RESEARCH AND ANALYSIS
Regional circular economy of building materials
Environmental and economic assessment combining Material Flow Analysis,
Input-Output Analyses, and Life Cycle Assessment
Ronny Meglin1,2Susanne Kytzia2Guillaume Habert1
1Department of Civil, Environmental and
Geomatic Engineering, ETH Zurich, Zurich,
2Institute for Civil and Environmental
EngineeringUniversity of Applied Sciences
Eastern Switzerland, Rapperswil, Switzerland
Ronny Meglin, Oberseestrasse 10, CH-8640
Editor Managing Review: Wei-Qiang Chen
This work was supported by the Swiss National
Science Foundation (SNSF) within the frame-
work of the National Research Programme
“Sustainable Economy: resource-friendly,
future-oriented, innovative”(NRP 73).
The construction industry is responsible for large quantities of construction and
demolition waste and almost 50% of the worldwide annual resource consumption,
putting the environment, its natural resources, and ecosystems under high pressure.
Therefore, governments are implementing regional policies that support a circular
economy (CE). But how do we know whether these developments will lead to a shift toward
a CE on a regional scale? How can we identify hotspots in a value chain and regional econ-
omy to support decision-makers and to develop regional policies?We propose an integrated
assessment method that considers indicators for environmental impacts and economic
benefits by combining Material Flow Analysis (MFA) and Life Cycle Assessment (LCA)
with Input-Output Analysis (IOA) as the connecting element. It provides the necessary
data and indicators for a holistic and comprehensive evaluation of a region or indus-
try. We demonstrate its benefits and limitations taking the Swiss canton of Aargovia
as an example. We analyze which processes in the material flow system of construc-
tion minerals are decisive for formulating mass-related or financial policies encourag-
ing a CE. We show that a shift toward a CE can only be captured by combining material
and money flows in a joined model, because a significant increase of services—mainly
waste management—is a core element in this development. It can only be covered suf-
ficiently by combining environmental and economic assessment. Our model captures
the degree to which a regional economy is advanced in the transition toward a CE to
compare different regions or analyze scenarios of future developments.
building materials, circular economy, Input-Output Analysis (IOA), Life Cycle Assessment (LCA),
Material Flow Analysis (MFA), regional assessment
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2021 The Authors. Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of Yale University
562 wileyonlinelibrary.com/journal/jiec Journal of Industrial Ecology 2022;26:562–576.
MEGLIN ET AL.563
The construction industry is responsible for almost 50% of the worldwide annual resource consumption (OECD, 2019). In 2011, 37 gigatons of non-
metallic mineral materials were extracted,with an expected increase to 86 gigatons in 2060. At the same time, the anthropogenic stock is a potential
source of raw materials for the construction industry. An average of 1.68 kg of construction and demolition waste (CDW) is “emitted” per person
and day,which can be used as secondary building material (Kaza et al., 2018). Considering the high consumption of natural resources and the equally
high production of CDW, the environment, naturalresources, and ecosystems are under high pressure (OECD, 2020; Pomponi & Moncaster, 2017).
For that reason, various governments and organizations aim to increase resource efficiency and establish a sustainable1economy (BAFU, 2020;
Ellen MacArthur Foundation, 2013; European Commission, 2020) including principles of a circular economy (CE)2. In Switzerland, for example, the
“Ordinance on the Avoidance and the Disposal of Waste” came into force in 2016, which aims “to encourage the sustainable use of natural raw
materials through the environmentally sustainable recovery of waste” (ADWO, 2020).
The increasingly dynamic developments represent a major challenge for the phlegmatic building material industry (Abuzeinab et al., 2017;
Giannoni et al., 2018). A large number of barriers and enablers affect these developments (Hart et al., 2019; Kliem & Scheidegger, 2020), such
as public policies (spatial planning, standards norms, etc.), but also alternative business models (Schaltegger et al., 2016). Traditional business mod-
els in the building materials industry link economic success to material turnover. The higher the material turnover, the higher the economic success
(Spoerri et al., 2009). This promotes an inefficient use of natural resources and contradicts macroeconomic objectives, such as circular material
flows and reduced material consumption. This ultimately leads companies to change their traditional linear business models to alternative models
(Bocken et al., 2016; Halme et al., 2007; Kliem & Scheidegger, 2020).
But how do we know whether these developments will lead to a transition toward a CE on a regional scale? How can we avoid unintended side effects
on economic growth and emissions, or problem shifts to other regions? How can we identify hotspots in a value chain and/or a regional economy to support
decision-makers and to develop regional policies?
These questions are particularly important because this fundamental transition to a CE also includes a change from a material-oriented economy
to a service-oriented economy (Stahel, 2016) which must not necessarily be regarded as sustainable (Geissdoerfer et al., 2017;Zink&Geyer,2017).
