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181 One of the more important ways that changes in trans- portation fuels and vehicle technologies could affect state DOTs is through their effects on vehicle cost and the marginal energy cost of travel, which would in turn influence aggre- gate volume and mode choice for passenger travel and goods movement. This appendix reviews past trends and future prospects for three additional factors strongly linked to both energy use and travel demand: population growth, economic growth, and land use. The last section in the appendix dis- cusses the anticipated effects of these variablesâin isolation or in combinationâon the future energy and transportation scenarios developed in Chapter 6. The intent is to ensure that the effects of evolving fuel sources and vehicle technologies on travel behavior are not considered in isolation but rather in concert with broader socio-demographic trends that may affect both energy use and travel patterns. G.1 Population Growth This appendix first considers past trends in population growth and projections for how the U.S. population is expected to change and expand in the coming decades. G.1.1 Historical Population Growth The United States is currently the third most populated country on earth, accounting for nearly 4.5% of the worldâs total population (Shrestha and Heisler 2011). The U.S. pop- ulation has more than doubled in size since 1950, reach- ing around 309 million people in 2010 (U.S. Census Bureau 2000, 2011). This corresponds to an average annual growth rate of 1.2%. Figure G.1 shows the fairly steady popula- tion growth experienced by the United States over the last 60 years. The population changes that have occurred over the pre- vious decades have made the United States one of the most diverse nations on earth. Key demographic factors affecting the size and composition of the U.S. population include: â¢ Birth rates. Birth rates in the decade following World War II rose dramatically, leading to the baby boom era. Since 1950, the long-run birthrate has gradually declined from 24 to about 14 births per 1,000 people per year in 2010. The U.S. Census Bureau predicts that birthrates in the United States will fall slightly lower, to 13 births per 1,000 people, by 2050 (Shrestha and Heisler 2011). â¢ Life expectancy. Advances in medicine and improved living conditions have contributed to longer life expec- tancies. In 2007, the average life expectancy at birth was 77.9 years (Shrestha and Heisler 2011). The U.S. Census Bureau expects that the average life expectancy will grow to 82.6 years by 2050 (Shrestha 2006). â¢ Net immigration. More people enter the United States than leave each year, further contributing to population growth. The U.S. Census Bureau predicts that net immi- gration will grow over time, from approximately 800 thou- sand people per year in 2015 to over 1.2 million people per year in 2060 (U.S. Census Bureau 2012). G.1.2 Future Population Projections The U.S. population is expected to continue to undergo structural shifts in the coming decades, as discussed by Cheeseman Day (undated): â¢ The U.S. population will continue to grow, but at a declin- ing rate. Between 1950 and 2010, the U.S. population grew at a rate of 1.2% per year. However, this rate is expected to slow to 0.8% per year over the next 40 years. The U.S. Census Bureau (2009 a, b, c, d) has provided a range of population projections based on assumptions about future net international migration, as shown in Figure G.2. The A p p e n d i x G Population, Economy, and Land Use
182 implied rates of U.S. population growth between 2010 and 2050 for the different scenarios range from 0.1% in the zero net-migration case to 1.0% in the highest net-migration case. â¢ The U.S. population will be older in the future. Due to increases in life expectancy, the average age of the popula- tion is expected to increase over time. â¢ The United States will become more diverse in the future. Non-Hispanic whites are expected to decline as a share of the U.S. population, while other races are expected to grow at a faster rate. Asian and Pacific Islanders are the fastest growing segments of the population in the United States. One of the most uncertain factors associated with the magni- tude of future growth of the U.S. population is the rate of immi- gration. Figure G.2 shows several future population projections prepared by the U.S. Census Bureau (2009 a, b, c, d) based on alternate assumptions about net migration rates. Depending on the scenario, the U.S. population could stabilize at around 320 million or could climb to over 450 million by 2050. The trends just described are at the national scale. However, they have and are likely to continue to play out differently in certain regions of the country. Table G.1 provides regional estimates of the U.S. population in 1950 and 2010 and pro- jections for 2030 developed by the U.S. Census Bureau (1995 and 2005). Historically, the western portion of the United States has grown at a faster rate than other parts of the country, fol- lowed by the South. The Northeast and Midwest have lagged in population growth. In the future, the western and south- ern portions of the country are expected to continue to grow faster than other portions of the country, with growth rates in these regions averaging 1.2% per year for the next 20 years. G.2 Economic Growth Potentially influential economic trends are now considered. The United States is still slowly emerging from its deepest eco- nomic slump since the Great Depression, but the decades since World War II have been generally characterized by economic prosperity. While there have been periods of downturn, they have tended to last only a few years and to be followed by lon- ger periods of growth. More recently, though, the nation has been experiencing several fundamental shifts that are likely to affect future economic growth and in turn transportation demand patterns. These include changes in economic focus, in workforce composition, and in the nationâs leadership role within the broader global economy. G.2.1 Historical Economic Trends Aggregate economic growth is most often characterized by changes in GDP. This is computed as an estimate of the market value of all final goods and services produced within a country over some specified period of timeâtypically a quarter or a year. To analyze how changes in GDP and other measures of economic activity change over time, economists Source: Compiled by authors from U.S. Census Bureau (2000, 2011) data. 0 50 100 150 200 250 300 350 1950 1960 1970 1980 1990 2000 2010 U. S. P op ul a on (m ill io ns ) Figure G.1. U.S. population 1950â2010. Figure G.2. U.S. Census population projections to 2050. Source: U.S. Census Bureau (2009 a, b, c, d). 0 50 100 150 200 250 300 350 400 450 500 2010 2020 2030 2040 2050 U. S. P op ul a on (m ill io ns ) High Net Internaonal Migraon Low Net Internaonal Migraon Constant Net Migraon Zero Net Migraon
