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Chapter 7
Urban Metabolism
Sybil Derrible, Lynette Cheah, Mohit Arora, and Lih Wei Yeow
Abstract Urban metabolism (UM) is fundamentally an accounting framework
whose goal is to quantify the inflows, outflows, and accumulation of resources
(such as materials and energy) in a city. The main goal of this chapter is to offer
an introduction to UM. First, a brief history of UM is provided. Three different
methods to perform an UM are then introduced: the first method takes a bottom-up
approach by collecting/estimating individual flows; the second method takes a top-
down approach by using nation-wide input–output data; and the third method takes a
hybrid approach. Subsequently, to illustrate the process of applying UM, a practical
case study is offered using the city-state of Singapore as an exemplar. Finally, current
and future opportunities and challenges of UM are discussed. Overall, by the early
twenty-first century, the development and application of UM have been relatively
slow, but this might change as more and better data sources become available and as
the world strives to become more sustainable and resilient.
7.1 Introduction
Water, electricity, gasoline, natural gas, food, concrete, and asphalt are some of the
energy and resources that are imported, consumed, stored, or exported to, in, and from
cities every day. Keeping track of these exchanges and processes can be extremely
challenging and is at the heart of urban metabolism (UM). The term metabolism
S. Derrible (B
)
University of Illinois at Chicago, Chicago, USA
e-mail: derrible@uic.edu
L. Cheah ·M. Arora ·L. W. Yeow
Singapore University of Technology and Design, Tampines, Singapore
e-mail: lynette@sutd.edu.sg
M. Arora
e-mail: arora_mohit@alumni.sutd.edu.sg
L. W. Yeow
e-mail: lihwei_yeow@alumni.sutd.edu.sg
© The Author(s) 2021
W.Shietal.(eds.),Urban Informatics, The Urban Book Series,
https://doi.org/10.1007/978-981- 15-8983-6_7
85
86 S. Derrible et al.
relates to how a human body converts nutrient intake into energy. The first attempt
at quantitative (human) metabolism accounting was probably developed in the early
seventeenth century where, in the first documented experiment, Sanctorius (1561–
1636) spent over 30 years weighing his dietary intake and bodily excretions on a
weighting chair to create a mass-balance sheet. Understanding that not everything
that is consumed is directly excreted, he concluded that a significant portion of his
consumption was lost through insensible perspiration via his skin (Eknoyan 1999).
Quantifying the metabolism of a city requires a similar methodological approach.
The origins of the modern form of UM date back to 1965 when Abel Wolman
wrote a ten-page article in Scientific American titled “The Metabolism of Cities”
(Wolman 1965). As a sanitary engineer, Wolman’s research interests delved into
pollution, recognizing that getting an account of the flows of resources inside and
outside of a city was key to solving the problem at its root. The concept then grew
in popularity in the early 2000s, notably aided by the rise of the global research
agenda toward sustainable development and the need to identify major consumers of
energy and emitters of greenhouse gases (GHG). Over the years, UM has grown in
its understanding into three main schools: Marxist ecology, industrial ecology, and
urban ecology (Newell and Cousins 2014). Marx defined UM as the characterization
of complex nature–society relationships that produce uneven outcomes; industrial
ecology looks at UM as stocks and flows of materials and energy; and urban ecology
looks at it as complex socio-ecological systems. More broadly, UM fits within the
realm of sociometabolism defined by Haberl et al. (2019) as “a systems approach to
study society–nature interactions at different spatiotemporal scales.”
Since its origin, UM has evolved significantly from a methodological point of view,
partly due to changes in data format and accessibility. Conceptually, UM remains
largely an accounting framework, as illustrated in Fig. 7.1, that includes inputs (I),
outputs (O), internal flows (Q), storage (S), and production (P) of water (W), energy
(E), material (M), and food (F). With its initial focus on resources and materials, UM
has evolved to account for energy (in addition to resources) and for the endogenous
processes occurring within cities (e.g., accounting for the production of food in
Fig. 7.1 Sketch of UM processes accounting for inputs (I), outputs (O), internal flows (Q), storage
(S), and production (P) of water (W), energy (E), material (M), and food (F)
7 Urban Metabolism 87
cities and for the internal reuse and recycling of materials), again in line with the
global sustainability effort. A commonly adopted definition of UM comes from
Kennedy et al. (2007) who defined it as: “the sum total of the technical and socio-
economic processes that occur in cities, resulting in growth, production of energy,
and elimination of waste.”
From a methodological viewpoint, following the industrial ecology way of
thinking, UM is largely inspired by material flow analysis (MFA), which for example
quantifies the flows of a particular material across industrial sectors. An account of
energy flows can then be added to the approach, thus giving material and energy flow
analysis (MEFA). Broadly, there are two main methods for studying the UM of a city:
the bottom-up method is based on directly collecting flow data from a city (e.g., how
much water is consumed), while the top-down method is based on economic input–
output data (e.g., from the United Nations International Trade Statistics Database,
also known as UN COMTRADE). Both techniques are presented in this chapter. In
addition, a hybrid approach combining bottom-up and top-down datasets has facili-
tated the development of several methods discussed in this chapter and categorized
as hybrid methods.
Ultimately, the volume of data available is the main limiting factor to what can be
included in an UM study. In spite of the fact that we have entered the era of big data,
UM involves such a large number of flows that data availability is arguably the main
reason why UM has not been applied more systematically to cities across the world.
New datasets and new UM methods might help partly tackle this issue, however, as
will be discussed. In fact, when it comes to urban informatics, UM holds a central
presence and has the potential to directly inform policies and designs to help cities
become more sustainable and resilient (Mohareb et al. 2016; Derrible 2019a).
In line with the general theme of this book, the main goal of this chapter is to give
a brief introduction to urban metabolism by:
•Offering a brief review of the history of urban metabolism;
•Introducing two methods to calculate the metabolism of a city;
•Applying UM to a practical case study (Singapore); and
•Discussing the future of urban metabolism.
The structure of the book chapter follows these goals sequentially. To learn more
about UM, the reader is referred to several important works (that inspired this
chapter), including Sustainable Urban Metabolism by Ferrão and Fernández (2013),
Understanding Urban Metabolism: A Tool for Urban Planning by Chrysoulakis et al.
(2014), Urban Engineering for Sustainability by Derrible (2019b), and the book
chapter “A Mathematical Description of Urban Metabolism” by Kennedy (2012).
For quicker references and data on cities, the reader is strongly recommended to
look at the Metabolism of Cities online platform accessible at https://metabolismof
cities.org/.
