1. School of Mathematics, Computer Science & Engineering
Analysing financial and related risks inherent in typical
funding structures used in roads’ infrastructure
development in England, and the current best management
practices.
by
Philip Caleb
A Dissertation Submitted in Partial Fulfilment of the
Requirements for the Degree of
MSc in Project Management, Finance and Risk
Supervisor: Visiting Professor R V Bruce
London
Final report: 13 September 2016

2. ABSTRACT
Financial risks are known to be heavily weighted against a project’s success. They
have a negative impact on the cash flow of a financial plan that can alter the project’s
financial life and limit its profitability. Infrastructure projects suffer from poorly
managed risks throughout the project’s lifecycle. The range of risks commonly in the
transport infrastructure projects are identified and allocated in the appropriate areas
of the project’s financial model.
This dissertation focuses on analysing the impact of financial risks to that of non-
financial risks (demand and volume) on transport infrastructure projects and to
comprehend how these risks directly affects the financial model’s projected cash
flows.
This dissertation has been met with a twin research strategy, with research aims
through an extensive study of the relative literature of past projects and the
implementation of practical research. The latter was carried out through a Case
Study on the Dartford River Crossing with the construction of a hypothetical financial
model and testing a hypothesis of whether financial risks has more of an impact than
non-financial risks. Assumed financial input values (Interest rates, Equity Debt rate,
Tax rate) were made to display the effects on the financial outputs (Debt Service
Cover Ratio, NPV, Debt Equity). This in turn produced risk distribution profiles
comparing the effect against the financial outputs using the @risk software.
The main conclusion drawn from this research was that the traffic demand and
volume risks impacts the optimal project cost structure than the financial risks
studied in this research.
Project Title: Analyzing financial risks inherent in
typical financing structures used in
infrastructure development in
England, and the current best
management practices.
Student: Philip Caleb
Supervisor: Mr. Rupert Bruce
Sponsor: only if applicable
Date: 13Sep16

3. Acknowledgements
Firstly, I would like to thank the Almighty God for giving me the strength and
perseverance to complete this dissertation.
A special thanks to my fiancé Miss Charlotte Ambrose for supporting me throughout
each stage of this dissertation process and being my anchor.
Also, I would like to express my most sincere gratitude to my project supervisor Prof.
Rupert Bruce for his support, guidance and patience along the way.
Lastly I would like to thank my colleagues for their input and support for throughout
the project life of the dissertation.

4. 1 Table of Contents
1 Introduction............................................................................................................ 1
1.1 Background............................................................................................................. 1
1.2 Financial risks......................................................................................................... 1
1.3 Aim and Objectives................................................................................................. 2
2 Literature Review................................................................................................... 3
2.1 Introduction............................................................................................................. 3
2.2 Transport Projects................................................................................................... 5
2.3 Public Private Partnership(PPP) Projects................................................................ 6
2.4 Financial Risks...................................................................................................... 10
2.4.1 Inflation.......................................................................................................... 11
2.4.2 Interest Rate Swap ........................................................................................ 13
2.4.3 Interest Rates ................................................................................................ 13
2.5 Demand Risks ...................................................................................................... 14
2.6 Environmental Risks ............................................................................................. 14
2.7 Financial modelling ............................................................................................... 15
3 Methodology ........................................................................................................ 18
3.1 Introduction........................................................................................................... 18
3.2 Research Strategy ................................................................................................ 19
3.3 Data Collection ..................................................................................................... 22
3.4 Framework for data analysis ................................................................................. 24
3.5 Limitations, outstanding issues ............................................................................. 26
3.6 Case Study details................................................................................................ 27
4 Findings & Analysis ............................................................................................. 29
4.1 Introduction........................................................................................................... 29
4.2 Case Study Findings: Description and Analysis .................................................... 29
4.2.1 NPV............................................................................................................... 30
4.2.2 Debt/Equity Ratio........................................................................................... 32
4.2.3 Debt Service Cover Ratio (DSCR) ................................................................. 34
4.3 Synthesis of Case Study Findings......................................................................... 36
Legend (for Table 3 above) ............................................................................................. 37
1 – Most impactful ........................................................................................................... 37
2 – Second most impactful .............................................................................................. 37
3 – Third most impactful .................................................................................................. 37
4 – Fourth most impactful ................................................................................................ 37
5 – Fifth most impactful ................................................................................................... 37
6 – Sixth most impactful .................................................................................................. 37

5. 5 Conclusions ......................................................................................................... 38
5.1 Introduction........................................................................................................... 38
5.2 Research Objectives: Summary of Findings and Conclusions .............................. 39
5.2.1 Research Objective 1: Risks found in Transport Infrastructure Projects......... 39
5.2.2 Research Objective 2: Allocation of the Financial Risks................................. 40
5.2.3 Research Objective 3: Risk Distribution Profiles ............................................ 41
5.2.4 Research Objective 4: Scope of Managing Financial Risks............................ 42
5.2.5 Research Objective 5: Recommendations ..................................................... 42
5.3 Self-Reflection ...................................................................................................... 43
Appendix A ………………………………………………………………………………45
References ………………………………………………………………………………50

6. List of Tables
Table 1: Effect of Inflation on a Project Cash Flow[15]……………………………...13
Table 2: Assumption of Financial Variables ………………………………………...46
List of Figures
Figure 1: Simplified Project Finance Structure[4]...................................................... 3
Figure 2: BOT Financial Flows[3] ............................................................................. 4
Figure 3: Risks in PPP projects by phase[6]............................................................. 8
Figure 4: Revised Risk Management Process for PPP project with Real Options[5]10
Figure 5: Qualitative data analysis for Financial risks within DRC .......................... 25
Figure 6: Total Acquisition Value (NPV) Output...................................................... 31
Figure 7: Total Acquisition Value(NPV) effect on Output Mean .............................. 32
Figure 8: Debt Equity Output.................................................................................. 33
Figure 9: Debt Equity Output Mean........................................................................ 33
Figure 10: DSCR Output Mean .............................................................................. 35

7. List of Abbreviations
PPP – Public Private Partnerships
PFI - Private Finance Initiative
BOT – Build-Operate-Transfer
CBA – Cost-Benefit Analysis
DSCR – Debt Service Cover Ratio
BCR – Benefit Cost Ratio
SMCP – Sustainability Management Certified Professional
SMC – Social Marginal Cost
DRC – Dartford River Crossing
CAPEX – Capital Expenditure
LRTA – Light Rail Transit Association
LD – Linkage Disequilibrium

8. 1
1 Introduction
1.1 Background
It is known that roadways most likely constituted the initial human demand for
infrastructure works[1]. The progression of civilisation either progressed or
declined around the standard of their road networks. The term infrastructure can
refer to a variety of industries with altering characteristics; usually it refers to the
following sectors of the economy – energy, transportation, water,
telecommunications and sanitation. The energy sectors generally refer to gas, oil,
electricity generation, transmission, distribution and petrochemicals. Airports,
ports, roads and rails are transportation infrastructure, while telecommunications
are affiliated with hardware mobile telephones and fixed lines. In this research
paper the financial risks will be analysed and compared with the effects of non-
financial risks affecting traditional infrastructure industries. These can either be
the demand and volume of traffic congestion, requiring an altering solution or
even Health and Safety issues that affect these projects.
Decision support within transport infrastructure planning is typically a complex
task of choosing between several competing project alternatives taking into
account a wide range of impacts. Conventional cost-benefit analysis (CBA) is a
generally acknowledged methodological approach providing the decision-makers
with an economic assessment of the project alternatives, expressed on a
monetary scale[2]
1.2 Financial risks
These are considered the risks that have a negative impact on the cash flow of a
financial plan in a way that endangers the project’s viability or limits
profitability[3]. Evaluating Public-Private Partnerships (PPPs) provides a broad
spectrum of risks affiliated with all projects. It is therefore necessary to say that
the risk management process considers the possibility of managerial flexibility,
giving way to real options as a solution to maximise the returns on the

9. 2
investments made. Our transport system is vital to the way we lead our lives, the
success of our economy, our wellbeing and our environment. As a nation we
benefit from a substantial transport network and services and as further
improvements are sought, it is essential that decision-makers have the fullest
information about all the impacts each option could have on our society,
economy, and environment; and how these align with decision-makers'
objectives[2]. For this dissertation, a hypothesis is formulated to display that
financial risks have more impact than any other risk in successful infrastructure
development projects.
1.3 Aim and Objectives
The overall aim is to evaluate the relative impact of financial risks compared
with non-financial risks that are typically faced within infrastructure development
projects in England. The sector to be studied in this research paper is
transportation infrastructure, with a focus on road links (as opposed to airport,
rail, or port projects).
The objectives to be pursued in order to fulfil this aim will comprise:
1. Identifying the range of risks commonly found in the transport infrastructure
sector;
2. Identifying the prevalent allocation of the financial risks in this sector;
3. Evaluating the various risk distribution profiles generated for a range of
financing risks;
4. Analysing the scope for managing the identified financial risks for an optimal
project cost structure;
5. Recommending a risk allocation matrix which optimises benefits to asset
owners and investors

