Reducing the external costs of food distribution in the UK
This chapter summarises the results of a study undertaken for the UK government which examined the opportunities for reducing the external costs of food distribution. These costs comprised environmental infrastructural and congestion elements. In consultation with an industry advisory group the researchers identified a ‘long list’ of over 60 measures 15 of which were subjected to external cost modelling. The chapter focuses on a short list of six of these measures which were considered to be practical and offer significant benefit. These included transport collaboration increasing maximum truck weight and size redesigning logistics systems and increasing the use of vehicle routing and telematics systems. It is estimated that the application of the six measures could cut the external cost of UK food distribution in 2012 by 17%
Campaigning by environmental groups has given the impression that sourcing food over long distances is unsustainable. They have highlighted the so-called ‘food miles’ issue and advocated a return to more localised sourcing (Sustain, 1999). ‘Food miles’ have been shown, however, to be only a partial, and in some cases misleading, measure of the environmental impact of the food supply chain (AEA Technology, 2005). This impact is affected not just by the distance travelled but also the nature of the transport. The long distance movement of food in full loads in a modern articulated vehicle along a motorway is less environmentally damaging than its distribution by van over short distances on urban roads. Lifecycle analysis also reveals that it can be environmentally beneficial to source food products from distant locations where the production operation uses less energy and emits less pollution (Mason et al., 2002).
Cutting the external costs of food distribution, therefore, involves more than simply returning to more localised supply systems. This is only one of a range of measures that can be applied to ‘green’ the movement of food products. In this chapter we summarise the results of a study commissioned by the British government to review these measures, quantify their environmental benefits and thus rank them in order of importance. The work was undertaken for the Food Industry Sustainable Strategy (FISS) Champions Group on Food Transport, which comprised representatives of the food industry and three government departments.
As the measures varied in their physical effects on particular externalities, such as air pollutants, noise and accidents, a common metric had to be found to compare them on a consistent basis. This metric was money, in other words the monetary valuation of the environmental and social impacts. Numerous attempts have been made in the UK to attach monetary values to the external costs of transport (e.g. Sansom, 1998; NERA et al., 2000; INFRAS, 2004; Piecyk and McKinnon, 2007). They have recently been reviewed by a study undertaken for the European Commission as part of a wider initiative to internalise these costs in higher taxes (CE Delft, 2007). The study reported in this chapter employed official valuations obtained from UK government departments. These related to six externalities: accidents, air quality, climate change, noise, traffic congestion and infrastructural wear.
The scope of the work was bounded by a ‘farm-to-shelf’ principle which excluded the movement of agricultural inputs upstream of the farm, consumer shopping trips and home deliveries of food. For the purposes of this research the food supply chain extended from the farm gate to the shop, passing through varying numbers of factories and warehouses along the way. Only food movements1 within the UK were considered and all the data analysed related to 2005.
Food transport externalities were calculated on a vehicle-km basis (or tonne-km for non-road modes). Data on the movement of food by road was obtained from the Continuing Survey of Road Goods Transport (Department for Transport, 2006). Figures for air pollution and CO2 emissions came from the National Atmospheric Emissions Inventory (NAEI) adjusted using information from NERA (2005) on the effects of vehicle weights. Monetary values for externalities came from various sources including DEFRA (2006), Department for Transport (2007a) and personal communication with government officials. Values for the cost of peak and off-peak congestion were obtained from the Strategic Rail Authority, SRA (2003), while a case study on urban road noise carried out in Stuttgart distinguished the valuation of night-time noise from that of daytime noise (Schmid et al., 2001). Table 22.1 presents estimates of the total cost of each of the six externalities in 2005. Congestion costs were by far the largest element in the calculation, followed infrastructure wear and accidents. Other environmental costs, associated with air pollution, global warming and noise, together accounted for less than 20% of the total.