Studies even show that in specific cases a CE can have greater environmental impacts (Blum et al., 2020; Bracquené et al., 2020)orevencreatea
linear economy lock-in (Coenen et al., 2015; Greer et al., 2021). Therefore, to identify suitable strategies and support decision-makers in the formu-
lation of sustainability policies, all responsible stakeholders must have a transparent and comprehensive decision basis for a better understanding
of an industry and region (Haas et al., 2016; Mayer et al., 2018).
In a federal state like Switzerland, the constraints for policies in each region are very different, especially in terms of resource availability, spatial
planning, or economic performance. For this reason, the implementation of CE policies remains with local actors (European Green Deal, 2021;Smol
et al., 2017; Virtanen et al., 2019). A model-based assessment on a regional scale enables policymakers to fall back on a unified basis and thus make
policy decisions that take a factor in sustainability considerations (Virtanen et al., 2019). A regional approach is seen to have a significant advantage
compared to a mere product-level approach (McCarthy et al., 2018; Vercalsteren et al., 2020).
In recent years, more and more scientists developed methods to evaluate CE from different perspectives. Recent review papers on CE in the
construction and CDW industry, highlighta single method focus on the product- or building-level, instead of regional or industry level (Hossain et al.,
2020). Most studies focus on resource efficiency of the construction industry (construction waste minimization and recycling) and neglect business
and economic perspectives (Lieder & Rashid, 2016; Parchomenko et al., 2019). Ghisellini et al. (2018, p. 636) conclude that “the environmental
impactsofapplying CE principles (...) are generallymuchmoreinvestigated (...)thaneconomic impacts.” López Ruiz et al. (2020, p. 12) also point
out, that “Research (...) has mainlyfocusedon aspects regardingreuseand recycling from anenvironmental performance perspective” and that
“integration of economic criteria is still limited.”
As there is no harmonized method available to quantitatively assess sustainability-aspects of a CE yet (Peña & Civit, 2020), it is necessary to
develop a more holistic multidisciplinary assessment methodology to evaluate the environmental, economic, and social aspects of CE and to con-
sider business perspectives, technological developments and policies (Haberl et al., 2016; Haupt & Hellweg, 2019; López Ruiz et al., 2020; Nußholz
et al., 2019). Such an assessment would improve communication with all stakeholders and provide a link to the Sustainable Development Goals
(United Nations, 2015) at all levels (Di Maio et al., 2017; Mayer et al., 2018).
We would like to close a part of this research gap and develop an applied instrument that evaluates the environmental and economic effects of
public policies on a region or a company in terms of a sustainable and circular building materials industry. We propose an assessment model, which
a. assess the economic and environmental impacts of the building materials industry of a region,
1Sustainability: Balanced integration of economic performance, social inclusiveness, and environmental resilience, to the benefit of current and future generations (Geissdoerfer et al., 2017).
2Circular economy: Regenerative system in which resource input and waste, emission, and energy leakage are minimised by slowing, closing, and narrowing material and energy loops through
long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, andrecycling (Geissdoerfer et al. (2017).
564 MEGLIN ET AL.
TAB LE 1 Types of analysis and associated issues of concern adapted from (Bringezu & Moriguchi, 2002;OECD,2008)
Issue of concern
Environmental impacts, supply security, technology
development within businesses, countries, regions
General environmental and economic impacts of materials
Objects of interest Substances Materials Products Businesses Economic activities Countries,regions
Type of analysis Substance Flow
b. identify key processes of the industry under study for promoting a CE on a regional scale,
c. compare the building materials industry of different regions with varying degrees of resource availability in terms of resource efficiency and
value creation and
d. to estimate the effects of a developing CE in a regional context.
In this paper, we present a model that combines proven methods and opens new possibilities for interpretation that would not be possible with
the individual methods. With the proposed novel combination, we can investigate a regional industry in detail and indicate the impacts of changing
material flows on the life cycle most relevant for generating value-added, causing emissions, and consuming natural resources on a regional level.
We conduct a case study of the Swiss canton of Aargovia to demonstrate the methods’ abilities and indicate its potential use for policymakers.
We can use the model to anticipate hotspots where the largest effects of public policies or changing materials flows can be expected. The results
highlight the impact of specific business models and their effects on the environmental and economic performance of a regional building materials
industry. These insights will help decision-makers formulate policies to promote a CE.
To analyze the circularity of an industry on a regional scale an integrated assessment model (IAM) based on complementary methods must be devel-
oped (Crawford et al., 2018; Loiseau et al., 2012; Moriguchi & Hashimoto, 2016; Säynäjoki et al., 2017; Singh et al., 2021; Teh et al., 2017). An IAM
addresses different stakeholders, who on the one hand have different background knowledge, and on the other hand, pursue different objectives.
This is aggravated by the fact that policies related to regional sustainability goals can have negativeeffects, especially on traditional economic activ-
ity. It is therefore of great importance for policymakers to consider conflicting economic and environmental constraints (Reif & Osberghaus, 2020).
