How to Valorise Research on the Effects of
Peak Oil for Urban Planning?
A Method to Investigate
Peak Oil Risks and
Mitigation
Dr. Susan Krumdieck
Associate 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
Meta Analysis of Petroleum Geology and Supply Experts
Expert Assessments of Peak Oil Year
Number
 r  y  2006)!  r
y 2006
P(y)  N 
 1 
(y  2006)!(r 1)!
Expert predictions of
Supply Decline Rate

2005
2010
2015
2020
2030
1.4%
2.0%
2.4%
4.0%
4.8%
6.7%
Peak Oil Issue: Probability
Meta Analysis of petroleum geology experts
40
Supply
probability
Raleigh Distribution
Monte-Carlo Simulation
Oil Production (Billion Barrels per year)
35
3%
30
15%
25
50%
50% Reduction by 2050
20
85%
15
97%
Historic
10
51960
1970
1980
1990
Projected
2000 2010
Year
2020
2030
2040
2050
(Krumdieck, Page, Dantas, 2010)
Long Range Fuel Supply Probability
Probability associated with scenarios of oil supply
issues.
Probability Scenario
World Oil Production (mbpd)*
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 averagedaily world oil productionwas 85 m bpd
Peak Oil: Impact
• Behaviour and Access to Activities
• Assets and Infrastructure Investments
Current Energy Use
For Current Travel Demand
Change in
Oil Supply
Future Energy Use
For Future Travel Demand
Oil Supply Decline Impact
Study of Adaptation
University of Canterbury, Christchurch
Students
$13,500 pa
Staff
$66,000 pa
Local Adaptive Capacity
Travel Behaviour Adaptation (% VKT)
• Travel Adaptive Capacity
Assessment Survey
(TACA Survey)
T ravel A daptive Capacity
Pressure f or Change of Travel Behaviour
(Price increase or other factor)
Travel Behaviour
Trips per week per 100 persons
800
Skateboard
Bicycle
Walking
Bus
Vehicle (passenger)
Vehicle (driver)
700
600
500
400
Students
5.6 litres/wk
34.7 km/wk
300
200
100
0
0-1
1-2
2-3
3-4
4-6
6-8
8-10
10-15
15-20
> 20
Distance Traveled
800
700
Staff
17.6 litres/wk
60.7 km/wk
600
500
400
300
200
100
0
0-1
1-2
2-3
3-4
4-6
6-8
8-10
10-15
15-20
> 20
Transport Energy
E  TD m, d  ECm, d  DBd
m
d
Adaptation in Travel Demand
Do you have an alternative?
Normal
Alternatives
Car use reduction
400
Other (e.g. taxi)
350
Electric bike
Rollerblades/skates
250
Bicycle
200
Walking
150
Bus or Park n ride (bus)
100
Vehicle (passenger)
50
Vehicle (driver)
20
>
0
-2
-1
5
15
Trip Distance (km)
10
-1
0
8
-8
6
-6
4
-4
3
-3
2
-2
1
-1
0
0
Trip Frequency
300
Travel Behaviour Adaptive Capacity
Christchurch
Energy
Reduction
Public Transport Adaptive Potential
Christchurch
Bus Routes
Bus
Potential
Council Urban Plan 2041
Christchurch Densification
45% higher fuel demand
than 2006
Christchurch Sprawl
95% higher fuel demand
than 2006
Risk to Essential Transport Activities
RECATS Method
40
Supply
probability
Oil Production (Billion Barrels per year)
35
Travel Activity
3%
30
15%
25
50%
20
Calculate Energy
Consumption
85%
15
97%
Historic
10
51960
1970
1980
1990
2000
Projected
2010
2020
2030
2040
2050
Year for Dunedin
Travel Demand Pattern
Energy Constraint
25%
E1
E2
E2< E1?
No
Modify
Travel Activity
W a lk
15%
Bik e
Bus
Ve hicle P a s se nge r
10%
Ve hicle Drive r
5%
Yes
Constrained Travel Activity
Calculate Risk
0
>2
0
-2
15
5
10
-1
10
8
Trip Distance [km]
8-
4
3-
6
3
2-
6-
2
1-
4-
1
0%
0-
Percentage of Trips
20%


T m, d, s  IW s


m d
s
Re  Pe 

1

m, d, s
 IW s   N ms  N ds  N tc    D m, d, s  IW s 
T
 m d s

m d
s
(Dantas et al, 2008)
Risk to Essential Activities
Canterbury Regional Fuel Use
95% Energy Increase
600
Greater Christchurch Fuel consumption
UDS Concentrated
UDS Dispersal
500
400
45% Increase
300
200
100
1990
2000
2010
2020
2030
2040
2050
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
Strategic Analysis to 2050
Urban Form Adaptations
Dense City
Active
Infrastructure Centre
Integrated
Current Urban
Urban Villages Form
Fuel, Vehicle, Behaviour Adaptations
100km Bikeways
3 L/100km
Fleet Efficiency
50% Biofuels
Synfuels
50% Electric
Vehicles
50 km of
Electric
Trolleys
Low Carbon
Lifestyle
Possible
Possible
No
No
No
No
Possible
Yes
Possible
Yes
Possible
No
Unlikely
No
Unlikely
golf carts only
Possible
No
Unlikely
Personal Travel
in Dunedin
• Technical
Feasibility
• Resource
Availability
• Economic
Viability
• Social
• Environment
• Asset Value
Yes
Possible
• Future Risk
Thank you for your attention
Engineering Research to
Investigate and Mitigate
Peak Oil Risks
Dr. Susan Krumdieck
Presentation to Walloon Parliament
26 April 2011
Namur, Belgium