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

Author’s 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

2

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

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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

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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.

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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

Urban

characteristics

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

Stocks

characteristics

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

13

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

5

50

19

25

Transport networks

10

44

22

29

5

28

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

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

Material groups

Materials

Buildings

Transport

networks

Energy and water

networks

Non-metallic

minerals

Concrete

x

Concrete block

x

Aggregates for asphalt concrete,

ballast and paving

x

Pit-run material

x

Stone

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

Metals

Steel

x

Cast iron

x

Aluminum

x

Zinc

x

Timber

x

Chipboard

x

Plastics

Polyvinyl chloride (PVC)

x

Polystyrene

x

Polyurethane

x

High density polyethylene (HDPE)

x

Other petroleum-

based materials

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

Geo-database by the French

National Institute of

Geographic and Forest

Information (IGN)

2

5

0

5

Fichiers fonciers

2014: data on

aboveground surfaces

x

Geo-database by the French

Ministry of Finance to

calculate property tax

amounts

7

5

0

8

Fichiers fonciers

2014: data on

underground surfaces

x

Idem aboveground surfaces

with lower reliability and

completeness (Lamé, 2013)

21

14

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

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

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

Geo-database by IGN

2

5

0

5

Leroy (2007a)

x

Surface areas of roads in Paris

by the City of Paris

21

14

0

28

Leroy (2007b)

x

Idem Leroy (2007a)

21

14

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

2

0

20

Hidalgo (2015)

x

PhD thesis. Information on the

material contents and

dimensions of metro tunnels.

7

14

0

15

Regan (2016)

x

Idem Hidalgo (2015) with

complementary information.

7

14

0

15

BD Topo (v 2.1 - 2014)

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

5

Lignes & Stations by

APUR (2014)

x

Geo-database by Paris

Urbanism Agency. Information

on metro and tramway tracks

with line numbering and

location of stations.

2

14

0

14

RATP (2016)

x

Information by the public

transport operator and

maintainer on the share of

underground tracks in the

total metro network length.

2

14

0

14

Lamé (2013)

x

PhD thesis. Information on the

dimensions of RER lines

tunnels.

7

14

0

15

Tilière and Viaud

(2012)

x

Information by an engineering

company designing railway

stations on high-speed lines

tunnels.

7

14

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

Geo-database by IGN. Surface

area of runways.

2

5

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

Geo-database by IGN.

Location and length of high

2

14

0

14

29

and very high voltage

overhead lines.

Leroy (2007b)

x

Information by the City of

Paris on the length of

underground electrical lines in

Paris.

21

5

14

0

25

RTE (2011)

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

0

9

RTE (2014)

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

8

Loubet (2012)

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

8

MOS 2012 by IAU

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

8

SIGEIF (2015)

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

Report to the French

parliament. Share of

underground lines in the

French electricity network.

2

0

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

Annual reports by electricity

and gas network manager

SIGEIF. Diameter and material

content of pipelines managed

by SIGEIF.

7

14

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

15

GRDF (2013)

x

Information by the gas

network manager GRDF on

the total length of pipelines in

the Paris region.

7

14

0

15

Leroy (2007b)

x

Information by the City of

Paris on the length of gas

pipelines in Paris.

21

14

0

28

30

MOS 2012 by IAU

x

Idem Electricity networks.

2

0

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

0

21

CPCU (2016)

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

8

DRIEE (2016)

x

Geo-database on heating and

cooling networks by Regional

and Interdepartmental

Directorate for the

Environment and Energy

(DRIEE). Length.

2

5

0

5

Drinking

water

networks

Rouvreau et al. (2012)

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

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

15

SEDIF (2013)

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

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

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

15

MOS 2012 by IAU

x

Idem Electricity networks.

2

0

41

Non potable

water

network

Mairie de Paris (2015)

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

8

APUR (2010)

x

Report by Paris Urbanism

Agency. Length of the non-

potable water network by

département.

7

5

0

9

Sewerage

network

Rouvreau et al. (2012)

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

Report by Seine-et-Marne

departmental council.

Material content of the

sewerage water networks in

Seine-et-Marne.

7

14

0

15

Agence de l’eau

Seine-Normandie

(2016)

x

See Drinking water networks.

