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Sustainable urban resource management depends essentially on a sound understanding of a city's resource flows. One established method for analyzing the urban metabolism (UM) is the Eurostat material flow analysis (MFA). However, for a comprehensive assessment of the UM, this method has its limitations. It does not account for all relevant resource flows, such as locally sourced resources, and it does not differentiate between flows that are associated with the city's resource consumption and resources that only pass through the city. This research sought to gain insights into the UM of Amsterdam by performing an MFA employing the Eurostat method. Modifications to that method were made to enhance its performance for comprehensive UM analyses. A case study of Amsterdam for the year 2012 was conducted and the results of the Eurostat and the modified Eurostat method were compared. The results show that Amsterdam's metabolism is dominated by water flows and by port-related throughput of fossil fuels. The modified Eurostat method provides a deeper understanding of the UM than the urban Eurostat MFA attributed to three major benefits of the proposed modifications. First, the MFA presents a more complete image of the flows in the UM. Second, the modified resource classification presents findings in more detail. Third, explicating throughput flows yields a much-improved insight into the nature of a city's imports, exports, and stock. Overall, these advancements provide a deeper understanding of the UM and make the MFA method more useful for sustainable urban resource management.
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RESEARCH AND ANALYSIS
Enhanced Performance of the Eurostat
Method for Comprehensive Assessment
of Urban Metabolism
A Material Flow Analysis of Amsterdam
Ilse M. Voskamp, Sven Stremke, Marc Spiller, Daniela Perrotti, Jan Peter van der Hoek,
and Huub H. M. Rijnaarts
Summary
Sustainable urban resource management depends essentially on a sound understanding of
a city’s resource flows. One established method for analyzing the urban metabolism (UM)
is the Eurostat material flow analysis (MFA). However, for a comprehensive assessment
of the UM, this method has its limitations. It does not account for all relevant resource
flows, such as locally sourced resources, and it does not differentiate between flows that
are associated with the city’s resource consumption and resources that only pass through
the city. This research sought to gain insights into the UM of Amsterdam by performing an
MFA employing the Eurostat method. Modifications to that method were made to enhance
its performance for comprehensive UM analyses. A case study of Amsterdam for the year
2012 was conducted and the results of the Eurostat and the modified Eurostat method
were compared. The results show that Amsterdam’s metabolism is dominated by water
flows and by port-related throughput of fossil fuels. The modified Eurostat method provides
a deeper understanding of the UM than the urban Eurostat MFA attributed to three major
benefits of the proposed modifications. First, the MFA presents a more complete image of
the flows in the UM. Second, the modified resource classification presents findings in more
detail. Third, explicating throughput flows yields a much-improved insight into the nature
of a city’s imports, expor ts, and stock. Overall, these advancements provide a deeper
understanding of the UM and make the MFA method more useful for sustainable urban
resource management.
Keywords:
Amsterdam
circular economy
industrial ecology
resource management
sustainable city
urban planning
Supporting information is linked
to this article on the JIE website
Introduction
Ongoing urbanization, resource depletion, and climate
change emphasize the need to design and plan cities that foster
sustainable urban resource management (Agudelo-Vera et al.
2011). In order to plan cities that generate less environmental
pressure, it is essential to understand how urban systems func-
Address correspondence to: Ilse M. Voskamp, Wageningen University and Research Centre, Landscape Architecture Group and sub-department of Environmental Technology,
P.O. Box 17, 6700 AA, Wageningen, the Netherlands. Email: ilse.voskamp@wur.nl
© 2016 by Yale University
DOI: 10.1111/jiec.12461 Editor managing review: Christopher Kennedy
Volume 21, Number 4
tion with respect to resource flows (Decker et al. 2000). One key
approach to researching the material flows and stocks of urban
systems is urban metabolism (UM) (Kennedy et al. 2011; Zhang
2013). The body of quantitative UM research includes studies
that assess particular subflows, such as food, energy, and water
(e.g., Kim and Barles 2012), and those that consider fluxes of
www.wileyonlinelibrary.com/journal/jie Journal of Industrial Ecology 887
RESEARCH AND ANALYSIS
Ta b l e 1 Comprehensive MFA studies of European cities
Specific MFA System Type of
Study City Base year method used boundary publication
Gorree et al.
2000
Amsterdam 1998 Amsterdam; boundaries
based on municipality
Report
Hendriks et al.
2000
Vienna Not specified Vienna city; boundaries not
further specified
Scientific article
Barrett et al.
2002
York 2000 Eurostat, Mass
Balance UK
City of York; boundaries not
further specified
Report
IWM (EB) 2002 London 2000 Mass Balance UK Greater London; boundaries
based on boroughs (34
included)
Report
Hammer and
Giljum 2006b
Hamburg,
Leipzig,
Vienna
H: 1992–2001
L: 1992–2001
V: 1995–2003
Eurostat City and Metropolitan
region; boundaries based
on administrative entities
(Eurostat NUTS system)
Report
Pom´
azi and
Szab´
o 2008
Budapest 2005 (Greater) Budapest;
boundaries not further
specified
Report
Barles 2009aParis 2003 Eurostat Paris municipality,
metropolitan area and
administrative region;
boundaries based on
administrative areas
Scientific article
Niza et al. 2009 Lisbon 2004 Eurostat Lisbon city; boundaries based
on municipality
Scientific article
Browne et al.
2011
Limerick 1992–2002 Eurostat Limerick City Region;
boundaries based on
electoral districts
Scientific article
Rosado et al.
2014
Lisbon 2003–2009 Eurostat Lisbon Metropolitan region;
boundaries based on
municipalities (18)
Scientific article
aThe study was also published in more detail as report (in French) (Barles 2007).
bThe methodology used is described in Hammer and colleagues (2003a).
MFA =material flow analysis.
particular elements, such as phosphorous, nitrogen, and specific
metals (e.g., Forkes 2007)(Kennedy et al. 2011; Zhang 2013).
However, only comprehensive analyses that account for all rel-
evant urban resource flows can provide a full understanding of
a city’s metabolism. This knowledge is essential for developing
resource management practices that do not shift the burden
of resource extraction and use from one resource to another
(Kenway et al. 2011). Only a few of such “comprehensive” UM
studies, which have quantified all or nearly all material flows
of cities, exist. Kennedy and Hoornweg (2012) found that only
20 relatively comprehensive UM studies have been published
since the work of Wolman (1965).
The limited number of comprehensive UM studies and the
lack of standardization among them restricts the opportuni-
ties for comparative research into metabolic similarities and
differences between cities (Decker et al. 2000). Yet, such com-
parative analyses offer opportunities to interpret city-specific
findings and enable decision makers to compare the effects
of measures and policies adopted in one city with those ap-
plied in other cities. Kennedy and colleagues (2015) show that
comparative research also contributes to a more general un-
derstanding of UM and the factors that affect it, advancing
the body of UM knowledge beyond predominantly city-specific
knowledge.
One of the key methods used for systematic assessment of
UM is material flow analysis (MFA) (Cast´
an Broto et al. 2012;
Zhang 2013). Also among MFAs there is a lack of standard-
ization, mainly attributed to the absence of standardized re-
source classifications (which goods, substances, and processes
are to be investigated) and established principles for setting
system boundaries. Kennedy and colleagues (2014) address the
need for conventions by proposing an indicator set for com-
prehensive UM studies of megacities aimed at standardizing
data collection and reporting. Another significant contribution
to the standardization of MFA is the method described in the
MFA guide of the European Statistical Office (Eurostat 2001).
The majority of comprehensive European urban MFA studies
are based on this Eurostat method (table 1). However, given
that the original Eurostat method was designed for MFAs on
the national level, each of the reviewed studies tailored the
888 Journal of Industrial Ecology
RESEARCH AND ANALYSIS
Eurostat method for application at the scale of the city (Bar-
les 2009; Browne et al. 2011; Hammer and Giljum 2006; Niza
et al. 2009; Rosado et al. 2014). In other words, there is no
standardized Eurostat method for urban MFAs.
Moreover, past application of the Eurostat method to cities
showed that the method has shortcomings regarding compre-
hensive assessment of UM. Partly, this is because the method ac-
counts for material owsratherthanresource flows. Even though
Eurostat states that water can be accounted for, none of the
studies quantifies drinking water and wastewater flows, except a
brief mention of water inputs for Lisbon by Niza and colleagues
(2009). Although one can argue that this is not only a short-
coming of the method but also of its application, the method’s
focus on material flows also limits the resource sourcing practices
it considers. The only locally sourced resources that the Eurostat
method accounts for are biomass, minerals, and fossil fuels, im-
plying that, for example, local generation of renewable energy
is not considered. Additionally, the Eurostat method falls short
in comprehensively accounting for local resource sourcing be-
cause of its black-box character. The method does not account
for processes that occur within the system boundary other than
sourcing of above-mentioned primary resources, effectively ex-
cluding sourcing of secondary resources from the analysis. How-
ever, insights into locally sourced resources are of particular im-
portance for sustainable resource management. Locally sourced
renewable and secondary resources can replace virgin, nonre-
newable resources and thus contribute to the transition toward
a more circular metabolism that reduces environmental pres-
sures (Agudelo-Vera et al. 2012). Another shortcoming of the
Eurostat method is that it does not differentiate between flows
that are associated with the city’s resource consumption and
resources that only pass through the city. Making this differ-
entiation is essential to avoid that trade-related flows through
a city are erroneously included as consumption flows, which
would lead to overestimating the actual resource consumption
of that city (Niza et al. 2009). Explicating throughput flows is of
particular importance for port cities given that previous studies
have shown that the material flows of such cities are dominated
by trade flows (Hammer et al. 2003b; Schulz 2007).
