System Dynamics energy model
for a sustainable transport system. Presented at the System Dynamics Society
Conference, Seoul.
Barlas, Y., 1996. Formal aspects of model validity and validation in system dynamics.
Syst. Dyn. Rev. 12, 183-210. doi:10.1002/(SICI)1099-
1727(199623)12:3<183::A ID-SDR103>3.0.C0;2-4
Ben-Akiva, M., Lerman, S., Ben-Akiva, 1985. Discrete Choice Analysis: Theory and
Application to Travel Demand. Mit Pr, Cambridge, Mass.
BenDor, T., Ford, A., 2006. Simulating a combination of feebates and scrappage
incentives to reduce automobile emissions. Energy 31, 1197-1214.
doi:10.1016/j.energy.2005.05.024
Bossel, H., 2007. Systems and models: complexity, dynamics, evolution, sustainability.
Books on Demand, Norderstedt.
Brownstone, D., Bunch, D.S., Train, K., 2000. Joint mixed logit models of stated and
revealed preferences for altemative-fuel vehicles. Transp. Res. Part B Methodol.
34, 315-338. doi:10.1016/S0191-2615(99)00031-4
Brownstone, D., Train, K., 1998. Forecasting new product penetration with flexible
substitution pattems. J. Econom. 89, 109-129. doi:10.1016/S0304-
4076(98)00057-8
Bunch, D.S., Bradley, M., Golob, T.F., Kitamura, R., Occhiuzzo, G.P., 1993. Demand
for clean-fuel vehicles in California: A discrete-choice stated preference pilot
project. Transp. Res. Part Policy Pract., Special Issue Energy and Global
Climate Change 27, 237-253. doi:10.1016/0965-8564(93)90062-P
Dahl, C.A., 2004. International Energy Markets: Understanding Pricing, Policies &
Profits. Pennwell Pub, Tulsa, Okla.
Dargay, J., Gately, D., Sommer, M., 2007. Vehicle Ownership and Income Growth,
Worldwide: 1960-2030. Energy J. 28, 143-170. doi:10.2307/41323125
Dieckhoff, C., Appelrath, H.-J., Fischedick, M., Grunwald, A., Hoffler, F., Mayer, C.,
Weimer-Jehle, W., 2014. Zur Interpretation von Energieszenarien. Deutsche
Akademie der Technikwissenschaften, Miinchen.
Dieckhoff, C., Fichtner, W., Grunwald, A., Meyer, S., Nast, M., Nierling, L., Renn, 0.,
VoB, A., Wietschel, M., 2011. Energieszenarien : Konstruktion, Bewertung und
Wirkung - “Anbieter” und “Nachfrager” im Dialog. KIT Scientific Publishing.
EVI, 2015. Global EV Outlook. 2015 Update. Electric Vehicles Initiative.
EVI, 2013. Global EV Outlook - Understanding the Electric Vehicle Landscape to
2020. Electric Vehicles Initiative.
21
Ford, A., 1995. Simulating the controllability of feebates. Syst. Dyn. Rev. 11, 3-29.
doi:10.1002/sdr.4260110103
Forrester, J.W., 1968. Principles of Systems. Pegasus Communications, Waltham, MA.
Forrester, J.W., 1961. Industrial Dynamics. MIT Press, Cambridge, Mass.
Forrester, J.W., 1958. Industrial Dynamics. Harv. Bus. Rev. 36, 37-66.
Gomez, J., Jochem, P., Fichtner, W., 2014. Car Technology Scenarios using System
Dynamics: Exploring Market Penetration and Energy Consumption in China,
Germany and US. In 2014 Proceedings of the Student Chapter, System
Dynamics Society.
Gomez, J., Jochem, P., Fichtner, W., 2013. The impact of electric vehicles on the global
oil demand and CO2 emissions. Presented at the W orld Conference on Transport
Research Society (WCTRS) Rio de Janeiro.
Hackbarth, A., Madlener, R., 2013. Consumer preferences for alternative fuel vehicles:
A discrete choice analysis. Transp. Res. Part Transp. Environ. 25, 5-17.
doi:10.1016/j.trd.2013.07.002
ICCT, 2012. Global Transportation Energy and Climate ROADMAP: The impact of
transportation policies and their potential to reduce oil consumption and
greenhouse gas emissions.