10. 3
2 Literature Review
2.1 Introduction
Project finance, is an appropriate method of long term financing for capital-
intensive industries where investment financed has a relatively predictable cash
flow, has played an important part in providing the funding required for the
change[4]. Below is a simplified typical project finance structure.
Figure 1: Simplified Project Finance Structure[4]
Transportation, infrastructure projects and facilities of public interest are of
bridges, airports, power plants, detention facilities, parking places and roadways
etc.[3]. Large infrastructure projects suffer from significant under management of
risk in practically all stages of the value chain and throughout their entire life cycle.
In particular, poor risk assessment and risk allocation, for example, through
contracts with the builders and financiers, early on in the concept and design
phase lead to higher materialised risks and private-financing shortages later on[1].
In transport projects, risks can vary from financial risks to non-financial risks. They

11. 4
can be categorised into four main risk domains. Technical risks include
construction risks (cost overruns or delays in completion), risks in the design of
tender documentation, and design risks. Commercial risks (demand risk) are also
identified as Risk mitigation in transport PPPs a risk domain, as well as political
and regulatory risks. Economic and financial risks represent a specific and
complex risk domain for assessment since they originate from uncertainties such
as economic growth, inflation rates, currency convertibility, and exchange rates
risks[5]. In the build and operate projects such as the Dartford River Crossing (as
described later in Chapter 3.6) a Build Operate Transfer(BOT) financial flow are
known practices. This is seen below:
Figure 2: BOT Financial Flows[3]
There has been a large amount of research done to investigate this topic, and the
paper Real option theory for risk mitigation in transport PPPs highlights a very
relevant solution in today’s world. It brought to light the fact that one of the most
important elements in the partnership between the public and private partners is
the risk allocation process[5]. With the selection of Public-Private Partnership
(PPP) projects, instead of traditional contracts, the risk sharing mechanism is more
prevalent, noting that the principle rule is to transfer the risks to the party that is
more capable of owning and managing them. The aim here is not to maximise the
risk transfer but to optimise it.
PPPs require in-depth analysis and allocation of a broad spectrum of risks which
include design and construction risk, operational risk, demand risk, technological
risk, political risk, to name a few. Risk management in PPPs is not static, but
rather dynamic, corresponding to the evolution of risks over time [6].

12. 5
Infrastructure projects are known for being high on governmental agendas and the
infrastructure-development pipeline is huge, however major infrastructure projects
have a history of being problematic. Overrunning costs, delays, failed
procurement, or unavailability of private financing are common factors[1] . There
are also Health and Safety Issues and demands that play a part in this.
2.2 Transport Projects
Transport projects can benefit dozens of towns and cities where objectives are to
tackle the congested local roads and improve key points in the strategic road
network as well as new initiatives to drive forward rail electrification[7] Thus the
need and application of highway projects is required for the progression of the
economy within a country. In the transport sector we value projects in terms of
their net worth, the difference between the value of their benefits and their costs,
both measured as far as possible in terms of monetary units[8]
This can of course lead to many questions; from what perspective, evaluation by
whom, for whom and at what stage etc. The stakeholders of highways and
infrastructure projects are the ones typically impacted by transport decisions,
whether these parties are the individual transport users, transport operators,
businesses, local residents, national, local taxpayers, land and property owners.
Each of these stakeholders will seek to assess the impact of a project from the
perspective of their own interest. But the perspective of transport evaluation needs
to be a social one, that is, one which takes account of significant impacts of the
project or policy on whoever is affected.[8]
To have a look at potential factors that affect successful transport projects, KPMG
did a feasibility study on various projects around the world, stating - Considerable
understanding of the city, its social and political context and the impact of the
project in question is necessary for a reliable and objective diagnosis. Many
elements need to be correct to deliver a successful project, of which effective
procurement and financing appeared to be the most important of the six success
factors identified. The effectiveness of procurement and financing is the
strongest predictor of success on all three of the success measures indicated[9].
Financing of transport projects being demonstrated as one of the most important

13. 6
success factors, the effect of volume and demand should not be negated as a
critical effect on infrastructure projects. There are problems of severe congestion
on urban roads: 89 per cent of transportation delay is estimated to be on urban
roads (Eddington, 2006). The Department for Transport (DfT) forecasts suggest
that congestion across the English road network as a whole will increase from by
27 per cent from 2003 to 2025 and 54 per cent by 2035 (Department of Transport
2012b). Congestion in England is among the worst in Europe and reflects
inadequate investment over previous decades (OECD, 2005)[10].
Investigating the paper “Success and failure in urban Transport Infrastructure
Projects”, the Manila Metro Rail Transit System has been impacted by various
financial variables that has possibly debunked the finances. There has been
something of a crisis in the public finances that has reinforced strong support for
‘BOT’ projects. The politicisation of fares (in particular) has worsened the finances
of LRTA (Light Rail Transit Association) (that operates Lines 1 and 2). The MRT3
(Metro Rail Transit Corporation) concession has required unexpectedly large
public financing that has caused serious problems. The hiatus over Manila Airport
BOT
(that remains in the courts several years after completion of the project) has
undermined confidence in such concessions[9].
2.3 Public Private Partnership(PPP) Projects
Public private partnerships (PPP) are, in general, agreements between two
parties, the public and the private sector, for delivery of services which were
traditionally provided by the public sector. These partnerships serve as a model for
overcoming budgetary shortfalls, i.e. for filling the gap between services required
by the society and available funds for delivery of those services. Transport is one
of major sectors in which the implementation of these types of agreements has
become a common approach in resolving the infrastructure issues[11].
Revenue generating projects like toll roads were usually funded by the public
sector, while the private sector was involved mainly in several phases of the

14. 7
project’s life, like construction of the highway section or scheduled maintenance
work. However PPP agreements enable the private sector to participate in the
project delivery through several crucial phases like design, building, finance, and
operation or a build-operate-transfer (BOT) scheme which is one of the most
common PPP models. For providing these services, the private sector is usually
entitled to collect tolls from users, although the public sector may provide an
annual payment directly to the private sector proportional to the highway traffic
volumes[11].
Finance for public infrastructure (roads, transport, public buildings, etc.) was
especially developed through the United Kingdom’s Private Finance Initiative
(“PFI”) from the early 1990s; such projects are now usually known as public–
private partnerships (“PPPs”)[4]. In today’s world infrastructure is primarily a public
sector issue, with investment made annually in infrastructure by the public sector
vastly exceeding that invested by the private sector. This said, even for the most
public of infrastructure service providers, private involvement forms an essential
part of successful service delivery, whether through construction contracts, service
agreements, delivery of goods or joint ventures[12].
World Bank (2008) provides an example of the risk distribution matrix for PPPs in
roads. This matrix defines twelve types of risk: design, site, construction, force
majeure, revenue, operation and maintenance (O&M), performance, external,
other market risk, political, default, and strategic risks[5].
The risks for infrastructure PPP projects can be divided into five main risk
categories: political, financial, construction, operational, and commercial risks.
Returns on investments in airport parking facilities depend on the demand for
parking which is related to a number of variables such as demand for air travel,
parking fees, and the availability of alternative modes[5]. The figure below
identifies the risks associated with PPP projects grouped by the project phase.

15. 8
Figure 3: Risks in PPP projects by phase[6]
Financial risks for this type of transport infrastructure are grouped into three
categories: project risk, competitive risk, and market risk. Discussing the nature of
risks in urban rail transit PPPs, Phang (2006) considers five risk categories:
general/project environment (force majeure, macroeconomic, legal risks), design
(change order, permits, untested technology risks), finance (interest rate,
exchange rate, counterparty risks), construction and procurement (acquisition and
right of way, construction delays, counterparty, health and safety, and cost
overruns risks), and O&M (ridership projection, cost overruning risks)[5].
They are a series of financial risks that, regardless of the sector in which projects
are executed, will affect the financial success of the work to be done. Market risks
for example are quite prevalent. Market risk deals with adverse price or volatility
that affects the assets contained in a firm's or fund's portfolio. It is the possibility
that sharp downward movements in market (stock, bond, commodity and
currency) prices will destroy a financial institution's capital base (i.e. sensitivity of a
bank's trading portfolios to changes in market prices), or the sensitivity of an asset
or open contract to a movement of the market. Secondly, it can also be defined as
the uncertainty of a financial institution's earnings, resulting from changes in
market conditions such as the price of an asset, interest rates, market volatility and

16. 9
market liquidity. It can be defined in absolute terms as a dollar amount or as a
relative amount against some benchmark[13]. The interest rate risk is measured
by past and present market volatility and the profile of the asset/liabilities of the
bank and its possible exposure through gap management, and it is controlled by
hedging (swaps, futures and options) the assets and liabilities and accurately
researching and quantifying pending changes and scenarios[13]. The risks of
operations will always be a factor to be considered, with chances that human or
machine can fail which will result in financial losses due to system failures,
securities processing, clearing issues and documentation deficiency. In the PPP
pipeline for England it is estimated that there are currently 36 PFI (Project
Finannce Initiaitve) projects in procurement in England with a combined capital
value of around GBP 4.7 billion. All of these projects are expected to reach
financial close within the next three years[14]. This demonstrates the necessity
and popularity of PPP projects.

17. 10
Figure 4: Revised Risk Management Process for PPP project with Real Options[5]
2.4 Financial Risks
External macroeconomic risks (also known as financial risks), namely inflation and
interest rate and currency exchange rate movements, do not relate to the project
in particular, but to the economic environment in which it operates. These risks
need to be analysed and mitigated in the same way as the more direct commercial
risks[15].