183 generally put prior-year estimates in real dollars (i.e., adjusted for inflation). Figure G.3 illustrates change in real GDP over time. Over the past 80 years, the U.S. economy has grown by over 1,500%, as measured by the change in real GDP, with an average annual growth rate of about 3.38%. However, growth has slowed more recently, in part due to the severity of the recent recession; over the past two decades (1992â2012), GDP increased by an average annual rate of around 2.51% (computed by authors from BEA 2013). Another commonly employed term, personal income, dif- fers from GDP in that it excludes economic activity that occurs in the United States that is owned by foreigners and includes U.S. economic activity that occurs in other countries. It repre- sents income received by a countryâs citizens or residents from all sources, including net earnings, property income, and per- sonal current transfer receipts. Personal income is also adjusted for depreciation and other factors (BEA 2007). When personal income is calculated in per-capita (i.e., per-person) terms, it provides a more useful measure of the income level of indi- viduals in the economy. In the transportation context, broadly speaking, GDP has the more significant impact on demand for goods movement, while personal income has a greater effect on passenger travel demand. Figure G.4 shows the trend in real per-capita personal income from 1930 to 2011 in the United States, in 2012 dollars adjust- ing for inflation (computations by authors based on data from BEA 2012 and BLS 2013). Over that time period, per-capita personal income grew by a little less than 400%, from almost $8,500 to $42,400, corresponding to a real annual growth rate of 1.98%. More recently, however, the rate of growth in per- capita personal income has slowed, in part reflecting the severity of the most recent recession; the average real growth rate over the past 20 years (1991â2011) was about 1.2%, while the average real growth rate over the past 10 years (2001â2011) was just 0.5%. Many factors have contributed to the United Statesâ eco- nomic growth. When comparing between countries or between regions within a country, economists often emphasize the fol- lowing three factors as particularly important: â¢ Human capital: the formal knowledge and skills of the labor force. Region 1950 2010 2030 Historical Annual Rate of Growth (1950â2010) Projected Annual Rate of Growth (2010â2030) Northeast 39,478,000 55,785,179 57,671,068 0.6% 0.2% Midwest 44,461,000 67,391,433 70,497,298 0.7% 0.2% South 47,197,000 113,583,614 143,269,337 1.5% 1.2% West 20,190,000 72,175,355 92,146,732 2.1% 1.2% United States 151,326,000 308,935,581 363,584,435 1.2% 0.8% Source: U.S. Census Bureau (1995 and 2005). Table G.1. Historical and future population estimates by region. Figure G.3. Real U.S. GDP, 1930â2012. Source: BEA (2013). $0 $3,000 $6,000 $9,000 $12,000 $15,000 $18,000 1930 1940 1950 1960 1970 1980 1990 2000 2010 GD P (B ill io ns 2 01 2 $)
184 developments could possibly lift U.S. economic growth to lev- els that surpass historical rates. It is also the case that economic growth could be distributed unevenly across the country. Fol- lowing the recent recession, for example, smaller cities in the oil and gas producing regions of the country have experienced the strongest records of economic growth, while sprawling metropolises and their smaller-city counterparts in the Sun- belt region have faced the greatest struggles (Florida 2012). G.3 Land Use Patterns Finally are considered past and potential future trends in land use, which is a term used to describe the purposes served by different parcels of land in an area. At the broadest level, land is often categorized as being either urbanized or unde- veloped. Urbanized land includes at least moderate develop- ment that can involve residential, commercial, or industrial uses. Undeveloped land, in contrast, has little if any physical construction and may include parkland, cropland, forests, wilderness areas, and the like. Within a large country such as the United States, the vast majority of land is undevel- oped rather than urbanized. Out of about 2.26 billion acres across the country, only around 66 million, or about 3%, were urbanized as of 1997. The pace of development has been rapid, though, with a four-fold increase over the second half of the twentieth century (Lubowski et al. 2002). Of the nationâs remaining open land, other major catego- ries are roughly 671 million acres of forestland; 614 million acres of grassland, pasture, and rangeland; 408 million acres of cropland; 313 million acres of special-use land such as parks and wildlife areas; and 197 million acres of miscella- neous nature such as tundra or swamps (Nickerson, Ebel, and Borchers 2011). The website NationalAtlas.gov (2013) reports that the federal government owns about 650 million acresâ roughly 30% of the nationâs land areaâwhich is reserved for national parks, national forests, national wildlife refuges, military reserves, and other public-interest uses. Another common differentiation of land use patterns is between urban, suburban, and rural. The U.S. Census Bureau began defining metropolitan statistical areas (MSAs)âwhich include both urban and suburban populationsâin 1910. Each MSA includes a densely developed central city area along with moderately dense communities around the cen- tral city. Taken together, center cities and their surrounding suburbs of a specified minimum density are described as urbanized, whereas areas falling outside of an MSA are gen- erally described as rural. Most MSA boundaries have changed over the years with the spread of urbanization; that is, land that was once classified as rural may be reclassified as urban- ized with the introduction of new development. This discussion is concerned with both the rate of develop- ment and the characteristics of the resulting land-use patterns. â¢ Physical capital: the machines, buildings, and infrastruc- ture that support the production of goods and services. â¢ Natural resources: access to the physical inputs (i.e., tim- ber, oil) used to produce goods and services. Among these, human capital is perhaps the most important driver of economic growth and, in turn, personal income. For a century, the United States expanded human capital signifi- cantly through education. Between 1875 and 1975, the aver- age years of education in the United States increased by seven grades (DeLong, Golden, and Katz 2003), although since then advances in educational attainment have begun to level off. Physical capital and natural resources are generally thought to be secondary drivers of growth relative to human capital. For example, despite dramatic increases in the size of the U.S. economy since World War II, the ratio of physical capital to output has remained relatively constant (DeLong, Golden, and Katz 2003). G.2.2 Future Economic Growth Projections of future U.S. economic growth, particularly over the long time horizon considered in this study (30 to 50 years), are highly uncertain. In its most recent reference-case energy projections, the EIA assumes that real U.S. GDP will grow at an annual rate of about 2.5% through 2040 (EIA 2013). Factoring in the EIAâs expected annual population growth rate of 0.9%, this would correspond to a growth rate in real per-capita GDP of just under 1.6%. Taking a more optimistic view, Dadush and Stancil (2009) project that the total U.S. economy will grow at a rate of 2.9% per year between 2009 and 2050. While such forecasts fall in line with historical growth rates, considering a wider range of scenarios seems prudent. Severe economic disruptions in various forms could lead to lower growth rates, while advances in technology or other Source: Computed by authors from BEA (2012) and BLS (2013). $0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000 $45,000 $50,000 1930 1940 1950 1960 1970 1980 1990 2000 2010 Pe rs on al In co m e (2 01 2 $s ) Figure G.4. Real U.S. per-capita personal income, 1930â2011.
