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Transportation, Energy, and Technology in the 21st Century
GCEP Advanced Transportation Workshop October 10-11, 2005 Stanford University
Andreas SchäferDepartment of Architecture and
Institute for Aviation and the EnvironmentUniversity of Cambridge
STRUCTURAL CHANGE IN THE ECONOMY
Source: Schäfer A., 2005, Structural Change in Energy Use, Energy Policy, 33(4): 429-437.
Sector shifts due to:1. Different income elasticities for goods and services produced by each of the three sectors2. Competitive advantage for each of the sector’s industries3. Changing needs of society
11 World-Regional Data series: 1971 - 1998
… AND IN THE ENERGY SYSTEM
Source: Schäfer A., 2005, Structural Change in Energy Use, Energy Policy, 33(4): 429-437.
0
10
20
30
40
50
60
70
80
90
100
0 5000 10000 15000 20000 25000GDP/cap, US$(1985)
Sha
re in
Fin
al E
nerg
y, %
Residential
0
10
20
30
40
50
60
70
80
90
100
0 5000 10000 15000 20000 25000GDP/cap, US$(1985)
Sha
re in
Fin
al E
nerg
y, %
Agriculture
0
10
20
30
40
50
60
70
80
90
100
0 5000 10000 15000 20000 25000GDP/cap, US$(1985)
Sha
re in
Fin
al E
nerg
y, %
Industry
0
10
20
30
40
50
60
70
80
90
100
0 5000 10000 15000 20000 25000GDP/cap, US$(1985)
Sha
re in
Fin
al E
nerg
y, %
Services
Transportation
Others
GLOBAL TRAVEL DEMAND MODEL
• Driving forces: growth in population and GDP/cap• Schäfer/Victor (2000):
– Historical dataset: 1960 - 1990– Constraints: fixed budgets of money and time, others– Balancing equations of travel time and speeds– Aggregate of urban and intercity transport
• Currently under development:– Historical dataset: 1950 - 2000– Multinominal LOGIT model for mode choice simulation
• Next generation:– Separation of urban and intercity travel– Mixed LOGIT model for mode choice simulation
TRAVEL BUDGETS: MICROMOTIVES …
Travel Time or Money Expenditures,Trip Rate, Trip Distance
% M
obilie
Pop
ulat
ion
… AND MACROBEHAVIOR: TTB
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 5000 10000 15000 20000GDP/cap, US$(1985)
Trav
el T
ime
Bud
egt,
h/ca
p/d
African Villages in:I Tanzania, 1986II Ghana, 1988
City Surveys: 1 Tianjin (China), 1993 2 Kazanlik (Bulgaria), 1965/66 3 Lima-Callao (Peru), 1965/66 4 Pskov (Former USSR), 1965/66 5 Maribor (Former Yugoslavia), 1965/66 6 Kragujevac (F. Yugoslavia), 1965/66 7 Torun (Poland), 1965/66 8 Gyoer (Hungary), 1965/66 9 Olomouc (Former CSFR), 1965/6610 Hoyerswerde (Former GDR), 1965/6611 Sao Paulo (Brazil), 198712 Sao Paulo (Brazil), 197713 Warsaw (Poland), 199314 6 Cities (France), 1965/6615 Osnabruck (Germany), 1965/6616 44 Cities (USA), 1965/6617 Jackson (USA), 1965/6618 Paris (France), 1976
I II
19 Paris (France), 198320 Paris (France), 199121 Sendai (Japan), 197222 Sapporo (Japan), 197223 Kanazawa (Japan), 197424 Kagoshima (Japan), 197425 Kumamoto (Japan), 197326 Hamamatsu (Japan), 197527 Fukui (Japan), 197728 Niigata (Japan), 197829 Hiroshima (Japan), 197830 Osaka (Japan), 198031 Tokyo (Japan), 198032 Osaka (Japan), 198533 Tokyo (Japan), 198534 Cities No. 21-29 in 198735 Tokyo (Japan), 199036 Osaka (Japan), 1990
National Travel Surveys:A Belgium, 1965/66B Austria, 1983C Great Britain, 1985/86D Germany, 1976E Netherlands, 1979F Great Britain, 1989/91G Finland, 1986H Netherlands, 1987I France, 1984J Germany, 1982K Netherlands, 1989L USA, 1990M Germany, 1989N Switzerland, 1984O Switzerland, 1989P Australia, 1986Q Singapore, 1991R Norway, 1985S Norway, 1992T Japan, 1987
2
1
3 4
57
89 10 14 15
161712
11
136
18 19 20
22 23
2425
26 2728
2930
31
3233
35
36A34
B
C
D
E F
GH
I
J K L
M
N
OP
Q
R ST
21
Source: Schäfer A. and D.G. Victor (2000) The Future Mobility of the World Population, Transportation Research A, 34(3): 171-205
STABILITY OF TRAVEL TIME BUDGET
NOTE: increase in time dedicated to sleep and leisure statistically significant @ 95% confidence, as opposed to change in other time allocations.Data source: Szalai et al.(1972), data from 11 countries, population between 18 and 65 years of age.