18. 11
The main financial risks that affect a project according to[15] are macro-economic
risks;
 interest rate risk;
 Interest rate swaps;
 swap credit risk;
 inflation rate risks;
 foreign exchange rates and currency risk.
The Paper [16] stated from research – “We found that most banks and institutions
are more comfortable bidding for smaller, less risky PPP deals while a few
expressed an interest in financing the construction risk for onshore wind. A similar
picture emerged with institutions. They are most comfortable with PPP and/or
brownfield assets with existing operating revenues. One large insurer said they
would consider buying equity in renewables when the asset had been built and
was generating a steady revenue stream. The ideal individual bank ticket sizes for
projects identified as attractive to banks and institutions is less than £200 million,
typically £100 million, and tenors range from five to seven years to cover
construction to as long as 30 years. Some banks said they prefer smaller tickets
and syndication for the larger deals. The buy-side indicated that they are
interested in investments of a similar scale but are happy to provide longer tenors.
As a result, there is deep liquidity available for these assets and competition is
fierce.” This is to show that regardless of how impactful, whether negatively or
positively, financial risks will not eschew asset investors once there is a steady
revenue stream generated.
2.4.1 Inflation
With inflation, this can sometimes either benefit or damage the finances of the
project company and its investors.
During the operating period, if inflation leads to higher operating costs than

19. 12
projected, the level of lenders’ cover ratios, and the return for investors may be
reduced. If the Project Company has a long-term Project Agreement under which
revenues are received on the basis of an agreed Tariff, some elements of the
Tariff may be indexed against inflation, thus substantially reducing any inflation
risk mismatch between costs and revenues (Equally, sales prices in a competitive
market should also reflect inflation) [15].
Inflation-Indexed financing is where it is possible to issue bonds where the coupon
(interest rate) is the total interest paid, that is linked to the total rate of inflation
[15]. This can be appropriate for a project company in the event that a long term
Project Agreement is made and most of the revenue is inflation linked. Inflation-
linked financing is beneficial in a low inflation environment, since it ensures a lower
cost of debt. However, a lower rate of inflation also reduces the growth in
revenues[15]. For interest rates, during the operating period, where a higher
interest rate usually leads to lower project cash flow, and therefore reduction in
cover ratios for the lenders lower returns for the investors. In cases where the
floating rate loans are used, Interest rate hedging arrangements are usually put in
place to mitigate the interest rate risk. The most common type of hedging used in
project finance is interest rate swaps; to a lesser extent interest rate caps, collars,
and other instruments are used; 100% of the risk may not need to be covered[15].
To define the economic convenience of the project the Extended Cost-Benefits
Analysis (CBA) is applied to the economic comparison with the neutral hypothesis
of not intervening. The extended CBA, based on the real option analysis, allows to
quantify, not only the economic value of the project, but also the value deriving
from the volatility, and requires the preliminary traditional CBA application[17]. To
give an example of the effect of inflation on a net cash flow over five years, two
scenarios were given. Table 1(A) ignores inflation and shows a total cash flow to
the investor of 350. But as Table 1(B) shows, if the inflated cash flow to the
investor shown is itself reduced by the rate of inflation[15]

20. 13
Table
1: Effect of Inflation on a Project Cash Flow[15]
2.4.2 Interest Rate Swap
Under an interest rate swap agreement (also known as a “coupon swap”) one
party exchanges an obligation for payment of interest on a floating rate basis to
make payment at a fixed rate, and the other party does the opposite. Banks in the
capital markets run large books of such interest rate swaps.
In project financing, a Project Company that has an obligation to pay interest at a
floating rate under its loan agrees to pay its counterpart (a bank or banks—the
“swap provider”) the difference between the floating rate and the agreed-upon
fixed rate if the floating rate is below this fixed rate, or will be paid by the swap
provider if the floating rate is above the fixed rate[15].
2.4.3 Interest Rates
The risk that changes in interest rates will result in financial losses related to
asset/liability management. It is measured by past and present market volatility
and the profile of the asset/liabilities of the bank and its possible exposure through
gap management, and it is controlled by hedging (swaps, futures and options) the
assets and liabilities and accurately researching and quantifying pending changes
and scenarios[18].

21. 14
2.5 Demand Risks
Demand based revenue risk: this risk is mostly related to congestion/scarcity
because it is associated with the non-linear variation of social marginal costs with
demand, and consequently with price, since according to the Sustainability
Management Certified Professional (SMCP) principles, user charges should be
equal to the social marginal costs caused by the correspondent transport
activity[19]. Indulging in the traffic congestion and disruption issues that are known
for the Dartford River Crossing within the UK, the Article on the Dartford Thurrock
Crossing Bill states - Because the design capacity has been exceeded, the
crossing is subject to major traffic congestion and disruption, particularly when
parts are closed because of accidents or bad weather. Though the Government
was adamant that the Queen Elizabeth II Bridge should be designed to avoid
closure due to high winds, the bridge has nevertheless had to close on occasions.
In February 2014, during the winter storms, it was closed on the 12th owing to
60 mph winds, and again on the evening of 13th–14th.
At busy times there was significant delay at the payment booths when these
existed. There are numerous junctions on either side of the crossing, and because
it is not under motorway restrictions, a high proportion of local traffic mixes with
long distance traffic, for example travelling from the North and Midlands onwards
to Continental Europe[20]
2.6 Environmental Risks
Future evolution of Social Marginal Cost (SMCs - which is is the total cost society
pays for the production of another unit or for taking further action in the economy):
some marginal costs present considerable uncertainty in the future. This is the
case of the environmental costs that will certainly change but in the medium and
long term their variation is high. Other externalities may be seen with a similar

22. 15
evolution to environment and it is very likely that as societies evolve new
externalities will be detected in the future[19].
In evaluating an example of the Dartford River Crossing, both social and physical
environmental factors impact the optimal cost structure of the project as an asset.
The need for infrastructure development links communities together generating
and propelling the economies in communities within the area.
The physical environmental conditions and risks are to be considered in the
portfolio of infrastructure projects. Revisiting the Dartford Crossing example, in the
event of harsh environmental conditions it can cost the project company dearly.
Though the Government was adamant that the Queen Elizabeth II Bridge should
be designed to avoid closure due to high winds, the bridge has nevertheless
had.to close on occasions. In February 2014, during the winter storms it was
closed on the 12th owing to 60 mph winds, and again on the evening of 13th–
14th[21].
2.7 Financial modelling
A financial model is used by investors to evaluate their returns and by lenders to
calculate the level of cover for their loans and to create a Base Case and
sensitivity calculations[22]. From this an adequate financial model is an essential
tool for financial evaluation of the project. The financial model covers the whole of
the Project Company’s operations, not just the project, and thus takes into
account, for example, tax and accounting issues that may affect the final cash flow
of the Project Company[22].
The input assumptions for the financial model for the Project Company can be
classified into five main areas;
• Macroeconomic assumptions – For interest rates and inflation background
assumptions are needed. At the bidding stage, the Public Authority should retain
that the same assumptions are carried out by all bidders if changes in these would
affect the Service fees;
• Project costs and funding structure;

23. 16
• Operating revenues and costs;
• Loan drawings and debt service; and
• Taxation and accounting[22].
The model’s initial purpose is to calculate the Service Fees being applied to
PPP/PFI projects, based on various ‘building blocks’ of inputs. The basis for the
inputs must be clearly documented. The standard way of doing this is for an
‘assumptions book’ to be compiled. This takes each line of the financial model and
sets out the source for the input (or the calculation based on these inputs) in that
line, with copies of the documentation to support this[23]. There are several factors
that need to be taken into account for the Financial model inputs. These are the
Project Contracts, which includes the expected and required completion of
construction, timing of payments or receipts, and calculation of bonuses or
penalties[22].
The documentation of the inputs must be clear, with compiling an assumption
books as the standard way of doing this. The purpose of this is to take each line of
the financial model which sets out the source for the input or calculation of that
particular line, with copies of the documentation to justify the values being used.
After Financial Close the model continues to be used[22];
 As a basis for lenders to review the changing long-term prospects for the
project and thus their continuing risk exposure;
 To price variations and compensation payments in the PPP Contract
 To calculate any Refinancing Gain (revising a payment schedule for repaying
debt. Mechanically, the old loan is paid off and replaced with a new loan
offering different terms. When a company refinances, it typically extends
the maturity date. Companies or individuals refinancing loans may have to pay
a penalty or fee[24]), to be shared between the Public Authority and the Project
Company; and
 As a budgeting tool for the Project Company.
Schedule and cost data are typically stored and modelled in separate
environments. This makes it difficult or impossible to accurately assess the

24. 17
impacts of changes in one or the other. Cost data, typically modelled in Excel, can
now be easily linked with formulas to the Excel view of the project schedule. You
can now see the impacts that changing costs have on your schedule, and vice
versa. It is easy to model the impact of potential risks of any kind on your bottom
line[18].

