Environmental Science & Policy 4 (2001) 307–318

Energy use and transport correlation linking personal and travelrelated energy uses to the urban structure

James Cooper a,*, Tim Ryley a, Austin Smyth a, Edward Granzow b

a Transport Research Institute, Napier Uni�ersity, 66 Spylaw Road, Edinburgh EH10 5BR, UKb Iguana Inc., PO Box 101, Crystal Bay, NV 89402, USA

Abstract

This paper considers relationships between sustainability objectives, transport, spatial design of the built environment andrational use of energy. Conventional transport modelling, housing supply and energy assessment tools are drawn together toprovide a common platform for appraisal of city-wide energy use by stationary and mobile sources. The paper outlinesmethodologies developed for the city of Belfast, Northern Ireland. The paper concludes by briefly reviewing the effect in termsof mobile and stationary energy consumption and travel behaviour of implementing sustainable policy measures in current andfuture years within the study area. © 2001 Elsevier Science Ltd. All rights reserved.

Keywords: Sustainability; Urban assessment; Energy; Transportation; United Kingdom

www.elsevier.com/locate/envsci

1. Introduction

Urban sustainability and a role for an Urban EnergyFramework.

Energy consumption and emissions levels are increas-ingly the focus of planning and economic policy mea-sures at various scales ranging from the Earth Summitat a global level, planning guidance at a UK level, andspecific measures at a city level. For the urban planner,there is increasing purpose to develop the city in asustainable manner. This paper considers energy use, inthe city of Belfast for a range of prototypical sustain-able policy scenarios. Key consumers of energy in anurban environment include transportation and homes.The desire to reduce the negative effects of transportincluding energy use and pollution, has provided astimulus for policies in the transport arena. Domesticconsumption has been the subject of design guidelines,concentrating on heating efficiency measures at ahousehold level. Aggregation of city-wide energy con-sumption figures provide a useful indicator againstwhich a range of policy instruments can be assessed ona consistent basis.

1.1. Transport based energy use

Urban travel consumes significant energy and is amajor contributor to harmful emissions. The effectsconsidered most environmentally damaging relate toground level emissions including the health effects ofexhaust fumes as well as their contribution to green-house gasses. It is common to see policy instrumentsintroduced to limit traffic flow or induce a change intravel behaviour including a modal shift from privatecar to public transport, to alleviate these effects. Fol-lowing the development of Environmental Impact As-sessment in the UK, transport analysis has tended toassociate energy and environmental measures withtraffic flow, following guidelines set out in currentDepartment of Environment, Transport and the Re-gions appraisal methods (Highways Agency, 1999).

Achieving behavioural change, however, has beenslow, in part due to the observed inelastic demand fortravel by private car. Changes in individual policyelements have resulted in only limited impact on travelbehaviour. Increases in fuel taxation in the UK high-light an inelastic demand for private vehicles (Goodwin1992), while experiences elsewhere suggest that fueltaxation on its own produces regional anomalies whererural communities experience greater financial impactsthan those in urban areas. (Dougher and Rayola, 1993).

* Corresponding author. Tel.: +44-131-4555156; fax: +44-131-4555141.

E-mail addresses: ja.cooper@napier.ac.uk (J. Cooper),a.smyth@napier.ac.uk (A. Smyth).

1462-9011/01/$ - see front matter © 2001 Elsevier Science Ltd. All rights reserved.PII: S 1 4 6 2 -9011 (01 )00030 -2

J. Cooper et al. / En�ironmental Science & Policy 4 (2001) 307–318308

Substantial increases in fuel taxation have resulted inlittle reduction in the use of cars. The same is true ofpublic transport enhancement. The UK underlyingtrend for bus use has been downwards, and althoughsuch measures may reduce the rate of decline they havemade relatively little impact on total public transportuse, and failed to halt the slide in the latter’s marketshare.

1.2. Domestic energy use

Households consume energy in a number of ways.� Space heating� Water heating� Cooking

Domestic energy use is an important consideration inurban sustainability; in the same way as transportenergy consumption adds to emissions and reducesreserves of fossil fuels. The typical policy approaches todomestic energy savings have been related to buildingcontrol standards, and proactive policies to encourag-ing householders to take remedial action by fittingimproved insulation. Technological innovation, includ-ing a wider use of natural gas heating systems hasimproved household energy efficiency. This has led toreduced emissions and energy consumption componentthan would otherwise have been the case.

