IMPROVING THE ENERGY EFFICIENCY & ENVIRONMENTAL

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

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.

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


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.


O i f G d MOverview of Goods Movement


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


GDP (Billions, 2000$)

Source: Corbett and Winebrake, 2009.

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).


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.

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.

M d l C iModal Comparisons


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


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.

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.


Connect Multiple Transportation Mode Networks at Intermodal Transfer FacilitiesIntermodal Transfer Facilities

Road Network Rail Network

Waterway Network Intermodal Transfer Facility


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.



Montreal to Cleveland (Ship 1) Montreal to Cleveland (Ship 2)

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

300400

CO2 Comparison

100200300

CO

2(k

g)

0100

Truck Only Rail Only Ship-Truck Rail-Truck


2500

CO2 Comparison

1000

1500

2000

O2

(kg)

0

500

1000

CO

Truck Only Rail Only Truck-Rail


O i i f M d Shif iOpportunities for Mode Shifting


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!


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.

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

Insights into fijk – U.S. Intermodal InfrastructureInfrastructure


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


air) courier modes

Source: CFS 2007.

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%.




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]




Assume that of the cargo that is compatible, infrastructure can only serve 70% of the ton-miles in


the short term [fijk ~0.70]


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


500 miles. Assume economic possibility exists for mode shifting for 35% of total truck trips. [pijk ~ 0.35].

P li i f P i Effi iPolicies for Promoting Efficiency


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 ●


Demand management ●

C l iConclusion


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


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/


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IMPROVING THE ENERGY EFFICIENCY & ENVIRONMENTAL