energy implications of high -speed passenger...
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Energy Implications of High-Speed Passenger Transportation: Examining Aviation, High-Speed Rail, and their Climate Impacts Regina Clewlow, PhD Candidate Engineering Systems Division Massachusetts Institute of Technology Committee: J. Sussman (Chair), H. Balakrishnan, M. Webster Behavior, Energy, and Climate Change Conference Washington, DC November 30, 2011
The Increasing Impact of High-Speed Transportation
Trend towards faster modes of transportation with GDP growth (Schafer, 2000). In the U.S., aviation is the dominant “high-speed” mode.
Under business-as-usual, global aviation emissions anticipated to grow by 300% by 2050 (IEA, 2008).
Climate policy analysis suggests that reductions in the aviation sector will be difficult to achieve (Winchester et al, 2011).
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U.S. Aviation Emissions Under Climate Policies
Economic and Environmental Effects of Cap-and-Trade Policies in Aviation, Joint University Program (JUP) Winter Progress Meeting, Federal Aviation Administration (FAA) Technical Center, Atlantic City, NJ, January 2010.
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Business as Usual
Most Stringent Policy Case
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Broad Research Overview
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Europe Empirical analysis of systemwide air traffic. Impacts of air-rail connectivity.
U.S. Scenario analysis of potential impacts of climate policy on high-speed transport.
China Experimental survey design and analysis of passenger preferences for intercity travel.
The European Case: Key Questions
How has HSR impacted air traffic demand?
How has HSR impacted total aviation system demand?
How does air-rail connectivity impact demand?
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Rail Journey Time
Air Traffic Decline vs. Rail Journey Time
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The European Case: An Econometric Analysis
Data: air traffic for 86 origin-destination pairs, 1996-2008.
Model structure:
Aviation Demand = α + β X + δ Rail Travel Time + ε
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Dimension Variable Aviation demand Air traffic Price/ fares Jet fuel Income Household income Population Density Distance Distance Rail competition Rail travel time
Key Variables
Key Findings: • Average reduction of 3% per year on short-haul routes. • Rail travel time improvements significantly impact short-
haul air traffic (coefficient estimate of 4.588). • Density. Cities with higher densities experience greater
reductions. • Jet fuel price has a significant negative affect (elasticity
range: -1.54 to -1.13).
The European Case: Analysis of Systemwide Air Traffic
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International traffic EU traffic National traffic
Key Findings: • Medium-haul markets have grown
significantly, fueled by expansion of low-cost carrier service.
• Airlines maintain certain levels of frequency in the short-haul markets to “feed” long-haul traffic.
• Emissions savings from short-haul traffic reductions are negated by significant growth in medium- and long-haul traffic.
Aviation CO2 Emission Equivalents of Flights Departing Major European Airports
Future Work: A Climate Policy Analysis of the U.S. Case
GDP
Climate Policies Fuel Prices
Electricity Prices
Electricity Mix
Rail Fares
Mode Choice
Air Fares
Aggregate Demand
Emissions
HSR Investment
Travel Times
Aviation Demand
HSR Demand Baseline
Aggregate Demand
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Back-Up Slides
Back-up slides
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(c) Annual Passengers: Charles de Gaulle
Paris CDG - Bordeaux Paris CDG - Lyon Saint ExuperyParis CDG - Montpellier Paris CDG - StrasbourgParis CDG - Nantes
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(d) O-D Flights: Charles de Gaulle
Paris CDG - Bordeaux Paris CDG - Lyon Saint ExuperyParis CDG - Montpellier Paris CDG - StrasbourgParis CDG - Nantes
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(a) Annual Passengers: Orly
Paris Orly - Bordeaux Paris Orly - Lyon Saint Exupery
Paris Orly - Montpellier Paris Orly - Strasbourg
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(b) O-D Flights: Orly
Paris Orly - Bordeaux Paris Orly - Lyon Saint Exupery
Paris Orly - Montpellier Paris Orly - Strasbourg
Europe: Paris CDG and ORY Air Passenger Capacity and Traffic
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The Chinese Case: An Overview Air passenger travel
increased at a rate of 16% per year between 1978 and 2009, and is projected to continue to increase at a rate of 12% (Civil Aviation Administration of China).
China now has the world's longest high-speed rail network with about 6,012 mi of track in service as of June 2011.
New Beijing-Shanghai high-speed rail corridor was opened on June 30, 2011.
Conducted an intercept survey at Beijing and Shanghai airports and train stations in July/August 2011.
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The Chinese Case: Survey Design and Data Collection
Intercept survey design: Demographic data (age, income,
etc.) Current choice attributes (# of bags,
trip purpose, etc.) Hypothetical choice data: 12 choice
sets in block of 4 questions.
Data collection: Intercept surveys collected at:
Beijing Airport Beijing South Rail Station Beijing Rail Station Shanghai Hongqiao Airport Shanghai Hongqiao Station
July 22 – August 4, 2011 349 rail surveys + 144 airport
surveys 565 total surveys
Household Characteristics
Household income
Home city, neighborhood (or zip code?)
Age
Household Characteristics (in the context of a trip)
Trip purpose
Number of travelers
Frequency of travel on this corridor (how many trips per year)
Travel Characteristics
Line haul time (station to station, airport to airport, terminal to terminal)
Access/ egress time (home to station/airport/terminal)
Frequency
Fare (by class)
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