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Climate mitigation through efficiency in the road freight transport sector: vehicle approach and policy recommendations

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 Sociology Roger W. Worth .

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Climate mitigation through efficiency in the road freight transport sector: vehicle approach and policy recommendations

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  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 Sociology Roger 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 CO2-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 L (litres of diesel fuel) - 1 Energy efficiency Road freight transport demand = X L/TKM TKM - 1 - 1 Veh. consumption Rate of loaded km Average load = X X L / VehKM KMloaded/VehKM KMloaded/TKM Energy consumed in road freight transport and performance indicators: some links

  7. Energy consumed and performance indicators: maincompany data

  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. Road freight performance and fuel use: French case 2004 Litres TKM Efficiency Fuel use Load km Mean load Total veh. weight billions billions l / tkm l / 100km % tkm/veh-km Trucks 242.4 30.2 0.080 32.0 72.0% 5.5 3.5 to 6.0 t. 1.2 0.044 0.269 15.1 61.5% 0.9 6.1 t à 10.9 t 14.6 0.789 0.185 21.3 73.2% 1.6 11.0 t à 19.0 t 145.4 17.49 0.083 29.8 75.8% 4.7 19.1 t à 21.0 t 3.2 0.385 0.084 35.4 75.8% 5.6 21.1 t et plus 78.0 11.47 0.068 42.7 61.5% 10.2 Road Tractors 538.8 1820.030 38.1 76.5% 16.8 Total 781.2 212.2 0.037 36.0 74.9% 13.1 Source: SESP (2007): TRM 2005

  10. Key performance indicator and efficiency in UK for articulated trucks >33t KPI Pallet National statistics Survey Indicators 1995 2000 2005 changes 2004 95-05 Load factor of loaded trip (%) 70 66 59 - 16.0 % 31 Empty running kilometres (%) 28.6 27.5 26.8 - 6.3 % 12.8 Mean vehicle payload (t) 11.68 11.36 11.32 - 2.7 % Fuel consumption (l/100km) 39.8 37.6 35.3 - 11.3 % Fuel efficiency (l/tkm) 0.034 0.033 0.031 - 7.9 % CO2 emission efficiency (g CO2/tkm) 89 87 82 - 7.9 % 92 to 155 Transport content (km/ton) 12.08 11.79 10.97 - 9.2 % Mean length of haul (km per trip) 142 135 124 - 12.7 % 156 Sources: Dft 2006: Road freight transport statistics 2006; Les Beaumont 2004: KPI Pallet survey

  11. Key performance indicators in the German base survey 2003 Total trucks 40t trucks sample <40t Indicators n=153 n=44 n=109 Mean load factor by weight in % (incl. empty runs) 44.2 43.0 44.7 Mean volume capacity utilisation in % 59.3 48.2 63.6 Mean empty runs in % of the total distance 17.4 20.3 16.3 Mean vehicle payload (t) 10.16 6.06 11.01 Mean vehicle age 3.1 4.4 2.5 Mean fuel consumption in l/100 km 31.6 24.9 33.1 Fuel efficiency in l / tkm 0.036 0.068 0.030 CO2 efficiency in g CO2 /tkm (means) 96 181 80 Source: Leonardi & Baumgartner 2004; NESTOR database (unpublished)

  12. Key performance and energy data for Spain, 1997 and 2003 1997 2003 Annual Indicators changes 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) 0.030 0.027 -1.4 Emission efficiency (g CO2 /tkm) 79.4 73.0 -1.4 Operativity (%) _ 79 _ Transport content (km/ton) 9,6 8,8 -1.4 Mean transport distance (km) 113.2 104.1 -1.3 Transport efficiency (t/veh) 11,8 11 -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 CO2 efficiency / energy intensity from five European samples CO2 efficiency / Country energy intensity Sources and comment UK 0.082 kg CO2 /tonne-km DfT 2006 (Articulated trucks >33t) UK 0.092 to 0.155 kg CO2/tonne-km Les Beaumont 2004, (trucks >40t) D 0.080 kg CO2 /tonne-km Leonardi and Baumgartner 2004 (40t trucks) ES 0.073 kg CO2 /tonne-km Pérez-Martínez 2005 (heavy trucks only) F 0.079 kg CO2 /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 methodcomparative analysis of the food KPI survey with the National survey in UK CSRGT Food 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

  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. Level of implementation of efficiency measures in 52 German companies 2003 Measure type Percent of firms in the survey Technical improvements (tyres, lubricants, aerodynamic) 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 Source: Leonardi and Baumgartner 2004 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?

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