The external costs varied substantially by vehicle type reflecting differences in the nature of their deliveries and the sensitivity of the environments in which they tend to operate (Fig. 22.1). For example, light rigid HGVs had the highest congestion cost per vehicle-km because much of their mileage is run during peak hours in urban environments. In contrast, articulated HGVs had a higher proportion of their external costs in the infrastructure and climate change categories owing to their heavier axle weights and higher fuel consumption per km.
The original objective of the research was to find a combination of measures that would reduce the total external cost of food transport in the UK by 20%. It was recognised that the choice of measures would be strongly influenced by the distribution of external costs across the six externalities, different transport modes and the various types of vehicle. The study also examined the barriers likely to inhibit the implementation of these measures.
Following consultation with various stakeholder groups, the long list of options was reduced to a short list of 15 measures whose impact on external costs was quantitatively modelled. The choice of options was mainly determined by the scale of potential environmental benefits and likelihood of implementation, but it was also influenced by the availability of data. Some options, which could yield significant environmental benefit, were excluded because no means could be found of quantifying their impact. There had to be sufficient information on each option to give the FISS Group confidence in the accuracy and realism of the results. The nature of the modelling, nevertheless, varied across the 15 short-listed options depending on the amount, type and quality of data available. Broadly speaking, the modelling was conducted at four levels of realism:
The option screening process also excluded measures which would have an indirect effect on food transport and/or have an adverse environmental effect on other parts of the food supply system. Examples of such measures were:
• Relocation of food shops to suburban or out-of-town locations: While this would reduce the external costs of freight deliveries it might substantially increase the frequency and length of car-borne shopping trips, transferring the external costs from freight to personal transport.
• Encouraging consumers to switch their demand to less transport-intensive products: This would raise wider issues about the sustainability of consumption patterns and, as discussed earlier, need not yield a net environmental benefit.
• Reducing the volume of packaging used on finished products to improve vehicle utilisation: This would have wider implications for production operations, product wastage and the recycling/disposal of packaging material
The short-listed options were considered and approved by the FISS Group before being tested against the ‘base case’ external cost values for 2005. Reductions in external costs were estimated for 2012, though the modelling assumed that there would be no change in traffic levels during the intervening period. As a result, the estimated reductions in external costs could be conservative. Options were, nevertheless, tested against low, central and high implementation scenarios, representing differences in the applicability of the measures to different sectors of the food industry and the potential for improving the take-up rate for measures within each sector.
Table 22.2 shows the three levels of estimated savings in external costs accruing from the 15 measures, expressed both as monetary values and as a percentage of the total external costs of food transport.
It was recognised that these six options were not mutually exclusive and, therefore, that their combined impact on total external costs would not necessarily be cumulative. Further modelling revealed that in combination these measures would reduce the external cost of domestic food transport by 17.3% (or £326 million per annum).
These options will be reviewed in descending order of the potential reduction in external costs. None of the options are exclusive to the food supply chain. Indeed some have already been more effectively applied in other sectors where their beneficial effect on the environment has been demonstrated.
Since 1991 new trucks and vans have had to meet tightening emission standards. Table 22.3 shows how limits on the amount of nitrogen oxides (NOx) and particulate matter (PM10) have been drastically reduced over the intervening period. The gradual renewal of companies’ fleets results in the upgrading of freight vehicles to higher emission standards. The NAEI contains forecasts of future vehicle emissions based on projected upgrades to these higher standards. For example, it is predicted that by 2012, 73% of vans will meet Euro IV standard, 48% of rigid HGVs will meet Euro V and 57% of articulated HGVs will conform to the Euro V standard. Allowance is also made for increases in the proportion of vans run on diesel fuel from 88% to 90%, offering fuel efficiency benefits.
