Dynamic modeling of Singapore’s urban resource flows: Historical trends and sustainable scenario development Tamas Abou-Abdo, Noel R. Davis, Jonathan S. Krones, Karen N. Welling, and John E. Fern´andez Abstract—The process of urbanization is one that is inextricably linked with the consumption of material, energy, and water resources. Urban metabolism provides a framework for characterizing the magnitudes of these urban resource requirements by considering the extended analogy of the biological metabolic process. In this study we propose a System Dynamics approach for linking the stocks and flows of urban metabolism with the socioeconomic activities of cities. We also present initial results from its application to the island city-state of Singapore. In the long term, we intend this technique of dynamic urban metabolism to be both descriptive and predictive, the former to better understand different historical modes of urban resource consumption, and the latter to inform strategies for resource efficient urban development in an increasingly resource-scarce world. Index Terms—Resource Efficiency, Singapore, System Dynamics, Urban Metabolism
I. I NTRODUCTION N 2007 the global demographic balance shifted so that for the first time in history, the majority of the people on Earth lived in cities [1]. Although this shift had been predicted for many years, its arrival was nevertheless non-trivial. Cities are open systems, unable to provide from their limited land area all of the physical resources required to sustain their population and industry. They are therefore dependent on both their hinterland and global supply chains to support their everincreasing appetites for resources. That cities are now home to a global majority and are growing at rates exceeding that of total population promises to exacerbate already strained natural resources worldwide. Urban metabolism is a methodology for examining the resource demands of cities. Following the general material flow analysis framework and informed by the term’s metaphorical antecedent, urban metabolism examines a) flows of resources into a city through imports and local resource extraction (ingestion); b) transformation and consumption of resources resulting in addition to stock, generation of economic value, or some other urban service (digestion); and c) export of products and resources and emissions of waste to air, land, or water (excretion) [2].
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This work is supported by the Singapore-MIT International Design Centre of 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 6303 6600, email:
[email protected]). T. Abou-Abdo, N. R. Davis, K. N. Welling, and Prof. J. E. Fern´andez are with 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 metabolism analysis to include an understanding of the dynamic elements of resource consumption in cities. We are initially focusing on Singapore but intend to develop a methodology generalizable to other global cities. Using System Dynamics modeling techniques, we link the stocks and flows of seven classes of resources—water, energy, construction materials, industrial minerals, biomass, petrochemicals, and finished products— with drivers and behaviors in the urban system. This latter set of factors is informed by an interpretation of the IPAT equation [3], which explains environmental impact (in our case, resource consumption) of a given system as a function of the population (P ) living within the system boundaries, the affluence (A) of that population, and the technology (T ) that that population uses to interact with the environment. P includes not only total population but also factors like labor force and birth rate. A includes per capita income as well as direct foreign investment and the contributions of the three main types of industry—primary (agriculture), secondary (manufacturing) and tertiary (service)—to overall Gross Urban Income (GUI).1 T , in this case, is multifaceted, containing elements such as construction techniques, resource intensity of local industry, available modes of transportation, etc. Spatial factors also play a role in shaping urban metabolism, providing an additional driver of urban land use. In this paper, we present the background and approach we are taking to the dynamics of urban metabolism, as well as its initial application to the flows of construction material and electricity in Singapore’s public housing sector. We use historical data to calibrate our models, which can be used to begin projecting future resource demand. Why Singapore? Singapore offers a number of attractive qualities to urban metabolism research. The first is data availability, often a barrier to traditional urban metabolism studies. Singapore, being a small, densely populated island, is resource poor, and consequently must import nearly all of that which it consumes (including a sizable percentage of its water). As a city-state, its city boundaries (which define the spatial scope of our research) are co-incident with its national boundaries, meaning that 1 This value is a direct analogue of Gross National Income, or Gross National Product, at the city scale.
everything crossing the boundary is recorded as international trade. There are therefore detailed inventories of material flows in and out of the city reaching back more than fifty years. Second, while many Western cities offer a similar quality of data, Singapore’s urbanization has occurred very recently. So not only is data available, it is available for the time period that tracks the development of the city from a small port to a legitimate global metropolis. Third, Singapore’s planning horizon has been from the outset long-term and under a specter of resource scarcity [4]. This has given rise to resource-efficient policies, particularly in the water arena, and provides many opportunities to learn about the linkages between urban development policies and their effect on resource consumption. The paucity of resources has also bred to a multi-disciplinary research community focused on urban sustainability.
