Dynamic modeling of Singapore’s urban resourceflows: Historical trends and sustainable scenario

developmentTamas Abou-Abdo, Noel R. Davis, Jonathan S. Krones, Karen N. Welling, and John E. Fernandez

Abstract—The process of urbanization is one that is inextrica-bly linked with the consumption of material, energy, and waterresources. Urban metabolism provides a framework for charac-terizing the magnitudes of these urban resource requirementsby considering the extended analogy of the biological metabolicprocess. In this study we propose a new System Dynamicsapproach for linking the stocks and flows of urban metabolismwith the socioeconomic activities of cities. We also present initialresults from its application to the island city-state of Singapore.In the long term, we intend this technique of dynamic urbanmetabolism to be one both descriptive and predictive, the formerto better understand different historical modes of urban resourceconsumption, and the latter to inform strategies for resourceefficient urban development in an increasingly resource-scarceworld.

Index Terms—resource stocks and flows, Singapore, SystemDynamics, urban metabolism

I. INTRODUCTION

IN 2007 the global demographic balance shifted so thatfor the first time in history, the majority of the people on

Earth lived in cities [1]. Although this shift had been predictedfor many years, its arrival was nevertheless non-trivial. Citiesare open systems, unable to provide from their limited landarea all of the physical resources required to sustain theirpopulation and industry. They are therefore dependent on boththeir hinterland and global supply chains to support their ever-increasing appetites for resources. Cities are now home to aglobal majority and are growing at rates exceeding that of totalpopulation, promising to exacerbate already strained naturalresources worldwide.

Urban metabolism is a methodology for examining the re-source demands of cities. Following the general material flowanalysis framework and informed by the term’s metaphoricalantecedent, urban metabolism examines a) flows of resourcesinto a city through imports and local resource extraction(ingestion); b) transformation and consumption of resourcesresulting in addition to stock, generation of economic value, orsome other urban service (digestion); and c) export of productsand resources and emissions of waste to air, land, or water(excretion).

This work is supported by the Singapore-MIT International Design Centreof the Singapore University of Technology and Design, www.sutd.edu.sg.

J. S. Krones is with the Singapore University of Technology and Design,Singapore 279623. (He is the corresponding author. Telephone: +65 63036600, email: jskrones@alum.mit.edu).

T. Abou-Abdo, N. R. Davis, K. Noiva Welling, and Prof. J. E. Fernandez arewith the Building Technology Group of the MIT Department of Architecture,Cambridge, MA 02139, USA.

Project objectives and approach

We seek to expand the capability of urban metabolismanalysis to include an understanding of the dynamic elementsof resource consumption in cities. We are initially focusing onSingapore but intend to develop a methodology generalizableto other global cities. Using System Dynamics modelingtechniques, we link the stocks and flows of seven classesof resources—water, energy, construction materials, industrialminerals, biomass, petrochemicals, and finished products—with drivers and behaviors in the urban system. This latterset of factors is informed by an interpretation of the IPATequation, which explains environmental impact (in our case,resource consumption) as a function of the population (P )living within a given system boundaries, the affluence (A) ofthat population, and the technology (T ) that that populationuses to interact with the environment. P includes not onlytotal population but also factors like labor force and birthrate. A includes per capita income as well as direct foreigninvestment and the contributions of the three main typesof industry—primary (agriculture), secondary (manufacturing)and tertiary (service)—to overall Gross Urban Income (GUI).1

T , in this case, is multifaceted, containing elements such asconstruction techniques, resource intensity of local industry,available modes of transportation, etc. Spatial factors alsoplay a role in shaping urban metabolism, which gives us anadditional driver of urban land use.

In this paper, we present the background and approach weare taking to the dynamics of urban metabolism, as well asits initial application to the flows of construction material andelectricity in Singapore’s residential housing sector. We usehistorical data to calibrate our models, which can be used tobegin projecting future resource demand.

Why Singapore?