Differences in approach exist between sustainabilityin transport policy and domestic energy use policies.Moreover, the links between household choices andtravel behaviour are not fully explored in the tradi-tional modelling approaches. The research developed anumber of links between housing preferences and travelin an attempt to separate consideration of these typi-cally interdependent choices.

Both the survey and the review of existing techniquesindicated a number of areas of common concern be-tween Housing Choices, Transport and Energy Use.Links identified included:

Housing location:� Transport demand.� Energy gains (solar gain).� Spatial patterns of demand for commercial and retail

services.Housing design:

� Density-transport demand, including public trans-port viability.

� Energy gains (building design).� Heating system requirements.

Transport infrastructure:� Accessibility.� Corridor design.� Potential for energy efficiency gains linked to site

design/layout.

1.3. Issues of common concern related to housing andtransportation

The establishment of the links (above) between hous-ing choice and transport infrastructure has informedsustainable development policy making. The UK Ur-ban Task Force reinforced the importance of theselinks by recommending guidelines on the developmentof housing densities and brownfield sites. These havebeen incorporated in updating Planning Policy Guid-ance notes (DETR, 1994).

There is a requirement for techniques to be availableto test the efficacy of strategies in both transport andhousing. Conventional traffic and urban planning mod-elling techniques do not appear to immediately offersuch a facility. It is against this background that theTransport Research Institute, Edinburgh and the Uni-versity of Ulster, investigated issues relating to trans-port, housing choice, and energy use in a study entitled‘Potential for co-ordinated strategies in transport, spa-tial development and rational use of energy in build-ings’ funded by the EPSRC Sustainable Citiesprogramme. The research work considered trade offsbetween choices in travel and housing, and provided anassessment of sustainable policy scenarios for the cityof Belfast. A key element of the research has been thedevelopment/enhancement of modelling tools allowingmeasurement of selected mobile and stationary energyimpacts across the city.

1.4. Study goals

The research set out to enable tests of sustainablepolicies to be performed with specific reference to en-ergy consumption. It included a number of modellingand related objectives:� Identify and implement models appropriate for the

assessment of energy use and transport.� Produce a common spatial framework for differing

datasets.� Identify areas of inadequacies in, or missing data.� Identify links between modelling processes.� Define and test policy scenarios.

2. Review of existing modelling techniques

2.1. Con�entional urban tra�el demand models ( fourstage)

Transport models are established tools in the plan-ning of cities. The traditional transport model describestraffic and travel conditions under a number of networkand flow characteristics, and is often used to assess theimpact on traffic flow of changes in the highway andpublic transport network (Ortuzar and Willumsen

J. Cooper et al. / En�ironmental Science & Policy 4 (2001) 307–318 309

1994). Belfast–Northern Ireland, the study area for thisresearch has been the subject of a number of validatedtransport models. These include the Alternative UrbanTransport Technologies (AUTT) model developed bythe research team. This provided a base model for thecurrent research. The AUTT model was developed as apart of research completed on behalf of the Departmentof the Environment for Northern Ireland and com-prises a 200+ zone traffic model of the Belfast UrbanArea (BUA) (Joint Universities Land Use TransportUnit, 1992; Ferguson et al., 1995). It has been enhancedto permit analysis of conditions prevailing in 1999.

2.2. Traffic emissions models

The most commonly applied methods of modellingurban transport energy consumption and emissions as-sess vehicular fuel efficiency and fuel calorific valuesover a range of driving conditions. Typical US exam-ples of such models include California’s EMFAC,which was designed to predict emissions factors (emis-sions per distance travelled) at a given average speedfor different vehicle types. In this way, the data fromforecasting models, combined with vehicle fuel efficien-cies, are used to project future transport emissions.Emissions models are based largely on traffic flows, andare therefore readily combined with a conventionaltransport model. They use outputs from the highwayassignment stage of a transport model as a basic input.Accuracy of the emissions model depends upon theaccuracy of data input from the transport model, andon the relationships assumed between traffic flow andvehicle mix.