The research presented in this article is motivated by the
need for more-comprehensive UM research and for the estab-
lishment of a comprehensive and standardized MFA method
that can inform urban policy makers and planners striving for
sustainable urban resource management. The objective of this
article is twofold:
I. to gain insights into the UM of Amsterdam by performing
an MFA employing the Eurostat method;
II. to identify modifications to the Eurostat method that will
enhance the performance of this method for comprehen-
sive UM analyses.
The article is structured as follows. First, the case study is
introduced. Second, the Eurostat MFA is introduced, and re-
sults of the Amsterdam Eurostat MFA are presented and bench-
marked against other European cities. Third, the modified Euro-
Ta b l e 2 Amsterdam: key statistics (2012)
Indicator Unit Value
Population (number) 790,000
Land area (km2) 219
Population density (ca/km2) 3,607
GDP (million euros) 56,912
Climate zone Moderate maritime
climate (Cf-climate)
Annual precipitation (mm/year) 900–925
Sources: CBS (2015), City of Amsterdam (2013), and KNMI (2011).
GDP =gross domestic product; km2=square kilometer; ca/km2=capita
per square kilometer; mm =millimeters.
Ta b l e 3 Summary of the 1998 Amsterdam MFA (in kt)
Category Input Accumulation Output
Total 138,431 1,802 136,824
Drinking water 99,402 27,651
Building materials 1,548
Industrial materials 4,401
Fossil fuels 2,691 34
Products 836 4,128
Waste 253a681
Storm water 22,000
O27,553
Water losses/effluent 94,695
CO27,732
H2O from combustion 1,511
Other emissions 392
Source: based on Gorree and colleagues (2000).
aIncludes imported waste only.
MFA =material flow analysis; O2=oxygen; CO2=carbon dioxide;
H2O=water.
stat MFA is presented and the findings of the Amsterdam MFA
using this modified method summarized, including a compari-
son with other European cities. Finally, the outcomes of both
MFAs are compared to ascertain whether the modified method
offers a richer understanding of the UM of Amsterdam.
Introduction to the Amsterdam Case
This research departs from a case study of Amsterdam, the
capital of the Netherlands (see table 2 for key statistics and
figure 1 for the location of the city). An earlier study of Ams-
terdam’s metabolism (Gorree et al. 2000) showed that this UM
is dominated by water flows (table 3). Water, fossil fuels (FFs),
and oxygen account for over 95% of all inputs. Outputs are
dominated by emissions of water and carbon dioxide (CO2).
The analysis did not employ the Eurostat method, but specified
Amsterdam’s inputs and outputs according to ten sectors.1The
1998 MFA also has its limitations. The analysis, for example,
excluded port-related material flows. Another limitation is that
the majority of findings are rough estimations based on indirect
data rather than data specific to Amsterdam.
Voskamp et al., Comprehensive Material Flow Analysis of Amsterdam 889
RESEARCH AND ANALYSIS
Belgium
Germany
The Netherlands
North Sea
Municipality
of Amsterdam
Harbor area
Other
built-up area
Built-up area
Amsterdam
Water
0 5 10km
Legend
Amsterdam
Airport Schiphol
Highway
Amsterdam
Amsterdam
Paris
Amsterdam
Hamburg
Lisbon
Limerick
Vienna
Leipzig
Figure 1 Location of Amsterdam in Europe and the Netherlands, and boundaries of Amsterdam municipality.
Eurostat MFA
Method
The Eurostat method that Hammer and colleagues tailored
for the urban scale (Hammer et al. 2003a; Hammer and Giljum
2006) was selected to perform the MFA of Amsterdam. This
method was chosen because Hammer and colleagues adapted
the original Eurostat method to a limited extent; they merely
explicated terminology so that the Eurostat method could be
applied to cities and regions. Moreover, because it was the first
Eurostat application on the urban scale, subsequent urban stud-
ies build on this work. It is also the only Eurostat-based urban
method that has been applied to more than one city. In the
following, we will refer to this method as the “urban Eurostat
method.” The method considers only flows that enter or orig-
inate from economic processes (production, consumption) as
inputs and outputs of the system. Moreover, processes that take
place within the system are not specified—the system is studied
890 Journal of Industrial Ecology
RESEARCH AND ANALYSIS
Figure 2 Material flows according to urban Eurostat method (based on Eurostat 2001).
as a “black box.” Figure 2 and table 4 specify the flows and
indicators considered by the urban Eurostat method. For
Amsterdam, we calculated local extraction, imports, exports,
and a physical trade balance (imports minus exports) in
kilotonnes (kt) (1 kt =106kilograms [kg]), which is consistent
with Hammer and Giljum (2006). Also in line with the
method, the system boundaries of the Amsterdam case study
correspond to those of an administrative entity, the Amsterdam
municipality (figure 1). The year 2012 was chosen as the base
year for the study because this was the most recent year for
which reasonably complete data sets were available (see the
Supporting Information available on the Journal’s website for
more insights on the data sets).
To facilitate interpretation of the findings, the results of
the Amsterdam MFA were benchmarked with Hamburg and
Vienna2(Hammer and Giljum 2006). Because the MFAs of
Hamburg and Vienna have the base year 2001, the results of
all three MFAs were normalized for the gross domestic prod-
uct (GDP) in the base year of each study. Also, the indicators
direct material input (DMI) and domestic material consump-
tion (DMC) were used for comparison, normalized per capita
and per GDP.
Results of the Urban Eurostat MFA
The results of the MFA using the urban Eurostat method
show that FFs and FF products dominate the material balance
of Amsterdam, representing 58.3% of total imports and 61.0%
of total exports (table 5). The predominance of FFs reflects that
the trans-shipment of coal and refined oil products is one of
the main activities of the Port of Amsterdam. The port is the
world’s largest gasoline port and Europe’s second-largest coal
port (Port of Amsterdam 2012a).3The comparison of Amster-
dam’s material flows with those of Hamburg and Vienna (ma-
terial flows/GDP) confirms that this is specific to Amsterdam
(table 5).
Further, the benchmark shows that Amsterdam’s total
imports and exports as well as its DMI/GDP are similar to
those of Hamburg, which also has an extensive harbor area
largely located within the city’s administrative boundaries.
The differences between the two cities in the types of materials
imported and exported show that the cities’ ports differ in their
core activities. For instance, the amounts of Hamburg’s imports
and exports of “other industry products” are 4 times those of
Amsterdam owing to Hamburg’s specialization in container
traffic (Merk and Hesse, 2012). Moreover, both Amsterdam
and Hamburg have a higher DMC/GDP than Vienna, which
may be explained by the medium- to long-term storage of large
shipments of bulk materials in these harbor cities. Because
the MFA is a yearly analysis, port-related bulk storage that
extends across different years (for instance, materials imported
in 2012 and exported in 2013) appears in the material balance
as consumption or addition to stock. This implies that the
material balance of a port city with substantial storage facilities,
such as Amsterdam, can show a rather high DMC as a result
of material storage for periods extending over different years.
The effect of this long-term storage on the yearly material
balance can only be addressed by means of longitudinal
studies over several years. In spite of the similarities in the
UM of Amsterdam and Hamburg, Amsterdam has less than
Voskamp et al., Comprehensive Material Flow Analysis of Amsterdam 891
RESEARCH AND ANALYSIS
Ta b l e 4 Definitions of terminology used by Eurostat
Term Definition
Unused local extraction Materials that were moved during extraction of resources, but do not enter the economic system; they are
considered to have no economic value (e.g., residuals from agricultural production).
Local extraction Materials that are extracted (through mining or agricultural production) from the natural environment
within the geographical boundaries of the system and that enter the economy
Imports Flows coming from another socioeconomic system
Flows to nature Flows that leave the economy and enter the environment
Exports Flows that leave the system, feeding another economy
Indirect flows Upstream resources that were required to harvest, process, and transport imported and exported resources
Material accumulation Addition of stock to the system when inputs exceed outputs
DMI Direct material input: the total amount of direct material inputs that have economic value. DMI equals local
extraction plus imports.
DMC Domestic material consumption: the total consumption of direct material flows of an economy. DMC equals
DMI minus exports.
Sources: Based on Eurostat (2001) and Hammer and colleagues (2003a).
DMI =direct material input; DMC =domestic material consumption.
half the number of inhabitants of Hamburg, resulting in a
DMI/capita and DMC/capita that are around twice that of
Hamburg.
Modified Urban Eurostat MFA
Method
A literature review and stakeholder consultations were used
to identify potential modifications to the urban Eurostat method
that will enhance the performance of this method for compre-
hensive analysis of UM. We first reviewed the five exclusively
Eurostat-based European studies (Barles 2009; Browne et al.
