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Exploring long-term building stock strategies in
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IOP Conf. Series: Earth and Environmental Science 1078 (2022) 012023
IOP Publishing
doi:10.1088/1755-1315/1078/1/012023
1
Exploring long-term building stock strategies in Switzerland
in line with IPCC carbon budgets
Y D Priore1,2, T Jusselme1 and G Habert2
1 Energy Institute, University of Applied Science of Western Switzerland (HEIA-FR,
HES-SO), Fribourg, Switzerland
2 Chair of Sustainable Construction, ETH Zürich, Zurich, Switzerland
priore@ibi.baug.ethz.ch
Abstract. Stringent limits and reduction strategies paths on greenhouse gas (GHG) emissions
are being defined at different levels for long-term temperature stabilization. Given the nearly
linear relationship between warming and cumulative net emissions, a carbon budget approach is
required to limit global warming, as stated by the IPCC. In this setting, the built environment, as
a cross-sectorial and transnational area of activity, plays a crucial role in today’s carbon
emissions and future reduction potentials. Previous research showed the need for effective and
aligned carbon-targets to support and guide all actors in the construction sector towards these
challenging global goals. In this context, previous research compared top-down derived carbon
budgets for the Swiss built environment with a preliminary estimation of future cumulative
emissions of the sector. Findings showed the misalignment of current best practices and the
significant magnitude of effort that would be required to comply with such objectives.
Nevertheless, limitations in the preliminary work emerged, such as the lack of dynamicity of the
parameters included in the model restricting the representativity of its results. The current paper
brings further this previous work by integrating the dynamic evolution of the energy supply, the
materials’ production, and the renovation rate. Results are then presented by mean of a parallel
coordinate interactive graph. This interactive component allows the parametric exploration of
the compliance with limited global budgets by varying the input parameters. This way the
influence of macro-level strategies to decarbonize the Swiss building stock can easily be
visualized with reference to the IPCC carbon budgets. Ultimately, the available interactive tool
might support policy makers in decisions taken at the building stock level.
Keywords: Carbon Budgets, Building Stock, Carbon targets, Emissions, Mitigation
1. Introduction
To limit global warming and thus achieve the set long-term temperature stabilization (well below 2°C
and pursuing efforts towards a 1.5°C limit) as defined by article 2 of the Paris Agreement [1], countries
must take immediate action to reduce and mitigate emissions. Although reaching a set goal of net-zero
emissions by midcentury (article 4 of the Paris Agreement) is essential to achieve the required balance
for our environment, limiting cumulative emissions over time is not to be forgotten. As stated in the
IPCC Special Report of 2018 [2]: “limiting global warming requires limiting the total cumulative global
anthropogenic emissions of CO2 since pre-industrial period, that is, staying within a total carbon
budget”. The quantification of global carbon budgets is an integral part of the work conducted by the
IPCC [2–4] and the latest values (2021) are used in this work. The concept of a limited remaining carbon
budget and its distribution to countries and sectors is presented in various works in the literature [5–7].
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In this context, buildings and related construction activities contribute to 38% of all energy-related CO2
emissions [8] and urgent reduction strategies are required.
The building stock is a complex dynamic system that evolves over time and needs, amongst others,
to accommodate a constantly increasing population. Although new buildings are becoming increasingly
energy efficient, the impact of the existing stock and the increasing impact of embodied emissions [9]
are still an unsolved long-term problem. Furthermore, building stock strategies often focus on only one
aspect of buildings’ emissions and forget the cross-impact that, for example, increasing deep renovations
can have on embodied emissions [10]. Especially, countries’ initiatives and incentives to tackle
renovations often lack this level of understanding and tend to focus on the reduction of operational
energy without considering the impact of the materials put in place. For example, the 110% superbonus
[11] in Italy requires renovations to pass at least two energy-efficiency categories but does not mention
the embodied impact of the work. Similarly, in Switzerland the “Programme Batiments”, regulated by
the Cantons, defines a framework of incentives to increase the energy efficiency of buildings but no
weight is given to the choice of materials. An urgent need to understand these relations, especially at a
policy making level, is identified and thus, the present work presents a way to explore the impact of
building stock strategies, enabling the possibility to set more informed long-term policies for our built
environment. This contribution builds upon previous work, presented in section 2.
2. State of the art
Previous work from the same authors [12] attempted a first static estimation of future cumulative
emissions of the Swiss building stock, highlighting the great misalignment of current best practices in
Swiss constructions with limited budgets. The first part of the previous research established a
methodology to allocate the IPCC global carbon budget to the operation and construction of Swiss
buildings. This methodology is retained in the current paper but updated with most recent global carbon
budgets [3]. The second part of the previous work estimated future cumulative emissions with static
parameters such as a constant 1% renovation rate till 2050. The static nature of the previous model
presented shortcomings in its representative value. A more realistic evolution of the parameters used in
the model, such as a gradual increase of the renovation rate, is needed to fully represent the possible
pathways of the building stock and its compliance with climate goals. Furthermore, the initial state of
the stock, in the previous work, was assessed as a best case scenario following the SIA 2040 targets
[13]. In this work, the initial impact of the stock is assessed in more depth to better represent the current
level of emissions.
The contribution presented in this paper builds upon this previous work and further develops a usable
final interactive graph to enable the simple exploration of the results. Cumulative emissions of the Swiss
building stock are calculated considering the gradual evolution of the renovation rate, the operational
emissions as well as the embodied emissions of construction works. The main aim of the current work
is to explore which macro strategies would allow the compliance with limited budgets until 2050. The
graphical and interactive representation, by means of a parallel coordinate graph, further allows the
exploration of the sensitivity of the parameters put in place, giving insight on the parameters we should
focus on to reach the challenging climate goals.