185 area easier, and the phenomenon of âwhite flightâ from cities to suburbs following desegregation of urban school systems. While none of these policies or trends was initiated with the specific aim of creating suburbanization, in concert they had that effect. This major shift toward suburbanization has been inter- twined with trends in housing construction, density, and the location of employers, all of which have affected how Ameri- cans travel. Before 1920, the shares of housing built in cen- ter cities, suburbs, and rural areas were roughly equal; in the 1940s, about 80% of housing was built in the center city and suburbs. By the 1960s, more than half of new housing units were in suburbs, and by the 1990s, 60% were in the suburbs (Williams 2004). Suburbanization was accompanied by an increase in the development of single-family houses as opposed to multifamily buildings, as well as a trend toward the design of larger single- family houses. In 1974 (the earliest year for which figures are available), just over half of all housing units built were single-family houses; by 2000, the share had risen to about 80% (U.S. Census Bureau, undated). In 1950, the size of an aver- age new house was 983 square feet; by 2000 it was 2,057 square feet [National Association of Home Builders (NAHB) 2006]. For single-family homes, average size has increased from 1,695 square feet in 1974 to 2,504 square feet in 2012 (U.S. Census Bureau, undated). A key effect of suburbanization is the decline in density of the urbanized area as a whole. In 1940, the average popula- tion density of a metropolitan area (including both center city and suburbs) was 8,654 persons per square mile. By 2000, that had fallen to 5,581 persons per square mile (Giuliano, Agarwal, and Redfearn 2008; Kim 2007). Density gradientsâ a measure of the rate at which population density declines with distance from the centerâhave also become less steep Of particular interest are the density of development (e.g., population or jobs per square mile) and degree to which dif- ferent land uses (e.g., residential, commercial, and industrial) are either mixed or segregated from one another. These char- acteristics have significant implications for both total travel demand and mode choice. G.3.1 Historical Land-Use Trends Before World War II, most of the U.S. population lived in rural areas. By 1950, however, more Americans lived in metropolitan areas than in rural areas; by 2000, the share of Americans in metropolitan areas had risen to 80% (Hobbs and Stoops 2002). Increasing urbanization, however, did not mean that Americans were flocking to the center cities, where the vast majority of metropolitan dwellers had lived before the war. Rather, Americans were drawn to the suburbs. In 1950, only 23% of Americans lived in areas characterized as suburban. By 2000, as shown in Figure G.5, that proportion had grown to half of the U.S. population. Many factors contributed to the rapid increase in suburban- ization following World War II. These include the Govern- ment Issue (GI) Bill that gave returning veterans access to inexpensive mortgages to purchase houses, the emergence of development companies (such as the famous Levittown) that sprung up to take advantage of greater interest in home ownership, the post-war baby boom that created a desire for larger homes in which to raise growing families, the federal mortgage interest deduction that made home ownership more affordable, the Federal Housing Administrationâs prac- tice of âred-liningâ that made it difficult to obtain mortgages for center city housing, the creation of the Interstate highway system that made commuting throughout a metropolitan Figure G.5. U.S. population by urban, suburban, and rural areas, 1950â2000. Source: Data from Hobbs and Stoops (2002). 0 50 100 150 200 250 300 1950 1960 1970 1980 1990 2000 U. S. P op ul a on (m ill io ns ) Rural Suburban Urban
186 cities whose growth relied largely on the real estate market itself will retrench, exurban areas with large amounts of single- family housing in single-use zoning will evolve into slums, and cities that successfully recentralize and attract knowledge workers will thrive (Myers and Gearin 2001, Leinberger 2008, Florida 2009). In terms of what this would mean for land use, much new development could take place along smart growth principles: more compact housing types, built to a greater extent near transit services, and in mixed-use communities that combine housing with employment and retail. However, it is also possible that the strong decentraliza- tion trends of the second half of the twentieth century may continue, given the slow pace of land use change and the institutional factors that make higher-density development difficult to build in many center cities and inner-ring sub- urbs. Established communities often resist adding additional housing or office space, fearing declines in property values and increased traffic; developers are accustomed to work- ing with large suburban parcels of land and do not wish to take on the additional requirements that infill development often entails; and the regional planning that can help areas grow in a more compact fashion is often undermined by local land-use controls, since zoning is typically a local prerogative (Downs 2005). It is also possible that the country could remain in a kind of holding pattern in which it stays mired in sluggish eco- nomic conditions and largely retains the land use patterns in existence today. It might be that the expected growth in population fails to emerge; immigration could slow because there are fewer jobs available, and people might have fewer children if unemployment remains high. Instead of forming new households, the future could witness more intergenera- tional households, in which adult children continue to live with their parents even as they have children themselves. This could lead to more overcrowding, defined as people sharing housing units that were built for a smaller number of occupants. Home ownership might also decline, leading to reduced demand for new houses. A recent analysis has already found that Americans are moving less now than at any point since World War II (Frey 2009). These are not either-or scenarios; some metropolitan regions could go one direction, developing more along preâWorld War II models, while others could continue to decentralize or stagnate. The trends described in the preceding section gen- erally held true for most metropolitan areas in the country, but there are always exceptionsâplaces that for one reason or another remained more centralized than the average. For the purposes of the scenarios in this report, however, the broadest trends and how they would affect transportation are of most interest. These scenarios assume that the United States recovers from the current recession eventually and continues growing due to suburbanization and the gradual expansion of metro- politan area boundaries (Kim 2007). Another result of the trend toward suburbanization is that the conversion of undeveloped land has occurred more rapidly than the population has grown; from 1960 to 2000, urbanized population grew by 80%, while urbanized land area grew by 130% (Nelson 2004). Following World War II, there began a shift toward the decentralization of employment locations as well. In 1950, the share of employment in the central county of metropoli- tan areas was between 45% and 50%; by the early 1990s, that share had declined to just 40% (Glaeser et al. 2001). Prevailing wisdom is that the workforce suburbanized and then employ- ers followed, although this is clearly a two-way process. While employment decentralization has occurred across the board in American cities, there is much diversity between metropolitan areas. Some areas have edge citiesâhigh con- centrations of