88 S. Derrible et al.
7.2 History of Urban Metabolism
As an accounting framework, UM is used to gain an understanding of the flows
between a city and its surrounding environment. As cities grew in size and as pollu-
tion levels increased significantly because of the Industrial Revolution—that notably
spurred the initial push for suburbanization (Hall 2002)—it was only a matter of
time before a technique like UM was developed. A first essay titled “Essay on the
Metabolism of Berlin” was written by Theodor Weyl in 1894 and quantified the flows
of nutrients in and out of Berlin (Lederer and Kral 2015). We can then see some traces
of UM in Patrick Geddes’s book “Cities in Evolution” (Geddes 1915). It was only
when more data started to be collected and become available, however, that UM
took its more modern form, and the rise of UM from sanitary engineering and in the
twentieth century is, therefore, not surprising. Issues related to data availability have
always been central to UM. In fact, even in his original article, Wolman could not
calculate the UM of an actual city, and instead estimated the UM of a hypothetical
American city of one million inhabitants, focusing on three inputs (water, food, and
fuel) and three outputs (sewage, solid waste, and air pollutants). Figure 7.2 shows
the original figure used by Wolman, which illustrates the large imports of water and
exports of sewage from a typical city.
Fig. 7.2 Wolman’s 1965 urban metabolism of a hypothetical American city of one million people,
focusing on water, food, and fuel as inputs and on sewage, solid waste, and air pollutants as outputs
7 Urban Metabolism 89
Fig. 7.3 UM of Brussels in the 1970s, Belgium. Adapted from Duvigneaud and Denaeyer-De Smet
(1977)
Perhaps the most famous of all early UM studies is the surprisingly exhaustive
case study of Brussels in the 1970s by Duvigneaud and Denaeyer-De Smet (1977).
The main figure from the study is shown in Fig. 7.3. One year after the Brussels study,
in 1978, Newcome et al. (1978) calculated the inflows and outflows of construction
materials and finished goods in Hong Kong for 1971, foreseeing the amazing growth
in demand for materials and resources for an increasingly wealthy and urban world.
In their article, Kennedy et al. (2007) report the UM of nine cities:
•US typical (Wolman’s study) in 1965
•Brussels (Belgium) in the 1970s
•Tokyo in 1970
•Hong Kong (China) in 1971 and 1997
•Sydney (Australia) in 1970 and 1990
•Toronto (Canada) in 1987 and 1999
•Vienna (Austria) in the 1990s
•London (United Kingdom) in 2000
•Cape Town (South Africa) in 2000.
Since the early 2000s, many more UM studies have been carried out, from Paris
(Barles 2009) to Ho Chi Minh City (ADB 2014), including one particularly large
study by Kennedy et al. (2015) that investigated the UM of 27 megacities. Significant
data requirements remain a limiting factor to calculate the UM of more cities. In the
90 S. Derrible et al.
next section, we will review two standard methods to estimate the metabolism of a
city.
7.3 Methods of Urban Metabolism
Estimating the flows in Fig. 7.1 can be done in many different ways. In fact, there
is no right technique as long the flows can be identified. Broadly, we can categorize
techniques in three groups: bottom-up, top-down, and hybrid methods. From the
bottom up, flows are investigated individually, for example, by contacting local water,
gas, and electricity utility companies. From the top down, economic input–output
(IO) data can be collected, often at the country scale, and then disaggregated to the
city scale.
The bottom-up approach is generally preferred because it tends to provide more
insights about a city; for example, to investigate differences between residential and
commercial consumption patterns. The bottom-up approach tends to be arguably
more accurate as well since disaggregating data from the national scale to the urban
scale can be challenging. Nevertheless, methodologically, the top-down approach
may be easier to apply and thus might be preferred in some instances. Other
approaches including using emergy, ecological, or environmental network analysis
and other methodological advancements have found lesser momentum but can be
powerful tools for UM study. The three groups of approaches are introduced in this
section.
7.3.1 Bottom-Up Methods
Identifying the flows in Fig. 7.1 from the bottom up can be done by asking the proper
authorities for data or by using some means to estimate them. Flows related to the
consumption of water, electricity, gas, and other resources can be collected from local
utility companies, for example. Flows related to the amount of water received from
precipitation can be collected from local weather stations. Nevertheless, collecting
these data can be challenging—local utility companies may not want to share data
or they may not have access to data in the first place. This section introduces some
of the ways these flows can be estimated.
Primarily, we will use the divide and conquer technique by breaking down a
problem into multiple parts; the general approach (not related to UM) is well
discussed by Mahajan (2014). This approach is greatly influenced by the IPAT
equation, initially developed by Ehrlich and Holdren (1971) and defined as
I=P·A·T(7.1)
7 Urban Metabolism 91
where I,P,A, and Tstand for impact, population, affluence, and technology, respec-
tively. Essentially, the end goal is to estimate total energy use or emissions (e.g.,
in watt-hours or Wh) and the problem is divided cleverly to play with units. For
example, if we are looking for the total energy use linked with water consumption in
liters [L], we can use the IPAT equation by estimating the average water consump-
tion per person and the average energy use per liter of water; in terms of units, we
get: [Wh] =[pers] ×[L/pers] ×[Wh/L]. In this section, we will cover four sectors:
materials, energy, water, and food. The chapter is greatly inspired by Kennedy (2012)
and more details can be found in Derrible’s (2019b) book.
7.3.1.1 Materials
Cities are physically composed of countless materials. While it is impossible to quan-
tify the flows of every material imported to or exported from a city, certain materials
are worth investigating. In particular, for many cities, the two giants are concrete for
buildings and asphalt for roads—in terms of weight, concrete production actually
tends to be the most produced material in the world, over oil and gas production
(Ashby 2013). In this section, we will see two ways to estimate these two materials,
but the methods can easily be extended to account for other materials such as steel
and other metals.
For buildings, we can try to divide the problem into estimating the floor space
available per person, A, in a city in [m2/pers], and the material intensity Mof a
building in tons per square meter (i.e., [t/m2]). Specifically, for building type i,the
stock Sof material m(e.g., concrete) can be estimated from
Si,m=P·Ai,m·Mi,m(7.2)
The units of the three variables on the right-hand side are [pers] ×[m2/pers] ×
[t/m2], thus giving us an answer in [t] (i.e., a weight). For roads, we can follow the
same procedure or instead try to estimate the proportion of roads space taken by unit
area in [km/km2]forA, using the following equation:
Si,m=D·Ai,m·Mi,m(7.3)
where Si,mis the stock of road type ifor material min [t], Dis the area of a city in
[km2], Ais the affluence of roads in [km/km2], and Mis the material intensity in
[t/km].
Results in units of weight can then be multiplied by an energy or carbon conversion
factor, for example, in [MWh/t] and [t CO2/t], respectively. These conversion factors
can be found in the literature. For example, the Circular Ecology group offers a fairly
extensive and free database accessible at https://www.circularecology.com/.Inthis
database, the energy and carbon conversion factors of concrete are 1.53 MWh/t and
92 S. Derrible et al.
0.95 t CO2/t, and the same factors for asphalt are 696.95 kWh/m2and 99 kg/m2—note
the difference of units between concrete and asphalt.