Overall it can be seen that the gradual adoption of these higher emissions standards significantly reduces the external cost of air pollution (Table 22.4). This measure has a very marginal effect on climate change, however, CO2 is not one of the exhaust gases controlled by Euro emission standards. On the contrary, in an effort to cut emissions of other pollutants, particularly NOx, vehicle manufacturers have had to sacrifice potential fuel efficiency gains and CO2 reductions. The climate change benefits quoted in Table 22.4 resulted mainly from the marginal switch from petrol to diesel fuel in the van fleet but also from improvements in the average fuel efficiency of new freight vehicles.
The high and low estimates for external cost savings from this option reflect the rate at which higher emission standards are adopted across the vehicle fleet transporting food products. The lower values assume that it will take two years longer to achieve the forecast emission level, while the high values assume that they will be reached two years earlier. The introduction of the low emissions zone in London, which penalises companies whose vehicles do not meet Euro III NOx standards, is promoting more rapid adoption of cleaner engines. On the other hand, during periods of economic recession, hauliers have less capital to invest in new trucks and often extend the vehicle replacement cycle.
There is a limit to the extent that any individual company can improve the efficiency of its transport operation. This limit can be relaxed when it is prepared to collaborate with other companies to consolidate loads, improve backloading and reschedule deliveries. In recent years, businesses in the food sector have shown increased willingness to explore opportunities for collaboration. The European Logistics Users Providers and Enablers Group (ELUPEG) and Efficient Consumer Response (ECR) (Institute of Grocery Distribution, 2003 and 2007) have been actively promoting transport collaboration among food businesses. The English Farming and Food Partnerships (EFFP) have also been working with companies such as ASDA, Tesco and McDonalds to help create more transparent and efficient supply chains (Anon, 2005).
Collaboration can take various forms. It can involve cooperation between manufacturers distributing their products to similar outlets. This ‘horizontal’ type of collaboration is well illustrated in the food sector by a collaborative initiative established by Douwe Egberts, Unipro and Masterfoods within the Dutch catering sector (Cruijssens, 2006). All three companies supply frozen products to catering outlets and had a 68% customer overlap. As distribution costs constituted a relatively high proportion of sales revenue it was agreed that joint distribution presented a commercially attractive opportunity. Overall, joint route planning and sharing of vehicles reduced total distance travelled by 31%, increased load factors by over 95% and permitted a 50% reduction in fleet size. Although the collaboration was financially motivated, it yielded major environmental benefits.
Also in the Netherlands, Unilever and Kimberley Clark have set up a joint distribution centre (DC) at Raamsdonksveer where they consolidate loads. This has achieved substantial savings in logistics costs, improved service quality and reduced environmental impact. In this case the distribution operation is outsourced to a logistics service provider (LSP) (Kuehne and Nagel). LSPs can play a critical role in collaborative initiatives. In the USA, collaborative transportation management (CTM) has been strongly advocated as a holistic process that brings together supply chain trading partners and logistics providers to drive inefficiencies out of the transport planning and execution process (VICS Logistics Committee, 2004). The objective of CTM is to improve vehicle utilisation through the sharing of information and forecasts and collaborative planning of freight movements. Carriers participating in CTM pilot projects reported reductions in empty mileage of 15% and fleet utilisation improvements of 33%.
Organisations belonging to ECR UK have trialled ‘collaborative multi-partner trunking’ which involves a retailer and a group of their suppliers working together to reduce empty running. It was estimated that across 20 organisations participating in the trial, a total of around 3.2 million vehiclekm could be saved. More detailed modelling of the potential savings in inner London from collaboration between Boots, Musgraves-Budgens-Londis and Sainsburys suggested that 2.5% of vehicle-km could be eliminated. This potential saving was constrained by restrictions on delivery times imposed by local authorities both at individual shops and at a zonal level. The case for easing these restrictions is discussed in Section 22.4.5.