Therefore, the goal of an IAM is not only to generate analytical insights but also to link the different stakeholders with a “common” language and
to create a common basis for decision-making (Kytzia, 2010; Mayer et al., 2018). This should allow us to
∙better estimate future developments (scenario analysis),
∙obtain initial findings in the context of policymaking (policy evaluation), and
∙establish a possible prioritization for further developments (Weyant et al., 1996).
2.1 Current state of assessment methods integrating economic and environmental goals
Bringezu and Moriguchi (2002) already proposed different methods for different objects of interest (Table 1).
Material Flow Analysis (MFA), Input-Output Analysis (IOA), and Life Cycle Assessment (LCA) have proven to be the most appropriate methods
to perform an environmental and economic assessment of products and systems (Bovea & Powell, 2016; Dossche et al., 2017; Hawkins et al., 2007;
Moriguchi & Hashimoto, 2016). Nevertheless, none of the methods can provide a comprehensive economic and ecological assessment of a complex
system in the context of a CE in isolation. Each method has different system boundaries, benchmarks, calculating techniques, and scopes, as shown in
Tabl e 2(Haaset al., 2016; Joshi, 1999; Nakamura et al., 2007). However, since these single methods are accepted and widely used by the professional
community, they provide the starting point for our study.
We propose an IAM that considers indicators for environmental impacts of building materials with indicators for regional economic benefits. We
use the different scopes and advantages of the respective methods combining MFA and LCA with an IOA as a connecting element. Our approach
will combine the different levels of interests (product level, regional level) and overcome the individual shortcomings presented in Table 2.
There are already attempts to combine different methods to investigate severalaspects on multiple levels. However, these methods use different
combinations as various objectives are pursued under different boundary conditions. For example, some studies using similar approaches to assess
MEGLIN ET AL.565
TAB LE 2 Comparison of methods
MFA IOA LCA
Description ∙Method to investigate the technical processes
of a socioeconomic system and its
dependencies in a defined boundary (space
∙Is performed according to the first law of
thermodynamics; the basic condition is that
the input must always equal the output
including all stock changes
∙Top-down economic tool for analyzing
interindustrial interdependencies in an
∙Describes the distribution of goods and
services by using a system of linear equations
∙Bottom-up methodological framework
encompassing all the impacts of a product
system from cradle to grave
∙A decision-support tool used to promote
sustainable management as well as
sustainable construction and to assess and
plan CE strategies
System definition ∙Functional or geographical ∙Geographical or political ∙Functional
Advantage ∙Flexibility regarding model assumptions
∙Mass balancing (filling data gaps)
∙Basis for impact assessment methods
∙Represents the whole economy/industry
∙Public data available (on nationwide level)
∙Possibility to extend to broaden the scope
(multiple regions MRIO or environmental
∙Detailed evaluation of a product
∙Availability of data
∙Monetary flows (e.g., services) are not
∙Low resolution due to high aggregation
∙partial simplifications and assumptions
∙Truncation error; subjectivedefinition of the
system boundary (e.g., End-of-Life [EoL]
∙Choice of allocation
Selection of key
(Brunner & Rechberger, 2017; Fischer-Kowalski
et al., 2011; Krausmann et al., 2018)
(Farhauer & Kröll, 2013; Joshi, 1999; Miller &
Blair, 2009; Schaffartzik et al., 2014; Suh,
2010; Weisz & Duchin, 2006)
(Frischknecht, 2020; Jolliet et al., 2016;Peña&
566 MEGLIN ET AL.
material efficiency and its links to service provision (Haberl et al., 2017; Pauliuk et al., 2020). Others use LCA-based combinations to investigate
the environmental impacts of individual regions or national economies (Dias et al., 2018; Kovanda, 2020; Lausselet et al., 2020; Lavers Westin et al.,
2019; Sigüenza et al., 2020). Input-output approaches are often used to enable a top-down investigation of specific regions or industries (Dias et al.,
2018; Kovanda, 2020; Teh et al., 2017). However, the proposed combination of MFA, LCA, and IOA, represents a novel combination in the context
of sustainability assessment (Sassanelli et al., 2019).
Combining MFA and LCA offers advantages for the assessment of complex systems, such as industrial sectors or regions (Laner & Rechberger,
2016; Lopes Silva et al., 2015). Laner and Rechberger (2016, p. 316) state, that “MFA can be applied both as a tool for environmental impact assess-
ment itself and as a basis for impact assessment methods such as life cycle impact assessment” but has a different perspective than LCA. Using
MFA as a basis for the analysis, we increase consistency, robustness, and transparency of the input data (Brunner & Rechberger, 2017). Therefore,
MFA-Data is used as Input for the bottom-up IOA for value chains on a regional scale and further evaluation in combination with LCA-data. This is
in line with Säynäjoki et al. (2017, p. 164) who state in their study, that “IO-LCA with comprehensive supply chain information is the better option
for LCA if the focus of the study is to understand the economy-wide implications of construction-sector products.” Also, Teh et al. (2017, p. 313)
conclude that the combination of LCA and IOA (i) “unites the precision of process-based LCA with the comprehensiveness of IOA” and (ii) solves
the“aggregationlimitationinIOA(...) bydisaggregating selectedsectorsintospecificproductstoprovidebettergranularity.”Thisisalsoconfirmed
by Aguilar-Hernandez et al. (2018), who propose to disaggregate products and sectors in more detailed categories to avoid the deficiency in the
resolution of IOA.