0
1
2
3
4
5
6
7
8
9
10
3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5Daily Working Time, h/cap/d
Tim
e de
dica
ted
to m
ajor
Act
iviti
es, h
/cap
/d
Sleep ( )
Eating ( )
Homemaking and childcare ( )
Leisure incl. study, participation, etc. ( )
Personal care ( )
Travel ( )
AGGREGATE TRAVEL BEHAVIOR: TMB
0
5
10
15
20
25
30
35
40
45
50
0 5000 10000 15000 20000GDP/cap, US$(1985)
Trav
el M
oney
Bud
get,
%
Thailand
TunisiaSri Lanka
Belgium
Denmark
France
FRG
Greece
Ireland
ItalyJapan
NL
Portugal
Spain
UK
USA
0
1
2
3
4
5
6
0 5 10 15 20 25 30 35 40 45 50Travel Money Expenditures, % total Expenditures
Shar
e of
Hou
seho
lds,
%
Source: Schäfer A. and D.G. Victor (2000) The Future Mobility of the World Population, Transportation Research A, 34(3): 171-205
GLOBAL MOBILITY TRENDS (1950-2000)
100
1,000
10,000
100,000
1,000,000
100 1,000 10,000 100,000 1,000,000
GDP/cap, US$(1996)
PKT/
cap
Centrally Planned AsiaLatin AmericaM. East, Nth&Sth AfricaOther Pacific AsiaSub-Saharan Africa
North AmericaPacific OECDWestern EuropeEastern EuropeFormer Soviet Union
South Asia
World
Source: A. Schäfer, Global Passenger Mobility Data Set, Version 1.0, University of Cambridge, Sept. 2005
Hypothetical Target Point at:600 km/h x 1.2 h/d x 365 d/yr = 262,800 km/yr
PUBLIC TRANSPORT (1950-2000)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
100 1000 10000 100000 1000000
PKT/cap
Shar
e of
Pub
lic T
rans
port
Mod
es in
PK
T
Centrally Planned AsiaLatin AmericaM. East, Nth&Sth AfricaOther Pacific AsiaSub-Saharan Africa
North AmericaPacific OECDWestern EuropeEastern EuropeFormer Soviet Union
South Asia
Source: A. Schäfer, Global Passenger Mobility Data Set, Version 1.0, University of Cambridge, Sept. 2005
LIGHT-DUTY VEHICLE TRAVEL (1950-2000)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
100 1,000 10,000 100,000 1,000,000PKT/cap
Shar
e of
Lig
ht D
uty
Vehi
cles
in P
KT
Centrally Planned AsiaLatin AmericaM. East, Nth&Sth AfricaOther Pacific AsiaSub-Saharan Africa
North AmericaPacific OECDWestern EuropeEastern EuropeFormer Soviet Union
South Asia
Source: A. Schäfer, Global Passenger Mobility Data Set, Version 1.0, University of Cambridge, Sept. 2005
HIGH-SPEED TRANSPORT (1950-2000)
Source: A. Schäfer, Global Passenger Mobility Data Set, Version 1.0, University of Cambridge, Sept. 2005
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
100 1,000 10,000 100,000 1,000,000
PKT/cap
Sha
re o
f Hig
h-S
peed
Mod
es in
PK
T
Centrally Planned AsiaLatin AmericaM. East, Nth&Sth AfricaOther Pacific AsiaSub-Saharan Africa
North AmericaPacific OECDWestern EuropeEastern EuropeFormer Soviet Union
South Asia
URBAN AND INTERCITY TRAVEL: U.S.
Source: A. Schäfer, Global Passenger Mobility Data Set, Version 1.0, University of Cambridge, Sept. 2005
HOWEVER: More careful separation between urban and intercity travel necessary!
