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Spatial characterization of construction material stocks: The case of the Paris region

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Better knowledge of the spatial distribution of anthropogenic stocks is key for the implementation of circular economy policies. This article focuses on the case of construction materials in the Paris region in 2013. A bottom-up approach using GIS modeling is applied to estimate and locate the stocks of 27 materials and 24 building archetypes, three transport networks and six energy and water networks. The stocks of three sub-urban areas inside the region (Paris, PC and GC) and three neighborhoods representative of urban forms are compared. The anthropogenic stocks of the Paris region amount to 204 t/capita and contain 96% of non-metallic minerals. Buildings make up 72% of the stocks. Materials located underground represent 47% of the stocks. The density of total stocks is much higher in central Paris (4.6 t/m² of the urbanized area) than in the outskirts of GC (0.7 t/m²). Higher stock density is associated with lower shares of single-family houses and networks in total stocks. This study suggests the possibility of systematically assessing stocks of construction materials using statistical data and GIS modeling over French regions and cities. As studying stocks with a bottom-up approach is data-intensive, to provide more accurate information, the availability and quality of data are crucial.
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Authors version of the article Augiseau, V., & Kim, E. (2021). Spatial characterization of
construction material stocks: the case of the Paris region. Resources, Conservation and
Recycling, 170, 105512.
Note : this document includes the supplementary materials (from page 25)
Corresponding author:
Vincent Augiseau
Project and research manager at CitéSource
81 mail François Mitterrand, 35000 RENNES, FRANCE
E-mail address: v.augiseau@citesource.fr
Co-author:
Eunhye Kim
Project and research manager at CitéSource
81 mail François Mitterrand, 35000 RENNES, FRANCE
E-mail address: e.kim@citesource.fr
Abstract
Better knowledge of the spatial distribution of anthropogenic stocks is key for the implementation of
circular economy policies. This article focuses on the case of construction materials in the Paris region
in 2013. A bottom-up approach using GIS modeling is applied to estimate and locate the stocks of 27
materials and 24 building archetypes, three transport networks and six energy and water networks.
The stocks of three sub-urban areas inside the region (Paris, PC and GC) and three neighborhoods
representative of urban forms are compared.
The anthropogenic stocks of the Paris region amount to 204 t/capita and contain 96% of non-metallic
minerals. Buildings make up 72% of the stocks. Materials located underground represent 47% of the
stocks. The density of total stocks is much higher in central Paris (4.6 t/m² of the urbanized area) than
in the outskirts of GC (0.7 t/m²). Higher stock density is associated with lower shares of single-family
houses and networks in total stocks.
This study suggests the possibility of systematically assessing stocks of construction materials using
statistical data and GIS modeling over French regions and cities. As studying stocks with a bottom-up
approach is data-intensive, to provide more accurate information, the availability and quality of data
are crucial.
Highlights
- Bottom-up approach using GIS modeling to estimate and locate stocks
- 27 materials in 24 types of buildings, 9 groups of transport, energy & water networks
- 204 t/capita, 96% of non-metallic minerals, 72% located in buildings, 47% underground
- 1% located in hibernating buildings and networks and 25% in permanent structures or dissipated
- Higher building density leads to higher stock density and lower share of networks
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Keywords
construction materials; anthropogenic stocks; urbanization; urban mining; circular economy
Graphical abstract
3
1. Introduction
1.1. Toward circular economy policies to reduce the environmental impacts of the construction
sector
Construction materials are the largest material inflows to the human society after water. Their global
consumption increased tenfold during the second half of the 20th century (Krausmann et al., 2009) and
could double by 2060 (OECD, 2018). The impact of this consumption on the environment is significant.
Resources used by the construction sector are mostly non-renewable and, in some cases, scarce, as
observed in the case of copper on a global level (Gordon et al., 2006) and sand on a local level (Peduzzi,
2014). The sector accounts also for 39% of energy and process-related carbon dioxide emissions, 11%
of which result from manufacturing construction materials (IEA, 2019). Construction and demolition
(C&D) waste are the first solid waste flows, and their recycling rate remains low (Krausmann et al.,
2017).
The construction sector is one of the target areas of the circular economy roadmap of the European
Union (EU, 2015; 2020). In France, the law against waste and for a circular economy sets out political
guidelines from 2020 for systematic reuse and recycling of construction materials. In this context, the
role of local authorities is considered crucial to implement circular economy policies at a local level
(Ministère de la transition écologique, 2018). Cities in France decide major orientations in local
transport and land development through their master plans. Moreover, they establish land use plans
which shape types and volumes of buildings.
1.2. Research on construction material stocks and its gap
By the end of the 1990s, many scientific research works were devoted to construction material stocks
and flows. Stocks were mostly studied at a national level as it is the case of 24 over the 31 publications
reviewed in Augiseau and Barles (2017). This scale is appropriate to assess the order of the magnitude
of the past and present stocks and to estimate, at a macroeconomic level, future anthropogenic stocks,
resource demand and potentially recyclable materials according to different policy scenarios
(Wiedenhofer et al., 2015). Some studies enriched material inventory and contributed to assessing the
material impact of policies. Residential buildings, for example, have been most studied among
buildings in this regard (Lanau et al., 2019). Since 2000, increasing literature addresses on
anthropogenic stocks at urban and regional scales, which improved the understanding of the urban
and regional stocks as described in Augiseau and Barles (2017) and Lanau et al. (2019). Fast-growing
literature also improves building material inventory (Heeren and Fishman, 2019) and the
methodological frameworks.
Stocks assessment methods are generally categorized into two main complementary approaches
(Augiseau and Barles, 2017; Lanau et al., 2019). First, the top-down approach is to quantify stocks as
the sum of annual net additions to stock over a long period for a defined socioeconomic system
(Eurostat, 2001). Second, the bottom-up approach consists of measuring the current dimensions of
the buildings and converting them into mass. The bottom-up approach is often used to study the urban
metabolism for two reasons. First, it gives insights into the inner structure of stocks (Lichtensteiger and
Baccini, 2008), which is key to analyze flows among socioeconomic sectors. Second, it is also
acknowledged for its usefulness for describing the spatial distribution of the stocks at a sub-urban
scale.
Kleemann et al. (2016) argue that spatial distribution of construction material stocks is key information
for the forecast of C&D waste flows, particularly for estimating their recoverable value and cost of
disposal. Stephan and Athanassiadis (2018) argue that precise information on the location of stocks
and flows in space and in time is essential to implement recovery scheme for industries and
4
stakeholders. For urban policymakers, identification of stocks at a relevant spatial scale is particularly
essential. For project owners and industries, identification of available secondary materials in space
and at a certain time point is key for estimating the market potentials, enabling synergies between
actors and implementing circular economy strategies (Stephan and Athanassiadis, 2016).
Heeren and Hellweg (2018) show that using geospatial data gives useful insights to understand the
temporal and spatial dynamics of building stocks development and material flows. To this purpose,
more and more research works have been conducted with a bottom-up approach using GIS modeling
(Schandl et al., 2020; Stephan and Athanassiadis, 2018). For European cities, Canton of Geneva
(Erkman 2005; Emmenegger and Frischknecht 2003), Orléans (Rouvreau et al., 2012), Vienna
(Kleemann et al., 2016; Lederer et al., 2016; Obernosterer et al., 1998), Bruxelles (Athanassiadis et al.,
2017) and Paris (Augiseau, 2017) are best-known stocks studies.
Localizing anthropogenic stocks and understanding their spatial organization on urban and regional
scales remain major research questions (Kleemann et al. 2017; Stephan and Athanassiadis 2016;
Heeren and Hellweg, 2018; Schandl et al., 2020; Stephan and Athanassiadis, 2018). Two types of
research gaps can be identified. First, it is difficult to compare different research cases because of
methodological differences, which shows the lack of a more systematic approach and therefore the
need for more comparable methodologies and databases (Athanassiadis et al., 2017). Second, as
Stephan and Athanassiadis (2016) claim, more accurate and comparable material intensity data are
still needed. Recent efforts to do so reflect this need, as shown by a database seed for community-
driven material-intensity research platforms (Heeren and Fishman, 2019).
Moreover, there is still a lack of research on implementing strategies based on results of stock
assessment at the urban scale, which is essential to enhancing synergy among actors (Augiseau and
Barles, 2017). The role of political decision makings and urban planning on stocks formation, such as
land use, urban form, and investment on infrastructures, seems major but is less studied (Wang et al.,
2018; Zhang et al., 2018). Schiller (2007) compares seven urban structure types in cities of Saxony
(Germany) and shows that material stocks in networks are higher in low building density areas. Huang
et al. (2017) show that the period of urban sprawl in Chinese cities matches with those of the growth
in per capita material intensity. Wang et al. (2018) show the intimate relationship between local
elections and road extension thus the growth of related material stocks. Schandl et al. (2020) show the
impact of urban planning, land-use changes and economic development on construction material
stocks by period of construction.
1.3. Objectives and plan
The study aims at assessing construction materials stocks in the Paris region (Ile-de-France) in 2013 at
the land plot level, using a bottom-up approach and GIS modeling. We focus on understanding the
spatial distribution of stocks in urban areas: the spatial distribution of total regional stocks, stocks by
building and network type, and by age of buildings at sub-urban area level. In addition, urban and
material stock indicators are presented and reviewed to observe the impact of urban forms on
construction material stocks. Methodologically, this study examines the possibility of a systemic stock
assessment using building and network topographic and land property databases.
This article organizes as follows. Section 2 introduces the Paris region case study and describes the
methods and data used to assess stocks. Section 3 presents results and provides insights on material
stocks and their characteristics. Section 4 discusses urban drivers resulting in differences in
construction materials stock distribution and composition. Data quality and the limit of GIS modeling
method are also discussed in this section.
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2. Materials and method
2.1. Study area
The Paris region extends over approximately 12,000 square kilometers and has a population of about
12 million in 2013, or 18% of the French population. It is an interesting case study for stocks analysis
for four reasons. First, it is an administrative division which is the subject of land planning and
resources management policies. The Regional Council is in charge of the Regional Master Plan (SDRIF)
which sets objectives from 2013 to 2030 for housing construction and transport network development.
The Council is also in charge of the C&D waste management plan (PRPGD). Another authority, the
Regional and Interdepartmental Office for the Environment and Energy (DRIEE) is in charge of the
regional planning scheme for quarries.
Secondly, as a good knowledge of the territory is required to elaborate and manage those policies,
many data sources on the Paris region are available, including open data sources produced by two land
and urban planning agencies: IAU (now called Institut Paris Region) and APUR.