2.2 Integrated environmental and economical assessment for CE
The model for an integrated environmental and economic assessment is based on an MFA covering all necessary processes and products of the
mineral building materials industry from raw material extraction to the end-of-life (EoL) phase (see figure S1 in Supporting Information S1). The
MFA is translated into a physical input-output table (PIOT), which represents the processes and interindustrial dependencies of the region under
investigation in mass units (denoted by a P). As the PIOT’s processes correspond exactly to the MFA processes, material flows in the MFA can be
transferred directly (see flow numbers in the MFA in figure 1, and the process numbers in the PIOT in figure S2 in Supporting Information S1).
This PIOT is used to set up an input-output model consisting of a final demand vector (Y), an input-output coefficient matrix (A), and an output
vector (X). The coefficient matrix (A) describes the system under investigation by showing the input needed for one unit of output of the respective
sector within the region. The output vector (X) corresponds to the sum of the final demand (Y) and the intermediate demand.
By multiplying the material flows with the respective costs/prices, we obtain the monetary input-output table (MIOT) in monetary units (denoted
by an M). The conversion into monetary units is intended to enable a consistent and more comprehensible assessment of environmental influ-
ences and “can improve our understanding of the effects of physical flows on the socioeconomic system” (Bruel et al., 2019, p. 17). Beaussier et al.
(2019, p. 409) also state, that “valuating environmental benefits into monetary units can facilitate communication with a broad audience and raise
societal awareness about environmental issues.” The conversion from physical to monetary units makes it necessary to change the position and flow
direction of various processes in the change of PIOT to MIOT. This is especially necessary for services, where material and money flows in the same
direction in contrast to goods/products where material is traded against money, leading to flows in opposite directions. For example, the imports
of excavated material and CDW, which are listed in the PIOT in the Input-matrix, are listed in the MIOT in the Output-matrix. This is because the
import leads to a positive monetary flow and must be credited to the result of the services landfill or waste management.
The relationship between input and output can be illustrated as follows using the core equation of IOA according to Leontief (1986)withn
XPorM vector of the output of each process in monetary or physical units of material, dimension n×1
YPorM vector of final demand of each process in physical or monetary units of material, dimension n×1
Iidentity matrix, dimension n×n
Amatrix of input coefficients for material based on monetary or physical relations, dimension n×n
Using formula (1), we can calculate the impact of a change in demand (ΔY) on the region/industry under consideration (ΔX) as follows:
MEGLIN ET AL.567
TAB LE 3 Results that can be obtained from the PIOT and MIOT
PIOT/physical IOA MIOT/monetary IOA
∙Material flows per sector
∙Mass related indicators (see Supporting Information) to
investigate material consumptions and recycling rates
∙Effects of changes in demand
∙Leontief-multipliers (see 2.5)
∙Revenue per sector
∙Value-added (VA) per sector (see 2.4)
∙Effects of changes in demand
∙Leontief-multipliers (see 2.5)
After this step, two separate, but linked input-output tables (IOTs) are available, one in physical units (PIOT), another in monetary units, and two
separate IOAs according to the Equation (1) in physical and monetary units. From these two data sets, the following results can be obtained and
used for further calculations:
The assessment of environmental burdens and economic impacts is based on the MIOT/monetary IOA to include products and services. In the
MIOT waste management services are represented as output flows of demolition and recycling processes and input flows to material production
and construction industries. This reflects the economic logic driving the MFA system. The monetary IOA represents the relation between the eco-
nomic value of goods and services providing a value proportional allocation in the assessment model. To link the material flows with the corre-
sponding emissions, the MIOT is extended with a vectorof defined environmental burden coefficients. The burden coefficients are defined for each
environmental burden under study in relation to each process’s output in monetary units. They are estimated based on the corresponding PIOT and
environmental impacts from databases (see Section 2.4). Therefore, an environmental vector b is introduced to Equation (2) leading to the following
eVector of environmental burdens by each process in the unit of the burden used for environmental assessment, dimension n×1
bVector of direct environmental burdens defined as the amount of burden (in the unit of the chosen burden) related to the input of each process
in monetary units, dimension n×1
In the same way, the change in value-added (VA) based on formula (2) can be calculated as follows:
ΔVA =va ×ΔXM=va ×(I−AM)−1×ΔYM(4)
VA Vector of value-added by each process in monetary units, dimension n×1
va Vector of value-added by each process related to the input of each process in monetary units obtained from the MIOT (Table 3), dimension
2.3 System boundary and functional unit
To define the system boundary and the functional unit, we focus on an explicit industry in a defined region. All necessary processes of the value
chain of the building materials under study (gravel, cement, concrete, and excavation material), from raw material extraction to the EoL-Phase are
included (see figure S1 in Supporting Information S1). However, the use phase of the building material in the building is neglected. To consider
the environmental impacts of imported raw materials or additives, we integrate a second region in the model as “Hinterland” (Kytzia et al., 2004;
Schiller et al., 2020). This “Hinterland” is not modeled in detail but provides the necessary imports directly connected to the value. The use of this
approach enables us to better illustrate dependencies, especially regarding the supply of raw materials. As functional unit, we chose the “output of
the buildings materials-industry in the region of interest over a specified period.”