Singapore offers a number of attractive qualities to urbanmetabolism research. The first is data availability, often abarrier to traditional urban metabolism studies. Singapore,being a small, densely populated island, is resource poor, andconsequently must import nearly all of that which it consumes(including a sizable percentage of its water). As a city-state, itscity boundaries (which define the spatial scope of our research)are co-incident with its national boundaries, meaning that

1This value is a direct analogue of Gross National Income, or GrossNational Product, at the city scale.

everything crossing the boundary is recorded as internationaltrade. There are therefore detailed inventories of material flowsin and out of the city reaching back more than fifty years.

Second, while many Western cities offer a similar qualityof data, Singapore’s urbanization has occurred very recently.So not only is there great data available, it is available forthe time period that tracks the development of the city from asmall port city to a legitimate global metropolis.

Third, Singapore’s planning horizon has been from theoutset long term and under the specter of resource scarcity [2].This has given rise to ingenious resource-efficiency policies,particularly in the water arena, and provides plenty of opportu-nities to learn about the linkages between urban developmentpolicies and their effect on resource consumption. The paucityof resources has also given rise to a rich research communityfocusing on urban sustainability.

II. PRIOR WORK

There is a rich canon of work in the field of urbanmetabolism, reaching at least as far back as to the first useof the term in the evaluation of an “average U.S. city” in1965 by Wolman [3]. In subsequent years numerous citiesworldwide have been examined, including but not limited toBrussels, Cape Town, Hamburg, Hong Kong, Lisbon, London,Singapore, Stockholm, Sydney, Tokyo, Toronto, and Vienna[4], [5]. Many of these examples of urban metabolism utilizea technique of material flow accounting (MFA) eventuallycodified by EUROSTAT [6].

Wolman’s original study examined parameters as they varyover time, but since then increased methodological detail—and concomitant difficulty in data acquisition—has led to agreater focus on static, “snapshot,” urban metabolism studies.There are departures from this norm, sometimes in the formof time-series urban metabolism, in which data sets tend to besimplified but nonetheless provide valuable information aboutlong-term trends. Notably, Singapore has been treated to a 41-year time series analysis of system inputs and outputs [7]–[10].This work by Schulz is extensive; he reports on aggregatedmaterial and energy flows [7], the linkage between theseaggregated flows and societal development [8], and the carbonfootprint associated with material and energy consumption[10]. Additionally, colleagues at the National University ofSingapore and the Yale School of Forestry and EnvironmentalSciences are conducting a detailed urban metabolism study ofSingapore’s built environment, which is complementary to ourown work.

Application of System Dynamics to the urban system isnearly as old as the modeling framework itself; Jay Forrester’sseminal 1969 text Urban Dynamics [11] demonstrated thevalue in using mathematical simulation to study complexurban system behavior. More recently, the technique has beenapplied to link environmental and resource pressures withurban behavior, for example, Guneralp and Seto’s detaileddynamic urban growth model that evaluates environmentalimpacts in Shenzhen, China [12] and Hu’s elegant model

Fig. 1: Energy and material resource demands in a rapidlyurbanizing scenario, from [14].

of the stock and flow dynamics of Chinese construction anddemolition waste [13].

Finally, prior work by Fernandez, also examining newconstruction in China, presents a generalized model of thematerial and energy resource demands of a city progressingthrough different stages of urbanization: rapid urbanization,stabilizing urbanization, and incremental densification. Thismodel is presented in Fig. 1, and informs our approach,particularly in Section IV.

III. DYNAMIC HYPOTHESES

An initial step in any System Dynamics exercise is thedevelopment of a dynamic hypothesis that guides the initialconstruction of the model. For this project we have developed asuite of dynamic hypotheses that we intend to prove illustrate ahigh-level understanding of the relationships between resourcestocks and flows and urban dynamics at multiple scales.A description of our hypotheses, which are used to designdetailed models of dynamic urban metabolism, follows.

