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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. SMC-7, NO. 4, APRIL 1977
A Systems View of Energy and Land Use
T. OWEN CARROLL, MEMBER, IEEE, ANDREW S. KYDES, JONATHAN B. SANBORN,
ROBERT NATHANS, AND PHILIP F. PALMEDO
Abstract-The role of land use as an underlying determinant of energydemand is often neglected. A systems framework to study land use andenergy utilization has been developed. With input from planning orga-nizations, consulting firms, and federal policy makers, the systemsmodels are being used to assess the impact of strategies to achieveenergy-conserving land use patterns.
I. INTRODUCTION
V IRTUALLY every current study of the energy prob-lems of this country has concluded that energy
conservation is not only the key to ameliorating our short-term energy supply problems but is also a necessarycondition for a satisfactory medium- and long-term energysupply-demand situation. Recent attention has naturallyconcentrated on those short-term conservation measuresthat can have immediate impact such as insulation retrofit,lowered speed limits, and actions resulting from priceincreases. While many of these measures can induce short-term reductions in energy demand, few deal with the under-lying regional growth forces which are continually drivingthe energy demand curve upward. To affect these structualelements that underlie energy growth, policies must beestablished to affect the evolution of current land usepatterns.
State or local governmental units and the variousagencies of the federal government whose policies influenceland use development throughout the country have thus farnot moved to implement measures designed to bring aboutenergy conserving uses of land. One reason appears to bethat while public policy for energy conservation originatesat the federal level, land use regulation has been recognizedas a prerogative of local, or sub-federal, governmental units.As a result, local and regional land and use developmentpatterns have often proceeded in a manner which placesthem in conflict with stated national goals and priorities. Asecond reason for the lack of effective policy in this area isthe lack of a sound methodological basis for relating theproduction and use of energy to the regional mix and spatialarrangements of industrial, residential, commercial, andpublic service land use activities. Not only is there a lack ofquantitative knowledge as to the extent of the energy savingsthat might be achieved through the use of alternative landuse plans, but there is relatively little sense of the com-
Manuscript received January 13, 1976; revised November 19, 1976.This work was supported by the Office of Conservation and Environ-ment, Federal Energy Administration.
T. 0. Carroll, R. Nathans, and J. B. Sanborn are with the Institutefor Energy Research, State University of New York, Stony Brook,NY 11794.
A. S. Kydes and P. F. Palmedo are with the National Center forEnergy Systems Analysis, Brookhaven National Laboratory, Upton,NY 11973.
patibility of land use development patterns which mightresult in reduced energy consumption with those thatwould, for example, reduce environmental degradation orlimit the use of other scarce resources. Consequently,regional community planning for future land use has not,up to now, incorpotated energy considerations. Moreover,even in regions where public officials, planners, and com-munity groups wish to modify land use patterns to reflectcurrent or projected energy shortages and increased prices,no effective decision tools are available to demonstrate thetradeoffs in terms of alternative patterns of land use.
In an effort to increase our general understanding of landuse and energy interdependence, the range of potentialsavings, the strategies and measures for achieving suchsavings, and the interaction between local and nationalenergy supply systems, the Federal Energy Administrationis sponsoring a Land Use Energy Utilization Project whichis intended to formulate and test a systems approach to thisgeneral area. The project has focused special attention onenergy conservation and the preparation of models whichpermit the examination of tradeoffs between land useactivity levels, mixes, and spatial arrangements, and thedemands upon the regional energy supply and distributionnetwork. The systems framework and models which havebeen developed during the course of the project have beenchosen to draw heavily from existing land use models andenergy system models which have found wide use withinplanning agencies. They are also designed to provide a finaloutput which is both understandable and implementable bythose in the planning profession. To assure realism in ouranalysis, we have used a specific region the Nassau-SuffolkCounty region on Long Island as an initial testing ground.The appropriateness of this choice stems primarily from thefact that it is typical of the fast growing suburban regionssurrounding many of the nation's largest cities. From an
energy perspective, it is in this type of region where therange of possible development patterns is, perhaps, themost extreme, and thus where it is most important to in-
corporate energy concerns into development objectives andpractices.
fn this paper we present a brief review of our overallapproach and results to date, and discuss some of thedifficulties inherent in a system view of land use and energyutilization.
It. SYSTEMS APPROACH TO LAND USEAND ENERGY UTILIZATION
In principle, the task of searching for and selectingstrategies and measures which will bring about energyconservation vis-'a-vis land use becomes that of under-
256
257CARROLL et al.: ENERGY AND LAND USE
/
//
NOTE: * Denote Intervention Strategies.