IEA, 2009. Transport, Energy and CO2: Moving Toward Sustainability.
IPCC, 2015. Climate Change 2014: Mitigation of Climate Change: Working Group III
Contribution to the IPCC Fifth Assessment Report. Cambridge University Press.
IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories.
Keith, D.R., 2012. Essays on the dynamics of alternative fuel vehicle adoption : insights
from the market for hybrid-electric vehicles in the United States (Thesis).
Massachusetts Institute of Technology.
Keles, D., Wietschel, M., Most, D., Rentz, O., 2008. Market penetration of fuel cell
vehicles — Analysis based on agent behaviour. Int. J. Hydrog. Energy 33, 4444
4455. doi:10.1016/.ijhydene.2008.04.061
Kieckhafer, K., 2013. Marktsimulation zur strategischen Planung von
Produktportfolios: Dargestellt am Beispiel innovativer Antriebe in der
Automobilindustrie, Auflage: 2013. ed. Springer Gabler, Wiesbaden.
Krail, M., 2009. System-Based Analysis of Income Distribution Impacts on Mobility
Behaviour. Nomos, Baden-Baden.
Kihn, A., Gléser, S., 2012. System-based feedback analysis of e-mobility diffusion in
China. Presented at the System Dynamics Society Conference, St. Gallen.
Martinez-Moyano, IJ., 2012. Documentation for model transparency. Syst. Dyn. Rev.
28, 199-208. doi:10.1002/sdr.1471
McFadden, D., Train, K., 2000. Mixed MNL models for discrete response. J. Appl.
Econom. 15, 447-470. doi:10.1002/1099-1255(200009/10)15:5<447::A ID-
JAE570>3.0.C0;2-1
Meadows, D.H., Wright, D., 2008. Thinking in systems: a primer. Chelsea Green Pub.,
White River Junction, Vt.
Meyer, G., 2009. Analyse und technisch-dkonomische Bewertung von Gesetzesfolgen
im Individualverkehr: dargestellt am Beispiel der Automobilindustrie Japans
und Deutschlands.
Most, D., Fichtner, W., Grunwald, A., 2009. Energiesystemanalyse : Tagungsband des
Workshops “Energiesystemanalyse” vom 27. November 2008 am KIT Zentrum
Energie, Karlsruhe. Universitat Karlsruhe Universitatsbibliothek, Karlsruhe.
22
Oil-price.net, 2015. Crude oil price [WWW Document]. URL http://www.oil-price.net
Park, S.Y., Kim, J.W., Lee, D.H., 2011. Development of a market penetration
forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure
and cost reduction effects. Energy Policy 39, 3307-3315.
doi:10.1016/j.enpol.2011.03.021
Rahmandad, H., Sterman, J.D., 2012. Reporting guidelines for simulation-based
research in social sciences. Syst. Dyn. Rev. 28, 396-411. doi:10.1002/sdr.1481
Randers, J., 1980. Elements of the System Dynamics Method. Pegasus
Communications.
Richardson, G.P., 2011. Reflections on the foundations of system dynamics. Syst. Dyn.
Rev. 27, 219-243. doi:10.1002/sdr.462
Richardson, G.P., 1991. Feedback Thought in Social Science and Systems Theory.
University of Pennsylvania Press, Philadelphia.
Schade, W., 2005. Strategic Sustainability Analysis: Concept and application for the
assessment of European Transport Policy, Auflage: 1. ed. Nomos, Baden-Baden.
Schipper, L., Marie-Lilliu, C., Gorham, R., 2000. Flexing the Link Between Transport
and Greenhouse Gas Emissions: A Path for the World Bank. Intemational
Energy Agency.
Schiihle, F., 2014. Die Marktdurchdringung der Elektromobilitat in Deutschland: Eine
Akzeptanz-und A bsatzprognose, Auflage: 1. ed. Hampp, R, Miinchen; Mering.