A more radical collaborative option was tested during the study. This involved the integration of two large supermarket chains’ distribution systems and sharing of DCs and vehicles fleets. This hypothetical analysis indicated that, if the existing DC locations and capacities were retained, lorry traffic could be cut by approximately 6%. Optimising the DC locations and capacities to supply the combined retail chains would raise this percentage to 10%. These reductions in lorry-km may seem rather low given the scale of restructuring required. This can be explained partly by the fact that the analysis only included outbound distribution to shops and was confined to two of the largest UK supermarket chains which generated a large proportion of full loads. A similar analysis conducted on smaller retail chains or food manufacturers would probably have revealed greater opportunity for combining part loads and cutting lorry-km.
On the basis of more realistic estimates of the degree of transport collaboration that might be achieved by 2012, potential savings in external costs might average around £60 million per annum (Table 22.5).
The main barriers to collaboration between suppliers and distributors in the food supply chain are often management culture and lack of trust. Equitable sharing of the costs and benefits and effective interorganisational exchange of information play a pivotal role in any collaborative scheme. Although the potential benefits of transport collaboration have been widely publicised for many years, it is only recently that many large companies in the food sector have begun to make a serious commitment to rationalising their transport operations on a collective basis (Institute of Grocery Distribution, 2009).
On roughly 6.5 of every 10 km that a truck travels carrying food products, the load is constrained by the cubic capacity of the vehicle (Table 22.6). Twice as many of these ‘laden-km’ are subject to a volume constraint as to a weight constraint. Increasing the size of vehicles can therefore create greater opportunities for load consolidation in the food sector than raising their maximum weight. Capacity can be expanded either vertically in double-deck vehicles or horizontally in longer vehicles. The study focused on the former option as this would not require any relaxation of current regulations. Two separate studies, sponsored by the UK government (Knight et al., 2008) and European Commission (Transport and Mobility Leuven, 2008) have examined the costs and benefits of increasing the length and weight of trucks.
Source: Department for Transport, 2006.
Unlike in much of mainland Europe where truck height is restricted to 4 or 4.2 m, there is no legal limit on the maximum height of lorries in the UK. By ‘custom and practice’ 5 m is considered to be the height limit across the trunk road network. This greater height clearance in the UK has allowed companies moving lower density products, such as packaged food, to double-deck their trailers and carry two layers of pallets or roll cages. Double-deck lorries are now extensively used in the UK food distribution system. In 2006, approximately 6% of the trailers operated by large British supermarket retailers were double decked (Institute of Grocery Distribution, 2007). These vehicles are well suited to fixed-route interdepot trunking, factory gate pickups and deliveries to larger supermarkets. Depending on the trailer configuration, double decks can carry 60–90% more product than a single deck vehicle. Tesco estimates that its double-deck trailers have 67% more carrying capacity than single decks. As they are heavier and have a poorer aerodynamic profile, double-deck vehicles consume more fuel per vehicle-km, but their overall energy efficiency (expressed as tonne-km or pallet-kms per litre of fuel) is much higher. One case study of a British retailer’s store delivery operations recorded fuel savings of 48% (Department for Transport, 2005a). Efforts have been made recently to improve the aerodynamic styling of double decks, although this reduces available space. One streamlined double-deck trailer sacrifices 9% of internal cubic capacity to achieve a 15% reduction in fuel consumption.
The main factors inhibiting the use of double-deck trailers are inadequate reception facilities at factories, DCs and shops, their higher capital costs and the limited proportion of loads that are large enough to exploit the extra vehicle capacity. Double decks account for around 5% of the articulated vehicle-km generated by major grocery retailers and wholesalers (Institute of Grocery Distribution, 2007). The modelling of potential external cost savings assumed that by 2012 this share would increase to 25% of food laden-km subject to a volume constraint (Table 22.7).