Fig. 1. Framework for search and selection of energy-conserving land use patterns.
standing and defining the relationships between sets ofpossible land use activities in a given region and the resultantenergy end-use demand and of determining the relativeimpact of such strategies and measures upon both regionaland national energy systems.A systems framework for structuring this search and
selection process is shown in Fig. 1. The diagram identifiesthe basic elements which enter into the land use energy
utilization linkages. Among the most important drivingforces for defining the final character of land use activitiesare the basic development goals of the region, such as
population, employment, industrial sales, etc. As with allregional land use planning procemses, they act as the startingpoint for estimating projected sets of land use activities.These targeted goals are not in themselves sufficient toproduce a set of projected land use activities. To do thisrequires a land use model and a more specific definition of
preferences with respect to mixes of industries, zoning, andthe spatial allocation of the major sectors: residential,commercial, industrial, and transportation. The functionof the land use model is to allocate land use activities bothspatially and among major land use categories in a mannerthat is consistent with regional development goals and theconstraints imposed by regional preferences. The level ofdetail expressed in either the regional goals or preferenceswhich serves as input to the land use model varies from oneregion to another. In many cases, they will be augmentedby constraints imposed by such relatively invariant localconditions as the physical characteristics of the region andexisting labor force mix, road networks, etc., and by what-ever information is known concerning consumer andindustrial preferences and projected levels of local economicactivities.Once a starting set of projected land use activities is
I
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, APRIL 1977
obtained, one can begin to estimate the projected totalenergy end-use demands for the region in question byutilizing energy intensity factors which are associated witheach of the activities included in the projected set. Thecharacterization and level of aggregation of these energyintensity factors, in addition to satisfying a number ofpractical criteria discussed below, must, of course, take intoaccount the limited domain of influence of planners andpolicy makers. For example, in most regions, it is probablysufficient to derive energy intensity factors for at most fourto six residential types and perhaps the same number in theindustrial and commercial sectors.To satisfy the requirements for analyzing the impacts of
the land use generated demands on the regional andnational energy systems, we must also include a distinctionbetween fuel specific and non-fuel specific energy demandsin our definition of the land use energy intensity factor. Thisdifferentiation is needed in order to explore alternatives inthe energy supply-distribution system for meeting theend-use energy demand.Although we have identified land use activities as the
primary determinants of energy demand, the production,processing, and distribution of energy are themselves landuse activities. Furthermore, because of their associatedenvironmental effects and physical requirements, regionalconditions can also act to limit the number and location ofthe energy processing and distribution facilities, e.g., powerplants or transmission lines. Regional preferences may alsobe expressed in the choice of energy conversion tech-nologies, e.g., nuclear versus coal-fired electric. Finally, thedemand for energy resulting from the land use activitiesalso affects and is affected by the national energy system.For these reasons, we also include a systematic descriptionof the regional energy supply-distribution system and itsfuel imports and exports in our scheme. The starting valuesand characteristics of the regional energy system are againbased on an internally consistent combination of pro-jections, estimates, and preferences which is provided forin the use of the energy supply-demand model describedbelow.
Using this framework, the process of searching for andselecting strategies and measures designed to bring aboutenergy-conserving land use practices reduces to that ofassessing their effectiveness in intervening in the land use
energy utilization system. This is accomplished by the use
of the land use and supply and demand models which allowone to alter the input parameters and determine the impacton the energy system. We have developed a variant of theLowry land use model which is described below [1]. Thisactivity-type approach is equivalent to that found inenvironmental management models which are used to
evaluate the impact of intervention strategies and measureson the resulting environmental quality [2].
Measures taken to alter land use activities so as to reduceend-use energy demand may result in the use of fuels whichare less efficient to produce or which are in short supply. Toevaluate the impact of fuel and technology substitutions,we use an energy supply-distribution-demand system model
indicated in Fig. 1, which is based upon the BrookhavenReference Energy System concept [3]. The regional ornational energy system may be represented in a networkformat of energy flows from alternate resources through thevarious energy conversion and delivery activities to specificend uses. All steps in the energy chain, including extraction,refining, conversion, storage, transmission, distribution,and end-use device, are represented. Each such process isdescribed by conversion efficiency, capital and operatingcost, and pollutant emissions. The construction of suchenergy system diagrams for selected years both present andin the future relies upon projected efficiencies, costs, andemissions, and also includes data based on projectedcapacities of conversion facilities, delivery and storagesystems, and the expected implementation of new energy-related technologies. Used in this manner, the function ofthe Reference Energy System is to present a systematicoverview of the regional energy system in order to assesscompatibility between energy supplies and demands.