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 5,000 10,000 15,000 20,000PKT/cap (Urban)
Sha
re in
Urb
an P
KT
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 5,000 10,000 15,000 20,000PKT/cap (Intercity)
Sha
re in
Inte
rcity
PK
T
Light-Duty Vehicles
Low-Speed Public Transport
Low-Speed Public Transport
Light-Duty Vehicles
High-Speed Transport
URBAN
INTERCITY
Source: Schäfer A., Victor D.G., 1997, “The Past and Future of Global Mobility”, Scientific American, October 1997, pp. 56–59.
GLOBAL MOBILITY: PAST, PRESENT, FUTURE
Based upon a travel time budget of 1.1 h/cap/d
CAN GROWTH IN WORLD PKT ENDURE?
• Availability of high-speed transport technology (⇒ supersonic transport)
• Traffic congestion (⇒ innovation and adaptation)• Transportation substitutes (⇒ complement vs.
substitute)• Energy resources (⇒ geographic location of
reserves)• Environment (⇒ impact of global warming
policies)
TRENDS IN ENERGY INTENSITY
Source: Schäfer A., work in progress
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970 1975 1980 1985 1990 1995 2000 2005
PA
X T
rave
l Ene
rgy
Inte
nsity
, MJ/
PKT
France
United States
Germany
Great Britain
OFFSETTING TRENDS+ E/PKTShift toward faster and more energy-intensive modesTrend toward larger and more powerful light-duty vehiclesDeclining occupancy rates in light-duty vehicles
- E/PKTFuel efficiency improve-ments of all modesRising occupancy rates (passenger load factors) in air traffic
Picture credit: http://wpni01.auroraquanta.com/pv/sumo
DETERMINANTS OF LDV ENERGY INTENSITY
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
E/PKTEi/VKTi
VKT/PKT
PKTi/PKT
Ene
rgy
Inte
nsity
, MJ/
PKT
1970-20021970-19791979-19911991-2002
United States
Source: Schäfer A., work in progress
WORLD PAX TRAVEL: CO2 EMISSIONS
Source: Schäfer A., Victor D., 1999, “Global Passenger Travel: Implications for CO2-Emissions”, Energy—The International Journal 24(8): 657-679
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1960 1970 1980 1990 2000 2010 2020 2030 2040 2050
Car
bon
Emis
sion
s, G
tC
High-Speed ModesRailwaysBusesAutomobiles
IndustrializedRegions
DevelopingRegions
ReformingRegions
HOW TO REDUCE LDV ENERGY USE?
• Demand and Supply Management – Car pooling, telecommuting, pricing measures to
induce time and/or mode shift, land-use planning, etc.
– Comparatively small potential, even if combined in packages
• Technology-Solutions– Increase energy efficiency– Single largest potential
LCA OF AUTO TECHNOLOGIES: MIT-STUDIES
Data Source: Weiss M., J.B. Heywood, A. Schäfer, and V. Natarajan (2002) A comparative Assessment of Advanced Fuel Cell Vehicles; Weiss M., J.B. Heywood, E. Drake, A. Schäfer, and F. AuYeung (2000) On the Road in 2020: A Well-to-Wheels Assessment on new Passenger Car Technologies.
0
10
20
30
40
50
60
70
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Life-Cycle Energy Use, MJ/km
