Green Car Future 2020: Scenario Implications For The European Automotive Industry

Cover Image: Copyright BMW AG, Munich, Germany
GREEN CAR FUTURE 2020
SCENARIO IMPLICATIONS FOR THE
EUROPEAN AUTOMOTIVE INDUSTRY
Sa ile s h Pa te l
M B A R es ea rc h P r oj ec t
Cass Bu si n es s S ch ool , Lo nd on

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GREEN CAR FUTURE 2020:
SCENARIO IMPLICATIONS FOR THE EUROPEAN AUTOMOTIVE INDUSTRY
Presented by:
Sailesh Patel
Reg. no. 100019537
In partial fulfilment of the:
Master of Business Administration (MBA) degree
Submitted for:
Business Mastery Project
Presented to:
Dr Mohan Sodhi
Cass Business School
Date: 30th September 2011
Word count: 14,435

GREEN CAR FUTURE 2020: SCENARIO IMPLICATIONS FOR THE EUROPEAN AUTOMOTIVE INDUSTRY
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Preface
This business research project on „Green Car Future 2020: Scenario implications for
the European Automotive Industry‟ has been conducted in partial fulfilment of the
full-time MBA programme at Cass Business School, City University, London.
The intent of this project is to provide advanced strategic foresight for the European
automotive industry, by exploring critical mid to long-term forces likely to affect
industry competitiveness, and developing plausible 2020 scenario world-views which
challenge existing policy and business strategies to facilitate enhanced strategic
options within a range of potential „green car‟ futures.
It is envisaged that the real test of this research‟s value will be its ability to prove
relevant to business leaders not only in 2011, but in each year to 2020, by moving
beyond historically-based trends and forecasting to provide robust scenario-based
insights that enable valuable strategic planning, thinking, and action for a globally-
competitive Europe.

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Contents
Preface................................................................................................................................ 3
Definitions ............................................................................................................................ 6
Executive summary............................................................................................................. 7
1 Introduction................................................................................................................ 12
2 Research methodology ............................................................................................ 14
2.1 Target stakeholders ............................................................................................ 14
2.2 Research objective ............................................................................................ 15
2.3 Research outline................................................................................................. 16
2.3.1 Research question....................................................................................... 16
2.3.2 Research method selection ....................................................................... 18
2.3.3 Research method approach ..................................................................... 22
2.3.4 Data collection and analysis...................................................................... 27
2.3.5 Research limitations..................................................................................... 31
3 Driving forces analysis ............................................................................................... 33
3.1 Scope .................................................................................................................. 33
3.2 Method................................................................................................................ 42
3.3 Trends and uncertainties.................................................................................... 58
4 Scenario development ............................................................................................. 63
4.1 Mapping uncertainty ......................................................................................... 63

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4.2 Scenarios............................................................................................................. 65
5 Scenario implications (2020)..................................................................................... 69
5.1 Opportunities ...................................................................................................... 69
5.2 Risks...................................................................................................................... 71
5.3 Opportunity horizon............................................................................................ 74
6 Recommendations (2011) ........................................................................................ 75
6.1 CAPIRE stakeholder groups ............................................................................... 75
6.2 European automotive industry.......................................................................... 78
Appendix........................................................................................................................... 80
Bibliography ...................................................................................................................... 82
Acknowledgements......................................................................................................... 86
About the author.............................................................................................................. 87

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Definitions
ACEA European Automobile Manufacturers‟ Association
BRIC Brazil, Russia, India, and China: ostensibly referred to as the
Emerging Market economies
CAPIRE Coordination Action PPP Implementation for Road-transport
Electrification
CARS21 Competitive Automotive Regulatory System for the 21st century
CORDIS Community Research and Development Information Service
EC European Commission
EPoSS The European technology Platform on Smart Systems integration
ERTRAC European Road Transport Research Advisory Council
EU European Union
EU27 The 27 member states of the European Union, comprising Austria,
Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia,
Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia,
Lithuania, Luxemburg, Malta, the Netherlands, Poland, Portugal,
Romania, Slovakia, Slovenia, Spain, Sweden and the United
Kingdom.
Europe 2020 The EU's growth strategy for the coming decade, aimed at
making the EU a smart, sustainable and inclusive economy.
ICE Internal Combustion Engine
Intelligent Car Initiative EU programme aimed at accelerating the deployment of
intelligent vehicle systems on European and international
markets.
LCV Light Commercial Vehicle
NAFTA North American Free Trade Agreement: trilateral trade bloc
comprising North America, Canada, and Mexico
OEM Original Equipment Manufacturer
PPP Public-Private Partnership
RTD (Programme for) Research and Technical Development

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Executive summary
Energy access and mass-adoption of the automobile have shaped our
environments and the way we live for much of the 20th century, creating unforeseen
opportunities for a global automotive industry to prosper and fail in often unforeseen
ways.
In 2011, the European automotive industry stands on the brink of uncertain and
potentially unprecedented change – the purpose of this report is to explore these
complex global and industry unknowns and explain possible outcomes in the form of
plausible, bounded industry scenarios, which will challenge incumbent and new
businesses to test the resilience of planned strategies, and to develop readiness and
recognise opportunities in alternative futures.
This report is presented for the interest of the 14 consortium members of CAPIRE, a
public-private research initiative within the framework of the European Green Cars
Initiative, one of the three Public-Private Partnerships (PPP) of the EC European
Economic Recovery Plan announced in 2008.
Due to time-constraints and limitations on publicly-available information, this report
predominantly addresses passenger car scenarios; however it is envisaged that the
analysis and implications will be of value to all members, irrespective of market
interest.
Europe‟s automotive industry faces paradigm challenges over the next decade,
during which four certain global macro forces prevail: imbalanced population
growth, resource demands, rebalancing of economic power, and universal access
to information. However, uncertain and dynamic forces – political, social,
technological, and many more – can have a profound impact on decision-making,
hence strong differences of opinion about the future prevail and uncertainty is high,
relative to business‟ ability to predict or adjust. These differences arise because trend
and forecasting methods are limited by their bias towards statistical mapping of
known relationships, with limited incorporation of new and unexpected dynamic
forces.

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There are therefore clear strategic benefits to moving beyond short to mid-term
methods, namely by mapping the development of known trends and the influence
of critical unknowns and uncertainties, such that the most likely disruptive industry
scenarios are explored, and their resultant implications used to test and develop
future policy and business strategy.
Proven methods of scenario planning were employed, adapting economic models
of national competitiveness and evidence-based research to identify and relate
critical industry driving forces and their outcomes. The following four „Green Car
Future 2020‟ scenarios were thus identified as most likely to influence the
international competitiveness of the European automotive industry:
‘Renaissance 2.0’: The European personal transport market becomes
the most sophisticated and demanding in the world, fuelled by
effective wealth generation strategies and innovation through
products and services; by comparison, the rest of the world are
suffering under the might of shared financial liabilities, whilst their
citizens face high unemployment and provide only undifferentiated,
price-sensitive markets.
‘Global Wealth’: In this scenario, the emerging economies transition to
a near-developed state, and connectedness between the East and
West facilitates effective wealth generation and redistribution. There is
much homogeneity between markets, and the concept of „national‟
automotive manufacturers becomes outdated as products and
service offerings merge to be provided by large multinationals.
‘Advanced Austerity’: Financial shocks hit hard and persist across the
world‟s economies, slowing growth and refocusing governments on
their own liabilities and debts. The international automotive industry
becomes an uncompetitive environment, except for small
entrepreneurs realising benefits from price-sensitive consumers.
‘Yin Yang’: Wealth from the West is redistributed to the East, as BRIC
nations collaborate to realise liberal and sustainable business
environments, fuelling indigenous wealth generation in products and
services. In contrast, the US and Europe are burdened by financial
shocks, poor access to energy, and a loss of knowledge workers to
opportunities in the East.
These scenarios are not predictions of the most likely automotive future, however
they provide a robust indication of how far the 2020 competitive landscape may –
due to critical driving forces - sustainably differ from that of 2011.