7.3.1.2 Energy
The UM of energy can include a number of sources since virtually every process
requires some kind of energy. Here, we divide total energy use into six sources:
buildings, transport, industry, construction, water pumping, and waste, such that:
IE=IE,buildings +IE,transport +IE,industry +IE,construction +IE,water pumping +IE,waste
(7.4)
where Iand Estand for impact and energy, respectively. Quantifying these six sources
of energy can be challenging, and other sources might exist depending on the scope of
the study. Ideally, data can be collected from local utilities. If not, individual sources
can be broken down into quantities that are simpler to estimate.
Energy use in buildings can be broken down into energy use for heating, cooling,
water heating, and light and appliances—about 50% of the energy used in buildings
is consumed for space conditioning (heating and cooling) and about 20% for water
heating, although values vary greatly, especially with climate. In the USA, data
for these four subcategories are available from the Department of Energy. Other
strategies are available in Derrible’s (2019b) book. For transport, we either need to
know how much fossil fuel was consumed and convert it into energy/emissions, or
we need to estimate the average distance traveled per vehicle type (e.g., car and bus)
and multiply it by an energy conversion factor. Local surveys are generally needed to
estimate distances traveled per vehicle type, although national surveys can help. In
the USA, the National Household Travel Survey offers US-wide travel pattern data,
and the Environmental Protection Agency (EPA) offers typical conversion factors
for distance traveled to carbon emissions.
For industry and construction, the flows can even be harder to estimate; this is
where the top-down approach might offer an alternative. For water pumping, energy
uses vary greatly based on several factors, including the topology of a city (i.e., hilly
vs. flat terrain). Chini and Stillwell (2018) have gathered and made available a large
database for the USA. Other values are available in the literature. We have to be a
little bit careful since some values in the literature might take into account the full
life cycle of a water distribution system (i.e., including the construction, operation,
and disposal of the water treatment plant and water distribution system), while many
others will not.
For waste, the quantity of waste generated as a weight must first be estimated (e.g.,
in [kg/y]). Urban-scale data are rarely available, but many countries offer national
per capita estimates that can be sufficient—the World Bank has also compiled a
significant database (Kaza et al. 2019). What may be more difficult is to get a break-
down of how much of the waste is recycled versus incinerated versus landfilled. Once
7 Urban Metabolism 93
achieved, however, the WAste Reduction Model (WARM) of the EPA offers carbon-
emission intensity values for different disposal strategies. Finally, some studies also
include natural energy inputs, such as the amount of energy received from the sun
(that was included in Fig. 7.3). Kennedy (2012) offered an equation which can be
referred to if needed. Ultimately, energy uses included in an UM study depend on
the scope of the study.
7.3.1.3 Water
As Wolman had already illustrated in his study, water is one of the largest resources
imported in a city, and water use is often included in UM studies. Moreover, although
energy use and carbon emissions linked to water use tend to be relatively small, water
is essential to generate electricity (i.e., Energy–Water Nexus) and for agriculture irri-
gation (i.e., to produce food), and monitoring water flows within an UM framework
is typically desirable.
In general, the overall water balance of a city can be captured by seven variables,
following the equation:
IW,precip +IW,pipe +IW,surface +IW,ground =OW,evap +OW,out +SW(7.5)
where IW, precip denotes natural inflow from precipitation,IW, pipe denotes pipe inflow,
IW, surface denotes net surface-water inflow (e.g., streams),I
W, ground denotes net
groundwater inflow,O
W,evap denotes water loss through evapotranspiration,O
W, out
denotes pipe outflow, and ΔSWdenotes annual change in water stored within the
city—typically close to 0 unless groundwater levels are changing, for example,
because of over pumping.
In Eq. (7.5), four variables are hydrological (precipitation, surface-water inflow,
groundwater inflow, and evaporation) and should be available from local weather
stations in most places. Pipe inflow relates directly to water use. Pipe outflow relates
both to water use and stormwater management. Pipe inflow tends to match water
use and accounts for both consumption and losses (e.g., through leaks). Estimating
water use can be challenging without adequate data, however. Leakage rates can
vary greatly from about 6% in some US cities to 50% in places like Rio de Janeiro
(Derrible 2019a). For water consumption, Kennedy (2012) proposed a method that
accounts for a base demand and a seasonal demand that was reproduced by Derrible
(2019b). Ideally, metered data from water-treatment plants can be collected since it
accounts for both consumption and leakage.
Pipe outflow can be broken down into three types: sanitary, stormwater, and
infiltrated wastewater (from groundwater aquifers that penetrate the sewer system).
Sanitary wastewater comes directly from water use, although the two quantities are
not equal since some of the water used is lost through leakage, some evaporates,
and some simply does not enter the sanitary sewer system (e.g., lawn watering);
Kennedy (2012) found that 20−25% of the water consumed in Toronto did not enter
94 S. Derrible et al.
the wastewater system. Here, again, data may be available from local wastewater
utilities. Stormwater and wastewater comprise mostly surface runoff that enters the
sewer system during heavy precipitation. Local wastewater utilities may have some
data here as well, depending on whether the sewer system is combined or separated.
Estimates of stormwater flows can also be generated through modeling, for example,
by using the Natural Resources Conservation Service curve number model. Infiltrated
wastewater flows are harder to estimate and may be negligible.
7.3.1.4 Food
Historically, food, as a specific sector, has rarely been included in UM studies.
Nonetheless, UM studies that focus on energy and water often include the amount
of energy and water used to prepare and dispose of food. Moreover, it may be more
difficult to collect data on food, but we can still think about ways to estimate the
UM related to food. First, the term food here includes both solid food and liquid
food. Packaged drinks, for example, can be accounted for here. Water use related to
food, such as water used in the kitchen, IW,Kit, can be included here, but we should
be careful not to double-count it if it was already included in the UM section related
to water.
Furthermore, food can be both imported into a city, IF, as well as produced within
a city, PF. In terms of exports, food waste, OF,FW , can either be disposed of in
landfills or it can be recycled (e.g., through composting). We can also account for the
carbon and water lost by transpiration and evaporation, OF,MET (where met stands
for metabolism), and for the water disposed of in the sanitary sewer, OF, S (unless
it is accounted for in the UM section related to wastewater). Altogether, we get the
following equation for the UM of food:
IF+PF+IW,Kit =OF,FW +OF,Met +OF,S(7.6)
All or only some of the variables in Eq. (7.6) may be available depending on
the scope of a study. In particular, food imports and exports may be available from
freight data sources. It might be more challenging to estimate the other variables. In
terms of units, food is generally expressed both as a weight in tons, although it could
be expressed as an energy in Wh or Joules with the proper conversion factors. This
is all we will cover in this section, but many more methods and techniques can be
imagined and applied to study UM from the bottom up. Now, we will switch to a
different conceptual approach to UM by estimating flows from the top down.