25. 18
3 Methodology
3.1 Introduction
This research study has entwined a variety of inter-related objectives and will be
adopting a Case Study methodology to illustrate these Objectives. The case study
is embarked on the Dartford River Crossing and this will be described in further
detail in Chapter 3.6. The objectives are;
1. Identifying the range of risks commonly found in the transport infrastructure sector;
2. Identifying the prevalent allocation of the financial risks in this sector;
3. Evaluating the various risk distribution profiles generated for a range of financing
risks;
4. Analysing the scope for managing the identified financial risks for an optimal
project cost structure; and
5. Recommending a risk allocation matrix which optimises benefits to asset owners
and investors
The assessment of the financial risks involved in the Dartford River Crossing
Bridge operations as an asset is the objective of this research paper with a
proposed solution. Financial and related risks that are found within a typical
funding structure that is used in roads, bridges structures, with a case study done
on the Dartford Crossing. This will be keenly examined, giving forecasted figures
of how the various micro and macroeconomic risks affect the Dartford Crossing
project. This research will specifically relate to the financial risks including currency
exchange rates, inflation and cost of capital (interest rates) in the context of
financial risks, whilst a different class of risks (operational) will be considered.
These are usually the unanticipated overruns within the construction or operation
costs.
A case study method was decided due to the limited time to complete such an
extensive study and the confidentiality of the financial information of the project.

26. 19
In chapter 3.2, the research strategy of evaluating a case study on the Dartford
Crossing Project will be attended. This is ideal since the project is currently an
ongoing one, and has been known to be in financial difficulty within recent years.
This can be re-iterated from a statement said by the then Prime Minister of the
United Kingdom, Mr. David Cameron. Mr Cameron said: "We will have to look at
all of these things. We obviously face a very difficult financial situation. But I quite
understand the local concern about this issue"[25]
The introduction of the free-flowing charging system operated by the French
company Sanef[26], poses a possible threat or benefit to the exchange risks that
the French company will incur for GBP charges. The exchange may be beneficial
with the strength of the GBP to the Euro, or the company may be at a
disadvantage with the post BREXIT effect beckoning.
In addressing the Pre-Financial Close stage of the Dartford River Crossing,
Formulating the financial provisions of the Project Contracts (including use as a
bidding model to calculate a Tariff if the Sponsors have to bid for the project,
checking Linkage Disequilibrium (LD) calculations etc.)[4] can be linked to
objective 4.
In the following chapter 3.3, the method of secondary data collection will be
discussed, highlighting the reasons for this method along with information on past
transport projects. Comparing and analysing the financial risks commonly found
with those transport projects, it can be seen that they are all quite common.
Chapter 3.4, will display the framework for the data analysis, analysing the findings
of the research.
Chapter 3.5, will highlight the limitations faced along with potential problems faced
during the practical research.
3.2 Research Strategy
The hypothesis proposed in Chapter 1 will be evaluated using a case study. The
particular case study is hypothetical (to an extent) because it cannot be tested in

27. 20
the real world, due to confidentiality requirements. It is known that the Department
for Transport’s executive agency: Highways England was/is considering disposing
of the asset known as the “Dartford River Crossing”. There is a reasonable
quantity of relevant data already available to construct a “phantom bid” to
purchase the asset from the government. Such a bid will have to assume project
and financing risks as laid down in Highways England’s conditions of contract.
When accessing the standardisation of PFI (Private Finance Initiative) Contracts,
the price and payment mechanism - The payment mechanism lies at the heart of
the Contract. It puts into financial effect the allocation of risk and responsibility
between the Authority and the Contractor. It determines the payments that the
Authority makes to the Contractor and establishes the incentives for the Contractor
to deliver the Service required in a manner that gives value for money[27].
The Contract must specify its duration. It will usually also specify a Service
Commencement Date to distinguish the time (if any) from the signing of the
Contract and before the Service Period from the Service Period itself[27].
This research strategy was chosen to aid in displaying the effects of the various
financial instruments. Financial risks are considered the risks that have a negative
impact on the cash flows of the financial plan in a way that endangers a project’s
viability or limits profitability[3]. In displaying the influence or effects the financial
instruments will have, the case study and research will be an exploratory one.
Historical research as a strategy is not entirely appropriate for this research paper,
since it is usually associated with looking at non-contemporary phenomena. This
research is interested in a contemporary phenomenon, which is the assessment of
financial risks attached to the Dartford River Crossing as an asset.
Given the nature of this research – an in depth study of financial risks, within the
operation of the Dartford River Crossing, where the stakeholder of Highways
England perspective is sought (the bidding of Dartford Crossing as a profitable
asset), and where the understanding of an absolute research on concrete
measurements is based on risk understanding of projects – a strategy that meets
the needs of this research case study. The case study approach provides the
focus that is required on this paper, emphasises depth of study, is based on the

28. 21
assumption that financial risks possess more of an impact than any other risks
associated with infrastructure and transport projects. These facets of case study
strategy fit ideally with the aim of objective 1 of this research paper: Identifying the
range of risks commonly found in the transport infrastructure sector, however the
financial risks compared to demand and volume risks will be analysed through risk
profiling with the hypothetical data used in the financial model.
So, the research undertaken incorporates a qualitative approach to produce, to an
extent, quantitative findings. This mixed methodology is nevertheless tightly
constrained in its approaches because, as stated already, the underlying case
studied is a “phantom exemplar”
This research strategy is therefore deliberately based on a single explanatory case
study; this is because transparent and established methods will be used to collect
empirical data (see next section) and secondly because creating a Financial Model
to develop the collected data allows rigorous analysis.
In parallel, this strategy also allows room for comparing what was discovered in
the Literature Review with the results of a case study[28]. It is believed that case
study approach meets the objective: ‘we would argue that a case study can be a
very worthwhile way of exploring existing theory and also provide a source of new
hypotheses[29].
The findings of the case study will be compared and contrasted with the Literature
Review findings in terms of views on the impact Interest rates, Inflation, Equity
rate, Debt Equity against the demand and volume the number of trucks and cars
per day.
In the initial stages a rigorous literature review was done on past infrastructure and
roadway projects around the world, to comprehend what other researchers have
found regarding financial risks. By selecting to carry out an experimental research,
testing the hypothesis formulated to display that financial risks have more impact
than other risk within successful infrastructure projects, a financial model with the
use of the @risk software was created to test whether the hypothesis is correct.

29. 22
3.3 Data Collection
To analyse the financial risks inherent in Transportation infrastructure projects, the
development of a Financial model will be produced. It is important that the financial
risks are allocated or highlighted in the prevalent locations of the financial model
and the project. The risk distribution profiles generated for a range of financing
risks will be calculated using the Palisade @Risk software. The United Kingdom,
for example, has identified an infrastructure pipeline of over 500 projects that is
worth more than £250 billion[1] ehich would indicate that proper financial
management is necessary. Therefore, the scope for managing the identified
financial risks for an optimal project cost structure is implemented.
This empirical research relates to the impact of financial risks compared to non-
financial risks such as demand and volume that affect transportation infrastructure
projects including the exemplar case study: Dartford River Crossing.
Secondary data is used, and as part of the qualitative approach this comes from
past projects, research papers, and journals relating to the subject matter. In order
to collect appropriate data for the financial model constructed to support the
quantitative approach, additional data is collected from published websites
including the DfT’s, Highways England, and the toll operator’s annual report and
accounts. Collecting this data allows a series of hypothetical inputs to be derived
for a financial model.
The binomial approach to option valuation, outlined by Guthrie (2009), can be
used conceptually to assess the value of a real option created by an infrastructure
project. We consider two separate cases relating to a major bridge (or other
transport) project. The examples have been chosen to show that consideration of
option values can either decrease or increase the likelihood of current investment
in the project relative to a decision based on standard CBA (Cost Benefit Analysis)
criteria. The standard CBA criterion in a capital unconstrained world is to invest if
the BCR (Benefit Cost Ratio) > 1, and not otherwise. (In a capital constrained
world, the criterion is to invest in projects with the highest BCRs.) The criterion to
invest if BCR > 1 is identical to a criterion to invest if the sum of discounted cash
flows (ΣDCF) > 0 [30]. This is to contrast the effect that interest rates, equity rate,
inflation, debt equity compared to demand and volume have on investment

30. 23
decision making. Using a model with an assumed project life of 10 years dating
from the 31st December 2016 until 31st December 2026, inputs with assumptions
on Operations (Car Toll, Truck Toll, Number of cars per day, Number of trucks per
day) and expenses (Maintenance, Management costs) to produce risk profile
analysis on the inputs. With two rows for traffic revenues, a forecasted total
revenue line overr the 10 years is calculated. The forecasted net cash flow, and
hence the NPV of the amount that would be paid for the benefit and also burden of
being the income recipient. Therefore, the key outputs being the debt service
cover ratio, Total Acquisition Value (NPV) and Debt Equity of this testable model.
This approach was selected since it was not practical to have the real data, which
mostly is confidential, in the short space of time allotted to complete this research
paper. This was used because of convenience for time issues and ideal for testing
a hypothesis. The focus of the research is to analyse the scope for managing and
identifying the impact of the identified financial risks for the optimal project cost
structure, giving a qualitative approach insight into risk evaluation, however not to
debunk the impact of non-financial risks.
To analyse the findings, the financial impacts, difficulties and solutions will be
patterned and compared to the impact of risks which are classified as non-financial
such as traffic volume and demand, and failed procurement. This will highlight the
most influential impact on major projects. The review of relevant literature
established that financial risk is an ongoing area where the financial inputs are
always sought to be minimised, and maximised outputs of profit lines, especially
within infrastructure projects. Therefore, the results of this study will be of interest
to those delving into purchasing assets with Transport projects such as the
Dartford River Crossing.
That being said, it is quite evident that each project will have its unique identity
with its individual set of mishaps and returns, with the generalised principles of the
input financial variables being the same. Empirical research in financial risks tends
to be undertaken via the mechanism of testable models or sensitive data if
available (examples include: [14]; [16]; [31]), resulting in probing qualitative data
rather than a preponderance of data. It is hoped here that the results of the case
study will provide the reader with a pertinent picture of the impact of financial risks

