1. Climate mitigation through efficiency in the road freight transport sector: vehicle approach and policy recommendations Jacques Leonardi Pedro J. Pérez-Martínez Transport Studies Group Christophe Rizet Dept. for Transport Economy and SociologyRoger W. Worth

2. Introduction and background Many open scientific questions and a wide debate on freight transport, energy and climate Domestic actions tackling climate change Dualities that would have to be linked: –Organisation and technology solutions –Impacts and measures –Survey methods and vehicles data –Company approach and policy approach –Decisions and limitations

3. Scientific questions How people behave with existing solutions? What are the main barriers for an implementation of mitigation strategies? What could we suggest to overcome them? A holistic approach is impossible  Define a feasible, pragmatic approach

4. Objectives of the vehicle approach to observe, quantify and understand energy consumption parameters and changes at a disaggregate vehicle level to understand how a behavioural change is leading to a net decrease in final energy use or CO 2 -emissions of the vehicle to understand how this change can be (potentially) supported by vehicle related measures taken by decision-makers in companies and in the public sector

5. Definition The vehicle approach is: Field oriented, but it needs modelisation to start Applying and defining survey methods Looking to impacts on transport & energy parameters Using interviews, data collection and statistics analysis

6. Energy consumed in road freight transport and performance indicators: some links Energy efficiency -1 L/TKM Energy consumed in Road freight transport L (litres of diesel fuel) Road freight transport demand TKM X Veh. consumption L / VehKM Rate of loaded km -1 KMloaded/VehKM Average load -1 KMloaded/TKM X X = =

7. Energy consumed and performance indicators: main company data Vehicle energy usel/100 km Gross Vehicle Weightt Load capacityt Volume capacity: Max nb of palets of the trucknumber Nb of palets of the payloadnumber Load factor by weight% Mean weight of one palet (density)kg Distance covered (per trip or per year)km Empty runningkm ou %

8. A comparative analysis: France, UK, Spain and Germany Main selection criteria for the choice of the comparisons presented is the data quality, notably the possibility to relate fuel use, tonne-km and vehicle type correctly in one sample Use of two types of data sources : –National statistics –Targeted surveys

9. Litres TKMEfficiencyFuel useLoad kmMean load Total veh. weightbillionsbillionsl / tkml / 100km%tkm/veh-km Trucks242.430.20.08032.072.0%5.5 3.5 to 6.0 t.1.20.0440.26915.161.5%0.9 6.1 t à 10.9 t14.60.7890.18521.373.2%1.6 11.0 t à 19.0 t145.417.490.08329.875.8%4.7 19.1 t à 21.0 t3.20.3850.08435.475.8%5.6 21.1 t et plus78.011.470.06842.761.5%10.2 Road Tractors538.81820.03038.176.5%16.8 Total781.2212.20.03736.074.9%13.1 Road freight performance and fuel use: French case 2004 Source: SESP (2007): TRM 2005

10. Key performance indicator and efficiency in UK for articulated trucks >33t KPI Pallet National statistics Survey Indicators199520002005changes 2004 95-05 Load factor of loaded trip (%)706659- 16.0 %31 Empty running kilometres (%)28.627.526.8- 6.3 %12.8 Mean vehicle payload (t)11.6811.3611.32- 2.7 % Fuel consumption (l/100km)39.837.635.3- 11.3 % Fuel efficiency (l/tkm)0.0340.0330.031- 7.9 % CO 2 emission efficiency (g CO 2 /tkm)898782- 7.9 %92 to 155 Transport content (km/ton)12.0811.7910.97- 9.2 % Mean length of haul (km per trip)142135124- 12.7 %156 Sources: Dft 2006: Road freight transport statistics 2006; Les Beaumont 2004: KPI Pallet survey

11. Totaltrucks 40t trucks sample <40t Indicators n=153n=44n=109 Mean load factor by weight in % (incl. empty runs)44.243.044.7 Mean volume capacity utilisation in %59.348.263.6 Mean empty runs in % of the total distance17.420.316.3 Mean vehicle payload (t)10.166.0611.01 Mean vehicle age3.14.42.5 Mean fuel consumption in l/100 km31.624.933.1 Fuel efficiency in l / tkm0.0360.0680.030 CO 2 efficiency in g CO 2 /tkm (means)9618180 Key performance indicators in the German base survey 2003 Source: Leonardi & Baumgartner 2004; NESTOR database (unpublished)

