Dunedin Peak Oil Vulnerability

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Transcription:

Dunedin Peak Oil Vulnerability Study and Strategic Transition Plan New Methods to Investigate Peak Oil Risks, Adaptation and Mitigation Dr. Susan Krumdieck Associate Professor Department of Mechanical Engineering University of Canterbury Christchurch, New Zealand IPENZ TG Conference 20 March 2012 Rotorua Dunedin Peak Oil Vulnerability July - Dec 2010 EAST Research Consultants Ltd. Krumdieck, Rendall, Watcharasukarn, Page 1

Peak Oil Issue Even if you believed it was an issue, what would you do about it? Stand and Deliver Understand Oil Supply as a Planning and Management Issue Assess Risks to Essential Activities & Goods Quantify Adaptation over Planning Time-Frames Strategic Transition Analysis, Design, and Re-Development Dantas, Krumdieck, Page 2

Peak Oil: Understanding the Issue Facts are Clear Probability and Time Frame (Campbell, 2004) Long Range Planning Issue Meta Analysis of petroleum geology gy experts 40 Barrels per year) 35 30 25 3% 15% Supply probability Raleigh Distribution Monte Carlo Simulation Oil Production (Billion 20 15 10 Historic Projected 50% 85% 97% 50% Reduction by 2050 5 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Year (Dantas et al., 2007, NZTA 311) (Krumdieck, et al. 2010, Trans Res A) 3

Impact Depends on Adaptation How do activity systems currently depend on fuel? What is the Adaptive Capacity? Current Energy Use For Current Travel Demand Change in Oil Supply Future Energy Use For Future Travel Demand Population - Origins & Destinations (StatsNZ Data) 4

VKT Demand Audit Annual Vehicle Kilometers Traveled Average Annual Kilometre es per Household Vehicle 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Central Dunedin North Dunedin Urban Suburbs Lifestyle Rural South Dunedin Maori Hill Roslyn Opoho North East Valley Green Island Ravensbourne Mosgiel Port Chalmers Fairfield Macandrew Bay Broad Bay-Portobello Brighton Company Bay Sawyers Bay Waitati Waikouaiti Outram (UC LATEE model using MOT WOF Data) Affordability Car Ownership Household Income Drive to Work High Vulnerability Less Vulnerable Low Vulnerability VIPER (Dodson & Sipe, 2005) analysis using Census Data 5

Travel Adaptive Capacity Survey Monday Work Drive CRV Home to Uni If you couldn t take your car, how many other ways do you have? Walk Bike Bus (Krumdieck, Page, Watcharasukarn, 2012, NZTA Research Report) Adaptive Capacity Assessment TACA Survey Behavior Trigger Price Elasticity Travel Adaptive Capacity Pressure for Change of Travel Behaviour (Price increase or other factor) (Watcharasukarn et al., 2011,Trans Res A) 6

Adaptability in Travel Demand Number of Trips Distance Mode 25% Travel Demand Pattern for Dunedin 20% 15% 10% Normal Adapted Walk Bike Bus Vehicle Passenger Vehicle Driver 5% 0% 0-1 1-2 2-3 3-4 4-6 6-8 Trip Distance [km] 8-10 10-15 15-20 > 20 Travel Adaptive Capacity Dunedin could reduce fuel use by 40% and not lose access to activities! Alternatives to Individual Car Trips in Dun as reported in TACA Survey August 2010 Trips/week Car Walk Car - No Alternative Car - Share a Ride km/week Litres/week Bus Bus Bike Walk Work from home 0% 20% 40% 60% 80% 100% Percentage of trips normally made by car 7

Active Mode Accessibility (Rendall et al., 2012, Trans Res Record) Active Mode Accessibility 8

Development Scenarios Urban Form Developments Technology Developments Behaviour Developments Alternatives Biofuels: 50% of petrol replaced Electric Cars: 50% of vehicles replaced 9

Current Urban Form: No Change Current Urban Form Car= 95%, Bus= 3.5%, 35% Walk= 0.9%, 09% Bike= 05% 0.5% Dense Urban Form Car= 85%, Bus= 5%, Walk= 6%, Bike= 4% 10

Development Scenario Integrated Urban Form - Urban Villages Car= 75%, Bus= 12%, Walk= 5%, Bike= 8% Development Scenario: Active Mode 100 km Bike Ways All children under 12 can get to their school by walking or by bike without being at risk from cars or trucks. 11

Development Scenarios: Public Transport 50 km of Electric Trolleybus Village Centres, CBD, Schools connected. Development Scenarios: Vehicle Turnover and Reduction High Efficiency Vehicles Fleet tefficiency i = 5 l/100k 2 litres/100km 1 car +1 scooter per household 4.5 litres/100km 11 litres/100km 17 litres/100km 12

Walk Bike Bus Vehicle Passenger Vehicle Driver 20/03/2012 Development Scenarios: Behaviour Adaptation Low-Carbon Lifestyle Vehicle = 25% Bus = 35% Walk= 30% Bike = 10% Bill Campbell ODT High Efficiency Vehicles and Scooters Risk to Essential Transport Activities RECATS Method 40 Oil Production (Billion Barrels per year) 35 30 25 20 15 10 Historic Projected Supply probability 3% 15% 50% 85% 97% Travel Activity Calculate Energy Consumption 25% 5 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Year Energy Constraint E 1 E 2 E 2< E 1? No Modify Travel Activity 20% 15% 10% 5% Yes Constrained Travel Activity Calculate Risk 0% 0-1 1-2 2-3 3-4 4-6 6-8 Trip Distance [km] 8-10 10-15 15-20 > 20 R e P e m T m, d, s m d s T m, d, s IW s IW s N ms N ds N tc D d s m d s m, d, s 1 IW s (Dantas et al, 2008, JEAST; NZTA Report) 13

Strategic Analysis to 2050 viour Adaptations Fuel, Vehicle, Beha 3 L/100km Fleet Efficiency 50% Biofuels Synfuels 50% Electric Vehicles 50 km of Electric Trolleys Low Carbon Lifestyle Urban Form Adaptations Active Infrastructure 100km Bikeways Possible Dense City Centre Possible Integrated Urban Villages Possible Current Urban Form Unlikely No No No No No Possible No Possible Unlikely golf carts only Possible Yes Yes Yes No Unlikely Possible Urban Form Changes + Alternatives Efficiency Not available Improvements High cost Not feasible High Risk of Doing Nothing Urban Form Changes + Behaviour Changes Strategic Analysis Opportunities Conclusion: There are no solutions, only choices 100% Share of Transport 75% 50% 25% 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Transportation System 1930-2050 Automobiles Public Transport Walk & Bike Scooters Biofuel & Electric 14

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 Dense Urban Centre 50% Reduction in Commuting over 10 km 50% Reduction in Commuting over 10 km Loft Apartments Pedestrian Zones Amenity Apartments Culture, Arts 15

Integrated Urban Villages Village Centres: Shopping, Medical Activity Areas Weekend Markets 30% local business growth 16