The expansion of retail chains, changing spatial distribution of demand, the reconfiguration of the upstream supply chains (e.g. partly as a result of the growth of imports) and company mergers can leave DCs in sub-optimal locations and/or with inadequate capacity. This impairs the efficiency of distribution operations, causing vehicle-km and the associated externalities to be higher than necessary. Relocating and resizing DCs, and reallocating stores between them, can significantly improve the efficiency of the system and reduce its environmental footprint. This ‘reoptimising’ of the system also needs to take account of seasonal fluctuations in throughput. Lack of capacity at peak times can result in the additional shuttling of product to/ from ‘overspill’ warehouses. One biscuit manufacturer, for example, had a single DC which was unable to hold its ambient Christmas stock. It used an external ambient storage facility about 16 km from the DC. This generated an extra 104 000 vehicle-km miles per annum, representing about 5.2% of the total. Another problem is that some companies do not revise their delivery boundaries frequently enough to take account of changes in the patterns of demand and road accessibility. Indeed, many companies have now abandoned fixed boundaries around DC service areas and plan deliveries across the system as a whole. This can achieve much higher levels of vehicle utilisation although, in the case of more complex systems, presents formidable analytical problems.
To assess the possible contribution of system redesign to external cost reduction, transport flows for four supermarket chains were modelled using data on DC and store locations, the volume of supplies moved, delivery frequencies, transport costs and other operating parameters, split by product categories. Several strategic options were tested for each of the supermarket chains:
Each of these strategic options would offer significant savings in vehiclekm, vehicle numbers and operating costs, with the second being the most beneficial to the environment (Table 22.8). The modelling revealed that simply by optimising DC size and store allocation, a reduction in vehiclekm of around 10.5% could be achieved. It should be noted too that this modelling only took account of outbound deliveries to shops. As generally happens in the planning of DC locations, no allowance was made for the geography of inbound flows into the DCs.
The results from this strategic modelling were factored into the externalities model on the assumption that distance savings would apply equally across all vehicle classes, types of route and time periods (Table 22.9). The logistical systems of food manufacturers and food service companies could be subjected to similar redesign to augment these external cost savings.
The main constraints on the redesign of logistics systems are the fixed nature of the assets, the additional capital investment required and the scale of the associated replanning and potential disruption. The re-sizing and relocation of DCs is clearly a longer term and more expensive option than the other five.
The rescheduling of road deliveries to the evening and night reduces their exposure to traffic congestion during the working day. Vehicles can then travel at more fuel efficient speeds, releasing less pollutants per kilometre travelled at times of day when there are few people on the street to inhale them. Accident risk is also reduced as there is less traffic on the roads and fewer pedestrians crossing them. The only externality which tends to increase with greater evening and night-time running is noise irritation, although in recent years this has reduced on a vehicle-km basis as a result of several trends:
• technological advances permitting the quietening of vehicles below legal limits: for example, one vehicle manufacturer has managed to reduce running noise to around 60 dB(A), well below the 78 dB(A) that a standard vehicle of comparable size creates when accelerating from stationary
• investment in on-vehicle noise mitigation systems such as air brake silencers, load restraint systems, quieter refrigeration units and the use of alternative fuels such as compressed natural gas (CNG)
As many factories, warehouses and even supermarkets are now open for 24 hours a day, the opportunity exists to send and receive deliveries around the clock. Even when premises are closed, unattended delivery systems can be used to give delivery staff secure access out-of-hours. The main constraints on evening/night-time delivery appear to be the rigidity of existing working practices and curfews imposed by local authorities. Many supermarkets in urban areas are close to noise-sensitive residential areas and have night-time delivery restrictions written into their planning consents. A survey by the British Retail Consortium (2004) found that 32% of retail outlets were affected by delivery curfews while the Freight Transport Association (2005a) estimated that over 40% of supermarkets throughout the UK were subject to some form of restriction on night delivery, usually between 10 pm and 7 am. For instance, 104 of the 360 supermarkets operated by the Morrisons supermarket chain were affected by night curfews.