Associated with the Reference Energy System are anumber of related models. The Brookhaven Energy SystemOptimization Model (BESOM), a variant of which is usedin the land use energy framework, is a linear programmingmodel which allows alternative pathways through theenergy system to be compared with respect to either costs,overall energy efficiencies, or total associated emissions,subject to such constraints as fuel mix, capacities of inter-mediate conversion facilities, etc. The value of the linearprogramming model in this case is not in its capability forhigh accuracy, but in its ability to capture the essentialstructure of a complex system, such as that involved in thesupply and utilization of energy, in a simple format wherethe assumptions and constraints are quite accessible. Inthe most simplified versions of this model, both the supplyof primary fuels and end-use energy demand are treated asexogenous inputs. In other versions, the model can be runin conjunction with an energy input-output model in orderto assure compatibility between projected energy flowswithin the energy system and the energy needs of individualsectors of the national economy [4]. Both the ReferenceEnergy System and its associated models have been usedextensively over the last several year sin connection withnational energy research and development planning.
III. LAND USE ENERGY SIMULATION MODEL
As noted above, the land use energy simulation modelwith integrated capability for generating energy demand,which we have developed, is a variant of the classic Lowrymodel [5]. Such a model framework captures two essentialfeatures of the land use/energy utilization interaction: 1)the spatial location of land use activity is explicit, and 2)transportation energy demand is determined as an integralpart of the spatial configuration.
Regional growth in the model is predicated upon an
industrial employment base and a representation of thetransportation infrastructure. Site-specific manufacturingand other industry dependent upon the interregional
258
CARROLL et al.: ENERGY AND LAND USE
transportation network or the availability of local resources,such as water, is termed basic industry. The region isdivided into tracts, and for basic industry, the employmentand acreage are specified for each tract. Using a tripdistribution function derived from the transportation net-work, which measures preference for travel in the region, aresidential population is spatially allocated consistent withindustrial employment opportunities [6]. Retail and othercommerical activity, such as offices and schools, measuredby employment opportunities, is also spatially distributed,again through use of the characteristics of the transportationnetwork for residential-commercial travel. Zoning andmeasures of agglomeration are expressed as constraintsupon location of activities in specified tracts, as are regionalplanning relations between households and employment.The sequence of regional development is portrayed throughappropriate intervention into growth in industrial employ-ment, zoning changes, housing mix, and modifications inthe transportation network representation.The model is adapted to the determination of energy
demands in several important respects. The residentialsector is disaggregated into types of housing within old andnew housing stocks for which energy demands differsignificantly. This facilitates examining the impact of thesingle/multifamily housing mix, whose complexity isrepresented in residential zoning-employment-travel inter-actions in the spatial allocation process. A linear program isutilized to establish the housing mix in each tract, in whichthe objective function expresses preferences for each typeof housing in the tract and the constraints reflect zoningrestrictions and land availability. Commercial sector energyis similarly associated with different types of retail activity.Industrial sector energy is determined through basicindustrial employment in the region.
Transportation energy is determined directly in themodel. Since the actual spatial allocation is tempered byzoning and agglomeration factors, the resulting land useconfiguration reflects the "constrained preferences" ofresidents with respect to travel. Actual industrial-resi-dential-commercial travel assignments by tract, trip distri-bution for different purposes, and impedance measures areutilized in the calculation of passenger miles of travel andenergy consumption [7]. Modal split is integrated into themodel through specific grid assignments with altered tripdistributions representative of accessibility to alternativemodes of transportation. Overall, the spatial land use
configuration both determines and is determined by thetransportation network so that travel patterns and asso-
ciated energy demand are explicit.For each major sector of land use in the model, the
energy demand can be estimated on the basis of surveys ofexisting informational and data sources [8]. The energy
intensity factors, end-use energy demands per unit, thusrepresent the averaged energy demands of a set of aggre-
gated activities. The units used to express the energy intensityfactor will vary from sector to sector. For the residentialsector, we use the household as the unit; in the commercialsector, square footage is used; in the industrial sector, the
RESIDENTIAL )(million Btu per household)
Heat (non-fuel specific)Electric
COMMERCIAL(thousand Btu per sq. ft)
Heat (non-fuel specific)Electric
INDUSTHIAL(million Btu per employee)(million Btu per dollar-
value added)
TRANSPORTATION(H)(Btu per vehicle mile)
SingleFami lyDetached
7534
Mall
8458
Light120
10.5
Autolo40
Low HighAttached Rise Rise
64 55 4631 29 29
Attached
10358
Medium
310
32
Strip Office
115 16858 50
Mining Paperana andMetals Chemicals
1170 1710
72 129
Bus676o
(1) 1300 ft2, R-11 ceiling/R-7 wall, 5500° days heating/1200° dayscooling.