31. 24
in comparison to demand and volume, adding to the tapestry of knowledge that is
elevating in and around the field of finance within Transport projects.
Given that a major focus of this research is to obtain a more in-depth
comprehension of the impact of macroeconomic risks compared to demand,
volume risks, this is grounded on demonstrating the sensitivity of the Dartford
River Crossing’s Value. The case study presents an excellent opportunity to
address surrounding issues of operational, project and financial risks concerning
the Dartford River Crossing and projects similar to it.
3.4 Framework for data analysis
A comprehensive financial model will be constructed with the financial variables
clearly identified, to allow for a Monte-Carlo simulation to be run on the relative
weight of impact of each finance risk. To show the weighted impact of each of the
variables that are tested, the tornado simulation graph is used to show the Cash
Value and debt Equity percentages each of the variables will have on the total
acquisition value. This is done with the assumption that this meets the Lenders’
Debt Service Cover Ratio.
To aid in focusing the financial model in terms of reflecting the main objectives of
this research and ease the analysis of the qualitative data, the financial input
variables will be structured according to the appropriate themes. The themes
reflect the overall aim and objectives in this research and also reiterate the primary
areas arising from the review of literature;
The financial risks in build-operate-transfer; A Risk-Management approach to
successful infrastructure project; Risk Management in Public-Private Partnership
Road Projects using the Real Options Theory; and to conclude, Real Option
Theory for risk mitigation in Transport PPPs. This is to compartmentalise the
various risks that would be associated with DRC and estimating a proposed
solution through objective 5 of Recommending a risk allocation matrix which would
optimise the benefits to asset owners and investors.

32. 25
Figure 5: Qualitative data analysis for Financial risks within DRC
Figure 5 demonstrates the graphical approach that will be advocated to analyse
data from the case study, based on the iterative process of description, analysis
and interpretation of the collected data, particularly with regard to extracting and
understanding emerging themes[28]. However, analysis of qualitative data is not a
linear activity and requires an iterative approach to capturing and understanding
themes and patterns (Miles and Huberman 1984; Creswell 1997)[28].
Since the data used for the financial model is hypothetical, and not having exact
financial figures poses some level of a hindrance, compared to having the exact
figures of the DRC. However, the resulting data will aid in the researcher’s aim of
collecting enriching, qualitative data.

33. 26
The use of the real option theory will be the financial factor in mitigating the risk
whilst doing an extensive research into the other risks that affect transport
infrastructure projects.
3.5 Limitations, outstanding issues
The limitation faced is the limited time for completion of the report.
With the creation of a hypothetical model – a test bed environment for assessing
the sensitivities of the model to key inputs that have been pre-determined as being
caused by financial risks such as Interest rate, Inflation, Currency exchange rate,
Equity rate compared to the impact of non-financial risks such as demand and
volume of the number of cars/trucks per day.
Due to the combination of the depth and sensitivity of the research topic, a case
study approach is adopted[32]. Although the lack of primary data sampling
inherent in the case study approach means the results cannot be generalised, the
results provide an adequate and detailed picture of the effects of the financial and
non-financial risks with the DRC. Analysis of this data gives a valuable insight into
the impact of these risks on the typical funding structures used in roads’
infrastructure development in England, and the current best management
practices.
Since a testable financial model was used with hypothetical data, the limitation and
potential problem is that the results will be hypothetical. The reason for this
approach is due to the fact that, sensitive and confidential data for the Dartford
River Crossing was very challenging to obtain.
In this research there are limitations as well as issues to implementing a case
study in an environment where the testable model can be used as a platform for
similar projects. The results of this study are aimed at being generalised to the
wider research community. The issue of reliability using this strategy will be of a
concern since the data is hypothetical; in particular with producing the risk
distribution profiles of the financial risks, based on assumed values. This is the

34. 27
only source of data collection. In the recent events involving BREXIT, where the
UK voted to leave the European Union, projected figures can see a decline in
traffic volume (Number of cars/trucks per day) over the next 10 years due to this
decision. This may lead to a decrease in European travellers throughout the UK,
leading to less demand and volume risk compared to financial risks.
Lender’s cash flow cover requirements. There is obviously a fundamental
difference between a project with a Project Agreement that provides reasonable
certainty of revenues and hence cash flow cover for debt service and a project
such as a merchant power plant (or Dartford River Crossing in this case) selling
into a competitive and comparatively unpredictable market with no form of hedging
of the revenue risks; it is evident that the latter type of project cannot raise the
same level of debt as the former[22].
One major limitation is the fact that, in pursuing a research topic that is heavily
finance based, coming from an engineering background this limits the core and
fundamental subject matter of finance.
3.6 Case Study details
The Dartford Crossing and until 1991 the Dartford Tunnel, is a major road crossing
of the River Thames in England, connecting Dartford in Kent to the south with
Thurrock in Essex to the north[33]. The crossing is the busiest in the United
Kingdom. The designed capacity has been exceeded and therefore the crossing is
subject to major traffic congestion and disruption, particularly when parts are
closed because of accidents or bad weather[20].
The Crossing is situated some 20 miles east of London. Two tunnels each 1.4
kilometres long carry four lanes of traffic northbound, and the Queen Elizabeth II
Bridge, 2.8 kilometres long carries four lanes of traffic southbound. Dartford River
Crossing's area of operational responsibility extends from Junction 31 in the north
to Junction 2 in the south, a distance of approximately 9 kilometres. The Dartford
River Crossing is open 24 hours a day, every day throughout the year[34].
A free-flow electronic charging system called Dart Charge began in November
2014 based on automatic number plate recognition. The charge can be paid online

35. 28
or phone in advance or by midnight the day after crossing, but can no longer be
paid in cash since the old toll booths have been removed[35].
The French tolling company Sanef was awarded the multi-million pound contract
to enforce a quicker system for using the Dartford Crossing[26]. The company
being the manager of the toll collection will be affected by the macroeconomic risk
of currency exchange risks in regards to the returns on their investment.

36. 29
4 Findings & Analysis
4.1 Introduction
This chapter will discuss the findings and results of the case study of the Dartford
River Crossing. A formidable quantity of relevant data already available was used to
construct a “phantom bid” to purchase the asset from the government. These
findings will comprise of the assumed financial inputs of Interest Rate, Equity Rate,
Inflation, number of cars/trucks per day, impact on the financial output of the Net
Present Value, Debt Equity and the Debt Service Cover Ratio of a hypothetical
financial model. This will entail identifying the risks today that in the anticipation for a
proposed sale of the Dartford River Crossing as an asset that would affect this
transaction. The risk distribution profiles will be produced by the use of Monte Carlo
simulation with the use of the @Risk software. Monte Carlo simulation is considered
to be the "best" method of sensitivity analysis. It comes up with infinite calculations
(expected values) given a number of constraints. Constraints are added and the
system generates random variables of inputs. From there, NPV is calculated. Rather
than generating just a few iterations, the simulation repeats the process numerous
times. From the numerous results, the expected value is then calculated[36].
4.2 Case Study Findings: Description and Analysis
The construction of a testable financial model was developed, with hypothetical data
to illustrate the effects of macroeconomic risks against that of the demand and
volume of the anticipated number of cars/trucks per day. The financial model can be
found in Appendix A of this research paper.
In the model there are general assumptions that are made, starting with a Model
Start Date being the 31-Dec-16 and end date 31-Dec-26 (over a 10-year period).
The inflation rate is assumed to be at 2%. The Revenue generated is placed under
the Operating assumptions. These comprise of the Car toll (£3.25), Truck Toll
(£5.25), Number of cars per day (80,000), Number of trucks per day (9,000) all
estimated at model start date, car growth rate (4%) and truck growth rate (2%). The

37. 30
expenses, consisting of Annual maintenance costs, Annual management costs,
Annual Capital Expenditure (Capex) and tax rate are all estimated and can be seen
in Table 2 of Assumption of Financial Variables. The financing assumptions
comprising of the input variables that are tested using Monte Carlo simulation to
create risk distribution profiles for the Net Present Value.
Table
2: Assumptions of Financial Variables
4.2.1 NPV
In the financial model the NPV (Total Acquisition Value in this case) was calculated
over a period of 10 years (Term of Debt). Using the @risk software (version 7.0) the
Data Type Data Units Name
General Assumptions
Model Start Date [input] [date] StartDate 31-Dec-16
End Date [input] [date] 31-Dec-26
inflation [input] [%] inf 2%
Operating Assumptions
Revenue
Car Toll [input] [£/car] CarToll 3.25
Truck Toll [input] [£/truck] TruckToll 5.25
Number of cars per day [input] [#] NoCarsperday 80000
Number of trucks per day [input] [#] Notrucksperday 9000
Number of cars growth rate (pa) [input] [%] CarGwthRate 4%
Number of trucks growth rate (pa) [input] [%] TruckGwthRate 2%
Expenses
Annual maintenance costs [input] [£000] AnnMaint 30,000
Annual management costs [input] [£000] AnnMgmt 5,000
Annual capex [input] [£000] AnnCapex 2,500
Tax rate [input] [%] TaxRate 30%
Financing
Debt Amount [input] [£000] TotalDebt 510,712
Interest rate [input] % IntRate 7.50%
Term of Debt(yrs) [input] # DebtTerm 10
Equity discount rate [input] % EquityRate 13%
Target Minimum DSCR [input] [x] TargetDSCR 1.25
Actual Minimum DSCR [calc] [x] ActualDSCR 1.26
0
Checks
DSCR Check [calc] Ok
NPV of Asset after 10 years [calc] [£000] NPV 806,052
Debt:Equity Value [x] Debt_Equity 63%