In addition to site-specific restrictions, there are zonal bans on night-time lorry movement. The largest area covered by such a ban in the UK is in London. Sainsbury’s has estimated that the London lorry ban results in its delivery vehicles travelling an additional 160 000 km per week because they must circumnavigate the restricted area to position vehicles for early morning store delivery, in many cases running this extra distance through environmentally sensitive areas.
Relaxing some of these regulations and other operational constraints would permit greater rescheduling of deliveries. This measure would yield both economic and environmental benefits (Cabinet Office, 2005). The modelling undertaken for this study suggested that the increase in noise costs would be marginal and far exceeded by congestion and accident savings (Table 22.10). It should be noted, however, that the noise costs related solely to moving vehicles and excluded the noise associated with loading and unloading operations at collection/delivery points. On the other hand, the analysis did not make allowance for reductions in emission costs accruing from vehicles running more fuel efficiently on congestion-free roads.
CVRS is now widely adopted by companies across the UK food supply chain. It allows them to reduce journey planning time, cut transit times, minimise vehicle-km, improve vehicle loading and save fuel (Department for Transport, 2005b). The software is usually calibrated to minimise transport costs and these costs are a function of both time and distance travelled. This does not necessarily minimise the environmental costs of the delivery. The available case study evidence suggests, however, that the application of CVRS generally reduces both vehicle-km and fuel consumption. Reductions in transport costs and distance travelled can be in the order of 5–10% (Eibl, 1996). The magnitude of the resulting saving partly depends on the quality of the previous system of manual vehicle routing and the nature of the transport operation. The potential benefits tend to increase with the number of drops and collections per delivery round, the range and complexity of delivery restrictions, the variability of collection and delivery times and the diversity of vehicle types and capacities. It should be noted, too, that some of the more popular CVRS packages now include modules which can plot routes that minimise carbon emissions.
An e-mail survey by the Freight Transport Association (2004) of a sample of member companies found that 22% were using some form of CVRS with a further 7% actively considering adopting it. This relatively low level of CVRS penetration is perhaps surprising since over 90% of CVRS users in the survey indicated that they were either ‘satisfied’ or ‘very satisfied’ with their choice of system and supplier. A large proportion of the users reported CVRS benefits which had obvious environmental spin-offs: improved efficiency (75%), reduced fuel cost (38%), better matching of demand and resource (38%) and reduced mileage (29%).
The level of CVRS adoption in the food sector may be higher than the Freight Transport Association average. Of 13 large retailers of grocery products profiled by the Institute of Grocery Distribution (2007), 11, accounting for 85% of total lorry-km, used CVRS systems. From industry consultation, however, it was concluded that across the food sector as a whole, CVRS could be more fully exploited to reduce the environmental impact of distribution. Evidence to support this claim came from a retrospective analysis of the routing of a large sample of food deliveries surveyed over a 48-hour period in May 2002 (as part of a so-called transport KPI audit) (Department for Transport, 2003; McKinnon and Ge, 2004). The actual routing was compared with the optimal routes constructed within seven delivery scenarios in which the length of driver’s shifts and delivery time windows at shops and warehouses varied (McKinnon et al., 2004). The researchers concluded that:
the actual pattern of delivery observed during the transport KPI survey for the sample fleets was sub-optimal. The degree of sub-optimality is difficult to determine as the KPI survey did not collect any information about opening times, delivery time windows or driver shifts. The analysis has shown, however, that if deliveries had been optimised within a realistic range of scheduling constraints, substantial reductions in vehicle-km, empty running, transit time and vehicle numbers could have been achieved. This would have translated into significant economic cost savings and environmental benefits (p.36).
Telematics can also enhance the efficiency of delivery operations by tracking vehicles, establishing communication with drivers and remotely monitoring various aspects of vehicle performance. By giving companies visibility of their vehicle fleets, it permits more effective planning and management of the transport operation (Department for Transport, 2007b). Routes can be replanned at short notice to take account of real-time information on traffic conditions and changing customer requirements. When used in combination with CVRS, a higher level of transport efficiency can be achieved with consequent benefits for the environment.
