improving the energy efficiency & environmental
TRANSCRIPT
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IMPROVING THE ENERGY EFFICIENCY & ENVIRONMENTAL PERFORMANCE OF ENVIRONMENTAL PERFORMANCE OF GOODS MOVEMENT
James J. Winebrake, PhD., Rochester Institute of TechnologyAsllomar 2009 Conference on Transportation and Energy
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Acknowledgementsg
Although I am presenting today, much of the Although I am presenting today, much of the material was developed jointly with Dr. James J. Corbett, University of Delaware., yI am indebted to the faculty and students in the Laboratory for Environmental Computing and y p gDecision Making at RIT, including Bryan Comer, Chris Prokop, Dr. Scott Hawker, and Dr. Karl Korfmacher
J. Winebrake, Asilomar, 2009.
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My Job Todayy y
Present energy and environmental attributes of goods gy gmovement from multiple modes
Discuss benefits from shifting from high energy-intensity modes to low energy-intensity modes
Assess overall opportunities for mode-shifting in a larger systems contextg y
Provoke policy-focused discussion
J. Winebrake, Asilomar, 2009.
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Working Hypothesisg yp
We can solve a large part of the energy and We can solve a large part of the energy and environmental problems of freight transportation by moving goods off trucks and onto trains and ships.g g p
VV.
J. Winebrake, Asilomar, 2009.
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O i f G d MOverview of Goods Movement
J. Winebrake, Asilomar, 2009.
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Goods Movement and GDP
4800
Ton‐Miles v. GDP for the U.S. (1987‐2005)
4400
4600
4800
3800
4000
4200
iles (Billions)
3400
3600
3800
Ton‐Mi
For every trillion dollar increase in GDP, we expect an additional 242 billion ton-miles.
3000
3200
6000 7000 8000 9000 10000 11000 12000
J. Winebrake, Asilomar, 2009.
GDP (Billions, 2000$)
Source: Corbett and Winebrake, 2009.
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6,000,000
U.S. Freight Transport by Mode, 1980‐2030
5,000,000s
3 000 000
4,000,000
Ton‐mile
s
2,000,000
3,000,000
Millions of
1,000,000
M
0
1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030
Truck Rail Domestic Shipping Air So rce B rea of Transportation Statistics Table 1 46bTruck Rail Domestic Shipping Air Source: Bureau of Transportation Statistics Table 1‐46b (1980‐2006); AEO 2009 (derived, 2007‐2030).
J. Winebrake, Asilomar, 2009.
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8000.0
Projected Energy Use in U.S. Freight Transport, 2006‐2030
6000.0
7000.0
4000 0
5000.0
n BT
U
AAGR:Freight truck =1 3%
3000.0
4000.0
Trillion -Freight truck =1.3%
-Freight rail = 0.9%-Air (freight carriers) = 2.8%-Domestic shipping = 0.9%
1000.0
2000.0
0.0
2005 2010 2015 2020 2025 2030
Truck Rail Domestic Shipping Air Source:AEO2009 Table 45Truck Rail Domestic Shipping Air Source:AEO 2009 Table 45
J. Winebrake, Asilomar, 2009.Source: AEO 2009, Table 45.
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Shipping,
Recreational Boats0.9%
Air9.8%
Military Use2.8%
Lubricants0.3%
Percentage of Energy‐Related Transportation CO2 Emissions by Mode, 2008
Total emissions from transportation
Shipping, Domestic1 2%
pp g,International
3.1%
from transportation~1.9 GtCO2eq/yr
Total emissions from all energy sectors
Rail, Freight2.2%
1.2% from all energy sectors~5.9 GtCO2eq/yr
Rail, Passenger0.3%
Light‐Duty Vehicles57.8%
B T i
Freight Trucks18.3%
Commercial Light Trucks
Bus Transportation1.0%
Trucks2.1%
J. Winebrake, Asilomar, 2009.Source: AEO 2009, Table 19.
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M d l C iModal Comparisons
J. Winebrake, Asilomar, 2009.
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5000Energy Intensity of US Freight Modes, 1980‐2006
4000
4500mile)
2500
3000
3500
(BTU
/ton
‐m
Note:These represent top-down averages and should
1500
2000
2500
gy Intensity not be used for blanket modal comparisons!
500
1000
1500
Energ
0
500
1980 1985 1990 1995 2000 2005
Truck Rail Domestic Shipping Source: Transportation Energy Data Book 27
J. Winebrake, Asilomar, 2009.
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Range of typical CO2 efficiencies for various cargo carriers
Cr de
General Cargo
LNG
Crude
NOTE I f f
Bulk
Chemical
Reefer NOTE: Impacts are a function of many factors related to route and modal characteristics.