7.3.2 Top-Down Methods
Bottom-up approaches for UM accounting often tend to be time consuming and data
intensive. As an alternative, most countries maintain data for economy-wide import,
7 Urban Metabolism 95
export, and production of resources, which can be tapped for an UM assessment.
A top-down approach primarily benefits from the availability of relevant data in
aggregate form. Often generating economy-wide insights on UM can be a powerful
tool to influence sustainability efforts at the national or regional scale. In addition, the
top-down approach tends to be easier to carry out and relies on international datasets,
which helps in making time-series assessments to track progress over time. This
section first provides a historical evolution of top-down economy-wide material flow
accounting. It also discusses resources categories, data sources, and the accounting
methods that can be chosen based on the scope and boundaries of an UM study.
7.3.2.1 General Approach
The MFA in an economy-wide (ew) exercise signifies the socioeconomic metabolism
of a territory. Even though this section provides a methodology for an ew-MFA, often
only partial accounts are performed, both in terms of materials and commodities
as well as inflows and trade, or outflows in some combinations. As illustrated in
Fig. 7.4, ew-MFA aims to assess the overall material inputs into a national economy,
material stock changes within the economic system, and the material outputs to
the external environment and economies (Krausmann et al. 2018). Such an exercise
aims to describe the total scale of socio-economic activities in physical quantities.
While initial efforts for ew-MFA were initiated in the 1990s in Austria, Japan, and
Germany, credit for leading the global comparative ew-MFA methodology has often
been assigned to a seminal study by Matthews et al. (2000). They assessed five coun-
tries, namely Austria, Netherlands, Germany, Japan and the USA, for their compre-
hensively mass-balanced material flows from 1975 to 1996, and they developed
material flow indicators.
Fig. 7.4 General framework of economy-wide MFA. Adopted and modified from Eurostat (2001)
and Krausmann et al. (2018)
96 S. Derrible et al.
In the same fashion, and to harmonize methodological details and indicators, Euro-
stat published its 2001 report “Economy-wide material flow accounts and derived
indicators: A methodological guide” (Eurostat 2001), which has evolved over the
years (Eurostat 2018) and which remains widely adopted for ew-MFA. For a step-
by-step procedure to perform ew-MFA, the reader can refer to the comprehensive
guide developed by Krausmann et al. (2018).
The basic concept of ew-MFA follows the mass-balance principle with a unit of
metric tons per year (i.e., [t/y]) where:
Input =Output +Additions to Stock−Removals from Stock
=Output +Net Stock Changes (7.7)
Covering over 70 material groups, a typical MFA approach aggregates four mate-
rial categories, namely biomass, metal ores, non-metallic minerals, and fossil energy
carriers. In terms of biophysical bases for society, these four major material categories
fulfill all the material and energy requirements for socio-economic metabolism such
as food, feed, energy, housing, and infrastructure, including all man-made artifacts.
Water and air are typically not accounted along with these four major groups of
materials, excluding the mass balancing items such as moisture.
Table 7.1 defines the main MFA parameters for input and output into the economy,
as well as for societal stocks. Most commonly, ew-MFA considers direct flows, which
are defined as flows crossing the system (national) boundary. Major direct material
flow categories include domestic extraction (DE) and imports on the input side, with
Table 7.1 MFA parameters and definition
Parameter Definition
Domestic extraction (DE) Used extraction of materials including solid, liquid, and gaseous raw
materials from the natural environment (excluding water and air)
Imports, exports All imported or exported commodities as weights (e.g., metric tons).
Traded commodities comprise of goods at all stages of processing
from basic commodities to highly processed products
Stocks Physical structures of society: humans, livestock, and manufactured
capital
Manufactured capital All in-use artifacts (buildings, infrastructures, and durable goods)
NAS Net additions to stock; year to year change of stocks
DPO Domestic processed output of wastes and emissions including
deliberately applied materials (e.g., fertilizers)
DPO* DPO excluding balancing flows of oxygen and water (i.e., the
fraction of DPO contained in DE)
Balancing flows Oxygen taken up during combustion and respiration and water
uptake by humans and livestock
Metabolic rate Material consumption per capita of population
Material intensity Material consumption per unit of GDP
7 Urban Metabolism 97
exports and domestic processed outputs (DPO) of waste and emissions on the output
side. DPO includes all waste and emissions from processing, manufacturing, use,
and final disposal of materials. Unused or indirect flows that do not become an input
for production or consumption are ignored. Because of the direct flows into and out
of an economy, there are net changes in the stocks, which are taken into consideration
to assess the physical growth. All accumulated materials in the form of manufactured
capital and discarded or demolished artefacts lead to a net addition to stock (NAS)
that can be positive or negative based on the overall balance. Negative NAS is rare
in growing cities and national economies.
Considering the mass balance nature of ew-MFA, it is important to account for the
water and air flows required in the processing and transformation of materials. Such
flows are categorized as balancing items on the input and output sides. These may
include water vapors for respiration, oxygen required for combustion of fossil fuels,
and atmospheric gases captured or transformed into commodities such as fertilizers.
These balancing items can be calculated using stoichiometric equations. Based on
these material flow categories, a national material balance for a given year can be
given by:
DE +Imports +Input Balancing Items =Exports +DPO
+Output Balancing Items +NAS (7.8)
In socioeconomic metabolism, material flows represent the pressure on the envi-
ronment from an economy. These pressures can be measured through aggregated
material flow indicators, which capture the socioeconomic sustainability of the
system being studied. Direct material input (DMI) measures the direct input of all
materials with an economic value and used in production and consumption activ-
ities. Domestic material consumption (DMC) provides all material inputs into an
economy that are destined to be consumed and eventually released into the environ-
ment as waste, representing domestic waste potential. Physical trade balance (PTB)
represents the balance of imports minus exports. These indicators are mathematically
defined by:
DMI =DE +Imports (7.9)
DMC =DE +Imports−Exports (7.10)
PTB =Imports−Exports (7.11)
For cross-country comparisons, material flow indicators require appropriate
measures to account for differences in size. Overall, material efficiency is assessed
by relating DMC to GDP. The ratio of DMC to GDP is defined as material intensity
while the ratio of GDP to DMC is defined as material productivity. The ratio of
material flows to total land area measures the scale of the physical economy to its
98 S. Derrible et al.
natural environment. The DE to DMC ratio measures the dependence of the physical
economy on domestic raw material supply. The proportion of import or export with
DMI measures the trade intensity for import or export for a physical economy.
