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Unformatted text preview: 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 CHAPTER 14 Systems Perspective on Transportation Energy 14-1 Overview This chapter uses the understanding of transportation energy technologies from the previous chapter as a basis for studying overall energy consumption and energy efficiency of transportation from a systems perspective. There are a number of possible factors that mitigate the ability of technological interventions to affect energy efficiency, and even ways in which technological changes can be undercut by the “rebound” effect. For many different categories of transportation system, the past several decades have seen a mixture of positive and negative effects at a systems level. Given the pressing energy and climate change issues of the twenty-first century, and the prominent role of transportation as a contributor to those problems, it is of growing importance to use systems tools to improve the energy efficiency and environmental performance of the transportation sector. Two possible tools for this purpose are (1) shifting transportation to more environmentally friendly “modes” (i.e., types) of transportation, and (2) rationalizing the system so that it uses fewer resources. The chapter concludes with a discussion of issues related to making a transition to a more sustainable system in the future. 14-2 Introduction By its essential nature, transportation, and in particular the use and conservation of energy in service of transportation, lends itself to taking a systems approach. All energy applications interact with each other to some extent as they function in their surroundings; for example, they compete for finite resources, and they emit wastes that the natural environment has a finite capacity to absorb. In the case of transportation systems, however, these interactions take on a special importance, because the sharing of common infrastructures (e.g., roads, railroads, seaports, airports, and so on) leads to the various units in the system influencing each other’s function and performance—sometimes significantly—whether it is motorists on an urban expressway, passengers in a train, or freight shipments moving through a distribution center. These interactions in turn affect how much energy is required to meet the needs of the system, as dictated by the level of congestion, the quality of maintenance of the system, or other factors. Over time, transportation system users make changes to the vehicles or the infrastructure in order to adapt to changing conditions in the network. Here again, systems effects will influence how well these adaptations work. Therefore, a systems perspective on transportation 421 Vanek_ch14-p421-464.indd 421 4/3/08 7:57:51 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 422 Chapter Fourteen energy use is a necessary and useful complement to the consideration of transportation technologies in Chap. 13. Transportation energy consumption originates in the propulsion system of the vehicle, whether it is an internal combustion engine, a jet engine, or the electric motor of a railway locomotive that is supplied with electricity from the grid. It is further influenced by other design choices of the vehicle, such as materials choices, which affect its overall weight, or styling, which affects its aerodynamic drag. Engineers in an R&D setting of a laboratory or design facility appreciate the benefit of designing a vehicle to be efficient, since reduced manufacturing cost or operating cost will make the product more appealing to the management of the company and to the prospective customer, respectively. However, the pursuit of efficiency must be weighed against other priorities, such as power or performance, and often in the pursuit of product sales it is the latter two criteria that are favored. The way in which the service of moving people and goods in the real world is delivered also affects total energy use, so that an identical vehicle may achieve different levels of energy efficiency in different situations, depending on the circumstances. Land use planning (i.e., the geographic location of amenities in a region), availability of transportation infrastructure, extent of congestion, and other factors all play a role. As an example of how a systems perspective can help us understand more accurately the likely outcome of changes in technology intended to address transportation energy problems, we can consider the rebound effect, as shown in Fig. 14-1. A common response to rising energy use in the transportation sector is to introduce policies aimed at improving the energy efficiency of vehicles. These policies cause manufacturers to R Maximum size of vehicle affordable to operate + Average vehicle size + + + Energy use per vehicle-km Level of transportation energy use + Amount of driving – – B Efficiency of engine and propulsion system + Policies to improve energy efficiency R + – Cost of driving FIGURE 14-1 Causal loop diagram of the relationship between energy efficiency policies, energy use per vehicle·km or vehicle · mi traveled, average vehicle size, and demand for driving. Vanek_ch14-p421-464.indd 422 4/3/08 7:57:52 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y develop more efficient engines and other drivetrain components, so that the drivetrain is able to move a vehicle the same distance with less fuel consumption. One part of the effect of this change is to reduce energy consumption per unit of distance, thereby influencing total energy use in a downward direction. However, there are two other effects. One effect is that the more efficient drivetrain makes larger vehicles more affordable, increasing average vehicle size. The second effect is that, from the laws of economics, if we make an activity such as driving cheaper, demand for that activity will increase, driving up the total amount of driving. These latter two effects both influence the amount of energy consumption in an upward direction The causal linkages in Fig. 14-1 show us three possible pathways from the step of increasing the level of “policies to improve energy efficiency” back to “level of transportation energy use,” but they do not tell us whether, in the end, a net improvement in the amount of energy use will result. The outcome depends in part on the circumstances in each situation where such a policy is tried. In many cases, a government that enacts such a policy may reduce overall energy consumption compared to a baseline “do-nothing” scenario, even after taking losses due to the rebound effect into account. However, some erosion of energy efficiency gains due to the rebound effect is almost inevitable, unless other policies specifically aimed at curbing growth in vehicle·km or vehicle size are instituted at the same time. Also, since the overall long-term baseline in most industrialized and emerging countries is a steady increase in transportation energy consumption year after year, energy efficiency policies may make reductions against the baseline but not be able to reduce overall energy consumption compared to its level at the beginning of the policy implementation. This example shows the value of looking at transportation energy use from a systems perspective, as will be further demonstrated in the remainder of this chapter. 423 14-3 Ways of Categorizing Transportation Systems Transportation systems can be categorized in a number of ways, and the category to which each system belong influences how it functions. In turn, the function of the system strongly influences energy requirements. The following is a typology of four transportation categories, where a classification on one level can be made independent of the other three (an illustrative example follows): • Function—passenger or freight: One general way to classify transportation is between passenger and freight transportation. Passenger transportation constitutes any movement of people (e.g., for work, errands, and tourism), including all luggage or personal effects pertaining to their travel, and freight constitutes the unaccompanied movement of goods other than luggage (e.g., bulk commodities, finished products, livestock, and mail and parcels.). In general, vehicles are dedicated to one form or the other, although there are exceptions to this rule, for instance, in the case of aviation, large commercial airliners frequently carry passengers on the main deck and airfreight on the lower deck (also known as the “lower lobe”) of the aircraft. • Modes—road, rail, water, air, and pipeline: Passenger and freight transportation can be further divided into one of these five major modes. For example, the road mode consists of cars, buses, and motorcycles on the passenger side, and vans and trucks on the freight side, which is sometimes called the “truck” mode. Broadly defined, the road mode can also include nonmotorized modes such as bicyclists Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 423 4/3/08 7:57:52 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 424 Chapter Fourteen and pedestrians. The water mode (also called the “marine” mode) includes movements of boats and ships on both inland bodies of water such as rivers, lakes, and canals, and on the open seas. All five modes exist in both a passenger and freight form, except for pipeline, which is used exclusively for freight, and primarily for the transport of energy products such as oil and natural gas. In addition to these major modes, a number of niche modes exist as well, such as an aerial tramway in a mountainous region that carries passengers and/or goods from the outside world to a remote community.1 The five major modes are, however, the only ones that consume a significant fraction of the world’s transportation energy budget. • Geographic scope—urban or intercity: Both passenger and freight transportation can be divided between urban and intercity transportation movements. On the passenger side, the word “urban” is used as an umbrella term that covers movements both in large urban areas and small communities, but that in each case are characterized by movements for work, commerce, or recreation over a short distance and carried out on a daily or regular basis. Intercity passenger trips are typically of a distance of 50 to 100 km (approximately 30 to 60 mi) or more between two distinct towns or cities. On the freight side, intercity movements entail the long-distance “trunk” movement of goods between population centers, where the goods are often combined into a larger unit such as a tractor-trailer or a freight train with multiple cars. Urban movements are those (at the endpoints of a trunk movement) that are used to gather shipments together for long-distance movement, or distribute them once they have arrived at their destination. In some cases, smaller vehicles (such as delivery vans or light trucks2) are used to carry out urban distribution movements, while in other cases, the same truck may be used for both intercity movement and urban collection and delivery activities. • Ownership—private or commercial: Transportation activity can be divided between private transportation movements, which consist of any activity in which the driver or operator owns the vehicle, and commercial movements, which entails the selling of the transportation service to passengers or shippers of freight by professional transportation providers (e.g., taxi companies, airlines, and forhire trucking companies). In the case of freight transportation by road, a fleet of trucks that is owned by a company for movement of products that it makes or sells itself is considered to be private transportation, even though the driver of the truck does not personally own the vehicle. For example, some large food retailers that operate chains of supermarkets may have their own private fleet, while others contract with for-hire firms to have this service carried out. As examples of application of these categories, a driver in her/his own personal automobile to work represents passenger transportation using the road mode for urban transportation in a privately owned vehicle. A railroad moving a shipping container from a port to an inland city represents freight transportation using the rail mode for intercity transportation in a for-hire vehicle. 1 Means of conveyance, such as aerial tramways, and cruise ships in which the purpose is sightseeing or tourism, and the passenger travels in a circuit without the intention of reaching the endpoint of the circuit as a destination, are generally not counted in transportation statistics. 2 In the remainder of this chapter, we use the term light truck to refer to pickup trucks, vans, minivans, and sport-utility vehicles (SUVs). Vanek_ch14-p421-464.indd 424 4/3/08 7:57:52 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y 425 14-4 Influence of Transportation Type on Energy Requirements The distinction between passenger and freight transportation is important because the movement of freight is strictly commercial in nature, while the movement of passengers entails more of a balance between competing factors including not only cost but also, in many cases, the comfort of the passenger, pleasure derived from the route of travel, or the image or status conveyed by the vehicle. When manufacturers, retailers, or other parties responsible for the overall cost of a product make decisions about freight, they determine the amount of protection needed for the product (e.g., protective packaging, and refrigeration) as well as the desired speed and reliability of delivery, and then seek out the least-cost solution that meets these requirements. There is therefore no incentive to spend extra on more expensive solutions, since the difference in cost will come directly out of the profitability of the product. When passengers make transportation choices, on the other hand, they may be more likely to spend extra on a larger, more comfortable vehicle, or more distant vacation destinations, so long as they have the economic means to do so. Especially in the case of middle- and upper-class populations in the industrial countries, some of whom have in recent decades greatly increased purchasing power, the attraction of energy-intensive transportation choices has made curbing the growth in overall transportation energy use more challenging. The effect of increasing wealth and more demanding requirements for transportation service spills over into the choice of mode as well. For example, in the case of freight, modes such as water and rail are on average more energy efficient because they allow the vehicle operator to consolidate more goods on a vehicle, they move with less stopping and starting, and also water and rails create less rolling resistance than, for example, rubber tires on roads. However, these modes also require greater coordination at the terminal points to consolidate and break apart shipments. From a transportation management point of view, shipment by road and air is a more agile option because the shipment reaches the destination more quickly and reliably, although the energy requirement is on average greater. An analogous argument can be made in the case of passenger transportation. Overall, the marketplace for both passenger and freight transportation has in recent years favored the higher service of road and air modes over the energy efficiency of rail and water, increasing total energy consumption. Lastly, one of the main effects of the geographic scope of transportation is to limit energy source options. In the case of passenger transportation, the majority of transportation activity is generated in urban movements. For many of these trips, it would be possible for travelers to use alternative energy options such as electric vehicles with batteries