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Implications for the competitiveness of the European automotive industry were
derived both from the scenario-building process, which is a strategic learning
process in itself, and from the final scenarios. Three key factors were identified as
most influential to the relative competitiveness of the industry: 1. Demand of the
European market; 2. Related and supporting industries (e.g. products and services);
3. Factor conditions, such as infrastructure and advanced conditions such as R&D.
Demand was predominantly influenced by the sophistication and size of market,
and identified to be the single largest determinant of competitiveness; one whose
relative development across international borders is significantly impacted by global
uncertainties. The future competitiveness of the European automotive industry is
therefore considered largely unknown. Faced with increasing competition from
emerging nations supported by government programmes, consideration must also
be given to the mutual reinforcement of international and national competitiveness.
Furthermore, as the automotive industry aims to transition into delivering products
and services, it must recognise that competitiveness in services may require different
factors to manufacturing competitiveness.
Accordingly, the top three scenario opportunities for each of the CAPIRE
stakeholder groups were identified as:
• Product and service innovation
• Development of supporting industries
• Manufacturing and delivery innovation
Automotive Manufacturers
and Suppliers
• Realisation of integrated transport model
• Sustainable energy efficiency gains
• Enhanced quality of life
City Transport Authorities
• Retail model innovation
• Supply chain innovation
• Lifestyle market penetration
Electricity Suppliers

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Conversely, the top three scenario risks for each of the CAPIRE stakeholder groups
were identified as:
Hence, recommendations for immediate action, with the intent of maximising value
capture opportunities and minimising risk consequences, both now and in the future,
were identified for CAPIRE stakeholders as below:
Automotive Manufacturers and Suppliers
I. Develop affordable, integrated personal transport solutions
II. Deliver differentiated products and services
III. Share innovation risk in non-core aspects; design and develop
for flexible breakeven volumes
I. Develop indispensable transport infrastructure and services
II. Improve accessibility and affordability whilst monetising service-
delivery capabilities
III. Maximise efficiency through integration, and learn from the best
I. Develop a sustainable relationship with consumers lifestyle
energy needs
II. Provide a differentiated, integrated energy service through
suitable channels
III. Develop capacity and flexibility
• Differentiation and affordability
• Investment costs
• Capacity and service quality
Automotive Manufacturers
and Suppliers
• Transport capacity
• Market cannibalisation
• Living costs
• Capacity utilisation
• Supply chain disruption
• Retail model resilience

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This report concludes by advising all European automotive industry stakeholders to
consider the following factors in order to enable 2020 competitiveness:
 Future planning
 Strategy
 Operations
 Competencies
 Tracking events
 Overcoming resistance
Through deeper understanding of these scenario implications, and implementation
of suggested recommendations, Europe‟s automotive industry will be better
prepared to innovate and develop agility to compete in the green car future of
2020 and beyond.

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1 Introduction
In an increasingly unpredictable, complex world, this report aims to answer the
strategic dilemma that is foremost for the European automotive industry: How to
compete in the green car future, 2020?
Presented with long-term uncertainty, industries and businesses tend to adopt one of
three strategic postures (Knowledge@Wharton 2009):
1. Do nothing (the „zero-future‟ option): caution rules, no attempts
are made to analyse, anticipate, or predict beyond the short-
term, and major decisions are deferred until the fog of
uncertainty clears.
2. Take a calculated bet on one particular future: a clear message
is presented, and bold action is taken that could play out as
brilliant, or fail through wishful thinking.
3. Separate knowns and unknowns, in order to explore possible
futures: scenarios, not predictions, are developed, focussing on
the development of agility and options.
Though postures (1) and (2) may prove successful, the risks for businesses committed
to such approaches are high, especially if the competitive business environment
undergoes irreversible and rapid change.
Hence, whilst acknowledging that „No one can definitively map the future, but we
can explore the possibilities in ways that are specifically intended to support
decision-making‟ (Shell International BV 2008) businesses that adopt posture (3) are
more likely to be competitively enabled to reduce risks, and expedite effective,
informed strategic decisions in order to maximise potential benefits.
To provide maximum business value to the European automotive industry, this
research will be split into four principal sections:
 Developing an optimal research method to identify key driving forces and
produce relevant, applicable scenarios.
 Understanding the key trends and driving forces of change in the
automotive industry, and identifying the impacts and uncertainties.

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 Constructing scenarios by mapping uncertainties, identifying themes, and
describing significant scenarios.
 Determining scenario implications and developing strategic
recommendations for the European automotive industry.
In doing so, it is hoped to provide a learning process; one which helps broaden and
deepen the understanding of stakeholders, and permits improved knowledge of the
trends and uncertainties which could impact the competitiveness of the European
automotive industry of 2020.

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2 Research methodology
2.1 Target stakeholders
This research is provided for business decision-makers in the European automotive
industry, with specific regard for the 14 consortium members of CAPIRE
(Coordination Action PPP Implementation for Road-transport Electrification), a
public-private research project within the framework of the European Green Cars
Initiative (EGCI), one of the three Public-Private Partnerships (PPP) of the EC
(European Commission) European Economic Recovery Plan announced in 2008.
CAPIRE aims to produce a dedicated roadmap based on an elaborated and deep
analysis of R&D needs, respective milestones and supporting measures. The project
focuses on the definition of potential flagship projects which could foster the
competitiveness of the European Automotive Industry in the domain of Transport
Electrification as well as in the development of technologies and services to reduce
the European CO2 footprint. (CAPIRE webmaster n.d.)
The 14 CAPIRE consortium members, home countries, and their main business
activities are summarised in Table 1.
CAPIRE member
Home
country Business activities
RENAULT France
Car, light commercial vehicle, and truck
manufacturer
AVL LIST Austria
The world's largest private independent
powertrain and instrumentation systems
developer
CRF Italy
Centro Richerche Fiat - Fiat Automobile
Group's research centre
Volvo Technology Corporation Sweden
Volvo Group's main technology research
centre
VDI/VDE-IT Germany
Providers of IT innovation and technology
services across automotive and other sectors
Robert Bosch GmbH Germany
Suppliers of technology and services in
industrial, automotive, construction, and
consumer goods
VALEO France
OEM module and systems supplier to the
automotive industry
IBERDROLA Spain
Number 1 Spanish energy company, and
number 1 global wind power provider

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CAPIRE member
Home
country Business activities
Transport for London England
Transport authority and operator for London's
public transport network
Continental Teves AG Germany
Auto supplier, covering Chassis & Safety,
Powertrain and Interior
HIDRIA d.d. Slovenia
Climate and automotive components
supplier
Procter & Gamble Eurocor Belgium
Household and personal products
manufacturer
TÜV Rheinland Consulting
GmbH Germany
Product development, certification, and
approval services
SOLARIS Bus & Coach Poland
Family-owned bus and tram manufacturer,
including hybrids (60% export activity)
Source: Author analysis of member websites
Table 1: CAPIRE consortium members and business activities
2.2 Research objective
This business-focussed project, supplementing existing technology R&D programmes,
aims to inform and enable European automotive industry decision-makers
responsible for implementing the CAPIRE roadmap.
The importance of the European automotive industry to Europe‟s future cannot be
underestimated. Following the 2008 financial crisis, the EC‟s „Responding to the crisis
in the European automotive industry‟ publication recognised:
“With an annual turnover of €780 billion and a value added of over
€140 billion, it [the European automotive industry] makes a major
contribution to the EU's GDP. It exports far more than it imports, with a
surplus of over € 60 billion on overall exports of €125 billion. In addition,
the sector plays a central part in tackling many of the key economic,
social and environmental challenges faced by Europe today, such as
sustainable mobility and safety.” (European Commission 2009b, p.3)
Therefore, the research objective of this project is to develop and evaluate the
business implications of plausible mid to long-term global scenarios likely to be of
highest sustained impact to the European automotive industry.

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2.3 Research outline
In order to distinguish relevant, sustainable outcomes which challenge and
constructively inform the future strategies of the European automotive industry, the
research process must be designed to efficiently and effectively explore critical
uncertainties.
The primary resources available for this project include but are not limited to:
 Approximately 300 hours of MBA student time (40 hrs per week)
 Academic supervision to direct research and review drafts
 City University library resources
 Publicly accessible information sources
In this section, a suitable research outline is determined by evaluating the five core
research elements illustrated in Figure 1.
Source: Author analysis
Figure 1: Research outline process
2.3.1 Research question
The research question for this project has been formulated in consideration of the
goals of the CAPIRE programme, the challenges facing the European automotive
industry, and the value of existing prevailing research.
A comprehensive literature review was performed, assessing the scope of existing
industry, academic, and public policy insights, trends, and foresights (see
Bibliography). Although a wealth of near-term insights and opinions can be readily
discerned, uncertainty about the future is commonly discernible as mid to long-term
foresights providing limited strategic direction and comparability, as illustrated by
the EC‟s sectoral overview of the European automotive industry:
Research
question
Research
method
selection
Research
method
approach
Data
collectionand
analysis
Research
limitations

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“In the medium-term, the technological competition in this industry is
set to intensify, with different fuel and energy concepts plus intelligent
transport playing a particularly important role. […] In the long-term, the
global market for motor vehicles is set to grow substantially” (European
Commission 2009a, pp.9–10)
From a business perspective, notable anchoring biases were observed, with
research tending to assume that a product-based retail model will prevail in future,
while incorporating the substitution of emergent „green‟ technological
developments and associated infrastructure to varying degrees. E.g. J.D. Power and
Associates 2010, Phil Gott 2008, Housely Carr 2011.J.D. Power and Associates 2010
Limited consideration is evidenced from published research for the substitution and
disruptive effect of alternative product, service, and transport models, or the
composite impact of political, economic, and social factors, for example.
Furthermore, consolidated research specifically evaluating the extent to which the
competitive position of the European automotive industry might be challenged is
notable by its absence.
Nonetheless, literature consensus is achieved on the core challenge facing the
global, and the European automotive industry – that of competing effectively in a
future in which sustainability and „green‟ (environmentally responsible)
considerations are no longer consumer preferences, but substantially business and
policy requirements.
Therefore, to inform and enable the European automotive industry, a timescale of 5-
10 years is identified as the strategic domain of critical uncertainty, urgently requiring
improved foresight to inform imminent mid to long-term competitiveness planning
and action (see Figure 2), hence the research question for this project is formulated
as: „Green Car Future 2020: Scenario implications for the European Automotive
Industry‟.