7.3.2.2 Data Sources
Several data sources exist to meet the data requirements needed to carry out an
ew-MFA; for example, to collect inflow, outflow, or domestic extraction. National
statistics and databases serve as the primary and most reliable data sources due to
their direct collection mechanisms. Multiple international databases with harmonized
values across countries and commodities also exist. In particular, the United Nations
International Trade Statistics Database (UN COMTRADE) remains one of the most
comprehensive datasets for international trade that provides monetary as well as
quantity data for import and export commodities. This dataset can be aligned with the
MFA computation tables based on the focus of the UM exercise for biomass, metals,
fossils or non-metallic minerals. In addition, the Food and Agriculture Organization
(FAO) maintains the FAOSTAT database for all biomass production and trade, which
is more detailed and reliable.
Table 7.2 provides major data sources for various material categories. It is impor-
tant to highlight that both the time scale (1917–2018) and the geographical coverage
Table 7.2 Major data sources for material flows in world economies
Material Flows Main source
Biomass (food, paper, wood,
timber, and products, etc.)
Production, import, export,
consumption
FAOSTAT, UN COMTRADE
Metals (steel, aluminum,
copper, etc.)
Production, import, export,
consumption
World Steel Association, The
Aluminum Association, British
Geological Survey, US
Geological Survey, UN
COMTRADE, UN Industrial
Commodity Statistics Yearbook
Non-metallic minerals (sand,
gravel, etc.)
Production, import, export UN COMTRADE, UN
Industrial Commodity Statistics
Yearbook, United States
Geological Survey
Cement Production, import, export,
consumption
CEMBUREAU, UN
COMTRADE, UN Industrial
Commodity Statistics Yearbook
Asphalt Production, import, export,
consumption
International Energy Agency
(IEA)
Fossil materials and petroleum
products (coal, crude and
refined oil, gas, etc.)
Import, export, consumption IEA, UN COMTRADE
7 Urban Metabolism 99
(from a few countries to worldwide) of these data sources vary significantly. Addi-
tional sources of data include scientific studies, reports, and surveys, which can be
very useful in certain cases.
For countries with limited datasets, several academic studies over the years have
led to a comprehensive understanding of socio-economic metabolism, leading to
significant datasets. Ongoing efforts in UM and industrial ecology communities have
resulted in data repositories such as the industrial ecology database at the Univer-
sity of Freiburg Germany (https://www.database.industrialecology.uni-freiburg.de/),
the UNEP MFA database (https://www.resourcepanel.org/global-material-flows-dat
abase,https://www.materialflows.net/), and the Eurostat MFA database (https://ec.
europa.eu/eurostat/web/environment/data/database).
In case of poor data quality for certain commodities or countries, various datasets
can be combined. When combining datasets for UM assessment, proper validation
processes should be followed. For instance, data for domestic extraction of primary
resources such as mining activities and food and vegetable production should ideally
be validated with national statistics. Data for consumption of non-metallic minerals
can be validated with consumption data for cement and asphalt. Likewise, gross
metal ore production can be estimated from metal production and ore grades data in
mining. Such exercises help in ensuring the mass balance of material flow. We now
move on to hybrid methods to perform a UM study.
7.3.3 Hybrid Methods
Based on the scope and boundary of an MFAstudy, raw material equivalents (all mate-
rials used in the production of a commodity) for traded commodities can be calcu-
lated based on life-cycle assessment (LCA), environmentally extended input–output
models, or by combining both. This is particularly useful for estimating consumption-
based indicators such as the material footprint of an economy. Multiregional input–
output (MRIO) models have been most widely used for sectoral resolution of physical
flows based on monetary inputs and outputs. Allocating physical amounts of material
extraction to products of final consumption can be carried out based on monetary
information about the economics and structure of a sector while considering global
processing chains and trade; however, challenges also exist (Krausmann et al. 2017a).
To estimate material and substance stocks, several extensions have been devel-
oped with varied temporal, sectoral, and spatial resolutions. Methodologically, it
includes top-down and bottom-up static or dynamic stock assessment models. The
basic concept of stock assessment depends on the service life of built-up stock and
stock renewal rates, which are estimated for stock building artifacts such as infras-
tructure, buildings, road networks, and vehicles (Fishman et al. 2014; Krausmann
et al. 2017b). Techniques such as geographical information systems and satellite-
based imaging have allowed for various advances in the measurement of stocks and
resource flows. In addition, hybrid approaches combine both the bottom-up and top-
down approaches for assessing the UM of a city. From an ecological system’s point
100 S. Derrible et al.
of view, the use of emergy and ecological network analysis (ENA) has found greater
interest.
The use of emergy originated in the 1950s through the pioneering work of the
Odum brothers on the energetic basis of ecology on Earth. Hau and Bakshi (2004)
suggest that emergy analysis “provides an ecocentric view of ecological and human
activities, which can be used for evaluating and improving industrial activities.” This
approach is fundamentally based on the principle that the sun is the primary source of
energy for all ecological and economic activities on earth. It considers tidal energy and
deep earth heat as additional non-solar sources of energy on Earth and converts them
into an objective matrix of energy quality that can be added altogether. As a result,
all direct or indirect energy required to manufacture or deliver any or all products and
services can be characterized in terms of solar energy equivalents. Emergy, hence,
is estimated based on energy required to perform a function or service, with solar
energy as the only source of energy (Odum 1996). As a scientific unit, emergy is
represented in terms of solar embodied joules, abbreviated as [sej]. To account for
energy transformations from high to low quality or into heat, the concept of solar
transformity has been developed. Solar transformity, as a measure of energy quality
or transformations, is defined as the solar emergy required to make one J of a service
or product (measured in [sej/J]). Mathematically,
M=τ·B(7.12)
where Mis emergy, τis transformity, and Bis available energy.
This equation provides a convenient way of estimating the emergy of commodi-
ties, resources, and services. Odum pioneered the estimation of transformity for
most inputs and, at the time of this writing, research still relies on Odum’s matrix
to estimate emergy. Total emergy input to the Earth can be derived from the sum of
emergy of solar exposure, tidal energy, and deep Earth heat. To estimate ecological
and metabolic pressures, emergy estimations can be carried out from the planetary
level to the product or city level. To integrate economic and ecosystems activities, it is
possible to estimate emergy of economic inputs based on the total emergy of a country
and its gross national economic product, thus allowing for an objective comparison.
The thermodynamic rigor behind this approach, the inclusion of ecological contribu-
tions in economic activities, and the ease of objective comparison based on a single
measurement unit are some of its major advantages. The reader should refer to Odum
(1996) for a detailed methodology.
As a different approach, modeling the complexity of nature–societal interactions
has been carried out in some studies through ecological network analysis and its vari-
ations. This approach develops urban metabolic networks between different actors
and assigns possible transformative processes to the flows (Fath et al. 2007). In
comparison to linear relationships, network analysis captures more realistic interac-
tions between various stakeholders and flows. However, complexity and assumptions
involved in network simulations are primarily data limited. The methodology has
evolved to capture the complete dynamics of urban metabolic activities. The scope
7 Urban Metabolism 101
and boundary of an urban metabolic network varies according to carbon emissions,
pollutants, energy, materials, nutrients, and other substances. Finally, several studies
have combined network analysis with emergy and MFA to provide robust compa-
rable results for cities such as Beijing and Vienna (Chen and Chen 2012; Zhang et al.
