Energy for travel: The influence of urban development patterns

Tmnspn Rcr. Vol. l6A. No. I. pp. 65-70, 1982 Printed in Crest Britain.

0191-2@7/82/01006u)61.lM/O 0 1982 Perpmon Press Ltd.

ENERGYFORTRAVEL:THEINFLUENCE OFURBANDEVELOPMENTPATTERNS" DALE Battelie Memorial

L. KEYES

Institute, Washington Operations, Washington, DC 20036, U.S.A.

2030 M St., NW,

(Received 14 October 1980; in revised jorm6 April 1981) Abstract-Rising energy prices and supply shortfalls have underscored the need to improve the energy efficiency of urban travel. Long run solutions may involve rearranging development patterns in urban areas, or at least locating new growth in ways which reduce the need to travel. To test the degree to which altering development patterns may effect transport energy savings, relationships between gasoline consumption and urban development characteristics were investigated in 49 U.S. metropolitan areas. The results suggest that cities of medium size with clusters of high residential densities are associated with lower levels of per capita gasoline consumption. However, it is unlikely that changes in current development patterns during the next two decades would be substantial enough to cause a significant reduction in U.S. energy consumption.

Concern for the efficient operation of cities has led urban planners and others to search for “optimal urban forms” toward which development patterns in metropolitan areas should be directed. Urban travel (and the energy consumed by this activity:) seems especially well suited for efficiency gains effected through the manipulation of land uses, since the spatial arrangements of “trip ends” (origins and potential destinations) are primary determinants of travel behavior. It follows logically that compact arrangements of urban activities (whether a product of transportation considerations or other influences) should produce travel and thus energy savings. But what is less clear is the magnitude of these savings and the role that metropolitan growth control can play in conserving energy used for transport. This paper develops quantitative estimates of the influence exerted by urban form on transportation energy consumption, based largely on an examination of gasoline consumption in a sample of metropolitan areas. The effects of both individual development characteristics and selected combinations or patterns of development are examined. Explanations for the observed relationships are sought by examining the way certain features of urban travel vary with development patterns. Finally, implications of the research findings for national energy policy are briefly discussed.

traveler is a function of (a) the spatial arrangement of desired destinations, (b) the pecuniary and opportunity costs of travel, (c) the income level of the traveler, and (d) the “supply” of travel opportunities in terms of the availability, speed, and comfort afforded by alternative modes (sidewalks, bicycles, autos, buses, taxis, or trains). The supply function, in turn, is specified by private and public investments in travel vehicles and transport corridors. Our interest in the energy consumed for transportation extends beyond the extensiveness of urban travel. The mode and speed of transport contributes in important ways to the energy consumed per mile of travel. Wide differences exist in energy efficiency among and within individual modes, with an approximate ranking from high to low efficiency (energy use per passenger/pedestrian mile, full occupancy) as follows: bicycle, walking, bus, electric train, auto. Efficiency also depends on the speed of travel, in terms of both the mean speed per trip and the variability about the mean; continuous travel at slow speeds is the most efficient, stop-and-go movement characteristic of downtown travel is the most consumptive per mile. The spatial patterning of urban activities could thus be associated in multiple ways with (and presumably influences) the amount of energy consumed by urban transportation. Several investigators have reported direct relationships between population size and density, on the one hand, and the amount of travel (vehicle miles traveled, VMT) on the other: smaller or more densely settled metropolitan areas are characterized by shorter and less frequent vehicular trips, and similar trends appear across neighborhoods within cities (Bellomo et al., 1970; Highway Research Board, 1977; and Wilbur Smith and Associates, 1968). Not only are destinations physically closer to residences, thus allowing households to substitute walking and bicycle travel for vehicular trips (Paaswell and Berechman, 19761, but road congestion and parking difficulties discourage automobile use. The latter is evidenced by a very pronounced decl-

THE DETERMINANTS OF ENERGY CONSUMED FOR URBAN TRAVEL

Very little urban travel is undertaken for the intrinsic enjoyment of travelit’rg. Instead, travelers seek to reach destinations where their demands for goods and services can be satisfied. The aggregate distance traveled per *The research reported in this paper was supported by funds from the following federal agencies: Energy and Research Development Administration (now Department of Energy), Federal Energy Administration (now Department of Energy), Environmental Protection Agency, Department of Transportation. Department of Housing and Urban Development. and Council on Environmental Quality. TRAl