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Source: Jones 2010
Figure 2: Domains of strategic uncertainty
2.3.2 Research method selection
To determine an appropriate research method, the common nature of the
challenge facing leaders in the automotive industry must be understood.
Schoemaker‟s research identifies strong parallels with CAPIRE members‟ interests:
“Senior executives commonly ponder such questions as "What might
give us continued competitive advantage?" and "What new product
or markets should we enter and how?" Both questions go to the heart
of a firm's strategic vision. […] Knowing the answers constitutes the
difference between muddling through and running the business from
day to day with confidence and foresight.” (Schoemaker 1997, p.43)
Thus, the dilemma for business is one of developing an appropriate strategic vision to
enable corporate planning for action within a range of possible future scenarios. To
inform this strategy a variety of research methods may be utilised to provide future
foresight, including:
 Statistical techniques
 Decision analysis
 Standard forecasting
 Scenario techniques
Schoemaker‟s 1991 research describes extensively the limitations of statistical
techniques based on historically-derived, albeit complex models, which incorporate

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multiple known variables within a simplified view of the world, or decision analysis
examining the effect of one variable whilst holding others constant. His research on
standard forecasting observed that:
“forecasts are often wrong (Smyth, 1983; Zarnowitz, 1984, 1985),
especially when dealing with the macro environment (Page, 1982;
Leamer, 1983) […] The more stable the game, the more valuable
forecasts are; the more unstable the environment, the more scenarios
may be of help.” (Schoemaker 1991, p.551)
Pierre Wack (1922-1997), a successful practitioner of scenario thinking at Royal Dutch
Shell, similarly identified the core risks of relying on forecasts alone:
“Forecasts are not always wrong; more often than not, they can be
reasonably accurate. And that is what makes them so dangerous.
They are usually constructed on the assumption that tomorrow's world
will be much like today's. They often work because the world does not
always change. But sooner or later forecasts will fail when they are
needed most: in anticipating major shifts in the business environment
that make whole strategies obsolete.” (Wack 1985)
Put simply, these traditional techniques produce outcomes which are to a large
extent bounded by the influence of known variables. For example, Thomas Watson,
chairman of IBM, predicted in 1943, "I think there is a world market for maybe five
computers." informed by his bounding of the future using existing market models.
However, if presented with scenarios that identify relevant and critical uncertainties,
it is unlikely that business leaders should fail to recognise and understand outcomes
in alternative futures. It is therefore advantageous to develop scenario techniques
that recognise and incorporate critical uncertainties and their driving forces, and
bounds their potential impacts, opportunities, and risks in the form of relevant
scenarios.
Referring to Figure 3, mapping differing degrees of uncertainty, businesses can seek
to improve their knowledge of the future by:
 Firstly, identifying and developing a better understanding of the impact of
known trends – stable driving forces that will affect us and others, which
we can predict quite clearly. e.g. population growth, universal data
access, etc.

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 Secondly, identifying and developing a better understanding of the
potential impact and driving forces of unknown trends – unstable forces
that will affect us, but that we cannot predict clearly. e.g. the price of oil,
recessionary recovery, demand for raw materials, etc.
 Thirdly, identifying uncertainties – relevant unknowns that could
significantly affect us - and developing an understanding of their potential
impact and driving forces. e.g. intelligent highways, government subsidies,
political succession, electromobility, etc.
Figure 3: Degrees of uncertainty
Scenario techniques can facilitate exploration of all the above degrees of
uncertainty, and to a large extent are concerned with the likely challenges and
outcomes of the unknown and uncertain. Though in use since the 1960s and 1970s, it
must be recognised that they are „a difficult technique to define and describe.‟ and
that their „very flexibility and widespread applicability seems to limit their general
acceptance in everyday organisational use.‟ (Verity 2003, p.186)
Furthermore, some practitioners (such as Shell) sometimes draw a distinction
between scenario thinking, which is aimed at the exploration of uncertainties, and
scenario planning, which is aimed at applying scenarios to support decision-making,
using probabilities, decision analysis, etc, to support other strategy methods.
Henceforth in this document, „scenario planning‟ will be used interchangeably to
refer to both scenario techniques of thinking and planning, as both elements will be
valuable in order to derive implications for the European automotive industry.
KNOWN
UNKNOWN UNCERTAIN

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Therefore, scenario planning to reduce uncertainty for the automotive industry
appears well-suited as a research method, as Verity, arguing for greater use of
scenario techniques in business environments that are increasingly complex,
changing, and global, notes Michael Porter‟s early advocacy of scenario
techniques to achieve competitive advantage:
“As Porter (1985) pointed out: „Every plan is based on an industry
scenario in one form or another, though the process is frequently an
implicit one‟. He was an advocate for practising scenario techniques in
strategy formulation. He recognised that the assumptions managers
used as background to generating their strategies were rarely made
explicit or challenged. By acknowledging the uncertainties, he foresaw
much improved strategy outcomes.” (Verity 2003, p.188)
Advancing Porter‟s research, Schoemaker further identifies industry and corporate-
level conditions favouring the use of scenario planning, as described in Table 2.
Source: Schoemaker 1997 p.48
Table 2: Business conditions favouring the use of scenario planning
Though these conditions may not reflect the views of all organisations in the
automotive industry, the symptoms have historical precedent and reflect the
uncertainties and difficulties increasingly likely to be felt by the European automotive
industry. Therefore, scenario planning is determined as an ideal research method to
achieve the objective of this project.

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2.3.3 Research method approach
Scenario planning is a flexible, non-prescriptive technique facilitating many different
research approaches, as its practitioners have noted, „There are different schools of
thought about what scenarios are for, how scenarios should be built and when they
should be used.‟ (Verity 2003, p.186)
Consequently, there are strong reasons why managers will resist or reject using
scenario techniques. Verity explains:
“One of the reasons why scenarios are not adopted more widely is this
divergent set of methodologies and the different emphases on
quantitative or qualitative input. It is difficult for managers to sift
through the different approaches and know, with confidence, which
one to adopt for which business issue.” (Verity 2003, p.187)
Hence, to effectively develop „Green Car Future 2020‟ scenarios and produce
implications that will be confidently received and used by leaders and managers in
the European automotive industry, this section will draw upon scenario practitioner
experience to clearly detail:
 What the scenarios will be for;
 How the scenarios will be built;
 When the scenarios should be used.
What the scenarios will be for ____________________________________________________
The scenarios produced by this report aim to serve two primary business needs for
CAPIRE members and the wider European automotive industry:
1. Strategic thinking: by exploring and highlighting large-scale forces that
could push the competitiveness of the European automotive industry in
markedly different directions
2. Strategic planning: by providing scenarios that encourage decisions
made today to play out well across several different scenarios
The scenarios will provide analysis of the driving forces affecting the competitiveness
of the European automotive industry within a range of critical uncertain futures.

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Company-specific decision scenarios will not be within the scope of this project. It is
important to emphasise that the scenarios will not be a prediction of the most
probable futures, but will aim to serve as strategic tools to permit learning,
exploration, and decision-making, best expressed by Schoemaker‟s analogy:
“Suppose you are planning to climb a mountain. Corporate planning
of the past would provide you with a detailed map, describing the
predetermined and constant elements of the terrain. Of course, this
traditional planning tool is very valuable and, indeed, indispensable in
this case. Just as geographical mapping is an honored art and
science, so corporate mapping can be very useful. However, it is
incomplete. First, the map is not the territory, but an incomplete and
distorted representation (any two-dimensional map distorts the earth's
surface). Second, it ignores the variable elements, such as weather,
landslides, animals, and other hikers. The most important of these
uncertainties is probably the weather, and one option is to gather
detailed meteorological data of past seasons, perhaps using
computer simulations.
Scenario planning, however, goes one step further. It simplifies the
avalanche of data into a limited number of possible states or
scenarios. Each scenario tells a story of how the various elements might
interact under certain conditions. Where relationships between
elements can be formalized, quantitative models can be developed.
Each scenario should be evaluated for internal consistency and
plausibility. For example, high visibility and heavy snow drifts are an
implausible combination. Although the boundaries of scenarios might
at times be fuzzy, a detailed and realistic narrative may direct your
attention to aspects you would otherwise overlook. Thus, a vivid snow
drift scenario (with low visibility) may highlight the need for skin
protection, goggles, food, supplies, radio, shelter, and so on.”
(Schoemaker 1997)
How the scenarios will be built____________________________________________________
A typical scenario-building exercise, illustrated in Figure 4, requires the definition of a
core issue, scope, and stakeholders (to provide context and a „test of relevance‟),
followed by identification of trends and uncertainties, which are then developed
into scenarios by various means, and disseminated to permit strategic thinking and
planning.