2.3. Household energy models

Estimation of energy use in buildings is the subject ofestablished measurement and modelling. The BuildingResearch Establishment industry standard model(BREDEM) is frequently employed in house buildingappraisal. BREDEM calculates space heating, waterheating and other energy consumption for each dwel-ling, incorporating building design, construction mate-rials (U-values), elevation and gains from external heatsources, including solar gain. The model is widely usedin new house building, and in appraisal of energy effi-ciency measures, such as remedial insulation. BRE-DEM provides an effective measure of individual dwel-ling energy use. However, the model requires large datainputs that may limit its use in large scale applications.

Alternative built area energy models include, Town-scope II, a small area assessment package developed bythe University of Liege, and the LT method, a modeldeveloped at Cambridge University, focusing on com-mercial property. Townscope provides for three mod-elling areas:

� Definition of the design concept, defines Urban Im-provement Zone.

� Input of urban design options.� Input of detailed building/design criteria.

The model charts the effect of the gains on surround-ing dwellings.

The LT method (Lighting and Thermal) is a tablebased assessment of non-residential building types(Baker and Steemers, 2000). The model provides aprediction of annual primary energy consumptionbased on:� Local climatic conditions.� Orientation of facade.� Area and type of glazing.� Obstructions due to adjacent buildings.� The inclusion of an atrium.� Occupancy and vacation patterns.� Lighting levels.� Internal gains.

Local climatic conditions and basic assumptions arederived from a division of climatic conditions betweenNorth and South in the UK. LT modelling provides aready assessment of non-residential energy use in com-parable conditions.

The study built upon a number of existing datasets.City-wide energy consumption had previously beenestimated for Belfast in a joint study by the Universityof Ulster and Belfast City Council, due to be calledthe BCCM model. The model generated aggregateenergy use and fuel type statistics for Belfast city wardsfrom the 1991 Northern Ireland census, providingestimates of energy use by fuel type by square kilo-metre.

In the case of the model types reviewed for thecurrent research the problems faced in their applicationrelated either to an insufficient level of geographic andarea differentiation of model output (BCCM model) orin a requirement for detailed design inputs and knowl-edge of site specific locational features which make citywide application of the LT and Townscope type prob-lematic. In addition, the BREDEM procedure is inap-propriate for city-wide application and to the scale ofanalysis demanded, given its intensive input require-ments. LT was not considered further, as the methodwas designed for use in non-residential conditions.

2.4. City co�erage and accuracy

The available models and datasets differed in scale,accuracy and coverage of Belfast. As the research setout to incorporate analysis across a range of policymeasures, the development of a common scale of analy-sis and city coverage were prerequisites for the mod-elling effort Table 1.

J. Cooper et al. / En�ironmental Science & Policy 4 (2001) 307–318310

2.4.1. Model co�erage

� The AUTT transport model provides zonal coverageof the Belfast Urban Area, comprising 182 TrafficAnalysis Zones, which include the city, as well ascontiguous built up areas in Lisburn, Holywood,and Newtownabbey.

� The Emissions/Energy Use model is an add on to the4 stage traffic model. The accuracy in scale andcoverage is thus the same as the 4-stage model.

� The BREDEM model provides a very high level ofaccuracy at individual dwelling level. The geographiccoverage of the model will also be affected by theextent of information input to the model. In practi-cal terms, it is not possible to apply BREDEM to alldwellings in the city.

� The BCCM dataset provides information aggregatedto a square kilometre base for and area equivalent to

the City of Belfast, but excluding much of the outly-ing BUA area Fig. 1.

3. Definition of an appropriate research framework

The measurement of city-wide energy use by bothresidential and transport consumers permitted testing aseries of policy measures promoting sustainable devel-opment. To achieve a common framework for assess-ment the research required consideration of differentmodel types and datasets.

3.1. Data integration

Iguanaspace, a windows based urban framework tooldeveloped by Iguana Inc, of California USA allowedthe team to established links between the various mod-

Fig. 1. Comparative coverage, traffic analysis zones and grid square data.

