How to Valorise Research on the Effects of Peak Oil for Urban Planning?

A Method to Investigate Peak Oil Risks and Mitigation

Dr. Susan KrumdieckAssociate Professor

Department of Mechanical Engineering

University of Canterbury

Christchurch, New Zealand

Presentation to Walloon Parliament 26 April 2011 Namur, Belgium

Peak Oil Issue

Even if you believed it was an issue, what would you do about it?

Research Developments 2000-2011

• Peak Oil as a Planning Issue• Transition Engineering for Mitigation

Risk Assessment and Mitigation

• Risk Assessment: Probability and Impact• Adaptive Capacity• Resilience• Re-Development• Strategic Development Planning

New Zealand Herald

Peak Oil: Understanding the Issue

• Not really a question of if • Probability

(Campbell, 2004)

Peak Oil: Probability

Expert Assessments of Peak Oil Year

Num

ber

2005 2010 2015 2020 2030

Expert predictions of Supply Decline Rate

1.4%2.0%2.4%4.0%4.8%6.7%

Meta Analysis of Petroleum Geology and Supply Experts

P(y) Nr y 2006)!

(y 2006)!(r 1)!

r 1 y 2006

Peak Oil Issue: Probability

Meta Analysis of petroleum geology experts

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 5

10

15

20

25

30

35

40

Year

Oil

Pro

du

ctio

n (

Bill

ion

Ba

rre

ls p

er

yea

r)

97%

85%

50%

15%

3%

Historic Projected

Supply probability

50% Reduction by 2050

Raleigh DistributionMonte-Carlo Simulation

(Krumdieck, Page, Dantas, 2010)

Long Range Fuel Supply Probability

World Oil Production (mbpd)* Probability Scenario 2020 2030 2040 2050

Unexpected Problems 54 38 27 19

No New Giant Oil Fields 60 45 38 26

Most Experts Agree 71 57 44 37

Discovery of Giant Fields 85 68 58 48

* For reference: 2008 average daily world oil production was 85 mbpd

Probability associated with scenarios of oil supply issues.

Peak Oil: Impact

• Behaviour and Access to Activities • Assets and Infrastructure Investments

Current Energy UseFor Current Travel Demand

Change in Oil Supply

Future Energy UseFor Future Travel Demand

Oil Supply Decline Impact

University of Canterbury, Christchurch

Students$13,500 pa

Staff$66,000 pa

Study of Adaptation

Local Adaptive Capacity

• Travel Adaptive Capacity Assessment Survey (TACA Survey)

Pressure for Change of Travel Behaviour (Price increase or other factor)

Tra

vel

Beh

avio

ur

Ad

ap

tati

on

(%

VK

T)

Tr avel Adaptive Capacity

0

100

200

300

400

500

600

700

800

0-1 1-2 2-3 3-4 4-6 6-8 8-10 10-15 15-20 > 20

Skateboard

Bicycle

Walking

Bus

Vehicle (passenger)

Vehicle (driver)

Travel Behaviour

Trips per week per 100 persons

Distance Traveled

Students5.6 litres/wk34.7 km/wk

Staff17.6 litres/wk60.7 km/wk

0

100

200

300

400

500

600

700

800

0-1 1-2 2-3 3-4 4-6 6-8 8-10 10-15 15-20 > 20

E TD m, d

d

m

ECm, d DBd

Transport Energy

Adaptation in Travel Demand

0

50

100

150

200

250

300

350

400

0 - 1

1 - 2

2 - 3

3 - 4

4 - 6

6 - 8

8 - 10

10 - 15

15 - 20

> 20

Trip Distance (km)

Trip

Fre

quen

cy

Other (e.g. taxi)

Electric bike

Rollerblades/skates

Bicycle

Walking

Bus or Park n ride (bus)

Vehicle (passenger)

Vehicle (driver)

NormalAlternatives

Do you have an alternative?

Car use reduction

Travel Behaviour Adaptive Capacity

Christchurch

Energy Reduction

Public Transport Adaptive Potential

Bus RoutesChristchurch

Bus Potential

Council Urban Plan 2041

Christchurch Densification Christchurch Sprawl

45% higher fuel demand than 2006

95% higher fuel demand than 2006

Risk to Essential Transport Activities

RECATS Method

(Dantas et al, 2008)

Travel Activity

Energy Constraint

Calculate Energy Consumption

E2< E1?Modify

Travel Activity

Constrained Travel ActivityCalculate Risk

Yes

NoE1

E2

0%

5%

10%

15%

20%

25%

0 - 1

1 - 2

2 - 3

3 - 4

4 - 6

6 - 8

8 - 1

0

10 -

15

15 -

20

> 20

Trip Distance [km]

Perc

en

tag

e o

f Tr

ips

Walk

Bike

Bus

Vehicle Passenger

Vehicle Driver

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 5

10

15

20

25

30

35

40

Year

Oil

Pro

du

ctio

n (

Bill

ion

Ba

rre

ls p

er

yea

r)

97%

85%

50%

15%

3%

Historic Projected

Supply probability

Re Pe

T m, d, s

s

IW s

d

m

T m, d, s

s

IW s

d

m

Nms Nds N tc D m, d, s

s

IW s

d

m

1

Risk to Essential Activities

1990 2000 2010 2020 2030 2040 2050

100

200

300

400

500

600

Greater Christchurch Fuel consumptionUDS ConcentratedUDS Dispersal

95% Energy Increase

45% Increase

Canterbury Regional Fuel Use

Mitigation and Planning

• Urban Form Developments• Urban Villages and Free Markets• Public Transport• Densification• Bike Infrastructure

• Technologies• Vehicles and Fuels

• Behaviours• Residential Location• Mode Choice

Strategic Analysis: Opportunities

Automobiles

Transportation System 1930 - 2050

Biofuel & Electric

Walk & Bike

Sh

are

of

Tran

sp

ort

50%

100%

75%

25%

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Public Transport

Scooters

Strategic Analysis to 2050

Urban Form Adaptations

Fuel

, Veh

icle

, Beh

avio

ur A

dapt

atio

ns

Active Infrastructure 100km Bikeways

Dense City Centre

Integrated Urban Villages

Current Urban Form

3 L/100kmFleet Efficiency

50% Biofuels Synfuels

50% Electric Vehicles

Low Carbon Lifestyle

50 km of Electric Trolleys

Possible

No No No No

NoUnlikely

golf carts onlyNo No

Unlikely

Unlikely

Yes Yes Yes

Possible Possible

Possible Possible

Possible

Possible

Personal Travel in Dunedin

• Technical Feasibility

• Resource Availability

• Economic Viability

• Social

• Environment

• Asset Value

• Future Risk

Thank you for your attentionThank you for your attentionEngineering Research to Investigate and Mitigate Peak Oil Risks

Dr. Susan Krumdieck

Presentation to Walloon Parliament 26 April 2011 Namur, Belgium