(21) This is end-use energy demand based upon 28 miles per gallon.At efficiency 0.2, gasoline consumption is 5200 Btu per vehicle mile.
unit is either industrial dollar value-added or employee;in the transportation sector, vehicle miles traveled is used.The energy intensity factors appropriate for the Northeast
in general are summarized in Table I. These are derivedfrom the survey work above and certain specialized energydata sources [8] correlated with engineering design cal-culations [9]. These correlations were used to normalize theenergy demands of different building structures to oneanother and to calibrate heating and cooling loads to specificclimatic conditions.To develop energy intensity factors for the industrial
sector required a separate analysis. We begin with a base ofnationally-averaged industrial energy utilization with 101industrial sectors [10]. This is aggregated to the 57-sectorBEA Level, which retains important distinctions betweenmany of the industries normally subsumed under a two-digit SIC Code. Input-output tables have been preparedwhich yield the "direct" energy demand coefficients, i.e.,the amounts of energy demand per dollar of sales for eachsector. Using cluster analysis, we examine the energy use
patterns in Fig. 2 with respect to energy and employees perdollar value added. This allows us to identify five majorindustrial groups whose energy intensity factors differsubstantially from one another. Light industry includes allof the light manufacturing industries. Medium industry iscomposed of a miscellany of personal consumption in-dustries including food and kindred products, textile goods,rubber and plastics, etc. Mining and metals includes fourindustries: stone and clay mining, nonferrous metal oremining and manufacturing, iron ore mining, and glassproducts. Paper and chemicals includes chemical andfertilizer production, paper and allied production, andmanufacture of stone and clay products. Synthetics includeplastics and synthetic materials production, chemical andchemically-related production, paving mixture, asphalt
TABLE IENERGY INTENSITY FACTORS FOR THE NORTHEAST
259
260 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, APRIL 1977
I I r I I I I I -I I I I i I I
* Plastics aSynthetic Matelrils
Chomical SF*rtililer*Mineral Mining
Iron Ore Min.
0 Stomn Clay Products
*Popor S Allied Products
* Primary Nonferrous Motal MfgIng
* Glass Sf GII -r
*Nonferrous Meltal Or* Mining
lass Products
* Stone S Cloy Mining
Points a Allied Products0 T* Leother
Brood & Narrow Fabrics
Food 8 Kindred Products * Rubber I * 1 0 Lumber S Wood ProductsMisc Plastic Products
Ordonc**Metal Containers AccssorisM
leanig* \ *ting *tol ProductsCleaninga , &^ T
's 1'Iisa* * * Printing S
PublIbig50 60 70 80 90 100 110
00*
Eloctronic* Household
Elcetronic FurnitureComponents
I I
* Wood Containers
9 *Apparel
120 130 140 150 460 170 ISO 190 200
Number of Employ"s Per Million Dollars Value Added
Fig. 2. Industrial energy/employment per value added.
felts and coatings production, and primary iron. Most oftheir energy is embodied in feedstocks. The energy in-tensities for this last group are very much specific to theindustry considered, and an average for use in Table I isnot appropriate.We should note that the energy intensity factors described
above are appropriate for analyzing intervention measures
directly impinging on the factors generating end-use energy
demand. When exploring the effect of supply constraints on
land use, further disaggregation of the end-use energy
demand is needed to reflect additional details of fuelconsumption patterns such as the load characteristics ofelectric devices.
IV. APPLICATIONS OF THE SYSTEMS FRAMEWORK
The land use energy simulation model was developed as a
tool for two types of applications. First, specific regions ofthe country can be analyzed to estimate the energy demandsof the region under various growth scenarios. The objecthere would be to analyze the long-term land use and energy
implications of changes in residential zoning. In the second
case, the computer model is intended to study the genericrelationships between energy utilization and "urban form."
A. Employment Distribution anid Energy Consunmption
Preliminary computer runs of the model are aimed at
exploring the generic relationship between energy demandand "basic" industrial employment dispersion in an urbansprawl situation. The results are suggestive and indicatethe need for further exploration with the model beforedefinitive statements can be made concerning the mag-
nitude and direction of the interactions.The model has been applied to a prototypical region with
675 square miles. The total basic employment in the regionwas held fixed, but the manner in which it was distributedradially around a preselected grid was allowed to varyaccording to the function
Er11 = E(ro)erlrowhere r is the radial distance from the central grid and ro isa constant which determines the dispersion of basic employ-ment in suburban regions. E(ro) is a constant with respect
0-
161
154
14C
i3(
12C
I I I
1-.
go
la
ca
.C
Ito
100
90
80
70
60
50
40
30
20
100 Drug, (
Tollet PrepOff ICe*
Tobacco, Mfg Machines
0 40 20 30 40
. ...