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Figure 4: Overview of typical scenario-building process
Numerous sources have been referred for guidance, however only Schoemaker
provides a comprehensive ten-step guide to scenario-building and dissemination,
which is adapted in Table 3 to provide an overview of the scenario planning
activities included within this project.
Scenario planning activity Implementation within ‘Green Car Future 2020’ project
1. Define the issues
Time frame: 2020 (incorporating changes in technology,
products, services, competition, markets, economies post-
2011)
Scope: scenario implications for the European automotive
industry
Issues: how to compete in the „green car‟ future 2020 –
understanding uncertainty to industry risks and opportunities
2. Identify the major
stakeholders
See Section 3
3. Identify basic trends
4. Identify key uncertainties
5. Construct initial scenarios
See Section 4
6. Check for consistency
and plausibility
7. Construct learning
scenarios
8. Identify research needs See Sections 5 and 6, including scenario implications and
Define focal issue
(test of relevance)
Identify trends and
uncertainties
Develop scenarios
Disseminatescenarios to
stakeholders
Use scenarios for strategic
thinking and planning

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Scenario planning activity Implementation within ‘Green Car Future 2020’ project
recommendations
9. Develop formal models Not in project scope
10. Create decision
scenarios Not in project scope
Source: adapted from Schoemaker 1997
Table 3: Scenario planning activities implemented in the ‘Green Car Future 2020’ project
The main purpose is to identify dynamics of critical uncertainties and potential
business environment states that might affect the competitiveness - for better, or
worse - of the European automotive industry. Table 4, adapted from Schoemaker‟s
experience of developing and applying scenarios in industry, provides the criteria
that will be used to direct and evaluate the quality of the final „Green Car Future
2020‟ scenarios.
How can you assess if your scenarios are good ones?
1. Objective: The scenarios should cover a wide range of possibilities and highlight
competing perspectives (within and outside the industry), while focusing on
interlinkages and the internal logic within each future.
2. Relevant: To have impact, your scenarios should connect directly with the mental
maps and key concerns of those who will use them (e.g., senior executives, middle
managers, etc.).
3. Consistent: The scenarios should be internally consistent (and be perceived as such)
to be effective.
4. Distinctive: The presented scenarios should be archetypal - that is, they should
describe generically different futures rather than variations on one theme.
5. Sustainable: Each scenario should ideally describe an equilibrium or a state in which
the business environment might exist for some length of time, as opposed to being
highly transient. It does an organisation little good to prepare for a possible future
state that will be quite short-lived.
Source: adapted from Schoemaker 1997 p.54
Table 4: Essential criteria for successful scenarios

26
When the scenarios should be used ______________________________________________
The industry-level scenarios, themes, and frameworks developed within this project
should be used by firm managers for four complementary purposes:
1. To test existing strategy: by applying industry level scenario planning to
existing firm-level strategy, managers will be able to test the resilience and
flexibility of their strategies within a range of critical futures. Resilient
strategies are those which are sustainable across multiple scenarios.
2. To develop strategy (existing and new): industry level scenario planning
may be used to develop understanding of opportunities and risks within
existing strategy, and to identify and test new strategic opportunities.
3. To develop organisational awareness: by propagating scenario themes
within the organisation, managers and employees can together benefit
from increased organisational awareness of risks and opportunities in
critical future scenarios.
4. To develop organisational agility: responsiveness during uncertainty is
often demanded of modern organisations; by propagating awareness of
future critical scenarios an organisation can begin preparing for
uncertainty, and learn to understand and recognise signals and signposts
of change.
Ultimately, scenario planning can help an organisation make better decisions, as
illustrated in Figure 5, by encouraging multiple perspectives, and striving for an
integrated analysis and understanding of critical future scenarios.

27
Figure 5: Generic levels of scenario analysis
2.3.4 Data collection and analysis
The data collection methods selected for this research will be aimed at supporting
the production of scenarios that:
 Cover a wide range of possibilities
 Highlight competing perspectives (within and outside the industry)
 Focus on interlinkages and the internal logic within each future
 Describe sustainable futures
As the „Green Car Future 2020‟ scenarios will, by definition, aim to explore
developmental (change) factors in relation to context, the data collection and
analysis method most appropriate to this project is the case study. Thomas (2011)

28
offers the following definition of case study: "Case studies are analyses of persons,
events, decisions, periods, projects, policies, institutions, or other systems that are
studied holistically by one or more methods.”
Yin (2003) further defines a case study as:
“an empirical inquiry that:
 investigates a contemporary phenomenon within its real-life
context; when
 the boundaries between phenomenon and context are not
clearly evident; and in which
 multiple sources of evidence are used.”
To analyse the perspectives, interlinkages, and driving forces influencing the
automotive industry in a plausible, cohesive manner, it will be necessary to obtain
and evaluate data from a variety of sources, in a method appropriate to the
subsequent scenario-based analysis. Rosenberg and Yates (2007 pp.449-450) outline
a framework for identifying and analysing data, as shown in Figure 6.
Source: Rosenberg and Yates 2007 p.449
Figure 6: Identifying data collection and selecting analysis methods
However, in contrast to Rosenberg and Yates‟ approach of selecting data analysis
strategies after collecting data, scenario planning requires that an analysis strategy
(the scenario building method) is selected before collecting data, after which the
required data collection methods may be identified. It is evident that scenario
building will require the collection of data supporting content, thematic, and
statistical analysis, necessitating both qualitative and quantitative input.

29
Due to time constraints, resource limitations, and the requirement to collect a large
range of unbiased data, only secondary research will be conducted. As far as is
reasonably possible, attempt will be made to use multiple sources of evidence, and
to aid scenario learning by identifying relevant assumptions and limitations of
collected data (in so doing, it may also be more readily feasible for others to
develop this research beyond the scope of this project).
It is envisaged that the nature and quantity of data required will inhibit the
derivation of purely quantitative models of relationships by which to determine
driving forces; similarly, the incorporation of qualitative data will necessitate a more
stochastic method. It is therefore reasonable to determine that a heuristic method of
scenario building will be required in order to efficiently identify and understand
significant driving forces and their relationships within the timescale of this project.
Henceforth, proven qualitative and quantitative models and frameworks will be
utilised for analysis and scenario building where possible, and where necessary,
logical, bounded approximations to models will be developed and applied.
In order to capture a full perspective of the driving forces affecting or capable of
affecting the European automotive industry, data collection will encompass the
field-of-vision proposed by Day and Schoemaker, illustrated in Figure 7, aimed at
helping organisations1 actively capture strategic signals from the periphery.
1 Although Day and Schoemaker‟s original intent was to help capture signals at the organisational
level, many of the areas identified are equivalent at industry level, and therefore these areas are
considered advantageous for identifying driving forces at industry level.

30
Source: Day and Schoemaker 2007 p.22
Figure 7: Capturing external and internal driving forces at the organisational level
Research will therefore be undertaken to identify the external and internal driving
forces affecting (or likely to affect) the competitiveness of the European automotive
industry by exploring the following five areas:
 Political, legal, social, and economic forces
 Emerging technologies and scientific developments
 Influencers and shapers
 Customers and channels
 Competitors and complementors
To provide perspective within each source, appropriate macro-level evidence from
economic, industry, and consumer perspectives will be sought, using academic,
industry, and available media sources. Where possible, insights and opinions will be
derived from multiple disciplines and industry thought leaders, thereby developing a
„richness of insight‟ that is encouraged and desired when conducting – as Shell have
done – workshop-based, global scenario building processes (Lawrence Wilkinson
n.d.). It is also considered advantageous to refer to published scenarios at the
economic, industry, and consumer foresight level, spanning multiple timelines, as

31
these research sources, despite their differing methods, will help identify core data
sources and further inform the application of scenario planning techniques.
Together, the above approaches are intended to reduce bias errors when exploring
areas we don‟t know (second class – see Figure 3), and areas which are unknown to
us (third class), as observed by Schoemaker:
“Various biases - overconfidence, narrow framing, the tendency to
look for confirming evidence-plague all three classes, but by far the
greatest havoc is caused by errors of the third kind (Russo &
Schoemaker 1992). Although no fail-proof techniques exist to avoid
them, much improvement can be garnered by focusing our attention
on classes two and three.” (Schoemaker 1997, p.68)
By reducing systemic errors when collecting data in the realm of uncertainty,
scenario planning is more likely to achieve effective understanding and learning
since, as Schoemaker (1997 p.68) described “it is essentially a study of our collective
ignorance.”
2.3.5 Research limitations
To achieve the aim of this research project using available resources, the research
scope is intentionally limited to focus on the driving forces affecting the passenger
car market only; LCVs (Light Commercial Vehicles) and buses will not be explicitly
analysed. This decision is considered appropriate because, during 2009:
 14 million passenger cars were produced in the EU by 17 countries, in
comparison with only 1 million LCVs produced by 10 EU countries.
 Truck production accounted for 0.25 million units, whilst buses represented
only 0.035 million units of production.
Source: ACEA 2009 p.70
Although EU production figures alone do not reflect the full interest of the European
automotive industry, the above data illustrates the greater significance of its car
production activities. Furthermore, as the market for LCVs and trucks is primarily
concerned with demand for transport of goods and not people, it is desirable to
separate this element from analysis of the market for cars and mobility, unless
determined as a key driving force. Nonetheless, it is envisaged that the analysis and

32
implications presented in this report will be of value to all members, irrespective of
market interest.
Implicit research limitations likely to influence this project will arise due to behavioural
bias; namely, the author of this report will, by definition, incorporate individual biases
into data collection, scenario development, and analysis, hence every attempt is
made to identify assumptions and minimise errors by seeking a broad range of
relevant perspectives. To mitigate against researcher bias and inexperience, the
final report will incorporate comments and changes advised by an experienced
academic professor.
Following the preliminary completion of this report, it may be desirable to solicit and
incorporate feedback from CAPIRE members, however to achieve these activities
within the project timescale is unfeasible, therefore this intention may instead be
considered as the scope of a follow-on research project.