SDS, 2014. System Dynamis Society. Introduction to System Dynamics [WWW
Document]. URL http://www.systemdynamics.org/what-is-s/
Senge, P.M., 2006. The Fifth Discipline. Crown Business, New Y ork.
Shepherd, S., Bonsall, P., Harrison, G., 2012. Factors affecting future demand for
electric vehicles: A model based study. Transp. Policy, URBAN TRANSPORT
INITIATIVES 20, 62-74. doi:10.1016/j-tranpol.2011.12.006
Shepherd, S., Emberger, G., 2010. Introduction to the special issue: system dynamics
and transportation. Syst. Dyn. Rev. 26, 193-194. doi:10.1002/sdr.454
Shepherd, S.P., 2014. A review of system dynamics models applied in transportation.
Transp. B Transp. Dyn. 2, 83-105. doi:10.1080/21680566.2014.916236
Sterman, J.D., 2002. All models are wrong: reflections on becoming a systems scientist.
Syst. Dyn. Rev. 18, 501-531. doi:10.1002/sdr.261
Sterman, J.D., 2000. Business Dynamics: Systems Thinking and Modeling for a
Complex World. McGraw- Hill/Irwin.
Struben, J., Sterman, J.D., 2008. Transition challenges for alternative fuel vehicle and
transportation systems. Environ. Plan. B Plan. Des.
Toyota, 2015. Toyota Mirai - The Tuming Point [WWW Document]. URL
http://www.toyota.com/fuelcell/ (accessed 3.18.15).
UN, 2012. World Population Prospects: The 2012 Revision. United Nations,
Department of Economic and Social Affairs. Population Division, Population
Estimates and Projections Section [WWW Document]. URL
http://esa.un.org/wpp/ (accessed 3.18.15).
UNECE, 2015. UNDA Project on CO2 emissions and ForFITS. United Nations
Economic Commission for Europe [WWW _ Document]. URL
http://www.unece.org/trans/theme_forfits.htm]
Walther, G., Wansart, J., Kieckhafer, K., Schnieder, E., Spengler, T.S., 2010. Impact
assessment in the automotive industry: mandatory market introduction of
23
altemative powertrain technologies. Syst. Dyn. Rev. 26, 239-261.
doi:10.1002/sdr.453
Wansart, J., 2012. Analyse von Strategien der Automobilindustrie zur Reduktion von
CO2-Flottenemissionen und zur Markteinfiihrung A lternativer A ntriebe: Ein. . .,
Auflage: 2012. ed. Springer Gabler, Wiesbaden.
WB, 2014. The World Bank Data. World Bank [WWW Document]. URL
http://data. worldbank.org/ (accessed 3.18.15).
Weikl, R., 2010. Simulation zur Abschatzung der Marktanteilsentwicklung
unterschiedlicher Antriebsvarianten am deutschen Fahrzeugmarkt: ein
systemdynamisches Modell zur Entscheidungsunterstiitzung in der strategischen
Marktanalyse / Robert Weikl. GUC, Verl. der Ges. fiir Unternehmensrechnung
und Controlling, Chemnitz.
Wolff, P. de, 1938. The Demand for Passenger Cars in the United States. Econometrica
6, 113-129. doi:10.2307/1907143
Ziegler, A., 2012. Individual characteristics and stated preferences for alternative energy
sources and propulsion technologies in vehicles: A discrete choice analysis for
Gemany. Transp. Res. Part Policy Pract. 46, 1372-1385.
doi:10.1016/j.tra.2012.05.016
24
APPENDIX
In line with suggestions by (Rahmandad and Sterman, 2012) (Martinez-Moyano, 2012)
on model transparency and reproducibility, this appendix contains the model
documentation using SDM-Doc. The version of the model used in this paper is available
(Vensim® Reader format) from the main author upon request.