or alternative liquid fuels that do not have as high of an energy density, because the distances between opportunities to recharge/refuel are not too great. It is also easier to connect vehicles such as buses or urban rail vehicles to a catenary grid (e.g., overhead wires), because the density of passenger demand is high enough to justify the cost. By contrast, the majority of freight transportation activity happens over long distances between cities, where the current expectation is that the vehicle or aircraft can travel for long periods between refueling stops. For the most densely traveled rail routes, electric catenary may be justified, but for other routes, rail locomotives must rely on liquid fuels stored onboard the vehicle between refueling stops in the same way that trucks, aircraft, or ships do. Vanek_ch14-p421-464.indd 425 4/3/08 7:57:53 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 426 Chapter Fourteen 14-5 Units for Measuring Transportation Energy Efficiency Transportation energy efficiency can be measured at various stages, from the testing of the equipment in the laboratory to the delivery of transportation services in the real world. Each successive stage introduces the possibility for ever greater numbers of intervening factors that can disrupt the smooth operation of a component, vehicle, or entire system, so that the potential for losses is increased, as shown in the following four stages: 1. Technical efficiency of components: A component of the drivetrain can be tested for its ability to transmit input to output energy. For example, the engine might be tested under steady-state, optimal conditions to determine what percent of the energy present in the fuel combusted is transferred to the rotation of the driveshaft. Engineers might evaluate losses in other drivetrain components in a similar way, that is, calculating efficiency on the basis of power out divided by power in. 2. Laboratory vehicle fuel economy: At this level of measurement of energy efficiency, vehicles are tested on a dynamometer to estimate fuel economy, where the dynamometer drive cycle is used to represent driving conditions in the real world. The results are given in units of city or highway mi/gal or km/L. For metric measurement of fuel consumption, the measure of “L/100 km” is commonly used. Laboratory testing recognizes that measuring technical efficiency of the drivetrain (approach #1) does not capture the use of the component in the vehicle, or the effect of parts of the vehicle that do not directly consume energy (e.g., the vehicle body). Estimation of fuel economy also results in a measure that incorporates times when the vehicle is not operating at optimal energy efficiency, for example, stop-and-go driving conditions. Lastly, prospective buyers of the vehicle want to know the effect of fuel economy on operating cost of the vehicle. Whereas a measure of technical efficiency of the drivetrain gives the buyer little information on this point, a measure of fuel economy can readily be translated into an estimated cost per year for fuel, if the buyer knows how far she/he typically drives in a year. 3. Real-world vehicle fuel efficiency or intensity: Government agencies typically report overall fuel efficiency (also referred to as energy intensity) for different vehicle classes (e.g., passenger cars, light trucks, and heavy-duty vehicles), in terms of energy consumption per unit of distance traveled. Thus fuel efficiency or intensity is the inverse of fuel economy, which is distance per energy. (L/100 km, introduced under approach #2, is a measure of fuel intensity.) These agencies use vehicle counts on selected roadways and modeling techniques to estimate total vehicle·km of travel, and the allocation of total annual transportation fuel sales to different transportation applications, as a basis for estimating actual kJ/vehicle·km (Btu/ton·mi in standard units).3 4. Real-world transportation service efficiency or intensity: Use of energy per vehicle·km as a measure of technological progress is imperfect because the purpose of the 3 For brevity, metric units are used in the remainder of this chapter. Conversion between standard and metric units are as follows: 1 passenger · mi = 1.6 passenger·km, 1 ton · mi = 1.45 tonne/km, 1 vehicle · mi = 1.6 vehicle · km. Vanek_ch14-p421-464.indd 426 4/3/08 7:57:53 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y transportation system is not the movement of vehicles but rather the delivery of transportation service, that is, the movement of passengers or freight. Movement of vehicles is a means to this end, but it is not the end itself. The quantity of transportation service is measured in units of passenger · km (pkm) and tonne · km (tkm, e.g., the movement of 1 passenger or 1 tonne of freight for a distance of 1 km, respectively) in order to incorporate the effect of distance on transportation intensity. In other words, the movement of 100 passengers for 100 km will require more energy, incur more wear-and-tear on transportation infrastructure, and so on, than the movement of 100 passengers for 1 km. As with vehicle · km statistics, total passenger · km and tonne · km are published by governments through a mixture of sampling and modeling. Thus it is possible to publish measures of transportation energy intensity in terms of kJ/passenger · km and kJ/tonne · km, respectively. These measures capture the effect of changing transportation practices on energy consumption: for instance, if vehicles are not loaded as fully and all other factors remain the same, energy intensity measured in energy per passenger · km or tonne · km will increase. The role of different measures in assessing the energy efficiency of the transportation system is summarized in Table 14-1. 427 Units Name Technical efficiency of components Laboratory vehicle fuel economy Metric Percent efficiency; kJ out per kJ in Standard Percent efficiency; Btu out per Btu in Description Laboratory testing of drivetrain components (engine, transmission, tires, etc.) Estimate of real-world energy consumption performance based on laboratory drive-cycle test. Real-world energy efficiency based on estimates of actual energy consumption and vehicle distance traveled Real-world energy efficiency based on actual energy consumption and passenger or freight distance traveled L/100 km, km/L mi/gal Real-world vehicle fuel efficiency or intensity kJ/vehicle· km Btu/vehicle·mi Real-world transportation service efficiency or intensity kJ/passenger · km, for passenger; kJ/tonne · km, for freight Btu/passenger·mi, for passenger; Btu/ton·mi, for freight TABLE 14-1 Levels of Measuring Transportation Energy Efficiency Vanek_ch14-p421-464.indd 427 4/3/08 7:57:53 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 428 Chapter Fourteen Comparison of laboratory vehicle fuel economy values (approach #2) for an entire fleet of cars in a country and real-world fuel efficiency (approach #3) typically reveals a fuel efficiency shortfall or gap between the predicted and actual fuel consumption. The laboratory fuel economy values can be used to predict the annual fuel consumption of each vehicle in the fleet, base on its rated fuel economy and estimated distance driven. The sum of all the vehicles in the national fleet gives one possible value for the amount of fuel consumed. When the real-world fuel efficiency is converted to units of fuel economy, it is not as high a value as the estimate based on laboratory fuel economy figures. The shortfall occurs because laboratory tests typically do not fully capture the negative effect of high-speed driving, traffic congestion, decline of fuel economy due to aging vehicles, inadequate owner maintenance practices (e.g., failure to keep tires sufficiently inflated), and other factors. 14-6 Recent Trends and Current Assessment of Energy Use in Transportation Systems In many industrialized countries, transportation is one of the fastest growing energy users, on a percentage basis. For example, Fig. 14-2 shows the change from 1970 to 2000 for the United States, indexed to a value of 1.00 in 1970. As shown, of the five principal sectors included (namely, freight transportation, passenger transportation, commercial, residential, and industrial), freight transportation had the largest growth with 120%, 2.40 2.20 Relative growth (1970 = 1.00) 2.00 Freight Commercial 1.80 All trans. Passenger 1.60 All energy Residential 1.40 Industrial 1.20 1.00 1970 1975 1980 1985 1990 1995 2000 FIGURE 14-2 Relative growth of U.S. energy consumption, indexed to 1970 = 1.00. Notes: 1970 values—Freight transportation = 3.7 EJ, Commercial = 9.3 EJ, All transportation = 17.8 EJ, Passenger transportation = 14.1 EJ, All energy = 70.2 EJ, Residential = 14.5 EJ, Industrial = 31.0 EJ. Conversion: 1 Quad = 1.055 EJ. (Source: Own calculations based on data from U.S. Department of Energy, 2003.) Vanek_ch14-p421-464.indd 428 4/3/08 7:57:54 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y while passenger transportation was third largest with 55%. Combined transportation (“All trans.” in Fig. 14-2) grew by 66%, while overall energy use (“All energy” in Fig. 14-2) grew by 49%. The U.S. energy consumption data show that the trend of relatively rapid increase in transportation energy use has been continuing for some time. There are many possible contributors to this phenomenon, including the rise of more automobile-dependent land-use patterns; a faster paced working life in some sectors of the economy, forcing workers to travel more and longer distances in each working week; or greater wealth, allowing citizens to spend more on purchase of travel services. This situation can be contrasted with that of other sectors. In the industrial sector, manufacturers either improved energy efficiency so as to cut costs, or moved manufacturing activities out of the United States and hence off of the U.S. energy consumption records. In the residential sector, higher energy costs encouraged a significant shift to more efficient buildings, better insulation, and more modern appliances. The pattern in many other industrial countries is similar to the one in Figure 14-2, with growth in energy consumption of sectors of special importance to a service economy (e.g., freight and commercial activities) outpacing that of other sectors, such as the industrial sector. There are, however, wide variations between countries in the total amount of transportation energy consumption or energy efficiency of transportation. As an example, Figs. 14-3 and 14-4 show the breakdown of total energy consumption for the United Kingdom and the United States, respectively. The modal percentages in the figures are for the combination of passenger and freight energy consumption. In both cases, the road and air modes are the largest energy consumers, although for the United States, the percent share for the road mode is somewhat higher than in the United Kingdom. (Pipeline energy consumption data were not available for the 429 United Kingdom Air 24% Water 2% Rail 1% Road 73% FIGURE 14-3 Breakdown of transportation energy consumption by mode, United Kingdom, 2005. Total = 2.49 EJ. Note: Total does not include 0.03 EJ energy equivalent of electricity attributed to the transpor tation sector. Pipeline energy consumption not included due to lack of data availability. (Source: Depar tment for Transpor t, 2006.) Vanek_ch14-p421-464.indd 429 4/3/08 7:57:54 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 430 Chapter Fourteen United States Pipeline 3% Water 5% Rail 2% Air 9% Road 81% FIGURE 14-4 Breakdown of transportation energy consumption by mode, United States, 2005. Total = 28.8 EJ. (Source: U.S. Department of Energy, 2007.) United Kingdom, but we believe that their inclusion would not change this outcome, given the compact geography of the United Kingdom.) Also, the per capita energy consumption for transportation of the United States is over twice as much as that of the United Kingdom (approximately 96 GJ/person, vs. approximate. 42 GJ/person). This difference can be explained partly in terms of factors beyond the control of the population, such as the large geographic expanse of the United States, and partly in terms of short- and long-term choices about land-use patterns, size of vehicles, and so on. These factors may also explain differences in average delivered energy efficiency of road vehicles for the two countries, as shown in Fig. 14-5. For the two categories shown, namely, passenger cars (excluding light trucks) and combination trucks (also known as articulated trucks, that is, a heavy truck consisting of a tractor and one or more detachable 14 Fuel efficiency [km/liter] 12 10 8 6 4 2 0 Psgr cars Comb. truck UK USA FIGURE 1 4-5 Comparison of average delivered fuel economy for the United Kingdom and United States, for passenger cars and combination trucks. (Sources: U.K. Department for Transport, 2006, for the United Kingdom; U.S. Department of Energy, for the United states.) Vanek_ch14-p421-464.indd 430 4/3/08 7:57:54 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y trailers), the U.K. fleet had an overall 19% and 14% efficiency advantage. Note that comparisons between the energy consumption and energy efficiency values for the two countries should be made carefully. The methods used to gather the underlying data, and then to calculate the values for the United Kingdom and the United States are not necessarily the same. Also, the relative contribution of natural factors (geography, climate, and the like) and human-controlled factors (transportation policy, fuel tax, decisions about land use, and the like) to the different values are not known. Therefore, while these data do suggest that the United States could study the United Kingdom for ideas on how to improve transportation energy consumption and efficiency, it does not necessarily hold that, by exactly replicating the conditions found in the United Kingdom, the United States would arrive at the same values of energy consumption per capita or per vehicle·km. 431 14-6-1 Passenger Transportation Energy Trends and Current Status The dominant user of passenger transportation energy in most industrial countries is the light-duty road vehicle (passenger cars and light trucks). In 2004, of the 19.7 EJ of energy attributed to passenger transportation in the United States, 84% was consumed by lightduty vehicles. Most of the remainder was consumed by passenger air travel. In other industrial countries, light-duty vehicles are almost always the largest consumer of energy, although in some cases railroads and not aircraft are the second largest user, depending on the intensity of use of the rail versus air system. Data on passenger transportation energy consumption for emerging countries are generally not available, but it is likely that in some countries with particularly low rates of private ownership of light-duty vehicles, the largest consumer of energy may be buses or passenger trains, since these modes become critical for urban or long-distance travel in the absence of personal cars. Delivered energy intensity of passenger modes in kJ/passenger·km depends both on the inherent technical efficiency of the vehicle and the load factors of the service provided (i.e., percent of available seats filled). Figure 14-6 shows energy intensity for three intercity passenger modes in the United States, namely, bus, air, and rail. Although the rail mode is generally very efficient when load factors are high, intercity rail in the 6000 Energy intensity [kJ/p-km] 5000 4000 Air 3000 2000 1000 0 1975 1980 1985 1990 1995 2000 2005 Rail Bus FIGURE 14-6 Intercity passenger transportation energy intensity in kJ/passenger·km, 1975 to 2005. Note: Data for bus energy intensity after 2000 were not available. (Source: U.S. Department