Fig. 1: Energy and material resource demands in a rapidly urbanizing scenario, from [15].
II. P RIOR W ORK There is a broad canon of work in the field of urban metabolism, reaching at least as far back as to the first use of the term in the evaluation of an “average U.S. city” in 1965 by Wolman [2]. In subsequent years numerous cities worldwide have been examined, including but not limited to Brussels, Cape Town, Hamburg, Hong Kong, Lisbon, London, Singapore, Stockholm, Sydney, Tokyo, Toronto, and Vienna [5], [6]. Many of these examples of urban metabolism utilize a technique of material flow accounting (MFA) eventually codified by EUROSTAT [7]. Wolman’s original study examined parameters as they vary over time, but since then increased methodological detail— and concomitant difficulty in data acquisition—has led to a greater focus on static, “snapshot,” urban metabolism studies. There are departures from this norm, sometimes in the form of time-series urban metabolism, in which data sets tend to be simplified but nonetheless provide valuable information about long-term trends. Notably, Singapore has been the subject of a 41-year time series analysis of system inputs and outputs [8]– [11]. Schulz reports on aggregated material and energy flows [8], the linkage between these aggregated flows and societal development [9], and the carbon footprint associated with material and energy consumption [11]. Additionally, colleagues at the National University of Singapore and the Yale School of Forestry and Environmental Sciences are conducting a detailed urban metabolism study of Singapore’s built environment, which is complementary to our own work. Application of System Dynamics to the urban system is nearly as old as the modeling framework itself; Jay Forrester’s seminal 1969 text Urban Dynamics [12] demonstrated the value in using mathematical simulation to study complex urban system behavior. More recently, the technique has been applied to link environmental and resource pressures with urban behavior, for example, G¨uneralp and Seto’s dynamic urban growth model that evaluates environmental impacts in Shenzhen, China [13] and Hu’s approach to the stock and flow dynamics of Chinese construction and demolition waste [14].
Finally, prior work by Fern´andez, also examining new construction in China, presents a generalized model of the material and energy resource demands of a city progressing through different stages of urbanization: rapid urbanization, stabilizing urbanization, and incremental densification [15]. This model is presented in Fig. 1, and informs our approach, particularly in Section IV. III. DYNAMIC H YPOTHESES An initial step in any System Dynamics exercise is the development of a dynamic hypothesis that guides the initial construction of the model. For this project we have developed a suite of dynamic hypotheses that could explain the relationships between resource stocks and flows and urban dynamics at multiple scales. A description of our hypotheses, which are used to design detailed models of dynamic urban metabolism, follows. A. City scale At the city level, we can identify four complementary relationships between resource consumption and generalized “urban behavior.” a) Urban growth demands more resource consumption. b) Consumption facilitates continued growth. c) Physical restrictions and bottlenecks on resource provision limit the rate of urban development. d) Contraction of the urban system reduces consumption. The Environmental Kuznets Curve [16] can also be applied to dynamic urban metabolism. This theory posits that as per capita income increases, per capita resource consumption first increases until, for various reasons, resource efficiency becomes an economic or social priority, at which point consumption decreases with increased income. In Singapore, this dynamic is illustrated by historical water consumption data [17]. As seen in Fig. 2, annual consumption peaked at about 115 m3 per capita when annual per capita GUI was approximately S$34,000, in the early 1990s. Since then,
A. Background
Fig. 2: Singapore per capita water consumption as a function of per capita Gross Urban Income, an illustration of the Environmental Kuznets Curve, from [17].
average per capita GUI has exceeded S$50,000 per year while per capita water consumption has fallen to 93 m3 per capita. B. Sectoral scale In general, we have chosen to divide the city into a number of 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 materials and resources fall into one of two categories: accumulation and throughput [18]. Accumulation resources are those that add to stock, generally the built environment: buildings, roadways, infrastructure, etc. Throughput resources are those that do not contribute appreciably to a stock-in-place, such as water, fuel, industrial minerals, and many household goods. The interaction between the two types of resource flows within a city provides an interesting growth dynamic. Accumulation resources are a capacity, like a water pipe, while throughput resources are a use factor. As use approaches available capacity, demand grows for consumption of accumulation resources, facilitating the continued growth of throughput materials. The specifics of this dynamic are subject to properties of each sector. IV. R ESOURCE F LOWS IN S INGAPORE P UBLIC H OUSING In this example of our modeling approach in Singapore, we have chosen the residential sector, which is dominated by public housing projects. In examining the data of historical public housing construction and residential energy use in Singapore, we have found that annual material and energy consumption follow curves similar to those illustrated in Fig. 1 for the three fundamental phases of urban development: rapid urbanization, stabilizing urbanization and incremental densification. Using a System Dynamics framework we have identified drivers and causal relationships that, in concert, describe the observed behavior.