Vol. 16. No I-E

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D. L. KEYES

ine in trip frequency with increased neighborhood density in New York for households with automobiles (Deutschman and Jaschik, 1968). So point number one is that people living in close proximity to each other (and, by inference, to commercial and employment opportunities) will travel fewer miles per year. The influence of density on the choice of transportation mode follows directly from this relationship. High population densities and the shorter distances between origins and destinations make possible the provision of mass transport modes. Evidence abounds to document this assertion with respect to buses and fixed rail transit (Wilbur Smith and Associates, 1968), and the references cited above document the higher levels of non-vehicular travel in high density, central city locations. Thus the second point: more efficient transport modes can be provided and utilized in compact urban settings. The third effect of urban spatial arrangements (as summarized thus far by the single descriptor “population density”) is, in an energy use sense, antithetical to the previous ones. High density, compact urban areas produce intermittent traffic flows-the most energy inefficient type of vehicular movement. Research at the Urban Institute has shown that average speed per automobile trip is negatively related to neighborhood density and positively to distance from the central business district (CBD) in six metropolitan areas (Neels et al., 1977).t Consequently, there is reason to believe that the efficiency gains accomplished by concentrating destinations and encouraging the use of more energy efficient travel modes may be offset, somewhat, by the congestion factor. These considerations form the foundation for the case linking spatial elements of urban development patterns with energy consumed for intra-metropolitan travel. The simple relationships discussed above describe average conditions; they are not expected to hold everywhere. The pattern of land development in urban areas as well as transportation systems found there are the product of historical as well as current trends. Thus, a static picture of any one metropolitan area could well reveal high density development of a recent vintage totally lacking in the expected mass transportation infrastructure. Likewise, older and now sparsely populated central city neighborhoods can be found withgood access to rail and bus transit. An equally important limitation to these general relationships is the inadequacy of population size and density as the sole measures of the pattern of development. Clearly these variables do not capture the many dimensions of spatial patterning important to trip-making and thus energy use-the clustering of activities in multiple nodes throughout the metropolitan region or the mixture of land uses within any given sub-region. Nor do these descriptions, alone, reflect the degree of geographic congruity between houses and jobs, which, as Zahavi (1976) and tThis study also provides additional supportive evidence for the effects of population size and density on trip length, trip frequency, and travel mode discussed in previous paragraphs.

others have pointed out, is highly correlated with commute distance; low density urban areas may be produced by a simultaneous dispersal of people and jobs, with much less impact on travel than a dispersion of one without the other. Likewise, a concentration of jobs near the urban center in large metropolitan areas frequently signals extensive commuting distances, since residential decentralization typically precedes job dispersal in these areas. Finally, the enunciated relationships are second-order with respect to energy use. Though the associations between population size/density and the various characteristics of urban travel have been demonstrated empirically, this evidence alone is not sufficient to confirm the suspected linkages to energy consumption.

PREVIOUS STUDIES OF VARIATIONS M ENERGY USED FOR URBAN TRAVEL

The preponderance of research in this area has focused on exploring the relationship between urban spatial structure and transportation energy use through simulation analysis. Difficulties in identifying and measuring the salient dimensions of urban structure for a large number of metropolitan areas have argued for a data parsimonious approach-hypothetical cities and simulated travel patterns. By and large, these studies have suggested that compact urban areas with extensive mass transit systems should offer significant advantages in energy efficiency when compared to those with dispersed settlement patterns more reliant on the automobile (Keyes, 1976; Keyes and Peterson, 1977). One,in fact, has suggested that the difference in energy used for travel may be as high as 50%, with the relative location of employment centers and residential areas the principal contributing factor (Edwards, 1975). Ultimately, of course, the believability of these findings rests on the general acceptance of the theory on which the simulations are based, and/or upon empirical verification of the simulation results. Stewart and Bennett were among the first to employ a purely empirical approach (Stewart and Bennett, 1975). Using 1972 gasoline oil sales by service stations reported in the Census of Business (U.S. Bureau of the Census, 1967), variations among 134 Standard Metropolitan Statistical Areas (SMSA’s) were related by regression analysis to various characteristics of these areas and their populations. Population size, population density, (both inside and outside the central city), and percentage change (l%O70) served together with city age and a rough measure of industrial character (identification by Rand-McNally as a “manufacturing” or “diversified” area) as descriptors of urban structure. A direct measure of transit usage (“per cent of workers using public transit for work trips”) was also included. Other explanatory variables included demographic, social or economic aspects of the population; unique characteristics presumed relevant to transporation energy use (e.g. tourist attractions); and the price of gasoline. Stewart and Bennett argued that per capita dollar sales of gasoline to SMSA residents should be lower in older, slower growing, or declining SMSA’s

