Modelling the future impact of freight transport on the environment - - PowerPoint PPT Presentation

Modelling the future impact of freight transport on the environment

Maja Piecyk Julia Edwards Alan McKinnon

5th September 2007

Green Logistics

• The Green Logistics project is a 4 year project funded by the Engineering

and Physical Sciences Research Council (EPSRC).

• The research team- a consortium of 6 UK universities (Leeds, Cardiff, Heriot-

Watt, Lancaster, Southampton and Westminster).

• The aim of GL project is to look at different ways to improve the economic,

environmental and social sustainability of the UK transport industry.

• Heriot-Watt University- leadership in WM2, WM8 and WM12.

www.greenlogistics.org

WM2- Understanding and forecasting Business-as-Usual (BAU) trends

Reason: To be able to predict the effects of future policies we need to project what the future would be like without any new interventions Time scale: January 2007- May 2008

Objectives of WM2:

Analyse business-as-usual trends in a series of key parameters which determine the environmental impact of freight movement Canvas expert opinion on future trends in these parameters (Focus Groups, Delphi survey) Construct a forecasting model capable of making baseline projections of these parameters

Value of goods produced / consumed Weight of goods produced / consumed Weight of goods transported by road Road tonnes-lifted Road tonne-kms Total vehicle-kms Energy-related externalities Fuel consumption Other noxious gases

CO2

Other externalities value density modal split average handling factor average length of haul average load on laden trips average % empty running vehicle carrying capacity by weight / volume vehicle utilisation

• n laden trips

Distribution of vehicle-kms by vehicle size, weight and type fuel efficiency Noise, vibration, accidents, visual intrusion Contribution to traffic congestion emissions per litre of fuel

• ther externalities per vehicle km

Similar analyses for

• ther modes

efficiency of vehicle routing Timing of deliveries Spatial pattern

• f delivery

supply chain structure level of backhaulage

• utputs

key ratios determinants

Supply chain structure Number of links in chain Average length of links Modal split % of freight moved by rail % of freight moved by water Vehicle utilisation Level of empty running Load factor on laden trips Fuel management Fuel efficiency Carbon content of fuel

Focus group research

Seven focus group workshops (March – June 2007) Organised and conducted in co-operation with Cardiff University (WM1) Five locations across UK to represent the intensity of logistics flows in Britain (London×2, Nottingham×2, Birmingham, Edinburgh, Cardiff) Sample:

156 invitations sent+ 21 more invitees 84 acceptances, 58 participants (acceptance rate 50%, attendance rate 35%, absenteeism rate 31%)

Focus group research

Key issues discussed What will be the business-as-usual trends to 2020? What will be the key drivers of these trends? Are changes likely to be gradual and / or dramatic? To what extent will trends vary between sectors? Participants Logistics experts from different types of organisations: shippers, enablers, carriers, trade bodies, customers and policy makers Representatives of 13 different industry sectors: retail, 3PLs, IT providers, waste and recycling, construction, health etc.

Focus group research

Analysis

Digital recordings of each session Notes taken at the event by the research team Detailed summary of the focus groups based on the notes and recordings Frequency tables

Results

Identification of different factors affecting key supply chain trends & parameters Better understanding of issues influencing different types of supply chains Results used to construct a Delphi questionnaire

Decoupling of economic growth and road freight traffic growth

80 90 100 110 120 130 140 150 1 9 9 9 1 9 2 9 3 9 4 9 5 9 6 9 7 9 8 9 9 2 1 2 3 4 5

Index value (1990 =100)

Gross Domestic Product road tonne-kms decoupling

Reasons for decoupling:

Changing composition of GDP (service-based industry) Offshoring of manufacturing, increase in imports Miniaturisation, lighter and higher value-density products Modal split Better stock management Displacement of freight to vans Growing penetration of the UK haulage market by foreign operators

The participants expected the decoupling trend to continue in the future.

Supply chain structure – handling factor

Factors influencing the handling factor:

Hub-and-spoke networks Consolidation initiatives:

Primary consolidation Urban delivery consolidation centres

E-commerce Reverse logistics Import of store-ready goods (DC bypass) Road pricing/ fuel prices/ congestion

1.0 1.5 2.0 2.5 3.0 3.5 4.0 1 9 7 2 1 9 7 4 1 9 7 6 1 9 7 8 1 9 8 1 9 8 2 1 9 8 4 1 9 8 6 1 9 8 8 1 9 9 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 2 2 2 4 Handling factor

All modes Road transport

Handling factor = tonnes-lifted ÷ weight of material inputs The handling factor represents the average number of links in the supply chain.

The overall effect is difficult to predict because the different trends contradict each other.