Thirdly, a recent shift from urban sprawl to urban renewal can be observed. Since 2012, most buildings
are constructed on already urbanized areas (Omhovère and Foulard, 2013). This situation is favorable
for urban mining according to Brunner's classification (2011). It differs from areas that are currently in
demographic decline (i.e. cities in Saxony: Schiller, 2007; Deilmann, 2009) or whose future population
will be stable or even declining (i.e. Japan: Fishman et al., 2014), as well as from areas that are growing
rapidly (i.e. Beijing: Hu et al., 2010).
Moreover, the region is divided into eight administrative subdivisions called départements which form
three intra-regional areas characterized by very different types of urbanization: Paris (also a city), three
départements surrounding Paris called Petite Couronne (PC), and four others called Grande Couronne
(GC). Paris is a very dense central area where high multi-family houses largely dominate, and which is
almost totally urbanized (Vincennes and Boulogne woods cover 7% of the city). PC has a lower
population density where single-family houses cover 30% of the urbanized area. In GC, only 18% of the
land is urbanized and single-family houses cover 38% of the urbanized area. Population densities within
each urbanized area strongly decline from Paris to GC. Figure 1 summarizes those characteristics.
6
Figure 1. Urban characteristics of Paris, Petite Couronne and Grande Couronne, 2013
Source: data from INSEE and MOS 2012, background from IGN
Difference between Paris, PC and GC come from different urbanization periods. Paris has several
historical layers, including Roman and medieval buildings and modern buildings. The city, within its
actual municipal boundary, saw its population quadrupled between 1800 and 1870 (Marchand, 1993).
During this period, Paris witnessed important urban regeneration, called the Haussmann's renovation.
Between 1949 and 1974, modern urban regeneration was done related to the construction of
Boulevard Périphérique and public facilities and housing supply (APUR, 2005).
PC started to develop with industrial and related residential use during the 2nd half of the 19th century
(Marchand, 1993). Heavy industry, such as energy and metal industries, as well as merchandise
transport facilities to match increasing Parisian consumption located in this area. At the beginning of
the 20th century, the urban area of the Paris region reached approximately the administrative limit of
the PC area (Lecoin, 1977). That means that it was densified by industrial and residential buildings in
this period. This area was also marked by high rise multi-family houses constructed between 1949 and
1974. Therefore, this area is characterized by a variety of building types.
Until the middle of the 20th century, GC remained mainly rural. Its urbanization is characterized with a
non-continuous and highly scattered urban form, so-called urban sprawl done during the 2nd half of
the 20th century. Between 1946 and 1974, its population significantly increased (Lecoin, 1977). During
the 1980s, the urbanization of the area accelerated with the development of a regional express railway
network and the construction of low-density single-family housing areas and new towns. The extension
of the urbanized area reached a peak of 27 km²/year between 1980 and 2000.
2.2. Overall method
This study focuses on anthropogenic material stocks of buildings and networks in the Paris region. It
includes 24 types of buildings and four great categories of networks. Some buildings and
infrastructures are excluded due to the lack of data as follows: sports facilities and classified historical
buildings, waterway networks, oil pipelines, telephones cables and fiber optic networks. It includes 15
non-metallic minerals, four metals, two timber materials, four plastics and two other petroleum-based
7
materials, a total of 27 materials. Pit-run material or soil located between the ground surface and
underground networks are excluded in our study (see section 1 in supplementary materials for more
details).
Stocks are estimated by a bottom-up approach. This approach is data-intensive, particularly for
material contents. Since complete knowledge of the latter is impossible, material intensities in
kilogram per gross floor surface area, are studied on a limited number of buildings. All buildings are
thus classified into several groups, also known as “building archetypes” (Lanau et al., 2019). The
definition of archetypes is to classify buildings and networks into several groups considered to have
the same material content and intensity. To obtain the material stocks of a building group, total floor
area of a building archetype, for example, is weighted by its average material intensity. For networks,
with the same approach, material intensities in kilogram per meter are used. They are multiplied by
network lengths to calculate masses.
The main data source to determine gross floor areas of buildings and lengths of networks is a national
topological data base called BD Topo by the French National Institute of Geographic and Forest
Information (IGN). The land property database, fichiers fonciers, by the French Ministry of Finance is
also used, mainly to assign building archetypes. The result on building material stocks is given at land
plot level, the finest spatial scale both for BD Topo and fichiers fonciers.
2.3. Material intensities
The data for material intensities of buildings and networks come from diverse sources. Two previous
French research projects funded by the National Research Agency (ANR) give the overview on data
availability. Duret et al. (2013; ANR-CONFLUENT) provide the data on transportation infrastructure
stocks from Lille case study. Rouvreau et al. (2012; ANR-ASURET) provide material contents of buildings
and some networks in Orléans city case. Data compiled with those two sources were completed by
local official documents (Leroy 2007a; 2007b; Mairie de Paris, 1993). Material intensities for zinc
rooftop are obtained from Umicore Bâtiment (2014). Material intensity for roads come from the two
research works cited and local data sources used as complementary sources which are described in
section 2.5.
2.4. Buildings
The method for assigning archetypes to buildings is often used in stock studies with a bottom-up
approach (Kleemann et al., 2016; Rouvreau et al., 2012; Stephan and Athanassiadis, 2018; Schandl et
al., 2020). We refer to Rouvreau et al. (2012) for defining building archetypes. Nevertheless, types are
adopted to better match with the Parisian case. Research by architect Graulière (2007) for the French
Ministry of Environment is used to set more detailed construction periods: it distinguishes major
construction periods considering large scale transformation of the construction system which followed
great changes in public policy, such as postwar reconstruction and the introduction of the first heat
regulation
1
. Three more periods of construction for commercial buildings and one more period for
industrial buildings are added. Second, we exclude four building archetypes which represent a small
share in the Paris region according to BD Topo. We classify all buildings of the region into 24 groups of
buildings with three criteria as follows: 1) building use, 2) construction period and 3) main materials in
walls.
The data sources to estimate gross floor areas come from BD Topo. The latter informs some
characteristics and the physical dimensions of buildings and infrastructures and covers almost all
1
The construction techniques used before 1914 are very heterogeneous (Graulière, 2007). 1948 corresponds to
the beginning of the massive reconstruction policy of the French State after WWII. In 1975, the first energy
regulations were adopted imposing minimum insulation.
8
buildings and infrastructures at national scale (see section 2 in SM). The total floor area for each
building is calculated by multiplying building site area and number of stories considering average
ceiling heights by archetype informed by Graulière (2007). The latter is also used to estimate an
average number of underground stories by archetype.
Although BD Topo database informs building physical dimensions particularly for ground surface, data
for the definition of building archetypes and for the estimation of underground building surface are
lacking. BD Topo is completed with data from fichiers fanciers. Its data on construction year (jannath),
major surface type (dteloc), main activity (cconac), main wall structure (dmatgm) and underground
area are used. When data for a building in fichiers fonciers are missing, available information at the
closest spatial scale is used to assign archetypes assuming a spatial homogeneity of buildings at a plot
or cadastral section level (for more details, see section 3.1 in SM). Figure 2 summarizes building
archetypes used in this study.
2.5. Infrastructures
The length and the size of ground networks such as roads and railways are known in BD Topo database.
Most underground networks are not included in this database and are known from local data sources.
APUR (2014b) informs on the length of metro lines. Energy and water networks are known by local
networks companies or DRIEE for heating and cooling networks. As most of the data on the lengths of
energy and water networks are only known at a regional scale, we allocate lengths by département
considering that their spatial distribution follows that of urbanized surfaces which are known by the
regional land use database MOS 2012. The network groups covered in this study are described as
follows.
2.5.1. Transport networks
30 road archetypes are defined according to Duret et al. (2013) and Rouvreau et al. (2012) and Mairie
de Paris (1993) for Paris with four criteria, such as location (in or outside Paris), road hierarchy and the
position relative to the ground.
Material intensity data comes from the two first sources which are completed by local data sources:
Office des Asphaltes (2001) for sidewalks and Centre d’Étude des Tunnels (1983) and D’Aloia et al.
(2015) for tunnels. For the case of the Paris municipality, considering the importance of the traditional
stone paving system and resulting specification in materials, two more road archetypes are added.
Their material intensities are defined according to Leroy (2007a; 2007b) and Mairie de Paris (1993).
14 types of railway archetypes are distinguished with three criteria. Tunnels located inside or outside
stations having different diameters are distinguished. Other underground areas for transit or sales
related to railways are excluded. For aerodromes, we assume that all aerodrome runways are of the
same type. Details are presented in sections 3.2 to 3.4 in SM.
2.5.2. Energy and water networks
The two main criteria to distinguish network archetypes are diameter and main material. Position
relative to the ground level, type of water flows and location are also considered to classify some
networks. A total of 40 types of energy and water networks are distinguished. Details are presented in
sections 3.5 to 3.10 in SM. Figure 2 summarizes network archetypes used in this study.
9
10
Figure 2. Building and network archetypes
Source: authors
2.6. Indicators
To compare stocks in different urban districts and spatial areas inside the region, we use the indicators
below:
- Building floor area to urbanized area ratio
- Single-family houses floor area to urbanized area ratio
- Population density (inhab./km²)
- Total stock density (t/m² of urbanized area)
- Share of single-family houses in total stocks (%)
- Share of networks in total stocks (%)
- Per capita stocks (t/capita)
- Per capita and per job stocks (t/capita+job)
Floor area ratio (or floor space index) is not selected as it not considered as the most appropriate
indicator to analyze urban forms according to Duany et al. (2001). Moreover, Fouchier (1997) shows
that the building floor area to urbanized area ratio is a more relevant indicator to study urban
structures in the Paris region. Urbanized area is calculated with MOS 2012 database and includes the
surface occupied by single-family houses, multi-family houses, all non-residential buildings and
facilities, transportation networks, as well as parks, gardens and sports fields (classified 5, 6, 7, 8, 9, 10
in MOS nomenclature).
Indicators are used to compare the anthropogenic material stocks to primary stocks in the region. The
Paris region has rich non-metallic mineral resources particularly natural alluvial aggregates, which is
highly demanded for concrete production (DRIEE et al., 2017). Timber is an important resource
considering its potential even if its actual extraction is small. The density of primary stocks is defined
as the mass of a resource divided by the surfaces occupied by the latter. For alluvial aggregates, it is
the mass of alluvial aggregates resources estimated in DRIRE Haute-Normandie (1999) divided by the
area occupied by regional resources according to DRIEE et al. (2017). For timber, it is the mass of timber
stock according to Inventaire National Forestier (2010) divided by the regional forest area known in
MOS 2012 database.