38. 31
Monte Carlo simulation is run using the Tornado simulation graph to give the
sensitivity analysis. Figure 6 below illustrates the Total Acquisition Value (NPV)
output and the financial variables such as the Equity rate, Interest rate, Inflation and
Tax rate, and the demand/variable of the number of cars and trucks per day,
displaying the effects on this value (NPV). These variables are displayed on the Y-
axis. On the X – axis the Total Acquisition Value (NPV) is represented in £££ value.
The values displayed are estimated yearly values.
Figure
6: Total Acquisition Value (NPV) Output
The main thought is that the longer the bar or the larger the coefficient, the greater
the impact that particular input has on the output that you are analysing. A positive
coefficient, with the bar extending to the right indicates that this input has a positive
impact: increasing this input will increase the output. A negative coefficient, with the
bar extending to the left, indicates that this input has a negative impact: increasing
this input will decrease the output[37].
It is elucidated that the variable that is the most impactful (having the highest
monetary value) is the Number of cars per day, which generates a value of
£177,818.70 (per year). The equity rate (Equity is the value of an asset less the
value of all liabilities on that asset[38]) is the variable that is second most impactful,
generating a loss of over £20,000 (per year). The interest rate is the third most
impactful, generating a loss of just under £20,000 (per year). It is then seen that the

39. 32
number of trucks per day can generate profits of £14,701.80 (per year). Inflation rate
(rate at which the general level of prices for goods and services is rising and,
consequently, the purchasing power of currency is falling[39]) generates a value of
£7,305.65 (per year). Tax rate (is the percentage at which an individual
or corporation is taxed[40]) is the least impactful and formulates a value of
£4,820.68 (per year). Figure 7 below illustrates the effect of the input variables
(financial and demand risks) on the Y- axis and the Total Acquisition Value (NPV)
on the X – axis. This represents the Inputs ranked by the effect of the output mean
of the Total Acquisition Value (NPV) in millions.
Figure 7: Total Acquisition Value(NPV) effect on Output Mean
4.2.2 Debt/Equity Ratio
Debt/Equity Ratio (D/E ratio) is a debt ratio used to measure a company's
financial leverage, calculated by dividing a company’s total liabilities by
its stockholders' equity. The D/E ratio indicates how much debt a company is using
to finance its assets, relative to the amount of value represented in the
shareholders’ equity[41]. In figure 8 below, the traffic volume of number of cars per
day gives the Debt Equity Ratio of -16.836%, hence once more having largest
impact on the optimal cost structure. The interest rate being the second most

40. 33
impactful giving a Debt Cover ratio of +1.9% on cost structure of the asset. The
Traffic of the number of estimated trucks per day giving a ratio -1.5%. The equity
rate, inflation and tax rate displaying that its effect would be less impactful than the
traffic volume and demand variables of the DRC asset.
Figure
8: Debt Equity Output
Figure 9: Debt Equity Output Mean

41. 34
4.2.3 Debt Service Cover Ratio (DSCR)
In corporate finance, the Debt-Service Coverage Ratio (DSCR) is a measure of
the cash flow available to pay current debt obligations. The ratio states net operating
income as a multiple of debt obligations due within one year, including interest,
principal, sinking-fund and lease payments[42].
A DSCR greater than 1 means the entity – whether a person, company or
government – has sufficient income to pay its current debt obligations. A DSCR less
than 1 means it does not[42].
In general, it is calculated by:
DSCR = Net Operating Income / Total Debt Service
A DSCR of less than 1 means negative cash flow. A DSCR of .95 means that there
is only enough net operating income to cover 95% of annual debt payments. For
example, in the context of personal finance, this would mean that the borrower
would have to delve into his or her own personal funds every month to keep the
project afloat. In general, lenders frown upon a negative cash flow, but some allow it
if the borrower has strong external income[42].
Figure 10 below illustrates the Debt Service Cover Ratio (DSCR) mean output and
the financial variables on the optimal cost structure.

42. 35
Figure 10: DSCR Output Mean
On the Y- axis the financial variables are represented and the mean debt service
cover ratio values shown on the X – axis.
The traffic volume variable comprising of the Number of cars per day, is seen as
being the most impactful variable, with a maximum DSCR of 1.8765 and minimum
of 0.63753. The baseline (a benchmark that is used as a foundation for measuring
or comparing current and past values[43]) DSCR from the estimated data is 1.2569.
This is displaying that with the projected traffic volume and having DSCR of 1.8739,
the DRC as an asset will be a formidable to purchase from the government. The
interest rate being the second most impactful variable, possessing a DSCR of
maximum value 1.3757 and minimum value of 1.1454. The variable of the traffic
volume of the number of trucks per day being the third most impactful with a
maximum DSCR of 1.3062 and minimum 1.2198. The Tax rate giving a DSCR
maximum value of 1.2926 and minimum 1.2303 inherently being the fourth most
impactful variable. The variable with the least impact is the rate of inflation giving a
maximum value of 1.2755 and minimum value of 1.2296.
The hypothetical debt service model agreed to by the lenders is the value of 1.25, of
which the debt service cover ratio determined here is that of 1.26. Since the value is
above 1 and is a ratio of 0.01 more than the DSCR, this illustrated that the project
company has sufficient income to pay its current debt obligations.

43. 36
4.3 Synthesis of Case Study Findings
The input variable with the most impact out of the three outputs of this case study –
NPV, Debt Equity Ratio and Debt Service Cover Ratio, is undisputedly the traffic
volume of the number of cars estimated daily. For the Total Acquisition Value, the
number of Cars per day input variable yields £177,818.70 (per year) for Regression
mapped values and with inputs ranked by the mean output yields maximum value of
£1,118,356.54 and minimum value of £496,248.53.
The Debt Equity ratio is seen to have been impacted the highest by the Traffic
volume/demand variable of the number of cars per day giving an output maximum
mean of 1.0652 and minimum mean of 0.45976 from the Inputs effect on out mean
values. This illustrates the fact that demand and volume within traffic congestion
impacts the cost of the asset severely. This in turn indicates that the projected
volume and traffic demand can finance the DRC asset. Table 3 below shows the
risk impact on the financial mean output values. These results show the project
company’s financial leverage, indicating the debt the company is using to finance
the asset relative to the amount of value represented in its shareholder’s equity.
Revisiting the Literature Review in Chapter 2.5, in evaluating the demand risks
within transport projects, the example given was the Dartford River Crossing.
Stating that, because the design capacity had been exceeded, the crossing is
subject to major traffic congestion and disruption. The traffic volume of the number
of cars highly reflects the enormous impact of the three output financial instruments
of the Total Acquisition Value (NPV), Debt Equity Ratio and Debt Service Cover
Ratio. This exhibits the fact that with the increasing volume of major traffic the
design of the tolling system had to be altered, hence the introduction of the free-
flow electronic charging system called Dart Charge which began in November 2014.
Being based on automatic number plate recognition of vehicles instead of manned
booths, refer Chapter 3.6 Case Study Details.

44. 37
Table 3 below displays the Risk Impact on the Financial Output Mean, with a legend
to follow.
Total
Acquisition
Value (NPV) –
Output Mean
Debt Equity
Ratio – Output
Mean
Debt Service
Cover Ratio
(DSCR) –
Output Mean
No cars per day 1 1 1
No trucks per
day
3 2 3
Interest Rate 4 3 2
Equity Rate 2 4 _
Inflation 6 5 4
Tax Rate 5 6 5
Table 3: Risk Impact (Risk Profile) on Financial Output Mean
Legend (for Table 3 above)
1 – Most impactful
2 – Second most impactful
3 – Third most impactful
4 – Fourth most impactful
5 – Fifth most impactful
6 – Sixth most impactful