I I I I I L I II I I L I I I I
I I
-
-
-
-
261CARROLL et al.: ENERGY AND LAND USE
-i2
z
U)
z
0.
zw
a
Ji
z0Un
CL
U)z
zw
a
0
D.
r (MILES FROM CENTER)
Fig. 3. Basic employment and population distribution (POP = 0.58million).
POPULATION(MILL IONS)
27 - 5
25 _
-2323
2.3
2)1
1.1519 _ 0.58
170.3 1.5 7.5
rO
Fig. 4. Daily work-trip mileage per household.
to r but is selected to obtain the proper total basic employ-ment in the region. If ro is very large, the basic employmentapproaches a uniform distribution, whereas as ro approacheszero the basic employment becomes more concentrated in a
"central business district." However, as shown in Fig. 3,the population distribution is somewhat less dependentupon the dispersion of employment opportunities. Resi-dential zoning restrictions in the region were held fixed forall runs with a uniform maximum density constraint Z,Hroughly equivalent to suburban sprawl. Single-familydetached and attached homes were permitted with a
preference for detached homes.Figs. 4 and 5 illustrate the complex tradeoff between
work-and-shopping-trip vehicle miles in each case. Central-ized employment (ro= 0.3) implies that work trip lengthsare relatively long, whereas shopping trips tend to remainrelatively short. For dispersed employment (ro = 7.5), i.e.,where a central region of high basic employment is sur-
rounded by significant levels of dispersed suburbanemployment, the graphs imply shorter work-trip lengthsbut longer shopping-trip lengths. The reason for theseshifts appears to be a result of the agglomeration con-
straints. Lower population densities cannot support
2.9U)
2.7
2.5
2>3- 0.3 1.5 7.5
Fig. 5. Daily shopping-trip mileage per household.
TABLE IIANNUAL ENERGY DEMAND PER CAPITA
POPULATION(millions) r TRANSPORTATION RESIDENTIAL TOTAL
.3 32.7 32.5 105.25 1.5 31.9 32.5 104.4
7.5 23.8 32.5 96.3
.3 25.6 32.9 98.52.3 1.5 24.4 33.7 98.1
7.5 20.6 33.7 94.3
.3 23.8 34.4 97.71.15 1.5 22.4 34.4 96.3
7.5 20.6 35.5 96.9
.3 22.8 34.9 97.7.58 1.5 21.2 35.1 96.3
7.5 20.6 36.0 96.9
commercial development except at a limited number ofsites. Overall, minimum vehicle miles per household occursfor low population areas having some modest suburbanemployment.The total annual per capita consumption is summarized
in Table II. Low, widely distributed populations (0.58million people with ro = 7.5) require 96.9 x 106 Btu/person, whereas large centralized populations (5 millionpeople with ro = 0.3) require 105.2 x 106 Btu/person forresidential plus transportation needs. This points to poten-tial savings which are achievable through careful choices ofland use patterns in a growing region. Table IL also indicatesthat growth in a region can be accomplished with eitherincreasing or decreasing per capita energy consumption.This suggests that existing communities which are rapidlygrowing have options over the next 20 years leading toeither increases or decreases in per capita energy con-sumption depending on the selected growth strategy.
B. Suffolk County-A Case StudySince most future growth on Long Island, both in terms
of land use development and population, is expected to takeplace in the Island's eastern areas, the focus of this case
study is to study land use energy interactions under al-ternative conditions of growth in Suffolk County.
Three regional scenarios were constructed to explore theenergy requirements of alternative growth patterns:
* Urban Sprawl (US),* Comprehensive Plan (CP),* Growth Centers (GC).
Continued urbanization and the development of largegrowth centers of concentrated land use and economic
- - l--TPOPULATION(MILLIONS)
50 -- O gt
2.3
0.58
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, APRIL 1977
activity represent opposite extremes of projected futuregrowth in the Nassau-Suffolk region. Their analysis out-lines the extremes of energy consumption patterns associ-ated with land use. On the other hand, the comprehensiveplan prepared by the Bi-County Commission [I1] providespractical guidelines for regional development consistentwith environmental and other factors. In each case, overallpopulation and employment projections remain the same,reflecting estimates for Suffolk County growth to the year2000.
TABLE IIISUFFOLK COUNTY LAND USE AND
ENERGY USAGE (10 Btu/yr
Basi Industry
Commerc iai
Residential
TransportationTotal
Per person (106Btu)
Case 1 Case 2( Sr,rawl ) (r. Flan
42.647.1
79.9
78.7248.5
105.6
42.6147 .1
76.056. ~
222.0
90.5
Suffolk Population and Employment (Thousands)
Year 1975 2000
Population 1300 2350Households 380 758Commercial Employment 258 516Basic Employment 178 355
The alternative land use scenarios differ primarily in thespatial allocation of basic employment opportunities andzoning constraints imposed upon residential location.'A summary of these allocations is given in Table III.