Table 1Spatial coverage characteristicsa

Characteristics model Spatial coverage Small area accuracy City wide accuracy Links to other models

Very goodGood LimitedEntire BUA4 Stage transport modelEntire BUA GoodTraffic emissions model Good Linked to 4 stage

BREDEM household energy model Dwellings only Very good Poor NonePoor ReasonableBCCM citywide energy model NoneBelfast city and some BUA

a Definition of areas covered: BCC, city of Belfast area, city council area, excludes Lisburn and outer Belfast areas; BUA, Belfast Urban Area,city and suburban satellite areas including, Lisburn, Holywood and Newtownabbey; BMA, Belfast Metropolitan Area, includes dormitory townsand villages in the Belfast city region.

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Fig. 2. Illustration of linkages established in the measurement process.

els within a purpose designed ‘shell’. Outputs from thetransportation model provided inputs to energy models.Assessment of small area housing developmentsprovided input to the transportation model.

The work distinguished between ‘Top-Down’analysis, where city wide data was disaggregated to azonal area, including the allocation of city wide BCCMenergy figures to the traffic analysis zones; and‘Bottom-Up’ Models, where disaggregated datainformed larger scale traffic analysis zones. Areas inwhich specific policies were to be tested were furthertested using BREDEM at a dwelling level.

3.2. Small area methodology

The development of sustainable housing wasconsidered most likely to occur at a localised level suchas energy efficient housing schemes, and planneddensification occurring in small areas. Assessment ofchanges limited to a single development provided anaccurate indication of potential changes in energyconsumption, and allowed a more detailed analysisthan possible at a zonal level.

3.3. Household approach to energy estimation, sur�eybased

In an attempt to improve upon the policy andbehavioural sensitivity of the domestic energyestimation procedures, a further element involved useof a household survey to generate energy estimates. Thestarting point is a household level sub-model, usingreported heating costs per year and main heating fueltype for each household from a household survey. The

mean annual heating cost for the sample (798households stated cost) was $443 per year. Fuel oil,used by most of the sample (84%), was the cheapest fueltype; it cost $432 per year, $40 cheaper than the nextcheapest alternative (electric heating). The heating costswere converted into energy consumption usingNorthern Ireland energy data on relative costs per unitof energy generated by fuel type. A cross-classificationprocedure, analogous to the type employed in tripgeneration within the four-stage urban travel model,was employed to generate estimates of (heating) energyconsumption by household energy type andsocio-economic group. This is described in Fig. 2.

4. Implementation of research design

4.1. Transfer from BCCM to TAZ data resolution

BCCM data was based on kilometre squares acrossmuch of the city area. A transfer methodology wasdeveloped where km2 energy values were re-assigned toTAZ zones on the basis of the proportion of the gridsquare within the traffic zone. A GIS procedure foridentifying polygon and grid square overlap providedproportion estimates for the transfer.

Energy Use, Zone i=� TEU i

= (SEU x ×%prop x i)

…(SEU y×%prop y i)

Where:

TEU i =Energy use for TAZ i

J. Cooper et al. / En�ironmental Science & Policy 4 (2001) 307–318312

SEU x =Energy use for Grid Square x

%prop x i

=Proportion of Grid Square x area in Traffic Zone i

Difficulties arose where a significant proportion ofthe traffic zone fell outside the area covered by gridsquare data area. Energy values could be extrapolateddirectly using this formula where data existed for theentire traffic zone area. In some instances, particularlyto the west of the city, km2 energy data was notavailable. In these instances an estimation procedurewas devised in which data was synthesised by relatingbuilding type to typical energy use values. Buildingenergy characteristics were matched against an on sitesurvey of city tracts outwith the grid square datasetarea to provide an estimated energy use figure. Aphotographic survey identified residential propertytypes within the area covered by the BCCM dataset;and matched these to similar property types for theremaining BUA areas. The combined energyconsumption dataset incorporating BCCM andsynthesised ‘external’ rates provided the basis forbackground ‘top-down’ assessment (Fig. 1).