of Energy, 2007.) Vanek_ch14-p421-464.indd 431 4/3/08 7:57:55 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 432 Chapter Fourteen United States suffers from low occupancy, so that energy intensity per passenger·km is only slightly lower than that of airline travel. By contrast, the bus mode is consistently more efficient than the other two. The air mode has been able to reduce energy intensity by 58% over the time period shown in Fig. 14-6, due both to improved aircraft and engine technology, and the use of yield management to maximize seat occupancy on flights. Of the three modes, only air had a significant share of total passenger·km; in 2004, bus and rail accounted for less than 2%, whereas air accounted for approximately 13%. Data on lightduty vehicle passenger energy intensity divided between intercity and urban geographic scope were not available, so intercity car travel does not appear in the figure. However, for comparison, combined urban and intercity car and light truck intensity values in 2004 were 2305 kJ/passenger·km and 2854 kJ/passenger·km, respectively. For the two U.S. modes for which data were available, air and rail, the trend in load factors is shown in Fig. 14-7. For commercial aircraft, load factors are given in terms of percent of seats filled on average for the years 1970 to 2000. However, for rail, these data are not available directly, so instead a measure of passenger·km delivered per traincar·km moved is used. Data are also available on train·km of movement, or distance traveled by entire trains, but traincar·km are used as a basis for this figure since they more closely reflect the capacity provided. Because two different measures of load factors are used for the two modes, the values are presented in relative terms indexed to a value of 1970 = 1. The graph shows that load factors have steadily increased for air from approximately 50% in 1970 to 72% in 2000, while for rail the number of passenger·km delivered per traincar·km of movement rose just 6% over the same period (from 14.2 to 15), although they reached a value as high as 20 in 1990. Note that for the rail mode the use of passenger·km/traincar·km is not entirely precise because the average size of traincars may change over time, but it is thought to be adequate for this comparison. The United States is unusual among the industrial nations for having a particularly low share of passenger · km and passenger energy consumption for bus and rail. Most other industrial nations have a higher share for these two modes, and also lower energy intensity for rail, since load factors are higher. 1.60 Relative value [1970 = 1.00] 1.40 1.20 Air 1.00 0.80 0.60 0.40 0.20 0.00 1970 Rail 1975 1980 1985 1990 1995 2000 FIGURE 14-7 Relative usage of available capacity for U.S. commercial air and passenger rail modes indexed to 1970 = 1.00. Notes: For air, 49.7% of seats were occupied in 1970; for rail, each traincar·km of movement delivered 14.2 passenger·km of service. (Source: U.S. Department of Energy.) Vanek_ch14-p421-464.indd 432 4/3/08 7:57:55 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y 433 Divisia Analysis of U.S. Car and Light Truck Energy Consumption: A Case Study Among passenger modes in the United States, the light-duty vehicle is the dominant user of transportation energy at present, and it has also been driving the rapid growth in transportation energy consumption over the last few decades. In 1970, light-duty vehicles consumed a total of 9.7 EJ of energy; in 2004, this amount had grown to 16.6 EJ, an increase of 70%. Divisia analysis (see Chap. 2) provides insight into factors that contributed to this trend, by considering the relative roles of the two main classes of light-duty vehicles, namely cars and light trucks. While both cars and light trucks in the United States may have reduced energy intensity in recent decades, in the case of cars, improving intensity has kept pace with growing vehicle · km, while in the case of light trucks it has not. As shown in Table 14-2, fuel consumption for cars rises only slightly from 257 to 277 billion L/annum, while fuel consumption for light trucks increased approximately fourfold from 47 to 200 billion L. A significant shift in purchasing habits by American drivers drove this trend: as Americans bought fewer passenger cars and more pickups, minivans, and SUVs, the total number of vehicle · km traveled by light trucks increased more than sevenfold, while the increase in vehicle · km for passenger cars was less than twofold. To carry out the Divisia analysis, we need to know the kilometers, fuel consumption, and fuel economy of the combined fleet of cars and light trucks in the base year of 1970. These are obtained by adding together the respective values given in Table 14-3; values from 2000 are also included in the table, for comparison. As shown, the overall fuel economy increases from 5.5 km/L to 8.5 km/L. Based on the increase in activity (kilometers of vehicle travel), the trended fuel consumption with no change in fuel economy would have been 737 billion L: 4.037 × 1012 km = 7.37 × 1011 L 5.5 km/L Cars Year 1970 1975 1980 1985 1990 1995 2000 bil. km. 1467 1654 1779 1995 2253 2301 2560 bil. L 257 280 265 271 263 258 277 Km/L 5.7 5.9 6.7 7.4 8.6 8.9 9.3 bil. km 197 322 466 626 920 1264 1477 Light Trucks bil. L 47 75 90 104 135 173 200 Km/L 4.2 4.3 5.2 6.0 6.8 7.3 7.4 Note: Figures include the sum of gasoline and diesel fuel gallons. (Source: U.S. Department of Energy, 2003.) TABLE 14-2 Vehicle·km traveled, liters of fuel consumption, and fuel economy of U.S. cars and light trucks 1970–2000. Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 433 4/3/08 7:57:56 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 434 Chapter Fourteen Year 1970 2000 bil. km. 1664 4037 bil. L 304 477 Km/L 5.5 8.5 TABLE 14-3 Vehicle·km Traveled, Liters of Fuel Consumption, and Fuel Economy of All U.S. Light-Duty Vehicles, 1970 and 2000 The Divisia analysis is completed by calculating the effect of overall efficiency changes (the combined energy intensity of cars and light trucks) and structural changes (the relative share of the two vehicle types). The results are shown in Fig. 14-8. Of the two types of changes, efficiency changes have the stronger effect, amounting to a change of −301 billion L in 2000. The increasing share of vehicle·km for light trucks, from 12% to 37% of the total between 1970 and 2000, has an upward effect on fuel consumption equivalent to 40 billion L in 2000. The two effects together explain the difference between actual and trended fuel consumption in the graph, that is, Eactual = Etrended + ΔEefficiency + ΔEstructure 477 = 737 − 300 + 40 7 [109 L fuel] Note that the results of the Divisia analysis do not explain the substantial rise in both activity and energy consumption over the period 1970 to 2000, nor do they tell us to what extent the upward pressure on fuel consumption due to rising vehicle·km could have been offset through increases in efficiency. One can surmise that, since vehicle·km increased by 143% over this period, it would have been difficult to prevent some amount of rise in absolute fuel consumption in any case—the average fuel economy would have needed to improve to 13.3 km/L to keep pace. The analysis results do tell us that improvements in efficiency accounted for most of the improvement against the trended projection of where fuel consumption would have been in 2000. Although 800 Fuel consumption [bill.liter] 600 400 200 0 1970 1975 1980 1985 1990 1995 2000 −200 −400 Trended Actual Structure Efficiency FIGURE 1 4-8 Divisia analysis of U.S. light-duty vehicle fuel consumption in billion liters, 1970 to 2000. Vanek_ch14-p421-464.indd 434 4/3/08 7:57:56 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y changes in the structure profoundly affected the nature of the light-duty vehicle market, with automakers selling many more light trucks, they only modestly increased fuel consumption relative to the trended value. 435 14-6-2 Freight Transportation Energy Trends and Current Status4 As mentioned above, freight transportation energy consumption is growing rapidly around the world, due to both the move toward a more global economy, and changes to domestic distribution systems within many countries. Taking the Unites States as an example, the freight sector energy consumption has been increasing by more than 20% each decade since 1970. The absolute value for freight rose 120% from 3.7 EJ in 1970 to 8.2 EJ in 2000, as shown in Fig. 14-2; although the latter value is smaller than that of some other sectors, 8.2 EJ is a large amount of energy in absolute terms, and the fact that it is almost entirely derived from petroleum and that its rate of consumption is on a rapidly increasing trend is cause for concern. In the freight sector, improvements in technology have in general been working in favor of saving energy, and growing demand and operational choices (e.g., by what mode to ship, how quickly, in what shipment size) have in general been putting upward pressure on energy consumption. On the one hand, freight vehicles have benefited from improved engine efficiency, use of lightweight materials, improved aerodynamics, and other advances. On the other hand, shippers of goods have increased their service expectations, so that a long-term modal shift toward faster and more energy-intensive modes, namely truck and airfreight, has taken place, especially for the more valuable finished products. Rail and water modes continue to move large volumes of bulk goods, such as energy products (coal, petroleum products) or bulk agricultural products (grains, feeds). However, the loss of market share of total tonne ·km of higher-value finished products for railroads over this period has been significant. For example, in 1977, railroads carried 45% of all food product tonne·km, which include any value added foods that have been canned, packaged, or prepared in some other way. (Grains and other unprocessed agricultural output are classified as agricultural products and are therefore not included in this figure.) By 1997, rail’s share of food product tonne ·km had fallen to 23%. This type of modal shift has been seen in many European countries and Japan as well. The increase in U.S. freight energy consumption is driven by the road mode, which grew from 1.6 EJ in 1970 to 5.4 EJ in 2000 (Fig. 14-9). Other modes including water, rail, and pipeline held more or less constant due to modest increases in total freight tonne·km combined with gradually increasing energy efficiency. In the case of the air mode, rapid reductions in energy intensity offset the rapid growth in demand for this mode over this time period, although there was an upturn in consumption from 1995 to 2000. At present, the truck mode is on average much more energy intensive than the water or rail modes. In the year 2000, the average energy intensity values for these three modes were 2301, 306, and 239 kJ/tonne·km, respectively. Note that this measure masks the impact of bulk goods on energy efficiency of water and rail, since they tend to be densely packed and move slowly, allowing these modes to achieve efficiency values that are not possible for more high value, time-sensitive goods. It is still more efficient to move high-value goods by rail than by truck, but the efficiency gain is not as great as 4 Parts of the discussion of energy use in freight transportation systems in this chapter are based on earlier work published in Vanek and Morlok (2000). The collaboration of Professor Emeritus Edward K. Morlok on these efforts is gratefully acknowledged. Vanek_ch14-p421-464.indd 435 4/3/08 7:57:56 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 436 Chapter Fourteen 6000 Annual consumption [PJ] 5000 4000 3000 2000 1000 0 1970 Road Pipeline Rail Water Air 1975 1980 1985 1990 1995 2000 FIGURE 14-9 Total U.S. freight transportation energy consumption by mode, 1970 to 2000. Note: Data point for water for the year 2000 is not included due to lack of a consistent data stream for the period 1995 to 2000. (Source: Own calculations, based on data from U.S. Dept. of Energy.) might be implied by the approximate 10:1 advantage base on the overall average modal energy intensities alone. The influence of the type of product or commodity on the efficiency of freight movement leads to an alternative approach to understanding freight energy consumption, namely, to divide total energy among different types of products or commodities (coal, food products, paper products, etc.). The commodity-based approach sheds light on the role of different commodities in generating consumption of freight energy, including the intensity of freight energy requirements, the trend over time for the product in question, and the contribution of the different freight transportation modes to the total freight energy for the product. This information can then be used to carry out more targeted improvement of freight energy efficiency (see Secs. 14-7-1 and 14-7-2). For example, it may be of particular interest to work on improving energy consumption with a sector of the economy that either uses a large fraction of the total freight energy budget, or is energy intensive relative to the value or weight of goods moved, or is increasing its consumption rapidly. A comparison of commodity-based analysis of intercity freight energy use in the United States and United Kingdom is given in Fig. 14-10(a) and (b). For the United Kingdom [Fig. 14-10(a)], 13 commodity groups are shown plus a miscellaneous shipments category (“Misc Products”). The data did not support disaggregation of rail and water modes in the United Kingdom by commodity. Therefore, the contribution of these modes is not included in the commodity disaggregate values, and instead is shown separately on the right side with hash-marked bars. The remaining values are then just for road energy consumption. For the United States [Fig. 14-10(b)], 14 commodity groups are disaggregated, including energy consumption for multiple modes (e.g., road, rail, and water) for each group. Commodities not included among the 14 are represented with an “other products” bar in the figure, which represent 24% of the total energy consumption shown in the figure. Both figures [Fig. 14-10(a) (b)] exclude pipeline energy consumption, energy use for urban movement of freight, and energy consumption in outbound international airfreight movements. Vanek_ch14-p421-464.indd 436 4/3/08 7:57:57 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y 90.0 80.0 Energy consumption [PJ] 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 C oa Te l xt W ile oo s d & Pu Tr lp an sp eq pt tls ts ry s s ts s s od ac he al pr in od in od m ai l W at er er al od uc od al ts il ne uc uc O M ic hi go pr g R 437 d ild M C et Ag fg pr d M Bu M M (a) United Kingdom 900 800 Energy consumption [PJ] 700 600 500 400 300 200 100 0 l ry (b) United States FIGURE 14-10 Freight energy consumption disaggregated by commodity for (a) United Kingdom, 1995, and (b) United States, 1993. Total energy: for U.K., 0.36 EJ; for U.S., 3.5 EJ. Note: Not all freight energy consumption of respective countries is covered in these figures; see text. A common point between the two figures is that in both countries, shipping of food products is a large consumer of energy, relative to other commodities. The agricultural products (United Kingdom) or farm products (United States) groups are also fairly large consumers of transportation energy, so the total energy balance for the entire delivery of food items from crops on the farm to the final consumer, as a fraction of total freight energy, is even larger: 24% and 22% of the total energy consumption covered in the two figures, respectively. This observation makes the case for working with the food