2.4 Economic and environmental assessment
The economic assessment of the region or company is based on revenue (income produced by the sector) and value-added (VA), which can be
extracted from the MIOT. The VA represents factor income generated by labor and capital on a regional scale. On a company scale, this factor income
568 MEGLIN ET AL.
TAB LE 4 The environmental indicators used in this study
Impact category Unit Description Source
kg CO2-eq. GWP (IPCC 2013, 100y) Emissions of processes/products over a chosen
period of time relative to that of CO2indicated
in kg CO2-equivalent
MJ CED Balances and adds up the consumption of primary
energy in relation to various sources,
distinguishing between renewable and
(Frischknecht et al.,
Ecological scarcity Ecopoints – Environmental impacts (e.g., climate change,
resource depletion) are weighted according to
the ecological objectives of the respective
country; summarized to create a
is analyzed for each process in the production chain (including internal transports) by subtracting material and energy costs from the revenueselling
To calculate the environmental impacts, we set up an LCA Inventory (LCI) based on the market data sets (including average transports) of all pro-
cesses (see Supporting Information). We assess the environmental impacts using the following indicators: global warming potential (GWP); cumu-
lative energy demand (CED) and the method of ecological scarcity (see Table 4). GWP and CED as impact categories have been selected as they
are the most frequently used in studies of the built environment and therefore allow comparison with other studies (Bovea & Powell, 2016). The
ecological scarcity is an end-point indicator that is used especially in the Swiss context of this study (Frischknecht & Büsser Knöpfel, 2013; Knoeri
et al., 2013).
Consumption of natural resources is directly assessed based on the PIOT. An overview of mass-related indicators can be found in the Supporting
Information. In an additional step, environmental indicators and mass-related indicators (per VA and per capita) help to connect to the UN SDGs
(United Nations, 2015), in particular SDGs 8, 9, and 12, and thus expand the spectrum of the evaluation.
2.5 Assessment of regional circularity
As mentioned before, aspects of CE are often assessed regarding resource efficiency and recycling and reuse of waste/secondary materials. This
does not capture the economic aspects of a CE. To capture those aspects, we suggest using indicators from regional economics: Leontief multipliers
calculated based on IOTs (Dubois, 2015; McLennan, 1995). In most studies, they are used to evaluate which processes/industries are pivotal to
economic activity in a region (Lenzen, 2003;Wen&Wang,2019) by indicating how much additional turnover is generated by an additional output
of a specific process/industry. We assume that these values increase as a regional economy advances in the transition toward a CE because linear
value chains are replaced by circular value chains. This increases the interlinkages within a regional economy and results in higher values for Leontief
multipliers for specific processes as well as the overall system.
To calculate the Leontief multipliers, we use the Leontief inverse matrices (I−APorM
)−1of the physical and monetary IOAs (Holub & Schnabl,
1994a, 1994b). Three different characteristic values (multipliers) can be distinguished here:
(i) The cell-value cij at the intersection of line iand column jof the Leontief inverse indicates the change in output of sector irequired to create
one additional unit of the respective sector jfor final demand.
(ii) The sum of the inverse coefficients cij of the column of a sector jindicates what all sectors (including sector j) must, directly and indirectly,
produce in addition for sector jto create one additional unit for final demand and
(iii) the sum of the inverse coefficients cij of the row of a sector iindicateswhatsectorimust generate in total, based on direct and indirect signals,
for each of the sectors to create one additional unit for final demand.
In the context of this study, we focus on the cell values cij and the column sum. The row multiplier is still shown in the results for completeness,
but not considered in detail.