In the urban sprawl case, industrial zoning and residentialdevelopment is assumed to continue according to the patternthat has clearly developed in western Nassau and easternSuffolk. Residential zoning constraints were establishedfrom 1975 land use. A tract was considered "developed" ifits residential density exceeded 2.5 dwelling units per acre.No further residential development of such tracts waspermitted.
Industrial growth in the urban sprawl scenario will followexisting patterns so that the spatial distribution of Suffolk'sbasic employment force remains unchanged, i.e., internal"basic" employment of Suffolk County in 1975 was simplyscaled up to the 355 400 basic jobs required to support apopulation of 2.35 million.The second scenario is based on the land use allocation
of the comprehensive plan. Commuting to employmentopportunities outside the region will not increase signif-icantly over present levels so that the 1975 commutingpatterns remain unchanged. This implies a large increasein internal basic employment which was allocated mainlyto middle and eastern Suffolk industrial zones describedin the comprehensive plan. These industrial areas have goodaccess to residential clusters and "centers."The residential density constraints are computed in a
straightforward way to be consistent with zoning andresidential densities in the 1985 comprehensive plan data.
' One brief technical note in the application of the systems approachand models is of interest. There are some five major tract variables,several regional totals, housing mixes, and the like within the land use
model. Tract size, which must be compatible with the scale of neighbor-hood travel, determines the magnitude of the computation problem.For the Long Island region, a three-mile square tract size yields about200 tracts. The land use simulation is then about 1200 equations with1400 unknowns. However, the structure is sparse, and only about50 000 word storage is needed. The linear program representation ofthe regional energy system is typically ten resources and about 20demands, some 150 variables, and 40 constraints. Overall, the com-
putation problem is manageable, but without care can expand beyondthe storage capability of many machines.
HOUSING BREAKDOWN (PerCent)
Single Fanily DetaChed
Single Family Attached
Low Rise
High Rise
PERSONAL TRANSPORTATION
Daily Work-Trip Distance**
DailY ShOP .Trip DiStanCe
310.23.86.7
35.814 .8
PERCENT DECREASE FROM CASE 1
Residential
Transportation
Tota1
69.35.4
19.4
5.7
23.814.6
65.C,.
'PO 5
c1- 3
12.61
6. 44.9
28.610.7
t Does not include social-recreational or truck; auto travel assumed.* Mileage traveled for work purposes on a weekday per household.** Total average shopping mileage daily per household.
Land designated as vacant, farmland, or parks and re-creation was designated as "unusable."The third case represents extreme clustering in which all
new basic employment after 1975 is allocated to four''centers.' Commutation is assumed to remain the same
as in the comprehensive plan above. Residential siting isconstrained to 1975 levels except to within a radius of aboutsix miles of these "centers." Tracts near these "centers"have very high residential-density constraints of 15 dwellingunits per acre, allowing low- and high-rise construction.These conditions create four large population "centers," or
cities, in the region.The major energy-related results of these runs are
summarized in Table [II. Significant shifts in energy con-
sumption patterns in the transportation sector result from
the spatial patterns of basic employment sites in thedifferent growth scenarios. In the urban-sprawl case, a largefraction, 13%, of the work force, must commute from variouslocations in New York City, more than 20 miles away. The
relocation of employment into Suffolk County in the otherscenarios not only shortens the work-trip length for those
employees whose place of employment has been changedbut also for those who continue to commute because of the
better availability of housing sites in the western part of the
county. For example, the average trip-length for a Queenscommuter in the urban sprawl scenario is 35 miles; for the
comprehensive plan, it is 25.8 miles. The small reduction in
work-trip mileage from the comprehensive plan to the
''center's' scenario is significant but not as large as that
ENERGY DEMAND> eCase 'Q( f'e:ntfrs )
'.
17l;. OI
5 8 . 1.
2i'7 . 9)
92. 7
262
CARROLL et al.: ENERGY AND LAND USE
from urban sprawl to comprehensive plan. Workersemployed in the more compact "centers" have shortertrip-lengths than those employed in the industrial corridorof the comprehensive plan.