A. City scale

At the city level, we can identify four relationships betweenresource consumption and generalized “urban behavior.”

a) Urban growth demands more resource consumption.b) Consumption facilitates continued growth.c) Physical restrictions and bottlenecks on resource provi-

sion limit the rate of urban development.d) Contraction of the urban system reduces consumption.The Environmental Kuznets Curve can also be applied to

dynamic urban metabolism. This theory posits that as percapita income increases, per capita resource consumptionfirst increases until, for various reasons, resource efficiencybecomes an economic or social priority, at which point percapita consumption decreases with increased per capita in-come. In Singapore, this dynamic is illustrated by per capitawater consumption as a function of per capita income. Asseen in Fig. 2, annual consumption peaked at about 115 m3

per capita when annual per capita GUI was approximately

Fig. 2: Singapore per capita water consumption as a functionof per capita Gross Urban Income, an illustration of theEnvironmental Kuznets Curve. Source: SingStat, SingaporeYearbook of Statistics, various years.

S$34,000, in the early 1990s. Since then, average per capitaGUI has exceeded S$50,000 per year while per capita waterconsumption has fallen to 93 m3 per capita.

B. Sectoral scale

In general, we have chosen to divide the city into a numberof sectors, based on a combination of functional and practical(i.e. data availability) considerations. These sectors are:

• Residential• Commercial• Industrial

• Transportation• Water• Energy

Within each of these sectors, stocks and flows of materialsand resources fall into one of two categories: accumulationand throughput. Accumulation resources are those that add tostock, generally the built environment: buildings, roadways,infrastructure, etc. Throughput resources are those that donot contribute appreciably to a stock-in-place, such as water,fuel, industrial minerals, and many household goods. Theinteraction between the two types of resource flows withina city provides an interesting growth dynamic. Accumulationresources are a capacity, like a water pipe, while throughputresources are a use factor. As use approaches available capac-ity, demand grows for consumption of accumulation resources,facilitating the continued growth of throughput materials. Thespecifics of this dynamic are subject to properties of eachsector.

IV. RESOURCE FLOWS IN SINGAPORE PUBLIC HOUSING

In this example of our modeling approach in Singapore,we have chosen the residential sector, which is dominated bypublic housing projects. In examining the data of historicalpublic housing construction and residential energy use inSingapore, we have found that annual material and energyconsumption follow curves similar to those illustrated in Fig.1 for the three fundamental phases of urban development:rapid urbanization, stabilizing urbanization and incrementaldensification. Using a System Dynamics framework we haveidentified drivers and causal relationships that, in concert,

describe the observed behavior. To a limited extent, we havebegun to use these models to project future resource demandby this sector.

A. Background

Singapore has a very large and successful public housingscheme, overseen by the Housing and Development Board(HDB ). Anemic post-war housing projects through the 1950sfailed to alleviate a growing housing shortage, which wasexacerbated by population growth following the country’sindependence in the early 1960s. In a series of extraordinaryfive-year building cycles, HDB eliminated the shortage andcreated the network of towns and planned neighborhoods thatare central to the structure of modern Singapore. By the mid-1970s nearly half of the population lived in HDB housing.This fraction peaked in 1989 at 88% and slowly leveled offto around 82% in 2006.2 HDBs have been characterized bylarge apartment buildings with simple, standardized layoutsin well-planned neighborhoods, although the model seems tobe changing as the Board adapts to compete with the privatecondominium housing that caters to an increasingly affluentSingaporean population.

Extensive data and details about the history of the HDBwere found in Annual Reports published by the agency since1960. Additional information was found in Singapore’s MasterPlans and associated publications as well as other governmentstatistical documents.

B. Material stocks and flows

The model development for the construction material stocksand flows began with the a general understanding of thedynamics of HDB unit construction, followed by four sub-models: supply, demand, floor area, and materials.

1) HDB construction dynamics : Fig. 3 illustrates ourapproach to HDB construction dynamics using a causal loopdiagram.

The first development phase, rapid urbanization, is char-acterized by exponential growth in material consumption.We explain this growth using two complementary reinforcingfeedback cycles, R1 and R2. R1 assumes that housing demandgreatly exceeds available supply, so as more initiation ofbuilding generates more supply, the cumulative constructionexperience increases labor capacity and material availability,enabling increased initiation of new construction.