33
3 Driving forces analysis
3.1 Scope
This section details the scope and method used to identify the competitive
environment, major stakeholders, basic trends, and key uncertainties.
Focal issue ______________________________________________________________________
The focal issue is to identify the significant drivers of change that could result in
critical scenarios likely to strategically impact the competitiveness of the European
automotive industry in 2020. An overview of the approach to developing scenarios is
shown in Figure 8.
Figure 8: Building blocks for scenarios
Drivers of change will include basic trends and key uncertainties, the significance of
which will be evaluated based on their likelihood of change and the sustained
impact this might have on the competitiveness of the European automotive industry.
Competitive environment ________________________________________________________

34
As the „Green Car Future 2020‟ project addresses the competitiveness of the
European automotive industry within a global environment, an understanding of the
scope and nature of the competitive context is warranted:
In 2005, the global automotive industry made over 66 million cars, vans, trucks and
buses. If vehicle manufacturing (only) was a country, it would have equated to be
the sixth largest economy in the world. (OICA 2006)
In 2011, the six major competing global automotive industry suppliers, defined by
economic interest and development are:
 The European automotive industry (EU)
 The North American automotive industry, including Mexico (NAFTA)
 The Japanese automotive industry
 The South Korean automotive industry
 The Emerging Markets automotive industries (BRIC)
The global market share by volume of each competitor is illustrated in Table 5,
describing relative 2009 passenger car production. Note that these figures do not re-
domicile overseas production by national car manufacturers, either through joint
venture production or wholly-owned overseas factories. Furthermore, there may be
a surplus between passenger car production and actual sales.
Source: OICA
Table 5: Passenger car production worldwide - 2009

35
The relative share of total global sales is shown in Figure 9, describing total worldwide
motor vehicle registrations for 2010, and illustrating how the EU represents a market
for one in four vehicles sold in the world.
Source: ACEA 2010 p.77
Figure 9: Motor vehicle registrations worldwide - 2010
Trade between economic blocs provides an important global import and export
market for each competitor‟s business activities. The European automotive industry is
a net exporter of business activities, generating net value for the EU as highlighted
by the EC:
“With an annual turnover of €780 billion and a value added of over
€140 billion, it [the European automotive industry] makes a major
contribution to the EU's GDP. It exports far more than it imports, with a
surplus of over € 60 billion on overall exports of €125 billion.” (European
Commission 2009b, p.3)
The importance of the industry to Europe‟s prosperity is underscored by the scope of
its network of business and economic activities, acknowledged by the EC:
“The European automotive industry is one of Europe's flagship
industries. It is a key driver of growth, exports, innovation and jobs. Its
impact filters down across a wide variety of other sectors. And it has a
particularly important cross border reach, with suppliers, manufacturers
and sales and servicing downstream creating a web of mutual interest
that touches every one of the EU's Member States.

36
As a consequence, the value of intra-Community trade in automotive
products is substantial with around € 360 billion in 2008. Any downturn in
the automotive sector therefore strongly affects other sectors and all
EU Member States. ” (European Commission 2009b, pp.2–3)
Highlighting the importance of exports, Figure 10 illustrates the relative contribution
by value of EU passenger car exports, whereby North America, Asia, and Eastern
Europe represent approximately 80% of total export value of €40 billion (down from
€60 billion in 2008).
Source: Eurostat
Figure 10: Relative value of EU passenger car exports – 2009
Overall, the domestic European market (illustrated as „EU‟ in Figure 9) represented
the largest market for the European automotive industry, registering 16.6 million
passenger cars in 2009, of which 2.3 million cars were imported from non-European
automotive industries, primarily Japan and South Korea, as shown by units of sales in
Table 6.

37
Source: Eurostat
Table 6: Origin of EU passenger car imports – 2009
However, when compared by value received (see Table 7), Japan (€8 billion) and
the Unites States (€3 billion) were the largest non-European industry competitors,
differences attributable to a higher average cost of cars sold.
Source: Eurostat
Table 7: Value of EU passenger car imports – 2009
Nevertheless, the European automotive industry remains a net exporter for the EU,
representing a trade balance of nearly €26 billion in 2009 (down from €40 billion in

38
2008), as shown in Table 8. Note that these figures classify geographically European,
non-EU countries within the European Free Trade Agreement (EFTA) - Turkey,
Switzerland, etc - as export markets.
Source: Eurostat
Table 8: Trade balance of EU automotive industry passenger car sales – 2009
‘Green’ context _________________________________________________________________
The US, Japan, China, Korea and the EU have given priority to reducing the climate
impact of automobile production and use by developing programmes which target
two outcomes:
1. Reducing primary energy requirements: Growing dependency on
primary (naturally sourced. i.e. non-fusion) energy sources suggest this
factor is very likely the most motivating one. “In the EU, 73% of all oil (and
about 30% of all primary energy) is consumed by the transport sector.”
(EGCI Ad-hoc Industrial Advisory Group 2008)
2. Reducing greenhouse gas effect on climate change: Vehicle emissions
are contributing to the increased concentration of gases that lead to
climate change; Figure 11 illustrates the global contribution of road
transport to total man-made CO2 emissions in 2007.

39
Source: OICA 2007
Figure 11: Global contribution of Road Transport to man-made CO2 emissions
These „electromobility‟ and „green car‟ programmes primarily focus on developing
integrated transport methods to reduce the energy and climate cost of personal
mobility, and the production of commercially-viable electric vehicles, which can be
powered from „clean‟ energy sources and are estimated to reduce energy demand
beyond that achievable by internal combustion engine (ICE) cars:
“A comparison with the situation ten years ago shows that in the last
decade the technological evolutions have radically changed the
impact of the electrical vehicle on primary energy consumption: from
about 30% higher primary energy consumption as compared to the
ICE in 1998 to about 25% energy savings in 2008. These figures do not
yet take into account the potential for energy harvesting e.g. by
modern low cost on-board photovoltaic technology.” (EGCI Ad-hoc
Industrial Advisory Group 2008)
Nonetheless, traditional fuelled vehicles are widely expected to prevail by 2020.
Figure 12 illustrates the relative proportion of climate impact during the production
and use of a typical vehicle in use for 8 to 10 years.
Source: WWF-UK 2006
Figure 11: Distribution of energy demand/carbon consumption throughout the life cycle of a
typical car
Major stakeholders ______________________________________________________________

40
Scenario stakeholders will be international or national groups or individuals who
have, or potentially will have, an interest in the drivers of change and their
implications for the European automotive industry, including those who could
significantly influence change.
Figure 12, adapted from Schoemaker (1991), provides an overview of the macro
and industry-level forces likely to affect industry, which will be utilised as a taxonomy
to identify influential stakeholders, with direct interest on driving forces inferred for
industry stakeholders and indirect interest for world and country stakeholders.
Figure 12: Typical industry-level driving forces
Interested and influential industry stakeholders include:
 Suppliers European automotive manufacturers2
 Customers Private transportation buyers
Business transportation buyers
2 The vehicle manufacturing business is highly complex. It is not limited to the assembly of vehicles and
production of engines. The same manufacturers engage in testing, sales/marketing and distribution,
maintenance, recycling and disposal. Most also make components – although the degree of vertical
integration varies. They also have separate finance arms. These finance companies can provide
finance to their dealer network, leasing activities and to final customers (captive finance). In recent
years these non-automotive parts of a vehicle manufacturer's business have tended to generate more
profits than their core manufacturing assembly operations and in some years may have been the only
source of profits, thereby supporting the loss making automotive arm.(IHS Global Insight 2009)

41
Public transportation buyers
 Competitors Non-European automotive manufacturers3
 Substitutes Public transport suppliers
Alternative forms of personal transport suppliers
Alternatives to travelling suppliers (e.g. e-retailers)
Table 9: Major automotive industry stakeholders
Potentially interested and influential world and country stakeholders include:
 Economic Related and supporting industries (e.g. energy)
Automotive industry employees
Aftermarket industry (servicing, parts, accessories)
Automotive industry investors
Aftermarket industry investors
Related and supporting industry investors
Externality industries (e.g. infrastructure)
Customer stakeholders (e.g. employers, etc)
 Political Governments
Politicians
Regulators and legislators
 Social Non-profit externality industries (e.g. healthcare)
Society at large (male, female, young to old)
 Technological Automotive industry suppliers
Aftermarket industry suppliers
Related and supporting industry suppliers
Public transport suppliers
Alternative forms of personal transport suppliers
Alternatives to travelling suppliers
Table 10: Major world and country stakeholders
3 There is no straightforward delineation of ownership interest between European and non-European
automotive manufacturers, as shared engineering, manufacturing, ownership, and joint ventures are
extremely common across the global automotive industry, as depicted by the „Tube Map‟
representations presented as Exhibits 1 (2005 environment) and 2 (2010) in the Appendix.

