Driving in the dark As economists must do ecpc Incorporating and - - PowerPoint PPT Presentation

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Driving in the dark As economists must do ecpc Incorporating and - - PowerPoint PPT Presentation

Feb 16, 2023 120 likes •375 views

Driving in the dark As economists must do ecpc Incorporating and assessing travel In demand uncertainty in in transport Lit Review in investment appraisals Establish method NZ Transport Agency research report 620 Case Study Anthony

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Driving in the dark

As economists must do

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In Incorporating and assessing travel demand uncertainty in in transport in investment appraisals

NZ Transport Agency research report 620 Anthony Byett, economic consultancy + project cba, Taupo Arthur Grimes, Motu, Wellington James Laird, Institute for Transport Studies, Leeds Paul Roberts, QTP Limited, Christchurch

Lit Review Establish method Case Study

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Note 3

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Uncertainty creates human responses

“For my part I know nothing with any certainty, but the sight of the stars makes me dream” Vincent van Gogh

  • Optimism v Fear
  • Impulsiveness v Procrastination
  • Anxiety: Suspense v Worry
  • Intolerance of uncertainty >> Compare to others
  • Clarity not Certainty

Various including Jim Collins 4

NEXT: what is uncertainty?

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Risk versus Uncertainty

Risk

KNOW probability distribution of future “PREDICT and ACT” Common within:

  • Insurance
  • Funds management
  • Bank dealings in financial instruments
  • R&D corporates
  • Oil companies

Uncertainty

DO NOT KNOW probability distribution “ANTICIPATE and ADAPT” Common within:

  • Climate change
  • Environment in general (flood risk,

water supply)

  • Telecommunications
  • Defense

5 See Knight (1921), Guthrie (2011), Chades et al (2015)

Lit Review

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More “uncertainty”

  • Walker et al (2010) – clear (enough) future, probable alternatives,

multiple plausible futures or future unknown (i.e. deep uncertainty)

  • Chapman and Ward (2011) – ambiguity, inherent, event or systemic
  • Kodukua and Papadesu (2006) – market-related v. project-specific
  • Boardman (2011) – collective v. private

General observation: uncertainty can be many-faceted

References as above 6

Lit Review

NEXT: how do others deal with uncertainty?

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Approaches to “uncertainty”

Lit Review

7 Personal broad overview

“Standard” CBA Apply risk-adjusted discount rate to expected cash flows, plus sensitivity testing Operations research Optimise amongst pathways, decision trees Financial valuation (including ROA) Estimate value using probability distributions, using market pricing and taking advantage of portfolios Institutional Recognise value is inherent in ‘rights’ and see contracts as opportunities to exploit uncertainty Risk management (including AM) Process to understand, manage, communicate and monitor risk Better Business Case Align to strategy, analyse volatility, consider wide set

  • f alternative actions, include risk in discount rate
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What can be learnt from finance?

  • Investors are risk averse and hence require an extra return for risk
  • Risk is dampened by diversification (and hence focus is on portfolios)
  • Current “fair value” equals discounted expected future returns
  • Real options show there is value in limiting unwanted outcomes
  • There is a general reliance on efficient market pricing
  • Risk is limited according to measures such as VaR e.g. limit risk to such that

returns will be above a threshold, say, 99% of the time

  • Hedging does not necessarily match 1-to-1 with liabilities (e.g. pension

liabilities hedged with equities)

  • A premium is paid for liquidity (a form of adaptability)

8 Personal observations

Lit Review

NEXT: economics is a study of choice

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Supply and demand

Note 9

Transport costs (TC) Freight traffic (tonnes)

A

TC0 TC1 X1 X0 Demand0,1

freight

Supply1 Supply0 ΔTCfreight

(a) Transport market

Product price (P) Output (tonnes)

B

P0 P1 Q1 Q0 Demand0,1

goods

Supply1

goods

Supply0

goods

ΔTCfreight

(b) Goods market

Wage (W) Labour (hours) W0 W1 L1 L0 Demand0

labour

Supply0,1

labour

Demand1

labour

C

(c) Labour market

D

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Travel demand uncertainty

  • 4-stage model
  • Trip generation, trip distribution, modal split and trip assignment
  • Forecast errors due to:
  • Input data errors, parameter estimation and/or model specification
  • Key uncertainties
  • Economic development (especially local development for short-term forecasts)
  • Local population growth
  • Technology and social effects on traffic demand
  • Mode share
  • Treatment of uncertainty
  • Hubris – ongoing process of forecasting improvement
  • Humility – show uncertainty and reduce sensitivity of decisions to forecasts

Lit Review

10 Ortuzar and Willumsen (2011), Willumsen (2014), Hartgen (2013)

NEXT: decision making

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Risk and Real options

  • Right but not obligation to invest (divest) in a real asset
  • Types: Defer, Abandon, Scale, Stage, Learn, Switch
  • Involves: a risky future, irreversible decisions, sequential decisions
  • Valuation based on probability distributions
  • By Black-Scholes, Binomial Lattice, Monte Carlo and/or Decision Tree
  • Decisions now can create, retain or extinguish real options
  • Real Options Analysis (ROA) can be on (a) valuation and/or (b) flexible

decision making

  • This project puts emphasis on “(b) flexible decision making”
  • Key aim: to harness uncertainty

Lit Review

11 Guthrie (2009), Kodukula and Papadesu (2006)

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Key real options

  • Option written to others to expand
  • Infrastructure investment undertaken
  • Property owners given increased value in option to develop – if demand and

complementary investment is sufficient

  • Option to learn
  • Major investment is delayed while learning activities undertaken
  • Investment scaled as uncertainty resolved (or at least thresholds reached)