Concurrently, R2 generates a stock of relatively low qualitybuildings and infrastructure. The fact that demand greatlyexceeds supply translates to a high level of urgency to build.This urgency in turn results in a decrease in quality andexpected service life of new buildings and infrastructure. Theeffect of this low-quality construction is initially a reduction ofmaterial consumption. After some time, the low quality stockbegins to reach the end of its service life, and its replacementdrives further material consumption.

In the second phase of development, stabilizing urban-ization, balancing loop B1 takes hold as supply begins to

2Source: SingStat, Singapore Yearbook of Statistics, various years.

Fig. 3: General causal loop diagram for material consumptionof rapid urbanization, stabilizing urbanization, and incrementaldensification.

satisfy demand. This depletes the stock of unmet demand,resulting in reduced initiation of new construction. Materialconsumption reaches its maximum level and begins to decline.Once demand is completely met, the only remaining materialconsumption is driven by the replacement of aging buildingsand infrastructure.

In the final development phase, incremental densification,unmet demand remains quite low, and reinforcing loop R2

begins to work in the opposite direction. Decreased urgencyto build results in an increase in quality of new construction,and a longer service lifetime. Over time, this results in lessdemolition and less need to replace existing structures. Itis the effect of this increased quality loop that continues todrive down material consumption even as the city continuesto densify.

2) Sub-model: Housing Supply: The construction and sup-ply of public housing is modeled with a simple stock and flowstructure containing two primary stocks: units in the pipelineand HDB housing stock. The single, first-order delay of unitsin the pipeline creates a lag between the decision to constructnew projects and their addition to the habitable stock. Thisdelay introduces both oscillation and overshoot behavior inthe flow of units completed, which corresponds to the recordeddata.

The rate at which new units can be initiated is a functionof the capacity of the construction labor force and materialavailability. In this model the size of the HDB housing stockand a maximum fractional initiation rate is used as a proxy forthe collective effect of these two terms. When demand greatlyexceeds supply, initiations occur at the maximum fractionalrate, and the number of initiations per time period grows withthe stock of units completed. As supply approaches demand,the initiation rate is reduced by a multiplier that falls from 1

to 0.3) Sub-model: Housing Demand: The absolute demand for

HDB housing, measured in households, is a function of Singa-pore’s resident population, the average number of residents perHDB household, and the percentage of residents who wish tolive in public housing. This demand is generated exogenouslyfrom recorded resident population data, calculated householdoccupancy data, and an estimated desire to live in HDBhousing as a percentage of the resident population.

The desirability curve is S-shaped and time dependent. Itis assumed to be at saturation (HDB desired by 100% of theresident population) from 1960-1980 when the affordabilityand relative quality of HDB housing far surpassed the (non-existent) private alternatives. The desirability curve declines atan increasing rate between 1980 and 2005 while the qualityand social status of private housing increase and growingpersonal income makes the private option more affordable tothe wealthier residents. After 2005 desirability begins to leveloff, goal-seeking towards the government stipulated limit of70% minimum resident occupancy in HDB housing. Whilethis curve is currently exogenous to the material consumptionmodel, it will in the future be modeled endogenously as a BassDiffusion structure of private housing adoption.

4) Sub-models: Housing Floor Area and Material: Thefinal two components of the model generate floor area andmaterial stocks. Both entities are modeled as co-flows [15] tothe HDB housing supply stock and flow structure, and bothshare the same structure. Inflow to the floor area stock isdriven by the HDB unit completion rate and a time-dependentfloor area per unit conversion factor. This conversion factor isexogenous and based on HDB construction data.

Similarly, the outflow from the floor area stock is the prod-uct of units demolished and the floor area per unit conversionfactor. However, in this flow the time-dependent floor area perunit conversion factor is sampled from the time when thatunit was constructed. Specifically, this time is calculated asthe current time less the current average building lifetime.