4.2. Small area de�elopments

The measurement of energy consumption withinsmall areas provided an additional input to the totalstationary energy consumption figures. A small area,equivalent to a new housing development or estate, wasused to test very specific and detailed housingcharacteristics, such as increased density per hectare,and improvements in building technologies such asimproved insulation and heating standards. Data inputsand building energy use characteristics (materialperformance) were developed in collaboration with aregional house builder, and validated with BREDEMoutputs from environmental consultants LlewellynDavies.

4.3. Small area methodology

The future sustainability of a city region will to avarying degree be dictated by the use and nature of newdevelopments within the region. In the UK, the needfor increasing densities of population has been recog-nised with John Prescott, the Deputy Prime Ministerand Secretary of State for Transport, the Environmentand the Regions, calling for a suburban density of40–50 households per hectare. This compares to afigure of 20 households per hectare under previousDepartment of the Environment for Northern Irelandguidelines applied in that UK region. Future progresstowards a more sustainable city will depend on largenumbers of decisions being taken at a local level and

relating to relatively small area typical development site(denoted as the small area plan). It is therefore desir-able to incorporate into the Belfast model the ability tomeasure the use of energy across the city at a level ofspatial disaggregation no less than the Traffic AnalysisZone (TAZ) level and preferably at a greater levelspatial resolution.

4.4. The de�elopment of the small area plans

The research design provided for such small areaplans to be the basis for the development of scenariosthat could be aggregated to a spatial level consistentwith the zonal transportation and energy models. Theareas selected for investigation were informed by anappreciation of available sites along the key corridorsidentified in the original AUTT study and reviewed toensure consistency with 1999 conditions.

4.5. Relationship between the small area de�elopmentsand the model structure

The small area plans relate to specific areas consid-ered suitable for built forms constructed on sustainabledevelopment principles. These area plans incorporateperformance indicators and explanatory variables relat-ing to transport, land use/built form and energy use.

The relationship between a sustainable developmentbeing promoted by a leading brownfield site developerand traffic analysis zone are apportioned as percent-ages. In cases where the new development covers morethan one traffic zone, the assessment is carried outseparately for each traffic zone and summed to give atotal energy use figure for the development under con-sideration. To calculate the energy efficiency gains re-sulting from the development in one zone, the energyefficiency is measured for that zone, plus the netchanges in population across the other zones (Table 2).

Similar calculations were undertaken across the cityfor the other small area development plans. Changes inenergy consumption resulting from policies applied tothe small area were then integrated into the zonalenergy consumption figure, and aggregated to give acity-wide consumption estimate.

4.6. Household sur�ey based statistical analysis

Domestic energy consumption was also estimatedfrom data incorporated in the household survey. Esti-mates were derived from a matrix of heating costs,shown below. Cross classification based relationshipswere developed between household survey variablesaffecting annual heating costs. The size of the house-hold and building were the found to be the maindeterminants of heating fuel costs. These variables weredefined as the number of people that make up a house

J. Cooper et al. / En�ironmental Science & Policy 4 (2001) 307–318 313

Table 2Basis for calculations: new build energy requirement from Carvill plans high energy use estimate

Zonal calculation 39Zone Rebuild% 3

2853 Remain 2767.41Households New buildBase 60Energy use Base 83 941 Remain 81422.77 New build 1511.4 gJ

29.42201 Remain 29.422012Base New buildEnergy efficiency 25.19 gJ/householdHhldsTotals 2827.41 Energy 82934.17 gJ 29.33221 gJ/household

Changes −25.59Hhlds Energy −1006.83 Efficiency −0.089815.68 Space 19.51 gJWaterEnergy requirement

hold, the number of bedrooms and the type of house(costs were considerably higher for detached houses).For those categories with too few cases to estimate astatistically reliable mean value, an estimate of themean cost was derived from values for other adjacentcross classified groups in the overall table (Table 3).

Census data for the categories in the matrix wasemployed to estimate the number of households in eachtraffic zone for each cell. The fuel mix for each trafficzone was taken from a House Condition Survey carriedout by the Northern Ireland Housing Executive(NIHE). Domestic energy consumption in tera jouleswas calculated from the fuel mix (Table 4).