suburban office and retail spaceâwhile others have more dispersed employment. The dominant employ- ment sector also has some impact on the form and patterns of decentralization; cities with higher shares of service employ- ment (such as banking) tend to be more centralized than those with higher shares of manufacturing (Glaeser et al. 2001). While these trends describe most of the post-war twenti- eth century, there is some evidence that certain trends have slowed more recently. Two-thirds of the 100 largest central cities added population in the first decade of the 2000s, help- ing to offset previous declines (Brookings Institution 2010). By 2008, housing prices per square foot were 40% to 200% higher for many urban areas than their suburban counter- parts (Leinberger 2008). However, decentralization continues to be the dominant trend. G.3.2 Future Land-Use Trends The future trajectory of development patterns in the United States is difficult to predict. Many observers have viewed decen- tralization as an unsustainable trend in the long run based on the externalities associated with automotive travel. The mortgage meltdown of 2008 provided some ammunition to this argu- ment, albeit from a financial standpoint rather than an envi- ronmental one. The recession of 2008 was brought on in large part by overextension in the for-sale housing marketâprices rose too quickly, too many new homes were built, prospective homeowners bought houses they could not realistically afford because mortgages were so readily available, and banks resold bundles of sub-prime mortgages as high-quality securities. Future historians may look at the housing crisis and ensu- ing recession as a key turning point in American land-use trends, the way that the end of World War II ushered in its own significant shifts. Some futurists argue that our economic geography is undergoing profound changes. Older industrial cities, in this view, will continue their decline, previous boom
187 between short-run elasticities (measuring changes that occur within a year or so) and longer-run elasticities (measuring changes that unfold over a few years or more). Longer-run elasticities can differ significantly from short-run elasticities because they allow more time for individuals and firms to alter decisions or behaviors in response to the change (for example, to purchase a more fuel-efficient vehicle in response to higher fuel prices). G.4.1 Effects on Fuels and Vehicle Technologies The future transportation energy scenarios developed for this study encompass the price of oil, conventional-vehicle fuel economy, the mix of alternative fuels in use, vehicle cost premiums, and the marginal per-mile energy cost of travel. Of these factors, the logical effects on the price of oil are most clear, even if they are likely to be quite modest. The potential effects on other factors are much more speculative. Price of oil. Population growth and economic expansion both correlate with increased travel, translating to greater aggregate fuel consumption. For example, based on their review of relevant studies, Goodwin, Dargay, and Hanly (2004) esti- mate that a 10% increase in average income leads to a 4% increase in fuel consumption over the short term and a 10% increase over the longer term. (The elasticity is higher over the longer term because higher incomes enable households to purchase more vehicles and to choose models that substitute greater size, power, and acceleration for fuel economy, but these purchase decisions unfold over multiple years.) Greater fuel consumption in turn puts upward pressure on the price of oil. In contrast, denser land-use patterns and greater mixing of land uses, as discussed at greater length subsequently, are associated with reduced vehicle travel. This could help reduce aggregate fuel consumption and in turn ease pressure on oil prices. Note, however, that oil is traded on a world market. As a result, changes in U.S. demand for oil stemming from future changes in population, economy, and land use may have only a modest influence on oil prices given the rapid increase in oil consumption within many of the emerging economies around the world. Vehicle fuel economy. The effects of population, economy, and land use on vehicle fuel economy are uncertain but not likely to be significant. For example, if growth in population or the economy leads to additional vehicle travel and higher oil prices, this could result in increased demand for vehicles with higher fuel economy. On the other hand, rising incomes in the past have often resulted in the purchase of faster and more powerful vehicles with lower fuel economy. In the com- ing years, however, as a result of significantly more-stringent CAFE standards, new vehicles will have to meet progressively in some fashion. However, some observers think that higher and more volatile oil prices, coupled perhaps with unsus- tainable government deficits or environmental crises, could lead to much more drastic changes in American life. Several recent books describe this prospect as a type of urban col- lapse, in which extremely high-priced or unavailable petro- leum means that urban areas cannot feed their populations or function as economic centers (Heinberg 2003, Kunstler 2005). Others see a more optimistic future, but still one in which more expensive gasoline leads to a radical restructur- ing of cities, more local production of food, and far less long- distance transport of goods and people (Newman, Beatley, and Boyer 2008; Steiner 2009). By nature, revolutionary change is more difficult to predict than evolutionary change, given that it is based on sharp dis- location rather than long-term trends. The changes described in the preceding paragraph are revolutionary, although none of the authors make hard-and-fast predictions about the pace of change, and some acknowledge that even wide- spread decline may take generations, not just a few years. The United States has certainly seen large-scale population migrations in the past, in some cases involving rapid growth due to economic opportunity (San Francisco grew 25-fold in 2 years during the gold rush) and in other cases involving mass departures based on economic hardship (about 2.5 mil- lion people left drought-ridden Midwestern farmlands in the 1930s dust bowl). So such major changes in populationâand with them, land useâare not unprecedented, but neither are they easy to predict. G.4 Potential Effects on Energy and Transportation This final section explores how future changes in popula- tion, the economy, and land useâindividually or in concertâ affect specific energy and transportation factors included in the scenarios developed in this study. The discussion draws on principled reasoning and findings from the literature, as appro- priate. As a general rule, the effects on transportation tend to be more direct and better studied; in contrast, the potential effects of changes in population, economy, and land use on some of the energy factors of interest, such as the future mix of different fuel types, are more speculative. Note that some of the studies referenced in this section rely on the economic concept of elasticities to report their results. A measure of elasticity provides a ratio that indicates how a percentage change in one variable of interest relates to a percentage change in another variable. For example, if the elasticity of fuel consumption with respect to changes in the price of fuel is estimated as â0.2, this indicates that a 10% increase in the cost of fuel should trigger a 2% reduction in fuel consumption. Economic analyses often further differentiate