Material inflow and outflow are driven by the constructionrate and material intensity of construction. Construction ma-terial intensity is in reality a time-dependent factor, althoughfor this model our data is based on a 1955 survey of buildingconstruction methods. Quantifying the evolution of HDB con-struction techniques is one of the primary next steps for thismodeling effort.

5) Preliminary Model Results: From its introduction in1960, the Housing Development Board (HDB) experiencedrapid and continued exponential growth for over two decadesto catch up with rapidly growing demand (Fig. 4). In theearly 1980s supply met demand resulting in peak in new unitinitiation and unit completion shortly thereafter. The delaybetween unit initiation and completion caused an overshootand oscillation scenario where the supply response fluctuatedabove and below new demand. Since then, new constructionand resulting material consumption have declined along anexponential curve driven by diminishing new demand andincreasing building lifetime.

Fig. 4: Preliminary results from the HDB growth model. (a)Unit completion rate, modeled results compared with actualdata from HDB Annual Reports. (b) Modeled supply anddemand of HDB units.

In general, overall material consumption (Fig. 5) follows atrend similar to that proposed by Fernandez (Fig. 1). It is onlythe oscillation generated by the delay between initiation andoccupancy of new HDB units that varies from the referencecurve. Most likely, these oscillations are magnified by our useof obsolete construction material data. Advances in concretestrength and other building advances would limit the size ofthose oscillations.

C. Energy flows

HDB housing is primarily powered by electricity. There ispiped and bottled natural gas available in many neighborhoodsbut it is used only for cooking, as there is no need for heatingsystems in Singapore’s tropical climate. Therefore this studyfocuses on the electrical energy consumption of householdsliving in HDB units.

1) Modeling method: Our household electricity modelbased on the power consumption of household appliances.We assume that people who purchase electrical appliancesuse them on a regular basis. Furthermore, we assume thatpeople replace their equipment to newer, more energy efficient,models when they are made available on the market.

Total electricity consumption is calculated with the follow-

Fig. 5: HDB material consumption estimate based on constant1958 housing construction data, overlaid with the hypotheticalmaterial intensity of urbanization curve from [14].

Fig. 6: Adoption curves for the six appliance types used in theHDB energy consumption model.

ing algorithm: ∑i

ai(t) · hi(t) · ci(t) (1)

where i is the type of appliance, a is adoption percentage,h is the number of HDB households, and c is the powerconsumption of each appliance. Each of these factors is afunction of time t.

Using historical product data and information from the Sin-gapore Department of Statistics, we first developed adoptioncurves for each type of electric appliance evaluated in themodel: lighting, refrigerators, washing machines, televisions,air conditioning, and computers. These are S-shaped growthcurves starting from date of introduction and terminating at asaturation percentage, with a growth rate given by statisticalsurveys. Adoption curves for the six appliance types from 1960to the present are given in Figure 6.

Following (1) and using data from primary sources or othersub-models, we arrive at HDB electricity consumption due toeach of six appliances, shown in Fig. 7. It is clear from thisdata that recently air conditioning loads have far surpassed

Fig. 7: Annual electricity consumption for each of the sixdomestic appliances in Singapore HDB housing from 1960to present.

loads from any of the other appliances in Singaporean house-holds.

V. FUTURE WORK

There are a number of important next steps for this researchproject. The approach outlined above for the residential sectorwill be replicated for the industrial and service sectors as well.

Interrelationships among the three kinds of resourcesWater!TransportationScenario projection

ACKNOWLEDGMENT

This research project would not be possible without support,guidance, and direction from a number of individuals inSingapore, at MIT, and elsewhere. In particular, the authorswould like to thank: Pragnya Alekal, Alek Cannan, Prof.Marian Chertow, Prof. Kua Harn-Wei, Prof. Burak Guneralp,Enrique Lopez Calva, Prof. Chris Magee, Melissa Sapuan,Dr. Niels Schulz, Melanie Tan, Dr. James Thompson, and Dr.Cecilia Tortajada.

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