3. Results
3.1. Building and network stocks at regional level
Regional building and infrastructure stocks account for 2,438 Mt, or 204 t/capita. As shown in Figure
3, non-metallic minerals are dominant making up 96% of the total stocks, in which concrete is major.
Concrete consists mainly of aggregates such as gravels and sand and locates massively in buildings and
to a lesser extent in roads and railways. Stone is the second major non-metallic mineral stock in the
Paris region accounting for 25% of the total stocks. Other materials as metal, wood and plastics
represent only a small portion of the total stocks. Nonferrous metals as zinc and aluminum share minor
part, their quantity is respectively 0.5 Mt and 0.3 Mt.
The material stocks of the Paris region are mainly located in buildings (Figure 3). Their stock accounts
for 1,751 Mt, or 146 t/capita. Transport, energy and water networks share 39% of the total stocks with
687 Mt, or 57 t/capita. Among building stocks, housing stocks are the most important, accounting for
more than half of the total. Roads dominate networks stocks. Local roads make up 14 % of total stocks
(see also tables 22 and 23 in SM).
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Half of the stocks locates underground. For buildings, 33% of the stocks are underground in which 11%
of them locate in foundation and 22% in basement. This share is more important for roads and railways
representing respectively 81 and 69%. Energy and water networks stocks are 100% underground
except for the electricity network (table 24 in SM).
Figure 3. Stocks by material, stocks by building and network, share of stocks located underground, the
Paris region, 2013, %
Source: authors
Stock density amounts to 0.2 t/m2 of the total regional area. The density reaches 0.9 t/m2, taking only
urbanized areas into account. Densities of anthropogenic stocks of aggregates in concrete and timber
reach respectively 0.3 t/m² and 0.02 t/m². Therefore, secondary stocks are denser than primary stocks
of natural alluvial aggregates and timber. It is 1.5 times higher for aggregates and 5 times higher for
timber. Besides, it can be noted that in terms of mass the anthropogenic stock of timber is almost four
times higher than the primary stock. The stock of aggregates in concrete is 30% lower than the stock
of natural alluvial aggregates in mass.
3.2. Stocks at sub-urban level: Paris, Petite Couronne and Grande Couronne
This section presents the results at urban scale dividing the Paris region into three main urban areas
defined in 2.1. section: Paris, PC and GC.
First, figure 4 shows that the building stock density strongly increases from the outskirts to the center
of the region, so as from GC to Paris.
12
Figure 4. Stock density of building ground surfaces per land plot, 2013, t/m²
Source: data: authors; map: from BD Topo by IGN
Table 1 compares the stocks in the three areas and relates stocks to urban characteristics observed
with the indicators defined in section 2.6: building floor area to urbanized area ratio, single-family
houses floor area to urbanized area ratio and population density. It shows a strong relation between
building floor area to urbanized area ratio and stock density: ratio and stock density are respectively 9
and 7 times higher in dense central Paris than in GC. Single-family houses form a minor part of the
urbanized surface as well as the stocks of Paris where they represent almost half of the urbanized areas
and a third of stocks in GC. Moreover, when the share of networks in total stocks in Paris is low, it
reaches 40 % in GC. PC is in an intermediate situation. Stocks by buildings and networks are detailed
in Table 25 in SM.
Comparison of population density and stocks per capita is more difficult. Indeed, while the population
density is lower in PC and GC than in Paris, stocks per capita are higher in GC but they are lower in PC
than in the central city. At first, this can be explained by the location of economic activities, as well as
public and tourism facilities in Paris. Indeed, per capita and per job stocks are lower in Paris than in PC.
Complementary data on public and tourism facilities could better show the impact of these activities
on stocks.
Table 1. Comparison of stocks in Paris, Petite Couronne and Grande Couronne
Indicators
Paris
Petite
Couronne
Grande
Couronne
Building floor area to urbanized area ratio
2.0
0.5
0.2
Single-family houses floor area to urbanized
area ratio
0.001
0.3
0.4
Population density within the urbanized area
(inhab./km²)
23,828
8,108
2,588
Total stock density (t/m² of urbanized area)
4.7
1.3
0.6
Share of single-family houses in total stocks (%)
1
16
29
Share of networks in total stocks (%)
10
18
40
Per capita stocks (t/capita)
197
159
245
Per capita and per job stocks (t/capita+job)
109
110
181
Source: authors
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Another important factor which impacts stocks is its age. Figure 5a compares stocks in multi-family
houses, single-family houses, and tertiary buildings among the three areas by construction period. In
Paris, for all types of buildings, around 50% of stocks are formed before 1914. In PC, the stocks formed
between 1914 and 1974 are dominant. However, for tertiary buildings, 74% of stocks are constituted
after 1975. In the case of GC, the age of the stocks is relatively diverse. Multi-family houses stock
formed pre-1914 and single-family houses stock between 1948-75 are most important. Combining the
periods, between 1948-74 and between 1975-2000, the stocks dated from the 2nd half of the 20th
century is the largest, with 71% for multi-family houses and 45% for single-family houses. Most of the
stocks in tertiary buildings in the area is formed after 1975.
The age of the stocks influences the material contents of buildings. Figure 5b illustrates the stocks in
multi-family houses, single-family houses, and tertiary buildings among the three areas by materials.
Paris has dominant stone stock and concrete is only the second material with 38% of the total stocks.
PC has the most important concrete stock accounting for around 60%. In GC, concrete is the main
material, but stone is the second, which is dominant in single-family houses. Buildings constructed in
stone have a higher material density per building floor area than concrete buildings.
Figure 5. Stocks in multi-family houses, single-family houses and tertiary buildings by construction
period (a) and material (b), Paris, Petite Couronne and Grande Couronne, 2013, %
Source: authors
3.3. Stocks at neighborhood level
To better illustrate how urban characteristics impact materials stocks, three neighborhoods are
compared. They are typical urban forms in the Paris region according to Fouchier (1997): 1. compact
Parisian Haussmannien developed in the 19th century with buildings in stone and brick; 2. typical high-
rise low-density multi-family houses in GC developed in the 1960s; 3. low-rise single-family houses
constructed in the 1990s with cul-de-sac roads in GC area. Haussmannien is mostly located in Paris,
low-density multi-family houses in Paris and PC, and single-family houses in PC and GC. Figure 6
illustrates the three neighborhood units.
14
Figure 6. Maps of three typical neighborhoods of the Paris region
Source: data: authors; map: BD Topo by IGN
Table 2 compares the three neighborhoods in terms of urban characteristics and stocks in buildings
and roads (parking and sidewalks included). Stocks in underground networks, accounting for a small
portion are ignored. First, a strong relation between building floor area to urbanized area ratio and
stock density can be observed. Second, Haussmannien neighborhood, which is only formed of multi-
family houses (often including commercial activities located in the first and second floors) has a very
minor share of material stocks in networks. On the contrary, the typical high-rise multi-family houses
and low-rise single houses neighborhoods have a relatively high portion of stocks in roads. Surprisingly,
despite the striking differences in urban form, the last two types of neighborhoods show similarities in
the ratio of material stocks between buildings and roads. Per capita and per job stocks are much higher
in Haussmannien. As observed for Paris, this partly results from the material contents of buildings.
Table 2. Comparison of material stocks in three typical neighborhoods of the Paris region
Indicators
Haussmannien
(Montholon)
Housing
scheme
(Ferme du
Temple)
Low-
density
housing
(Parc de
Sénart)
Urban
characteristics
Building floor area to urbanized area ratio
4.3
0.7
0.4
Population-employment density (inhab. + jobs/km²)
50,675
17,701
4,072
Stocks
characteristics
Total stock density (t/m² of urbanized area)
9.2
2.0
0.5
Share of networks in total stocks (%)
3
36
34
Per capita and per job stocks (t/capita+job)
182
110
123
Source: authors
15
4. Discussion
4.1. Study contribution
Our research suggests GIS modeling for stock estimation with a bottom-up approach. Such approach
enables spatial and content differentiation of results which is the main advantage compared to the
top-down approach (Lanau et al., 2019). However, the bottom-up approach often applies to smaller
or more limited building types (e.g., residential buildings or one network type) because it requires data-
intensive tasks (Lanau et al., 2019).
This study presented the possibility of systematic stocks evaluation on the scale of cities and regions
using two databases. Building Topological Database (BD Topo) for determining accurate building
physical dimensions and Land property database (fichers fonciers) for determining building archetypes.
We propose the possibility of systemic assessment of building and network stocks using those two
main databases at city and regional levels across France. Models based on the number of dwellings
use data on the average surface of a dwelling. Observation of construction statistics in France since
2000 show that constructed dwelling surfaces vary by year and city (Augiseau, 2017). Therefore, using
a topological database enables more detailed building gross area estimation.
We also demonstrated the possibility of using the land property database, which is mainly used for
real estate and land-use research, for the study of construction material stocks. This source provides
detailed data on building use, construction period and main materials, but had never been used in
scientific research on building stocks. In addition, using this database, it is possible to reduce data-
intensive tasks and efficiently allocate building archetypes on a regional scale. Of course, this approach
has limitations to be described in detail in Section 4.5.
Finally, this study provides empirical findings of existing urban mines in the Paris region and, in
particular, the spatial distribution of stocks in its three urban areas. We have provided insight into
urban characteristics that have a great impact on stock characteristics. The main building types,
dominant construction periods and materials vary greatly between Paris, PC and GC, and the stock
density in Paris is twice that of PC and eight times that of GC. Regional-level observation shows that
this difference is even greater when comparing Paris's dense neighborhood with GC's neighborhood.
Knowledge of building and network material stocks obtained here is a prerequisite for the next step of
material flow analysis in the Paris region (paper in preparation; see also Augiseau, 2017).
4.2. Relationship between urban characteristics and the spatial distribution of stocks
Cities require high consumption for resources, but at the same time have a high potential for secondary
resources reuse and recycling (Swilling et al., 2013). Stock density is often considered as a relevant
indicator to analyze secondary resources potential (Schiller, 2007; Kleemann et al., 2016). Urban stock
densities are generally higher than those of national stocks which vary from 0.01 to 0.2 t/m2 (Stephan
and Athanassiadis, 2016; Lanau et al., 2019).
Schiller (2007) analyzed the relation between building stocks and network stocks in Saxony (Germany)
and showed that the lower the residential building density in a selected area, the greater the share of
material stock in infrastructure. The correlation between building stock density and the share of
networks in total stocks is also observed at the sub-urban level in the Paris region and shows consistent
result with Schiller (2007). Paris city’s stocks density is ten times higher than the one in GC and it has
a lower share of networks in total stocks.