Energy for travel: the influence of urban development patterns and those with (a) smaller populations, (b) more compact development patterns (greater percentage of people in the central city), (c) higher population densities, (d) diversified economies, and (e) high transit patronage. Of course, several of the variables are expected to be highly interdependent as noted previously. The authors found, however, that, among these variables, only SMSA size, growth, and transit usage were statistically significant, and size had an effect opposite from that postulated. One is forced to conclude from these results that (a) the pattern of metropolitan development does not affect gasoline consumption or (b) the measures of urban form adopted by Stewart and Bennett do not adequately detect the energy-sensitive features of metropolitan development. Another possible explanation is that sales of gasoline and oil products in an SMSA do not significantly reflect gasoline actually consumed by SMSA residents for personal travel. Sales to non-residents or to residents for non-travel purposes could obscure underlying relationships. Stewart and Bennett examined the first possibility using restaurant and hotel/motel receipts to rank SMSA’s by their relative attractiveness as tourist or entertainment centers. They found that high ranking SMSA’s could not be distinguished from low ranking ones with respect to gasoline sales. The second possibility, though not tested explicitly, also does not appear a likely explanation; most fuels used to transport goods or for non-highway activities are non-gasoline petroleum derivatives. In a more recent study, Kenneth Small employed a slightly different approach but arrived at essentially the same conclusion (Small, 1980). Drawing from information on typical commuting patterns within and between central cities and suburbs in all major SMSA’s, Small concludes that even an increase in the real price of gasoline of $1.00 would encourage few residents to relocate. Moreover, what changes may be effected would likely be toward metropolitan decentralization. However, this study utilized data at a relatively high level of geographic aggregation (a central city-suburban distinction was the only intra-metropolitan disaggregation for all SMSA’s except Cleveland), thus obscurring potentially important distinctions at a finer scale. A REFINED ANALYSIS Despite the lack of positive results from the Stewart and Bennett study. their approach is attractive. The use of *The use of actual consumption rather than dollar sales also avoids the inherent correlation between sales and price. $As indicated. the standard practice of normalizing the standard deviation with respect 10 the mean has been followed. As with many variables. high average densities are likely 10 be associated with high standard deviations. A relative measure of variability allows for comparisons among SMSA’s with different average densities. §The density values were computed using data from both the Bureau of the Census (population data) (U.S. Bureau of the Census, 1970) and the Nat&al Planning Data Corporation (land area of each census tract) (National Planning Data Corporation). As noted. values were obtained by aggregating census tract level data.

67

gasoline sales as the dependent variable allows a test of the central hypothesis without the need to make inferences from intervening travel variables. The analyses of linkages between energy and metropolitan development reported in this paper improve upon this feature of the Stewart and Bennett approach by employing a somewhat more direct measure of energy use (gasoline consumption in gallons from the 1972 Census of Retail Trade (U.S. Bureau of the Census, 1975) rather than sales in dollars) as the dependent variab1e.t More importantly, the explanatory variables in the model specification are better descriptors of features of metropolitan development sensitive to the quantity of energy used for travel. The key development variables chosen are as follows: (i) Total population in the “urbanized region” (that contiguous area of each SMSA with a population density of at least 100 people per square mile); (ii) Population density of the urbanized region; (iii) The proportion of the urbanized region population living in census tracts of 10,000 or more people pre square mile; (iv) A regional population clustering index (the standard deviation of census tract population densities divided by the mean density); (v) The proportion of total SMSA employment located in the CBD. The first variable is designed simply to capture whatever inefficiencies of scale may exist in urban travel. The second, third and fourth variables measure different aspects of compactness; the second reflects region-wide average spatial propinquity, and the fourth measures the extent to which more localized compactness can be discerned-a high degree of variability in density among census tracts indicates one or more clusters or nodes of higher residential density.4 The other density variable (proportion of the population in tracts of 10,000 or more people per square mile) attempts to stratify the degree of compactness using a density threshold often cited as the minimum needed to support mass transit. The last development variable is a proxy for the degree of central focus in the spatial distribution of jobs. These development variables were selected from a feasible set of descriptors on the basis of their ability to identify commonly recognized development characteristics in a sample of SMSA’s. Five different measures of population density as well as two alternative expressions of population and job density gradients were tested. The population variables selected were evaluated at the census tract level. As a result, they are not unduly sensitive to the way jurisdictional boundaries are drawn (as are the variables employed by Stewart and Bennett) or to the spatial direction in which variations in density are measured. As such, they more nearly reflect the degree of actual population clustering in metropolitan areas. The employment descriptor is not as robust a variable, but, as with the measures of population clustering, it can be evaluated with easily obtained data from the Bureau of the Census.§ Other variables used to complete the regression model include: (i) The miles of freeways and roads with four or more lanes; (ii) The line miles of bus, commuter travel, and travel routes: (iii) Household income levels in 1972 (real values for a subsample of SMSA’s, nominal values

