Review of Efforts to Induce Changes in Freight mode Shares

This section provides an overview of policies aimed at inducing mode/vehicle shifts at national and urban level freight transportation. National level policies focus on a range of modal shifts, while urban level policies aim at various truck sizes, as explained below.

National Level Policies

Freight mode/vehicle policies are typically designed to combat negative externalities such as: congestion, deterioration of infrastructure, air and noise pollution, and accidents. The implementation of these types of policies may also result in externalities and unintended consequences. This section discusses relevant policies and their impacts.

A few studies concentrate on the factors that influence shippers or carriers to shift from road to rail mode. Policies that may not be intended to cause mode shift, may inadvertently cause a shift from a more sustainable mode to one that causes more emissions and environmental impacts. For example, the British government set the goal of increasing rail mode share by 80% by the year 2010, as a means of reducing the negative impacts caused by road transport. The need for this intervention was due to policies that had had the opposite effect, such as improvements in roads, fuel costs, and fewer investments in rail and other sustainable modes. The reasons for the preference for road in freight identified by Woodburn (2003) are: pressure from customers for just-in time delivery, which causes low-volume flow; road accessibility to all locations; poor level of service offered by rail to customers; and lack of funding for new and improved rail infrastructure. It was also found that rail could serve as a potential mode to 37% of freight in 5 years, provided supply issues are addressed (Woodburn 2003).

Dewey et al. (2002) study the potential of subsidy and tax policies to correct the market’s misallocation of freight shipments between the trucking and railroad industries, the existing equilibrium in the surface freight transportation market, and the optimal subsidy. With a sensitivity analysis the authors conclude that subsidizing the shipment of freight by railroad reduced truck shipments, and improved economic efficiency by more than enough to offset the loss in efficiency caused by collecting the revenues to pay the subsidy.

Stewart et al. (2008) study the impact of rail to truck mode shifts on pavement structures in three major corridors in the U.S. The mode shift to truck is often due to the closure of short-line railways, and growth in economic activities (new industries) in the area. Asset management techniques are used to estimate the cost of damage to pavement due to increased truck traffic. This study estimates that twelve out of 32 pavement sections in Wisconsin and South Railway (WSOR) line, and 18 out of 48 pavement sections in Michigan face reductions in lifespan. The viability of modes is also addressed in this study. In the case of the Minnesota Prairie Line closure, the new ethanol plants increased truck volume by 400%, making 11 out of 14 pavement sections inadequate to accommodate such traffic. The rail can’t be replaced due to high volumes, and the volatility of the ethanol.

McAuley (2010) evaluates the cost of externalities due to rail and road freight on four major corridors in Australia, showing that rail mode causes fewer externalities than road. The externalities evaluated are shown in Table 30. The table gives the costs of externalities in cents per ton-kilometer (ton-km) for rail and road freight movement. The total externalities per ton-km are greater for road mode compared to rail mode. For road and rail freight, the major externalities are accidents and GHG emissions. But the costs of accidents and GHG emissions for rail freight is much lower compared to those of road freight. This study concludes that road freight is underpriced relative to rail due to externalities, and suggests the mode shift to rail as one of the solutions to mitigate negative externalities.

Table 12

Gleason et al. (2005) evaluate the net benefits associated with increasing the utilization of rail mode to 100% of its capacity from 25%. The benefits include the reduction in the costs of such externalities as congestion, accidents, pollution, noise, and infrastructure damage, and the costs associated with fuel savings. The utilization to 100% of the capacity increases the freight rail mode from 14% to 56%. This study finds that the major reasons behind underutilization of rail capacity are: the weight ceiling is less than other lines, and limited trans-loading facilities. The major benefit in terms of reduction in CO2 emissions, would be $388,000 every year. The total net benefits are evaluated as $13.5 million every year. This study also evaluates the boost to the economy in terms of employment, as every million dollar of revenues from rail annually creates 11.4 new jobs. The transportation of hazardous materials by rail is more efficient, which is not considered in this study.

Beuthe et al. (2002) study the benefits associated with the new modal split occurring after internalizing the external costs of road users by changing the pricing policy. First this paper evaluates the following externalities: congestion, pollution, accidents, noise and damages to infrastructure, and makes the users responsible for these externalities. With the new pricing, the modal split is simulated using NODUS virtual network methodology (a systematic G.I.S. designed for analyzing freight transportation over long distances); optimizing generalized costs over links on a virtual network. The results show decreases in congestion costs by 44%, pollution costs by 14%, accident costs by 24%, and a 20% decrease in noise pollution costs, and a 27% decrease in damages in the 1995 traffic scenario.