industry to make sure that food distribution is as energy efficient as possible. Vanek_ch14-p421-464.indd 437 pr Pu ods lp /p ap er M in Pe er al tr/ s co al pr Fa od rm s pr od uc ts C he m ic W al oo s d pr Fo od od s pr od uc O ts th er pr od s s re pt s ile od pa ne ui Te xt Ap ac hi eq pr C oa l tl M sp M Tr an Fa b Pr im M tl Fo o is c pr M 4/3/08 7:57:57 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 438 Chapter Fourteen Applying Freight Energy Consumption by Product Type to Life Cycle Energy Use It is also possible to use commodity-disaggregate energy consumption to look at freight in context of the energy life cycle of products. Table 14-4 provides a partial life cycle analysis for total energy consumption of selected commodity groups (the analysis is partial because energy data on extraction, end use, and disposal sages were not available). For 10 commodity groups where comparable numbers were available, the freight energy consumption values from Figure 14-10(b) are added to production energy consumption for the product sector in question, as estimated by the U.S. Department of Energy, to give the combined energy consumption shown. The percentage values are then the percent of the combined value attributed to each stage. As shown, the food, lumber, and apparel sectors have relatively high percent values for the transportation side, suggesting that attention to freight energy requirements is an important part of life cycle energy efficiency in these sectors. By contrast, sectors requiring intensive manufacturing processes including paper products, chemical products, and various types of products based on metallurgy and metalworking have smaller percent shares for freight. The treatment of life cycle analysis in this example is simplistic in that it deals with aggregate energy consumption for production and transportation in entire sectors, without tracking the specific life cycle analyses of particular products within that sector. Also, boundaries around energy consumption are imprecise; some products become components in other products, such as textiles in apparel, and some of the energy consumption in both the production and transportation stages of many specific products in the sectors represented occurs outside the U.S. borders and therefore outside of the data sets that are used as a basis for the table. A more complete life cycle analysis would consider freight energy consumption for specific products as part of a whole-life view of the product from raw material to finished item, with a geographic scope not limited to energy consumption within the borders of the United States. Combined Commodity Fabricated metal products Transport equipment Pulp/paper Primary metal products Petroleum/coal products Lumber/wood products Chemicals Food/kindred products Textile mill products Apparel & textile prods PJ 405 458 2831 2775 2547 784 2232 1618 326 78 Percent Share Production 80% 74% 93% 94% 91% 61% 88% 62% 89% 59% Transport 20% 26% 7% 6% 9% 39% 12% 38% 11% 41% (Source: U.S. Department of Energy, for production energy consumption; own calculations, for freight energy consumption.) TABLE 14-4 Comparison of Production and Transportation Stage Energy Consumption Values for 10 Representative U.S. Commodity Sectors, 1993. Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 438 4/3/08 7:57:57 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y In conclusion, the role of freight modes and commodities moved by freight vehicles are important elements for understanding what is driving the current status and growth trend in freight transportation. For passenger transportation as well, an understanding of the amount of passenger·km travel demand and the vehicles chosen to meet this demand is useful for explaining energy consumption trend. Since the long-term trend in energy consumption is upward, there is clearly scope for strategies that will reduce energy consumption, CO2 emissions, and other types of pollution. In the next section, we explore some specific avenues for achieving this end for both freight and passenger transportation. 439 14-7 Applying a Systems Approach to Transportation Energy In the previous section, we reviewed the ways in which total transportation energy consumption, for both passenger and freight transportation, is an interaction between the vehicle technology and the way in which it is used. In this section we look at ways in which taking a systems approach to transportation energy can lead to possible solutions for curbing the growth in energy demand. The solutions covered include the following: 1. Modal shifting: Meeting a given demand for transportation services while increasing the modal share of the most efficient modes to reduce energy consumption. 2. Rationalizing transportation services: Meeting a given demand for transportation services while reducing total vehicle·km, passenger·km, or tonne·km, using optimization or other planning tools to deliver the service more efficiently. 14-7-1 Modal Shifting to More Efficient Modes The practice of shifting transportation demand to more efficient modes can lead to energy savings for both passenger and freight transportation. By taking a more holistic view of transportation systems, it is possible to use the most energy intensive modes such as cars and trucks less, and use more energy efficient modes such as buses or trains more. This practice assumes that the more efficient modes will have a high enough load factor (e.g., percent loading relative to maximum capacity) to achieve energy savings. As shown in Figs. 14-6 and 14-7 for the case of intercity rail in the United States, low load factors can reduce the energy efficiency of modes that are generally thought to be advantageous. In this section, the examples of (1) urban public transportation, (2) personal transportation choices, and (3) intermodal freight transportation are used to illustrate how modal shifting can be put into practice. Modal Shifting to Public Transportation in Urban Regions In recent times, many urban regions around the world have attempted to create a shift in modal choice by modernizing and expanding public transportation systems. Historically, as car ownership has grown over the past decades, public transportation systems have lost some share of passenger·km to the private automobile in many countries, so efforts are now being made to win back modal share for buses, subways, and other forms of public transportation. The focus of many of these programs is not just on energy efficiency and protecting the environment, but also on improving livability of cities. Public transportation systems can move passengers rapidly from origin to destination, without the delays common to Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 439 4/3/08 7:57:58 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 440 Chapter Fourteen travel by car in congested urban arteries. In an ideal situation, the mix of public transportation and car travel is rebalanced to the point that congestion is eliminated. Residents then choose between public transportation and use of their own car, depending on the nature of their trip, but in either case, there is no congestion, and travel times are predictable. Since the public transportation system must be well utilized in order to achieve this rebalancing, overall energy consumption decreases, so that the system-wide energy per passenger·km is also reduced. As an additional benefit, some types of public transportation can use renewable energy, further reducing CO2 emissions. Some types of public transportation can also reduce the amount of space required for transportation infrastructure, thereby preserving more green space. For example, at maximum capacity, a subway system can carry more passengers per track in each direction than an urban expressway can carry per lane in each direction. Public transportation comes in several forms, each with specific characteristics in terms of cost and maximum capacity, as follows: • Heavy-rail transit: These systems include subway and commuter trains that have few or no at-grade crossings with streets, and run underground or on elevated passageways. Subway systems in New York, London, Tokyo, and so on. are examples. • Light-rail transit (LRT): These systems comprise vehicles that run on rails but are typically smaller than those used in heavy-rail transit (see Fig.14-11). They also use a mixture of guideways separated from street traffic and mixed in traffic, as well as occasional use of tunnels or overpasses to improve flow at key points. FIGURE 14-11 Bombardier “Flexity” Light-Rail Transit (LRT) vehicle at a stop in Geneva, Switzerland. In recent years the LRT railcar industry has pushed the maximum length of LRT vehicles while still allowing them to navigate street traffic where necessary. This development increases both maximum passenger capacity during peak periods and financial productivity of the asset, since the driver’s wages are distributed among a larger passenger base, all other things equal. Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 440 4/3/08 7:57:58 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y They are less expensive to build per kilometer of track than heavy rail. There are numerous examples around the world, including Manila, Philippines; Manchester, England; and Portland, Oregon, United States. • Bus rapid transit (BRT): These systems resemble light rail in their use of bus-only roadways separated from street traffic, but the use of buses instead of rail vehicles reduces cost and simplifies the extension of bus rapid transit routes from the busway to and from local streets (see Fig. 14-12). Examples include Curitiba, Brazil; Bogota, Colombia; and Pittsburgh, Pennsylvania, United States. • Street transit: These systems include buses, tramways, and trolleys (i.e., electrically powered rail vehicles that operate using overhead catenary), and trolleybuses (also known as trackless trolleys), that operate entirely in the presence of street traffic. These systems are the least expensive to build and maintain, but they also provide the slowest service and are the most susceptible to congestion. Of the four types mentioned, heavy-rail and street transit are the oldest, while LRT and BRT are developed more recently. The reason for their emergence can be illustrated using a cost versus level-of-service (also known as LOS) diagram, as shown in Fig. 14-13. At the midpoint of the twentieth century, public transportation primarily offered only heavyrail and street transit systems. Between these two lay a gap in terms of service offering, where, for many cities, heavy-rail systems were too expensive, and street transit was too slow, to compete with the private automobile for passengers. LRT and BRT reduce the total system cost compared to heavy-rail, but they also provide a measure of rapid transit, in which passengers travel faster than the stop-and-go speeds of street traffic. Since the 1960s, many cities of 1 million or less population that could not afford heavy-rail systems 441 FIGURE 14-12 Bus Rapid Transit, Miami, United States. The busway for this BRT system resembles an LRT or subway system in that the roadway is exclusively for buses and street traffic crosses the route of the busway on an overpass, rather than at grade level, as shown in this photograph. Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 441 4/3/08 7:57:59 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 442 Chapter Fourteen Heavy rail (subway) Increasing cost Light rail Bus rapid transit Street transit Decreasing Level-of-Service (LOS) FIGURE 14-13 Cost versus level-of-service tradeoff for public transportation systems. (Photo: Jon Bell. Reprinted with permission.) built LRT or BRT instead, and today, the number of cities in the world that operate LRT and/or BRT has surpassed the number with heavy-rail systems. In the very large cities as well, LRT and BRT can serve certain niches, for example on the periphery of a city where demand is not high enough to justify a heavy-rail system. Example 14-1 illustrates the potential effect of modal shifting to public transportation on energy consumption and efficiency. Example 14-1 A metropolitan region generates 7.5 billion passenger · km of transportation demand per year and is experiencing 2% per annum growth in total passenger · km, and a 0.5% decline in energy intensity of passenger · km. Current energy intensity of automobile travel and public transportation is 2200 kJ/passenger · km and 1000 kJ/passenger · km, respectively, and share of passenger · km is 90% and 10%, respectively, for those two modes. For the purposes of this example, ignore all other types of transportation. Suppose the government institutes a comprehensive transportation program that increases the passenger · km share for public transportation by 5 percentage points over 10 years. Calculate (a) the baseline energy consumption in the present year, (b) the baseline energy consumption after 10 years, and (c) the reduction in energy consumption in the 10th year relative to the baseline in that year due to the policy. Solution (a) For the current year, the total energy consumption is the sum of the consumption for the two modes, as follows: kJ ⎞ ⎛ −12 PJ ⎞ ⎛ For car: (7.5 × 109 pkm)(0.9) ⎜ 2200 ⎟ ⎝10 kJ ⎟ = 14.85 PJ pkm ⎠ ⎜ ⎠ ⎝ kJ ⎞ ⎛ −12 PJ ⎞ ⎛ 10 For public trans.: (7.5 × 109 pkm)(0.1) ⎜1000 = 0.75 PJ pkm ⎟ ⎜ kJ ⎟ ⎠ ⎝ ⎠⎝ The sum of these two values is 15.6 PJ. Vanek_ch14-p421-464.indd 442 4/3/08 7:58:00 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y (b) For the 10th year, we account for the growth in passenger · km as follows: 443 (7.5 × 10 9 pkm (1 + 0.02) = 9.14 × 109 pkm 10 ) Modal energy intensities have also changed, and these are recalculated as kJ ⎞ kJ ⎛ For car: ⎜ 2200 (1 − 0.005)10 = 2092 pkm ⎟ pkm ⎝ ⎠ kJ ⎞ kJ ⎛ For public trans.: ⎜1000 (1 − 0.005)10 = 951 pkm ⎟ pkm ⎝ ⎠ Recalculating the energy consumption by repeating the calculation in part (a) with the increased number of passenger · km and reduced energy intensity gives 17.22 PJ for car, 0.87 PJ for public transport, and a total value of 17.22 + 0.87 = 18.09 PJ for the entire system. (c) This alternative assumes that in the 10th year the modal split is 85% car / 15% public transport. We can therefore recalculate as in part (a) using the new values: kJ ⎞ ⎛ −12 PJ ⎞ ⎛ For car: (9.14 × 109 pkm)(0.85) ⎜ 2092 ⎟ ⎟ ⎜10 kJ ⎠ = 16.26 PJ pkm ⎠ ⎝ ⎝ kJ ⎞ ⎛ −12 PJ ⎞ ⎛ For public trans.: (9.14 × 109 pkm)(0.15) ⎜ 951 ⎜10 kJ ⎠ = 1.30 PJ ⎟ pkm ⎟ ⎝ ⎝ ⎠ The combined total is 17.56 PJ. Therefore, the energy reduction is 18.09 − 17.56 = 0.52 PJ. Discussion The results of this example show the challenge of curbing growth in transportation energy consumption in the face of continually expanding passenger · km values. Despite a 50% increase in the amount of public transportation over 10 years, energy consumption is higher in year 10 than it is in the current year. Nevertheless, public transportation has made a measurable reduction in energy use of 0.52 PJ, which is equivalent to 21% of the 2.49 PJ growth in energy use that occurs with no increase in public transportation. One limitation of this type of analysis is that it assumes each unit of passenger · km shifted to public transportation will achieve energy savings based on the difference between the average energy intensity of the two transportation options. For a more accurate estimate, the analyst might build a computer model of the transportation network that includes the modeling of residents’ modal choices for different types of trips. The analyst would first verify that the model reproduces the baseline system in the real world within some degree of accuracy, and then impose the new expanded public transportation system on the model to evaluate the new modal split