D. L. KEYES

68

for the total sample); (iv) Average gasoline price levels in 1972. The first two are direct measures of the extensiveness of the highway and transit systems, and the last two are key control variables. Values for the transportation system variables were taken from the 1972 National Transportation Survey conducted by the U.S. Department of Transportation (U.S. Department of Transportation, 1972). They represent measurements or estimates made by local transportation planners for geographic areas corresponding to estimated 1990 urbanized areas (Bureau of the Census definitions of “urbanized area”) in each SMSA. Values for household income were obtained from the U.S. Bureaus of the Census and Labor Statistics (U.S. Bureau of the Census, 1973 and U.S. Bureau of Labor Statistics, 1973), and price data were taken from The Oil and Gas Journal (Oil and Gas Journal, 1972). MODELSPECIFICATIONSAND EMPIRICALRESULTS

Various models employing the above descriptors are specified. The basic model is structured as a linear combination of explanatory variables: Per Capita Gasoline Consumption

=b,,tb,

development variables

This implies a simple, non-interactive influence on gasoline consumption of each set of variables, and of each variable within each set. Undoubtedly, a rather high level of intercorrelation exists among variables in the first set and. perhaps (as the rationale for linkages between urban development and energy use laid out earlier suggests), between variables in the first and second sets. Were every variable to be included in the model, the statistical strength of each would be substantially reduced due to multicolinearity. Therefore, only a subset of all possible explanatory variables is included in any specification: the relative importance of any one variable is judged from the pattern of results across all models. The SMSA’s selected for purposes of evaluating the empirical models are subsets of a 106member sample constructed to reflect the distribution of all SMSA’s (above 100,000population) by size and by the four major geographic regions. The actual number of SMSA’s employed to test each model varies from 15 to 49, reflecting the availability of data for the various explanatory variables among the initial 106 SMSA’s. The models evaluated and the results of multiple stepwise regression analyses are shown in Table 1. The first two are the reference equations. The regression results indicate that approx. 40-50% of the variations in per capita gasoline consumption among over 40 SMSA’s can be explained by the development variables. Overall, metropolitan areas with high average densities, one or more higher density nodes, and many residents living in neighborhoods of 10,000 or more people per square mile,