Stocks located in seven urban areas including Paris were compared in Athanassiadis et al. (2017).
However, it appeared that data were missing to better understand differences. To illustrate this, we
will focus here on the Paris region, Canton of Geneva and Orléans presented in Table 3. Those three
16
European urban areas have approximately the same per capita stocks of around 200 t/capita.
Nevertheless, both in terms of densities of stocks and population, Canton of Geneva has 1.5 times
higher density and Orléans 4 times higher density than the Paris region, meaning that the spatial
distribution of population and stocks are different. Comparing the Paris region to the two other areas,
we can observe that: 1. the share of networks in stocks grows as the share of building stock density
decreases; 2. the lower the network share, the higher the share of single-family houses. However,
those relations are not observed between Geneva and Orléans. Indeed, the Paris region and Canton of
Geneva include low dense outskirt area, while Orléans includes only its dense urban center. Moreover,
the age of buildings and networks and their material contents differ.
Table 3. Comparison of stocks in the Paris region, Canton of Geneva and Orléans
Paris region in
2013
Canton of Geneva
(Switzerland) in 2000
Orléans (France) in
2004-2006
Source
authors
Faist Emmenegger and
Frischknecht (2003)
Rouvreau et al.
(2012)
Built works and materials
see section 2.2
Buildings, road and rail
networks; electricity,
gas and water networks
Gravel/sand, asphalt
concrete, concrete,
brick, plastic, wood,
iron, copper, aluminum
Buildings, road and
rail networks;
electricity, gas and
water networks
Minerals, metals,
wood, plastics,
asphalt
Population density (inhab./km²)
991
1,450
4,121
Total stock density (t/m²)
0.2
0.3
0.8
Share of single-family houses in total stocks (%)
20
10
8
Share of networks in total stocks (%)
28
16
17
Per capita stocks (t/capita)
204
209
192
Source: authors' calculation from Faist Emmenegger and Frischknecht (2003) and Rouvreau et al.
(2012)
4.3. Stocks study for urban planning and decision making for a circular economy
The construction sector is acknowledged for its strong potential for circular economy policies in the EU
(EU, 2015; 2020). Cities and urban regions have a crucial role in the implementation of circular
economy policies, particularly regarding their ability in urban development and infrastructure policies.
However, urban planning and resource management policies in the Paris region remain to a large
extent partitioned. This lack of coordination between circular economic strategies led by cities and
urban planning is also observed by Petit-Boix and Leipold (2018) and Obersteg et al. (2019).
Urban planning could ensure that materials flows are limited by giving priority to the refurbishment of
buildings (IRP, 2018). As urban renewal becomes dominant in the Paris region (Omhovère and Foulard,
2013), it is becoming strategically important to utilize existing regional urban mines for urban
reconstruction. Circular economy policies should focus on adapting the existing built area as more than
three-quarters of the stocks currently present in developed countries will still be present in 2050
(Pomponi and Moncaster, 2017).
Our study informs stocks of secondary resources in mass, material composition and location in the
Paris region at different spatial levels which are adapted to the scales of the urban planning and land
use plans set by local inter-municipal authorities. The knowledge of the stocks at various scales such
as building, district, city and region is essential for local circular economy policies making and
implementation (Schandl et al., 2020; Stephan and Athanassiadis, 2016).
17
Comparison of typical urban forms in the region by Fouchier (1997) showed that areas with a lower
building density had a lower population density. Traisnel (2001) showed that these areas usually have
a higher energy consumption per capita. Our study shows that they have higher materials stocks per
capita for networks. Further comparison of stocks in typical urban forms could bring useful information
to urban planners.
4.4. Secondary resources potential
Secondary resources need to be analyzed in terms of availability for extraction. To carry such study,
Winterstetter et al. (2015) set two categories: in-use stocks and obsolete stocks, also called hibernating
stocks in the literature. The latter, which are directly available for extraction, represent a minor part
of the stocks in the Paris region. Indeed, 0.5% of stocks of housing and offices buildings are in
hibernation according to data on surfaces from population census in 2013 by INSEE, SDES (2013) and
ORIE (2013) converted into mass. Unoperated railways, as distinguished by BD Topo, represent 1.4%
of the railway network stock. Though the stocks in hibernation in other buildings and networks cannot
be estimated due to missing data, these buildings and networks form only a small part of total stocks.
Therefore, hibernating stocks form less than 1% of total stocks.
On the other hand, about a quarter of the materials in the anthropogenic stocks will probably not be
extracted as it is located in permanent structures or is dissipated. Indeed, according to Hashimoto et
al. (2009), permanent structures have a low probability of being demolished. It can be considered very
roughly that 20% of the buildings in the Paris region belong to a protected heritage, i.e. 14% of total
stocks. Permanent structures also include tunnels which make up 1% of stocks. Dissipated materials
are generally left in the ground during reconstruction or demolition. If one considers very roughly that
a quarter of the materials in buildings foundations as well as a quarter of road bases and subbases
courses will be left in place, dissipated materials represent 9% of total stocks. These very rough
estimates require further study.
4.5. Limitation of the study and uncertainty
The mass of per capita stock of the Paris region falls within the range of the previous stock studies
showing our results are consistent with other studies. Like all models, our analysis has limitations and
uncertainties due to the assumptions made and the lack of data. This section discusses first the
limitation related to the scope of the study, second, the uncertainty related to the method used and
finally, the uncertainly due to data quality.
4.5.1. System boundary and non-accounted materials
Excavation and backfilling materials from construction work are acknowledged as important due to
their significant mass in C&D waste. However, urban subsoil materials were excluded from our study.
Those materials, in Paris for example, originated from soil movement from one place to another
(Fernandez, 2018). They could therefore be considered as anthropogenic and call further study.
Our study excludes embankments included by Tanikawa et al. (2015). This network probably has non-
negligible material stock in the Paris region considering its important role in merchandise transport.
We also ignored materials located in hibernating networks which raise materials recovery issues (Krook
et al., 2011; Wallsten et al., 2013).
Several non-ferrous metals and the stock located in urban subsoil were excluded in the analysis. The
material overlaying underground networks are also excluded. This material can comprise aggregates
from quarries or natural soil, which means they can be both natural and anthropogenic stock.
18
4.5.2. Uncertainty related to the method
Uncertainty related to the method comes from the parameters used, the definition of archetypes and
the determination of gross floor areas and network lengths.
As building archetype definition greatly influences the results, it is a source of uncertainty. The building
stocks were obtained by 24 building archetypes. In our study, residential buildings have more
categories than non-residential buildings such as tertiary and industrial buildings. Particularly,
commercial and industrial buildings have two archetypes classified by age which are not sufficient to
have fine results for these stocks. The buildings dated before 1914 have just one archetype, although
they are highly heterogeneous in structures and materials.
For networks, the definition of archetypes as well as the assumption made to determine their length
can lead to uncertainty. For road networks, the definition of archetypes depends on the availability of
data. Therefore, geotechnical characteristics or construction periods, which may be important for
materials, were not considered.
The simple assumptions we made to determine the length of underground networks due to the lack
of spatialized data lead to a large uncertainty for this category of stocks. However, they represent only
a very small share in the total stocks.
4.5.3. Uncertainty related to data quality
Following the method presented in Laner et al. (2016), coefficients of variation for each data source
are estimated. As summarized in Table 4, data on material content have higher coefficients than data
for assigning an archetype or estimating dimensions. Among data on material content, road network
and aerodrome runways have the highest coefficients due to their low temporal correlation. For
buildings, data for assigning an archetype or estimating dimensions have higher coefficients. This
results from the lower reliability and temporal correlation of data, in particular for those used to
estimate underground surfaces. For energy and water networks, with the exceptions of heating and
cooling networks and the non-potable water network, high coefficients result from the lower reliability
of data. Details are presented in sections 2 and 6 in SM.
Table 4. Coefficients of variation of the data sources for buildings and networks
Data on material content
Data for assigning an archetype or
estimating dimensions
Minimum
Maximu
m
Media
n
Averag
e
Minimu
m
Maximu
m
Media
n
Averag
e
Buildings
17
17
17
17
5
50
19
25
Transport networks
10
44
22
29
5
28
14
14
Energy and water
networks
8
25
21
19
5
42
15
19
All buildings and
networks
8
44
22
24
5
50
15
19
Source: authors
As fichiers fonciers had never been used for other stocks studies, their quality was further assessed.
First, data on construction periods were compared with those of a database on the city of Paris: APUR
(2014a). The latter contains data from fichiers fonciers that were corrected by using four other sources.
Our comparison shows that periods differ for only 1% of building plots. Secondly, we conducted a field
survey on 100 building plots located in two neighborhoods with different urban characteristics (Picpus
in Paris XII and Trèfles in Sevran). It shows that information on the construction period is missing for
19
10% of building plots and appears to be wrong for 5% of plots. Data on the main material in walls are
missing for 15% and wrong for 15% of total land plots.
The quality of the data sources on material contents could be better assessed by cross-referencing
data with estimates or measurements performed during construction or demolition projects as in
Kleemann et al. (2016). It could also be complemented by comparing French sources with foreign ones
as initiated in Athanassiadis et al. (2017). This could contribute to the database set by Heeren and
Fishman (2019). Quality of data for assigning archetypes or estimating dimensions can be further
assessed by conducting field studies.
For instance, a site survey conducted at a neighborhood scale in Est Ensemble in PC area (CitéSource
and Neo-Eco, 2019) shows differences between actual and modeled buildings. In the case of structural
materials, such as concrete, stone and brick, the difference is due to an overestimate of the modeled
underground surfaces and associated material intensities. Within a building archetype, buildings
observed have highly heterogeneous nonstructural materials, even if the share of these materials is
relatively small in total stocks.
Finally, as recommended by Laner et al. (2016) and applied in studies like Mesta et al. (2019),
uncertainty analysis should be complemented in future work by a statistical approach using Monte
Carlo simulations.
Conclusion
In this study, we quantify and localize construction material stocks in the Paris region. The stocks are
estimated by building and network archetypes using national topographical database and land
property database (BD Topo, fichiers fonciers).
Three neighborhoods and three urban areas within the region are compared. The results show a strong
relation between building density (observed with building floor area to urbanized area ratio) and stock
density. They also demonstrate that higher stock density is associated with lower shares of single-
family houses and networks in total stocks.
