IMPROVING ENERGY EFFICIENCY IN ROAD FREIGHT TRANSPORT SECTOR: THE - - PowerPoint PPT Presentation

IMPROVING ENERGY EFFICIENCY IN ROAD FREIGHT TRANSPORT SECTOR:

THE APPLICATION OF A VEHICLE APPROACH

Jacques Leonardi, Michael Browne, Julian Allen Pedro J. Pérez-Martínez

Transport Studies Group

Christophe Rizet

• Dept. for Transport Economy and Sociology

Roger W. Worth

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

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

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

• r 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

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

Energy consumed and performance indicators: main company data

Vehicle energy use l/100 km Gross Vehicle Weight t Load capacity t Volume capacity: Max nb of palets of the truck number Nb of palets of the payload number Load factor by weight % Mean weight of one palet (density) kg Distance covered (per trip or per year) km Empty running km ou %

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

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 538.8 182 0.030 38.1 76.5% 16.8 Total 781.2 212.2 0.037 36.0 74.9% 13.1

Road freight performance and fuel use: French case 2004

Source: SESP (2007): TRM 2005

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

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)

Key performance indicators in the German base survey 2003

Key performance and energy data for Spain 2003

Source: Pérez-Martínez 2005, SGT 2005 Load Performan ce Length of haul Vehicle kilometer Empty running Mean load Mean fuel use CO2 intensity Vehicle type and gvw (t) Tonnes (103) tkm (106) km km (106) % km tonnes (tkm/ load km) l/100 km g CO2/ tkm Rigid vehicles 742,205 24,859 33 7,827 29.2% 4.5 30 228.4 3,6-7 t 52,816 2,729 52 1,958 31.4% 2.0 25 473.5 7,1-10 t 115,064 5,376 47 2,222 29.9% 3.4 28 305.5 10,1-14 t 327,009 7,444 23 1,875 26.8% 5.4 28 186.2 14,1-18 t 180,284 6,238 35 1,295 26.0% 6.5 28 153.4 18,1-20 t 23,637 347 15 59 29.2% 8.2 30 135.8 > 20 t 43,395 2,725 63 417 35.6% 10.1 32 129.4 Artics 1,102,040 165,468 150 18,060 19.1% 11.3 30 91.2 3,6-24 t 411,086 76,373 186 8,722 18.4% 10.7 30 90.4 24,1-26 t 492,578 72,305 147 7,670 19.2% 11.7 33 92.4 >26 t 198,376 16,790 85 1,668 22.1% 12.9 34 89.2

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 0.091 kg CO2 /tonne-km

Pérez-Martínez 2005 (all artics)

F 0.079 kg CO2 /tonne-km

SESP 2006 (Articulated trucks only)

Why these differences and similarities?

• Different transport patterns in the four

countries?

• Different samples?
• Different survey methods?

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

Accuracy of data gathering method

comparative 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

Energy conversion and emission factors

Emission factors Combustion only Combustion + supply Volume in litres = kg = kgoe = kg C eq = kg eq CO2 = kg C eq = kg eq CO2 Diesel 1 0.845 0.845 0.726 2.664 0.804 2.951 Gasoline 1 0.755 0.791 0.649 2.380 0.774 2.841 Heavy fuel 1000 1000 952 859 3153 968 3553 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 kilometres 282,5/x mpg = y l/100km Carbon equivalent and CO2 equivalent 1 kg C eq = 3,67 kg CO2 eq

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
• riginal primary data from surveys, in other cases, on

secondary, calculated data from at least two different sources

• Unclear why France shows 16 tonnes for HGV mean

load and other countries only 11 t?

‘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)

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’ ?

Level of implementation of efficiency measures in 52 German companies 2003

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?