have lower gasoline consumption levels; those with large populations and higher employment levels in the CBD are associated with higher levels of gasoline use. This accords well with the hypothesis constructed earlier. Large metropolitan areas, especially those with centrally focused employment opportunities, require greater distances for residents to reach travel destinations. A centrally focused employment pattern may also encourage mass transit, but this effect appears to be minor. Spatial compactness and population clustering clearly lead to less-than-average levels of gasoline consumption. Of note is the strong correlation between population size and regional density (r = 0.6 in the sample). For this reason, large SMSA’s may actually show lower paer capita gasoline consumption levels. This may explain the inverse relationship between gasoline sales and population size observed by Stewart and Bennett, if their measure of regional density were not adequate in capturing the individual effects of density and population clustering. The effect reported for household income in Table 1 is rather erratic. In principle, one should be careful to measure income effects in terms of real income, that is, income adjusted for inter-metropolitan differences in price levels. In the one sub-sample for which this was possible (eqn (3)), the income effects are significantly positive, as one would predict. Nominal income (income unadjusted for price differentials), however, was not a statistically significant determinant of gasoline sales in any of the equations. The fourth equation is designed to test the effect of gasoline prices. The estimated equation shows that higher retail gasoline prices depress consumption, but the coefficient of the price variable is significant only at the 0.10 level. The small sample size does not permit interpretation beyond acknowledgement that the signs and approximate size of the coefficients of the important explanatory variables remain stable in the various subsamples. It was argued earlier that urban form exerts its influence partly through intermediate associations with characteristics of the transportation network. In addition to the positive association between residential density and transit service, job centralization (or, more accurately, separation of jobs and homes) may be associated with more highway miles per capita. ,Explicit introduction of highway and transit terms should provide a means to test these assumptions. Looking at the results of the fifth regression, we see that both HIDENS and CBDJOB decline in importance after inclusion of the transportation variables, thus offering support for this hypothesis. The coefficients of the transportation variables are significant and have the expected signs. Accounting for their independent effect on gasoline consumption raises the amount of explained variation in gasoline sales among SMSA’s to 56%. Thus far, the effects of urban development characteristics on gasoline consumption have been discussed largely in qualitative terms. A gasoline-efficient city has been depicted as modest in size, but still large enough to have a sizable proportion of its population living in

Energy for travel: the influence of urban development patterns

69

Table I. Results of the gasoline consumption analyses 1.

Ix2 = 0.47 N = 49

G i

453

I’OP -

+ 0.69

C = 261

+ 1.88

POP -

(2.72)**’

3.

R2 = 0.79 N =

4.

C = 127

+

(3.88)*‘*

R2 = 0.53

G = SO8 +

=

259

+

2.00

POP

-

(2.95)**’

‘tie

Notes:

coefficients

parentheses

of to

332

346

160

the

200

IIIDENS

l

IIIIll!NS

POP --

CDDJOB + 4.01

(3.26)“.

+

IllDENS

363

each

N-IIIIINC

-

(0.33)

CDDJOD

+

(corrected

for t

(-1

(2.30)”

degtess

statistics

which ate respectively.

PRICli

N-IIIIINC + 109 IllWAYS - 60.5

(0.13)

equation;

8.27 (1.54)’

1.01

(1.23)

gallons of gasoline pet person pet of Retail Trade 1972, Miscellaneous

of

freedom)

and

sample

THNST .Efl)*

sires

aIpnr

in

appear

in parentheses below each tegtessignificant at the 0.10, 0.05, end 0.01 levels Units and data sources for the variables as

year in 1972 (U.S. Bureau of the Census. Census YSubjects, HC72-S-3, IJSGI’U, Wasbiogton. D.(.. : 1975).

population in the developed area in hundred thousands. (U.S. Bureau of the Census. 1970 Census of PoPulation, USGPO, Washington. D.C., as interpreted by Notional Data Planning Corp. and the author).

lIlDENS --

proportion

CLUS’IXR -CIIUJOB --

the

of population

coefficient

of

in census

dispersion

of

nominal Census.

R-IPIINC --

teal

cost

of

HIWAYS --

TRNST --

average Joutnnl,

of

ID,000

tract

persons/square

densities

CBD (U.S.

about

mile the

Bureau of Census,

ot mote

regional Census

(Ibid.)

average of

housebold

income

(N-HlINC/CLI)

living

retail price of December 1972)

miles of urbanized Sutvey)

gasoline

in

thousands

of

(Ibid.)

Retail

household income (median value) in thousands of dollars (U.S. Bureau of Collnty and City Data Book, 1972, USGPO, Washington, D.C.: 1973.)

index (U.S. Bureau of and Comparative Indexes for Selected Washington, D.C.: June 1973)

PRICE --

tracts census

proportion of .SMSA jobs located in the Trade. Major Uetail Centers 1972

N-IUIING --

CL1 --

II-IIIIINC CBDJOB + 2.99 (2.79)”

+ 907

‘llwsc coefficients sion coefficient. ate indicated by an l, l*, and ***, show ate: G --

CBDJDD

(3.10)**’

(1.94)..

determination of

3.93

l

592 CBDJOB + 6.08 N-IUIINC (O.tl9) (2.36)*‘*

IIIDENS + 893

(-2.10)9’

left

CLllSlBH

(3.49)***

(-1.54)’

(2.28)..

(;

148

(-4.33)“.