2011; Hammer and Giljum 2006; Niza et al. 2009; Rosado
et al. 2014). These studies examined six different cities (loca-
tions shown in figure 1). We then modified the original Eu-
rostat resource classification in the light of insights obtained
from the literature review. To verify the proposed modifica-
tions, we organized stakeholder workshops. Representatives
from academic, societal, and industry partners4were grouped
according to their expertise, each group focusing on one of
the four main types of resources (organic materials, inorganic
materials, water, and energy). The groups then discussed the
modified resource classification and evaluated its completeness
and appropriateness for the Amsterdam case. Subsequently, the
stakeholder input was processed and the resulting resource clas-
sification was used during data collection. Insights that arose
during data collection and processing were shared in a subse-
quent meeting and the classification was updated accordingly.
The process resulted in seven modifications to the urban Eu-
rostat method (summarized in figure 3 and discussed below).
Finally, the modified method was used to quantify the 2012
UM of Amsterdam.
A full comparison of the findings of the modified MFA with
Vienna and Hamburg was not feasible because of the lack of
similar MFAs of these two cities. Instead, the amount of locally
generated renewable energy (in total, per GDP, and per capita)
for 2012 (Hamburg) and 20115(Vienna) was used as an exem-
plary category for benchmarking the cities on the basis of the
modified MFA method.
Data
Almost all resource flows of the modified resource classifi-
cation could be quantified using reliable data sets containing
local data on Amsterdam for 2012 (see the Supporting Infor-
mation on the Web). To identify and obtain these data sets,
the stakeholders’ commitment and workshops played a cru-
cial role. Industrial and construction waste, however, could
not be quantified because no data were accessible. Apart from
CO2emissions, detailed information on emissions was not
available either. This made it unfeasible to quantify all flows
to nature. Consequently, net addition to stock could not be
calculated. To enable comparison between the different re-
source flows, all flows were expressed in the same unit (kilo-
tonne), which is also the most widely used unit in (urban)
Eurostat MFAs. Locally sourced energy was expressed both
in kilotonnes and Joules, because the latter is the most ap-
propriate metric for energy. To convert locally sourced en-
ergy from Joules to tonnes, we used tonnes of oil equivalents
(toe).6
Modifications
Including Drinking Water and Wastewater Flows
In order to gain comprehensive understanding of a UM,
drinking water and wastewater flows must be accounted for.
Indeed, Eurostat (2001) advises quantifying water flows, but
presenting these separately from the other material flows be-
cause of the magnitude of these flows. None of the reviewed
UM studies, however, included drinking water and wastewater
flows in their analysis. The modified urban Eurostat method
therefore incorporates drinking water and wastewater flows in
the resource classification.
892 Journal of Industrial Ecology
RESEARCH AND ANALYSIS
Ta b l e 5 UM of Amsterdam according to the urban Eurostat MFA (in kt) and compared with Hamburg and Vienna for DMI, DMC, and
material flows (t/GDP)
Characteristics Amsterdam Hamburg Vienna
Base year of study 2012 2001 2003
Population 790,000 1,726,000 1,590,000
Land area (km2) 219 755 415
Population density (capita/km2) 3,607 2,286 3,831
GDP (million euro) 56,912 70,994 56,728
GDP/capita (euro) 72,041 41,132 35,678
Material flows (kt) (t/GDP) (t/GDP) (t/GDP)
INPUTS
Total local extraction 24 0.4 2.5 4.6
Biomass 24 0.4 2.1 2.8
Agricultural 3 0.0 1.4 2.5
Forestry 4 0.1 0.1 0.3
Grazing 17 0.3 0.6
Fishery – – –
Minerals – 1.8
Metal ores
Industrial minerals
Construction minerals 1.8
Fossil fuels and fossil products 0.4
Coal – – –
Crude oil 0.4
Natural gas – – – –
Total imports 82,322 1,446 1,415 444
Biomass and biomass products 10,177 179 235 51
Total ores and industrial minerals 12,631 222 352 132
Fossil fuels and fossil products 48,025 844 294 153
Chemical products 5,827 102 113 10
Other industry products 4,533 78 318
Other imports 1,129 20 104 98
OUTPUTS
Total exports 69,583 1,223 1,219 309
Biomass and biomass products 10,083 177 217 32
Total ores and industrial minerals 9,644 169 282 91
Fossil fuels and fossil products 42,420 745 183 89
Chemical products 1,988 35 146 17
Other industry products 4,199 74 295
Other exports 1,250 22 95 80
PHYSICAL TRADE BALANCE 12,738
Biomass and biomass products 94
Ores and industrial minerals 2,987
Fossil fuels and fossil products 5,605
Chemical products 3,839
Other industry products 334
Other exports –121
Indicators
DMI/GDP (t/million euro) 1,491 1,417 449
DMI/capita (t) 104 58 16
DMC/GDP (t/million euro) 231 198 140
DMC/capita (t) 16 8.2 5.0
Note: UM =urban metabolism; MFA =material flow analysis; kt =kilotonnes; DMI =direct material input; DMC =domestic material consumption;
t/GDP =tonnes per gross domestic product; km2=square kilometers; t =tonnes. City characteristics in base year of study.
Voskamp et al., Comprehensive Material Flow Analysis of Amsterdam 893
RESEARCH AND ANALYSIS
Figure 3 Resource flows according to the modified urban Eurostat method.
Accounting for Storm Water and Groundwater Entering
the Sewer
Stakeholders pointed out that not only drinking water and
wastewater should be quantified, but storm water that enters the
combined sewer as well as groundwater seeping into the sewer7
should also be accounted for. Both flows increase the amount
of wastewater entering the socioeconomic system at wastewater
treatment plants. These flows are therefore considered local
sourcing of resources.8
Incorporating Renewable Energy (Local Sourcing, Import,
Export)
Another category of locally sourced resources8included in
the modified urban Eurostat classification is renewable energy.
The urban Eurostat method includes only FFs and FF products
(including electricity) as energy flows. At present, however,
generating energy from renewable sources is at the forefront of
“green” agendas of most Western cities, and these internally
generated flows are crucial when aiming to reduce urban envi-
ronmental pressures. Hence, it is essential to include renewable
energy in the modified method.
Categorizing Waste According to its Origin, Type,
Process, and Location of Treatment
Both literature and stakeholders indicated that a detailed
classification of waste flows is desirable. Barles (2009) empha-
sizes that the original Eurostat method falls short of providing
insight into urban waste generation because urban waste
treatment is often located outside the municipal boundaries,
which means that waste is classified as an export. Therefore,
Barles (2009) proposes categorizing exported waste explicitly
and indicating the share of the exported waste that is recycled
(external recycling). Browne and colleagues (2011), Niza
and colleagues (2009), and Rosado and colleagues (2014)
introduced waste categories that refer to the type of waste.
Additionally, Niza and colleagues (2009) provide an analysis of
the waste composition and waste treatment (recycling, incin-
eration, and controlled landfill). Stakeholders pointed out that
it is relevant to indicate whether waste is locally generated or
imported, because Amsterdam’s waste incinerator treats waste
from other municipalities in the Netherlands and from abroad.
The modified method categorizes waste according to its origin
(imported vs. locally generated), type (municipal solid waste,
industrial waste, construction and demolition waste, and other
waste), treatment method (landfilling, incineration without
energy recovery, incineration with energy recovery, recycling,
composting, anaerobic digestion, and method unknown), and
destination location of treatment (local treatment vs. export
for treatment outside the system).
Incorporating Recovered Materials and Energy from
Waste as a Local Sourcing Category
Materials and energy recovered from local treatment pro-
cesses are included in an additional local sourcing of secondary
resources category. This modification ensures that practices for
sourcing secondary resources other than recycling, namely, en-
ergy recovery from incineration and organic materials from
composting and anaerobic digestion, are revealed. These
894 Journal of Industrial Ecology
RESEARCH AND ANALYSIS
practices have to be accounted for, given that they are essential
processes in making the transition toward a more circular UM
and sustainable cities.
Classifying Import and Export Flows in Less Aggregated
Categories
We agree with Rosado and colleagues (2014) that the Euro-
stat representation of imports and exports in six categories is too
broad. Presenting the MFA results in this aggregated manner
hampers interpretation of the findings and comparative analysis
with other cities. To increase the usability of the MFA results
for informing sustainable urban resource management practices
and policies, the modified urban Eurostat method presents re-
source flows in less aggregated categories. Import and export are
described in 32 categories instead of six.
Explicating Throughput Flows
Niza and colleagues (2009) and Rosado and colleagues
(2014) discuss the issue of “flows crossing through” the city
of Lisbon in their MFAs. Explicating trade-related flows that
are not consumed or processed within the city, but simply pass
through the city (throughput), can help to avoid overestima-
tion of local resource consumption. Nevertheless, excluding
throughput flows entirely from the MFA (e.g., Niza et al. 2009)
or only stating the combined overall amount of throughputs
(e.g., Rosado et al. 2014) results in an incomplete picture of
UM. Therefore, the resource classification of the modified ur-
ban Eurostat method explicates, for each resource flow, what
share of the import is exported at the same quality during the
same year. This share is labeled as throughput. This modification
is, in particular, important for port cities such as Amsterdam,
where throughput flows are key to the city’s economy.
