Transportation

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GCAM contains a detailed representation of transportation energy use and service demands, with the sector divided into three service demands: passenger, freight, and international shipping (see Figure 1). The overall approach is described in Kim et al. (2006)<ref>Kim, S.H., J.A. Edmonds, J. Lurz, S.J. Smith, and M. Wise (2006). “The ObjECTS Framework for Integrated Assessment: Hybrid Modeling of Transportation.” Energy Journal 27: 63-91.</ref>; a brief summary follows. Passenger services are represented in terms of passenger-kilometers, and freight and international shipping are represented in tonne-kilometers. Service intensities are represented in kilojoules per passenger- or tonne-kilometer, and are derived from exogenous vehicle intensities (i.e., kJ per passenger- or tonne-km) divided by exogenous load factors (i.e., persons or tonnes per vehicle). Costs are in dollars per passenger- or tonne-kilometer.  Demands for passenger and freight transport services are determined by a constant elasticity function with endogenously determined transport service prices, income or income per capita, population, and price and income elasticities.<br>

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Figure 1. GCAM transportation representation.<br>

The base-year calibration of the transportation sector in all regions is broadly described below; see the electronic annex of Kyle and Kim (2011)<ref>Kyle, P., and S.H. Kim. 2011. Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands. Energy Policy 39(5): 3012-3024.</ref> for a more detailed description. The method starts with IEA (2007)<ref>IEA. 2007. Energy Balances of non-OECD Countries and Energy Balances of OECD Countries. International Energy Agency, Paris, France.</ref> estimates of energy consumption by region and mode (road, rail, domestic ship, international ship, and air). Rail, air, and road energy consumption is disaggregated to passenger and freight services, and passenger road energy is further disaggregated into light-duty vehicles and buses. These disaggregations are performed using a wide variety of country-level inventories of passenger and freight transportation (see Table 1), along with exogenous assumptions of vehicle intensities and load factors. It is generally assumed that the vehicle intensities are equal to the corresponding values for the United States, according to the Transportation Energy Data Book (Davis et al. 2008).<ref>Davis, S.C., S.W. Diegel, and R.G. Boundy. 2008. Transportation Energy Data Book. Oak Ridge National Laboratory, ORNL-6981.</ref> However, for light-duty vehicles, region-specific estimates are used, based on available data on average fuel economy by region (e.g., Shafer et al. 2009<ref>Schafer, A., J.B. Heywood, H.D. Jacoby, and I.A. Waitz, 2009. Transportation in a Climate-Constrained World. The MIT Press, Cambridge, MA, USA</ref>; ICCT 2009<ref>ICCT. 2009. Policy Update #3: Notice of Proposed Rulemaking to Establish Vehicle GHG Emissions and Fuel Economy Standards. International Council on Clean Transportation, September 30, 2009.</ref>; Zhou 2007<ref>Zhou, N. 2007. What do India’s transport energy data tell us? A bottom-up assessment of energy demand in India transportation sector. Lawrence Berkeley National Laboratory. Presented at the 8th ACEEE Summer Study, June 2007, Pacific Grove, California.</ref>).

Table 1. Main data sources used for disaggregation of passenger and freight service and energy, by GCAM region

<br>Light-duty vehicle load factors are assumed to be about 1.6 persons per vehicle in the U.S. (Davis et al. 2008), and 2.5 in much of the developing world (Schafer et al. 2009), and are assumed to decrease linearly with income until the value of 1.6 persons per vehicle is reached. Bus load factors are similarly assumed to decrease with GDP, which (all else equal) tends to increase the service intensity of this mode as GDP increases. Light-duty vehicle fuel intensities are assumed to have some regional heterogeneity in the base year (based on ICCT, 2009), but to converge by 2095 in all regions at about 6 L per 100 km in the reference scenario, and 4 L per 100 km in the advanced scenario (values based on Schafer et al., 2009).<br>

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As with other sectors in GCAM, nested logit competition is used to allocate shares among competing modes and transportation technologies. Specific vehicle technologies (e.g. electric and ICE vehicles) compete within modes (e.g. LDV, bus). Competition at both vehicle and modal levels takes place on the basis of costs, but the modal competition in the passenger sector also considers time value costs, derived in each period from the average modal door-to-door speed, and the average regional wage rate. As per-capita GDP increases, the increasing wage rate places a premium on faster modes, while also increasing the total cost of passenger transport service. This allows the model to be consistent with historical patterns of modal shares in all regions going forward, without exceeding a reasonable daily time travel budget, generally between 1 and 1.5 hours per person per day (see, e.g., Schafer and Victor 2000<ref>Schafer, A., and D.G. Victor, 2000. The future mobility of the world population. Transportation Research Part A 34, 171-205.</ref>; Schafer et al. 2009). The cost of time in transit constrains the growth in passenger transport service as incomes rise. While high-speed modes (air and high-speed rail) claim greater shares as GDP increases, in no regions do they become dominant, as much of the transportation service demand is local. On this note, while high-speed rail is allowed in all regions starting in 2020, its growth is exogenously constrained by the relatively small portion of total service demands to which it would apply.

<br>Non-fuel costs in transportation in base years for the U.S. are from Davis et al. (2008) and the Bureau of Transportation Statistics (2007)<ref>Bureau of Transportation Statistics. 2007. Data and Statistics. Research and Innovative Technology Administration, U.S. Department of Transportation. http://www.bts.gov/programs/%3C/ref%3E, and are applied to all other regions, and assumed constant through 2095. Fuel cell vehicles are assigned costs that are 15 percent higher than ICE vehicles in 2020, dropping to 10 percent in 2050 (reference technology scenarios). Electric vehicles are assigned costs that are 10 percent higher than ICE vehicles in 2020, and drop to 5 percent in 2050 (reference technology scenarios). In advanced technology scenarios, it is assumed that electric and fuel cell vehicle non-energy costs drop to match those of ICE vehicles.

References

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