Managing urban material and energy flows is at the center of urban environmental policies for cities
facing emerging resource scarcity and aiming at reducing waste disposal. Knowledge of anthropogenic
stocks and their resource potential is essential to develop circular economy strategies that would
reduce inflows and outflows and substitute primary resources by secondary resources. More systemic
approach to urban mining calls for collaborative research involving multiple authors.
Acknowledgement
This research was conducted during the PhD thesis of Vincent Augiseau in the field of urban and
regional planning from 2014 to 2017. It was supervised by Sabine Barles of Géographie-Cités laboratory
in Université Paris 1 - Panthéon Sorbonne. The research was funded by the Regional Council of the
Paris region and the Regional and Interdepartmental Office for the Environment and Energy of Ile-de-
France (DRIEE). This research work is continued in the framework of studies conducted by CitéSource.
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25
Spatial characterization of construction material stocks: the case of the Paris region
Supplementary Materials
1. Scope
Table 1. Scope of buildings and networks
Groups of buildings
or networks
Partially or totally
included
If partially included, buildings and networks excluded
due to incomplete data
Buildings
Buildings
Partially included
Sport facilities: buildings dedicated to art,
entertainment and recreation; agricultural buildings;
greenhouses; silos; tolls; sport field stands; historical
and religious buildings; underground car parks; light
constructions, huts, meadows, awnings; sport grounds
*
Transport
networks
Road network
Partially included
Bridges (excluding the binder courses and the surface
courses overlying bridges); noise barriers; stairs
Railway network
Partially included
Tunnels: spaces in stations for traffic and sales; bridges
and viaducts (excluding rails, sleepers and ballast
located on bridges and viaducts); marshalling yards
Aerodrome runways
Partially included
Grass runways
River network
Not included
/
Energy
and water
networks
Electricity networks
Partially included
Pylons; transformers; wind turbines
Gas networks
Fully included
/
Heating and cooling
networks
Fully included
/
Drinking water
networks
Partially included
Aqueducts (excluding pipelines); water towers and
other water reservoirs
Non-potable water
network
Partially included
Aqueducts (excluding pipelines)
Sewerage networks
Partially included
Sewerage treatment plants
Pipeline
transportation of
dangerous goods
networks
Not included
/
Telecommunication
networks (telephone
cable and optic fiber)
Not included
/
*7 types of buildings according to BD Topo are excluded from the scope of this study due to their very
variable average floor-to-floor height and their low share in the total volume of buildings in the region:
sport buildings (1% of total volume), agricultural buildings (0.6%), greenhouses (0.4%), silos (0.1%),
tolls (0.000002%), sport ground stands (0.1%), historical and religious buildings (0.7%). Buildings
dedicated to art, entertainment, and recreational activities according to fichiers fonciers are also
excluded for the same reasons.
Source: authors
26
Table 2. Scope of materials
Materials
Buildings
Transport
networks
Energy and water
networks
Concrete
x
x
x
Concrete block
x
Aggregates for asphalt concrete,
ballast and paving
x
Pit-run material
x
Stone
x
x
x
Masonry
x
Solid clay brick
x
Hollow clay brick
x
Clay tile
x
Tiling
x
Glass
x
Plaster (including plaster and plaster-
based adhesive mortar)
x
Mortar and mineral plaster (including
polymer-supported plaster)
x
Mineral wool
x
Asbestos cement
x
Steel
x
x
Cast iron
x
Aluminum
x
x
Zinc
x
Timber
x
x
Chipboard
x
Polyvinyl chloride (PVC)
x
x
Polystyrene
x
Polyurethane
x
x
High density polyethylene (HDPE)
x
Mastic asphalt
x
Asphalt
x
Source: authors
27
2. Data analysis
Table 3. Coefficients of variation used for the Pedigree Matrix
Data quality indicators
Sensitivity level
Coefficient of variation
Score: 1
Score: 2
Score: 3
Score: 4
Reliability
/
2.3
6.8
20.6
62.3
Completeness/temporal
correlation/geographic
correlation/other correlation
Highly sensitive
0
4.5
13.7
41.3
Medium
sensitive
0
2.3
6.8
20.6
Not sensitive
0
1.1
3.4
10.3
Source: Laner et al. (2016)
Table 4. Presentation and coefficients of variation of each selected data source
Data source
Data use
For
mat
Short description
Coefficients of variation in %
Data on material content
Data for assigning a type
Data for estimating the dimensions
Geo-database
Reliability
Completeness
Temporal correlation
Geographical
correlation
Other correlation
Total coefficient of variation
Buildings
Rouvreau et al. (2012)
x
Research project ASURET on
the case study of Orléans
7
5
14
5
0
17
Umicore Bâtiment
(2014)
x
Technical information on zinc
roofs
2
14
0
2
0
14
BD Topo (v 2.1 - 2014)
x
x
x
Geo-database by the French
National Institute of
Geographic and Forest
Information (IGN)
2
5
0
0
0
5
Fichiers fonciers
2014: data on
aboveground surfaces
x
x
x
Geo-database by the French
Ministry of Finance to
calculate property tax
amounts
7
5
0
0
0
8
Fichiers fonciers
2014: data on
underground surfaces
x
x
x
Idem aboveground surfaces
with lower reliability and
completeness (Lamé, 2013)
21
14
0
0
0
25
Graulière (2007)
x
Survey on building archetypes
by architect Graulière for the
French Ministry of
Environment
21
14
41
14
0
50
Road network
Duret et al. (2013)
x
Research project CONFLUENT
on the case study of Lille
7
5
5
5
0
10
Rouvreau et al. (2012)
x
See Buildings
21
5
0
5
0
22
Mairie de Paris (1993)
x
Technical information on
roads in Paris by the City of
Paris
7
14
41
0
0
44
Office des asphaltes
(2001)
x
Technical information on
sidewalks by the French
professional union of asphalt
material producers
2
14
41
2
0
44
28
CETU (1983)
x
Technical standards by the
French technical center for
tunnels
2
14
41
2
0
44
BD Topo (v 2.1 - 2014)
x
x
x
Geo-database by IGN
2
5
0
0
0
5
Leroy (2007a)
x
x
Surface areas of roads in Paris
by the City of Paris
21
14
14
0
0
28
Leroy (2007b)
x
x
Idem Leroy (2007a)
21
14
14
0
0
28
Railway
network
Rouvreau et al. (2012)
x
General information: see
Buildings. Material content of
high-speed lines and main
tracks based on modeling.
21
5
0
5
0
22
CIMBETON (2004)
x
Technical information by the
French technical center for
concrete. Complementary
data on tramway platforms.
2
14
14
2
0
20
Hidalgo (2015)
x
x
x
PhD thesis. Information on the
material contents and
dimensions of metro tunnels.
7
14
0
0
0
15
Regan (2016)
x
x
x
Idem Hidalgo (2015) with
complementary information.
7
14
0
0
0
15
BD Topo (v 2.1 - 2014)
x
x
x
Geo-database by IGN. For
each railway section: length,
number of tracks, position in
the railway network hierarchy
and position relative to the
ground. Subway and tram
tracks not distinguished.
2
5
0
0
0
5
Lignes & Stations by
APUR (2014)
x
x
x
Geo-database by Paris
Urbanism Agency. Information
on metro and tramway tracks
with line numbering and
location of stations.
2
14
0
0
0
14
RATP (2016)
x
x
Information by the public
transport operator and
maintainer on the share of
underground tracks in the
total metro network length.
2
14
0
0
0
14
Lamé (2013)
x
x
PhD thesis. Information on the
dimensions of RER lines
tunnels.
7
14
0
0
0
15
Tilière and Viaud
(2012)
x
x
Information by an engineering
company designing railway
stations on high-speed lines
tunnels.
7
14
0
0
0
15
Aerodrome
runways
STBA (1983)
x
Technical standards for
aerodrome runways by the
French technical center for air
bases.
7
5
41
2
0
42
Duret et al. (2013)
x
General information: see Road
network. Data on the material
content of highways used as a
basis to calculate the content
of aerodrome runways.
7
41
5
2
14
44
BD Topo (v 2.1 - 2014)
x
x
x
Geo-database by IGN. Surface
area of runways.
2
5
0
0
0
5
Electricity
networks
Rouvreau et al. (2012)
x
General information: see
Buildings. Material content of
electrical lines based on
information from a producer.
21
5
0
5
0
22
BD Topo (v 2.1 - 2014)
x
x
Geo-database by IGN.
Location and length of high
2
14
0
0
0
14
29
and very high voltage
overhead lines.
Leroy (2007b)
x
x
Information by the City of
Paris on the length of
underground electrical lines in
Paris.
21
5
14
0
0
25
RTE (2011)
x
x
Information by the high
voltage electricity network
manager RTE on the total
length of high and extra-high
voltage lines in the Paris
region.
7
5
5
0
0
9
RTE (2014)
x
x
Information by the high
voltage electricity network
manager RTE on the total
length of medium and low
voltage lines in the Paris
region.
7
5
0
0
0
8
Loubet (2012)
x
x
Information by the medium
and low voltage electricity
network manager ERDF
(Enedis) on the length of all
voltage lines by groups of
départements.
7
5
0
0
0
8
MOS 2012 by IAU
x
x
Geo-database by the regional
land and urban planning
agency (Institut Paris Region).
Information on the surface
area of urbanised areas used
to calculate the network
length by département.
7
5
0
0
0
8
SIGEIF (2015)
x
x
Annual report by electricity
and gas network manager
SIGEIF (mainly located in
Petite Couronne). Share of
underground lines in the
electricity network managed
by SIGEIF.
7
5
41
5
0
42
Kert (2001)
x
x
Report to the French
parliament. Share of
underground lines in the
French electricity network.
2
0
0
0
41
41
Gas networks
Rouvreau et al. (2012)
x
General information: see
Buildings. Material content of
gas pipelines based on
information from producers.
21
5
0
14
0
25
SIGEIF (2013; 2014)
x
x
Annual reports by electricity
and gas network manager
SIGEIF. Diameter and material
content of pipelines managed
by SIGEIF.
7
14
0
0
0
15
GRTgaz (2013)
x
Information by the high-
pressure gas network manager
GRTgaz on the total length of
high-pressure pipelines in the
Paris region.
7
14
0
0
0
15
GRDF (2013)
x
Information by the gas
network manager GRDF on
the total length of pipelines in
the Paris region.
7
14
0
0
0
15
Leroy (2007b)
x
Information by the City of
Paris on the length of gas
pipelines in Paris.
21
14
14
0
0
28
30
MOS 2012 by IAU
x
x
Idem Electricity networks.
2
0
0
0
41
41
Heating and
cooling
networks
Fröling et al. (2004)
x
Environmental assessment of
heating networks in Sweden.