Supply chain structure – average length of haul

average haul length

20 40 60 80 100 120 140 1 9 5 3 1 9 5 6 1 9 5 9 1 9 6 2 1 9 6 5 1 9 6 8 1 9 7 1 1 9 7 4 1 9 7 7 1 9 8 1 9 8 3 1 9 8 6 1 9 8 9 1 9 9 2 1 9 9 5 1 9 9 8 2 1 2 4

Index value 1985=100

35km 87 km

Average length of the links in the supply chain

Factors influencing the average length of haul:

• Centralisation vs. decentralisation
• Geographical extend of sourcing
• Hub-and-spoke networks
• IT systems (CVRS, satellite tracking)
• Road pricing/ fuel prices/ congestion
• Working Time Directive / Drivers’ Hours Rules
• Expanding port hinterlands

Again, the overall effect is difficult to predict because the different trends contradict each other.

Freight modal split

0% 20% 40% 60% 80% 100% 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004

% of tonne-kms moved by different transport modes

Road Rail Water Pipeline

% of freight moved by rail % of freight moved by waterborne modes

Issues affecting rail freight transport:

• Suitability of rail to move particular products
• Reliability and vulnerability of the rail network
• Capacity problems with existing infrastructure
• Fuel prices, road charging and congestion of road

infrastructure

• Need for ‘real’ Government policies
• Potential use for container traffic
• Flexibility issues
• Ability to support JIT replenishment

Factors affecting coastal shipping:

• Development of coastal ro-ro services
• Feeder movements from the deep sea ports
• Relative cost
• Competition between rail and coastal shipping
• Consolidation initiatives (loading hubs) e.g. timber

Participants did not anticipate any major changes in the share of the rail freight transport. However, the share of the coastal shipping services is likely to increase.

Vehicle utilisation – empty running

20 22 24 26 28 30 32 34 36 1 9 7 3 7 5 7 7 7 9 8 1 8 3 8 5 8 7 8 9 9 1 9 3 9 5 9 7 9 9 ' 1 ' 3 5

%

• f lorry-km

s run em pty

% of empty running

% of truck-kms run empty Factors influencing empty running:

• Technology (Telematics, CVRS)
• Working Time Directive / Drivers’ Hours Rules
• Consolidation / collaboration initiatives
• Hidden empty running e.g. empty containers
• Reverse logistics
• Freight exchanges / online matching services
• Increasing costs of transport
• Need to prioritise outbound delivery
• Waste regulations

Participants anticipated empty running to fluctuate around the present level.

Vehicle utilisation – lading factor

0.54 0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 1 9 9 9 1 9 2 9 3 9 4 9 5 9 6 9 7 9 8 9 9 2 1 2 3 Lading Factor

A weight-based measure % of available capacity utilised

• n laden trips

Factors influencing vehicle loading:

• Consolidation / collaboration initiatives
• JIT / lower inventory levels
• Need for more space-efficient packaging/

handling equipment

• Loads are volume-limited
• Demands from the retailers
• Increase in max weight and size of lorries
• Business is service- rather than cost-driven

Again participants were not expecting significant changes to the loading factor of vehicles.

Fuel management

0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Index value 1990 = 100

tonne-kms per litre vehicle-kms per litre

Fuel efficiency: average kms per litre Carbon intensity: average CO2 per litre

Factors affecting fuel management:

• SAFED training/ fuel efficiency programmes
• New Euro emission standards
• Night- time delivery / ‘out of hours’ operation
• Technology (Telematics, speed limiters,

cruise control devices)

• Alternative fuels – bio-diesel, electric trucks
• Fuel prices- if oil becomes very expensive

companies will switch to alternative fuels

• Electricity- the infrastructure already exists

Participants generally had concerns regarding the future use of biodiesel.

Spreadsheet model

Developed in Excel Data sources:

Government statistics, reports and other publications (DfT, DEFRA) Statistics and publications from trade bodies (FTA, RHA) NAEI data (National Atmospheric Emissions Inventory) Other sources (UK and EU projects etc.)

Initial modelling Delphi study – quantification of possible changes in key parameters Final modelling based on the results from the Delphi survey Synthesis of results in final report

Initial results

• Three theoretical scenarios
• Cost data sourced from reports

published by DfT and DEFRA

• Cost of carbon (in 2005 prices):

low estimate = £43.95 per tonne medium estimate = £82.59 per tonne high estimate = £159.88 per tonne

• Stern Review ≈ £232 per tonne (2005

prices)

Internalisation of the external cost of road freight transport

Taxes: VED, fuel duty + VAT Externalities: air pollution, noise, congestion, accidents and damage to roads/bridges

If congestion costs are not included the total external costs of road freight transport are almost fully internalised (91%).

Next step- Delphi questionnaire

September 2007-December 2007 Purpose: to quantify the trends identified by focus group participants Two rounds Expected sample size ≈ 100 responses Assuming response rate of 25% ≈ 400 questionnaires will be distributed Target group: participants of the focus groups who expressed an interest, senior managers, policy makers, academics, supply chain experts etc.