42
3.2 Method
This section identifies the desired requirements of driving forces, evaluates typical
methods and their limitations for identifying industry driving forces, and evaluates
and proposes an evidence-based industry competition model to effectively identify
relevant driving forces and determine their influence on competitiveness.
Driving force requirements _______________________________________________________
To reliably output good quality scenarios meeting the criteria specified by
Schoemaker (see Table 3), the driving forces – that is to say, the inputs required for
the scenario building process, and the model-building methodology - should aim to
meet equivalent, supporting criteria. Every future is comprised of known, unknown,
and uncertain driving forces that can alter, inhibit, or enhance each other in
complex and chaotic ways. It is therefore desirable for scenario driving forces to
exhibit the characteristics described in Table 11.
1. Objective by identifying unbiased knowns, unknowns, and
uncertainties.
2. Relevant by relating to the competitiveness of the European
automotive industry in an international environment.
3. Consistent by considering the effect of all stakeholders to evaluate
competitiveness.
4. Distinctive by discerning the driving force of significant uncertainties
which could affect the competitiveness of the European
automotive industry.
5. Sustainable by identifying influences which may persist for an
impactful period of time (minimum 2 yrs).
Table 11: Desired characteristics of ‘Green Car Future 2020’ scenario driving forces
Typical identification methods, scenarios, and limitations __________________________
Typical scenario planning methods aim to resolve complex, global uncertainties by
using formal or intuitive methods, with each approach determining the nature and
scope of driving forces required as inputs to the scenario-building process.

43
Formal approaches seek to develop, quantitative models from economic theory, an
example of which is shown in Figure 14. Intuitive methods, as practised by Shell,
involve teams up to 250, with roles spanning Scenario Director, Planning, Analysis,
Synthesis (Editor & Research Leader), Primary Recipients, Specialist Contributors, and
External Recipients (Shell International BV 2008).
Source: Global Insight, Inc 2007
Figure 14: Example of typical industry-level scenario driving forces
It is undesirable and impractical for this project to develop and utilise purely
quantitative, formal methods, at risk of merely reproducing existing research, and
restricting the ability scenarios to effectively convince business level strategists.
Similarly, typical methods for identifying and analysing driving forces for intuitive
methods of scenario building – as shown in Figures 15 and 16 - require subjective and
time-consuming iterations and analysis to identify dependencies, relationships, and
criticalities within scenarios, without demonstrating a consistently objective model of
scenario building.

44
Source: Schoemaker 1991
Figure 15: Example of typical intuitive identification of driving forces
Source: Schoemaker 1991
Figure 16: Example of typical intuitive identification of key uncertainties

45
Hence, variation in focal issues, stakeholders, and a lack of consistent
methodologies result in the generation of bespoke, global level scenarios, each
identifying and interpreting critical driving forces subject to the capabilities of the
scenario-building team, as Verity acknowledges:
“building the team to participate in a scenario exercise appears to be
more crucial to the outcome than is the case using other planning
tools. It is a methodology that requires multiple inputs in order to
generate thought-stimulating outcomes.” (Verity 2003)
It is therefore unlikely that high-level, global scenarios will capture a full range of the
driving forces directly influencing industry and businesses, requiring secondary
interpretation and translation in order to benefit CAPIRE members. Schoemaker
discerns that:
“the scenarios will generally be at too aggregate a level to be directly
linked to an option's consequences. They first need to be translated
from the macro-economic or political level to the industry and firm
level.” (Schoemaker 1991, p.559)
Therefore, whilst published scenarios such as ‟Future Agenda: the world in 2020‟
(Jones 2010) serve to identify global automotive industry challenges, they do not
identify and relate the driving forces that may affect the relative competitiveness of
each national automotive industry, and hence there is a distinct need for an
method of producing more relevant scenarios.
Desired method _________________________________________________________________
To more effectively identify driving forces affecting the competitiveness of the
European automotive industry, it is desirable to utilise an evidence-based model of
scenario building – as shown in Figure 17 - that reliably relates the dynamics of:
 Stakeholder influences
 Business factors (qualitative and quantitative)
 National industry competitiveness in an international environment

46
Figure 17: Desired model of scenario building to determine international competitiveness
Furthermore, it is extremely desirable to capture the diversity and influence of
qualitative factors, whilst minimising undue human bias that quantitative scenario-
builders strive to avoid, described below:
“Although all scenarios should be treated equally, the reality is that
they typically are not. Both scenario generating teams and executives
gravitate toward the scenarios that they find to be “most interesting,”
which typically reflect corporate cultural biases and wishful thinking.
The use of probabilities actually forces scenario teams and executives
to examine likely scenarios that they might otherwise dismiss as
plausible but not attractive.” (Futuring Associates LLC n.d.)
In so doing, the final quality of scenarios produced is less reliant on utilising an
experienced and reputed team of scenario builders, and instead facilitates a more
robust method of identifying driving forces, evaluating impacts, and determining
implications. Improved transparency would also permit the re-use, development,
and refinement of driving force identification and analysis.
Porter’s ‘National Diamond’ – a suitable model of national competitiveness?________
Literature research was performed, identifying Michael Porter‟s study-based
„National Diamond‟ (M. E. Porter 1998) as a suitable analytical model of national
competitiveness, placing the ability of firms at the heart of local, regional, or
national competitiveness by inter-relating the dynamic effect of four interdependent
variables (the „diamond‟), and two supporting enablers, as the factors supporting
local, national, and global competitive success.
Unidentified
influences
(Internationaland
National)
Dynamicmodel
of national
industry
competitiveness
IdentifiedDriving
Forces
(International
competiveness)
International
competitiveness
scenarios

47
Figure 18: Michael Porter’s ‘National Diamond’
For the „diamond‟ to positively impact competitive performance usually requires
that all four sets of influences are present in a „home‟ environment; conversely, the
relative weakness of factors (when compared to competitor environments) may
indicate a condition limiting ultimate competitive potential. „Clustering‟ by industry
and/or geography was determined to provide a reinforcement of overall
competitiveness through knowledge transfer and internalisation of industries.
Government enabling – through increased rivalry, etc, as opposed to subsidising - of
a more competitive environment further strengthens national competitiveness, whilst
„Chance‟ or luck is essentially the stochastic element within the model, albeit one
which scenario planning aims to minimise.
A summary of the main influences on the „National Diamond‟ is presented in Table
12.
Demand conditions: Porter places particular emphasis on the role of home demand in
providing the impetus for 'upgrading' competitive advantage. Firms are typically most
sensitive to the needs of their closest customers, hence the characteristics of home demand
are particularly important in shaping the differentiation attributes of domestically-made
products and in creating pressures for innovation and quality. Porter places particular

48
emphasis on the role of sophisticated and demanding domestic customers, noting the
influence of highly-discerning home buyers on the development of the Japanese camera
industry, and the German passion for durable, high-performance cars as a factor in German
dominance of the world luxury-car market.
Related and supporting industries: One of the most pervasive findings of the study was the
tendency for the successful industries within each country to be grouped into 'clusters' of
related and supporting industries. Economies which are external to individual firms and
industries are internalized within the industry cluster. Technological leadership by the U.S.
semiconductor industry during the period up until the mid-1980s provided the basis for U.S.
success in computers and several other technically-advanced electronic products.
Factor conditions: Advanced factors are the most significant for competitive advantage
and, unlike factors whose supply depends upon exogenous 'endowment' advanced factors
are a product of investment by individuals, companies, and governments. The relationship
between basic and advanced factors is complex. Basic factors can provide initial
advantages which are subsequently extended and reinforced through more advanced
factors, conversely, disadvantages in basic factors can create pressures to invest in
advanced factors.
Firm strategy, structure and rivalry: Within this broad set of influences, the most interesting
relationship which Porter identifies is between domestic rivalry and the creation and
persistence of competitive advantage. Rivalry is critically important in pressuring firms to cut
costs, improve quality, and innovate. Because competition between domestic firms.
Source: Grant 1991
Table 12: Summary of the four core influences on Porter’s ‘National Diamond’
Supporting its application as a model for the European automotive industry‟s future
international competiveness, Grant‟s (1991) assessment of Porter‟s model recognises:
“First, Porter's integration of the theory of competitive strategy with
theory of international trade and comparative advantage extends the
analysis of strategy formulation to an international environment.
Second, Porter's emphasis upon innovation and 'upgrading' as central
to the creation and sustaining of competitive advantage represents
further steps towards the reformulation of the strategy model within a
dynamic context.” (Grant 1991)

49
This is an important assessment, because it identifies that the model may be applied
to compare the advantage of different environmental states, as scenarios seek to
evaluate and describe. This in contrast to Porter‟s earlier „Five Forces‟ strategy
model, which based competitive advantage upon achieving an ideal position of
cost and differentiation, which has clear weaknesses when applied to a complex,
competitive environment undergoing constant and regular change.
Research evidence and considerations __________________________________________
Porter's analytical framework was developed through studying competitive
performance among 10 countries (United States, West Germany, Italy, United
Kingdom, Sweden, Switzerland, Denmark, Japan, Korea, and Singapore), each
involving between 5 and 19 industry cases.
These countries‟ conditions and cases may not accurately represent the
competitive environment of the European automotive industry and anticipated
scenarios, therefore further research performed to evaluate relevant applications of
the framework, identifying the following evidence and considerations:
1. Porter’s diamond holds true for the automotive industry
Evaluating performance as Profits as a Percentage of Revenues and Profits as a
Percentage of Assets, Sledge (2005) found a positive correlation for the three non-
firm influences defined and measured as shown in Table 13, with the statistical
significance of demand over twice as large as that for related & supporting
industries (representing a vital network clustering effect), and investment in
upgrading and maintaining advanced factors noted as a necessity for the
maintenance of global competitive rankings.
Automotive industry research further supports these conclusions (N Boudette 2003),
and its application, Sledge notes, has also been found to hold true for the banking
industry‟s firm performance.
Definition Representative measurement
1. Demand Consumer sophistication and home market
demand. Generally, demand conditions are
Weighting of: percent of home
market enrolled in higher