There is also a significant change in the residential energyconsumption caused by the shift away from the single-familyhomes toward the higher-density types. The housing break-down in the urban sprawl case is similar to the presentbreakdown in Suffolk and is clearly a result of the zoningimposed. The change in mix occurring in the comprehensiveplan case is a result of clustering. Zoning encourages theemergence of clusters in the appropriate locations. Second,residential areas in the comprehensive plan are easilyaccessible from employment sites.Commercial and basic energy utilization were inten-
tionally held constant in these runs in order to effect aclear-cut comparison of other factors associated with landuse development patterns.Two points are noteworthy regarding the overall savings
in energy demonstrated under the comprehensive plan andthe continued sprawl scenarios. The first is the large potentialsavings achievable in the transportation area as a result ofthe careful interspersion of "basic" employment andresidential sites (and zoning). Secondly, the bulk of thesavings in both the transportation and residential sectorswas achieved within the guidelines of the comprehensiveplan and under entirely reasonable assumptions. Finally,although the comprehensive plan was not initially designedto produce savings, it is clear that substantial energybenefits result from the creation of clustered and/or compactresidential and commerical sectors if accessible from nearbyemployment sites.
V. CONCLUDING REMARKSFor policy makers, the systems framework and models
offer the mechanism for exploring a wide range of strategiesrelated to land use and energy conservation. The resultingpotential energy savings can be estimated either for actualregions or for idealized communities by operating themodels in a simulation mode. The results of such runs areuseful in providing guidelines for the design of new landuse developments which are intended to conserve energy.Implicit in this approach to analyzing the potential energysavings to be derived from future land use patterns is theassumption of an extended time horizon. Interventionstrategies initiated in the next year will only produce resultsafter a number of years. Both the time lag involved and themagnitude of the energy conserved will depend, of course,on the rate of development of the region in question.During this time, new technologies may be introducedwhich themselves may affect either the final distribution ofland use activities, the energy consumed by individualactivities, or the regional system of energy supply.While the impact of technological development is not a
formal part of this study, we should note that the systemsframework allows some of the effects of the introduction ofnew technologies to be considered. For example, theintroduction of new construction materials or insulation
standards can be included by estimating their effect on therelevant energy intensity factors. The same is true withregard to the introduction of new manufacturing processeswhich may lead to revised values for the energy intensityfactors in the industrial area. The framework also allowsfor the introduction of new intermediate energy conversionprocesses in the description of the regional energy supply-distribution systems. Working closely with selected planningagencies and organizations, planning consulting firms, theFederal Energy Administration staff, we have begun toutilize this conceptual framework for analysis of prototypicregions in the nation and begun to delineate the kinds ofstrategies that will lead to energy conservation by iden-tifying those points in the regional land use energyutilization system where intervention will bring aboutchanges in existing practices and trends. Strategies arebeing designed: 1) to reduce or modify land use activitylevels, 2) to focus on changing the mix of land use activities,and 3) to directly alter the spatial configuration of landuses. In some cases, strategies are compatible with presenttrends in land use planning; in other cases, they are not.Consequently, the selection of appropriate strategies andmeasures requires not simply analytic models, but alsoextensions beyond the models to identify the specific setof actions needed to implement energy-conserving land usepatterns and the social and/or institutional barriers whichmight lie in the path of implementation.
ACKNOWLEDGMENTWe are indebted to Dr. Lee Koppelman, Executive
Director of the Nassau-Suffolk Bi-County RegionalPlanning Board, and to his staff for their generous time andeffort spent with us.
REFERENCES[1] S. Lowry, "A model of metropolis," RAND Corporation:
MR-4035-RC. Santa Monica, CA, 1964.[2] C. S. Russel and W. W. Spofford, "A quantitative framework for
residuals management," Environmental Quality: Theory & Methodin the Social Sciences, Johns Hopkins Press, 1972; B. T. Bower,"Studies of residuals management in industry," prepared for theConference on Economics of the Environment, November, 1973;and B. T. Bower and W. R. D. Sewell, "Selecting strategies forair quality management," Resource Paper #1, Department ofEnergy, Mines, & Resources, Ottawa, Canada, 1971.
[3] E. A. Cherniavsky, "Brookhaven energy system optimizationmodel," Brookhaven National Laboratory, Energy SystemsGroup, Report #BNL 19569, 1974.
[4] D. J. Behling, W. Marcuse, M. Swift, and R. Tessmer, Jr., "Atwo-level iterative model for estimating inter-fuel substitutioneffects," Summer Computer Simulation Conference, San Fran-cisco, CA, July 21-23, 1975.
[5] Op. cit. [I]; M. J. Blatty, "Models and projections of the spaceeconomy," Town Planning Review, 41:2. 1971; J. R. Stubbs andB. Barber, "The Lowry model," Spatial Policies for RegionalDevelopment, Tech. Report #10, American-Yugoslav Project,Ljubjana, Yugoslavia, 1970.
[61 W. L. Garrison, "Estimates of the parameters of spatial inter-action," Papers and Proceedings of the Regional SciencesAssociation, Vol. 2, 1956.