Singapore has a very large and successful public housing program, overseen by the Housing and Development Board (HDB ). Post-war housing projects through the 1950s failed to alleviate a growing housing shortage, which was exacerbated by population growth following the country’s independence in the early 1960s. In a series of five-year building cycles, HDB eliminated the shortage and created the network of towns and planned neighborhoods that are central to the form and function 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 off to around 82% in 2006 [17]. HDBs are characterized by large apartment buildings with simple, standardized layouts in well-planned neighborhoods, although the model might be changing as the Board adapts to compete with the private condominium housing that caters to an increasingly affluent Singaporean population. Extensive data and details about the history of the HDB were found in Annual Reports published by the agency since 1960. Additional information was found in Singapore’s Master Plans and associated publications as well as other government statistical documents. B. Material stocks and flows The model development for the construction material stocks and flows began with the a general understanding of the dynamics of HDB unit construction, followed by four submodels: supply, demand, floor area, and materials. 1) HDB construction dynamics: Fig. 3 illustrates our approach to HDB construction dynamics using a causal loop diagram. The first development phase, rapid urbanization, is characterized by exponential growth in material consumption. We explain this growth using two complementary reinforcing feedback cycles, R1 and R2 . R1 assumes that housing demand greatly exceeds available supply, so as more initiation of building generates more supply, the cumulative construction experience increases labor capacity and material availability, enabling increased initiation of new construction. Concurrently, R2 generates a stock of relatively low quality buildings and infrastructure. The fact that demand greatly exceeds supply translates to a high level of urgency to build. This urgency in turn results in a decrease in quality and expected service life of new buildings and infrastructure. The effect of this low-quality construction is initially a reduction of material consumption. After some time, the low quality stock begins to reach the end of its service life, and its replacement drives further material consumption. In the second phase of development, stabilizing urbanization, balancing loop B1 takes hold as supply begins to satisfy demand. This depletes the stock of unmet demand, resulting in reduced initiation of new construction. Material consumption reaches its maximum level and begins to decline. Once demand is completely met, the only remaining material
Fig. 3: General causal loop diagram for material consumption of rapid urbanization, stabilizing urbanization, and incremental densification.
consumption is driven by the replacement of aging buildings and 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 urgency to build results in an increase in quality of new construction, and a longer service lifetime. Over time, this results in less demolition and less need to replace existing structures. It is the effect of this increased quality loop that continues to drive down material consumption even as the city continues to densify. 2) Sub-model: Housing Supply: The construction and supply of public housing is modeled with a simple stock and flow structure containing two primary stocks: units in the pipeline and HDB housing stock. The single, first-order delay of units in the pipeline creates a lag between the decision to construct new projects and their addition to the habitable stock. This delay introduces both oscillation and overshoot behavior in the flow of units completed, which corresponds to the recorded data. The rate at which new units can be initiated is a function of the capacity of the construction labor force and material availability. In this model the size of the HDB housing stock and a maximum fractional initiation rate is used as a proxy for the collective effect of these two terms. When demand greatly exceeds supply, initiations occur at the maximum fractional rate, and the number of initiations per time period grows with the 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 Singapore’s resident population, the average number of
residents per household, and the percentage of residents who wish to live in public housing. This demand is generated exogenous to the model from recorded resident population data, calculated household occupancy data, and an estimation of the desire to live in HDB housing as a percentage of the resident population. The desirability curve is S-shaped and time dependent. It is assumed to be at saturation (HDB desired by 100% of the resident population) from 1960–1980 when the affordability and relative quality of HDB housing far surpassed the private alternatives. The desirability curve declines at an increasing rate between 1980 and 2005 while the quality and social status of private housing increase and growing personal income makes the private option more affordable to the wealthier residents. After 2005 desirability begins to level off, goalseeking towards the government stipulated limit of 70% minimum resident occupancy in HDB housing. While this curve is currently exogenous to the material consumption model, it will in the future be modeled endogenously as a Bass Diffusion structure of private housing adoption. 