2.6 Case study
To demonstrate the functionality of the model, we perform a regional assessment of the Swiss canton Aargovia for the year 2018. We chose this
canton because there is a high availability of raw materials on the one hand, but also a high construction activity. It covers all the major material
MEGLIN ET AL.569
flows considered in this study and is therefore considered an adequate sample region. Key figures for the assessment can be found in the Supporting
2.6.1 Data of material flows
The material flows are obtained from the database “KAR-Modell” (Rubli, 2020). The KAR-Model is an institutionalized database of regional and
interregional material flows of building materials (K =Kies/gravel; A =Aushub/excavated material; R =Rückbaumaterial/CDW) of various cantons
in Switzerland. As the KAR-Model does not provide detailed data sets for material flows in concrete production and building industries, we addi-
tionally use data provided by studies that examine the material flows of the Swiss building stock in detail (Gauchet al., 2016; F. Guerra & Kast, 2015;
Rubli, 2016), as well as regional statistics (Kiefer et al., 2020). Key numbers used for this assessment can be found in the Supporting Information.
2.6.2 Economic data
Economic data are used from national statistics of the federal government or regional statistics of the canton. Average prices were determined
based on the public price lists of around 170 building materials producers throughout Switzerland (see the Supporting Information). These prices,
as well as costs and internal price structures, were validated in interviews with various producers. By grouping the individual sectors according to
the Swiss General Classification of Economic Activities (NOGA), we can make a more detailed analysis of the turnover and VA (see the Supporting
Information). Using key figures from the Swiss value-added statistics (BfS, 2020) we can estimate personnel costs, other operating costs, and the
amount of amortizations in addition to expenditure on goods and materials. These values, even if these are only rough estimates based on statistics
averaged over Switzerland, allow us to assess an industry in more detail and compare it with other sectors.
2.6.3 Environmental coefficients
The environmental indicators are calculated based on datasets from ecoinvent (Wernet et al., 2016). In the case of construction and demolition pro-
cesses, no data records are available in ecoinvent. Therefore, processes from German Database Ökobaudat (BMI, 2020) were used instead. Note,
that for the construction and demolition processes, values for ecological scarcity were not available. The recycling processes of mixed and concrete
granulate are set up according to Tschümperlin et al. (2020) using datasets from ecoinvent. We use an economic allocation approach based on pub-
lished average values to include the emissions of alternative and secondary raw materials in cement production (see the Supporting Information).
Alternative fuels are allocated according to the average fuel mix of the Swiss cement production. Transportation is accounted according to the
respective Swiss market data sets. Electricity is modeled according to the average Swiss electricity mix which is available in ecoinvent. The detailed
environmental impacts of the considered inputs can be found in the Supporting Information.
3.1 Economic and environmental results
The results of the assessment are visualized in Figure 1. The detailed results can be found in the Supporting Information. Based on these diagrams,
we can identify hotspots in the industry under study but also processes with increased resistance to change caused by policies. We assume that
resistance to change correlates with VA—creating jobs and capital income in the region.
The evaluation of material output in comparison to VA (Figure 1a) shows that various sectors have a high material turnover but generate only
little VA. These sectors can thus be identified as intermediate sectors, where policies are likely to have only a minor effect on the regional economy.
The sectors “Cement plant,” “Buildings” and “Gravel pits” can be identified as hotspots. They handle large amounts of material and generate high
VA. This also means that these sectors are likely to show high resistance to policy-induced changes in material flows. The position of the sectors
“Demolition” and “Recycling Plant” in the figure indicates that the recycling of demolition material still creates far less VA compared to sectors
using primary materials. This suggests that these sectors are more open to policies stimulating alternative business models.
From an environmental perspective (Figure 1b–d), the cement sector is the single most important process in the Canton of Aargovia, as it
is home to two of the six Swiss cement plants. For this reason, the environmental impacts of the cement sector are of paramount importance
in this canton. The remaining sectors, especially the gravel pits, gravel plants, and quarries show higher environmental impacts, as they are
not only resource-intensive but also transport-intensive. It should be noted that due to the data situation (see Section 2.6), the environmental
570 MEGLIN ET AL.
FIGURE 1 Results of the assessment: (a) Comparison of sectors in terms of material output and VA; (b) comparison of sectors in terms of GWP
and VA; (c) comparison of sectors in terms of CED and VA; (d) comparison of sectors in terms of eco-scarcity and VA; for better readability, the
x-axes in (b), (c), and (d) have been scaled to logarithmic ones. Underlying data used to create this figure can be found in Supporting Information S1,
impacts of the construction processes (building and infrastructure) can only be mapped to a limited extent and should therefore be treated with
3.2 Assessment of regional circularity
The two tables below show the physical (Table 5) and monetary (Table 6) Leontief inverse matrix and the multipliers described in 2.5. A high aggre-
gated multiplier indicates a high degree of interdependence in the industry, while a multiplier of 1.0 rather indicates a “pass-through sector.” The
column multiplier of the sector “Demolition” in the physical matrix shows a value of 3.973, which indicates that for one unit of output the sector
“Demolition” triggers a material turnover of 3.973 in the entire system. It indicates that “Demolition” is a key process for the material manage-
ment system and of paramount importance in the transition toward CE. Yet, this does not reflect economic reality. The construction industry is
represented in the physical matrix as a “supplier” to demolition, although the economic relation is vice versa. Demolition supplies the construction
industry with a waste management service. This becomes visible only in the monetary inverse. In consequence, the multiplier for demolition in the
monetary Leontief-Inverse is much lower. It only amounts to 1.176 because demolition is connected only with the recycling plant and the landfill.