Lit Review

12 Various

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Uncertainty and AM

  • “Adaptive management (AM) is an iterative process of reducing

uncertainty through time by learning by doing and monitoring”

  • Typically deals with small number of unknowns
  • Learning can be active or passive
  • Entails:
  • Structuring uncertainty
  • Learning by doing
  • Sequential decisions
  • Decisions taken to create flexibility
  • Typically adapting to triggers
  • E.g. a self-learning dyke
  • Key aim: to reduce uncertainty

Lit Review

13 Chades et al (2015), Walker et al (2013), Lawrence (2017)

manage monitor learn

  • bjective

Statet decisiont Statet+1 Statet+2 decisiont+1 Don’t knowt+1 Don’t knowt

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Our recommended process

Establish method

1 Define the issue FRAME 2 Estimate the status quo and business and usual (BAU) scenario 3 Identify key drivers of uncertainty 4 Create short-list of alternative investment opportunities 5 Draw decision tree for each alternative MODEL 6 Probe uncertainties EVALUATE 7 Crudely estimate indicative payoffs 8 Establish threshold(s) that favour one alternative over another THEN DECIDE

14 Multiple influences

NEXT: examples

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Case I. Auckland Northern Busway (SH1)

Historical example, involves High Occupancy Vehicles (HOV)

  • Uncertainties:
  • PT demand north to/from CBD
  • Total traffic flow north to/from CBD
  • Employment & Work locations

(likewise tertiary education)

  • Population & Resident locations
  • Future network requirements
  • Additional Harbour Crossing (AWHC)
  • Rail on north shore
  • Options:
  • To switch
  • To learn and expand
  • Future-proofing

Highlight: Option to switch

  • Example of ‘insurance’ or

‘protecting the downside’

  • In this case allow use of HOV
  • Akin to distribution of outcomes

being no longer symmetrical

  • i.e. average BCR is higher

Case Study

15 Informed by various pre- and post-busway reports

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Tree for ‘learning’

TreeAge, PrecsionTree 16

Case Study

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Auckland Northern Busway

Learnings:

  • Project did involve real options
  • Including endogenous learning
  • Decision tree was insightful
  • Crude estimates of real option values possible (but not essential)
  • Real option approach helped structure uncertainty

Case Study

17 Personal observations

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Case II. Kaimai Ranges (SH29)

Hypothetical, involves Ports of Auckland (POA), Tauranga (POT)

  • Uncertainties
  • Mix of POA/POT expansion
  • Hamilton inland port
  • General local economic development
  • Options:
  • To expand

Highlight: Option to expand (for

  • thers)
  • Example of ‘transformational

infrastructure’

  • In this case, expansion near POT

and non-port expansion near POA

  • White elephant a possibility
  • Implies seek option to switch
  • Or defer until ALARP
  • Also many instances when only

‘modest’ benefits likely

  • Suggests twofold BCR
  • Base BCR and With-Option BCR

Case Study

18 NZ Transport Agency research report 608

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Kaimai Ranges

Learnings:

  • Again decision tree was insightful
  • Emphasised importance of attention to

a) Probability of ‘prosperity’ scenario

  • And how to improve this probability

b) Outcome if ‘prosperity’ scenario did not emerge

  • And how to improve the BCR of these other scenarios

Case Study

19 Personal observations

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Tree for ‘prosperity’

TreeAge, PrecsionTree 20

Case Study

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Case III. Auckland Light Rail

An “on-the-table” example, involves Public Transport (PT)

  • Uncertainties
  • Traffic implications of technology
  • Uptake of PT
  • Tolerance of PT in Queen Street
  • Bus tunnel requirement in CBD
  • Development around Dominion Rd
  • Effect of current/mooted changes

(e.g. Waterview, congestion charges)

  • Options
  • To delay and learn (and then expand
  • r abandon)
  • To switch
  • To future-proof

Highlight: Option to delay and learn

  • Example of ‘buying time’ and

‘building ramps’

  • In this case, potentially use buses

to learn PT uptake

  • Reduces white elephant possibility
  • Enables resolution of multiple

uncertainties while leaving option for LRT or BRT open

  • i.e. an adaptive solution

Case Study

21 Auckland Transport (2016) Auckland Central Access Plan Programme Business Case

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Auckland Light Rail

Learnings:

  • Many real options potentially exist
  • Highlights importance of
  • Interdependencies
  • Adaptive abilities

Case Study

22 Personal observations

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More on option to delay

  • Key component of adaptive management
  • Delay until uncertainty removed
  • Either resolved passively
  • Or by active investment
  • Major advantage of delay is avoidance of large unnecessary

investment

  • But disadvantage of running cost of delay (e.g. congestion)
  • Not required to resolve uncertainty completely but just sufficiently to

be able to make decision

  • “Switching value” = the threshold where the investor decision would change

23 Various

Case Study

NEXT: summary

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Concluding comments

“Insights not simply numbers”

  • Important to understand whether unknowns are ‘risk’ or ‘uncertain’
  • Risk enables valuation
  • Uncertainty shifts the focus to ‘process’
  • Although process is also useful when addressing risk
  • Process required on big projects is to consider options and

adaptiveness

  • Process suggested fits within current BBC and CBA approaches
  • By emphasising exploration of alternatives and scenarios

24 Personal observations

NEXT: questions