and total energy consumption. Such a modeling exercise gives more reliable results but requires a far greater amount of effort. Modal Shifting of Personal Transportation Choices In the previous section, the objective of modal shifting relied on a substantial commitment by local and regional governments to provide public transportation service as an alternative to travel by car. Even without using public transportation, however, the individual traveler can take steps to shift modes in her/his personal choices. One of the simplest ways of achieving this end is for travelers who drive to own different vehicles for different purposes, assuming they have the necessary financial means. For these travelers, especially those with higher incomes, it may be practical to own a smaller vehicle for single-occupant work or nonwork trips, and a larger vehicle such as a van, SUV, or truck for occasions where the motorist is either carrying a large Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 443 4/3/08 7:58:00 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 444 Chapter Fourteen amount of goods or has several passengers on board. The situation of the driver traveling by himself or herself alone is termed a single-occupant vehicle (SOV), while a vehicle with a large number of passengers is a high-occupancy vehicle (HOV), which in many urban areas have access to special HOV lanes that allow congestion-free travel on urban expressways. From an energy efficiency point of view, it is desirable to avoid larger vehicles, and especially light trucks, traveling as SOVs. For example, a SUV that delivers 15 MPG (6.34 km/L or 15.8 L/100 km) during urban travel with a single occupant has an intensity of approximately 5000 kJ/passenger·km, which is much higher than the average U.S. value given above. There is anecdotal evidence that, with the increase in gasoline costs in the U.S. market since 2005, drivers who own both a compact car and a light truck increasingly are choosing the compact car for single-occupant travel in order to reduce their fuel expenditures. Outside of highway-capable light-duty vehicles (i.e., vehicles able to travel at the full range of speed limits, including expressway speeds), other options exist that can further allow the motorist to mode-shift toward the travel solution that meets the needs in the most efficient way possible: • Limited-use vehicles: Small battery-electric or gasoline vehicles with limited top speed and range that are useful for short trips in an urban setting (see Chap. 13). Some of these vehicles have necessary signaling and safety equipment to be road legal. In other situations, a non-road legal vehicle can be used on separate roadways, such as in golf communities where residents use golf carts within the community for golf, shopping, and other amenities. • Motorcycles, scooters, and other motorized two- and three-wheel vehicles: Where the motorist is traveling alone and does not have many goods to carry, these vehicles provide a very energy-efficient option for local travel. • Nonmotorized modes: Travel by bicycle or on foot by definition uses no energy, and additionally provide health benefits to the traveler through cardiovascular exercise, whenever the weather is suitable. In many instances, it is not the lack of a vehicle but rather the lack of bicycle- and pedestrian-friendly infrastructure that prevents their greater use. In response, many urban regions have been expanding and improving sidewalks, bike lanes and bike paths, road shoulders, multiuse trails, and bicycle rental facilities to provide a better nonmotorized alternative for work and nonwork travel (see Fig. 14-14). In some cases, national government funding is available, such as the Chester Creek Rail Trail project near Philadelphia, PA, which in 2007 received funding from the U.S. Environmental Protection Agency’s Congestion Mitigation and Air Quality (CMAQ) program. While the appeal of having different options for different types of trips is clear, many travelers find it impossible to create these opportunities for themselves, both for reasons of the additional capital cost of owning different vehicles and the practical requirement for space to store all the various vehicles in and around one’s residence. The need to occasionally carry a large amount of goods or a large number of passengers often dictates that the individual who can only afford one vehicle purchase a large one, and then travel at most times with “excess capacity” in terms of passenger seats or volume of cargo space. The excess capacity in turn translates into additional weight that must be moved around when the vehicle is in use, which increases energy consumption. Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 444 4/3/08 7:58:01 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y 445 FIGURE 14-14 Bicycles at an automated public rental facility, Paris, France, 2007. One way to expand the use of bicycles is to make them available for short-term rental at low cost. In this system implemented in Paris, rental facilities are located throughout the city to dispense and collect bicycles. Once users join as members, they may release a bicycle with a swipe card at any location, and then return it at the same location or another. One practical solution that is expanding in many urban areas of Europe and North America is car-sharing, where individuals join an organization that rents vehicles for short periods of time and for short distances, and makes them available in distributed locations around the urban area so that they are easy to access from one’s home address. In this way, car-sharing is different from the rental car industry, which is geared more toward rental of cars for periods of 24 hours or longer, and for longer distances. Carsharing gives the member access to specific types of vehicles at the times when they need them (e.g., compact cars for solo trips and minivans or light trucks for moving large amounts of goods) without the need to own and maintain the vehicle on one’s own premises. Matching the size of the vehicle to the needs of the trip in this way helps to reduce energy consumption. Members of car-sharing organizations may join in addition to owning their own car, but for those who join as a substitute for car ownership, car-sharing encourages the individual to diversify choice of transportation modes, since they no longer have the “sunk cost” of owning a vehicle. Modal Shifting of Freight Transportation The most important concept in expanding the use of energy efficient modes in freight transportation today is the concept of intermodalism, or the creation of a seamless freight transportation service that delivers the shipment from origin to destination with a high LOS while using different modes for different segments of the journey where each mode has a comparative advantage. The freight industry has come to recognize that, over long distances, modes such as rail (and in certain situations water) have attractive Vanek_ch14-p421-464.indd 445 4/3/08 7:58:01 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 446 Chapter Fourteen advantages, in terms not only of energy efficiency but also reduced cost and labor requirement. At the same time, most shipments today must at some point move by truck, since trucks provide the most convenient access to origins and destinations. Some locations, such as large manufacturing plants or mining facilities, have direct access to the rail and/or water network. However, in today’s dynamic economy, locations change rapidly, and it is much more practical to link a new facility to the road network than to the rail or water network. Also, for small- and medium-sized facilities, it is often more financially attractive to both the business and the transportation operators to send shipments via truck to the nearest transit point to the rail or water modes, rather than incur the high cost of building a dedicated rail/water facility on site. A number of systems exist to transfer shipments from the road to other modes at the intermodal transit point, as follows: • Roll-on roll-off: The single-body truck or tractor-trailer enters and leaves the railcar or marine vessel as a complete unit. • Loading of truck trailers: The trailer is separated from the truck tractor and loaded onto the railcar (also known as “piggyback” rail service). Separate loading of trailers onto marine vessels is less common, though possible. • Loading of shipping containers: When arriving by road to a rail or marine intermodal facility, the chassis (wheeled underbody that allows a shipping container to move over the road) is removed and then the container is loaded onto the railcar or marine vessel. Direct loading/unloading between rail and marine is also possible. Modern intermodal railcars allow double-stacking of containers on railcars to maximize the productivity of each train. On the basis of these different systems, a wide variety of applications are possible. For example, between Salerno, Italy and Valencia, Spain, the European Union has been supporting the development of a roll-on roll-off service aimed at trucks that would allow them to avoid driving along the perimeter of the Mediterranean Sea via France between the two countries. Although a motorway exists along the land route, it is circuitous and passes through a number of highly populated areas, so it is desirable to transfer some truck traffic to the seas. In a different European location, the Swiss government has developed a network of roll-on roll-off trains for trucks to allow them to transit the Alpine region without driving. The main driver of this program is the reduction of air pollution in an environmentally sensitive region, but there are also CO2-reduction benefits, since the Swiss railways are almost entirely electrified and Swiss electricity comes from hydro and nuclear power. One of the largest intermodal freight shipping operations in the world today is the shipping of containers and truck trailers in continental North America, along the rail networks of Canada, the United States, and Mexico (see Fig. 14-15). In this market, some freight shipments move very long distances (2000 to 3000 km or more) to and from population centers in the interior to shipping ports along the Atlantic and Pacific. Over these long distances, it makes financial sense to bundle a large number of shipments onto a single train so as to save energy and labor costs, as a single double-stack train with crew of three in the locomotive consist can replace 300 or more trucks each carrying one container. Through the 1990s, this industry saw double-digit percent annual growth in the number of containers moved as the railroads improved service quality and an increasing number of shippers took advantage of the cost savings available from this service. Vanek_ch14-p421-464.indd 446 4/3/08 7:58:03 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y 447 FIGURE 14-15 Double-stack intermodal train carrying shipping containers. (Photo: BNSF Railroad. Reprinted with permission.) Energy savings from intermodalism come from moving a given shipment via rail or water instead of over the road, reducing energy consumption on each tonne·km of movement. Intermodal shipments sometimes require truck movements between the origin/destination and the intermodal terminal that are not necessary when the shipment moves by truck directly from origin to destination using the intercity highway network. Nevertheless, for most long distance movements where intermodal transportation is financially competitive with direct shipment by truck, the energy savings can be substantial. Taking the case of the U.S. intermodal network during the 1990s and 2000s, these movements may have had an energy intensity on the order of 880 to 1690 kJ/tonne·km (1200 to 2320 Btu/ton·mi), compared to an average value of 2100 kJ/ tonne·km (2900 Btu/ton·mi) for trucking. Based on the growth in use of intermodal services over the period 1980 to 2005, energy savings attributed to moving shipments by intermodal transportation compared to moving the same shipments by truck under a midrange efficiency scenario (1285 kJ/tonne·km, or 1752 Btu/ton·mi) rose from 14 PJ to 80 PJ, as shown in Fig. 14-16. The latter quantity of energy is equivalent to approximately 15% of all rail energy use in the United States in 2005. The expansion of intermodalism requires financial investment in the necessary equipment, including not only vehicles and rights-of-way (rail lines or waterways), but also intermodal transfer facilities that make possible the smooth transition from one mode to another. If either transfer points or long-distance corridors do not function correctly, then the entire system becomes unattractive to shippers, and the potential to save energy is lost. On the other hand, intermodal freight is a win-win situation for governments, shippers, rail and marine operators, and even for trucking firms, who can profit from transferring segments of truck freight movements to the rail or water modes: for all of these entities, it brings the benefits of taking pressure off the overburdened and overcongested road network. From an energy perspective, it also brings the benefit of moving freight at lower energy intensity. At the present time, there is every indication that governments and the private sector will continue to invest in these systems and at the same time advance the underlying technology to make it even more competitive. Vanek_ch14-p421-464.indd 447 4/3/08 7:58:03 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 448 Chapter Fourteen 140 120 Energy savings [PJ] 100 80 60 40 20 0 1980 1985 1990 1995 2000 2005 FIGURE 14-16 Low-, mid-, and high-range for energy savings achieved by shifting freight from road to intermodal in United States, 1980–2005. 14-7-2 Rationalizing Transportation Systems to Improve Energy Efficiency The discussion of modal shifting in the previous section takes the demand for moving freight or passengers as measured in passenger·km or tonne·km as fixed, and seeks modal alternatives that will deliver the required transportation service with reduced energy consumption. In this section, we consider the “other half of the equation,” namely, the reduction of vehicle·km, passenger·km, or tonne·km through strategies that streamline or “rationalize” transportation movements while still meeting the needs of the customer. As a simple illustration, bus transit system operators often rationalize their service on weekends by reducing the number of buses per hour on a bus route, since otherwise the number of bus vehicle·km required would be excessive compared to the number of customers using the bus. Since the emergence of the railroads in the nineteenth century, it has been the pursuit of transportation planning at many levels of society to use assets (vehicles, railcars, and the like) in an optimal way so as to maximize the movement of passengers and freight, minimize travel time, minimize costs, and so on. In recent years, the development of information technology (IT) systems has greatly expanded this capability. IT systems can help transportation planners route freight trains over rail networks, assign empty taxi cabs to new customers or to key waiting points, match mobility impaired passengers to paratransit service, route delivery vehicles bringing parcels to addressees in a metropolitan area, and so on. While shipping clerks and other human operators developed pencil-and-paper solutions to these problems in the era prior to the digital age, IT systems can solve complex problems more quickly and with superior results, often by using some type of optimization (see Chap. 2). Rationalizing systems in this way translates into energy savings, since the required passenger·km or tonne·km of demand are met with fewer vehicle·km of movement. With very few exceptions, the solution to the optimal vehicle planning problem that minimizes energy consumption is also the one that minimizes financial cost by reducing capital, labor, and maintenance costs, and is therefore the most attractive to both public and private enterprises. In some cases, IT systems and optimization may also be used to reduce underlying demand for passenger·km and tonne·km. Although it was stated above that the latter Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 448 4/3/08 7:58:04 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y measures are the underlying service that transportation service delivers, they are in turn derived from the public’s need for access to goods and services. If this need can be met while reducing total passenger·km/tonne·km through locational choices, both cost and energy consumption can be further reduced. For example, a large retail firm that sells their products through a chain of outlet stores in a region with growing demand may be able to reduce total shipping cost by developing a source for certain products that is closer to the market. Assuming the new source does not cost more or consume more energy on the production side than the original one, the total cost of the product is reduced due to lower shipping costs, and energy is saved thanks to fewer tonne·km of movement required. 