45. 38
5 Conclusions
5.1 Introduction
The overall aim of this research was to evaluate the relative impacts of financial
risks compared with non-financial risks that are typically faced by infrastructure
development projects in England. The sector that was studied in this research paper
is transportation infrastructure, with a focus on road links (as opposed to airport, rail,
or port projects). The specific research objectives that were pursued in order to fulfil
the aim comprised of;
1. Identifying the range of risks commonly found in the transport
infrastructure sector;
2. Identifying the prevalent allocation of the financial risks in this sector;
3. Evaluating the various risk distribution profiles generated for a range of
financing risks;
4. Analysing the scope for managing the identified financial risks for an
optimal project cost structure; and
5. Recommending a risk allocation matrix which optimises benefits to asset
owners and investors.
In this Conclusion chapter this will be compartmentalised into sub-sections. The first
will be Chapter 5.2 Research Objectives which will revisit the research objectives
listed above, summarising the findings of this research work and offer warranted
conclusions based on the findings. The previous Chapter – Case Study Findings
was large and it is necessary to summarise it within this Chapter. Future research
recommendations will be discussed to aid in terms of how to progress this research
study. Consequentially, the contribution of this research to the impact of financial
and non-financial risks on transportation projects focusing on road links will be
clarified and display more insight into the field of study. Additionally, a section
reflecting on the research process that has been applied is included. By adopting
this structure, it is intended to reflect on whether or not the objectives stated at the

46. 39
start of this research have been met, including consideration of the value of this
study. Guidance will be offered on how this research work can be progressed[28].
5.2 Research Objectives: Summary of Findings and Conclusions
5.2.1 Research Objective 1: Risks found in Transport Infrastructure Projects
The literature review identified and broke down the various risks found within
transport projects, stating that risks vary from financial to non-financial risks. This
was shown by categorising the risks into four main risk domains; Technical risks
inclusive of construction risks (cost overruns or delays in completion); design risks;
commercial risks (demand risks); political; and regulatory risks. The economic and
financial risks (Technical risks), which usually originate from uncertainties such as
economic growth, inflation rates, currency convertibility and exchange risks, along
with interest rates and equity rates are the financial risks examined for this research.
The non-financial risks sought for this research is that of demand, the traffic volume
relevant to transport projects. In the case study of the Dartford River Crossing, it is
exhibited that the demand risk of the number of cars in usage of the DRC is said to
be the most impactful, and exceeds those of the financial risks. In past projects it
has be shown that the impact of the demand risk supersedes financial risks.
Statistics projected by the Department of Transport (DfT) suggested that congestion
across the entire English road network will increase from 2003 levels by 27 per cent
by 2025 and 54 per cent by 2035[10], as stated in Chapter 2 Literature Review.
Similarly, looking at the Dartford River Crossing and identifying risks such as
political risks that can play a monumental role in various large scaled transport
projects, has not impeded on the operational services of the DRC.
Since Market risk deals with the adverse price or volatility that affects assets
contained in a firm or project company’s portfolio, with the recent events of the
political BREXIT results and the uncertainty of the stability of the economy, Market
risk now becomes viable in the DRC’s portfolio.

47. 40
The conclusion that can be drawn in this research on risks found in Transport
infrastructure projects is that each project has its own identity, and the risks
attached to the transport projects may vary based on location, government stability
and economic stability (value of local currency). In relation to the DRC, three main
risks domain applies to, that of the technical risks, design risks and commercial risks
as stated earlier in the chapter (omitting political and regulatory risks).
5.2.2 Research Objective 2: Allocation of the Financial Risks
The pertinent financial risks that were researched and identified are the
macroeconomic risks. These comprised of inflation rate risk, interest rate, interest
rate swaps, swap credit risk and currency exchange rate movements. For the
currency exchange rate movements, this would not relate to the project in particular,
however would relate to the economic environment in which it is operating. In
looking at the case study DRC, the financial risks that impacted the project from the
hypothetical Financial model are the interest rate, equity rate and inflation as
illustrated in Table 3 Chapter 4. The LIBOR rate estimated at 0.75%, being the first
step to calculating the interest rates on financed loans[44], impacted the outcome of
the targeted Debt Service Cover Ratio, which is that of 1.25. The actual DSCR
calculated was 1.26 which signified that despite not meeting the exact value, the
DRC as a project company can adequately manage its borrowing cost if needed for
operations. With this DSCR value being above 1 it indicates a positive cash flow
from the operations of the project despite the various financial risks affecting the
project cost structure.
The sectors that transport infrastructure projects are classed into are either public or
private, in which the finance for public infrastructure (roads, transport, public
buildings etc.) was known to be developed through the UK’s Private Finance
Initiative (PFI) as stated in Chapter 2.3. The DRC was initially founded as a Private
Finance Initiative scheme which proved to be a better funding solution by being a
more cost-effective than publicly-financed alternatives. The prevalent risk that the

48. 41
French tolling company (Sanef) will face with managing the free-flow electronic
charging system will be exchange rate risk, with the conversion from GBP to Euro
as stated in Chapter 3.6 of the Case Study Findings, and the incurring Interest rate,
equity rate and inflation rates.
The Case study approach has led to the conclusion that on the research allocation
of financial risks within the transport sector, that the decision-makers should
evaluate, identify, mitigate and control key financial risks during the various stages
of Transport Projects such as DRC. The risks are not always deemed misfortunates,
however opportunities for the private concessionaire and the government can arise.
Exchange risks being a value asset for the Sanef company, with revenue converting
from GBP to Euros.
5.2.3 Research Objective 3: Risk Distribution Profiles
The risk distribution profiles will generate estimation as to how a wide range of
financial and demand/volume risks are expected to be forecast or influence the long
term results with regards to its; returns, volatility and covariance. In having a look at
the risk Impact (risk profile) financial output mean table (Table 3), it was quite
evident that the demand and volume risk impacted heavily on the Total Acquisition
Value (NPV), Debt Equity Ratio and the Debt Service Cover Ratio of the cost
structure than any of the various risks. The financial risks estimates are collectively
regarded as the Capital Market Assumptions and are produced following thorough
analysis on the current market yields within the hypothetical financial model.
From this, a gamut of asset allocations that can periodically increase in risk from a
level of 1 to 6 risk levels (1 – most impactful, 6 – least most impactful) is produced.
The methods for doing this are based on Modern Portfolio Theory techniques to
derive efficient portfolios, which maximise expected returns for any given degree of
risk and when plotted collectively, form an efficient frontier[45].

49. 42
5.2.4 Research Objective 4: Scope of Managing Financial Risks
The literature review gave an in-depth analysis of adequate risk management
processes in PPPs, which is fundamental in the assurance of the project’s success.
The personnel in managerial roles should be prompt to identify, evaluate, control
and isolate the key/major risks during the various phases of a PPP project. Since
proper front-to-end project planning in its entirety entails modelling the project’s risk
profile so it can be managed during execution and aggressively mitigate the risks
that emerge. In this case study with it being abundantly evident that the demand and
volume of cars, being the highest of risks to control or mitigate, the introduction and
operation of the free-flow charging system was introduced.
The main conclusion and lesson that can be drawn from this research objective is
that the solution is to recognise what risks are inherent to a project and what extent
of leverage there is to shape the risk profile before the majority of the resources are
committed.
5.2.5 Research Objective 5: Recommending a Risk allocation matrix to
benefit asset owners
Revisiting conclusion 1, which states that each project has its own identity, and the
risks attached to the transport projects may vary based on location, government
stability and economic stability, which the recommendation would be to instil a
thorough research on the need and the demand for linking communities and cities
together. The use of historical data as a medium for development of financial
models and tools for the valuation of major transport projects which in turn can
protect both the public and private sectors from unexpected losses is vital. It
enables project planners to see past trends and apply them to future projects.
From conclusion 3, stating that a gamut of asset allocations that can periodically
increase in risk from levels 1 through to 6 (table 3), a recommended method to

50. 43
minimise the risks and maximise the expected returns is based on Modern Portfolio
Theory techniques stated in Research Objective 3. This is to derive efficient
portfolios, which maximise the expected returns for any given degree of risk, to form
an efficient frontier once plotted accurately.
The conclusion drawn from research objective 4, encompassing a solution to
recognise which risks are inherent to a project and what extent of leverage there is
to shape the risk profile before the majority of the resources are committed. The
application of financial tools such as real options is a recommended solution to
combat this conclusion. Risk is involved in any economic related activities and
trading. This shows that one may have to, or incline to, make a judgement involving
committing funds based on prediction of future uncertainty. With hindsight, one
might or might not regret taking that position. An option is a financial instrument
giving one the right but not the obligation to make a specified transaction at (or by) a
specified date at a specified price[46].
To conclude in summary of the research findings, the hypothesis tested has proved
to be false since the risk of demand and volume impacted the output financial
variable the most.
5.3 Self-Reflection
In choosing a finance related topic, it posted an uphill battle for myself, coming from
an Engineering background. This was done as an interest to seek employment
within the financial industry. However, this was not deterring as formulating the aims
and objectives with efficacious guidance from the project Supervisor helped in my
progression and understanding in the subject matter. Testing a hypothesis as to
whether financial risks have more of an impact than non-financial risks such as
demand and volume, made a narrowing effect on exactly the objectives set out to be
achieved.

51. 44
This was proven otherwise from the overall research; it was shown from the
financial model with the use of the Palisade @Risk software that the demand risk
has a larger impact than financial risks on transport projects.
This was a painstaking process filled with every bit of emotion during the period of
this dissertation, and the feeling of not exactly knowing if one is going down the right
path was the biggest challenge of them all.
In the data collection chapter, having used a hypothetical testable financial model
that was assumed to be unbalanced, as the data itself was not based on figures
from the Dartford River Crossing project. Nevertheless, this was sufficed as the
model is only used to give testable and projected data.
In closing one major limitation of this project, is that of time. Inadequate time to
produce a more substantial finding.