Life
-Cyc
le G
HG
Em
issi
ons,
gCE
/km
Primary Energy: ZERO-CARBON FUELS
2001 Ref. (Gsl.)
2020 Baseline (Gsl.)
2020 Advanced (Gsl.)
2020 Advanced (Dsl.)
2020 Hybrid (CNG)
2020 Hybrid (Gsl.)2020 Hybrid (Dsl.)
2020 Hybrid FC (H2(CH4))
2020 Hybrid FC (Gsl.)
Technologies:Baseline: HSST unibody, advanced ICE, slightly reduced driving resistancesAdvanced: Al-body, advanced ICE,strongly reduced driving re-sistancesHybrid/FC: Al-body, ad-vanced drivetrain,strongly red. driv.resistances
Source: Schäfer A., 2000, “Carbon Dioxide Emissions from World Passenger Transport: Reduction Options”, Transportation Research Record 1738: Energy, Air Quality, and Fuels, paper no. 00-1182, pp. 20-29.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1960 1970 1980 1990 2000 2010 2020 2030 2040 2050Year
Car
bon
Em
issi
ons,
GtC
ReferenceEmissions
Zero-Carbon Fuels
Drastic Automobile Fuel Efficiency Improvements and Natural Gas
Drastic Automobile Fuel Efficiency Improvements
THE PROMISE OF ZERO-CARBON FUELS
BARRIERS TO TECHNOLOGY SOLUTIONS
• Long time scales for “sensible” fleet impact of new, fuel-saving technology (20 – 35 years, depending on technology; 50+ years for zero-carbon fuels)
• High implicit consumer discount rates (≈ 3 year amortization period for fuel-saving technology)
• Thus, need for policy measures: Optimum introduction of technology (U.S. case study)
TECHNOLOGY-DYNAMICS: MODEL SYSTEM
ModalSplit
Models
MARKAL(Technology)
EPPA(Economy)
Substitution Elasticities, AEEI
Tran
spor
t Dem
and
TechnologyDynamics
Technology Data Base
StructuralC
hange
Fuel Prices
Mode Shares
Transport Demand
Simulation Stage
Calibration Stage
Source: Schäfer A., Jacoby H.D., Technology Detail in a Multi-Sector CGE Model: Transport under Climate Policy, Energy Economics, 27(1): 1-24.
MARKAL cost-minimization similar to utility-maximization if vehicle attributes other than energy use and costs don’t change
ALTERNATIVE AUTO-TECHNOLOGIES (US)Tech.Name Characteristic Fuel Efficieny / Use Investments, US$ MPG L/100km Cumulative Annualized+
Gas. Costs Baseline 22.1 10.6 0 0.0 Packaging Improvements 27.2 8.66 -87 -37.8 Engine Management 27.9 8.45 -70 -46.4 Zero-Cost
Lower Cd, RRC 29.0 8.12 -13 -49.2 CVT 32.2 7.30 215 -27.7 Material Substitution, interior 32.6 7.21 244 -23.9 LoCost HSSt, unibody 33.6 7.00 350 -2.8
PowTrain1 Higher Compression Ratio 34.1 6.91 404 9.4 VVLT & Cylinder Deactivation 36.0 6.54 694 82.3 PowTrain2 EPS, Red. of parasitic Losses 36.8 6.39 865 130.3
Alum Al-intensive 42.1 5.58 2102 492.8 VVLT & Cyl. D., no Brake Drag 45.2 5.20 2719 675.6 Hybrid Hybrid Vehicle 53.6 4.39 4342 1168.2
Fuel efficiency based upon real driving conditions, i.e. U.S. FTP-75 Driving Cycle / 1.21 Based upon Capital Recovery Factor of 34%/yr, a gasoline price of U.S.$ 1.20/GAL, and an annual driving distance of 10,960 mi (17,630 km). Data source: Austin T.C., Dulla R.G., Carlson T.R., 1997, Automotive Fuel Economy Improvement Potential Using Cost-Effective Design Changes, Draft #3, prepared for the American Automobile Manufacturers Association, Sierra Research, Inc., Sacramento, CA. Austin T.C., Dulla R.G., Carlson T.R.,1999, Alternative and Future Technologies for Reducing Greenhouse Gas Emissions from Road Vehicles, report prepared for the “Transportation Table Subgroup on Road Vehicle Technology and Fuels”, Sierra Research, Inc., Sacramento, CA.
AUTOMOBLE TECHNOLOGY-DYNAMICS
Source: Schäfer A., Jacoby H.D., Technology Detail in a Multi-Sector CGE Model: Transport under Climate Policy, Energy Economics, 27(1): 1-24.