Results of the Modified Urban Eurostat MFA
The results of the modified urban Eurostat MFA (see ta-
ble 6) show that the material balance of Amsterdam is domi-
nated by throughput flows. Around 77% of imported FFs and
FF products, for example, are throughput. A share of the re-
maining 23% of imported FFs may also pass through the city
without being used. FFs are, namely, stocked in the harbor for
lengthy periods of time, causing export to occur in a later year
than import. Because import and export take place in different
years, these flows are not labeled as throughput. Among the im-
ported FF that is utilized locally, is coal that feeds the coal-fired
power plant in Amsterdam. The electricity generated by that
plant exceeds local consumption, making the city virtually self-
sufficient in terms of electricity generation and a net exporter of
electricity.
Similar to FFs and FF products, the main share of biomass
and ores and industrial mineral flows cannot be ascribed to local
consumption. The MFA reveals that 81% of biomass imports
and 47% of ores and industrial mineral imports are through-
put flows to the hinterland. These throughput flows result from
port activities, considering that the Port of Amsterdam primar-
ily handles (besides FFs) bulk shipments related to minerals,
recycling, and agribulk (Port of Amsterdam 2012b). These bulk
shipments amount to 8,267 kt of imports and 6,404 kt of ex-
ports of industrial/construction minerals (e.g., stone, sand, and
gravel) and to large flows of plant and animal products, which
mainly consist of raw materials for fodder.
In the modified urban MFA study, major additional inputs
and outputs appear as a result of the inclusion of water flows
(shown in more detail in figure 4). Water imports amount
to a total of 81,354 kt, the same order of magnitude as all
other imports combined. The input side reveals two sources for
Figure 4 Overview of water flows (in kt) in Amsterdam’s metabolism (excluding industry water).
Voskamp et al., Comprehensive Material Flow Analysis of Amsterdam 895
RESEARCH AND ANALYSIS
Ta b l e 6 Results of the MFA Amsterdam 2012 using the modified urban Eurostat method
Resource classification Input Internal flow Output
(kt) (TJ) (kt) (TJ)
Local sourcing of primary resources (input) and
secondary resources (internal flow)
19,014 675
Water 18,965
Storm water inflow into sewer 12,483
Groundwater infiltration into sewer 6,482
Biomass 24
Agricultural 3
Forestry 4
Grazing 17
Fishery –
Minerals –
Metal ores
Industrial minerals
Construction minerals
Fossil fuels
Coal –
Crude oil
Natural gas
Renewable energy 25 1,010 58 2,408
Electricity 14 572 46 1,929
Wind energy 14 560
Solar energy: PV 0 12
Incineration of green waste 44 1,846
Biogas combustion 2 83
Heat and cold 11 438 12 479
Solar heat n.a. n.a.
(Geo)thermal 7 288
Incineration of green waste 6 231
Biogas combustion 5 214
Biomass–fueled stoves and boilers 1 34
Cold extracted from surface water 4 150
Fuels 0 0
Hydrogen 0 0
Recovered materials and energy from waste 617
Materials 403
Rubber products 30
Metals 25
Nonmetal minerals 344
Fertilizer 4
Electricity and heat 44 1,842
Electricity from waste incineration 39 1,633
Heat from waste incineration 5 209
Fuels 170 7,138
Fuel pellets from industrial waste 60 2,512
Biodiesel (second generation) 110 4,605
Green gas from sludge digestion 0 21
(Continued)
896 Journal of Industrial Ecology
RESEARCH AND ANALYSIS
Ta b l e 6 Continued
Resource classification Input (kt) Internal flow (kt) Output (kt)
Import/throughput/export 163,742 61,873 81,967
Piped water 81,354 5,438 12,296
Industry water 1,932
Drinking water 71,098 5,438 12,296
Pretreated 24,978 –
Treated 46,120 5,438 12,296
Wastewater (untreated) 8,324
Biomass and biomass products 10,246 8,339 10,111
Agricultural and Fishery 9,672 8,117 9,700
Crops 1,114 843 951
Plant and animal products 8,504 7,245 8,720
Leather and clothing 54 29 29
Forestry 539 222 397
Raw wood, cork, and rubber 311 115 275
Wooden, cork, and rubber (semimanufactured)
products
41 0 1
Paper and board 152 107 107
Sewage sludge 35 14
Ores and industrial minerals 12,631 5,937 9,644
Metal ores 262 184 428
Metallic (semi)manufactured products 1,720 734 2,324
Industrial and construction minerals 8,267 4,532 6,404
Building materials 2,383 486 487
Fossil fuels and fossil products 48,019 37,025 42,470
Solid fuels 15,501 10,802 10,802
Hard coal and derivatives 15,318 10,771 10,771
Peat and derivatives 183 31 31
Liquid and gaseous fuels 32,478 26,223 31,668
Crude oil and derivatives 31,701 26,223 31,668
Natural gas 777 0
Other: heat 40 0
Chemical products 5,827 1,760 1,988
Cellulose and paper waste 361 52 268
Chemical raw materials 4,723 1,119 1,130
Chemical products 743 590 590
Other industry products 4,017 2,873 4,190
Machines and vehicles 1,098 804 898
Other goods 2,919 2,069 3,293
Waste 516 – 9
Municipal solid waste 516 9
To be landfilled 0
To be incinerated with energy recovery 516 2
To be recycled 7
Industrial waste n.a. n.a. n.a.
Construction and demolition waste n.a. n.a. n.a.
Other n.a. n.a. n.a.
(Continued)
Voskamp et al., Comprehensive Material Flow Analysis of Amsterdam 897
RESEARCH AND ANALYSIS
Ta b l e 6 Continued
Resource classification Input (kt) Internal flow (kt) Output (kt)
Other import/export 1,132 501 1,259
Fertilizers 868 366 1,102
Renewable electricity 1 9
Cold extracted from surface water 2
Unknown 261 135 148
Local waste treatment 1,625
Municipal solid waste 926
Landfilled
Incinerated without energy recovery
Incinerated with energy recovery 889
Household waste locally generated 272
Commercial waste locally generated 21
Imported waste 516
Sewage sludge 80
Recycled 34
Composted
Anaerobically digested 3
Industrial waste 699
Incinerated with energy recovery 599
Anaerobically digested 100
Other n.a.
Construction and Demolition waste n.a.
Other n.a.
Flows to nature 92,737
Emissions to air 4,712
CO24,712
Waste landfilled
Emissions to water 83,256
Treated waste water discharge 81,324
Industry water 1,932
Dissipative flows 4,769
Drinking water 4,769
Nonrevenue water 2,730
Household loss 2,039
Note: “0” is a rounded number, whereas “—” means not existing and “n.a.” means not assessed. Locally sourced secondary resources and locally treated
waste are presented as internal flows within the socioeconomic system to avoid double counting. All sewage sludge is stated as the weight at 21% dry
matter content, which is the final weight after dewatering. Locally produced biofuel does not appear as an export because it is blended with fuels from
fossil-fuel origin and is accounted for in the category “crude oil and derivatives.” Findings are structured according to the resource classificationofthe
modified urban Eurostat method (summarized in figure 3).
MFA =material flow analysis; kt =kilotonnes; TJ =terajoules; PV =photovoltaic; CO2=carbon dioxide.
drinking water: water that is imported at drinking water quality
and pretreated water that is processed by the drinking water
treatment plant within the municipality to produce drinking
water. The drinking water exports comprise water that this
treatment plant supplies to nearby municipalities (6,858 kt) as
well as drinking water that is distributed by the Amsterdam
drinking water network to these municipalities (throughput of
5,438 kt). Around 92% of the drinking water distributed in
Amsterdam ends up at the two wastewater treatment plants,
attributable to nonrevenue water and household losses.9In
898 Journal of Industrial Ecology
RESEARCH AND ANALYSIS
Ta b l e 7 Locally generated renewable energy in Amsterdam (2012), Hamburg (2012), and Vienna (2011)
Energy source Amsterdam Hamburg Vienna
Renewable energy (TJ) 3,384 11,329 7,239
Electricity 2,501 6,149 4,586
Wind energy 560 294 42
Solar energy: PV 12 184
Incineration of biomass and green waste 1,846 4,887 585
Biogas and landfill gas combustion 83 782 36
Hydropower 2 3,923
Heat and cold 883 4,887 2,653
(Geo)thermal 288 – –
Incineration of biomass and green waste 231 4,887 2,653
Biogas combustion 214
Cold extracted from surface water 150
Other 293 –
Renewable energy per GDP (GJ/million euros) 59 123 93
Renewable energy per capita (MJ/capita) 4,284 6,532 4,182
Note: “0” is a rounded number, whereas “—” means not existing. The category “biomass-fueled stoves and boilers” is excluded from the Amsterdam results
because it is also not considered for Hamburg and Vienna. For Hamburg, only energy totals for biomass incineration were known; half was allocated to
electricity and half to heat.
TJ =terajoules; PV =photovoltaic; GDP =gross domestic product; GJ =gigajoules; MJ =megajoules.
addition, the influent of the wastewater treatment plants con-
sists of 8,324 kt of wastewater generated outside Amsterdam
and 18,704 kt of water that originates from storm water inflow
and infiltration of groundwater into the sewer. After treatment,
water is discharged to surface water (flow to nature).