Only in this way, the service of “Demolition” becomes visible. This reinforces the statement mentioned before, that monetary data should be used
for a realistic evaluation of a service-oriented CE. However, this high physical multiplier shows the importance of the sector “Demolition” in terms
of CE and material usage. The demolition process triggers all other processes in the construction industry and a higher rate of demolition and use of
CDW would therefore probably strengthen the linkage.
In the monetary matrix (Table 6), building construction shows the highest column multiplier of 2.06. This means an increase in construction
spending of CHF 1 million triggers a total output of the construction industry of CHF 2.06 million. It can be concluded that the construction of
buildings has the greatest influence on achieving a CE. The same applies to the “concrete plant” sector or the sector “gravel plant,” which has a
MEGLIN ET AL.571
TAB LE 5 Physical Leontief inverse matrix of the building materials industry in Aargovia in 2018 and the Leontief multipliers
To/to (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(1) Gravel pits 1.000 0.896 0.265 0.459 0.384 0.449 0.421 0.013 3.887
(2) Gravel plant 1.000 0.296 0.512 0.429 0.501 0.470 0.014 3.222
(3) Recycling plant 1.084 0.115 0.152 0.120 0.134 0.004 1.610
(4) Quarries 0.033 1.000 0.668 0.147 0.094 0.021 0.053 0.002 2.017
(5) Cement plant 0.050 1.000 0.220 0.141 0.031 0.079 0.002 1.523
(6) Concrete plant 0.232 1.025 0.657 0.144 0.367 0.011 2.435
(7) Buildings 0.313 0.033 1.044 0.073 0.496 0.015 1.974
(8) Infrastructures 0.405 0.043 0.057 1.094 0.642 0.020 2.260
(9) Demolition 0.717 0.076 0.101 0.167 1.138 0.035 2.235
(10) Terrain 0.338 0.347 0.109 0.190 0.127 0.206 0.167 0.178 0.173 1.000 0.695 3.532
(11) Landfill 1.000 1.000
Column-multiplier 1.338 2.243 3.503 1.190 1.795 2.836 3.225 2.778 3.973 1.000 1.811
TAB LE 6 Monetary Leontief inverse matrix of the building materials industry in Aargovia in 2018 and the Leontief multipliers
CHF/CHF (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(1) Gravel pits 1.000 0.660 0.107 0.165 0.053 1.986
(2) Gravel plant 1.000 0.162 0.088 0.069 1.319
(3) Recycling plant 1.000 0.018 0.028 0.029 0.159 1.233
(4) Quarries 1.000 0.064 0.028 0.030 0.002 1.160
(5) Cement plant 1.000 0.435 0.184 0.019 1.638
(6) Concrete plant 1.000 0.423 0.043 1.466
(7) Buildings 1.000 1.000
(8) Infrastructures 1.000 1.000
(9) Demolition 0.074 0.132 1.000 1.205
(10) Terrain 0.011 0.002 0.001 0.001 1.000 1.014
(11) Landfill 0.067 0.007 0.017 1.000 1.091
Column-multiplier 1.000 1.671 1.000 1.000 1.064 1.751 2.060 1.354 1.176 1.000 1.000
multiplier of 1.795 and 1.671, respectively. Here, too, it can be assumed based on the multipliers and the linkages, that policies considering those
sectors have high leverage in the building materials industry.
To evaluate the leverage effect of policies on the sector in more detail, we can use Equations (3)and(4) from Section 2.2 to calculate the changes
in impacts when the demand Ychanges. As an example, we will use the sector “Buildings,” and the induced output of CHF 2.06 million CHF for an
investment of CHF 1 million. Figure 2shows in which sectors this investment would havethe greatest impact. For VA, the sector “buildings” itself has
the dominant impact, followed by gravel pits and concrete plants. Furthermore, the cement plant contributes the largest share to the environmental
impacts. However, a large share of the environmental impacts, especially the ecological scarcity, is caused by the gravel pits and plants.