Modal shift is highly effective in reducing external costs to society. Wiederkehr et al. (2004) determine the definition, criteria, potential policies and their effects for an Environmentally Sustainable Transport (EST) project initiated by Organization for Economic Co-operation and Development (OECD) countries. Based on the case studies from nine OECD countries, this study concludes that 40% of the EST can be achieved by technological improvements, while the remaining 60% should be achieved from demand-side changes and modal shifts to sustainable modes. The criteria for EST are based on the emissions of CO2, NOx, VOC, particulate matter, noise and land-use. The emissions of CO2 from transportation should be 20% to 50%, and emissions of NOx, and VOC should be less than 10% of such emissions in 1990. The PM-10 should be reduced to 55- 99% of the 1990 levels. The noise levels should not be more than 55 decibels (dB) during the day, and 45 dB at night. The land use should be such that regional objectives of air, water, and eco-system protection are met.

To meet the above criteria, the primary solution is switching from less to more sustainable modes, accompanied by the relative decrease in the unsustainable modes. This goal is called the modal split of EST,and planned for 2030. The goals of the modal split of EST in 2030 include 60% of ton-km of freight transported by rail or combined modes, and 20% of ton-km transported by waterways. In addition, the train and waterways should incorporate increased capacities, speeds and more sustainable fuel sources, such as electric or hydrogen cell-based fuel. Even the production of electricity has to be made efficient and mostly from renewable energy sources. This change requires targeted investments in sustainable modes and energy, combined with policy regulations.

The modal split of EST in 2030, compared with projections from the current modal trends and externalities have been evaluated until 2015. Accidents are the major externality in both cases. In EST, the total cost of externalities except congestion is reduced by 37% by 2015. In 2015, for nine countries, the cost of externalities is 5% of the GDP in the current scenario, and 2% of the GDP in EST. Modal shifts alone could cause a dramatic decrease in external costs. The current cost of externalities, which is evaluated as 6% of the GDP, is due to the past modal shifts that caused the current modal patterns. The modal split of EST in 2030 has its own negative impacts on the economy, such as reductions in GDP growth and the employment rate, but compared to the estimated reduction in externalities these negative impacts are minimal.

Urban Freight Policies

One type of policy that has had unintended consequences is truck size restrictions, or the banning of larger trucks from urban centers. These types of policies are implemented with the aim of decreasing congestion, which results in decreased pollution and increased safety for road users. However, the implementation of this policy results in larger shipments that would typically be delivered in a large truck, being broken down into smaller shipment sizes and delivered in smaller trucks, to comply with the regulation. The policy therefore results in more than one small truck replacing a large truck to compensate for the reduction in capacity.

This concept was explored in research conducted by Holguín-Veras et al. (2013), which compared the performance of large and small trucks delivering in the urban area, in relation to the social costs produced. The results indicated that when using social cost as the determinant for which truck class is optimal, it is essential to not only analyze the amount of social cost generated, but also to include the cargo productivity and the substitution effect between the various classes. Using this method, the analysis is able to capture the true response to restricting larger trucks. The amount of cargo will not be reduced to limit the truck traffic, as this will have negative effects on the economy; therefore, what will occur is the replacement of the larger truck with a number of small trucks that will be able to cover the amount of cargo that is being delivered. So even though the aim of the implementation of the policy is to reduce congestion and the resulting negative externalities from congestion, the net effect may result in the opposite, and actually increase social costs due to an increase in Vehicle Miles Traveled (VMT) by small trucks.

Using the Oakland, California network as a case study, and a combination of traffic micro-simulation, econometric modeling, optimization techniques, and valuation for the externalities to determine the optimal mix of traffic and the isocost substitution rate, which was complemented with a sensitivity analysis to evaluate the effects on social cost. Application of the optimization formulation to the base-case scenario indicated that the use of only large trucks would provide the optimal traffic mix that would minimize social costs. The use of only large trucks when the shipment requires leads to: social cost savings of 2.76%; a reduction in average congestion time by 3.84% or $76,151/day; and reductions in various pollutants of from 0.02% to 8.68% (Holguín-Veras et al. 2013).

The sensitivity analysis done using various combinations of small and large truck payloads indicated that the most crucial factor that contributes to the social costs is the substitution rate between small and large trucks, which was expected based on theory. The isocost substitution rate was found to be 1.6. In other words, if the average payload for a small truck is less than 62.5% (1/1.6) of the average payload of a large truck, then the use of large trucks would result in a reduction in social costs. If this is not the case, then small trucks are the better choice. The results of the study are important for policy-making, as they combat the assumption that minimizing large truck traffic will result in a decrease in social costs without factoring in the substitution effects. Therefore, the take-away for government and planning agencies is to assess these substitution effects to have a broader, more conclusive view of social costs during the decision-making process (Holguín-Veras et al. 2013).

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