188 and more congested travel conditions can greatly reduce vehicle fuel economy (Barth and Boriboonsomsin 2009). This could be offset, however, if greater land-use density enabled increased adoption of limited-range electric vehicles or stimulated the purchase of smaller cars more generally. Here again, then, there is considerable uncertainty. G.4.2 Effects on Travel Demand In contrast to energy factors, for which the causal relation- ships are much less clear, the interactions between popula- tion, the economy, land use, and travel patterns have been studied extensively and are therefore easier to anticipate. Here are discussed how changes in these variables would be likely to influence future passenger vehicle travel, truck travel, and mode share for transit and other non-automotive alternatives. Passenger vehicle travel. When the population grows, total passenger vehicle travel generally expands as well. There are, however, important variations in travel behavior among different demographic groups that could moderate the over- all effect of population growth on travel. For example, retired persons, recent immigrants, and those in lower-income households tend to drive less than their younger, more accul- turated, or more affluent counterparts (Santos et al. 2011). If population growth stems mainly from longer life expec- tancies, higher rates of immigration, or a higher birth rate among lower-income families than among the wealthy, then the growth in vehicle travel could be less, proportionately, than the increase in population would initially suggest. The relationship between the economy and the demand for transportation is well documented. Increases in per- sonal income, which tend to rise in proportion to GDP, are associated with an increase in vehicle ownership and overall personal travel (Santos et al. 2011). Drawing on the litera- ture on income elasticities and travel, Goodwin, Dargay, and Hanly (2004) suggest that a 10% increase or decrease in aver- age income will cause the vehicle miles traveled to grow or decline by 2% in the short run and by 5% over the longer run. It is worth noting, however, that increases in VMT are not strictly caused by economic growth. Rather, the relation- ship can work in both directions, with some additional travel resulting from economic growth and some economic growth produced by greater travel (Pozdena 2009). There has been considerable attention to the relationship between land use and travel as well. The evidence suggests that three aspects of local land-use affect travel in consistent and measurable ways: â¢ Density. This attribute, which refers to the number of people living or working within a given area and can be characterized with such measures as dwelling units per higher levels of fuel economy, culminating in an average of 54.5 miles per gallon by 2025. In the context of rapidly escalating CAFE requirements, it is not clear that changes in population, the economy, or land use will have much addi- tional effect. Alternative fuels. As with fuel economy, likely effects on the use of alternative fuels are also speculative. If changes in population or economy result in higher oil prices, this could make alternative fuels comparatively more attractive. As noted previously, however, any effects on gasoline or diesel costs are likely to be muted in the context of broader world- wide trends in supply and demand. It is also possible that denser land use could enable greater adoption of electric vehicles, even with their current range limitations. Vehicle cost. One could make plausible arguments about how changes in population, the economy, or land use might influence vehicle cost. For example, a combination of high population growth and high economic growth, as described previously, might lead to rising oil prices, in turn increasing demand for alternative fuels. A robust economy, in turn, could allow for increased investment in fuel and vehicle technology research and development, which might lead to breakthroughs that reduce the premiums for, say, electric or hydrogen vehi- cles. As another example, a trend toward increased land-use density might lead residents to purchase smaller, and often more affordable, vehicles because they are easier to maneuver and park in dense urban areas. On the other hand, the uptake rate for electric vehicles, which entail significant cost premi- ums, could be higher in urban areas where range limitations are less constraining to typical travel patterns. In short, there is significant uncertainty regarding whether and how future trends in population, the economy, and land use might affect vehicle cost, and little in the way of available evidence to clarify expectations. Marginal cost of travel. Following this logical sequence, growth in population and the economy could result in increased fuel demand and higher oil prices. Higher prices for gasoline and diesel would in turn provide an incentive for consumers to purchase more fuel-efficient models, potentially reducing the marginal cost of travel. Higher prices could also trigger greater research and development funding for alternative fuels and vehicle technologies. If such investments led to breakthroughs that significantly reduce the vehicle premium costs associated with electric, natural gas, or hydrogen fuel- cell vehicles, in turn stimulating mass market adoption, this could also help reduce the marginal cost of travel. While such technologies currently involve much higher vehicle purchase prices, they promise much lower per-mile energy costs in return. In contrast, a shift to higher land-use den- sity could increase the marginal cost of travel. This is because higher population and employment density generally leads to increased levels of traffic congestion (Sorensen et al. 2008),
189 tives. Some recent research has examined this question, and one study found that neighborhood characteristics are more important predictors of behavior than self-selection (Cao 2009), but this remains an area of ongoing inquiry. Second, the results of any efforts to modify land use pat- terns with the aim of reducing VMT may take decades to unfold. As one recent review notes, âVMT savings will be slow to develop, however, if only because the existing build- ing stock is highly durable; therefore, opportunities to build more compactly are limited largely to new housing as it is built to accommodate a growing population and to replace the small percentage of existing units that are scrapped each yearâ (TRB 2009, p. 5â6). Still, it is clear that higher densities, mixed use, and cen- trality are associated with lower VMT. Higher densities and mixed uses lead to shorter and fewer driving trips, in part because origins and destinations are closer together. The aforementioned TRB review estimated that doubling resi- dential density across a metropolitan area could lower house- hold VMT by about 5% to 12% (TRB 2009); if coupled with higher employment density, significant public transportation improvements, greater mixing of uses, and demand man- agement strategies, the reduction could be as much as 25%. Greater centrality, in turn, can reduce VMT by enabling more trips to be taken on transit; one study of over 100 urbanized areas found that a doubling in centrality was associated with a 15% reduction in VMT (Bento et al. 2005). To illustrate the potential effects of land use on travel behavior, Figure G.6 graphs housing density against VMT per household for a sampling of smaller neighborhoods drawn from three cities in the United States: Chicago, San Francisco, and Los Angeles. Despite the varying land-use patterns of these three cities, there is a generally consistent relationship between density and VMT when viewed at the neighborhood level (Holtzclaw et al. 2002). acre or jobs per hectare, is known to have a strong effect on individual travel behavior. Higher levels of density gener- ally correlate with reduced automobility and increased use of alternative modes such as transit, walking, and biking. Density has been shown