7

14

0

15

Cité de l’eau et de

l’assainissement

(2016)

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

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

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

15

MOS 2012 by IAU

x

Idem Electricity networks.

2

0

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

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

128.6

0.0

14.4

0.0

1.4

2.4

74.9

0.0

2.7

0.0

Brick framed multi-family

houses before 1914

1,512.5

0.0

155.4

0.0

14.4

0.0

1.4

3.1

92.3

0.0

4.1

0.0

Stone framed multi-family

houses 1914-1947

1,754.5

0.0

128.6

0.0

14.4

0.0

1.4

2.4

53.8

0.0

2.7

0.0

Brick framed multi-family

houses 1914-1947

0.0

1262.3

0.0

514.3

0.0

29.8

0.0

1.7

24.7

0.0

21.3

0.0

1.7

0.0

Multi-family houses 1948-

1974

0.0

1,417.2

178.5

63.8

0.0

29.8

0.0

2.6

1.8

6.0

0.0

29.8

0.0

1.7

1.0

0.0

5.8

Multi-family houses 1975-

2000

0.0

1,302.6

0.0

28.5

0.0

3.2

1.6

19.4

0.0

34.5

0.0

2.1

1.3

2.6

17.0

Concrete framed multi-family

houses since 2001

0.0

1,572.9

0.0

34.3

39.4

2.6

0.2

3.6

0.0

17.7

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

29.4

40.1

2.6

12.8

3.6

0.0

14.1

0.0

1.8

0.0

1.2

15.5

Single-family houses before

1914

1,684.8

0.0

30.0

0.0

33.8

0.0

14.4

0.0

1.4

3.4

91.0

0.0

Single-family houses 1914-

1947

1,684.8

0.0

30.0

0.0

33.8

0.0

14.4

0.0

1.4

3.4

91.0

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

Concrete framed single-

family houses 1975-2000

0.0

499.3

325.7

0.0

67.5

0.0

29.9

0.0

2.1

7.2

102.8

0.0

9.3

0.0

1.4

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

1.4

0.0

Concrete framed single-

family houses since 2001

0.0

724.9

332.9

0.0

58.3

0.0

41.4

198.5

2.1

4.8

3.4

12.0

12.2

0.0

1.6

6.8

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

Timber framed single-family

houses since 2001

0.0

430.3

46.5

0.0

56.9

0.0

44.3

16.7

1.8

9.2

5.1

32.6

9.0

0.0

0.6

3.2

0.0

Shopping malls and buildings

dedicated to transport and

storage

0.0

400.0

0.0

1.8

0.0

2.0

4.0

0.0

76.0

0.0

Other commercial and

institutional buildings before

1914

1,754.5

0.0

128.6

0.0

14.4

0.0

1.4

2.4

53.8

0.0

2.7

0.0

33

Type

Stone

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

30.0

0.0

14.4

0.0

1.4

1.3

73.4

0.0

2.7

0.0

Other commercial and

institutional buildings 1948-

1974

0.0

1,417.2

178.5

63.8

0.0

29.8

0.0

2.6

1.8

6.0

0.0

29.8

0.0

1.7

1.0

0.0

5.8

Other commercial and

institutional buildings 1975-

2000

0.0

1,302.6

0.0

28.5

0.0

3.2

1.6

19.4

0.0

34.5

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

3.9

3.1

4.6

0.7

0.6

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

0.5

0.0

65.8

0.0

Industrial buildings since

1948

0.0

400.0

0.0

1.1

0.0

0.6

4.0

0.0

83.4

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

Aggregates

(for

asphalt

concrete)

451

338

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

0.04

6

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

Steel

/

85 kg/m3

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

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

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

47,459

5,058

0

Masonry

Solid clay brick

33,124

6,350

0

3,878

549

Hollow clay

brick

0

4,679

0

Clay tile

0

15,29

1

0

Tiling

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

76

0

Timber

13,152

29,30

6

0

2,380

0

Chipboard

0

398

0

Polyvinyl

chloride

585

172

0

103

0

Polystyrene

467

144

0

66

0

Polyurethane

337

0

88

0

High density

polyethylene

42

Mastic asphalt

3,490

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

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

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

Aerodrome runways

83

Electricity networks

68

Gas networks

100

Heating and cooling networks

100

Drinking water networks

100

Non-potable water network

100

Sewerage networks

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

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

Abstract and Figures