The domestic energy consumption levels for eachtraffic zone were adjusted to be consistent with thetop-down model, the latter included water heating,cooking, lighting and appliances in addition to spaceheating (divided by 0.69). The value of 0.69 was takenfrom a report by the Northern Ireland Housing Execu-tive (NIHE, 1997).

4.7. De�elopment of policy scenarios

Energy consumption and emissions rates were calcu-lated for a number of policy scenarios on a city-widebasis. Scenarios were based on prototypical sustainablepolicy initiatives, both for housing and transport (Table6).

4.7.1. Sustainable housing policiesDensification of the city refers specifically to the

number of inhabitants per unit area, where higherdensity areas are more likely to produce walking jour-neys, and be suited to public transport use. A review ofdensity levels sets out bands of gross sustainable densi-ties (Chapman, 1995):

90–120 persons per Minimum for public trans-hectare port network

Minimum for ‘walking city’persons per300hectare

225–300 Minimum for sustainablepersons perhectare city

The research adopted a gross level of 40 persons perhectare, considered a mid range level, as a minimum

equivalent to a level required to encourage sustainabledevelopment.

Two policy scenarios relating to population densitywere tested. The first, a city-wide densification was feltto be unlikely in practical terms. The second considereddensification applied in corridors selected for publictransport enhancement.

4.7.2. Road user chargingThe application of road charging is receiving increas-

ing attention both as a measure to encourage sustain-able use of transport, and to mitigate some externaleffects of transport. Differing methods of charging ex-ist, each with different impacts on traffic levels andnetwork behaviour. These are summarised as:� Cordon tolls, manual and electronic.� Area licensing.

One policy option assessed the effect of a prototypi-cal cordon toll. An additional user charge was appliedat a distance of 5–7 km from the city on all accessroutes.

4.7.3. Parking chargesThe development of parking charges as a traffic

measure are investigated within the research. Addi-tional parking charges, such as work place parkingcharges or local tax levies are the focus of increasing

Table 3Heating costs according to household size, employment type and thenumber of bedroomsa

Number of bedroomsEmploymentHH size

One or two Three or more

26 $330Blue collar 34 $347One48 $395White collar 22 $424

Other 16 $452 18 $452

Blue collarTwo 26 $344 106 $40597 $433White collar 19 $305

Other 10 $372 55 $462

7 $435Blue collar 125 $488Three or more116 $521White collar 5 $470

Other 2 $416 55 $490

a Those in italics are estimates inferred from the relationshipsbetween other variables.

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Table 4Estimated heating (Sutherland, 1998) energy consumption by type

kWh per house perSutherland estimate-CostMean cost pa from Proportion of sample inper useful kWh (pence)household survey NIHE survey (%)annum

1.95Central heating gas 19897.44$557 1.1Fuel oil $432 1.35 22296.3 36.1Solid fuel 2.52$521 14404.76 42.9

3.54 9293.79$473 12.0Electric (storage)9.09 3619.36Electric (fixed) 2.4$473

Fig. 3. Areas of policy application.

interest on the UK. The research developed a scenariowhere an additional charge equivalent to £1.50 per citycentre parking was applied as a policy option. Parkingcharges were applied to all car trips terminatingin a central area but not applied for transitingjourneys.

4.7.4. Ad�anced urban transport (AUT)Public transport is seen as more sustainable in its use

of energy than private transport. Improving the levelsand usage of public transport reduces per capita energyconsumption, and alleviates private car emissions. AUTrefers to the implementation of corridor based routes

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Table 5Review of findings, transport network and mobile energy

Parking charges Network performance Pk hour energy mJScenario: dense corridors AUT Cordon tollYear/scenario

Highway miles Index Transit miles Index

1 435 317 100 336 508 100 7 276 244019992 1 172 765 81 280 116 83 5 945 254×1999

1 623 655 106 358 268 106 8 231 011×1999 34 1 482 007 103 322 200 96 7 513 950× × ×19995 1 047 123 72 271 027 81 5 308 321× ×1999

1 460 587 101 375 852 112 7 404 349×1999 6 ×××

1 632 615 100 323 813 100 8 276 4332030 01 406 569 86 270 143 83 7 130 5082030 ×2