52. 45
APPENDIX A
Financial Model – Assumptions Sheet

53. 46
Assumptions
Data Type Data Units Name
General Assumptions
Model Start Date [input] [date] StartDate 31-Dec-16
End Date [input] [date] 31-Dec-26
inflation [input] [%] inf 2%
Operating Assumptions
Revenue
Car Toll [input] [£/car] CarToll 3.25
Truck Toll [input] [£/truck] TruckToll 5.25
Number of cars per day [input] [#] NoCarsperday 80000 At model start date
Number of trucks per day [input] [#] Notrucksperday 9000 At model start date
Number of cars growth rate (pa) [input] [%] CarGwthRate 4%
Number of trucks growth rate (pa) [input] [%] TruckGwthRate 2%
Expenses
Annual maintenance costs [input] [£000] AnnMaint 30,000
Annual management costs [input] [£000] AnnMgmt 5,000
Annual capex [input] [£000] AnnCapex 2,500
Tax rate [input] [%] TaxRate 30%
Financing
Debt Amount [input] [£000] TotalDebt 510,712
Interest rate [input] % IntRate 7.50% LIBOR rate of 0.75%
Term of Debt(yrs) [input] # DebtTerm 10
Equity discount rate [input] % EquityRate 13%
Target Minimum DSCR [input] [x] TargetDSCR 1.25 Min
Actual Minimum DSCR [calc] [x] ActualDSCR 1.26
0
Checks
DSCR Check [calc] Ok
NPV of Asset after 10 years [calc] [£000] NPV 806,052
Debt:Equity Value [x] Debt_Equity 63%
Scenario Analysis
Unit
Avg Cars per day [#] 99890.81
Avg Trucks per day [#] 10051.84
Interest Rate [%]
Equity Rate [%]
Inflation [%]
Total Debt [£000]
Debt:Equity [x]

54. 47
Financial Model – Calculations of NPV, Debt: Equity and DSCR

55. 48

56. 49

57. 50
REFERENCES
[1] F. Beckers, E. Silva, N. Chiara, A. Flesch, J. Maly, and U. Stegemann, “A
risk-management approach to a successful infrastructure project,” McKinsey Work.
Pap. Risk, no. 52, p. 18, 2013.
[2] I. Ambrasaite, M. B. Barfod, and K. B. Salling, “MCDA and risk analysis in
transport infrastructure appraisals: The Rail Baltica case,” Procedia - Soc. Behav.
Sci., vol. 20, pp. 944–953, 2011.
[3] Y. Xenidis and D. Angelides, “The financial risks in build‐ operate‐ transfer
projects,” Constr. Manag. Econ., vol. 23, no. 4, pp. 431–441, 2005.
[4] E. R. Yescombe, “Principles of Project Finance, Chpater 2, What Is Project
Finance?,” pp. 5–19, 2002.
[5] R. Pellegrino, “Real option theory for risk mitigation in transport PPPs,” Built
Environ. Proj. Asset Manag., vol. 3, no. 2, pp. 199–213, 2013.
[6] N. Carbonara, N. Costantino, L. Gunnigan, and R. Pellegrino, “Risk
Management in PPP projects : an empirical study on the motorway sector,” vol. 1.
[7] “Road and rail projects to boost local and regional transport - Press releases
- GOV.UK.” [Online]. Available: https://www.gov.uk/government/news/road-and-rail-
projects-to-boost-local-and-regional-transport--2.
[8] “Notes on the Economic Evaluation of Transport Projects A FRAMEWORK
FOR THE ECONOMIC EVALUATION OF TRANSPORT PROJECTS,” 2005.
[9] R. Allport, R. Brown, S. Glaister, and T. Travers, “Success and failure in
urban transport infrastructure projects,” no. May, pp. 1–191, 2008.
[10] I. Muhammad and N. Javhria, “Infrastructure and growth,” Pak. Dev. Rev.,
vol. 50, no. 4, pp. 355–364, 2011.
[11] N. Vajdić and I. Damnjanović, “Risk Management in Public-Private
Partnership Road Projects Using the Real Options Theory,” … Manag. Compet.
June, vol. 2011, pp. 155–160, 2011.
[12] Jeffrey Delmon, Private Sector Investment in Infrastructure: Project Finance,

58. 51
PPP Projects ... - Jeffrey Delmon - Google Books. 2009.
[13] “analysing financing risk.” .
[14] R. I. Framework, “Kosovo PPP Units and Related Institutional Framework.”
[15] E. R. Yescombe, Macro-Economic Risks. 2014.
[16] a N. I. Investment, “Financing the UK ’ s infrastructure,” 2009.
[17] F. Cucchiella, I. D. Adamo, and M. Gastaldi, “Highway project management
through real option,” Int. Conf. Appl. Econ. – ICOAE 2008, pp. 217–223, 2008.
[18] @risk Analysis Overview Tutorials, “Analysing Financial Risks,” 2011.
[19] R. Mac??rio, “Future challenges for transport infrastructure pricing in PPP
arrangements,” Res. Transp. Econ., vol. 30, no. 1, pp. 145–154, 2010.
[20] P. Woodman, “Dartford-Thurrock River Crossing toll charges to rise,” Indep.,
2012.
[21] “Updated: 30mph speed restriction on QEII bridge at Dartford crossing (From
Echo).” [Online]. Available: http://www.echo-
news.co.uk/news/local_news/basildon/11012099.Drivers_warned_of_QE2_Bridge_c
losure_this_evening/?ref=rss.
[22] E. R. Yescombe, “Financial Structuring,” Princ. Proj. Financ., pp. 143–170,
2014.
[23] E. R. Yescombe, “Financial Hedging,” Public-Private Partnerships, pp. 171–
201, 2007.
[24] “Refinance.” [Online]. Available:
http://www.investopedia.com/terms/r/refinance.asp.
[25] “BBC News - Dartford Crossing future in doubt.” [Online]. Available:
http://news.bbc.co.uk/1/hi/uk_politics/election_2010/england/8644336.stm.
[26] “French firm Sanef awarded Dartford Crossing free-flow charging system
contract.” [Online]. Available: http://www.kentonline.co.uk/dartford/news/crossing-
deal-6624/.
[27] H. Treasury, Standardisation of PFI contracts, no. April. 2004.

59. 52
[28] John Biggam, Succeeding with your Master’s Dissertation, 3rd ed. 2015.
[29] S. Mark, P. Lewis, and A. Thornhill, Research Methods for Business
Students, Fourth. 2007.
[30] A. Grimes, “Building Bridges : Treating a New Transport Link as a Real
Option,” Public Policy, no. December, pp. 1–24, 2011.
[31] OECD and World Bank, “Risk and Return Characteristics of Infrastructure
Investment in Low Income Countries,” no. September, 2015.
[32] K. R. Yin, Case Study Research: Design and Methods, 5th ed. 2003.
[33] R. Simpson, Dartford River Crossing: Innovation in Civil Engineering.
Dartford Town Archive.
[34] “Dartford Technology: Communications - Dartford River Crossing.” [Online].
Available: http://www.dartfordarchive.org.uk/technology/communication_drc.shtml.
[35] “Dart Charge: Dartford Crossing remote payment - News stories - GOV.UK.”
[Online]. Available: https://www.gov.uk/government/news/dart-charge-dartford-
crossing-remote-payment.
[36] “Risk-Analysis Techniques - CFA Level 1 | Investopedia.” [Online]. Available:
http://www.investopedia.com/exam-guide/cfa-level-1/corporate-finance/risk-analysis-
techniques.asp.
[37] “Interpreting Regression Coefficients in Tornado Graphs - Palisade
Knowledge Base.” [Online]. Available:
http://kb.palisade.com/index.php?pg=kb.page&id=138.
[38] “Equity Definition | Investopedia.” [Online]. Available:
http://www.investopedia.com/terms/e/equity.asp?ad=dirN&qo=investopediaSiteSear
ch&qsrc=0&o=40186.
[39] “Inflation Definition | Investopedia.” [Online]. Available:
http://www.investopedia.com/terms/i/inflation.asp?ad=dirN&qo=investopediaSiteSea
rch&qsrc=0&o=40186.
[40] “Tax Rate Definition | Investopedia.” [Online]. Available:
http://www.investopedia.com/terms/t/taxrate.asp?ad=dirN&qo=investopediaSiteSear
ch&qsrc=0&o=40186.

60. 53
[41] “Debt/Equity Ratio Definition | Investopedia.” [Online]. Available:
http://www.investopedia.com/terms/d/debtequityratio.asp?ad=dirN&qo=serpSearchT
opBox&qsrc=1&o=40186.
[42] “Debt-Service Coverage Ratio (DSCR).” [Online]. Available:
http://www.investopedia.com/terms/d/dscr.asp.
[43] “Baseline Definition | Investopedia.” [Online]. Available:
http://www.investopedia.com/terms/b/baseline.asp?ad=dirN&qo=serpSearchTopBox
&qsrc=1&o=40186.
[44] “London Interbank Offered Rate (LIBOR) Definition | Investopedia.” [Online].
Available: http://www.investopedia.com/terms/l/libor.asp.
[45] “Assessing suitability & the FCA Fund risk profiling and Dynamic
Planner ® explained.”
[46] N. H. Bingham and M. T. March, “PLANNING,” no. March, pp. 38–40, 2011.