Almost all municipal solid waste generated within Ams-
terdam is incinerated locally at the waste-to-energy plant. In
2012, this plant incinerated 293 kt of locally generated house-
hold/commercial waste and 516 kt of municipal solid waste
from outside Amsterdam. A total of 192 kt of waste was
imported from adjacent municipalities and 324 kt from abroad.
In addition, 599 kt of industrial waste were incinerated. Recy-
cling of municipal solid waste occurs mainly outside Amster-
dam (34 kt of locally recycled vs. 7 kt exported to be recycled),
whereas anaerobic digestion takes place within Amsterdam.
The latter predominantly comprises digestion of sewage sludge
and biogenic industrial waste. This process yields biogas that is
used for the production of an equivalent of 83 terajoules (TJ)
of electricity and 214 TJ of heat, as well as 4 kt of fertilizer.
Further, the modified urban MFA shows that, in 2012, the
equivalent of 24 kt of biomass, 83 kt (3,418 TJ) of renew-
able energy, and 617 kt of secondary resources were sourced
locally. They appear as relatively small flows because the large
volumes of imports and exports dominate the overall picture.
Local biomass extraction, for example, amounts to as little as
0.2% of the total biomass import whereas it does equal 18% of
the net biomass import. Likewise, the renewable energy sourced
locally equals as much as 7% of the electricity and gas consumed
in Amsterdam. Of the 12,398 TJ of energy generated locally,
around 8% (1,010 kt) is generated on the basis of biophysical
flows (from water, wind, solar, and geothermal energy), 19%
(2,408 kt) is derived from biomass, and the remaining 72%
(8,980 kt) originates from secondary resources.
The comparison with Vienna and Hamburg (see table 7)
shows that the total amount of renewable energy generated
in Amsterdam is relatively low and that the cities use renew-
able energy from different sources. However, normalized per
inhabitant, Amsterdam generates an equal amount as Vienna
and two thirds of the renewable energy generated in Hamburg.
In terms of sources, energy generation from biomass is rela-
tively low in Amsterdam, in particular, compared with Ham-
burg. Amsterdam generates a relatively high amount of wind
energy, whereas Vienna strongly relies on hydropower. This
suggests that Amsterdam’s delta location and the presence of
the river Danube in Vienna are key explanatory variables for
the renewable energy performance of both cities. These find-
ings highlight that city characteristics are valuable to under-
stand a city’s UM and to explain metabolic differences between
cities.
MFA Comparison and Discussion
In this section, the results of the two MFAs are compared
on the basis of the modifications made to improve the urban
Eurostat method. Moreover, findings are discussed in light of the
1998 MFA of Amsterdam (Gorree et al. 2000). The comparison
of the urban Eurostat MFA and the modified MFA shows that
the latter quantified a large amount of additional inputs and
outputs. This is mainly attributed to the inclusion of drinking
water and wastewater in the modified MFA. Accounting for
storm water and groundwater entering the sewer increased the
total amount of locally sourced resources by more than 800
times. The results of the modified MFA confirm the finding of
the 1998 MFA (Gorree et al. 2000) that water flows make up
a significant share of Amsterdam’s metabolism. In comparison
with 1998, the input of industry water increased by around one
Voskamp et al., Comprehensive Material Flow Analysis of Amsterdam 899
RESEARCH AND ANALYSIS
third (1,932 kt in 2012 vs. 1,540 kt in 1998), whereas drinking
water consumption decreased from 63,632 to 56,074 kt, despite
a population increase of 72,000 people. The amount of treated
wastewater also decreased (from 82,400 to 81,324 kt), but the
difference is much less because, in 2012, additional wastewater
imports took place.
With the inclusion of locally generated renewable energy,
the MFA now provides insight into biophysical flows and
geochemical processes that are starting to sustain the local
economy. Although these additional local sourcing categories
resulted in relatively small changes in the overall balance for
2012, Amsterdam’s internal sourcing of renewable energy is
likely to increase in the near future. It is the city’s ambition to in-
crease the amount of per capita generation of renewable energy
by 20% in 2020, compared to 2013 (City of Amsterdam 2014).
Such ambitions support the need to account for renewable
energy as standard flow in UM analyses (see also Kennedy et al.
2014). To improve the integration of biophysical processes that
support a system’s economy into MFA, the definition of system
boundaries is an important issue to be addressed. Emergy-based
analysis may offer a relevant framework in this regard. As the
emergy10 synthesis of Beijing shows (Zhang et al. 2009), the
socioeconomic system is considered a subsystem embedded in
the whole “urban metabolic system.” This system definition
acknowledges that cities require the biophysical environment
within and outside of their administrative boundaries to sus-
tain their metabolism. When aiming for a circular UM, it is
crucial to take such a systems perspective and to acknowledge
that the densely populated space within urban administrative
boundaries is insufficient to realize self-sufficiency.
The advanced classification of waste flows allows comparison
of Amsterdam’s 2012 waste flows with those of 1998 (Gorree
et al. 2000). This comparison shows that the household waste
generated in Amsterdam decreased from 308 to 272 kt, equiv-
alent to a decrease from 429 kg per capita (kg/ca) to 344 kg/ca.
Nonetheless, over the past 14 years, the amount of recycled
municipal solid waste increased only slightly, from 39 to 41 kt.
The waste imported from adjacent municipalities is in the same
order of magnitude for both years. Yet, in 1998, no waste was
imported from abroad and the amount of industrial waste incin-
erated was less than half the amount in 2012 (259 vs. 599 kt).
Explicating sourcing of secondary resources from inter-
nal waste treatment processes increases understanding of the
metabolic processes occurring within the system boundary.
Cases were identified in which synergetic resource recovery
solutions have been implemented, such as the conversion of
biogas derived from wastewater sludge digestion into electric-
ity and heat. This example confirms that including water and
wastewater in the UM analysis and increasing the transparency
of the system is critical to reveal the potential to source sec-
ondary resources.
Because of the more specific classification of flows, the mod-
ified urban Eurostat MFA provides more detailed information
on Amsterdam’s metabolism than the urban Eurostat MFA.
This detailed classification enables a more thorough analysis of
which material flows affect the material balance of a city, em-
phasizing case-specific consumption and production processes,
and allows for a more in-depth comparison of cities. Although
the detailed classification proved useful for identifying city
characteristics that cause metabolic differences between cities,
benchmarking cities on their degree of sustainable resource
management also requires new performance indicators. These
indicators should, for example, explicate what share of DMI
and DMC is nonrenewable and what share is renewable. A
distinction should be made between direct nonrenewable ma-
terial inputs/consumption and direct renewable material in-
puts/consumption.
Indicating throughput flows helped to reveal that Amster-
dam’s material balance shows the so-called Rotterdam-Antwerp
effect (Peffekoven 1994). This means that materials are at-
tributed as imports and exports to port cities, even if these cities
are not the final destination of those materials, but rather a tran-
sit location where materials are discharged and trans-shipped.
This knowledge is of special value to local decision makers be-
cause it reveals that the metabolism of the city is dominated
by trade flows instead of consumer-related consumption flows.
More important, it brings to the attention that the envisioned
increased circularity (the “circular economy”) of Amsterdam
will affect the metabolism of the city only to a limited extent.
In the transition toward a circular economy and a sustainable
metabolism, it is therefore important to take the harbor and its
trade-related flows into consideration.
Conclusions and Recommendations
The first objective of this research was to gain insight into
the UM of Amsterdam by performing an MFA of this city. The
results of this case study show that Amsterdam’s metabolism is
dominated by water flows and port-related throughput of FFs
and FF products. Although the city’s environmental pressure
has decreased since 1998 in terms of per capita waste genera-
tion and drinking water consumption, the majority of the city’s
municipal waste is still incinerated.
The second objective of this research was to enhance the per-
formance of the Eurostat method for comprehensive UM analy-
ses. On the basis of the Amsterdam case study, we can conclude
that the findings of the modified urban Eurostat method provide
a deeper understanding of a city’s metabolism than those of the
original urban Eurostat MFA. These modifications resulted in
(at least) three benefits. First, the adjustments and additions to
the resource classification result in an MFA that presents a more
complete image of resource flows. The modifications result in
23 additional local sourcing categories and a significant amount
of additional inputs and outputs to Amsterdam’s material bal-
ance. Second, the modified resource classification presents find-
ings in more detail. Benchmarking selected results showed that
this can be valuable for interpreting the MFA findings and
identifying explanatory variables for metabolic differences and
similarities between cities. Third, explicating throughput flows
reveals a much-improved insight into the nature of a city’s
imports, exports, and stock. In cases of harbor cities such as
Amsterdam, it can disclose the significance of port-related flows
900 Journal of Industrial Ecology
RESEARCH AND ANALYSIS
and long-term harbor stocks on the overall material balance of
the city.
In order to support decision makers in bringing about a tran-
sition toward a more circular UM, additional work will be re-
quired. This work includes analyses of how much of a city’s
resource demands can be met by local sourcing. In other words,
the potential of key biophysical and geochemical processes
within the city as well as the potential of stocks to serve as
urban mines have to be determined. Another important chal-
lenge for future research is to unravel the UM within the black
box, to provide insight both into the temporal and spatial dy-
namics of resource flows and interconnectedness between flows.