Surveys by the Freight Transport Association (2005b) of telematics usage among member companies found that it almost doubled between 2002 and 2004, rising from 17% to 33%. An analysis of the logistical profiles of 13 large grocery distributors indicated that eight (62%) employed vehicle tracking, a similar number had installed in-cab communication and five (38%) were using real-time traffic information in their delivery planning. Research by McKinnon et al. (2004), however, found that some of the companies that had installed telematics systems in their vehicles were using only a small part of the available functionality. On the other hand, some larger businesses were deploying them in a more sophisticated way to improve transport collaboration. In 2007, for example, Coca Cola, Master-foods and Procter & Gamble jointly launched the Skylark Project which used vehicle tracking to identify and exploit opportunities for shared use of vehicles.
On the basis of the available evidence and industry consultation, the study concluded that there was still considerable scope for increasing both the diffusion of telematics technology and the average environmental benefit obtained from individual applications. This is reflected in the estimated external cost savings for this ‘green logistics’ option (Table 22.11).
Barriers to the uptake of CVRS and telematics include a lack of technical knowledge, particularly of the potential benefits, scepticism about new technology, the fragmentation of the road haulage industry and cost. Steady reductions in the real cost of both the hardware and software are, nevertheless, making these systems more affordable, while their functionality and user-friendiless is improving.
The research summarised in this chapter identified a series of sustainable logistics measures and attempted to quantify their impact on the external costs of food distribution in the UK. It was undertaken for the UK government in close cooperation with an industry group which provided both advice and data. The original goal was to find a set of measures which collectively would reduce the external costs by a fifth. The short list of six measures that was finally selected would, in combination, cut these costs by around 17% by 2012 against a 2005 baseline (DEFRA, 2007). The choice of measures was based mainly on their potential contribution to the reduction in external costs, but also took account of the likelihood of them being implemented and the availability of data to model their impact. No attempt was made to estimate the financial cost of applying these measures. In most cases, however, they would yield both economic and environmental benefits and be likely to prove self-financing within a relatively short payback period.
The FISS Group excluded from the final short list two other measures which have generated much discussion in recent years: localised sourcing of food and modal shift to rail and waterborne transport. The former option was largely excluded because a full-life analysis would be required to determine the net environmental impact, not only of changes to transport but also to related production, storage and handling operations. This was beyond the scope of the research. The exclusion of local sourcing also helped to deflect attention from the ‘food miles’ issue which has tended to dominate much of the debate on the sustainability of food supplies in recent years.
The modal shift option was not short listed for more detailed analysis because at the time of the study very little food was moved by rail and most food retailers and manufacturers did not consider it to be a viable logistical option in the UK. Over the past three years, however, two major retailers, Tesco and ASDA, have begun to make significant use of rail for long haul deliveries, while Tesco has also started moving wine and spirits by canal. This highlights the need to keep the range of sustainability measures under review and be prepared to expand the list of priorities.
Finally, the results of the modelling are very sensitive to the monetary values attached to the various externalities. The values will vary through time as the relative severity of the various environmental problems changes. As congestion worsens, the marginal cost of adding extra lorry journeys to the road network rises, while the cost of CO2 emissions is projected to increase as efforts intensify to decarbonise the economy. The analysis will therefore require regular updating to provide policymakers and logistics managers with the advice they require to maintain the ‘greening’ of food transport operations.
The authors are grateful to the Department for the Environment, Food and Rural Affairs (DEFRA) for funding the study and granting permission for the main results to summarised in this chapter. They also wish to acknowledge the support of Aecom, the main contractor on the project, and the members of the FISS Champions’ Group on Food Transport who provided valuable advice and data. Responsibility for the accuracy of this summary, nevertheless, rests with the authors.
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1Although reference is made to food, the analysis covered both food and drink products.