50
associated with a country's level of
economic development.
education, and national share
of international automotive
revenues.
2. Related &
Supporting
Industries
Oil and gas, chemicals, metals, mining and
crude oil production, insurance, electronics,
building materials and glass, banks and
petroleum refining.
National share of international
related & supporting industry
revenues.
3. Factor
Conditions
Naturally occurring and man-made
endowments, such as physical infrastructure
and knowledge resources. Porter states that
advanced factors are more significant than
basic ones and specialized factors are more
relevant than general ones.
Weighting of: number of internet
hosts per 1,000 people (a proxy
for communications
infrastructure), and number of
U.S. patent filings.
Source: adapted from Sledge 2005, pp.24–26
Table 13: Ranking of significant driving forces affecting the relative competitiveness of a
national automotive industry
2. The main drivers of competitive advantage are determined by a country’s stage
of national development
Table 14 summarises Porter‟s identification of the main drivers of development as a
country develops its competitive advantage.
Source: adapted from Grant 1991, p.540
Table 14: The stages of national competitive development (Porter)

51
Sledge‟s (2005) research, comparing the development of 1960 and 1985 data for 49
countries, identified demand as a significant catalyst for development:
“The differences between industrialized and developing countries are
strong enough to suggest that specific features of the dynamic
diamond, such as the demand conditions, might be significantly more
important in one stage of a country's development than in others.”
(Sledge 2005)
3. The role of ‘government’ may not be optional in developing countries
Porter originally considered „government‟ as an optional factor in his Diamond
model, however Sagheer et al‟s (2007) assessment of India and Thailand‟s shrimp
industry competitiveness considered government influence essential in developing
country scenarios, relating Thailand‟s success to, collaboration, commitment, and
flexibility its environment:
“Thailand's national government is seen to be more collaborative as
compared to that of its Indian counterpart. Programmes implemented
by the Thai government working with the industry at interactive levels
have yielded positive results. The focus of the Thai government to push
this high revenue earning food industry to higher productivity and
income earning levels and has brought commitment and sustained
efforts from all stakeholders in the industry.
While there are opportunities for India, Thailand has proven itself to be
a country where human interventions - at government level,
entrepreneurial level etc have brought about positive results.”
(Sagheer et al. Jan-Jun2007)
Essentially, a firm is only able to leverage competitive advantage when there is a
supporting enabling environment, either nationally or internationally.
4. National and international determinants of competitiveness can be mutually
reinforcing for business
The important conclusion of Porter‟s analysis is that a firm or industry's
competitiveness may or may not result primarily from influences at the national or
subnational level. Sledge (2005) recognises the limitations for business and policy of
focussing only on national competitiveness:

52
“Porter's exclusive focus on national determinants of global
competitive success may turn the attention of managers and public
policy makers away from the need to monitor international threats and
opportunities, as well as the requirement to build up strengths and
reduce weaknesses in foreign nations. Such an inward diversion of
focus in the long run would serve neither the affected firms or industries
nor the home nations involved.
Because competition can be either from domestic or international
companies it is not enough to consider only businesses within a country
market as potential competitors.” (Sledge 2005)
Whilst, Cartwright‟s (1991) research paper identifies that there is limited usefulness of
focusing on national determinants of competitiveness for home-based industries that
export a high proportion of their production.
To achieve resolution, it may be most appropriate to consider Rugman and
Verbeke‟s (1993) attempt to operationalise Porter's framework to incorporate the
modern theory of the multinational enterprise:
“In fact, it may be precisely a synergistic combination of determinants
at the national or subnational level and determinants at the foreign or
global level that may generate international success. In this case, it
would be sensible to attribute international competitiveness primarily to
a mutual reinforcement among national and international factors
leading to success” (Rugman & Verbeke 1993)
5. Local clustering may be more influential to the performance of services, whereas
manufacturing may be more reliant on national and international networks
McDonald„s (2007) study of UK industry identified benefits to clustering services:
“Cluster policies to promote a variety of economic and social
objectives may require a focus on employment growth and
improvements in income distribution rather than the development of
international competitiveness. However, the results from the present
study indicate that clusters in the services and MCRB [media,
computer-related and biotechnology] sectors are more likely than
manufacturing clusters to deliver employment growth.” (McDonald et
al. 2007)
However, McDonald also acknowledges research identifying dependencies for the
manufacturing sector, noting “Consideration should also be given to the possibility

53
that local networks may not be as important as links to national and international
networks, particularly for ﬂows of information and knowledge.” (Hendry et al. 2000)
Modelling assumptions __________________________________________________________
In consideration of presented theory, evidence, scenario objectives, and good
modelling practise, Table 13 identifies the assumptions that will be used to identify
relevant driving forces and evaluate their influence on the competitiveness of the
European automotive industry.
Assumption 1 Driving forces will be identified from the following five areas of vision: 1.
Political, legal, social, and economic forces, 2. Emerging technologies
and scientific developments, 3. Influencers and shapers, 4. Customers
and channels, 5. Competitors and complementors
Assumption 2 Driving forces will aim to fulfil all of the desired characteristics
described in Table 11.
Assumption 3 Firm strategy, structure and rivalry will be considered neutral, and will
not be considered as an influence on the relative competitiveness of
Europe or the rest of the world.
Assumption 4 Driving forces will be considered to influence only demand, related &
supporting industries, and factor conditions.
Assumption 5 Driving forces will be considered to affect a maximum of two out of
three factors of competitiveness.
Assumption 6 The outcome of driving forces will be determined in consideration of
theory, evidence, and the conditional influence of unknowns and
uncertainties. Each driving force can have a neutral, positive, or
negative effect on the factors of competiveness.
Assumption 7 The degree of certainty will be determined as follows, with regards to
the influence of the driving force on the factors of competitiveness
influencing the European automotive industry in 2020:
 Known: the outcome of this force on relative competitiveness is

54
predetermined and unconditional on other forces.
 Unknown: this force represents a known influence on the
current (2011) business environment and is reasonably
expected to remain significant in 2020, however its outcome is
unknown, possibly due to conditional relationships.
 Uncertain: this force represents an uncertain driving force
(weak in 2011 or new in 2020) which could significantly
influence the 2020 environment. Uncertain forces are most
likely to be dependent on conditional relationships.
Table 15: Assumptions used to identify driving forces and their influence on competitiveness
Modelling example______________________________________________________________
Figures 19 to 22 provide a „before‟ and „after‟ example to illustrate how the inter-
relationship between knowns, unknowns, and uncertainties can be applied to
determine (one of) the potential future competitive outcomes of Europe against the
rest of the world. Note how future competitiveness is dependent on the conditional
interaction between expected and potential influencers of outcome. (Note: see
Table 16 for Key.)

55
Figure 19: Schematic representation of Porter’s ‘National Diamond’
Figure 20: The focal issue - Porter’s ‘National Diamond’ can describe current European auto
industry competitiveness, but how can relative 2020 competitiveness be determined?
Automotive industry competitiveness, 2020Automotive industry competitiveness, 2011
Rest of
World
+
++ Europe
Rest of
World
?
?? Europe
Knowns
Unknowns
Uncertainties

56
Figure 21: The expected and potential influence of knowns, unknowns, and uncertainties is
identified
Rest of
World
?
?? EuropeRest of
World
+
++ Europe
NEuropeKnown: population growth
Unknown: oil price
[ ]
[ ]Europe
[ - ]
[ - ]EuropeUncertainty: „double dip‟ recession

57
Figure 22: The uncertainty and unknown are defined, conditional outcomes determined, and
the net effect on the European auto industry’s relative competitiveness is identified
Rest of
World
N
-EuropeRest of
World
+
++ Europe
NEuropeKnown: population growth
Unknown: oil price
+
-Europe
-
-EuropeUncertainty: „double dip‟ recession
2020: High oil price realised
2020: „double dip‟ affects world

58
3.3 Trends and uncertainties
The key driving forces affecting the European automotive industry were identified
and their expected or potential influence on industry competitiveness determined,
using the methods detailed in Section 2 and assumptions in Table 15. Table 16
presents the trends and uncertainties determined as most influential to the
competitiveness of the European automotive industry in „Green Car Future 2020‟.