to affect travel at both the local and regional level. â¢ Mixing of land uses. This describes the degree to which res- idential, commercial, retail, and other land uses have been located within close proximity of one another, which can help reduce the distances between the origins and destina- tions for some trips. The mixing of land uses is complex to measure, and most studies have focused on the neighbor- hood level. While available evidence suggests that greater mixing of land uses can help reduce per-capita passenger vehicle travel, the effect does not appear to be as strong as that of overall density. â¢ Centralization. This term relates to the percentage of employment or residences that are located in the center city as opposed to the outskirts. Broadly speaking, regions can be centralized, with a high concentration in the cen- ter city; decentralized, with few or no such concentrations; or multi-centric, with multiple smaller centers scattered throughout a region. At the regional level, some studies have found that higher degrees of centralization are asso- ciated with reduced per-capita vehicle travel. While the broad findings outlined are generally consis- tent across many studies, two caveats should be noted. First, the study of land use and travel interactions presents a self- selection bias problem. That is, it is methodologically chal- lenging to determine whether individuals in higher-density, mixed-use areas travel less by car in response to the environ- ment in which they are situated, or instead if individuals who prefer to drive less choose to move to such neighborhoods because they provide better non-automotive travel alterna- Source: Based on data from Holtzclaw et al. (2002, Figure 5). 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 0 50 100 150 200 An nu al V M T pe r H ou se ho ld Households per Residenal Acre Figure G.6. Relationship between residential density and VMT.
190 With respect to the mixing of land uses, most studies have focused at the neighborhood level, where it is easier to develop suitable metrics. Ewing and Cervero (2001) reviewed 14 studies that assessed the impact of neighborhood-scale land use. While the studies looked at slightly different vari- ables, they all compared the travel behavior of residents of neighborhoods with more traditional land-use patterns (mixed uses, good transit service, and pedestrian-friendly infrastructure) to those of suburban neighborhoods (exclu- sively residential, less transit service, and not pedestrian- friendly) with similar socio-economic demographics. The residents of traditional neighborhoods made fewer trips, shorter trips, and used transit and nonmotorized modes at higher levels. Freight trucking. Holding other factors constant, a larger population will consume more goods and services, in turn leading to additional truck travel. Growth in the econ- omy, likewise, results in additional trade flows and greater demand for goods movement via trucks and other modes. Bennathan, Fraser, and Thompson (1992), for example, used cross-sectional data for different developed countries to derive estimates of the elasticity of truck travel with respect to changes in GDP. Their results indicate an elasticity of approximately 1.02, suggesting that freight VMT increases one-for-one with GDP. The effects of changes in land use on truck travel, in contrast, are less clear. On the one hand, the reduction in distances between origins and destinations associated with higher density could reduce the length of some truck trips. On the other hand, trucking is typically the most efficient mode for goods movement within an urban context; as such, a shift toward larger and denser metro- politan regions might conceivably act to increase the overall mode share for trucking. References Barth, M. and K. Boriboonsomsin. 2009 (Fall). âTraffic Congestion and Greenhouse Gases.â Access, 35: 2â9. BEA. 2007. Measuring the Economy: A Primer on GDP and the National Income and Product Accounts. BEA. 2012. SA1-3 Personal income summary. http://www.bea.gov/ iTable/iTable.cfm?ReqID=70&step=1&isuri=1&acrdn=4 (accessed March 18, 2013). BEA. 2013. Current-Dollar and Real Gross Domestic Product. National Economic Accounts. http://www.bea.gov/national/ (accessed March 18, 2013). Bento, A. M., M. L. Cropper, A. M. Mobarak and K. Vinha. 2005. âThe Effects of Urban Spatial Structure on Travel Demand in the United States.â Review of Economics and Statistics, 873: 466â478. BLS. 2013. Consumer Price Index: All Urban Consumers â (CPI-U). ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt (accessed March 18, 2013). Bennathan, E., J. Fraser, and L. S. Thompson. 1992. What Determines Demand for Freight Transport? WPS 998. Infrastructure and Urban Development Department, The World Bank. An analysis of land use and transportation at the regional scale by van de Coevering and Schwanen (2006), which looked at many of the same cities examined in an earlier study by Newman and Kenworthy (1989), found that higher regional density was associated with fewer vehicle miles traveled and fewer commute trips by car. Study results also indicated that higher employment density in the center city is associated with lower VMT, although this effect was weaker, and that a greater concentration of residents in the center city is associ- ated with higher use of transit. Although most prior work has focused on residential den- sity, employment density can also affect travel behavior. One study by Chatman (2003) found that for each additional 10,000 employees within a square mile, average per-capita vehicle travel declines by about a half mile per day; for each additional 1,000 employees per square mile, the probability that an employee will drive to work rather than relying on an alternate mode declines by about 3%. Use of transit. Growth in the population should lead to more travel, including more trips by transit and other alter- native modes, but this does not necessarily result in a greater mode share for transit. As noted earlier, though, travel behav- ior varies among different segments of the population. For instance, transit use tends to be higher among lower-income groups (Santos et al. 2011) and recent immigrants. Thus, higher birthrates among lower-income families or higher- than-expected rates of immigration could contribute to an increased mode share for transit. Growth in the economy also leads to increased travel, although here again there are potentially conflicting effects on transit mode share. Assuming that increases in social prosperity are broadly distributed, the mode share for transit could decrease; evidence from the literature indicates that as real income levels rise, individuals are less likely to rely on transit and more likely to drive (Santos et al. 2011). On the other hand, recent decades have witnessed growing economic inequality, accompanied by declines in inflation-adjusted income for many lower- and middle-income families. If this pattern persists, reduced income among a sizable share of the U.S. population could translate to an increase in mode share for transit and other non-automotive modes of travel. Turning to land use, both density and the mixing of uses are known to have an effect on mode split. In denser areas, origins and destinations are often closer together, making it possible to choose non-automotive modes for a greater share of trips. Higher density also increases the pool of potential transit users within a given area or corridor, making if more financially fea- sible to develop faster transit options such as subways, light rail, and bus rapid transit with dedicated right-of-way. Finally, traffic congestion is generally more intense and parking rates can be considerably higher in dense urban areas, making tran- sit more attractive in comparison (Sorensen et al. 2008).