12. Key performance and energy data for Spain, 1997 and 2003 19972003Annual Indicatorschanges in % Load factor - loaded operations (%)_80_ Load factor - total operations (%)_40_ Operation factor (km/operation)_69,5_ Empty running operations (%)_47_ Empty running kilometres (%)_26_ Fuel efficiency (l/tkm)0.0300.027-1.4 Emission efficiency (g CO 2 /tkm)79.473.0-1.4 Operativity (%)_79_ Transport content (km/ton)9,68,8-1.4 Mean transport distance (km)113.2104.1-1.3 Transport efficiency (t/veh)11,811-1.1 Source: Pérez-Martínez 2005, SGT 2005

13. Contribution of different vehicle types and services to Key Transport Performance Indicators in Spain 2003 Source: Pérez-Martínez 2005

14. Comparison of CO 2 efficiency / energy intensity from five European samples CO 2 efficiency / Country energy intensitySources and comment UK0.082 kg CO 2 /tonne-km DfT 2006 (Articulated trucks >33t) UK 0.092 to 0.155 kg CO 2 /tonne-km Les Beaumont 2004, (trucks >40t) D 0.080 kg CO 2 /tonne-km Leonardi and Baumgartner 2004 (40t trucks) ES0.073 kg CO 2 /tonne-km Pérez-Martínez 2005 (heavy trucks only) F0.079 kg CO 2 /tonne-km SESP 2006 (Articulated trucks only)

15. Why these differences and similarities? Different transport patterns in the four countries? Different samples? Different survey methods?

16. Transport, traffic and national business conditions (typical logistics decision parameters) Commodity types Type of transport operation Trip distance Fleet size and truck types Driving conditions

17. Accuracy of data gathering method comparative analysis of the food KPI survey with the National survey in UK CSRGTFood KPI survey Full loading % by weight 13% 11% Full loading % by volume 37% 31% % Empty running 19% 22% Average vehicle loading factor 53% 56% Average fuel efficiency: (km/l) All road freight operations Small rigid (2 axles) 7.5 t 4.0 4.1 Medium rigid (2 axles) 7.5–18 t 3.6 3.7 (7.5–14 t)–3.3 (14–17 t) Large rigid (>2 axles) >18 t 3.1 2.9 (17–25 t) 32 t articulated vehicle (4 axles) 3.2 3.2 (<33 t) 38–44 t articulated vehicle (>4 axles) 2.9 2.9 (>33 t) Source: McKinnon and Ge 2004; Continuing Survey of Road Goods Transport: CSRGT

18. Energy conversion and emission factors Emission factors Combustion only Combustion + supply Volume in litres = kg = kgoe = kg C eq = kg eq CO 2 = kg C eq = kg eq CO 2 Diesel10.845 0.7262.6640.8042.951 Gasoline10.7550.7910.6492.3800.7742.841 Heavy fuel1000 95285931539683553 Source : Ademe: Bilan Carbone - guide des facteurs d'émissions, version 5.0, jan 2007, pp. 18 à 21. Miles per gallon and litres per 100 kilometres282,5/x mpg = y l/100km Carbon equivalent and CO 2 equivalent1 kg C eq = 3,67 kg CO 2 eq

19. Limitations Several limitations are hampering the quality of the comparative study The surveys were not designed for the purpose of this study, but were aiming at establishing other scientific results and reports in some cases, the efficiency indicator was build on original primary data from surveys, in other cases, on secondary, calculated data from at least two different sources

20. ‘everything else remains stable’ One central condition for scientific comparison is that ”..” excepting the differences in the objects of the analysis. This situation is not given, since business conditions and countries economies are changing from year to year. Therefore many external factors, not related to vehicles, and not mentioned in the explanations, could have been influencing the results: –Influence of cabotage, –logistics decision making and –other non technological factors discussed in McKinnon (2003)

21. Measure typePercent of firms in the survey Technical improvements (tyres, lubricants, aerodynamic)53.8 Driver training51.9 Informal co-operation40.4 Scheduling with IT23.1 On-board-systems17.3 Others15,4 Shift to rail/ship15.4 Scheduling with IT and telematics9.6 Stacking area optimisation software5.8 Formal co-operation 3.8 Source: Leonardi and Baumgartner 2004 Level of implementation of efficiency measures in 52 German companies 2003 How to influence, help or incite companies to take decisions? Is this a no policy area because investments are ‘for free’ ?

22. Conclusion Surprising similarities in the aggregated efficiency indicators Use of Key Performance Indicators in National or targeted surveys are the dominating methodologies in the studies presented Data from National Statistics are widely used Potential critical points are: –How to best evaluate the impacts of the measures at the company level and avoiding pitfalls? –How to ensure that positive effects on efficiency can be repeated in other companies? –How to support companies through public policy?