Material content of heating
networks according to their
diameter.
7
5
14
14
0
21
CPCU (2016)
x
x
Report by the heating network
manager CPCU (mainly located
in Paris and Petite Couronne).
Diameters of the pipes
managed by CPCU.
7
5
0
0
0
8
DRIEE (2016)
x
x
Geo-database on heating and
cooling networks by Regional
and Interdepartmental
Directorate for the
Environment and Energy
(DRIEE). Length.
2
5
0
0
0
5
Drinking
water
networks
Rouvreau et al. (2012)
x
x
x
General information: see
Buildings. Material content by
type of drinking water
pipelines and share of each
type in the total network of
Orléans. Based on information
by water network manager in
Orléans.
21
5
0
14
0
25
CG Seine-et-Marne
(2012)
x
x
x
Report by Seine-et-Marne
departmental council. Length
and material content of the
drinking water (and sewerage)
networks in Seine-et-Marne.
7
14
0
0
0
15
SEDIF (2013)
x
x
Report by the drinking water
network manager SEDIF (Paris
region excluding Paris). Length
and diameter of the pipes
managed by SEDIF.
7
14
0
0
0
15
Enquête sur l’eau
2008 on EIDER
x
Survey by the French Ministry
of Agriculture. Total length of
drinking water (and sewerage)
networks in the Paris region.
7
5
14
0
0
16
Eau de Paris (2015)
x
Report by the drinking water
network manager of Paris.
Length of the pipes managed
by Eau de Paris.
7
14
0
0
0
15
MOS 2012 by IAU
x
x
Idem Electricity networks.
2
0
0
0
41
41
Non potable
water
network
Mairie de Paris (2015)
x
x
x
Report by the City of Paris.
Total length and material
content of the non-potable
water network in the Paris
region.
7
5
0
0
0
8
APUR (2010)
x
Report by Paris Urbanism
Agency. Length of the non-
potable water network by
département.
7
5
5
0
0
9
Sewerage
network
Rouvreau et al. (2012)
x
x
x
General information: see
Buildings. Material content by
type of sewerage water
pipelines and share of each
type in the total network of
Orléans. Based on information
by sewerage network
manager in Orléans.
21
5
0
14
0
25
31
CG Seine-et-Marne
(2012)
x
x
Report by Seine-et-Marne
departmental council.
Material content of the
sewerage water networks in
Seine-et-Marne.
7
14
0
0
0
15
Agence de l’eau
Seine-Normandie
(2016)
x
x
See Drinking water networks.
7
14
0
0
0
15
Cité de l’eau et de
l’assainissement
(2016)
x
x
Information from the water
agency of Seine-Normandie.
Length of the sewerage
network in Paris, Hauts-de-
Seine, Seine-Saint-Denis, Val-
de-Marne. For Paris: diameter
of the pipes.
7
14
0
0
0
15
Enquête sur l’eau
2008 on EIDER
x
Report by Seine-et-Marne
departmental council. Length
of the sewerage water
networks in Seine-et-Marne.
7
5
14
0
0
16
CG Seine-et-Marne
(2016)
x
Information by the sewerage
water network manager SIAAP
(Paris region). Length and
diameter of the networks
managed by SIAAP by groups
of départements.
7
14
0
0
0
15
MOS 2012 by IAU
x
x
Idem Electricity networks.
2
0
0
0
41
41
Source: authors
3. Methods to estimate dimensions and material contents
3.1. Buildings
Table 5. Methods for estimating building surfaces
Above or
underground
Data sources
Method
Aboveground
BD Topo and
Graulière
(2007)
Calculation of the volume of each building according to BD Topo (floor area x height).
Definition of an average floor-to-floor height by building archetype (Graulière, 2007)
(ceiling height according to Graulière, 2007; assumption of 20 cm floor thickness for
all types).
Division of the volume of each building according to BD Topo by the floor-to-floor
height of the building archetype.
Basement
BD Topo,
Graulière
(2007), fichiers
fonciers
Definition of an average number of underground levels by building archetype and
urban area (Paris, Petite Couronne, Grande Couronne) according to Graulière (2007)
and fichiers fonciers.
Multiplication the floor area of the building according to BD Topo by the number of
levels of the building archetype.
Source: authors
Table 6. Material contents for aboveground surfaces by building archetype and material, kg/m² gross
floor area
(next pages)
32
Type
Stone
Concrete
Concrete
block
Terracotta
solid brick
Hollow
terracotta
brick
Clay tile
Ceramic tile
Plaster *
Mortar &
mineral
plaster**
Glass
Mineral wool
Timber
Chipboard
Steel
Aluminum
Zinc
Poly-Vinyl
Chloride
Styrofoam
***
Polyurethane
Mastic
asphalt
Stone framed multi-family
houses before 1914
1,754.5
0.0
0.0
128.6
0.0
0.0
0.0
14.4
0.0
1.4
2.4
74.9
0.0
0.0
0.0
2.7
0.0
0.0
0.0
0.0
Brick framed multi-family
houses before 1914
1,512.5
0.0
0.0
155.4
0.0
0.0
0.0
14.4
0.0
1.4
3.1
92.3
0.0
0.0
0.0
4.1
0.0
0.0
0.0
0.0
Stone framed multi-family
houses 1914-1947
1,754.5
0.0
0.0
128.6
0.0
0.0
0.0
14.4
0.0
1.4
2.4
53.8
0.0
0.0
0.0
2.7
0.0
0.0
0.0
0.0
Brick framed multi-family
houses 1914-1947
0.0
1262.3
0.0
514.3
0.0
0.0
0.0
29.8
0.0
1.7
1.7
24.7
0.0
21.3
0.0
1.7
0.0
0.0
0.0
0.0
Multi-family houses 1948-
1974
0.0
1,417.2
178.5
63.8
0.0
0.0
0.0
29.8
0.0
2.6
1.8
6.0
0.0
29.8
0.0
0.0
1.7
1.0
0.0
5.8
Multi-family houses 1975-
2000
0.0
1,302.6
0.0
0.0
0.0
0.0
0.0
28.5
0.0
3.2
1.6
19.4
0.0
34.5
0.0
0.0
2.1
1.3
2.6
17.0
Concrete framed multi-family
houses since 2001
0.0
1,572.9
0.0
0.0
0.0
0.0
0.0
34.3
39.4
2.6
0.2
3.6
0.0
17.7
0.0
0.0
1.8
3.8
1.2
15.5
Brick framed multi-family
houses since 2001
0.0
1,448.0
0.0
89.8
0.0
0.0
0.0
29.4
40.1
2.6
12.8
3.6
0.0
14.1
0.0
0.0
1.8
0.0
1.2
15.5
Single-family houses before
1914
1,684.8
0.0
0.0
30.0
0.0
33.8
0.0
14.4
0.0
1.4
3.4
91.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Single-family houses 1914-
1947
1,684.8
0.0
0.0
30.0
0.0
33.8
0.0
14.4
0.0
1.4
3.4
91.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Single-family houses 1948-
1974
546.4
227.7
145.7
30.0
0.0
33.8
0.0
14.4
0.0
1.4
3.4
91.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Concrete framed single-
family houses 1975-2000
0.0
499.3
325.7
0.0
0.0
67.5
0.0
29.9
0.0
2.1
7.2
102.8
0.0
9.3
0.0
0.0
1.4
0.0
0.0
0.0
Brick framed single-family
houses 1975-2000
0.0
499.3
0.0
176.5
0.0
67.5
0.0
29.9
0.0
2.1
7.2
102.8
0.0
9.3
0.0
0.0
1.4
0.0
0.0
0.0
Concrete framed single-
family houses since 2001
0.0
724.9
332.9
0.0
0.0
58.3
0.0
41.4
198.5
2.1
4.8
3.4
12.0
12.2
0.0
0.0
1.6
6.8
0.0
0.0
Brick framed single-family
houses since 2001
0.0
851.8
47.8
51.4
503.7
59.4
0.0
93.5
64.7
2.3
4.9
3.4
12.3
13.5
0.9
0.0
0.2
5.0
0.0
0.0
Timber framed single-family
houses since 2001
0.0
430.3
46.5
0.0
0.0
56.9
0.0
44.3
16.7
1.8
9.2
5.1
32.6
9.0
0.0
0.0
0.6
3.2
0.0
0.0
Shopping malls and buildings
dedicated to transport and
storage
0.0
400.0
0.0
0.0
0.0
0.0
0.0
1.8
0.0
2.0
4.0
0.0
0.0
76.0
0.0
0.0
0.0
0.0
0.0
0.0
Other commercial and
institutional buildings before
1914
1,754.5
0.0
0.0
128.6
0.0
0.0
0.0
14.4
0.0
1.4
2.4
53.8
0.0
0.0
0.0
2.7
0.0
0.0
0.0
0.0
33
Type
Stone
Concrete
Concrete
block
Terracotta
solid brick
Hollow
terracotta
brick
Clay tile
Ceramic tile
Plaster *
Mortar &
mineral
plaster**
Glass
Mineral wool
Timber
Chipboard
Steel
Aluminum
Zinc
Poly-Vinyl
Chloride
Styrofoam
***
Polyurethane
Mastic
asphalt
Other commercial and
institutional buildings 1914-
1947
1,441.8
0.0
0.0
30.0
0.0
0.0
0.0
14.4
0.0
1.4
1.3
73.4
0.0
0.0
0.0
2.7
0.0
0.0
0.0
0.0
Other commercial and
institutional buildings 1948-
1974
0.0
1,417.2
178.5
63.8
0.0
0.0
0.0
29.8
0.0
2.6
1.8
6.0
0.0
29.8
0.0
0.0
1.7
1.0
0.0
5.8
Other commercial and
institutional buildings 1975-
2000
0.0
1,302.6
0.0
0.0
0.0
0.0
0.0
28.5
0.0
3.2
1.6
19.4
0.0
34.5
0.0
0.0
2.1
1.3
2.6
17.0
Other commercial and
institutional buildings since
2001
0.0
1,423.5
3.8
0.0
0.0
0.0
3.9
3.1
4.6
0.7
0.6
0.0
0.0
53.9
0.2
0.0
0.1
0.2
0.0
11.1
Industrial buildings before
1948
0.0
500.0
0.0
285.2
0.0
0.0
0.0
0.0
0.0
0.5
0.0
0.0
0.0
65.8
0.0
0.0
0.0
0.0
0.0
0.0
Industrial buildings since
1948
0.0
400.0
0.0
0.0
0.0
0.0
0.0
1.1
0.0
0.6
4.0
0.0
0.0
83.4
0.0
0.0
0.0
0.0
0.0
33.0
* including plaster and mortar glue plaster coating
** including polymer-supported coating
*** including expanded polystyrene
Source: authors adapted from Rouvreau et al. (2012)
34
Table 7. Material contents for underground surfaces by building type, kg/m² gross floor area
Type
Material content
Stone framed multi-family houses before 1914
Stone: 1,312.2
Brick framed multi-family houses before 1914
Stone framed multi-family houses 1914-1947
Stone: 1,312.2
Brick framed multi-family houses 1914-1947
Multi-family houses 1948-1974
Concrete: 1,381.3
Concrete block: 178.5
Steel: 29.8
Multi-family houses 1975-2000
Concrete framed multi-family houses since 2001
Brick framed multi-family houses since 2001
Single-family houses before 1914
Stone: 1,457.1
Single-family houses 1914-1947
Single-family houses 1948-1974
Concrete: 300.0
Concrete block: 260.6
Steel: 6.0
Concrete framed single-family houses 1975-2000
Brick framed single-family houses 1975-2000
Concrete framed single-family houses since 2001
Brick framed single-family houses since 2001
Timber framed single-family houses since 2001
Shopping malls and buildings dedicated to transport and
storage
Concrete: 1,381.3
Concrete block: 178.5
Steel: 29.8 (idem other commercial buildings since 1948)
Other commercial and institutional buildings before 1914
Stone: 1,312.2
Other commercial and institutional buildings 1914-1947
Stone: 1,312.2
Other commercial and institutional buildings 1948-1974
Concrete: 1,381.3
Concrete block: 178.5
Steel: 29.8
Other commercial and institutional buildings 1975-2000
Other commercial and institutional buildings since 2001
Industrial buildings before 1948
Concrete: 1,381.3
Concrete block: 178.5
Steel: 29.8 (idem other commercial buildings since 1948)
Industrial buildings since 1948
Source: authors
3.2. Road network
- Outside Paris: roads: surface of each section according to BD Topo; pavements: half of the
surface of roads of importance 5 according to BD Topo.
