CO2 reduction and energy efficiency in German road freight traffic

© Willi Betz

Dr. Jacques Leonardi

MPI – Max Planck Institute forMeteorology, Hamburg, Germanyleonardi@dkrz.de

Contents

1. Trends 2. Baseline survey3. Case study container transportation 4. Scheduling and telematics survey5. Research needs and policy

recommandations

Case Study Germany, Project NESTOR

• Duration: 7/2002-09/2005

• Aknowledgements: – Prof. Dr. Hartmut Grassl

– Michael Baumgartner, Dr. Ingo Möller, Oliver Krusch

– All the firms involved

1970 2001

Road

Short seashipping

Freight transport in EU-15 Modal performance in bn tkm/year Source: EU DG Tren (2004): Transport statistics

1200

1000

800

600

400

200

0

Rail

Inland waterways

Pipeline

Inland air traffic2%

rail traffic1%

Inland shipping1%

Passenger transport, road

67%

Road freight traffic 29%

CO2 emissions according to traffic sectors

Germany 2000 (Source: UBA 2003)

Fuel consumption trends in road transportation Germany 1991-2001(Source: DIW 2003)

0

10000

20000

30000

40000

50000

60000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Passenger Transport

Freight Transport

mio. liter

NESTOR baseline survey & analysis

Objectives: - On site measurements of CO2 efficiency

- Identification/quantification of factors of influence

- Potential analysis

Sample and Survey:– 220 companies questioned (Feb.-May 2003)– 363 driver and 65 manager questionnaires sent– Responses from 38 companies = 46% returns– 168 driver datasets obtained (one dataset = one tank filling) – 153 valid datasets with complete coupled information on t, km, fuel

consumption and volume capacity utilisation as a %

Performance in sample Germany road freight 20001,668,193 tkm 347,000 million tkm

tkm = metric tonne × km sample / total = 1 / 219,000

CO2 efficiency in tkm / kg CO2

0

5

10

15

20

25

30

tkm

/kg

CO

2

least efficient transport0.81

most efficient transport: 26

transports with low efficiency (< 5 tkm/kg CO2)

are responsible for 6 % of total CO2 emissions,8,4% of km and 1,6% of tkm performance

mean CO2 efficiency: 10.4

CO2 efficiency in relation to load capacity utilisation and vehicle size class

r2 = 0.8837

r2 = 0.7899

r² = 0.9539

0

0,2

0,4

0,6

0,8

1

1,2

1,4

0 20 40 60 80 100

Average load factor (in % of maximum carrying capacity)

CO

2 e

ffici

en

cy (

in k

g C

O 2

/ tkm

)

vehicle load class 7.5 t

vehicle load class 12-25 t

vehicle load class 35-44 t

trend for 35-44 t

trend for 12-25 t

trend for 7.5 t

Average CO2 efficiency per economic sectors

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Parcel Serv. Container Trade Others Not specified Average

kg CO2/km kg CO2/tkm

Average CO2 emissions and size of companies

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

small <11 vehicles medium <51 large > 51 vehicles

kg CO2/km

kg CO2/tkm

Driver evaluation of volume capacity utilisation and CO2 efficiency

0

5

10

15

20

25

30

CO

2 e

ffic

ien

cy in

tkm

/ k

g C

O 2

0

20

40

60

80

100

Vo

lum

e u

tilis

atio

n r

atio

in %

-

CO2 efficiency in tkm / kg CO2

Mean volume utilisation in %

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Volume

Gewicht

Utilisation ratio for volume and weight

High efficiency potential

Efficiency of vehicle use in freight transport

New indicator mass-kilometers (mkm) and new ratio tkm/mkm were defined as values for measuring efficient vehicle use (Evu).

To calculate Evu and tkm/mkm (mass-kilometers):

Evu = tkm/[(t2 + t1) × km]

t1 = payload

t2 = empty load

Efficiency of vehicle use (Evu)

0

5

10

15

20

25

30

vertically coupled vehicle data

tkm

pe

r kg

CO

2

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

tkm

/mkm

i

CO2-efficiency in tkm/kg CO2

Efficiency of vehicle use in tkm/mkm

Efficiency of vehicle use (Evu)

r2 = 0,8626

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0 5 10 15 20 25 30

tkm/ kg CO2

tkm

/ m

km i

CO2 efficiency (E) is a factor strongly influenced by the

efficiency of vehicle usage (Evu), driver behaviour (d), speed

(s) and route (r) parameters.

E = Evu × d × s × r

In our sample, data from heavy trucks shows a high correlation (r²=0.91) for E and Evu.