191 Kuntsler, J. H. 2005. The Long Emergency: Surviving the Converging Catastrophes of the Twenty-First Century. Atlantic Monthly Press. Leinberger, C. B. 2008. âThe Next Slum?â The Atlantic. http://www.the atlantic.com/magazine/archive/2008/03/the-next-slum/306653/ (accessed March 18, 2013). Lubowski, R. N., M. Vesterby, S. Bucholtz, A. Baez, and M. J. Roberts. 2002. âMajor Uses of Land in the United States.â Economic Infor- mation Bulletin No. (EIB-14). Economic Research Service, U.S. Department of Agriculture. Myers, D. and E. Gearin. 2001. âCurrent Preferences and Future Demand for Denser Residential Environments.â Housing Policy Debate, 12 (4): 633â659. NAHB. 2006. Housing Facts, Figures and Trends. Washington, D.C. NationalAtlas.gov. 2013. Federal Lands and Indian Reservations. http://nationalatlas.gov/printable/fedlands.html (accessed March 15, 2013). Nelson, A. C. 2004. Toward a New Metropolis: The Opportunity to Rebuild America. Brookings Institution, Washington, D.C. Newman, P., T. Beatley, and H. Boyer. 2008. Resilient Cities: Responding to Peak Oil and Climate Change. Island Press. Newman, P. and J. Kenworthy. 1989. âGasoline Consumption and Cit- ies: A Comparison of U.S. Cities with a Global Survey.â Journal of the American Planning Association, 55 (1): 24â37. Nickerson, C., R. Ebel, and A. Borchers. 2011. âMajor Land Uses in the United States, 2007.â Economic Information Bulletin No. EIB-89. Economic Research Service, U.S. Department of Agriculture. Pozdena, R. 2009. âDriving the Economy: Automotive Travel, Economic Growth, and the Risks of Global Warming Regulations.â Working Paper. Cascade Policy Institute, Portland. Santos, A., N. McGuckin, H. Y. Nakamoto, D. Gray, and S. Liss. 2011. Summary of Travel Trends: 2009 National Household Travel Sur- vey. Federal Highway Administration. Shrestha, L. B. 2006 (May 5). The Changing Demographic Profile of the United States. Report to Congress. Congressional Research Service. Shrestha, L. B. and E. J. Heisler. 2011 (March 31). The Changing Demo- graphic Profile of the United States (Update). Report to Congress. Congressional Research Service. Sorensen, P., M. Wachs, E. Y. Min, A. Kofner, L. Ecola, M. Hanson, A. Yoh, T. Light, and J. Griffin. 2008. Moving Los Angeles: Short-Term Policy Options for Improving Transportation. RAND Corporation, Santa Monica. Steiner, C. 2009. $20 Per Gallon: How the Inevitable Rise in the Price of Gas- oline Will Change Our Lives for the Better. Grand Central Publishing. TRB. 2009. Special Report 298: Driving and the Built Environment: The Effects of Compact Development on Motorized Travel, Energy Use, and CO2 Emissions. Transportation Research Board of the National Academies, Washington, D.C. U.S. Census Bureau. Undated. Table Q-6. New Privately Owned Housing Units Completed in the United States, by Intent and Design. Charac- teristics of New Housing. http://www.census.gov/construction/nrc/ pdf/compsusintenta.pdf (accessed March 21, 2013). U.S. Census Bureau. 1995. Intercensal Estimates of the Total Resident Population of States: 1950 to 1960. Population Estimates. http:// www.census.gov/popest/data/state/asrh/1980s/tables/st5060ts.txt (accessed March 20, 2013). U.S. Census Bureau. 2000. Historical National Population Estimates: July 1, 1900 to July 1, 1999. Population Estimates. http://www.census. gov/popest/data/national/totals/pre-1980/tables/popclockest.txt (accessed March 18, 2013). U.S. Census Bureau. 2005. Table 6: Interim Projections: Total Popula- tion for Regions, Divisions, and States: 2000 to 2030. 2005 Interim Brookings Institution. 2010. State of Metropolitan America: On the Front Lines of Demographic Transformation. Brookings Institution Metropolitan Policy Program. Washington, D.C. Cao, X. 2009. âDisentangling the Influence of Neighborhood Type and Self-Selection on Driving Behavior: an Application of Sample Selection Model.â Transportation, 36 (2): 207â222. Chatman, D. 2003. âHow Density and Mixed Uses at the Workplace Affect Personal Commercial Travel and Commute Mode Choice.â Transportation Research Record: Journal of the Transportation Research Board, No. 1831: 193â201. Transportation Research Board of the National Academies, Washington, D.C. Cheeseman Day, J. Undated. National Population Projections. http:// www.census.gov/prod/1/pop/profile/95/2_ps.pdf (accessed March 18, 2013). Dadush, U. and B. Stancil. 2009. âThe G20 in 2050.â International Eco- nomic Bulletin. http://www.carnegieendowment.org/publications/ index.cfm?fa=view&id=24195 (accessed March 18, 2013). DeLong, J. B., C. Golden, and L. F. Katz. 2003. âSustaining U.S. Economic Growth.â In: H. J. Aaron, J. M. Lindsay, and P. S. Nivola (eds.), Agen- da for the Nation. Brookings Institution Press, Washington, D.C. Downs, A. 2005. âSmart Growth: Why We Discuss It More than We Do It.â Journal of the American Planning Association, 71 (4): 367â378. EIA. 2013. Annual Energy Outlook 2013. Ewing, R. and R. Cervero. 