0500
100015002000250030003500400045005000
Pas
seng
er-k
m, b
illion
0500
100015002000250030003500400045005000
1995 2000 2005 2010 2015 2020 2025 2030
Pas
seng
er-k
m, b
illio
n
1995 2000 2005 2010 2015 2020 2025 2030
95 FltZeroCostLoCostPowTrn1PowTrn2AlumHybridAll
D: Kyoto+ CaseC: Kyoto Case
A: Calibration Case B: Reference Case
Reference Case Automobile Pkm
Kyoto+ Automobile PkmDemand Loss
Reference Case Automobile Pkm
Kyoto Automobile Pkm
VEHICLE TECHNOLOGY-DYNAMICS
Source: Schäfer A., Jacoby H.D., Vehicle Technology Dynamics under CO2-Constraint: A General Equilibrium Analysis, forthcoming in: Energy Policy
00.51.01.52.02.53.03.54.04.55.0
Pas
seng
er-k
m, t
rillio
n
1995 2000 2005 2010 2015 2020 2025 2030
95 Flt
ZeroCost
LoCost
PowTrn1PowTrn2
AlumHybrid
Reference Case Automobile Pkm
Loss
Reference Case Automobile Pkm
Total Fleet
1995 2000 2005 2010 2015 2020 2025 2030 1995 2000 2005 2010 2015 2020 2025 2030
0
0.5
1.0
1.5
2.0
2.5
3.0
Gas
eolin
e R
etai
l Pric
e, U
S$/
L
Reference Case Kyoto Case Kyoto+ Case
Demand
95 Flt
ZeroCost
Total Fleet
95 Flt
ZeroCost
LoCost PowTrn1
DemandLoss
Total Fleet
Ton-
km, t
rillio
nP
asse
nger
-km
, tril
lion
Total Fleet
Total Fleet
95 Flt 95 FltLoCost
LoCost
PowTrn
Alum
Reference Case Personal Truck Pkm
0
5
10
15
20
25
Ene
rgy
Use
, EJ
A
B
C
D
E
Household Own Supplied Transportation
Purchased Transportation
Household Own Supplied Transportation
Purchased Transportation
Household Own Supplied Transportation
Purchased Transportation
MARKALEPPA
MARKALEPPA
MARKALEPPA
LoCost
Total Fleet
95 Flt
Reference Case Personal Truck Pkm
95 FltLoCost
Advanced
Total FleetTotal Fleet
Advanced
LoCost
95 Flt
Total Fleet
Advanced
95 Flt
LoCost
00.51.01.52.02.53.03.54.04.55.0
00.51.01.52.02.53.03.54.04.55.0
A BRIEF LOOK AT AIR TRAFFIC
Source: Lee J.J., Lukachko S.P., Waitz I.A., Schäfer A., 2001, “Historical And Future Trends In Aircraft Performance, Cost, and Emissions”, Annual Review of Energy and the Environment 2001, 26: 167-200.
ASK: Available Seat-KM
ENERGY INTENSITY VS. DEMAND
Sources: Lee J.J., Lukachko S.P., Waitz I.A., Schäfer A., 2001, “Historical And Future Trends In Aircraft Performance, Cost, and Emissions”, Annual Review of Energy and the Environment 2001, 26: 167-200. Jamin S., Schäfer A., Ben-Akiva M.E., Waitz I.A., 2004, Aviation Emissions and Abatement Policies in the United States: A City Pair Analysis, Transportation Research D, 9(4): 294-314.
Projected (global) growth in A/C-PKT: 3 – 12 X yr 2000-level
% Energy intensity change by 2050:
Low High Aircraft Technology † -25 -45 PAX Load Factor -10 -10 High-Speed Rail ‡ -1 -1 Direct Flight Routings 0 -11 TOTAL -33 -56 † Due to fleet turnover, average aircraft technology in 2050 corresponds to average new aircraft technology in 2025 (ultimately dependent on fleet growth) ‡ High-speed rail substitutes 50% of A/C-PKT in 10 U.S. high-density corridors with a cumulative great circle distance of 16,700km
SUMMARY
• As a direct consequence of economic development, transportation is becoming the major final energy consumer and carbon dioxide emitter
• Strong forces in transportation system: rising income translates into rising travel demand and shift toward faster, more energy-intensive modes ⇒ rising significance of air travel
• Decoupling of travel demand from economic growth unlikely, at least during the next decades
• Zero-carbon fuels necessary to decouple carbon emissions from transport demand; however, time scales are 50+ years for significant fleet impact.
SUMMARY, CONTD.
• Significant fuel efficiency improvements are expensive and consumer amortization periods for fuel-saving technology are short ⇒ public policy measures to move more fuel-efficient vehicles into the market.
• If accomplished via fuel price increases (e.g., through a uniform carbon tax across all sectors – the economically most efficient way), then:– Very high fuel prices (taxes) are necessary to achieve a
sensible emission reduction (⇒ public acceptance)– Marginal emission reduction costs may be lower in other
sectors than transportation; thus further increase in the relative importance of transportation in energy use and emissions
– Thus, some combination of policy measures necessary