3 ×2030 1 842 743 113 349 413 108 9 341 6632030 4 × × × 1 661 793 102 303 356 94 8 424 349

5 ×2030 × 1 408 609 86 283 902 88 7 140 8506 × × × × 1 678 004 103 359 1192030 111 8 506 530

Table 6Illustration of small area impacts

Location Household energy (gJ) /TAZ Traffic Flow (V/h) Energy (mJ) CO2 (g/h) HC (g/h) NOx (g/h)

54.3 21260 302 3.77 31 4 4Yorkgate31.75 9368 104Holywood arches 10 77 9 683.52 25725 53Shore road M2 6.1 49 5 414.2 614 1684Bolanic 120 962 110 81

Holywood 43.36 2178 594 55 443 51 38

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Table 7Citywide energy totals in response to scenarios testeda

CitywideMobileModel run Stationary

Emergy-mobile Population AAD/ppn perEnergy-mobile PA (gJ) AAD (gJ) AAD (mJ) ppn AAD/ppn per AAD/hh/d Total energymJ-AADcapita (mJ)AAD (mJ) capita (mJ)peak (mJ)

1.60E+00 4.40E+04 4.40E+071999 5.39E+050 81.82 3.36E+01 168.757.28E+06 7.28E+07 5.39E+05 135.111.66E+00 455E+04 455E+07 5.72E+05 79.55 3.28E+01103.98 136.785.72E+051999 2 5.95E+06 5.95E+071.60E+00 440E+04 440E+07 5.39E+05 81.82 3.36E+011999 186.483 8.23E+06 8.23E+07 5.39E+05 152.831.66E+00 455E+04 455E+07 5.72E+05 79.55 3.28E+01131.42 164.211999 5.72E+057.51E+077.51E+0641.66E+00 455E+04 455E+07 5.72E+051999 79.555 3.28E+01 125.645.31E+06 5.31E+07 5.72E+05 93.841.66E+00 455E+04 455E+07 5.72E+05 79.55 3.28E+01129.50 162.301999 5.72E+057.40E+077.40E+0661.71E+00 469E+04 469E+07 5.75E+05 81.82 3.36E+012030 177.650 8.28E+06 8.28E+07 5.75E+05 144.001.76E+00 482E+04 482E+07 5.75E+05 83.80 3.45E+01124.07 158.612030 5.75E+057.13E+077.13E+0621.71E+00 469E+04 469E+07 5.02E+05 77.91 3.21E+01 187.272030 3 9.34E+06 9.34E+07 6.02E+05 155.781.76E+00 482E+04 482E+07 5.02E+05 79.99 3.30E+01189.92 172.896.02E+052030 4 8.42E+06 8.42E+071.76E+00 482E+04 482E+07 5.02E+05 79.99 3.30E+01 151.572030 5 7.14E+06 7.14E+07 6.02E+05 118.801.76E+00 482E+04 482E+07 5.02E+05 79.99 3.30E+01 174.25141.282030 6 8.51E+06 8.51E+07 6.02E+05

a Abbre�iations: AAD, annual average day; Hhld, household; ppn, population.

J. Cooper et al. / En�ironmental Science & Policy 4 (2001) 307–318 317

using light rail or advanced bus technologies and pri-ority measures Fig. 3.

5. Summary of model outputs

5.1. Mobile energy and tra�el beha�iour impacts

Modelling focused on combinations of policy mea-sures which reflect the potential offered by currentand future developments in transport and housing.Each element was tested individually to illustrate sep-arate effects of the policies, and combined to give anindication of city-wide energy impacts likely to berealised by comprehensive prototypical strategiesTable 5.

Policy scenarios were assessed against a do nothingsituation indicative for 1999 and 2030. Densificationof housing along public transport corridors produceda significant reduction in the movement of trafficthrough the network and reduction in total city en-ergy use. Cordon Tolls as implemented under optionthree, do not promote city-wide sustainability. A ring-cordon toll produced increased traffic flows as vehi-cles diverted from original routes to avoid paying anadditional user charge.