High spatial and temporal resolutions will show more precisely
where and when resources are present in the system, offering
opportunities for coupling demand and supply and optimizing
the sourcing of secondary resources. These can be valuable fields
of further research to support the planning and design of sus-
tainable urban environments.
Acknowledgments
This article is an outcome of the Urban Pulse research project
under the auspice of the Amsterdam Institute for Advanced
Metropolitan Solutions (AMS). In Urban Pulse, academic, so-
cietal, and industry partners aim to acquire an understanding
of the spatial and temporal dynamics of resource flows in Am-
sterdam. We are grateful for the contributions made by our
project partners and thank our colleagues Adrie van’t Veer and
Monique Jansen (Wageningen University, Landscape Archi-
tecture Group) for helping us with the figures.
Notes
1. The following ten sectors were considered: industry, gas, water,
electricity, transport, building and construction, consumers, of-
fices, waste treatment, and sewage treatment.
2. Hammer and colleagues (2003a) also studied the UM of Leipzig
using this method, but this MFA was excluded from the compar-
ative analysis because it was conducted at the metropolitan scale
level.
3. The MFAs only take into account the material imports and ex-
ports attributed to the port area that is located within Amsterdam
municipality. However, the Port of Amsterdam as an entity covers
the entire port region, including the ports of Zaanstad, Beverwijk,
and IJmuiden.
4. Affiliations of the stakeholders involved were: AEB Amsterdam,
City of Amsterdam (Department of Urban Planning and Sustain-
ability); Delft University of Technology (Engineering Systems and
Services, Water Management); Port of Amsterdam; Waag Society,
institute for art, science and technology, Amsterdam; Wagenin-
gen University and Research Center (subdepartment of Environ-
mental Technology; Laboratory of Geo-information Science and
Remote Sensing; Landscape Architecture Group; LEI research in-
stitute); Waternet.
5. No 2012 data were available for Vienna.
6. One tonne of oil equivalent (toe) equals 41.868 gigajoules
(GJ).
7. Because many of the Amsterdam sewers are below the water table
and pipes can crack, groundwater seeps into the sewer system.
8. Although “local extraction” is a suitable category in which to
include new categories of locally sourced primary resources, “lo-
cal extraction” is an inappropriate term to describe local renew-
able energy generation, which is one of the additional categories.
Therefore, the “local extraction” has been renamed “local sourcing
of primary resources” in the modified urban Eurostat method.
9. Nonrevenue water includes leakage from the drinking water distri-
bution system and drinking water used as fire extinguishing water;
household loss includes water used for watering plants and evapo-
ration from laundry and cooking.
10. Emergy is defined as the total amount of solar energy that is used
directly and indirectly to make a product or a service (Odum
1996). Emergy-based analyses use solar equivalent joule (SEJ) as a
common metric.
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About the Authors
Ilse Voskamp is a researcher, Marc Spiller is a lecturer,
and Huub Rijnaarts is a professor at Wageningen University
in Wageningen, the Netherlands. Sven Stremke is an assistant
professor at Wageningen University and principal investigator
at the Amsterdam Institute for Advanced Metropolitan So-
lutions, Amsterdam, the Netherlands. Daniela Perrotti was a
visiting fellow at Wageningen University at the time the article
was written. She is currently a visiting scholar at Aalto Univer-
sity, Aalto, Finland. Jan Peter van der Hoek is executive officer
at Waternet, Amsterdam, and a professor at Delft University of
Technology, Delft, the Netherlands.
Supporting Information
Supporting information is linked to this article on the JIE website:
Supporting Information S1: This supporting information includes detailed information on the material flows databases
used in the study.
902 Journal of Industrial Ecology
... In all the studies examined, the EW-MFA method (Eurostat, 2001) is the most consistent in terms of flow categories and had the highest number of case studies. Initially designed for nationwide analysis, the EW-MFA has become a foundation for UM studies in the European Union and has been adapted to city and regional levels including, for example, Lisbon (Rosado et al., 2014), Cape Town (Hoekman & von Blottnitz, 2017), Amsterdam (Voskamp et al., 2017), Rennes and Le Mans (Bahers et al., 2019), Stockholm (Papageorgiou et al., 2020), and regions in European countries (Bianchi et al., 2020). All material flows required for the establishment, operation, and maintenance of socioeconomic biophysical stocks are quantified by EW-MFA (Mayer et al., 2016). ...
... Ecological Indicators 136 (2022) 108593 given their significant impact on the urban economy (Voskamp et al. 2017). Supporting and cultural ES were not included in the review since their assessment requires a different, multilayered modeling approach (Maes et al., 2012) which differs substantially from the one used in our framework development (cf. ...
... A significant portion of these studies (16 out of the total) develop an analysis of the water cycle or quantify water flows in an urban context. For example, this includes studies that developed an extended EW-MFA including water inputs and outputs (Voskamp et al., 2017), other UM methods (Ngo & Pataki, 2007), a mass-balance framework for waterflow classification (Kenway et al., 2011), and the assessment of rainwater from collection systems in buildings (Morales-Pinzón et al., 2015;Winker et al., 2018). ...
Article
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The growing evidence-base demonstrating that cities are responsible for accelerated natural-resource erosion and the exacerbated impacts of atmospheric emissions on climate change suggest the need for more systemic resource-use mitigation strategies at the urban scale. Nowadays, ecosystem service analysis provides an extensive reservoir of techniques and strategies to optimize the metabolism of cities through enhanced resource cycling and emission abatement. However, this reservoir is largely untapped in urban metabolism research despite substantial progress in ecosystem service knowledge and classification. In response to this knowledge gap, in this article we propose an integrated urban metabolism and ecosystem service framework to extend Economy-Wide Material Flow Analysis (EW-MFA). The framework utilizes “Pressures”, “Drivers” and “State” indicators to describe the relationships between anthropogenic and natural systems. A set of indicators was compiled from previous urban metabolism and ecosystem service studies to provide a shared and adaptable set of assessment categories across the two areas to jointly measure ecosystem services and resource flows. Through the proposed framework, interdependencies and causal relationships between ecosystem service assessment and EW-MFA flow categories can be identified. The focus of the paper is on elaborating the conceptual foundations of the framework and its analytical characteristics.
... The need for more granular data regarding the waste content has also been acknowl- The same data characteristics are relevant in regional assessments when the total amount of potentially available secondary resources is compared with the current primary resource demands. To evaluate the feasibility of circular strategies, high spatial (Voskamp et al., 2016) The geopolitical scale is also an important aspect while considering closing material loops. While Graedel et al. (2019) argue that no country anywhere has a 7.2.2 ...
... This MFA depiction traces flows but they are not to scale and do not include water quality. Voskamp et al. (2017) use MFA to analyse Amsterdam's current water flows and compare them to natural resource use in 1998 to measure progress toward Amsterdam's goals ( Figure 2.19). Arora et al. (2022) develop a demand-and discharge-driven water circularity assessment framework for cities which integrates anthropogenic water flow data based on the water demand in an urban system and treated wastewater discharge for primary water demand substitution. ...
Thesis
Whether there is enough water in California to meet the needs of residents, businesses, agriculture, and the natural environment, now and in the future, is important to Californians. One of the tools used to assess water availability in California is the water budget, which quantifies how much water enters and leaves the state, and how it is used or stored each year. While this information is useful for tracking quantity, it does not provide any information regarding the quality of the water. The objective of this thesis is to determine whether a method can be established for defining the quality of the water in a water budget in California. To do so requires determining whether a method can be established for creating a scale of water quality using the applicable water quality definitions for the types of water in a water budget in California. That requires determining how water quality is defined. This thesis introduces a six-step method for creating a scale of water quality categories that includes water found in both the natural and built environments in California. The method involves: selecting a geographical context; collecting water quality data applicable to the selected location; compiling water quality parameter data; organising water quality parameters in a matrix; ordering the rows of water quality parameter data values to form categories of water quality; and documenting data sources and notes. This thesis also introduces a seven-step method for creating a water budget, in the form of a modified mass flow diagram, that depicts the quality of each quantity of water. The method involves: delineating the system boundary for the water balance: selecting the water budget time period to be used for analysis; collecting water quantity data applicable to the selected system boundary and time period; drawing a modified mass flow diagram; selecting and assigning a colour code to the selected water quality scale; applying the colour code representing water quality to the modified mass flow diagram; and ordering diagram slices by level of water quality. The findings indicate that a water budget that includes water quality allows for areas of more efficient use, alternatives to over-extraction, and opportunities for reuse to be identified. Viewing the quantities and qualities depicted together on the same graphic allows like quantities and qualities to be matched, revealing opportunities for meeting demand using different water sources. Adding water quality to water budgets may not only show areas where there is room for improvement, but also depict areas where there are resources and opportunities that might not have been visually obvious from a table of numbers.