2009). As a practical case study, we will now turn to the UM of Singapore.
7.4 A Case Study: The Metabolism of Singapore
Singapore has unique characteristics that makes it a good case study for showcasing
the methodologies of UM. In 2016, the small and dense city-state in Southeast Asia
housed 5.6 million people on a total land area of 720 km2and imported most of its
material, food, and energy requirements. Unlike many other cities, the city-state has
clear national and urban boundaries that coincide with each other (Abou-Abdo et al.
2011). Thus, all flows in and out of the city are classified as international trade and
are well documented at Singapore’s highly regulated ports of entry. Moreover, water
flows in Singapore are highly managed by the Public Utilities Board (PUB), making
for relatively easy accounting. Stormwater and used water are collected in “separate
storm and sanitary sewer systems” (Irvine et al. 2014), which channel stormwater
and surface runoff to rivers and reservoirs, and used water to water treatment plants
(Tortajada et al. 2013). The water distribution network is robust, with “[no] illegal
connections, and all water connections are metered” (Tortajada and Buurman 2017).
The study of Singapore’s UM from the perspective of material flows began with
Schulz (2007), who used physical trade flows and other data sources to conduct
an ew-MFA, as described in the previous section. The flows of biomass, construc-
tion materials, industrial minerals, fossil fuels, and semi- and final products were
analyzed over a 41-year period from 1962 to 2003. The study found that DMC
“remained closely coupled to economic activity,” rising in tandem with Singapore’s
massive economic growth since independence. Chertow et al. (2011) continued this
work into the years 2000, 2004, and 2008, and have expanded the scope of flows to
include emissions, waste, and recycling. The authors found large variations in DMC
of between 14 and 55 metric tons per capita, which is mainly explained by variations
in the import of construction minerals. Other UM studies in Singapore include an
analysis of phosphorus flows (Pearce and Chertow 2017), and stocks and flows of
concrete and steel in residential buildings (Arora et al. 2019). Beyond the analysis
of material flows, system dynamics have been used to study urban resource flows
(Abou-Abdo et al. 2011) and water (Welling 2011), while Tan et al. (2019) use exergy
and ecological network analysis to study Singapore’s resource effectiveness.
As an illustration of UM methods, this section adopts the simpler top-down
approach to estimate the UM of Singapore in 2016, owing to the fact that as a city-
state, national data do not need to be disaggregated to the urban scale. A wide range of
data sources was used, such as international trade statistics from UN COMTRADE,
data from the Food and Agriculture Organization (FAO), the International Energy
Agency (IEA), and Singapore’s Department of Statistics. The physical flows reported
102 S. Derrible et al.
by these data sources are combined and adjusted to achieve mass balance. From
these balanced flows, the key metabolism indicators, such as DMI and DMC (Euro-
stat 2001), are calculated and compared with the same indicators during Singapore’s
independence in 1965 (Schulz 2007).
Figure 7.5 shows the material flows of Singapore’s economy in 2016. In total,
270.3 million metric tons of material were imported, with a large majority being
fossil fuels (187.2 Mt, 69%) followed by non-metallic minerals (65 Mt, 24%), which
are mainly used for constructing buildings and infrastructure, such as the 9,308
lane-kilometer long road network (Government of Singapore 2019). As a major oil
trading and refining hub, most of the fossil fuels it imports are in the form of crude
oil, which is traded or refined into other petroleum products for export (160.8 Mt).
As a small island with no natural resources and limited options for renewable energy
(NCCS 2019), 95% of Singapore’s electricity is generated from the combustion of
imported natural gas. A small proportion of energy is also produced from solar power
and waste-to-energy facilities that produce energy from incinerating waste (MEWR
2019). Of the 48.6 TWh of electricity consumed in 2016, the largest share was
by the manufacturing industry (38%), followed by businesses in the commerce and
services sector (36%), and households (16%) (Singstat 2019). Altogether, oil refining,
electricity generation and the 956,430 motor vehicles (Land Transport Authority,
2018)—most of which run on fossil fuels—contributed 51.5 Mt of greenhouse gases
(CO2equivalent) emitted into the air in 2016 (MEWR 2019).
Fig. 7.5 Metabolism of Singapore in 2016. Major flows of materials (in million metric tons, Mt),
water, and energy are displayed, along with several key statistics. Data on water flows, recycling,
and greenhouse gas emissions obtained from MEWR (2019). Singapore skyline by Kiraan on
VectorStock
7 Urban Metabolism 103
With a total renewable water resource (TRWR) per capita of 105.1 m3/year, Singa-
pore is considered to be facing absolute water scarcity (Food and Agriculture Orga-
nization 2014,2019). Even though Singapore is located just one degree north of
the equator and receives more than two meters of rainfall per year (weather.gov.sg
2019), its small size gives little room for water catchment sufficient to meet its water
demand. Historically reliant on its closest neighbor for water imports, Singapore has
invested heavily in water recycling (locally branded as NEWater) and desalination to
“close the water loop” (PUB 2016) and achieve self-sufficiency in water resources.
Investments in water recycling have resulted in the significant secondary flow of
water that makes up more than 25% of all the water sent to the end-users.
Table 7.3 shows how Singapore’s UM has grown since independence from 1965 to
2016. Except for DE, which has virtually disappeared relative to the other indicators,
all other indicators in 2016 have increased by 5–7 times their values in 1965, with
imports growing the most from 6.8 to 48.2 metric tons per capita. Fossil fuels have
always made up the bulk of Singapore’s imports and exports, although the share
of fossil fuels in total exports has increased while the opposite is true for imports.
These metabolic indicators show the phenomenal growth of the material flows of
Singapore, which occurred in tandem with Singapore’s rise from a predominantly
agricultural economy to a global one with manufacturing, oil refining, and service
industries.