449 System-Wide Rationalization of Transportation Demand: A Case Study of U.S. Freight Transportation Patterns Advances in vehicle technology and IT systems have acted as a two-edged sword in regard to their effect on energy consumption. The same technologies that allow transportation professionals to plan operations in an optimal way also enable passengers and goods to travel reliably over longer distances and at a lower cost. Even at the longest distances, journey times can be planned with greater precision than ever before. It is little wonder that not just the total number of trips being made or amount of goods being shipped has increased but the average length of trip or shipment has increased as well, driving up energy consumption. Transportation statistics consistently point in this direction. To take the example of freight transportation in the United States, from 1993 to 2002 the average shipment distance grew from 230 to 318 km. Along with estimates of total tonne·km of activity, U.S. government agencies also track total tonnes of freight originated in the system, which grew from 10.9 to 12.8 billion tonnes over the same period, or 18%. At the same time, tonne·km grew from 3.5 to 4.6 trillion, or 30%. Thus the average number of tonne·km for every tonne originated is also growing, from 323 in 1993 to 355 in 2002. A similar phenomenon has been observed in the United Kingdom and other European countries, and the term spatial spreading has been coined to describe it. A possible way to counteract this trend and curb the related growth in energy consumption is to take the scale of the optimization technique up to the next level, and consider not just optimizing the activities of individual firms internally, but optimizing the transportation choices of multiple firms that make up a product sector, so as to achieve larger gains. Analysis of government-gathered commodity flow data, or volume of freight of different types along major corridors between regions, can be used to develop a graphical representation of the spatial patterns of freight flows. The resulting database can in turn be used to identify opportunities to rationalize the system. To illustrate the concept, we have created a demonstration network using 1993 data on paper product movements from the U.S. Bureau of Transportation Statistics between four major producing regions and five major markets, as shown in Table 14-5. In large part, the data suggest a pattern where geographic distance already influences the amount of flow, and thus the volumes of flow are fairly rational, for example, the largest source of paper products for California is Oregon, and for Illinois is Wisconsin, which in both cases are adjacent states. However, there are also smaller volumes of flows moving very long distances, including from Maine to California or Oregon to New York. The pattern in Table 14-5 suggests that, by producing the right mix of paper products closer to the market for which they are destined, total tonne·km of freight could be Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 449 4/3/08 7:58:05 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 450 Chapter Fourteen Origins Destinations CA NY FL IL NJ OR 1904 67 0 40 0 WI 243 257 79 1230 85 GA 122 424 1258 335 293 ME 55 435 55 404 114 Note: Abbreviations are as follows: Oregon = OR, Georgia = GA, ME = Maine, and Wisconsin = WI), along with five major destination states for paper products (California = CA, New York = NY, Florida = FL, Illinois = IL , and New Jersey = NJ). TABLE 14-5 Flows of Paper Products between Select U.S. Origins and Destinations in 1000 tonnes, 1993. reduced while still delivering enough product to meet consumer demand. As a preliminary indication of the potential to reduce, Table 14-6 shows an alternative pattern where the flows have been rearranged using optimization such that the same tonnes of product are produced in each origin, and the same number delivered to the destinations, but the amount of generated tonne·km is minimized. Many of the very longest flows are eliminated in this alternative solution, leading to reduced tonne ·km requirements. Based on typical modal share among road, rail, and water movements, and average energy intensities, the estimated energy requirements for moving the 7.4 million tonnes of paper products in the base case is 13.4 PJ. The resulting change in transportation pattern would, if it were instituted entirely, eliminate 2.3 billion tonne·km of paper products movement, which is equivalent to 3.1 PJ, or approximately 200,000 tonnes of CO2 saved per annum. As with any optimization modeling exercise, the above results must be interpreted carefully. In many instances, there may be commercial reasons why it is important to source a specific product in a given location, regardless of distance. Therefore, one would not expect the improved pattern to be reproduced exactly. Also, from an energy life cycle perspective, there may be efficiency advantages to large-scale, centralized production that offset the extra energy requirements for shipping long distances. However, a process of studying spatial patterns, and then governments and private firms working together to From To CA NY FL IL NJ TABLE 14-6 requirement. OR 2011 0 0 0 0 WI 0 0 0 1894 0 GA 313 120 1393 115 491 ME 0 1064 0 0 0 Flows of Paper Products after optimizing to reduce tonne-km Vanek_ch14-p421-464.indd 450 4/3/08 7:58:05 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y transform the information contained therein into adjustments to rationalize the system can make a contribution to reducing energy consumption. Such changes could be beneficial, because the amount of energy consumption involved, even in a selection of product movements used in this example, is quite large. The figure of 13.4 PJ quoted above is small relative to the total freight energy figure of 8.2 EJ for the year 2000 from Figure 14-2, but it is equivalent to the entire energy budget of a small developing country. (Total energy consumption across all end uses for Sierra Leone in 2004 was 14 PJ.) Therefore, reducing energy consumption by 5 or 10% through a more geographically compact freight flow pattern could lead to worthwhile reductions in energy consumption. Lastly, the example presented in Tables 14-5 and 14-6 held the amount of paper products produced in each origin fixed, and reduced transportation energy consumption by rationalizing the destinations for those products. Additional gains could be reaped from expanding local and regional production, so that more demand can be met without going outside of a region for the supply. For example, in the Table 14-5, 18% of the 2.3 million tonnes arriving in California in the base case come from other distant parts of the United States. With expansion of paper products manufacturing along the west coast, including states such as California, Oregon, and Washington, more of the demand for products could be met within the region, and the shipment of products from the East Coast to California might be reduced. 451 14-8 Understanding Transition Pathways for New Technology Many analyses of potential energy savings from changes to the transportation system, including the preceding analysis of paper product movements, are carried out on a comparative static basis, meaning that a comparison is made between two static solutions, one before some change is made and one after. While this approach is useful for quickly obtaining a preliminary estimate of potential benefits of changes, and may be the only option where data of adequate quality do not exist to support a more sophisticated model, it also ignores the transition effects of going from one state to another. Some of the limitations of “static analyses” that ignore transition effects include • Changes to baseline conditions during transition period are overlooked: Transitions involving energy technology for transportation systems inevitably require long time horizons. During these time spans, all the underlying factors in a static analysis are subject to change, including total demand for energy, the efficiency of the incumbent technology, amount of greenhouse gas(GHG) emissions, and so on. Furthermore, in addressing situations such as climate change or potential petroleum shortages, the timing of when the energy savings and CO2 reductions are achieved is important. A comparative static analysis does not shed light on these issues. • The nature of the transition itself can shape the eventual outcome: Certain factors that act on the system during the transition may act as an obstacle to its completion in the way that is expected in the static analysis. Projections that do not consider the transition as a possible barrier may overstate the benefits of the change. • Where transitions depend on government policy for support, the transition may fail to take root permanently: While superior technological performance drives some transition (e.g., from paper-and-pencil to IT systems in transportation operations planning and management), others require the intervention of government Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 451 4/3/08 7:58:05 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 452 Chapter Fourteen through tax policy, subsidies, or regulations. Some transitions may require the permanent intervention of government in order for the technology to attract customers, which may or may not be financially or politically sustainable. Others may require an intervention long enough for the transition to “take root,” and will fail if it is too short. Where transitions fail for these reasons, the effect on energy consumption will not be the one that the static analysis predicts. Fortunately, the research community has enough historical experience with technological transitions (e.g., the influx of a new, superior technology into a market, displacing an incumbent technology, or the government-led phasing out of an undesirable product) that we understand to some degree the shape that these transitions take, the forces at work during the transition, and even the future shape that a transition will take based on its early path. Where analysts can obtain the necessary data, and have the time and resources to carry out a more careful, dynamic study of the transition, more accurate results are possible. Tools such as the logistics function or triangle curve are applied to mapping the transition pathway of new products and systems over time, leading to a more realistic understanding of when and to what extent energy savings will occur. Example 14-2 illustrates this process. Example 14-2 Using the growth in hybrid sales in the United States shown in Fig. 13-7 as a starting point, consider the transition pathway to hybrid penetration into the fleet, in the following way. Suppose that the number of hybrid electric vehicles (HEVs) in the fleet, which starts in the year 2000 with 9350 vehicles added, eventually tapers off to 13,000,000 units, and that each hybrid averages 15 km/L fuel efficiency, versus 8.5 km/L for the internal combustion engine vehicle (ICEV) alternative. Consider the year 2000 to be the year t = 0. Sales through the year 2006, in order, number 20,287, 35,000, 47,500, 88,000, 20,7000, and 25,3000. Assume that for each new sale another car is scrapped, so that the overall size of this segment of the fleet does not change, and that each car drives 16,000 km/year. (A) Use the logistics function to calculate in which year (call it year N) the number of hybrids in the fleet surpasses 99% of the 13M target. (B) Calculate the cumulative fuel savings in liters for the shift to HEVs from years 0 to N, if the new cars are assumed to all be available on day 1 of each new year. For the years 2000 to 2006, use the sales estimated from the logistics curve model, rather than the actual sales, to calculate the number of vehicles in the fleet. (C) Compare the savings to the situation where a fleet of 13 million ICEVs is instantaneously transformed into HEVs at the beginning of year 0, and then driven to the end of year N. Solution (a) We begin by converting sales numbers into cumulative numbers in the fleet. The following table provides this information through year 2006: No. Vehicles Year 2000 2001 2002 2003 2004 2005 2006 Sales 9,350 20,287 35,000 47,500 88,000 207,000 253,000 Cumulative 9,350 29,637 64,637 112,137 200,137 407,137 660,137 Vanek_ch14-p421-464.indd 452 4/3/08 7:58:06 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y 8.0E + 05 70E + 05. 6.0E + 05 Units of HEVs in fleet 50E + 05. 4.0E + 05 3.0E + 05 2.0E + 05 1.0E + 05 0.0E + 00 2000 Actual Modeled 453 2001 2002 2003 2004 2005 2006 FIGURE 14-17 Actual versus modeled number of HEVs in fleet, 2000 to 2006. We obtain all necessary parameters for the logistics function as follows. Based on full penetration of the 13 million market, a = b = 1. The presence of 9350 in year 0 leads to a value of p0 = (9350)/(1.3 × 107) = 0.00072. The value of x is solved in a spreadsheet by calculating the error between observed and model values for years 2000 to 2006, and then minimizing the square of the error terms, resulting in x = 1.29. For example, for year t = 2, a ⋅ p0 0.00072 = 0.00307 = bp0 + ( a − bp0 )e( − at/x ) 0.00072 + (1 − 0.00072)e( −2/1.38) p(2) = HEV penetration in 2002: (1.3 × 107 )(0.00307 ) = 39, 920 i Plotting actual versus modeled penetration through 2006 gives the curve shown in Figure 14-17: In year N = 17, the penetration surpasses 99%: 0.00072 = 0.988 0.00072 + (1 − 0.00072)e( −16/1.38) 0.00072 = 0.994 0.00072 + (1 − 0.00072)e( −17/1.38) p(16) = p(17 ) = (b) Since the total fleet size is constant at 13 million, savings accrue from replacing some fraction of the fleet with hybrids. In each year, the baseline fuel consumption with no HEV penetration is (1.3 × 107 vehicles)(16,000 km/year) = 2.45 × 1010 L/year 8.5 km/L Vanek_ch14-p421-464.indd 453 4/3/08 7:58:06 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 454 Chapter Fourteen The savings in each year is then the baseline minus the actual consumption, based on the mix of HEVs and ICEVs in the fleet. Taking year 2 as an example again, the HEV and ICEV fuel consumption values are, respectively: (3.992 × 10 5 vehicles)(16,000 km/year) = 4.26 × 107 L/year 15 km/L (1.3 × 107 − 3.992 × 10 5 vehicles)(16,000 km/year) = 2.44 × 1010 L/year 8.5 km/L The total fuel consumption is therefore little changed from the baseline, at 2.444 × 1010 L. Repeating the calculation of annual savings for each year and summing to quantify cumulative savings gives the following graph of fuel saved from 2000 through 2017, as shown in Fig. 14-18: Based on these calculations, at the end of year 17, the savings has reached 8.01 × 1010 L. (c) An instantaneous transition to 13 million HEVs gives the following energy consumption per annum: (1.3 × 107 vehicles)(16,000 km/year) = 1.39 × 1010 L/year 15 km/L On this basis, the annual savings is (2.45 × 1010) − (1.39 × 1010) = 1.06 × 1010 L/year. Including the savings in year 0, the project has an 18-year time span. Therefore the cumulative savings is (1.06 × 10 10)(18 years) = 1.91 × 10 11 L o f fuel. Discussion Comparing the results of parts (B) and (C) in the example shows that during the time of the project, considering the transition period results in a 58% reduction in the projected savings. Thus the benefits of reducing the consumption of petroleum or emissions of CO2 would accrue much more slowly when one takes into account a realistic amount of time for the new vehicle technology to penetrate the market. 