... Urban Metabolism studies are mainly based on two approaches. The first one is based on energy equivalence, according to Odum (1983) [25], and the second one is based on the flow of material resources [26][27][28]. Urban metabolism research has also been linked to urban sustainability indicators, urban greenhouse gas emissions, Urban Metabolism mathematical models and policy analysis, and sustainable urban form design and landscape planning [10,22]. The approach to Rural-Urban Metabolism is mainly based on the second one. ...
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Cities are responsible for about 75% of the global greenhouse gas emissions. Various materials and energy sources, which are mostly produced by the rural areas rather than the cities, are consumed by the cities, and their waste is released back into the rural areas, thereby causing evident environmental damages. The Rural–Urban Metabolism approach can offer a comprehensive tool to understand the flux of resources that cross the urban environments and plan for more sustainable cities. Considering the strength of the relationship between the urban and rural areas, this paper offers a new perspective regarding the Rural–Urban Metabolism and its application in the Autonomous Province of Trento is discussed. The methodological approach consists of four main steps: data collection and management to support strategic territorial/urban plans; data assessment to critically evaluate the existing context; data mapping to visualize the data and territorial dynamics; and finally, the definition of the strategic and integrated development plan and actions. The Rural–Urban Metabolism proved to be a strategic approach for urban planning and design to monitor the flow of it, assess the impacts of it and promote more sustainable and circular urban policies.
... The same data characteristics are relevant in regional assessments when the total amount of potentially available secondary resources are compared with the current primary resource demands. To evaluate the feasibility of circular strategies both high spatial (Voskamp et al., 2016) and high temporal (Akram et al., 2019) resolutions of material flow data are necessary. Understanding the spatial and temporal dynamics that can be influenced by external governmental incentives also serves the design of circular supply chains Yu et al. (2021). ...
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As appointed in the EU Circular Economy Action Plan, cities and regions in EU member countries start accompanying their circular economy strategies by monitoring frameworks, often called Circular Economy Monitors (CEM). Having the task to assess the performance towards the achievement of set targets and to steer decision-making, CEMs need to rely on a multitude of statistics and datasets. Waste statistics play an important role in circular economy monitoring as they provide insights into the remaining linear part of the economy. The collection of waste statistics is mandated by the European Commission which provides general guidelines on data collection and processing. The Netherlands has one of the most detailed waste registries among the EU countries. The country’s largest metropolitan region, Amsterdam, is currently building a CEM which tracks progress over time towards the set goals, highlights which areas need improvement and estimates target feasibility. This paper uses the Amsterdam CEM as a case-study to explore how the existing system of waste registration in the Netherlands is able to support decision-making. The data is explored with the help of four queries that relate to the CEM’s goals and require data mapping to be answered. The data mapping and analysis process has revealed several limitations present in the waste data collection and a number of gaps present in current circular economy research and data analysis. At the same time, the available data already supports significant insights into the status quo of the current waste system and provides opportunities for circular economy monitoring.
... The same data characteristics are relevant in regional assessments when the total amount of potentially available secondary resources are compared with the current primary resource demands. To evaluate the feasibility of circular strategies both high spatial (Voskamp et al., 2016) and high temporal (Akram et al., 2019) resolutions of material flow data are necessary. Understanding the spatial and temporal dynamics that can be influenced by external governmental incentives also serves the design of circular supply chains Yu et al. (2021). ...
Preprint
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As appointed in the EU Circular Economy Action Plan, cities and regions in EU member countries start accompanying their circular economy strategies by monitoring frameworks, often called Circular Economy Monitors (CEM). Having the task to assess the performance towards the achievement of set targets and to steer decision-making, CEMs need to rely on a multitude of statistics and datasets. Waste statistics play an important role in circular economy monitoring as they provide insights into the remaining linear part of the economy. The collection of waste statistics is mandated by the European Commission which provides general guidelines on data collection and processing. The Netherlands has one of the most detailed waste registries among the EU countries. The country’s largest metropolitan region, Amsterdam, is currently building a CEM which tracks progress over time towards the set goals, highlights which areas need improvement and estimates target feasibility. This paper uses the Amsterdam CEM as a case-study to explore how the existing system of waste registration in the Netherlands is able to support decision-making. The data is explored with the help of four queries that relate to the CEM’s goals and require data mapping to be answered. The data mapping and analysis process has revealed several limitations present in the waste data collection and a number of gaps present in current circular economy research and data analysis. At the same time, the available data already supports significant insights into the status quo of the current waste system and provides opportunities for circular economy monitoring.
... Corona et al. (2019) proposed another classification based on the review of 19 metrics and evaluation methods (Input/Output analysis, MFA, and LCA) distinguishing between the measurement of the degree of circularity and the assessment of the effects of circularity. Material Flow Analysis (MFA) for instance can be in turn a prerequisite for the calculation of circularity metrics (Franklin-Johnson et al. 2016) or a method used to assess the circularity of systems at different scales (Haas et al. 2015;Voskamp et al. 2017;Lonca et al. 2020). Specific reviews, for example at company scale (Vinante et al. 2021), are also proposed. ...
Article
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PurposeThe built environment is a key sector for the transition towards a so-called circular economy, contributing to solve the global environmental challenges humanity is facing. As buildings interact with other sectors like transport and energy, a systemic approach is needed to assess the environmental relevance of circular economy practices. The purpose of this study is to develop and test an approach for the evaluation of overall environmental performance of urban projects.Methods Combining Material Flow Analysis (MFA), the Material Circularity Indicator (MCI), and Life Cycle Assessment (LCA) indicators allows for relating means (material recovery) and performance (protection of human health, biodiversity, and resources).Results and discussionThis study shows the ability of LCA to evaluate circular economy practices at the scale of an urban project. It also highlights LCA’s limitations and shows that research is needed to improve resource depletion evaluation and biogenic carbon accounting in eco-design LCA tools. Results show that at the project scale, the MCI, one of the major circular indicators in use today, and MFA provide interesting information complementary to LCA but do not successfully evaluate the environmental performance of circular practices.Conclusions Circularity indicators are complementary to LCA indicators and should not replace them in the eco-design process. Rather than setting circularity targets for a project, it is advisable to set environmental targets so that designers use circularity combined with other means to reach these targets in a systemic way. The choice and implementation of environmentally sound circular actions and strategies are at stake.
Chapter
Despite rising awareness concerning climate change, global anthropic impacts on the environment are forecasted to increase in the overcoming years, exceeding our planet’s ecological limits. The accelerating pace of climate degradation calls for a quick and efficient response from our societies, should we have a chance to limit the impacts of global warming. Being main nodes of over-consumption and pollution, thus having a high potential for footprint reduction, cities are crucial actors for climate mitigation. Hence, to successfully achieve a transition towards real sustainability, knowledge transfer needs to happen from the cities that are aiming towards life-respecting planetary boundaries to other urban regions worldwide. Although gaining momentum in the literature, a life respecting Earth’s Carrying Capacity (ECC) is not yet explicitly nor widely set as the ultimate goal for cities wanting to realistically face climate change. This article’s purpose is to reflect on the identification of cities actively aiming for ECC and point out the various obstacles to this goal. A misrepresentation of cities’ impact, both induced by misused sustainability terms and incomplete assessment methodology, is found to be hindering cities from reducing their footprint with the efforts needed to adequately face climate change. To that extent, it is crucial that ECC becomes a wider used target for cities, and that compliant assessment methods along with more holistic indicators are used to evaluate and monitor their progress. Finally, other technical issues regarding the incompleteness of standards, accessibility, and representativeness of qualitative data must be addressed.
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Circular economy (CE) has gained relevance as a new economic-environmental paradigm. Despite their key role in this model, managing cities towards CE has taken different meanings, approaches, concepts and methods. Therefore, this study aims to clarify circularity approaches to urban areas, by identifying main trends and exploring potential organization into a framework for policymakers and urban managers. We first conducted a systematic literature review (SLR) to understand limits and divergences when spatially expressing circularity. Four approaches covering different possibilities regarding circularity in urban areas stood out: (i) specific flows within a circular city; (ii) flows integration for resource looping; (iii) planning the transition from linear to circular cities; and (iv) concepts of circular or regenerative urban areas. We then hypothesized that one of these concepts, the Cradle to Cradle (C2C) approach, embraces the multiplicity of quantitative and qualitative requisites needed for developing circular urban areas. Our contribution, in the second part of the paper, organizes the requisites and indicators raised during the SLR according to the C2C principles into a draft framework to enable optimization and integration of different flows with human activities to various urban and socioeconomic contexts.