Product
LPG
Container
Road
Rail
RoRo / Vehicle
0 50 100 150 200 250g CO2 / ton*km
Source: Buhaug, et al. 2009 J. Winebrake, Asilomar, 2009.
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S E l U i GIFTSome Examples Using GIFT
Th G ti l I t d l F i ht T t ti (GIFT) The Geospatial Intermodal Freight Transportation (GIFT) model is a model jointly developed by the Rochester Institute of Technology and the University of Delaware, gy y ,with funding support from US DOT/MARAD, Great Lakes Maritime Research Institute, ARB, among others.
J. Winebrake, Asilomar, 2009.
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Connect Multiple Transportation Mode Networks at Intermodal Transfer FacilitiesIntermodal Transfer Facilities
Road Network Rail Network
Waterway Network Intermodal Transfer Facility
J. Winebrake, Asilomar, 2009.
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Define Economic, Energy, Time and Environmental Costs of Traversing Each Network Segment and Transfer
Truck Segment “Costs”…NOxCO2EnergyOperating
CostTimeDistance
Truck Segment Costs
ESRI ArcGIS Network Analyst finds N y“Shortest” (least cost) routes.
J. Winebrake, Asilomar, 2009.
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J. Winebrake, Asilomar, 2009.
Montreal to Cleveland (Ship 1) Montreal to Cleveland (Ship 2)
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Emissions and Time of Delivery Tradeoffs Montreal to Cleveland
400
600
2(k
g)
0
200
T k Shi (DR) R il Shi (EJ)
CO
2
Truck Ship (DR) Rail Ship (EJ)
80
40
60
80
e-of
y
(hrs
)
0
20
40
Tim
eD
eliv
er
J. Winebrake, Asilomar, 2009.Truck Ship (DR) Rail Ship (EJ)
D
Mode
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300400
CO2 Comparison
100200300
CO
2(k
g)
0100
Truck Only Rail Only Ship-Truck Rail-Truck
J. Winebrake, Asilomar, 2009.
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2500
CO2 Comparison
1000
1500
2000
O2
(kg)
0
500
1000
CO
Truck Only Rail Only Truck-Rail
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O i i f M d Shif iOpportunities for Mode Shifting
J. Winebrake, Asilomar, 2009.
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The IF-TOLD Mitigation Framework: A C t t f M d Shifti Di iA Context for Mode Shifting DiscussionsThe IF-TOLD “six-legged cow”:ggIntermodalism/mode-shifting –use of efficient modesFuels use of low carbon fuelsFuels – use of low carbon fuelsTechnology – application of efficient technologiesOperations – best practices in operator behaviorLogistics – improve supply chain managementDemand – reduce how much STUFF we consume
Even a six-legged cow can moveall legs – dynamic, balancing!
J. Winebrake, Asilomar, 2009.
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Opportunities for Mode-Shiftingpp g
( )[ ]∑ ⋅⋅⋅=Δ EEpfcWE ( )[ ]∑ −⋅⋅⋅=Δk
jiijkijkijkikij EEpfcWE
ΔEij = energy savings due to modal shift from i to jWik = work done by mode i for commodity k (ton-miles) c = shipment compatibility fraction of i to j for k (cargo)cijk = shipment compatibility fraction of i to j for k (cargo)fijk = shipment feasibility fraction of i to j for k (infrastructure)pijk = shipment practicality fraction of i to j for k (economic)Ei = energy intensity factor for i (Btu/ton-mile)Ej = energy intensity factor for j (Btu/ton-mile)
J. Winebrake, Asilomar, 2009.Also need to account for intermodal transfer penalties.
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Insights into cijk – Cargo Characteristics Percentage of Goods Movement in U.S. by Commodity (Sample) and
Mode (2002 CFS Data)
g ijk g
Nonmetallic mineral products
Elect. and electrical equip.
Misc. manufactured products 1.1%
1.0%
4.3%
Coal
Basic chemicals
p
3.7%
21.9%
1 8%
Other agricultural products
Gravel and crushed stone
Nonmetallic minerals1.8%
3.4%
3.5%
Live animals and live fish
Cereal grains8.4%
0.1%
0% 50% 100%Truck Rail Water Other/Unknown
J. Winebrake, Asilomar, 2009.Source: CFS 2002, Ton-Miles by Commodity and Mode
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Insights into fijk – U.S. Intermodal InfrastructureInfrastructure
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1800
Average Miles per Shipment by Mode for the U.S. (2007)
1400
1600
1000
1200
Shipmen
t
600
800
Miles pe
r S
200
400
0Truck Rail Shallow
draftGreat Lakes Deep draft Air (included
truck and Parcel,
U.S.P.S. or Other and unknown
J. Winebrake, Asilomar, 2009.
air) courier modes
Source: CFS 2007.