- Paris: roads: 1. calculation of the total surface in Paris for each position in the network
hierarchy and position relative to the ground according to BD Topo; 2. multiplication of each
surface by the share of each road structure according to Leroy (2007a, 2007b) and Mairie de
Paris (1993); pavements: surface according to Leroy (2007a).
- Tunnels: half volume of a tube with an internal diameter equal to the width of the roadway
according to BD Topo and with a thickness of 0.2 m for paved roads and paths and 0.4 for
other roads.
Table 8. Thicknesses and material contents of road sections other than types specific to Paris and
excluding materials in pavements, tunnels and bridges, m and kg/m²
35
Course
Material
Motorways
Regional and main
roads
Local roads
Gravel roads and
paths
Thickness
Content
Thickness
Content
Thickness
Content
Thickness
Content
Surface
course
Asphalt
(for
asphalt
concrete)
0.2
29
0.15
22
0.15
22
0
0
Aggregates
(for
asphalt
concrete)
451
338
338
0
0
Seat
layer
Aggregates
(excluding
asphalt
concrete)
0.5
1,000
0.4
800
0.2
400
0.1
250
Shape
layer
Pit-run
material
0.8
1,440
0.6
1,080
0.5
900
0.3
750
Total
1.5
2,920
1.15
2,240
0.85
1,660
0.4
1,000
Source: authors
Table 9. Thicknesses and material contents of road sections specific to Paris excluding materials in
pavements, tunnels and bridges, m and kg/m²
Course
Material
Regional and main
roads: asphalt
concrete over
mosaïque paving
Regional and main
roads: échantillon
paving
Local roads: asphalt
concrete over
mosaïque paving
Local roads:
uncovered
échantillon paving
Thickness
Content
Thickness
Content
Thickness
Content
Thickness
Content
Surface
course
Asphalt (for
asphalt
concrete)
0.04
6
0
0
0.04
6
0
0
Aggregates
(for asphalt
concrete)
90
0
90
0
Paving stones
0.08
142
0.08
142
0.08
142
0.08
142
Aggregates
(sand used as
filler)
0.04
107
0.04
107
0.04
107
0.04
107
Base
course
Cement
concrete
0.2
440
0.2
440
0.2
440
0.2
440
Subbase
course
Pit-run
material
0.6
1,080
0.6
1,080
0.5
900
0.5
900
Total
0.96
1,865
0.9
1,769
0.95
1,685
0.8
1,589
Source: authors
36
Table 10. Thicknesses and material contents of pavements, tunnels and binder courses and wearing
courses of pavements over bridges, m, kg per linear metre, kg/m3
Material
Sidewalk
Tunnel (excluding roadways)
Binder course and surface
course for roadways over
bridges
Thickness
(m)
Content
(kg/m²)
Thickness (m)
Content
(kg/m3)
Thickness
Content
Asphalt (for asphalt
concrete)
0.02
3
/
/
Idem road type
Aggregates (for asphalt
concrete)
45
/
/
Cement concrete
0.1
220
Paths: 0.2;
other roads:
0.4
2,400 kg/m3
0
0
Steel
/
/
85 kg/m3
0
0
Stone (border)
/
100 kg per
linear metre
of road
/
/
Idem road type
Source: authors
3.3. Railway network
- Line for high-speed trains, main, service and non-operating tracks, funicular: length of each
section according to BD Topo.
- Metro and tramway: total length in the region according to Ligne de transport en commun
and breakdown by département and position relative to the ground according to RATP
(2016).
- Tunnel diameters: metro lines other than number 14 according to Hidalgo (2015) and Regan
(2016); metro line 14 and Regional Express Network (RER) according to Lamé (2013); high-
speed lines according to de Tilière and Viaud (2012).
- Tunnel lengths at stations: average lengths according to sources idem Tunnel diameters and
number of stations according to Stations de transport en commun.
Table 11. Dimensions and material contents of rail sections excluding materials located in tunnels, m,
kg/m of track
Component
Material
High-speed lines
Main tracks, service
tracks, non-operated
tracks, metro
Tramway and funicular
Dimension
Content
Dimension
Content
Dimension
Content
Rail
Steel
/
120
/
120
/
120
Tie
Timber
1.7 ties per
linear metre
135
(timber)
1.7 ties per
linear metre
135
/
/
Concrete
/
/
/
/
/
/
Ballast
Aggregates
Thickness:
0.4; width:
2.5
1,800
Thickness:
0.4; width:
2.5
1,350
/
/
Platform
Concrete
/
/
/
/
Thickness:
0.35; width:
2.5
1,925
Total
/
2,055
/
1,605
/
2,045
Source: authors
37
Note: these values are applied to linear metres of railway track (a single section according to BD Topo
may include several tracks).
Table 12. Dimensions and material contents of railway tunnels, m, kg/m linear of rail section
Out or in
station
Material
High-speed lines
Main tracks, service
tracks, non-
operating tracks
Metro - excluding
line 14
Metro - line 14
Dimension
Density
Dimension
Density
Dimension
Density
Dimension
Density
Out of
station
Masonry
(stone and
mortar)
/
/
/
/
Diameter
5.2;
thickness
0.7
25,936
/
/
Concrete
Inner
diameter
9.25;
thickness
0.5
36,738
Inner
diameter
9;
thickness
0.4
28,335
Inner
diameter
5.2;
thickness
0.6
27,882
Inner
diameter
8.6;
thickness
0.4
25,130
Steel
1,301
/
1,004
/
/
961
In stations
Masonry
(stone and
mortar)
/
/
/
/
Inner
diameter
10.4;
thickness
0.7; length
75
48,796
/
/
Concrete
/
/
Inner
diameter
18;
thickness
0.4; length
120
55,465
Inner
diameter
10.4;
thickness
0.6; length
75
52,455
Inner
diameter
17.2;
thickness
0.4; length
120
53,053
Steel
/
/
1,964
/
/
1,879
Source: authors
Note: these values are applied to linear metres of rail sections which generally comprise two tracks
(two-way traffic).
3.4. Aerodrome runways
Table 13. Thickness and material contents of aerodrome runways, m and kg/m²
Layer
Material
Thickness
Density
Binder course and
wearing course
Asphalt (for asphalt concrete)
0.18
26
Aggregates (for asphalt concrete)
406
Seat layer
Aggregates (excluding asphalt
concrete)
0.4
960
Shape layer
Pit-run material
0.6
1,080
Total
1.18
2,472
Source: authors
38
3.5. Electricity networks
- Paris: total length according to Leroy (2007b); length of high-voltage lines according to (RTE,
2014); length of low-voltage lines deduced (assuming they are all underground).
- Outside Paris: length of the high and very high voltage underground network in the region
according to RTE (2011) and length of the networks for all voltages in the region according to
Loubet (2012); then breakdown by département in proportion to urbanised areas according to
MOS 2012; length of the high voltage overhead network per département according to BD
Topo; length of the low-voltage network per département deduced from the length of the all-
voltage network and the high and very high voltage networks; lengths of underground low-
voltage lines by département according to coefficients from SIGEIF (2013) and Kert (2001).
Table 14. Material contents for electricity networks, kg/m linear
Material
Overhead high and
very high voltage lines
Underground high and
extra-high voltage lines
Overhead low-voltage
lines
Underground low-
voltage lines
Type of
cable
Content
Type of
cable
Content
Type of
cable
Content
Type of
cable
Content
Aluminum
HTA
3x240+95
4.2
HTA
3x240+95
4.2
BT 3x240
3.8
BT 3x240
3.8
HDPE
/
/
Cable
tube
1.8
/
/
Cable
tube
1.8
Total
/
4.2
/
6
/
3.8
/
5.6
Source: authors
3.6. Gas networks
- Paris: length according to Leroy (2007b).
- Outside Paris: lengths in the region according to GRTgaz (2013) and GRDF (2013); deduction
of the length in Paris and breakdown by département according to MOS 2012; breakdown by
material for each département according to coefficients from SIGEIF (2013; 2014).
Table 15. Material contents for gas networks, kg/m linear
Material
300 mm steel pipe
300 mm ductile iron
pipe
150 mm high density
polyethylene pipe
150 mm steel pipe
Steel
70
/
/
20
Ductile iron
/
83
/
/
HDPE
/
/
6,8
/
Total
70
83
6,8
20
Source: authors
39
3.7. Heating and cooling networks
- Length per network according to DRIEE (2016) database and breakdown by diameter
according to coefficients from CPCU (2016).