Nonetheless, Singapore is not alone in its trajectory. Other cities have also expe-
rienced great increases in material consumption per capita in the past century
(Kennedy et al. 2007). For example, the total material consumption per capita in
Hong Kong increased by 141% from 2.9 metric tons in 1971 to 7.0 metric tons in 1997
(Warren-Rhodes and Koenig 2001). While cities around the world are growing and
reaching new economic heights, will the trend of increasing material consumption
and intensity continue without bounds? If the theory of the Environmental Kuznets
Table 7.3 Comparison of Singapore’s UM indicators from 1965 to 2016
Indicators (metric tons per capita) 1965a
(Schulz 2007)
2016b%-change
Imports 6.8 48.2 612
Fossil fuels (% of total) 4.9 (72%) 33.4 (69%) 582
Domestic extraction (DE) 1.4 0.07 −95
Direct material input (DMI) 8.2 48.3 489
Exports 5.0 31.0 522
Fossil fuels (% of total) 4.3 (87%) 28.7 (93%) 561
Domestic material consumption (DMC) 3.2 17.3 441
Population (million) 1.89 5.6 196
GDP per capita (S$, 2015) 5804 77,754 1240
aValues estimated from figures published by Schulz (2007)
bThis study
104 S. Derrible et al.
Curve (EKC) holds, environmental impacts would decline as societies become more
affluent. Empirical support for the theory is mixed. DMI, DMC, and DPO were found
to correlate poorly with GDP per capita for affluent industrial economies (Fischer-
Kowalski and Amann 2001), with similarly poor correlations for water use and solid
waste production in megacities from 2001 to 2011 (Kennedy et al. 2015). On the
other hand, the latter found that energy use is growing at half the rate of economic
growth, with London even reducing its electricity consumption per capita while its
GDP grew. Returning to the case of Singapore, DMC grew at less than half the
rate of GDP growth from 1965 to 2016 (Table 7.3). Furthermore, Abou-Abdo et al.
(2011) presented evidence of per capita water consumption for Singapore following
the EKC, reaching a peak in the early 1990s with water consumption at 115 m3per
capita and a gross urban income of about S$34,000.
The material footprints of cities are direct consequences of their metabolism; to
recall the definition of Kennedy et al. (2007): “the sum total of the technical and
socio-economic processes.” Analyzing the flows of material and energy into, within,
and out of cities provides us with a glimpse under the hood of the engine that keeps
our cities running. These flows also serve as fingerprints of our cities, reflecting
the unique circumstances—past and present—that drive their continuing growth and
adaptation.
7.5 Urban Metabolism Applications, Challenges,
and Opportunities
The study of UM has been considered for the purposes of urban planning and urban
infrastructure planning. The study of resource stocks and flow exchanges in cities
offers a perspective for urban systems analysis, and a potential to understand self-
sufficiency, efficiency, and resilience. The merit of UM lies in examining resource
requirements, availability, rates of change, and accumulation. It offers an under-
standing of sources (inflows) required to sustain growth, or the abilities of the city
to regulate flows, assimilate or treat waste, and capture emissions. As a communica-
tions tool, UM can also be used to convey the consumption of resources within cities
and allude to limits to growth. Many cities are in fact resource sinks, often accu-
mulating material stocks, and requiring continuous inflows. While UM studies help
profile the past and current status of urban systems, many UM studies have not led
to actionable recommendations beyond the initial assessment. One main criticism of
UM is that since it fundamentally offers a retrospective view of resource stocks and
flows, it has to be coupled with other approaches in order to consider opportunities
for achieving resource efficiency. UM studies therefore provide diagnosis but are
missing a prescription to follow. John et al. (2019) found that two-thirds of 221 UM
studies followed a problem-oriented approach to characterize the metabolism of the
system and understand risks, as opposed to seeking ways to solve the challenges
uncovered.
7 Urban Metabolism 105
This limitation of UM is partly due to its systems perspective, which masks many
complex interactions that take place within cities and cannot yet be adequately
captured. It, therefore, lacks visibility about which actors are driving the flows,
where the flows occur, and the underlying usage and consumption patterns. Without
a view on the causes and drivers for resource flows, this makes it difficult to extract
details on specific infrastructure systems, levers of control, and to consider how to
manage, let alone optimize. Many UM scholars have, therefore, highlighted the need
to advance the field of practice beyond accounting, assessment, and reporting, to
guidance for designing, optimizing, and decision making.
A number of studies have suggested options to couple UM with notions of sustain-
able design, in order to translate the assessment into practical urban design and
planning. Examples include:
•The European BRIDGE research project (2011) developed a GIS-based decision-
support UM assessment tool that evaluates urban planning alternatives. The
research team emphasized a need for UM to focus on the local scale.
•González et al. (2013) used UM to assess the sustainability impact of urban
planning alternatives, such as building types or the location of transportation and
infrastructural developments.
•Thomson and Newman (2018) explored the influence of different urban forms
on resource inflows, and waste and emissions outflows, for the city of Perth,
Australia.
•In a comparative study of UM of different megacities, Han et al. (2018) considered
the industrial structures of cities and suggested that the pursuit of service industries
instead of manufacturing can allow cities to achieve green growth.
As the field advances, we see four challenges in the further application of UM:
1. As mentioned, unless the internal flows within cities are adequately portrayed
in UM, it will be difficult to translate the findings into intervention options.
Pincetl et al. (2012) suggested to connect metabolism studies with the actors
driving their dynamics. They also highlighted a need to consider the internal
political, economic, and social processes within cities, to better understand the
complexities of possible change. The aim is to better understand “socioeconomic
and policy drivers that govern the flows and patterns.”
2. The quantities or qualities of energy and material flowing through cities may not
always be the right metric of concern, nor are they all that matter. The forces
driving resource consumption are the demands for services derived from these
resources, or the utility obtained. There is a need to capture the value of the
services derived, and not just the amounts of resources. Carreón and Worrell
(2018) argue for the consideration of energy services, and drivers of them, in
UM research.
3. The study of UM remains highly constrained by the availability of quality data.
Most existing UM studies cover a limited set of resources—materials (particu-
larly metals), energy, water, and nutrients. Analyses are also usually limited to
a single time period (e.g., a single year). Moreover, Currie and Musango (2017)
106 S. Derrible et al.
highlighted that UM studies have generally been limited to the cities in the Global
North, given the lack of data elsewhere.
4. While there have been attempts to carry out comparative UM studies across cities
(including those by Currie and Musango 2017; Han et al. 2018), it is generally
difficult to compare UM studies without a standard approach. Beloin-Saint-Pierre
et al. (2017) reported on the lack of consistency on assessment methods. Zhang
et al. (2015) recommended the establishment of “a multilevel, unified, and stan-
dardized system of categories to support the creation of consistent inventory
databases,” which can guide comparative analysis. Even so, the harmonization
of efforts will likely remain highly challenging given disparate and often missing
datasets.
Despite these challenges, we see related opportunities to advance the field in
several ways. Most essentially, new data sources are becoming more available to
better examine urban systems. This allows for disaggregated UM that (i) operates
at finer temporal resolutions, (ii) is spatially explicit, and (iii) integrates relevant
sources of information. Enabled by pervasive sensing and improved communica-
tions technologies, time-series data on the building-, district- and even city-level are
increasingly available, such as real-time electricity use, individual mobility patterns,
water use, and management tools. With the shortening of the timescale of analysis,
it is possible to monitor and track resource consumption more carefully. This also
allows for understanding rates of change, to better understand the timescale of impacts
and potential interventions. In this direction, Shahrokni et al. (2015) proposed what
they termed smart urban metabolism, which is capable of integrating UM concepts
with information and communication technologies (ICT) and smart-city technolo-
gies, thus enabling user-generated automated data collection, real-time analytics, and
feedback for city planners.
