Modelling the future impact of freight transport on the environment Maja Piecyk Julia Edwards Alan McKinnon 5 th 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 value density Modal split Weight of goods % of freight moved by rail produced / consumed Similar analyses for modal split other modes % of freight moved by water Weight of goods transported by road average handling factor supply chain structure Road tonnes-lifted efficiency of vehicle routing Supply chain structure average length of haul Road tonne-kms vehicle carrying capacity Number of links in chain by weight / volume average load on laden trips vehicle utilisation Average length of links on laden trips average % empty running level of backhaulage Total vehicle-kms Distribution of vehicle-kms by vehicle size, weight and type Vehicle utilisation Timing of Spatial pattern fuel efficiency deliveries of delivery Level of empty running Fuel consumption Contribution to Load factor on laden trips traffic congestion Other externalities Energy-related externalities other externalities per vehicle km CO 2 emissions per litre of fuel Fuel management Noise, vibration, accidents, visual intrusion Other noxious gases Fuel efficiency Carbon content of fuel outputs key ratios determinants
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 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. 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?
Focus group research Analysis Results � Digital recordings of each � Identification of different session factors affecting key supply chain trends & parameters � Notes taken at the event by the research team � Better understanding of issues influencing different types of � Detailed summary of the supply chains focus groups based on the notes and recordings � Results used to construct a Delphi questionnaire � Frequency tables
Decoupling of economic growth and road freight traffic growth Reasons for decoupling: 150 Gross Domestic Product 140 � Changing composition of GDP Index value (1990 =100) 130 decoupling (service-based industry) 120 � Offshoring of manufacturing, 110 road tonne-kms increase in imports 100 � Miniaturisation, lighter and higher 90 value-density products 80 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 9 0 � Modal split 1 2 � Better stock management � Displacement of freight to vans The participants expected the � Growing penetration of the UK decoupling trend to continue haulage market by foreign operators in the future.
Supply chain structure – handling factor Factors influencing the handling 4.0 factor: 3.5 Road transport � Hub-and-spoke networks 3.0 Handling factor � Consolidation initiatives: 2.5 � Primary consolidation 2.0 All modes � Urban delivery consolidation centres 1.5 � E-commerce 1.0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 7 7 7 7 8 8 8 8 8 9 9 9 9 9 0 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 � Reverse logistics 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 Handling factor = tonnes-lifted ÷ � Import of store-ready goods (DC bypass) weight of material inputs � Road pricing/ fuel prices/ congestion The handling factor represents the The overall effect is difficult to predict because average number of links in the the different trends contradict each other. supply chain.
Supply chain structure – average length of haul Average length of the links in Factors influencing the average length of haul: the supply chain • Centralisation vs. decentralisation • Geographical extend of sourcing 140 • Hub-and-spoke networks 120 87 km Index value 1985=100 • IT systems (CVRS, satellite tracking) average haul length 100 • Road pricing/ fuel prices/ congestion 80 • Working Time Directive / Drivers’ Hours Rules 35km 60 • Expanding port hinterlands 40 20 3 6 9 2 5 8 1 4 7 0 3 6 9 2 5 8 1 4 5 5 5 6 6 6 7 7 7 8 8 8 8 9 9 9 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 Again, the overall effect is difficult to predict because the different trends contradict each other.
Freight modal split Issues affecting rail freight transport: • Suitability of rail to move particular products % of tonne-kms moved by different transport modes • Reliability and vulnerability of the rail network 100% • Capacity problems with existing infrastructure • Fuel prices, road charging and congestion of road 80% infrastructure 60% • Need for ‘real’ Government policies 40% • Potential use for container traffic • Flexibility issues 20% • Ability to support JIT replenishment 0% 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 Factors affecting coastal shipping: Road Rail Water Pipeline • Development of coastal ro-ro services • Feeder movements from the deep sea ports % of freight moved by rail • Relative cost • Competition between rail and coastal shipping % of freight moved by waterborne modes • 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 36 Factors influencing empty running: 34 pty 32 % of empty running s run em • Technology (Telematics, CVRS) 30 28 • Working Time Directive / Drivers’ Hours Rules of lorry-km 26 • Consolidation / collaboration initiatives 24 % • Hidden empty running e.g. empty containers 22 20 • Reverse logistics 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 7 7 7 8 8 8 8 8 9 9 9 9 9 0 0 0 9 ' ' 1 • Freight exchanges / online matching services % of truck-kms run empty • 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 A weight-based measure Factors influencing vehicle loading: % of available capacity utilised on laden trips • Consolidation / collaboration initiatives • JIT / lower inventory levels 0.64 • Need for more space-efficient packaging/ 0.63 0.62 Lading Factor 0.61 handling equipment 0.6 0.59 • Loads are volume-limited 0.58 0.57 • Demands from the retailers 0.56 0.55 • Increase in max weight and size of lorries 0.54 0 1 2 3 4 5 6 7 8 9 0 1 2 3 9 9 9 9 9 9 9 9 9 9 0 0 0 0 9 0 • Business is service- rather than cost-driven 1 2 Again participants were not expecting significant changes to the loading factor of vehicles.
Fuel management Fuel efficiency : average kms per Factors affecting fuel management: litre Carbon intensity : average CO2 • SAFED training/ fuel efficiency programmes per litre • New Euro emission standards • Night- time delivery / ‘out of hours’ operation • Technology (Telematics, speed limiters, 1.3 cruise control devices) 1.25 tonne-kms per litre 1.2 • Alternative fuels – bio-diesel, electric trucks 1.15 Index value 1990 = 100 • Fuel prices- if oil becomes very expensive 1.1 1.05 companies will switch to alternative fuels vehicle-kms per litre 1 • Electricity- the infrastructure already exists 0.95 0.9 0.85 Participants generally had concerns 0.8 regarding the future use of biodiesel. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
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