Model Summary
Model Assessment Results
‘Model information Number
‘312
Toil Number of Varables
(Level+Smooth+Delay Variables) 176.4%)
Feel eae ee mes
Teer :
Vanables with Source Information 73(23.4%)
Yona bisects am
i fine i 277 (88.8%)
ae —_—
pase aanae a
Time Unit Year
a oF
tart
Fepoted ins eal mee
naee ‘
Tea Fully Formulated ‘Yes
eae ae
Vis Westie SNe
[Undocumentec Equations: 912927)
Eun ebeaDa and : ae
Unavailable
ee A
Ce a
ee z
a a ag 7
ee
Eeuescoa Ba Ste a
Ceca een at i
Pee eae Coe
TET
Model Code
Note that, due to space constraints, only selected equations are shown below. The list
contains the code for the following subscripts: Germany and Gasoline (G). The full
model documentation (including the complete list of equations) can be obtained by
running the model using the SDM-Doc tool.
25
birth rate[G ermany] = FRACTIONAL BIRTH RATE[]*Population[]
death rate[G ermany] = Population[]/LIFETIME EX PECTANCY []
FRACTIONAL BIRTH RATE[Gemmany] = 0.0131196
FRACTIONAL GDP GROWTH RATE[Germany] = 0.0105939
GDP[Germany] = [GDP growth rate[] dt + [INITIAL GDP[]]
GDP growth rate[Germany] = FRACTIONAL GDP GROWTH RATE[]}*GDP[]
GDP per capita[Germany] = GDP[]/Population[]
INITIAL GDP[Germany] = 2.94843e+012
INITIAL POPULATION[Germany] = 8.35125e+007
LIFETIME EXPECTANCY [Germany] = 70
Population[Germany] = Jbirth rate[]-death rate[] dt + [INITIAL POPULATION[]]
annual VKT by car{Germany] = daily VKT by car{}*365
AVERAGE TRIP DISTANCE[Germany] = 18.06
car occupancy rate[Germany] = 1.2
daily VKT by car[Germany] = TRIPS PER DAY BY CAR[]*AVERAGE TRIP DISTANCE[]
PKM by car{Germany] = car occupancy rate[]*annual V KT by car{]
TRIPS PER DAY BY CAR[Germany] = 1.82
ADJUSTMENT TIME (Year) =1
ageing[Germany,G] = New Car Stock[]/AVERAGE AGEING TIME[]
AVERAGE AGEING TIME[Germany,G] = 1
AVERAGE LIFETIME[Germany,G] = 14
BETA COEF[Germany] =-25
car ownership ratio[Germany] = CAR SATURATION LEVEL[]*EXP(BETA COEF[]*EXP(GAMMA
COEF[]*coef GDP per cap[]))
CAR SATURATION LEVEL[Germany] = 557
coef GDP per cap[Germany] = GDP per capita[]/in thousand[]
divergence between projected and simulated car stock[Germany] = (projected car stock[]-total car
stock[])/ADJUSTMENT TIME
FIRST SALES RATE[Germany] =0
GAMMA COEF[Germany] = -0.169167
26
INITIAL CAR[Germany,G] = 3.3e+007
INITIAL NEW CAR[Germany,G] = 1e+006
market share first sales[G ermany,G] = exp U[]/denominator{Germany]
New Car Stock[Germany,G] = Jsales rate[]-ageing[] dt + [INITIAL NEW CART]
Older Car Stock[Germany,G] = Jageing[]-scrappage rate[] dt + [INITIAL CAR[]]
Population[Germany] = Jbirth rate[]-death rate[] dt + [INITIAL POPULATION[]]
projected car stock[Germany] = car ownership ratio[]/1000*Population[]
replacement sales[Germany,G] = scrappage rate[]*SHARE OF TECHNOLOGY
SWITCHING[Germany]
sales rate[Germany,G] = (market share first sales[]*FIRST SALES RATE[Germany])+(market share first
sales[]*divergence between projected and simulated car stock[Germany])+replacement sales[]
scrappage rate[Germany,G] = Older Car Stock[]/AV ERAGE LIFETIME[]
SHARE OF TECHNOLOGY SWITCHING[Germany] = 0.5
total car stock[Germany] = total new car stock[]+total older car stock[]
total car stock by tech[Germany,G] = New Car Stock[]+Older Car Stock[]
total new car stock[Germany] = }(New Car Stock[])
total older car stock[Germany] = )\(Older Car Stock[])
total sales[Germany] = }(sales rate[])
total scrappage[Germany] = }(scrappage rate[])
27