POP -

1.66

N = 22

DENS -

(-4.17)***

POP -

1.89

15

7.15 (1.66)’

(1.04)

the

dollars

Labor Statistics, “Autumn 1972 Urban Family Budgets Urban Areas. ” USUL: 73-253, U.S. Dept. of lahot.

in the

central

city

freeways and other toads with 4 ot mate nten” (U.S. Department of Transportation.

line miles of bus, conweutet train, and train routes utbnni zed atea” (U.S. Department of Transportation,

in cents

pet

lanes pet 1,000 1974 National

gallon

--(Oil

and Gas

persons ill the Transportation

“1990

pet 1,000 persons in the “1990 1974 National Transportation

Survey)

several high-density areas, and it has a relatively uniform distribution of jobs and population (i.e. a low degree of job centralization). What can be said about the magnitude of these effects? The coefficients for the development characteristic variables. in the fifth equation of Table 1 indicate that an increase in population of 500,000 (average size in the sample is about 1,140,000), a decrease of 0.05 in the proportion of metropolitan residents living in high-density areas (average value in the sample is 0.15), or an increase of 0.03 in the proportion of jobs located in the CBD (sample average is 0.09) would each increase per capita gasoline consumption by ten gallons or about 3% at the mean. Even a simultaneous shift of both HIDENS and CBDJOB in the direction of energy conservation by a full standard deviation would reduce per capita gasoline use by less than 40gal per person,

per year. or

approx. 12%. Changes in area-wide development patterns of this magnitude could only be brought about by drastic alterations in the location and density of new development. Where modifications to the transport system accompanied development changes, the impact on gasoline consumption would be greater, but still rather modest. And if energy consumed by transit vehicles (typically diesel fuel or electricity) were included, the estimated savings in terms of total energy consumed for travel would be even less.

POLICY IMPLICATIONS

Modest as the savings may seem, the manipulation of development patterns may still prove an efficacious conservation strategy in relative terms. To put these savings in perspective, the effectiveness of various strategies

70

D. L. KEYES

designed to conserve transportation energy can be compared. Hirst and Moyers have estimated that urban travel accounts for approx. 8% of national energy use (Hirst and Moyers, 1973). Assuming that a decrease of IO-IS% in energy consumed per person for urban travel is feasible from changes in urban development patterns alone, national energy consumption would decrease by 0.81.2% per year once the changes had been realized. If this could be accomplished by the end of the century, by which time national consumption should reach 110-120 quadrillion Btu’s per year, then the annual savings would equal approx. I quad per year.+ This compares with an estimated one year’s savings of 7.8 quads by the end of the century from achieving the 1985 fuel efficiency standard for new cars of 27.5 mpg (Altshuler, 1979). Doubling the real price of gasoline is likely to reduce auto gasoline consumption by 50% or more (a one year savings of about four quads by the end of the century), depending on the assumed long term price elasticity of gasoline demand (-0.5 to-0.9) (Wildhorn ef al., 1974; Chase Econometric Associates, 1974). Furthermore, the institution of strategies based on efficiency standards or gasoline price increases is likely to condition a land use approach. An increase in automobile fuel efficiency will shrink the difference between development patterns. On the other hand, price rises, while not affecting the magnitude of differences in transportation energy use among development patterns, diminish the importance of public control over land use in general; areas will naturally grow toward more efficient forms as energy costs become a larger fraction of consumer expenditures. Dramatic increases in gasoline prices will be required to cause a perceptable movement in this direction if the past is a guide to future consumer behavior, but such increases are clearly possible in an environment of uncertain petroleum supplies.

SUMMARY

OF FINDINGS

Systematic differences in the level and type of travel by residents and thus energy use are clearly observable among metropolitan areas of different development patterns. Energy efficient SMSA’s have high residential densities and similar spatial distributions of jobs and homes. Among SMSA’s with these characteristics, the smaller ones are the least energy consumptive. However, the observed differentials in transportation energy use are modest when compared to the energy savings estimated for other conservation strategies. To achieve even the relatively small savings anticipated from these findings, large changes in historical urban development patterns would be necessary.

tNational energy consumption by the end of the century has been estimated at between 110 and 120 quads per year (U.S. Department of the Energy, 1979).

REFERENCES

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Consumption and Urban’StrucSfudies. Department of Civil

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Research

Society

Meeting.