[7] U.S. Dept. of Transportation, Urban Mass Transit Adm., Officeof Transit Planning, Planning Methodology & Technical SupportDivision, Washington, DC 20590.
[8] Energy/Environmental Data Group, "Energy model data base,"BNL 19200, Brookhaven National Laboratory, Upton, NewYork; Federal Energy Administration, "Project Independence,Residential and Commerical Energy Use Patterns 1970-1990"(November 1974), U.S.G.P.O. Washington, DC; Stanford
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Research Institute, "Patterns of energy consumption in the U.S.,"January 1972, U.S.G.P.O., Washington, DC.
[9] ASHRAE, Handbook of Fundamentals, American Society ofHeating, Refrigerating and Air-Conditioning Engineers, Inc.,New York 1972; Gate "Energy parameter handbook," GateInformation Service, San Antonio, TX, June 1971; Load-Characteristic Subcommittee of the Planning Committee, "Resi-dential central air-conditioning study," LILCO. Survey IE-4,3-69, December 1969.
[10] R. L. Knecht and C. W. Bullard, "End uses of energy in the U.S.
economy, 1967," CAC Document No. 145, Center for AdvancedComputation, University of Illinois at Urbana-Champaign,Urbana, IL, 1975; W. A. Reardon, "Input-outptLt analysis ofU.S. energy consumption," Enetrgy Modelinig, Resources for theFuture, Inc., Washington, DC, 1973; R. A. Herendeen, "Anenergy input--output matrix for the United States, 1963: user'sguide," CAC Document No. 69, University of Illinois at Urbana-Champaign, Urbana, IL, 1973.
[11] L. W. Hall, Chairman, Nassau-Suffolk Regional Planning Board,Transportation, June 1970.
On the Role of Systems Analysis in Aiding
Countries Facing Acute Food Shortages
THOMAS J. MANETSCH, MEMBER, IEEE
Abstract-Modern systems analysis can play an important role indeveloping strategies and systems for reducing human misery andstarvation in countries undergoing food crises. A "survival model" isdescribed that permits the model user to explore various strategiesfor managing available food as a simulated country undergoes a foodcrisis. Model results are presented that indicate that some strategiesmake much better use of available food than others and lead to signif-icantly higher survival rates in the afflicted population. Model resultssuggest several specific areas where systems analysts can contributemeaningfully to the effective use of available food resources in feedingpeople undergoing acute food shortage.
INTRODUCTION
RECENT DEPLETIONS of world grain reserves,vagaries of world weather, high prices of food grains,
energy, and fertilizer coupled with bulging populations in a
number of countries lead many to the conclusion that anumber of developing countries are but a bad harvest or
two away from large scale starvation. Work goes on to
expand food production and control populations in de-veloping countries, but this new situation raises two furtherquestions.
Given that food crises are likely in a number of countries,how can these countries manage the food they do have as
to minimize the adverse consequences of an acute foodshortage?
Given that some countries in the world undergo foodcrises, how can international donors of limited food aidadminister this aid effectively where the objective again is
to minimize the adverse consequences of acute foodshortages ?
These are complex questions involving management ofextremely large dynamic systems. It is the thesis of this
Manuscript received October 8, 1976; revised December 2, 1976.This work was supported in part by the U.S. Agency for InternationalDevelopment.The author is with the Department of Electrical Engineering and
Systems Science, Michigan State University, East Lansing, MI 48824.
paper that modern systems analysis can play an importantrole in
a) the development of strategies that make effective useof available food supplies during times of crises,
b) the development of systems for implementing desiredstrategies during times of food crises.
The perspective here will be a short-run emergency onethat seeks to deal effectively with food crises as they evolveand progress over a period of 1-2 years. While it is im-perative to simultaneously address the longer run problemsof population control and expansion of food supply, theseareas are outside the scope of this paper. These latter issueshave received and are receiving much more attention thanthe short-run emergency questions discussed here.The paper will describe in some detail a "survival model"
that has been developed to stuidy some alternative strategiesfor coping with short-run food shortages and the varyingimpacts of these upon measures of human welfare. Themodel and results obtained from it will suggest several areaswhere systems analysts working with specialists in areassuch as human nutrition, rural development, and healthcan contribute significantly to the reduction of humanmisery during times offood shortage in developing countries.
DESCRIPTION OF A SURVIVAL MODEL
In order to address these shorter run questions, a simula-tion model has been developed of a country undergoing a
food crisis. While the model contains elements that are
common to a number of countries, it does not at this timeadequately represent a specific country. The purposes ofthis simulation exercise are to expand understanding of thecomplex issues involved, to shed light on some strategiesthat might be more effective than others in coping withfood crises, and to suggest further work that would lead to
more effective management of food crises in developing
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