The mapping of resource flows for a more spatially explicit UM analysis is another
potential area of development. By moving beyond scalar quantities, this allows for an
understanding of the direction and distribution of internal flows within the city. Impact
arises from the distributed nature of activities that drive the demand for resources,
resulting in flows. Planners can then consider the resource efficiency implications
of land use or infrastructure location decisions. Voskamp et al. (2018) also recom-
mended finer spatio-temporal resolution for monitoring energy and water flows,
arguing that this is required in order to develop interventions to optimize resource
flows. There is also the opportunity to integrate different types of information at the
disaggregated level to evaluate UM. Related sources of information and tools include
supply chain data (e.g., transaction data from enterprise resource planning systems)
or building information modeling (BIM) data. Researchers have even used satellite
and night-light imagery (Xie and Weng 2016), GIS tools (Li and Kwan 2018), and
freight transportation surveys (Yeow and Cheah 2019) to better examine UM.
Furthermore, data concerning different resources can be fused or integrated to
allow analysts a better understanding of the interdependencies and relationships
between different resource flows, as opposed to examining individual resources
separately. Exploring the interactions between water consumption and energy use
7 Urban Metabolism 107
Fig. 7.6 Hybrid Sankey diagram of 2011 U.S. water and energy flows. Source U.S. Department of
Energy
(water–energy nexus), or linking resource demand with urban activities can aid with
holistic policy decision-making and integrated resource management. Hamiche et al.
(2016) conducted a review of the water–energy nexus to reveal the complex links
between water and electricity generation. Movahedi and Derrible (2020) studied
the interrelationships between water, electricity, and gas consumption in large-scale
buildings in New York City. Figure 7.6 shows a hybrid Sankey diagram depicting
interconnected water and energy flows in the United States in 2011, developed by
the US Department of Energy (Bauer et al. 2014).
Finally, UM analysis may progress from a descriptive approach toward a more
prescriptive one, when it is considered in simulations of resource flows through cities,
allowing the analyst an opportunity to test potential interventions. Figure 7.7 shows
the potential evolution of the field, advancing toward more disaggregated analysis
with finer temporal and spatial resolution, and eventually using real-time data to
offer predictions on the state of the system. With live data streams, one can monitor
demand and regulate resource flows in or near real time. This would be analogous to
real-time system monitoring, even with the possibility of feedback and control. Such
advances are already becoming available at the scale of individual buildings and
even neighborhoods, with the possibility of scaling up to virtual city representations
in the form of the city’s digital twin, albeit with greater complexity. For instance,
in the Virtual Singapore project, a digital twin of the city has been developed with
the intention for urban planners to simulate alternative policies (Wall 2019). When
108 S. Derrible et al.
Fig. 7.7 Envisioned developments in the field of urban metabolism
available, such virtual representations of a city’s metabolism allow for an opportunity
to better monitor, manage, and optimize resource use. In the future, the metabolism
of cities can even be predicted and self-regulated.
Ultimately, the coupling of urban metabolism portrayal with sustainable urban
planning and design can provide both a comprehensive diagnosis, as well as the
capabilities to consider solutions. This allows stakeholders to explore impact miti-
gation pathways, and consider strategies to achieve sustainable urban renewal and
growth. Cities and their metabolism are an outcome of the agglomeration of the
complex behaviors of their residents. The study of UM monitors the pulse of the
city, allowing insights and actions toward greater urban sustainability.
7.6 Conclusions
From its humble beginnings in quantifying flows of nutrients in and out of Berlin and
in sanitary engineering, UM has evolved to become an established field whose main
goal is to quantify the inflows, outflows, and production of energy and resources to,
from, and in cities. In this chapter, a short history of UM was first offered, notably
recalling Wolman’s findings from his 1965 study. Because of the significant number
of flows that need to be estimated, carrying out a UM is not necessarily straight-
forward. Methodologically, the goal is primarily to perform a Material and Energy
Flow Analysis (MEFA) of a city. In this chapter, two main families of UM approaches
were described. The first family attempts to calculate UM from the bottom up by
either collecting or estimating individual flows, such as quantifying the amount of
water consumed. The second family takes a top-down approach by leveraging and
7 Urban Metabolism 109
disaggregating nation-wide economic input-output data sources. Finally, some hybrid
methods exist to pursue UM studies, including one that utilizes concepts of emergy
and another that utilizes concepts of ecological network analysis.
As a practical case study, the UM of Singapore was then studied. As a city-state,
Singapore is particularly interesting since both bottom-up and top-down approaches
can be adopted. The exercise led to the development of Fig. 7.5 that offers an inter-
esting and insightful snapshot of the material and energy flows that entered or exited
Singapore in 2016. Subsequently, the applications, opportunities, and challenges of
UM were reviewed. In particular, one main challenge of UM resides in the fact that
it is purely an accounting method and it does not directly lead to the development
of appropriate designs and policies to tackle specific problems. In contrast, as more
numerous and larger data sources are becoming available, it is becoming increasingly
possible to perform UM in much finer spatiotemporal resolutions.
Overall, the development and use of UM have evolved relatively slowly in the past
century, but significant advances are likely to emerge in the future. On the one hand,
more and better data sources are becoming available; on the other hand, cities around
the world are striving to become more sustainable and resilient. UM, therefore, offers
significant opportunities to help understand how energy and resources are being
consumed and, therefore, can contribute to inform better designs and policies to
radically change how people live in cities in the twenty-first century.
Acknowledgements This research was supported, in part, by the United States National Science
Foundation (NSF) CAREER Award 1551731 and by the Singapore University of Technology and
Design (SUTD) graduate research fellowship from the Ministry of Education, Singapore. The
authors would also like to acknowledge the OeAD Ernst Mach Grant (Award# ICM-2018-09903)
and thank Professor Fridolin Krausmann from the Institute for Social Ecology Vienna.
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Sybil Derrible is an Associate Professor in Civil Engineering
at the University of Illinois at Chicago and Director of the
Complex and Sustainable Urban Networks Laboratory. He is
the author of Urban Engineering for Sustainability (MIT Press,
2019) and an Associate Editor for the ASCE Journal of Infras-
tructure Systems.
Lynette Cheah is an Associate Professor of Engineering
Systems at the Singapore University of Technology and Design.
She leads the Sustainable Urban Mobility research group,
which develops data-driven models and tools to reduce the
environmental impacts of passenger and urban freight transport.
Mohit Arora is a Research Associate in Sustainable Built Envi-
ronment at the University of Edinburgh and Imperial College
London. His research combines Circular Economy Strategies
with Development Engineering for a low carbon future.
114 S. Derrible et al.
Lih Wei Yeow is a Senior Research Assistant at the Singa-
pore University of Technology and Design. He works with the
Sustainable Urban Mobility research group and is interested in
urban systems and their interactions.
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