5.E + 11 4.E + 11 4.E + 11 3.E + 11 3.E + 11 2.E + 11 2.E + 11 1.E + 11 5.E + 10 0.E + 00 2000 Cumulative A nnual Cumulative fuel savings [liters] 9.00E + 10 8.50E + 10 8.00E + 10 7.50E + 10 7.00E + 10 6.00E + 10 5.50E + 10 5.00E + 10 4.50E + 10 4.00E + 10 3.50E + 10 3.00E + 10 2.50E + 10 2.00E + 10 1.50E + 10 1.00E + 10 5.00E + 09 0.00E + 00 2002 2004 2006 2008 2010 2012 2014 2016 Annual fuel savings [liters] 4/3/08 7:58:06 PM 6.50E + 10 FIGURE 14-18 Annual and cumulative fuel savings, 2000-2017. Vanek_ch14-p421-464.indd 454 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y More realism could be added to this example by taking into account other real-world factors. Both HEV and ICEV fuel economy are likely to change over time. Also, stratifying the fleet by vehicle type, and then tracking sales and fuel economy of each vehicle type would make the projections of fuel savings more realistic. Furthermore, by the end of year 17, vehicles purchased in year 1 would likely be at the end of their useful lifetime, so an accounting of the turnover of both types of vehicles as part of the model would add accuracy. Over very long time spans, it may not be possible to predict a single “correct” pathway for the unfolding of a technological transition, in which case a “scenario approach” is useful for considering alternative combinations of factors to bracket the range of possible pathways for indicators of interest such as total energy required or total CO2 emitted. For example, a complete transition to a propulsion technology for transportation that emits no net emission of CO2 to the atmosphere but meets all of the demand on the planet could last until sometime between 2050 and 2100, or beyond. Figure 14-19 shows a transition scenario for the introduction of fuel cell vehicles (FCVs), followed by nonfossil resources to power the FCVs, for the worldwide fleet of passenger cars from 1960 to 2090, based on assumptions about the relative fuel efficiency of internal combustion engine vehicles (ICVs). Thus there are three stages, each color coded in the figure: • • • FF (fossil fuel) for ICV: Energy required for fossil fuels to power ICVs, which is the current technology. FF for FCV: During the first phase of ramping up the presence of FCVs in the world vehicle market, the hydrogen required is derived from fossil fuels. RE(renewable energy) for FCV: The hydrogen for the FCVs is derived from renewable energy, or some other source that does not emit net CO2 to the atmosphere. 455 The pathway shows that if FCVs only enter the market in 2030 and it takes this technology 60 years until 2090 to completely replace ICVs, then energy required from fossil fuels will continue to grow until 2050, reaching a value of approximately 41 EJ, compared to 27 EJ in the year 2000. Furthermore, Sume of for Fuel Conlce 5.00E + 10 4.50E + 10 4.00E + 10 3.50E + 10 Energy (GJ) 3.00E + 10 2.50E + 10 2.00E + 10 1.50E + 10 1.00E + 10 5.00E + 09 Fossil fuels for FCVs Fossil fuels for ICEVs Renewables for FCVs 0.00E + 00 1960 1970 1980 1990 2000 2010 2020 2040 2030 2050 2060 2070 2080 2090 FIGURE 14-19 Scenario for the influx of hydrogen fuel cell vehicles and carbon-free transportation energy in the twenty-first century, with estimate of world energy requirement. Note: This figure is a result of collaboration with Julien Pestiaux and Audun Ingvarsson, M. Eng students, 2003–04, whose contribution is gratefully acknowledged. Vanek_ch14-p421-464.indd 455 4/3/08 7:58:07 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 456 Chapter Fourteen a transition to hydrogen from renewable energy starting in 2050 and lasting 60 years (off the right side of the figure) would still require 10 EJ from fossil fuels for making hydrogen in the year 2090. The overall lesson from this scenario is that if the transition to alternative-fuel vehicles (AFVs) without CO2 emissions starts many decades from now, and present growth in worldwide vehicle · km continues, there is cause for concern that fossil fuel use and CO2 emissions from transportation may grow substantially before they begin to decline. The FCV is chosen for the scenario analysis in Figure 14-19 for illustrative purposes only; other AFV technologies could be analyzed in the same way. Another good candidate is the plug-in HEV, which might use a mixture of fossil and nonfossil electricity in a “bridge period” through the middle of the century until sufficient CO2-free electricity generating capacity could be developed to power the world’s fleet of plug-in HEVs. 14-9 Toward a Policy for Future Transportation Energy from a Systems Perspective In Chaps. 13 and 14, we have discussed a range of energy technologies for propulsion in transportation, and also various systems effects that influence the implementation of these technologies in a transportation system. In this concluding section we combine technology and systems perspective into a discussion of policies for transportation energy in the future, focusing on two topics: (1) a portfolio of options for a metropolitan region seeking more energy-efficient transportation, and (2) a plan for allocating emerging energy resources and technologies to different transportation functions, modes, and geographic domains. 14-9-1 Metropolitan Region Energy Efficiency Plan In this section a preliminary concept is presented for incorporating all of the options that are discussed in the body of this chapter that are available in the near to medium term (on the order of 10 to 30 years) into a transportation energy plan for a metropolitan region. This plan applies to a typical metropolitan region (hereafter referred to as “the region”) in an industrialized country in Europe, North America, or Asia, which seeks to reduce total transportation energy consumption across the board. The term metropolitan region implies the urban core of the city and surrounding suburban and exurban developments out to the perimeter of the developed area. For many cities, this definition includes both the area within the politically-defined city limits, and the built-up areas surrounding the city limits. The plan focuses on cities in industrialized countries because of their high per capita transportation energy use: Many cities in the emerging countries could also benefit from some parts of the plan. Notwithstanding the limitations on static analyses discussed above, the plan is static in nature, suggesting end targets for reducing energy consumption from each option in percentage terms, but not considering how long implementation might take or what might happen to baseline energy consumption in the mean time. The plan incorporates measures for both passenger and freight transportation. On the passenger side, options including the transformation of the light-duty vehicle fleet to more efficient models of vehicles, expansion of the use of public transportation, development of the use of limited-use vehicles and nonmotorized modes, and changes to land use patterns that can be implemented in the short to medium term. On the freight side, the region works with firms that provide movement of goods to, from, and within the region to use more energy-efficient modes, and also works with businesses Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 456 4/3/08 7:58:07 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y within and outside of the region to replace some of the distant sources of goods with others that are closer. The plan does not include technologies such as fuel cell vehicles that may not be available for some time. The summary of the plan is shown in Table 14-7. In the table, the percentage values given are compared to the future baseline value of total transportation energy consumption. The range of values shown are the author’s assessment of a plausible range of savings values, since no previous study was identified that considered all alternatives available to a metropolitan region; individual statistics are provided to support components of the plan, where possible. Several points can be made about the table: • The plan in the table does not address the potential increase in baseline passenger·km and tonne ·km that might occur during the 10 to 30 year implementation period. As in Example 14-1, it is possible for an efficiency option to reduce energy consumption relative to the future baseline, and still have the resulting future value be higher than the current value. In a region experiencing rapid population growth, increases in population and growing demand per capita might wash out all the improvements achieved by the plan. If this happened, the region would be better off than if no steps had been taken, but no closer to reducing the absolute value of its transportation energy consumption footprint. • The implementation of not just one of the options, but most or all of them, however, makes it more likely that the absolute amount of energy consumption will decrease. • The upper bounds of the percentage range for each option are intentionally made to be ambitious. It is likely that the twin challenges of reducing CO2 emissions and preserving petroleum resources will motivate cities to move toward unprecedented levels of reductions. • Although the projected percent savings are higher for some options than others, none of them are mutually exclusive of one another, so all are worthy of pursuit. • No attempt is made to calculate a total energy savings value on the basis of the percent values provided. Such an analysis would require a careful treatment of the interaction between different options (e.g., once the fleet has been transformed into a more efficient one, the total energy savings available from modal shifting away from light-duty vehicles is less than the baseline), and is beyond the scope of this text. In conclusion, the table does not result in a calculation of how much energy could be saved overall by the region. However, as an initial impression, the values in the table support a potential overall reduction ranging from 20 to 40%. These savings would lead to substantial reductions in CO2 emissions, even if most of the remaining demand for motorized transportation was powered by fossil fuels. Over the longer term, the region might shift the remaining transportation energy consumption over to a source that did not increase CO2 in the atmosphere (using the types of technologies discussed in Chap. 13). 457 14-9-2 Allocating Emerging Energy Sources and Technologies to Transportation Sectors In the future, and especially over the longer term beyond 20 or 30 years, alternative fuels such as biofuels, electricity, and/or hydrogen are expected to expand into the marketplace Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 457 4/3/08 7:58:07 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 Function Passenger Efficiency Option Improved efficiency of lightduty fleet: more efficient ICEVs, hybrids, plug-in hybrids, and the like. Potential Savings 10–25% Rationale Current and proposed improvements in fuel efficiency standards in all major auto markets of the world; efficiency improvement and rate of market penetration by HEVs during the period 2000–2007. Up to 50% reduction in energy use possible for U.S. public transportation compared to car/ passenger·km. 5–15% figure assumes limits ability to add transit and/or carpools to all areas of region. Ability to reduce energy consumptions assumes sufficient load factors in alternatives to cars. 30–40% of all trips in Amsterdam, Netherlands, by bicycle. Rate of bicycle trips in Melbourne, Australia, increases fourfold in 20 years. Untapped market for electric LUVs in industrialized cities. California “smart growth” plan could achieve 3–10% reduction in vehicle·km and energy use compared to baseline by 2020.5 Smart growth concept is widely applicable in United States and other countries. Projection based on savings per tonne·km moved by intermodal vs. truck, and maximum applicability of intermodal (not all sources of goods are served by intermodal service) Types of products most easily substituted (foods, farm products, certain building products, etc.) and their relative contribution to freight energy consumption—see Figs. 1410(a) and (b). Expanded (quasi-)public transportation: new rapid transit systems, expanded bus service, support for carpooling and vanpooling.∗ 5–15% Motorcycles, LUVs & nonmotorized modes: motorcycles, mopeds, scooters, limited-use EVs, expanded use of bicycling & walking through improved infrastructure Short- and medium-term land use changes: locally available shops and facilities, telecommuting centers, revitalizing traditional shopping districts Freight Encouraging modal shifting: greater use of intermodalism, supporting the development of intermodal terminals within the region Rationalizing freight demand: substituting near for far sources, developing local resources, e.g., local food production 2–10% 2–15% 2–10% 2–10% Percentage reduction figures are for total urban transportation energy consumption relative to future value for baseline (“do-nothing”) scenario; see explanation in text. ∗ Note that carpools and vanpools rely on collaboration between private individuals to share vehicles, but can be supported by local and regional governments, and are therefore labeled quasi-public in this option. TABLE 14-7 Range of Possible Percent Reduction Values for Energy Consumption from Efficiency Options Available to a Metropolitan Region 5 Estimated savings published by California Energy Commission (2001). 