59
Driving forces affecting the European automotive industry (2011 to 2020)
Scope
Degreeofcertainty
Relative influence
on EU auto industry
competitiveness
Key: Scope I International Expected outcome N Neutral (no relative advantage)
E / R EU / Rest Of World + / - More or Less competitive
Degree of certainty K Known Potential outcome [ ] More or Less competitive
(see Figure 3) UK Unknown (where driving force is [ + ] More competitive
UC Uncertain Unknown or Uncertain) [ - ] Less competitive
Demand
Related&
Supporting
Industries
Factor
Conditions
POLITICAL, LEGAL, SOCIAL, AND ECONOMIC FORCES
Political
Energy security concerns. E UK [ ] [ ]
Electrical mobility prioritisation in the US, Japan, China, Korea. R UK [ ] [ ]
Chinese government continues to subsidise purchase of domestically manufactured electric vehicles. R UK [ - ] [ - ]
Russian energy companies develop preferred supply agreement with China. R UC [ ] [ - ]
Chinese government privacy requirements restrict business access to personal location information. R UK [ ]
European restrictions imposed on number of cars per household. E UC [ + ] [ - ]
Legal
Imposition of regulation on EU manufacturers without prior consultation. R UC [ - ]
Widespread intellectual property abuse of premium European brands and products in BRIC nations. R UC [ - ] [ - ]
Security and privacy of individual location restricts implementation of „smart‟ private transport infrastructure; consumers favour public
infrastructure.
E UC [ - ] [ + ]
Significant patents monopolised by non-European consortium (e.g. US-Korea), resulting in license charges R UC [ - ]
Severe penalties for breach of intellectual property set new precedent for Chinese manufacturing industry. R UC [ + ]
Social
Healthcare costs in Europe prioritised in order to support increase in retirement age. E UC [ + ]
Corporate Social Responsibility becomes significant decision-factor in consumer purchases. I UC [ + ] [ + ]
Economic
China‟s GDP expected to overtake US. R UC [ + ]
Imbalanced population growth. (Jones 2010) I K N -
Rebalancing of economic power. (Jones 2010) I K [ ] [ - ]
Overcapacity in the manufacturing sector coupled with a shortage of “human capital” in a time of ageing populations and falling
birth rates. (Cass Business School 2011)
I K [ ]
Global versus local: as companies become more global they centralise, squeezing regional working. (Cass Business School 2011) I UK [ ]

60
Scope
Degreeofcertainty
Relative influence
on EU auto industry
competitiveness
Demand
Related&
Supporting
Industries
Factor
Conditions
„Double-dip‟ recession in Developed economies. I UC [ - ] [ - ]
Overcapacity in European manufacturing sector. E UK [ ] [ ]
Overcapacity in Chinese manufacturing sector. R UK [ ] [ - ]
Oil price. I UK [ ] [ ]
China becomes an increasingly service-based economy. (Sir Martin Sorrell 2011) R K [ ]
70% chance that Chinese GDP growth slows by 2015 when per-head GDP reaches around $16,740 on a purchasing-power-parity
basis, reflecting historical trends. (Economist 2011)
R UC [ - ] [ - ]
Around 60% to 80 % of new private vehicles continue to be bought using some form of credit requiring financing. E UK [ ] [ ]
Personal credit availability in BRIC countries restricting uptake of premium-priced vehicles. R UK [ - ] [ + ]
Resource demands. (Jones 2010) I UC [ ] [ ]
Commodity prices will likely trend upward, with a lot of volatility, and global imbalances will worsen. (Nariman Behravesh 2011) I UC [ ] [ ]
The momentum of global growth is strong enough to withstand these “known unknowns”. (Nariman Behravesh 2011) I UK [ ] [ ]
EMERGING TECHNOLOGIES AND SCIENTIFIC DEVELOPMENTS
Emerging technologies
Low cost modular vehicle architecture and factory permits licensed production from developing economies. (Gordon Murray Design
n.d.)
R UK N [ + ]
Breakthrough high-density, fast-charge battery technology commercialised in China. R UK [ + ]
International standards for interoperability of „smart‟ personal transport. I UC [ ] [ ]
BRIC experience in value-driven manufacturing and services facilitates new, resource-efficient manufacturing and service innovation. R UK [ - ]
Commercialisation of hydrogen fuel cells. I UK [ + ]
European commercialisation of „hub motors‟ permitting flexible vehicle architecture. E UK [ + ] [ + ]
Scientific developments
Commercialisation of hydrogen storage in solid form. E.g. carbon nanotubes. E UC [ + ]

61
Scope
Degreeofcertainty
Relative influence
on EU auto industry
competitiveness
Demand
Related&
Supporting
Industries
Factor
Conditions
INFLUENCERS AND SHAPERS
Mobility tax levied on individuals for all transport types – public, private, business. E UC [ ] [ + ]
Battery supply security to European industry. E UC [ ] [ ]
Prohibitive „recycling tax‟ on end-of-life electric vehicles. E UK [ - ] [ + ]
Manufacturers‟ ability to successfully market technologically innovative „green car‟ products. (Cass Business School 2011) I UK [ ] [ ]
Manufacturers‟ ability to successfully market technologically innovative „green car‟ services. E UC [ ] [ ]
European mass-adoption of mobility as a service. E UC [ + ] [ + ]
RoW (China esp.) mass-adoption of mobility as a service. R UC [ + ]
Too much power in the hands of procurement. There‟s a limit to what you can do to cut costs. I UK [ ] [ ]
End of life recycling responsibility for manufacturers imposed outside Europe. R UC [ ] [ - ]
International agreement on standards for energy generation emissions, coupled with upgrading and replacement costs for old and
„dirty‟ infrastructure.
I UC [ + ]
Perceived range limitations and incompatibilities between energy infrastructures limits uptake of non-gasoline powered transport. I UC [ ] [ ]
Security of personal data. I UC [ ]
High battery costs continue to require consumers to decide between rental or outright purchase. I UC [ - ] [ ]
Consumers embrace „crowd sourced‟ navigation systems in densely-packed Western cities, with value captured by telecoms
providers or software entrepreneurs. BRIC adoption limited by infrastructure and restrictions on access to public information.
I UC [ + ] [ + ]
CU STOMERS AND CHANNELS
Challenge of deriving „consumer surplus‟ by differentiating electric powertrains. I UC [ ] [ ]
Universal access to information. (Jones 2010) I K N [ ]
Significant differences in expected market share of electric cars. I UC [ ] [ ]
Increasing sales to business users. (Arthur D. Little 2010) E UC [ + ]
EU products not positioned particularly well for price competition. (European Commission 2009a, p.12)
EU products positioned in premium segments only. (European Commission 2009a, p.12) I UK [ ]

62
Scope
Degreeofcertainty
Relative influence
on EU auto industry
competitiveness
Demand
Related&
Supporting
Industries
Factor
Conditions
Most of the increase in global demand will come from China, India, or Russia. (European Commission 2009a, p.12) R UC [ + ]
Negative consumer reaction to battery performance of Chinese manufactured vehicles. I UK [ + ]
Consumers delaying demand until provision of full integration of green cars and electromobility. I UK [ - ] [ - ]
COMPETI TORS AND COMPLEMENTORS
Governments in BRIC countries become biggest customers for supply of „mass transit‟ smart personal transport. R UK [ - ] [ + ]
Battery demand for consumer electronics restricts supply for electric vehicles. (Martin LaMonica n.d.) I UK [ - ] [ + ]
Main competitors to Europe are Japan, US, and South Korea. (European Commission 2009a, p.12) I UC [ ]
Commercial hydrogen fuelling infrastructure gains foothold in European market; national governments subsidise conversion of existing
ICE cars to run on „dual fuel‟ sources.
E UC N [ + ]
Governments and electricity companies introduce levies on electric car charging during peak hours. E UC [ - ] [ - ]
Diversification of energy companies into electricity market to hedge against loss of income from petrol and diesel. (Viren Doshi,
Robert Oushoorn, and Robert Famulok 2011)
I UC [ - ] [ + ]
Source: Author analysis unless otherwise specified
Scope, Degree of certainty, and influence on competitiveness: Author analysis
Table 16: Driving forces affecting the international competitiveness of the European automotive industry

63
4 Scenario development
This section determines the extent of competitive uncertainty for the European
automotive industry in 2020, identifies distinct themes of the competitive
environment, and presents potential scenarios characterising critical outcomes.
4.1 Mapping uncertainty
To effectively navigate uncertainty, it is necessary to map the extent of uncertainty
and its critical underlying forces, just as a good sailor would do by identifying land,
sea, and the currents and winds that can present opportunity or extreme hardship.
Extent of competitiveness ________________________________________________________
The „Green Car Future 2020‟ presents two distinct extents of international
competitiveness for the European automotive industry: one in which it can fully
maximise its competitive advantage against the rest of the world (best case), and
one in which it is comprehensively disadvantaged against the a strong „rest of the
world‟ automotive industry (worst case), as shown in Figure 22 (see Figure 19 for key).
Figure 22: The extent of competitiveness for the European automotive industry in 2020
The most significant factor determining relative competitiveness is European market
demand, both its sophistication and size, whilst factor conditions, and related and
supporting industries play important contributing roles. The extents depicted do not
Competitive advantage: Rest of World
+
++ Rest of
World
-
-- Europe
European automotive industry competitiveness, 2020
Competitive advantage: Europe
-
-- Rest of
World
+
++ Europe

Green Car Future 2020: Scenario Implications For The European Automotive Industry

Green Car Future 2020: Scenario Implications For The European Automotive Industry

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