Every model is a simplification of reality and highly depends on the data used and assumptions made. The Material Flow data have a certain
amount of uncertainty as it is based on general statistics and expert opinions. Our main data source for MFA, the KAR-model, however, has been
validated over several years. Considering the calculation of environmental impacts, the existing data sets also have a significant influence on the
uncertainty. For example, there is little data available on the environmental impacts of construction and demolition processes. However, by using
periodically updated and peer-reviewed data of institutional LCA databases, we assume uncertainties related to the data quality to be low in
572 MEGLIN ET AL.
FIGURE 2 Share of economic and environmental impacts from the increase in demand in the building construction sector by CHF 1 million;
Underlying data used to create this figure can be found in Supporting Information S1, table S12
the context of this study. Further uncertainty arises in the economic evaluation, as average market prices were used for calculation. In the con-
struction industry, however, prices vary from region to region and sometimes from project to project, so that a certain degree of uncertainty is
transferred to the model. Since the data basis for prices is based on public price lists or a few data points from official statistics, prices are the
largest source of uncertainty for the assessment model in this study. Reliable statistics on construction and material costs would improve the
Nevertheless, the model is not about detailed product comparisons, but about a general evaluation of regions and industrial sectors to identify
hotspots and general changes in material flows and the related impacts. It can therefore be assumed that the assumptions derived from the model
analysis are justified and that the identified uncertainties do not have a significant impact on the results and the final evaluation. Ultimately, the
developed model is not primarily intended to evaluate the sustainability of an industry. Rather, it is designed to distill the necessary data and indica-
tors and to identify hotspots to support decision-makers, and develop regional policies. For these use cases, instead of high-precision data, general
insights for policymakers are relevant.
The consideration in this study is limited to a regional building materials industry. In principle, the processes of the model can be divided into raw
material extraction, intermediate product, and end product. Thus, we believe, that this model can be transferred to other regional heavy industries
with a high proportion of raw material extraction (e.g.,metal). In a very complex high-tech industry, however, the model would reach its limits due to
the many sub-sectors and global value chains.
5DISCUSSION AND CONCLUSION
Bruel et al. (2019) have pointed out, that we have to go beyond the physical study of material flows by studying the socioeconomic consequences
of flows. With our model, we are taking a first step toward a better understanding of the environmental and economic aspects of the transition
toward a service-oriented CE and indicate the critical hotspots for policymakers. Our model offers the possibility to provide all necessary data for
a holistic decision basis and to assess different regions and formulate specific policies promoting CE on a regional level (Smol et al., 2017;Virtanen
et al., 2019). This was made possible by the combination of the established methods MFA, IOA, and LCA and the calculation of environmental and
economic indicators on a physical and monetary basis. The range of different indicators enables a transparent and comprehensive evaluation on
different levels, a necessity highlighted by several scholars (Harris et al., 2021; Haupt & Hellweg, 2019; Nikolaou et al., 2021).
Through an exemplary assessment of the canton of Aargovia, we could demonstrate the benefits of the model. We were able to show which sec-
tors in the building materials industry are pivotal in the transition toward CE and where political incentives and business model innovations could
have the greatest effect (Dubois, 2015;Wen&Wang,2019). Mass-related policies (e.g., green public procurement or recycling quotas) must be
related to material-intensive sectors to achieve the greatest effect. Values of the physical Leontief multipliers identified demolition and recycling
plants as material-intensive sectors. This confirms current development in CDW-legislation in Switzerland (ADWO, 2020) that requires 100% recy-
cling of all mineral CDW. However, the monetary Leontief multipliers show, that incentive-based environmental policies (e.g.,a levy on virgin gravel
to improve CDW’s financial competitiveness) would rather be applied to the sectors of building construction, concrete plants, and gravel plants
since financial decisions have a greater influence there. The use of secondary resources highly depends on purchase decisions in these sectors.
Public policies in Switzerland start to recognize this and new legislation on public tender encourages including criteria for sustainable procurement
(UREK, 2020). Our findings support this initiative and could help to focus it more clearly on CE.
MEGLIN ET AL.573
Building on these initial findings, we see further applications of the model. For example, a comparison of different regions could identify relevant
regional characteristics and constraints. These would help to formulate tailor-made and efficient regional policies to address different challenges
and barriers (B. C. Guerra & Leite, 2021; Hart et al., 2019). The model provides the basis for recommendations for actors from politics and admin-
istration for the development of measures and instruments for a CE in the building materials industry and the promotion of efficient use of mineral
raw materials (Wilts et al., 2016). Another potential option would be scenario analyses, which would make it possible to consider the different
processes in the building materials industry. This is particularly important in the context of various possible strategies of the cement and concrete
industry to minimize raw material demand and reduce CO2emissions (Favier et al., 2018;Habertetal.,2020; Obrist et al., 2021). For this, the
model provides improved decision bases for companies in the construction industry to further develop their business models toward sustainable
The authors would like to thank the reviewers for their comments, which contributed to the enrichment of this paper, and Dr. Simon Hoell and
Daniel Kliem for their feedback on earlier drafts of the paper.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The data that supports the findings of this study are available in the supporting information of this article.
Ronny Meglin https://orcid.org/0000-0001-8730-1196
Guillaume Habert https://orcid.org/0000-0003- 3533-7896
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How to cite this article: Meglin R, Kytzia S, Habert G. Regional circular economy of building materials: Environmental and economic
assessment combining Material Flow Analysis, Input-Output Analyses, and Life Cycle Assessment. J Ind Ecol. 2022;26:562–576.