4) Sub-models: Housing Floor Area and Material: The final two components of the model generate floor area and material stocks. Both entities are modeled as co-flows [19] to the HDB housing supply stock and flow structure, and both share the same structure. Inflow to the floor area stock is driven by the HDB unit completion rate and a time-dependent floor area per unit conversion factor. This conversion factor is exogenous and based on HDB construction data [20]. Similarly, the outflow from the floor area stock is the product of the number of units demolished and the floor area per unit conversion factor from the time when that unit was constructed. Material inflow and outflow are driven by the construction rate and material intensity of construction. Construction material intensity is in reality a time-dependent factor, although for this model our data is based on a 1955 survey of construction resource demands in Singapore [21]. Quantifying the evolution of HDB construction techniques is one of the primary next steps for this modeling effort. 5) Preliminary Model Results: From its introduction in 1960, the Housing Development Board (HDB) experienced exponential growth for over two decades to catch up with rapidly growing demand (Fig. 4). In the early 1980s supply met demand resulting in a peak in new unit initiation and unit completion shortly thereafter. The delay between unit initiation and completion caused an overshoot and oscillation scenario where the supply response fluctuated above and below new demand. Since then, new construction and resulting material consumption have declined along an exponential curve driven by diminishing new demand and increasing building lifetime. In general, overall material consumption (Fig. 5) follows a trend similar to that proposed by Fern´andez [15]. It is the oscillation generated by the delay between initiation and occupancy of new HDB units that is the main variant from the reference curve.
C. Energy flows HDB housing is primarily powered by electricity. There is piped and bottled natural gas available in many neighborhoods but it is used only for cooking, as there is no need for heating systems in Singapore’s tropical climate. Therefore this study focuses on the electrical energy consumption of households living in HDB units. 1) Modeling method: Our household electricity model is based on the power consumption of household appliances. We assume that people who purchase electrical appliances use them on a regular basis. Furthermore, we assume that people replace their equipment with more energy efficient models when they are made available on the market. Total electricity consumption is calculated with the following formula: X ai (t) · hi (t) · ci (t) (1) i
Fig. 4: Preliminary results from the HDB growth model. (a) Unit completion rate, modeled results compared with actual data from HDB Annual Reports. (b) Modeled supply and demand of HDB units.
where i is the type of appliance, a is adoption percentage, h is the number of HDB households, and c is the power consumption of each appliance. Each of these factors is a function of time t. Using historical product data and information from the Singapore Department of Statistics, we first developed adoption curves for each type of electric appliance evaluated in the model: lighting, refrigerators, washing machines, televisions, air conditioning, and computers. These are S-shaped growth curves starting from date of introduction and terminating at a saturation percentage, with a growth rate given by statistical surveys. Adoption curves for the six appliance types from 1960 to the present are given in Figure 6.
Fig. 6: Adoption curves for the six appliance types used in the HDB energy consumption model.
Fig. 5: HDB material consumption estimate based on constant 1955 housing construction data [21], overlaid with the hypothetical material intensity of urbanization curve from [15].
2) Preliminary results: Following (1) and using data from primary sources or other sub-models, we arrive at HDB electricity consumption due to each of six appliances, shown in Fig. 7. It is clear from this data that recently air conditioning loads have far surpassed loads from any of the other appliances in Singaporean households.
R EFERENCES
Fig. 7: Annual electricity consumption for each of the six domestic appliances in Singapore HDB housing from 1960 to present.
V. F UTURE W ORK The models described above are just initial steps in the development of a holistic understanding of the dynamics of urban metabolism. Future plans for this project include the following steps: • refinement of the residential sector model; • models for the industrial and service sectors based on the general structure presented in this paper; • verification of our dynamic hypotheses using empirical data, such as in Fig. 2; • utility and transportation sector models; • model integration based on shared socioeconomic drivers and resource interdependencies (e.g. water intensity of electricity, material intensity of water infrastructure); and • scenario projection, testing existing “green development” plans from the Singapore government for their effects on total resource demand. ACKNOWLEDGMENT This research project would not be possible without support, guidance, and direction from a number of individuals in Singapore, at MIT, and elsewhere. In particular, the authors would like to thank: Pragnya Alekal, Alek Cannan, Prof. Marian Chertow, Prof. Kua Harn-Wei, Prof. B¨urak Guneralp, Enrique Lopez Calva, Prof. Chris Magee, David Quinn, Melissa Sapuan, Dr. Niels Schulz, Melanie Tan, Dr. James Thompson, and Dr. Cecilia Tortajada.
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