2001. âTravel and the Built Environment: A Synthesis.â Transportation Research Record: Journal of the Transpor- tation Research Board, No. 1780: 87â114. Transportation Research Board of the National Academies, Washington, D.C. Florida, R.. 2009. âHow the Crash Will Reshape America.â The Atlantic. http://www.theatlantic.com/magazine/archive/2009/03/how-the- crash-will-reshape-america/307293/ (accessed March 18, 2013). Florida, R. 2012. âThe Uneven Geography of U.S. Economic Growth.â The Atlantic Cities. http://www.theatlanticcities.com/jobs-and- economy/2012/10/uneven-geography-economic-growth/3067/ (accessed March 18, 2013). Frey, W. H. 2009. The Great American Migration Slowdown: Regional and Metropolitan Dimensions. Brookings Institution, Washington, D.C. Giuliano, G., A. Agarwal and C. Redfearn. 2008. âMetropolitan Spatial Trends in Employment and Housing: Literature Review.â Paper pre- pared for the Committee on the Relationships among Development Patterns, Vehicle Miles Traveled, and Energy Consumption of the Transportation Research Board and the Division on Engineering and Physical Sciences of the National Academies, Washington, D.C. Glaeser, E. L., M. E. Kahn, R. Arnott, and C. Mayer. 2001. âDecentral- ized Employment and the Transformation of the American City [with Comments].â Brookings-Wharton Papers on Urban Affairs, pp. 1â63. Goodwin, P., J. Dargay, and M. Hanly. 2004. âElasticity of Road Traf- fic and Fuel Consumption with Respect to Price and Income: A Review.â Transport Reviews, 24 (3): 275â292. Heinberg, R. 2003. The Partyâs Over: Oil, War, and the Fate of Industrial Societies. New Society Publishers. Hobbs, F. and N. Stoops. 2002. Demographic Trends in the 20th Century. CENSR-4. U.S. Department of Commerce. Holtzclaw, J., R. Clear, H. Dittmar, D. Goldstein, and P. Haas. 2002. âLocation Efficiency: Neighborhood and Socioeconomic Charac- teristics Determine Auto Ownership and Use-Studies of Chicago, Los Angeles, and San Francisco.â Transportation Planning and Tech- nology, 25 (1): 1â27. Kim, S. 2007. âChanges in the Nature of Urban Spatial Structure in the United States, 1890â2000.â Journal of Regional Science, 47 (2): 273â287.
192 U.S. Census Bureau. 2009d. Table 1-Z. Projections of the Population and Components of Change for the United States: 2010 to 2050 Zero Net International Migration Series (NP2009-T1-Z). 2009 National Population Projections (Supplemental): Summary Tables: Zero Net International Migration Series. http://www.census.gov/ population/projections/data/national/2009/2009znmsSumTabs. html (accessed March 20, 2013). U.S. Census Bureau. 2011. Table 1. Intercensal Estimates of the Resi- dent Population by Sex and Age for the United States: April 1, 2000 to July 1, 2010. US-EST00INT-01. Population Estimates. http:// www.census.gov/popest/data/intercensal/national/nat2010.html (accessed March 18, 2013). U.S. Census Bureau. 2012. Table 1. Projections of the Population and Components of Change for the United States: 2015 to 2060 (NP2012-T1). 2012 National Population Projections: Summary Tables. http://www.census.gov/population/projections/data/ national/2012/summarytables.html (accessed March 18, 2013). van de Coevering, P. and T. Schwanen. 2006. âRe-evaluating the Impact of Urban Form on Travel Patterns in Europe and North-America.â Transport Policy, 1 3(3): 229â239. Williams, B. T. 2004. These Old Houses: 2001. H121/04-1. U.S. Census Bureau; Office of Policy Development and Research, U.S. Depart- ment of Housing and Urban Development; and Economics and Statistics Administration, U.S. Department of Commerce. State Population Projections. http://www.census.gov/population/ projections/data/state/projectionsagesex.html (accessed March 20, 2013). U.S. Census Bureau. 2009a. Table 1-C. Projections of the Population and Components of Change for the United States: 2010 to 2050 Constant Net International Migration Series (NP2009-T1-C). 2009 National Population Projections (Supplemental): Summary Tables: Con- stant Net International Migration Series. http://www.census.gov/ population/projections/data/national/2009/2009cnmsSumTabs. html (accessed March 20, 2013). U.S. Census Bureau. 2009b. Table 1-H. Projections of the Population and Components of Change for the United States: 2010 to 2050 High Net International Migration Series (NP2009-T1-H). 2009 National Population Projections (Supplemental): Summary Tables: High Net International Migration Series. http://www.census.gov/ population/projections/data/national/2009/2009hnmsSumTabs. html (accessed March 20, 2013). U.S. Census Bureau. 2009c. Table 1-L. Projections of the Population and Components of Change for the United States: 2010 to 2050 Low Net International Migration Series (NP2009-T1-L). 2009 National Population Projections (Supplemental): Summary Tables: Low Net International Migration Series. http://www.census.gov/population/ projections/data/national/2009/2009lnmsSumTabs.html (accessed March 20, 2013).