Table 16. Material contents for heating and cooling networks, kg/m linear
Material
500 mm pipe
100 mm pipe
Steel
78
10
Polyethylene
24.2
2.4
Polyurethane
14.4
2.2
Total
116.6
14.6
Source: authors
3.8. Drinking water networks
- Paris: length of the network according to Eau de Paris (2015).
- Seine-et-Marne: length of the network according to CG Seine-et-Marne (2012).
- Other départements: length in the region according to EIDER, deduction of Paris and Seine-
et-Marne, breakdown by département according to MOS 2012; breakdown by water flow,
diameter and material for each département according to coefficients from Rouvreau et al.
(2012), SEDIF (2013), CG Seine-et-Marne (2012).
Table 17. Material contents for drinking water networks with high flow, kg/m linear
Material
1,000 mm concrete
pipe
500 mm steel pipe
300 mm steel pipe
300 mm cast iron pipe
Concrete
539
/
/
/
Steel
/
135
55
/
Cast iron
/
/
/
81
Total
539
135
55
81
Source: authors
Table 18. Material contents for drinking water networks with medium or low flow, kg/m linear
Material
300 mm cast
iron pipe
150 mm cast
iron pipe
150 mm PVC
pipe
150 mm HDPE
pipe
150 mm
asbestos
cement pipe
80 mm HDPE
pipe
Concrete
/
/
/
/
/
/
Steel
/
/
/
/
/
/
Cast iron
81
33.5
/
/
/
/
PVC
/
/
6.4
/
/
/
HDPE
/
/
/
6.8
/
2.2
Asbestos
cement
/
/
/
/
150
/
Total
81
33.5
6.4
6.8
150
2.2
Source: authors
40
3.9. Non-potable water network
- Network length per département according to APUR (2010) and breakdown by material for
each département according to coefficients from Mairie de Paris (2015).
Table 19. Material contents for the non-potable water network, kg/m linear
Material
Cast iron (diameter 300 mm)
Concrete with steel cylinder (diameter
1,000 mm)
Cast iron
81
/
Concrete (in concrete with steel
cylinder)
/
710
Total
81
710
Source: authors
3.10. Sewerage networks
- Paris and Petite Couronne: length of the network according to Agence de l’eau Seine-
Normandie (2016).
- Seine-et-Marne: length of the network according to CG Seine-et-Marne (2016).
- Other départements: length in the region according to EIDER, deduction of the 5
départements with known lengths, breakdown by département according to MOS 2012;
breakdown by diameter and material for each département according to coefficients from
previous sources and Cité de l'eau et de l'assainissement (2016).
Table 20. Material contents for sewerage networks in Paris and Petite Couronne, kg/m linear
Material
4,000 mm concrete
pipe
2,500 mm concrete
pipe
1,200 mm concrete
pipe
1,200 mm cast iron
pipe
Concrete
8,400
5,270
1,650
/
Cast iron
/
/
/
500
Stone (gutter)
250
250
250
250
Total
8,400 (+ 250)
5,270 (+ 250)
1,650 (+ 250)
81 (+ 250)
Source: authors
Table 21. Material contents for sewerage networks in Grande Couronne, kg/m linear
Material
2,500 mm
concrete pipe
1,200 mm
concrete pipe
1,200 mm cast
iron pipe
315 mm HDPE
pipe
150 mm PVC pipe
Concrete
5 270
1 650
/
/
/
Steel
/
/
/
/
/
Cast iron
/
/
500
/
/
HDPE
/
/
/
25
/
PVC
/
/
/
/
6.4
Stone (gutter)
250
250
250
250
250
Total
5,270 (+ 250)
1,650 (+ 250)
500 (+ 250)
25 (+ 250)
6.4 (+ 250)
Source: authors
41
5. Results
Table 22. Stocks in buildings by material and building and network, the Paris region, 2013, kt
Multi-
family
houses -
abovegr
ound
Multi-
family
houses -
undergr
ound
Single
-
family
house
s -
above
groun
d
Sing
le-
fam
ily
hou
ses -
und
ergr
oun
d
Shopp
ing
malls
and
buildi
ngs
for
transp
ort
and
storag
e -
above
groun
d
Shop
ping
malls
and
buildi
ngs
for
trans
port
and
stora
ge -
under
groun
d
Other
comme
rcial
and
instituti
onal
building
s -
abovegr
ound
Other
comme
rcial
and
instituti
onal
buildin
gs -
underg
round
Industri
al
building
s -
abovegr
ound
Industri
al
buildin
gs -
undergr
ound
Concrete
452,872
81,210
85,80
4
7,82
1
12,57
7
1,804
124,315
26,241
76,543
5,583
Concrete block
28,498
10,495
45,56
3
6,79
3
0
233
2,529
3,265
0
721
Aggregates for
asphalt
concrete,
ballast and
paving
Pit-run material
Stone
232,952
22,975
256,8
26
22,4
01
0
0
47,459
5,058
0
0
Masonry
Solid clay brick
33,124
6,350
0
3,878
549
Hollow clay
brick
0
4,679
0
0
0
Clay tile
0
15,29
1
0
0
0
Tiling
0
0
0
163
0
Glass
1,080
553
63
216
114
Plaster
11,652
7,268
57
1,921
208
Mortar and
mineral plaster
1,744
3,475
0
195
0
Mineral wool
811
1,575
126
166
756
Asbestos
cement
Steel
9,560
1,749
1,245
156
2,388
39
3,876
544
15,883
120
Cast iron
Aluminum
1
4
0
8
0
Zinc
395
0
0
76
0
Timber
13,152
29,30
6
0
2,380
0
Chipboard
0
398
0
0
0
Polyvinyl
chloride
585
172
0
103
0
Polystyrene
467
144
0
66
0
Polyurethane
337
0
0
88
0
High density
polyethylene
42
Mastic asphalt
3,490
0
0
1,140
6,235
Asphalt
Total
790,717
116,429
458,6
52
37,1
72
15,21
0
2,076
188,581
35,108
100,288
6,424
Source: authors
Table 23. Stocks in networks by material and building and network, the Paris region, 2013, kt
Road
netw
ork -
exclu
ding
sidew
alks
and
tunne
ls
Road
netw
ork -
sidew
alks
Ro
ad
net
wo
rk -
tun
nel
s
Rail
way
net
wor
k -
excl
udin
g
tunn
els
Railw
ay
netw
ork -
tunn
els
A
er
o
dr
o
m
e
ru
n
w
ay
s
Elect
ricity
netw
orks -
over
head
Electri
city
netwo
rks -
under
groun
d
Gas
net
wor
ks
Hea
ting
and
cool
ing
net
wor
ks
Drin
king
wat
er
net
wor
ks
Non-
potab
le
water
netw
ork
Se
wer
age
net
wor
ks
Concrete
3,066
20,61
9
2,8
10
321
13,05
8
598
11
52,
310
Concrete block
Aggregates for
asphalt
concrete,
ballast and
paving
211,4
36
4,229
8,85
3
1
6,
1
9
1
Pit-run
material
307,9
23
1
2,
8
0
0
Stone
986
11,58
4
671
Masonry
6,769
Solid clay brick
Hollow clay
brick
Clay tile
Tiling
Glass
Plaster
Mortar and
mineral plaster
Mineral wool
Asbestos
cement
250
Steel
100
779
205
278
33
176
Cast iron
61
823
233
2,0
36
Aluminum
122
178
Zinc
Timber
854
Chipboard
Polyvinyl
chloride
53
16
43
Polystyrene
Polyurethane
6
High density
polyethylene
85
121
9
37
92
Mastic asphalt
Asphalt
5,591
270
3
0
7
Total
529,0
02
36,70
1
2,9
10
10,8
07
20,03
2
2
9,
2
9
8
122
262
460
48
1,93
6
244
55,
125
Source: authors
Table 24. Share of the stock of each group of building and network located underground, the Paris
region, 2013, %.
Buildings:
foundation
s
Buildings:
basement
s
Roads:
base and
subbase
courses
Roads
and
railways:
tunnels
Energy and
water
networks
located
undergroun
d
Total
Multi-family houses
22
13
35
Single-family houses
17
7
24
Shopping malls and buildings for
transport and storage
56
12
68
Other commercial and institutional
buildings
23
16
38
Industrial buildings
37
6
43
Road network
80
1
81
Railway network
69
69
Aerodrome runways
83
83
Electricity networks
68
68
Gas networks
100
100
Heating and cooling networks
100
100
Drinking water networks
100
100
Non-potable water network
100
100
Sewerage networks
100
100
Total
16
8
20
1
2
47
Source: authors
44
Table 25. Stocks by building and network, Paris, Petite Couronne and Grande Couronne, 2013, kt
Paris
Petite
Couronne
Grande
Couronne
the Paris
region
Buildings
Multi-family houses
309,050
352,593
245,503
907,146
Single-family houses
4257
115,595
375,972
495,823
Shopping malls and buildings for
transport and storage
1,294
4,761
11,231
17,286
Other commercial and institutional
buildings
77,553
80,957
65,178
223,689
Industrial buildings
2,892
34,618
69,202
106,713
Sub-total
395,047
588,524
767,086
1,750,657
Transport
networks
Road network
25,210
103,063
440,340
568,613
Railway network
13,716
8,867
8,256
30,839
Aerodrome runways
57
6,982
22,258
29,298
Sub-total
38,983
118,913
470,854
628,750
Energy and
water
networks
Electricity networks
57
89
239
385
Gas networks
33
91
336
460
Heating and cooling networks
15
19
14
48
Drinking water networks
105
395
1,436
1,936
Non-potable water network
153
91
0
244
Sewerage networks
3,965
6,965
44,196
55,125
Sub-total
4,329
7,649
46,221
58,198
All buildings and networks
438,358
715,086
1,284,161
2,437,605
Source: authors
45
6. Discussion
Table 26. Minimum, maximum, median and average coefficients of variation by building and network
Data on material content
Data for assigning a type or estimating
dimensions
Minimu
m
Maximu
m
Media
n
Averag
e
Minimu
m
Maximu
m
Media
n
Averag
e
Buildings
Aboveground
surfaces
17
17
17
17
5
50
8
21
Underground
surfaces
17
17
17
17
14
50
25
30
Sub-total
17
17
17
17
5
50
19
25
Transport
networks
Road network
10
44
44
33
5
28
28
21
Railway network
15
22
17
18
5
15
14
13
Aerodrome
runways
42
44
43
43
5
5
5
5
Sub-total
10
44
22
29
5
28
14
14
Energy
and
water
networks
Electricity
networks
22
22
22
22
8
42
12
20
Gas networks
15
25
20
20
15
41
22
25
Heating and
cooling
networks
<