458 Vanek_ch14-p421-464.indd 458 4/3/08 7:58:07 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y to provide a carbon-free substitute for petroleum products. It makes sense to allocate each to its application of comparative advantage, especially taking into account the function, mode, and geographic scope of the transportation application in question. Current technological characteristics suggest that electricity and/or hydrogen will hold an advantage in urban transportation, where the distances traveled between opportunities to recharge are not as great. Whether a HFCV bus or a plug-in HEV, either one is well suited for urban movements during the day followed by recharging at night. These applications also favor the new infrastructure needed to support the energy source, since there is a high concentration of vehicles in each urban region that can more easily support the infrastructure cost of each new recharging station or hydrogen filling station. Assuming that a cost-effective means of generating carbonfree electricity or hydrogen can be developed, there will not be a space constraint on generating large quantities of these energy carriers for transportation. Therefore, it should be possible to meet the large energy demand that urban transportation requires with these sources. Electricity and hydrogen in the urban setting may have additional benefits for the electric grid. For example, plug-in hybrids might dock at workplace charging stations and discharge electricity stored the previous night into the grid during the middle of the day, helping to meet peak electric demand, especially on sunny summer days. Similarly, HFCVs might take a supply of hydrogen supplied to the workplace parking facility and convert it to electricity to be supplied to the grid rather than used for propulsion. Biofuels, by contrast, have an advantage for long-distance use in that they can more easily match the long range per refueling of petroleum-derived gasoline or diesel, and are therefore well suited for intercity travel. In the United States, for example, there is a good match between the 5 to 6 EJ/year energy requirement for the surface freight transportation system and the maximum output from robustly developed biofuel production facilities that may be possible in the future without harming the capability to grow enough food. A situation may emerge where the biofuel output is largely allocated to freight, and is able to meet most or all of the intercity demand. However, biofuels alone may not be able to meet both freight and intercity passenger energy demand. At the time of this writing, it is not clear how this gap might eventually be filled, but it is possible that the expanded use of electric catenary on the railroads might help to ease some of the pressure, since the electricity could come from diverse sources, and the railroads might increase their volume of both freight and passenger transportation. Turning to air transportation, the aviation industry has made little progress so far in developing an alternative fuel for jet-powered aviation. This is in part due to the difficult conditions under which jets operate at cruising altitude, where it is more difficult for alternative fuels to function well because of cold temperatures. Also, the higher cost of alternative fuel would have a large economic effect on the industry, since energy costs are an especially large part of the overall operating cost of commercial jetliners. Nevertheless, the industry is interested in developing alternatives over the long term to assure its survival. An early milestone was the use for the first time in 2006 of a mixture of traditional jet fuel and Fischer-Tropsch synthetic fuel derived from natural gas in a U.S. Air Force jet during a routine training flight. Given the difficulty of providing an alternative to petroleum-derived jet fuel, one strategy is to give aviation priority access to petroleum resources and emphasize the development of alternative fuels for other transportation modes so as to extend the lifetime of the petroleum supply. 459 Vanek_ch14-p421-464.indd 459 4/3/08 7:58:08 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 460 Chapter Fourteen Lastly, barring unanticipated technological developments, we can expect that progress will be slow in making large reduction in the absolute amount of CO2 emitted by worldwide transportation up to the middle of the twenty-first century. More efficient technologies that use petroleum may curb or reverse the growth in emissions, and systems-wide changes such modal shifting may contribute as well. However, carbon-free transportation energy systems that can meet the majority or most of the world’s demand are still some time away. One possible interim strategy is to focus extra effort on nontransportation CO2 emissions reduction, using options discussed in Chaps. 6 to 12, to offset emissions from the transportation sector. Summary Reducing energy consumption and GHG emissions from transportation has both a technological and a systems component, and the systems effects strongly influence the total energy required. Transportation systems can be understood from many perspectives, including function, mode, geographic scope, and ownership. Energy efficiency can in turn be evaluated using measures such as kJ/vehicle · km, kJ/ passenger · km, and kJ/tonne · km, depending on the circumstances. Although world energy use has been growing steadily in recent decades, both passenger and freight transportation energy consumption have been on an even more rapidly increasing trend, as modern land use patterns encourage the use of the automobile, and the global economy allows goods to move all around the world at increasing speeds. Much effort is currently being made to reverse this trend through government actions, for example, to encourage use of more environment-friendly modes such as rail and water. As we look to introduce new technologies in the future to reduce energy consumption, we should take into account transition effects that influence the rate at which technologies can enter the market and the rate at which energy can be conserved. References California Energy Commission (2001). California Smart Growth Energy Savings: MPO Survey Findings. Consultant Report Prepared by Parsons-Brinckerhoff. California Energy Commission, Sacramento, CA. Department for Transport (2006). Transport Statistics Great Britain. DfT, London. Vanek, F., and E. Morlok (2000). “Reducing US Freight Energy Use Through Commodity Based Analysis: Justification and Implementation.” Transportation Research Part D, Vol. 5, No. 1, pp. 11–29. Bibliography Davis, S., and S. Diegel (2003). Transportation Energy Data Book, 22nd ed. Oak Ridge National Laboratories, Oak Ridge, TN. Davis, S., and S. Diegel (2007). Transportation Energy Data Book, 26th Edition. Oak Ridge National Laboratories, Oak Ridge, TN. Vanek_ch14-p421-464.indd Vanek_ch14-p421-464.indd 460 4/3/08 7:58:08 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y Earth Policy Institute (2002). Eco-Economy Indicators: Bicycle Production breaks 100 million. Earth Policy Institute, Washington, DC. Web resource, available at org/Indicators/indicator11.htm. Accessed Nov. 14, 2007. Greene, D., and Y Fan. (1995). “Transportation energy intensity trends: 1972–1992.” Transportation Research Record, No. 1475, pp. 10–19. Greene, D. (1996). Transportation and Energy. Eno Foundation, Washington, DC. Lovins, A., and B. Williams (2001). “From Fuel Cells to a Hydrogen Based Economy: How Vehicle Design is Crucial to a New Energy Infrastructure. Fortnightly: The North American Utilities Business Magazine. Vol. 139, No. 4, pp. 12–22. Schaefer, A. (1998). “The Global Demand for Motorized Mobility.” Transportation Research Part A, Vol. 32, No. 6, pp. 455–477. Schipper, L., L. Scholl, and L. Price. (1997) “Energy Use and Carbon Emissions from Freight in 10 Industrialized Countries: An Analysis of Trends from 1973 to 1992.” Transportation Research Part D. Vol. 2, No. 1, pp. 57–75. Leiby, P., and J Rubin (2004). “Understanding the transition to new fuels and vehicles: Lessons learned from analysis and experience of alternative fuel and hybrid vehicles.” Chapter 14 in D. Sperling and J. Cannon, Eds., The Hydrogen Energy Transition: Moving Toward the Post-Petroleum Age in Transportation , pp. 191–212. U.S. Bureau of Transportation Statistics (1996). Commodity Flow Survey 1993: United States. U.S. Department of Transportation, Washington, DC. U.S. Bureau of Transportation Statistics (2004). Commodity Flow Survey 2002: United States. U.S. Department of Transportation, Washington, DC. U.S. Dept. of Energy (1994). Manufacturing Consumption of Energy 1991. Energy Information Agency, USDOE, Washington, DC. Vanek, F, and J. Campbell (1999). “UK Road Freight Energy Use by Product: Trends and Analysis from 1985 to 1995.” Transport Policy, Vol. 6, pp. 237–246. Vanek, F. (2001). “Growth of Exports from Developing Countries: Implications for Freight Trends and Ecological Impact.” Futures, Vol. 33, pp.393–406. 461 Exercises 1. A retail firm operates a decentralized distribution system (factory to warehouse to shop) in which the system uses six smaller warehouses distributed around a region to receive a product from manufacturer (called primary distribution”) and then send product to retail outlets (called secondary distribution). The firm is offered the opportunity to shift to a centralized system in which each unit of product will still undergo primary and secondary distribution, but now there will only be one warehouse in the middle of the region. The transportation of the product incurs financial cost and energy consumption per vehicle · km (v · km) of movement. In the case of the warehouse costs, inventory costs are incurred by virtue of needing to keep stock on hand in the warehouse between the time that the stock is purchased and the time it is sold, and energy is consumed to operate the warehouse. Assume that all other costs and rates of energy consumption are the same for either option. a. Based on the data given, determine whether the decentralized or centralized system is preferred. b. What is the environmental dilemma underlying this decision? Discuss. Vanek_ch14-p421-464.indd 461 4/3/08 7:58:08 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 462 Chapter Fourteen Data: Warehouse Costs & Energy Use Per Warehouse Inventory Cost ($1000/year) Decentralized Centralized 190 710 Energy (GJ/year) 155 730 Transportation Volume Generated Per Year Per Warehouse(1000 vehicle · km/year) Primary Decentralized Centralized 35 181 Secondary 82 645 Transportation Cost and Energy Use Cost ($/1000 v·km) All shipments 1500 Energy MJ/1000 v·km 19,000 2. The standard unit equivalent of the data given in Table 14-2 is given below. Carry out a Divisia analysis and produce a figure showing the influence of efficiency and structure, with years from 1970 to 2000 on the x-axis and billion gallons of fuel on the y-axis. Passenger Cars 10 mi 1970 1975 1980 1985 1990 1995 2000 917 1034 1112 1247 1408 1438 1600 9 Light Trucks 10 mi 123 201 291 391 575 790 923 9 10 gal fuel 68 74 70 71.5 69.6 68.1 73.1 9 109 gal fuel 12.3 19.8 23.8 27.4 35.6 45.6 52.9 3. Show that the data in Table 14-2 result in the Divisia analysis graph shown in Fig. 14-7. 4. Below are the data given in class showing the trends for U.S. trucks and railroads for the period 1980 to 2000, showing volume in billion tonne · km and energy in EJ. Use Divisia decomposition to create a table and a graph for the period 1980 to 2000, showing four curves: (1) actual fuel consumption, (2) trended fuel consumption, and the contribution of (3) energy intensity, and (4) structural changes to the difference between actual and trended fuel consumption. Vanek_ch14-p421-464.indd 462 4/3/08 7:58:09 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 S y s t e m s P e r s p e c t i v e o n Tr a n s p o r t a t i o n E n e r g y T·km Year 1980 1985 1990 1995 2000 Truck billion 836 925 1045 1194 1524 Rail billion 1342 1342 1565 1733 2168 Truck EJ 1.89 1.96 2.17 2.40 3.24 Energy Rail EJ 0.583 0.485 0.478 0.469 0.555 463 5. A food products company ships foods from three production plants to four markets as follows. The capacity of plants at Boise, Dubuque, and Charleston is 2200 tonnes, 3000 tonnes, and 2000 tonnes, respectively. The demand at San Francisco, New York, Miami, and St. Louis is 2002 tonnes, 1784 tonnes, 1355 tonnes, and 1972 tonnes, respectively. The mode of shipment is by truck, and the energy intensity is 2200 kJ/tonne · km. A table of distances between cities is given below. a. What is the allocation of shipments from plants to markets that meets all demands, does not exceed supplies available at any plant, and minimizes energy consumption? What is the value of energy consumption in this case? b. Suppose that for all routes with distance of 1500 km or more, an intermodal rail service is made available with energy intensity of 1400 kJ/tonne · km. Recalculate problem 14-5a. What is the new value of energy consumption? Does the shipment pattern from part 14-5a change? Data: Boise SanFran. New York Miami St Louis 1427 5038 5458 3855 Dubuque 3763 1685 2262 390 Charleston 4299 1782 548 1431 6. Park rangers in a region live in communities of the region and commute 5 days/week to four regional parks. In a day, each ranger must make one round trip from her/his home to one of the forests and back again. Due to the need for flexibility on the job, the rangers must drive in their own cars and cannot carpool. The number of rangers in Bayside, Mountainview, Springfield, and City Heights is 24, 28, 22, and 42, respectively. The number of rangers required in the parks of Sandy Beach, Lookout Mountain, Lone Tree, and Endless Forest is 23, 41, 19, and 33, respectively. The rangers’ cars average 9.3 km/L fuel economy, and the one-way distances are given in the table below. a. In one allocation of rangers to parks, the rangers rotate through the four parks over a 4-week period such that the amount of time spent in each park is proportional to the percent of slots represented by that park. For example, if a park requires 20% of the rangers on any given day, then each ranger will spend 20% of their trips going to that park, to the nearest whole number. Calculate the liters of fuel consumption per month for this allocation. b. The regional park supervisor decides to reduce energy consumption by reallocating rangers to minimize monthly driving. What is the new value of fuel consumption per month for this allocation? How much fuel is saved compared to the solution in 14-6a? Vanek_ch14-p421-464.indd 463 4/3/08 7:58:09 PM 7 3/8 x 9 1/4 Technical / Energy Systems Engineering / Vanek / 0071495932 / Chapter 14 464 Chapter Fourteen c. From a job satisfaction point of view, what is the limitation on the solution in 14-6b? How might it be addressed? Discuss. d. The use of SOV driving in parts 14-6a and 14-6b, while allowing flexibility, could be criticized for excessive fuel consumption, especially given the environmental focus of the regional parks organization. Describe a program that might facilitate carpooling while still meeting the demand for rangers and allowing some level of flexibility in vehicle use. (One-way Kilometers) Sandy Beach Lookout Mountain Lone Tree Endless Forest Bayside 41 50 80 33 Mountainview 33 23 65 48 Springfield 50 22 49 45 City Heights 62 51 76 19 7. Revisit Example 14-2, using the same fuel economy values for ICEVs and HEVs, but this time considering the entire U.S. passenger car fleet. Suppose that HEVs achieve 50% penetration of the national fleet by 2040. The fleet in 2000 consisted of 134 million vehicles; assume this number is fixed for the duration of the transition. Compare this transition to a scenario where there is no influx of HEVs. a. Calculate the cumulative fuel savings for the period of 2000 to 2040 using a triangle function with peak rate of change in 2020. b. Calculate the cumulative fuel savings for the period of 2000 to 2040 using a logistics function. Fit the logistics function to the data in years 2000, 2006, and 2040 only, with the assumed value in 2040 of 50% of the fleet, that is, 67 million vehicles. c. Compare the results from calculations in 14-7a and 14-7b. Discuss the differences. 8. Repeat Exercise 14-7, but this time with changing overall fleet size and fuel efficiency. Assume that both the fleet size and average fuel economy grow linearly at the average annual rate from 1980 to 2000. Also, assume that HEV fuel economy improves by the same percent each year as the ICEV fleet for the period of 2000 to 2040. Obtain the necessary data to extrapolate rates of change from the internet or other source. Vanek_ch14-p421-464.indd 464 4/3/08 7:58:09 PM ...
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This note was uploaded on 12/01/2010 for the course CEE 3040 taught by Professor Stedinger during the Fall '08 term at Cornell University (Engineering School).

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