Combining measures appears the most effectivestrategy for promoting sustainability. Densificationand Advanced Urban Transport together generated72% of the original vehicle miles produced under thedo minimum scenario. The travel behaviour and mo-bile energy impacts were combined with estimates forstationary energy to produce an overall assessment ofthe impacts of policies on energy use.

5.2. Extending the analysis

5.2.1. Stationary and mobile energy impacts of urbandesign and transport policies

The small area study allowed an assessment of sta-tionary and mobile energy use and traffic flows inselected locations throughout the urban area. Energysavings and traffic flow changes were identified bylocation (Table 6), and used to inform zonal and city-wide energy figures (Table 7). City wide energyfigures have been produced for mobile, transportbased, and stationary, household based, sources. Thetotals were combined to give a per capita and city-wide energy figure measured in mega joules for anaverage day.

Energy savings achieved as a result of policy imple-mentation vary by area dependent upon original den-sities and housing characteristics. The calculationsincorporated assumed population movements within

and into the city consistent with the observed propen-sity of people to remain within individual city areas.Densification was achieved by identifying diversionwithin an area, and relocation into the city from ex-ternal areas on a typical 60:40 ratio.

Revised travel behaviour and traffic impacts werethen estimated by inputting new zonal populationcharacteristics to the city-wide model to produce anet change in movement and energy consumption.Given that densification implies a larger total popula-tion within the modelled area an appropriate indica-tor for comparing strategy impacts on energyconsumption is on a per capita basis.

The energy use per capita shows an overall reduc-tion in energy consumption in most scenarios. Thecity demonstrates two potential energy levels per cap-ita, with or without densification. The latter can beregarded as equivalent to effective promotion ofhigher densities in development sites over a protractedperiod and on a consistent basis. The effect excludesany other savings to be realised by, for instance, im-proved material construction techniques.

Changes in lifestyles, in particular the formation ofsmaller household units, is reflected in differences be-tween 1999 and 2030 per capita energy use. Increas-ing demand for accommodation for the single personand small households increases individual energyconsumption.

City-wide energy use estimates combine stationaryand mobile elements. Combining Densification andAdvanced Urban Transport, scenario five, appears toproduce the most desirable impacts on city wide en-ergy use in both 1999 and 2030 scenarios, outper-forming tolls alone, scenario 3, and combined withtolls, scenario 4, in both years. Other forms of roaduser charging, may generate a more favourable over-all energy consumption, than the prototypical ringcordon tested. However, any combination that in-creases actual vehicle miles is likely to have a nega-tive impact on the urban consumption of mobileenergy.

In terms of domestic stationary consumption, fur-ther attention to development design type may in-crease savings achieved through densification. Thetesting of such developments is likely to be most real-istic where energy efficient design requirements areimposed through building controls, e.g. insulationstandards.

6. Conclusions

With the increasing realisation of the needs of com-munities to develop in a manner that allows for eco-nomic growth, while minimising ecological andenvironmental harm, the relationship between urban

J. Cooper et al. / En�ironmental Science & Policy 4 (2001) 307–318318

form and energy use has increasingly come to the forein informing transportation and land use planning. Thisstudy set out to develop tools to enable measurement ofcity-wide energy consumption as part of assessment ofsustainable development policies.

A move toward efficient housing patterns is possibleat a small scale without producing negative impacts onenergy use at a strategic highway and public transportnetwork level. However, certain contemporary, andhighly publicised, transport policies are called intoquestion. For instance, cordon tolls may increase over-all traffic mileage by diverting traffic aroundboundaries. The resulting increase in energy consump-tion may negate the beneficial effect of public transportenhancement along corridors within the cordonboundaries. The findings suggest that a combination ofexisting modelling tools can offer a cost effective ap-proach to the analysis of mobile and stationary energyrelated primary and secondary effects.

Acknowledgements

The project team would like to thank the Engineeringand Physical Science Research Council (EPSRC) of theUnited Kingdom for the project funding that made thestudy on which this paper is based possible. We wouldalso like to thank Professor Brian Norton Dean of theFaculty of Engineering at the University of Ulster atJordanstown for his assistance in the review of poten-

tial approaches and advice on development of the en-ergy modelling procedures used.

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