Technical Report
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Steden zijn plaatsen waar veel mensen op een relatief klein oppervlak bij elkaar leven. De bevolking van de stad moet van voedsel, water en energie worden voorzien, ze moeten de mogelijkheid en de ruimte hebben om arbeid te verrichten en te recreëren, en de afvalstoffen die ze produceren moeten worden afgevoerd. Als men met een enigszins abstracte bril naar de stad als geheel kijkt vertoont ze veel overeenkomsten met een organisme, of liever met een kolonie van organismen zoals een mierennest: in het nest stromen energierijke stoffen en bouwstoffen binnen die worden gebruikt om de organismen binnen het nest in leven te houden, bij het onderhoud en de uitbreiding van het nest en bij (en dat is uniek voor een stad) de productie van goederen. In dit proces, dat als het metabolisme van het nest c.q. de stad kan worden gezien, wordt zuurstof uit de atmosfeer verbruikt voor de verbranding van voedsel (energiedragers) en komen emissies, met name kooldioxide en water, en afvalstoffen vrij. Het metabolisme van de stad kan een belangrijk aanknopingspunt vormen voor het milieubeleid in de stad, met name voor een beleid gericht op dematerialisatie. Wanneer de stromen van stoffen, materialen en producten binnen de stad gekwantificeerd zijn kunnen middels een analyse van deze stromen bepaalde probleemstromen worden geïdentificeerd worden. Verder wordt duidelijk hoe bepaalde materiaalstromen in de stad met elkaar samenhangen. Vervolgens kunnen de processen waardoor deze probleemstromen worden veroorzaakt onder de loep worden genomen en kunnen mogelijkheden voor sturing van deze processen worden onderzocht. In deze studie zijn de materiaalstromen die samenhangen met het metabolisme van de stad Amsterdam gekwantificeerd. In termen van massa vormt water veruit de belangrijkste stroom. Zelfs de instroom van energiedragers en de emissies die bij de verbranding van deze energiedragers plaatsvinden vallen daarbij in het niet. In totaal stroomt er jaarlijks bijna 100 miljard kg drinkwater de stad binnen. Ter vergelijking: deze jaarlijkse instroom is groter dan de totale massa van alle bouwwerken in de stad! Dit water wordt, samen met het regenwater, weer afgevoerd via het rioolstelsel en gezuiverd in RWZIs. In tegenstelling tot het natuurlijk analoog wordt slechts 2 procent van het water gebruikt voor het metabolisme van de organismen in het nest, de rest wordt gebruikt voor hygiënische doeleinden. Schoon drinkwater is een schaars goed. Ondanks dat het in Nederland in overvloed aanwezig lijkt is er toch sprake van lokale tekorten, vooral in de zomermaanden. Daarnaast zijn voor de zuivering van drinkwater vaak energie en chemicaliën nodig. Zeker gezien de omvang van de stroom is het nuttig om zo efficiënt mogelijk omspringen met drinkwater. Te denken valt bijvoorbeeld aan zuiniger wasmachines, douchekoppen etc., maar ook aan het vervangen van drinkwater door water van mindere kwaliteit of door tweedehands water voor bepaalde toepassingen zoals het doorspoelen van het toilet. Na water zijn de stromen die samenhangen met de energievoorziening de grootste stromen. Deze stromen zijn meer dan een factor tien kleiner dan de waterstromen. Het gaat met name om de instroom van fossiele brandstoffen (2,7 miljard kg) en de voor verbranding benodigd zuurstof (7,6 miljard kg), en om de uitstroom van de verbrandingsproducten kooldioxide (ongeveer 7,7 miljard kg) en verbrandingswater (2,5 miljard kg). De verbranding van de fossiele brandstoffen vindt met name plaats tijdens de elektriciteitsopwekking en bij de warmtevoorziening, in het transport en in de bedrijven. Fossiele brandstoffen zijn in principe een uitputbare hulpbron, daarnaast levert de verbranding van fossiele brandstoffen een belangrijke bijdrage aan problemen zoals klimaatsverandering, verzuring, vermesting en smogvorming. Ook voor deze op een na grootste categorie stromen geldt dat een efficiënt gebruik door middel van zuinige apparatuur en vervoermiddelen de omvang van de stromen kan verlagen. Verder kan gedacht worden aan het vervangen van fossiele brandstoffen door alternatieve energiebronnen zoals zonne- en windenergie.
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Significance Our quantification of energy and material flows for the world’s 27 megacities is a major undertaking, not previously achieved. The sheer magnitude of these flows (e.g., 9% of global electricity, 10% of gasoline; 13% of solid waste) shows the importance of megacities in addressing global environmental challenges. In aggregate the resource flows through megacities are consistent with scaling laws for cities. Statistical relations are established for electricity use, heating/industrial fuels, ground transportation, water consumption, waste generation, and steel production in terms of heating-degree days, urban form, economic activity, and population growth. Analysis at the microscale shows that electricity use is strongly correlated with building floor area, explaining the macroscale correlation between per capita electricity use and urbanized area per capita.
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We introduce a new 'multi-layered' indicator set for urban metabolism (UM) studies in megacities. The indicator set is designed for gathering information on the definition (spatial boundaries, constituent cities, population, economy), biophysical characteristics (climate, population density, building floor area), and metabolic flows (water, waste, materials, and all types of energy) of megacities. In addition, it addresses the role of utilities in the provision of services and regulatory actions that, along with public governance, may influence (and/or control) the urban metabolism. In the article, we give background context to the growth and development of megacities, their overarching socio-economic issues, and the definition of their boundaries. Two methodologies to define megacity boundaries are compared, showing that the definition of 'megacity' is not trivial and that further investigation is needed to establish a baseline for comparison of urban metabolism data. Use of the standardized indicator set will ease inter-city comparisons of urban metabolism, whilst enhancing knowledge of megacities and their transformation into sustainable systems.
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Urban metabolism studies have been established for only a few cities worldwide, and difficulties obtaining adequate statistical data are universal. Constraints and peculiarities call for innovative methods to quantify the materials entering and leaving city boundaries. Such methods include the extrapolation of data at the country or the region level based, namely, on sales, population, commuters, workers, and waste produced. The work described in this article offers a new methodology developed specifically for quantifying urban material flows, making possible the regular compilation of data pertinent to the characterization of a city's metabolism. This methodology was tested in a case study that characterized the urban metabolism of the city of Lisbon by quantifying Lisbon's material balance for 2004. With this aim, four variables were characterized and linked to material flows associated with the city: absolute consumption of materials/products per category, throughput of materials in the urban system per material category, material intensity of economic activities, and waste flows per treatment technology. Results show that annual material consumption in Lisbon totals 11.223 million tonnes (20 tonnes per capita), and material outputs sum 2.149 million tonnes. Nonrenewable resources represent almost 80% of the total material consumption, and renewables consumption (biomass) constitutes only 18% of the total consumption. The remaining portion is made up of nonspecified materials. A seemingly excessive consumption amount of nonrenewable materials compared to renewables may be the result of a large investment in building construction and a significant shift toward private car traveling, to the detriment of public transportation.
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The knowledge of urban metabolism process is a major step towards the design of sustainable development schemes and environmental management. This study systematic simulated and analyzed the mechanisms of Beijing urban ecosystem from a thermodynamic point of view. This assessment model review and compile existing data and studies on environmental issues available primarily at some sources, which include resource accounting and environmental impact assessment. The direct and indirect emergy demand was assessed based on airborne and waterborne pollutants dilution patterns, and concepts of Life Cycle Impact Assessment followed the DALY and PDF methods. Results pointed out (1) the development of economy in Beijing was closely correlated with the consumption of the nonrenewable resources and exerting rising load on environment; (2) of the total emergy use by the economic system, the imported nonrenewable resources from other province contribute most with increasing use from imported nonrenewable resources; (3) the rapid growth of society fixed capital investment drave Beijing's economic development and GDP'S growing; (4) emissions greatly reduced the sustainability of the urban metabolic system by pulling resources for damage repair and for replacement of lost natural and human-made capital. Such a knowledge is a necessary pre-requisite to perform a reliable cost-benefit evaluation of urban sustainability strategies, and provide guidance to policy decisions.
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With accelerating global changes, cities have to cope with growing pressures, especially for resource supply. Cities may be considered as resources reservoirs and producers of secondary resources. This paper introduces the concept of urban harvesting as a management tool to change inefficient linear urban resource usage and waste production into sustainable urban metabolism. The Urban Harvest concept includes urban metabolism and closing urban cycles by harvesting urban resources. The purpose of this study was to quantify the potentials to harvest water and energy at different scales. We investigated potentials for the Netherlands. Results show that at national scale, potentials can cover up to 100% of electricity demand, 55% of heat demand and 52% of tap water demand. At neighborhood level, similar percentages were found for energy. Only 43% of water demand was achieved, due to fact that treatment measures were not considered. These results indicate the large potential of cities as providers of their own resources. Therefore urban resources management is a key element of future city design towards more resilient cities.
Article
This article describes a new methodological framework to account for urban material flows and stocks, using material flow accounting (MFA) as the underlying method. The proposed model, urban metabolism analyst (UMAn), bridges seven major gaps in previous urban metabolism studies: lack of a unified methodology; lack of material flows data at the urban level; limited categorizations of material types; limited results about material flows as they are related to economic activities; limited understanding of the origin and destination of flows; lack of understanding about the dynamics of added stock; and lack of knowledge about the magnitude of the flow of materials that are imported and then, to a great extent, exported. To explore and validate the UMAn model, a case study of the Lisbon Metropolitan Area was used. An annual time series of material flows from 2003 to 2009 is disaggregated by the model into 28 material types, 55 economic activity categories, and 18 municipalities. Additionally, an annual projection of the obsolescence of materials for 2010–2050 was performed. The results of the case study validate the proposed methodology, which broadens the contribution of existing urban MFA studies and presents pioneering information in the field of urban metabolism. In particular, the model associates material flows with economic activities and their spatial location within the urban area.