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Estimating Mode Shifting Potentialg g( )[ ]∑ −⋅⋅⋅=Δ
kjiijkijkijkikij EEpfcWE
TruckRailWaterIntermodalUSPS/ParcelOtherModal Shift
Consider total ton-miles as a gridded box, where each cell is
J. Winebrake, Asilomar, 2009.equivalent to 1%.
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Estimating Mode Shifting Potentialg g( )[ ]∑ −⋅⋅⋅=Δ
kjiijkijkijkikij EEpfcWE
TruckRailWaterIntermodalUSPS/ParcelOtherModal Shift
Assume that about 50% of the cargo currently moved by truck is compatible with rail or ship due to physical
ti f t l di l i ti t [ 0 50]J. Winebrake, Asilomar, 2009.
properties, safety, loading logistics, etc. [cijk ~ 0.50]
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Estimating Mode Shifting Potentialg g( )[ ]∑ −⋅⋅⋅=Δ
kjiijkijkijkikij EEpfcWE
TruckRailWaterIntermodalUSPS/ParcelOtherModal Shift
Assume that of the cargo that is compatible, infrastructure can only serve 70% of the ton-miles in
J. Winebrake, Asilomar, 2009.
the short term [fijk ~0.70]
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Estimating Mode Shifting Potentialg g( )[ ]∑ −⋅⋅⋅=Δ
kjiijkijkijkikij EEpfcWEUnder these assumptions, p
there is potential to move ~ 5% of the total ton-miles (~12% of truck ton-miles) from truck to rail/ship. If
truck is ~5 times more energy intense than il/ hi h hi i li rail/ship, then this implies
~8% reduction in energy consumption.
Average distance by truck is 200 miles. Assume that ~50% of the ton-miles shipped > 200 miles and 25% are > 500 miles. Assume economic possibility exists for mode
J. Winebrake, Asilomar, 2009.
500 miles. Assume economic possibility exists for mode shifting for 35% of total truck trips. [pijk ~ 0.35].
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P li i f P i Effi iPolicies for Promoting Efficiency
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Policy Options dalis
m
ogy
ons
s
y p
Inte
rmod
Fuel
Tech
nolo
Ope
ratio
Logi
stic
s
Dem
and
Policy Options I F T O L DEfficiency standards ● ● ●
TaxesTaxes ● ● ● ● ● ●
Subsidies ● ● ●
Technology mandates ●gyInfrastructure investment ● ●
R&D investment ● ●
/Alternative/LC fuels ● ●
Size/weight restrictions ● ● ●
Demand management ●
J. Winebrake, Asilomar, 2009.
Demand management ●
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C l iConclusion
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Conclusion
Modal shifts offer large side-by-side benefitsModal shifts offer large side by side benefitsSystem benefits vary depending on vessel, vehicle, locomotive, and route characteristics and are locomotive, and route characteristics and are constrained by compatibility, feasibility, and practicality – research needed herep ySuite of policy options should be considered recognizing freight sector as a systemWedge analysis needed for freight sector that looks at potential for the IF-TOLD set of elements
J. Winebrake, Asilomar, 2009.
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Citations
Corbett, J. J.; Winebrake, J.J.; “The impact of globalisation on international iti t t ti it P t t d d f t ti ” i maritime transport activity: Past trends and future perspectives,” in
Globalisation, Transport, and Environment, edited by N. A. Braathen, Organisation for Economic Cooperation and Development (OECD), Paris, forthcoming.Buhaug, Ø.; Corbett, J. J.; Endresen, Ø.; Eyring, V.; Faber, J.; Hanayama, S.; Lee, D. S.; Lee, D.; Lindstad, H.; Mjelde, A.; Pålsson, C.; Wanquing, W.; Winebrake, J. J.; Yoshida, K. Updated Study on Greenhouse Gas Emissions from Ships; International Maritime Organization (IMO) London, UK, 2009.( )Energy Information Administration (2009). Annual Energy Outlook 2009. Washington, DC; available at http://www.eia.doe.gov/oiaf/aeo/index.htmlB f T t ti St ti ti C dit Fl S (2002 d Bureau of Transportation Statistics, Commodity Flow Survey (2002 and 2007), available at http://www.bts.gov/programs/commodity_flow_survey/
J. Winebrake, Asilomar, 2009.