CO2 efficiency and efficiency of vehicle use

(No systematically coupled data on “time“ and “financial“ efficiency available)

Evu factors: choice of vehicle class and payload / empty weight

t2 = 13,5 tempty weight

t1 = 20,1 t payload

t1 / t2 = 1,48E(vu-max) = 0,598

t2 = 11 tempty weight

t1 = 29 t payload

t1 / t2 = 2,63E(vu-max) = 0,725

Sample averageGermany 2003

Best truck available2003

t1 / t2 = 0,47E(vu-max) = 0,32

t2 = 5,1 t

t1 = 2,4 t

Worst casein sample Germany 2003

Implementation of existing CO2 reduction measures in 52 German road freight firms

measure type % of firms

Technical improvements 53.8

Driver training 51.9

Informal co-operation 40.4

Scheduling with IT 23.1

On-board systems 17.3

others 15.4

Shift to rail/ship 15.4

Scheduling with IT and telematics 9.6

Stacking area optimisation software 5.8

Formal co-operation 3.8

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

< 0,2 0.2-0.25 0.25-0.3 0.3-0.35 0.35-0.4 0.4-0.45 0.45-0.5 0.5-0.55 > 0.550

10

20

30

40

50

60

70

80

Total distance in km (left scale)

Total performance in tonne-km (/10) (left scale)

Total CO2-emissions in kg (left scale)

Efficiency of vehicle use in tkm/mkm (x100) (right scale)

CO2-efficiency in tonne-km per kg CO2 (right scale)

Classes of efficiency in vehicle use, in tkm/mkm

mkm = (empty load + payload)*km

Potential for improving efficiency (1)

Potential for improving efficiency (2) and decoupling emissions from GDP

• Assumption: If all companies below 0.5 tkm/mkm mean were to implement efficiency measures, they could reach the mark of 0.5– Best company in survey has an average mark of 0.56– e.g. lightest vehicle for parcel delivery: 11 t

and load factor: 70% (=German average 2003) 0.5

• Result:Potential overall reduction of CO2 emissions (and fuel use) of 20.8% for heavy trucks

Fuel consumption in short and long haulage Case study Hamburg, container hinterland traffic

0

5

10

15

20

25

30

35

40

short haulage trucks long haulage trucks

19 companies, market share 38%, mean values for 2002

l/100km

Fuel costs in short and long haulage Case study Hamburg, container hinterland traffic

• short distance– 10-15% of total annual costs

• long distance– 10-30% of total annual costs

Container transportation chain (1) Physical transport harbour - hinterland

upstream / downstreamfrom / to the HINTERLAND

Container packing station

Port stack areaFull Container

Load

Container depot

Kai

Sea-Sea Transhipment center

OverseasContainer

Ports

EU Short SeaPorts

Shipper

Recipient

HARBOUR areaLandSea

Sea Transport Land Transport

Port System ForwardingRecieving

ShipperRecipient

Terminal Operators

Agent

ShipOwner

Carrier

Traditional organisation form

Actual integrated forms

Agent

TerminalOperators Shipper

Recipient Carrier

ShipOwner

Container transportation chain (2) Information and control

Scheduling andtelematics survey

Objective: Quantify impacts on transport efficiency and CO2 efficiency in German trucking companies in 2003

© Mercedes Fleetboard gmbH

Survey design and sample

79 firms with IT system questioned. Not including firms with major changes in efficiency as a result of other measures such as goods change, co-operation etc.

7 respondents with all needed data + 11 companies with IT schedule but partial data

7 companiesPerformance: 36 million km and 1.1 million t /year Fuel consumption: 12.5 million liter /year Market share: about 0.5 % of total German road freight transport 2002

Effect of IT scheduling on t, km and fuel use

Sample with ITscheduling

ReferenceGerman mean 30-40 t trucks

94

96

98

100

102

104

106

108

110

Before After

94

96

98

100

102

104

106

108

110

2000 2001

Total load (t)Total distance (km)Total fuel use (l)Mean fuel use (l/100km)

% changes

3 types of scheduling systems in use

• Manual scheduling without IT• IT based scheduling• IT based scheduling with telematics

for data communication and routing

• indicators tkm/mkm or CO2/tkm missing need for further R&D

Advantages offered by IT based scheduling system

• Enhances the transparency of companies and vehicle activities

• Functions as management information system • Allows higher vehicle capacity utilisation rate• Reduces average transport distance• Helps identify less profitable clients• Accounts the real origins of variable costs

Perceived advantages of an IT based logistic scheduling system with telematics

• Couples fuel consumption, mileage, vehicle, driver and time data

• Further increase of the vehicle capacity utilisation rate NEVER perceived: additional payload gains

• Further reduction of mean trip distance with routing information

• Reduced information transfer errors (codes etc.)• Improved driver training• Control of drivers

Necessary research into the factors influencing fuel consumption and transport efficiency

Vehicle Empty loadEURO NormTruck body components weightOther technology (tyres, oil, etc.)Fuel type (bio-diesel)Load class, age etc.

Organisation, Payload and payload factorlogistics Hauling capacity and traffic volume

Management decisions, service quality etc.Constraints, barriers such as time, finance, etc.

Road, trip and route Road type (highway, etc.)GradientItinerary choice, IT scheduling

Traffic Free traffic, traffic congestion, etc.Speed

Driver Driving behaviourVerification of maintenance

Weather Temperature, wind, precipitation

Instruments and measures proposed for decoupling road freight transport impacts and

economic growth

• Facilitate implementation and diffusion through enhanced market transparency would enable decoupling from GDP

• Incentives for CO2-efficient companies/drivers (?)

• Research on further technology effects on efficiency• Design a new policy approach for facilitating non-

technological efficiency measures in logistics (?)

Conclusions

• The level of efficiency is lower than expected • Increased fuel efficiency is a logistic challenge

• 20 % CO2 reduction is potentially feasible for heavy trucks in Germany