1. CO 2 reduction and energy efficiency in German road freight traffic © Willi Betz Dr. Jacques Leonardi MPI – Max Planck Institute for Meteorology, Hamburg, Germany leonardi@dkrz.de

2. Contents 1.Trends 2.Baseline survey 3.Case study container transportation 4.Scheduling and telematics survey 5.Research needs and policy recommandations

3. 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

4. 19702001 Road Short sea shipping 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

5. CO 2 emissions according to traffic sectors Germany 2000 (Source: UBA 2003)

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

7. NESTOR baseline survey & analysis Objectives: - On site measurements of CO 2 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 sampleGermany road freight 2000 1,668,193 tkm347,000 million tkm tkm = metric tonne × km sample / total = 1 / 219,000

8. CO 2 efficiency in tkm / kg CO 2

9. CO 2 efficiency in relation to load capacity utilisation and vehicle size class

10. Average CO 2 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.ContainerTradeOthersNot specifiedAverage kg CO 2 /kmkg CO 2 /tkm

11. Average CO 2 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 <51large > 51 vehicles kg CO 2 /km kg CO 2 /tkm

12. Driver evaluation of volume capacity utilisation and CO 2 efficiency

13. Utilisation ratio for volume and weight High efficiency potential

14. 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 (E vu ). To calculate E vu and tkm/mkm (mass-kilometers): E vu = tkm/[(t 2 + t 1 ) × km] t 1 = payload t 2 = empty load

15. Efficiency of vehicle use (E vu )

17. CO 2 efficiency (E) is a factor strongly influenced by the efficiency of vehicle usage (E vu ), driver behaviour (d), speed (s) and route (r) parameters. E = E vu × d × s × r In our sample, data from heavy trucks shows a high correlation (r²=0.91) for E and E vu. CO 2 efficiency and efficiency of vehicle use (No systematically coupled data on “time“ and “financial“ efficiency available)

18. E vu factors: choice of vehicle class and payload / empty weight t 2 = 13,5 t empty weight t 1 = 20,1 t payload t 1 / t 2 = 1,48 E(vu-max) = 0,598 t 2 = 11 t empty weight t 1 = 29 t payload t 1 / t 2 = 2,63 E(vu-max) = 0,725 Sample average Germany 2003 Best truck available 2003 t 1 / t 2 = 0,47 E(vu-max) = 0,32 t 2 = 5,1 t t 1 = 2,4 t Worst case in sample Germany 2003

19. Implementation of existing CO 2 reduction measures in 52 German road freight firms

20. Potential for improving efficiency (1)

21. 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 CO 2 emissions (and fuel use) of 20.8% for heavy trucks

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

23. 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

24. Container transportation chain (1) Physical transport harbour - hinterland upstream / downstream from / to the HINTERLAND Container packing station Port stack area Full Container Load Container depot Kai Sea-Sea Transhipment center Overseas Container Ports EU Short Sea Ports Shipper Recipient HARBOUR area Land Sea

25. Sea Transport Land Transport Port System Forwarding Recieving Shipper Recipient Terminal Operators Agent Ship Owner Carrier Traditional organisation form Actual integrated forms Agent Terminal Operators Shipper Recipient Carrier Ship Owner Container transportation chain (2) Information and control

26. Scheduling and telematics survey Objective: Quantify impacts on transport efficiency and CO 2 efficiency in German trucking companies in 2003 © Mercedes Fleetboard gmbH

27. 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 companies Performance: 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

28. Effect of IT scheduling on t, km and fuel use Sample with IT scheduling Reference German mean 30-40 t trucks

29. 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 CO 2 /tkm missing  need for further R&D

30. 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

31. 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

32. Necessary research into the factors influencing fuel consumption and transport efficiency VehicleEmpty load EURO Norm Truck body components weight Other technology (tyres, oil, etc.) Fuel type (bio-diesel) Load class, age etc. Organisation, Payload and payload factor logisticsHauling capacity and traffic volume Management decisions, service quality etc. Constraints, barriers such as time, finance, etc. Road, trip and routeRoad type (highway, etc.) Gradient Itinerary choice, IT scheduling Traffic Free traffic, traffic congestion, etc. Speed DriverDriving behaviour Verification of maintenance WeatherTemperature, wind, precipitation

33. 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 CO 2 -efficient companies/drivers (?) Research on further technology effects on efficiency Design a new policy approach for facilitating non- technological efficiency measures in logistics (?)

34. Conclusions The level of efficiency is lower than expected Increased fuel efficiency is a logistic challenge 20 % CO 2 reduction is potentially feasible for heavy trucks in Germany