In conclusion, this paper focuses on building stocks emissions and their ability to comply, or not,
with a limited carbon budget allowance for the sector till 2050. Furthermore, it allows the exploration
of various long-term building stock strategies in Switzerland in line with climate goals. The estimation
of cumulative emissions of buildings is made possible through a building stock model. As the scope of
this work was to create a simple model with easily accessible data, the level of detail of the model is
limited to its purpose.
3. Methodology
This section presents the methodology used to, initially build a simplified model with few input
parameters, then estimate emissions till 2050 and finally automatically explore different scenarios. The
construction of the graphical final output is presented in subsection 3.3.
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3.1. Model components and structure
A simplified top-down and statistical building stock model was built using the programming language
Python [14]. As presented in Figure 1 the model can be separated in three main “blocks”. The first one
incorporates the development of surfaces composing the stock in terms of square meters. The second
one includes greenhouse gas emissions related to every square meter of the stock in kgCO2-eq./m2. The
changes in surfaces and related emissions are calculated yearly and finally added up in the last block of
the model in terms of cumulative emissions over the studied period in Mt.CO2-eq. The outputs of one
block feed the other block through predefined relations as shown in Figure 1. The model is further
composed of two main elements with varying functions. First, input data, mainly retrieved by national
statistics or literature, are mainly used to characterize the initial state of the building stock. Secondly,
variable and dynamic parameters, defined as possible targets to be explored by the model. The number
of possible values (limited for computational reasons) for each variable parameter is shown in Figure 1
with the value “n” under each parameter. Further details on these parameters are found in section 3.2. .
All blocks are explained in more details in the following subsections, where input data, assumptions,
relations, and sources are presented.
Figure 1. Graphical representation of the model (“n” represents the number of possible values).
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3.1.1. Surfaces. The Swiss building stock in this work is represented in terms of existing (non-
renovated), renovated, and newly built surfaces. Considering surfaces instead of buildings makes the
model easier to manage as no distinction is made between different typologies but is limited in terms of
level of detail. The main assumptions behind these surfaces and their evolution are shown in the previous
research [12] and are here summarized in Table 1. Table 1 further presents the dependency on variable
and dynamic parameters used in this contribution. In summary, every year of the studied period, a part
of the existing stock is renovated while a part stays untouched and new surfaces are added. Renovated
and non-renovated surfaces are calculated with a renovation rate that varies every year depending on the
renovation rate target and goal year chosen for the scenario. Newly built surfaces are, instead,
independent of variable parameters and calculated based on the increasing population over the studied
period.
Table 1. Definition of surfaces in the model, input data, sources, and variable relations.
Input data Sources Dependency on variable and
dynamic parameter
Non-renovated
surfaces
Initial stock 2018
= 392 945 800 m2
[15]
Renovation rate target
Goal year
Renovated
surfaces
Initial stock 2018
= 392 945 800 m2
Renovation rate target
Goal year
Newly built
surfaces
Population 2018 = 8 525 611
Population 2050 = 10 440 600
Average new dwellings per
1000 inhabitant = 6
Average surface per dwelling =
99m
2
[15]
/
3.1.2. Emissions. Emissions from the building stock are subdivided into operational emissions and
embodied emissions. Each set of surfaces, outputted from the previous block, is related to each type of
emissions according to the methodology presented in previous work [12] and updated values that are
summarized in Table 2. Input data in this case represent the current emissions of buildings and
construction works in Switzerland. Most values are derived with a top-down approach from total
national territorial emissions and imported emissions for materials in construction in relation to surfaces
affected from it. Consequently, the average values presented do not refer to a specific new or renovated
construction. Operational emissions of new buildings are instead estimated analysing consumption
levels and energy carriers for buildings built in the last year in the cantonal energy certification scheme
database [16]. Operational emissions of renovated buildings are adapted by using the same ratio used
by the SIA 2040 [13]. Although the operational emissions of renovated buildings do not directly refer
to a specific strategy, they do refer to a representative average renovation work as presented in the
literature [17]. Average operational emissions of the existing stock remain unchanged and are not
affected by a variable parameter. It was here assumed that, although buildings are being renovated, no
order is defined (ex: renovating first buildings with highest impact), therefore the average emissions of
the stock will not be affected. The initial inputs estimate a current share of embodied and operational
emissions of 77%/23% and 56%/44% for new and renovated buildings respectively. These shares are
very close to the current targets proposed by the SIA 2040 [13] in Switzerland as well as shares found
in the literature [17].
Table 2. Definition of emissions in the model, input data, sources, and variable relations.
Input data
Sources
Dependency on variable and
dynamic parameter
Operation of non-
renovated surfaces
Average operational emissions
of existing stock
= 28 kgCO2eq/m
2
.year
[12]
/
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Operation of
renovated surfaces
Current average operational
emissions of renovated buildings
= 5.8 kgCO2eq/m
2
.year
[13,16]
Operation renovated
target
Goal year
Embodied of
renovated surfaces
Current average embodied
emissions of renovated buildings
= 440 kgCO2eq/m
2
[12]
Embodied renovated
target
Goal year
Operation of new
surfaces
Current average operational
emissions of new buildings
= 3.5 kgCO2eq/m
2
.year
[16]
Operation new target
Goal year
Embodied of new
surfaces
Current average embodied
emissions of new buildings
= 696 kgCO2eq/m
2
[12]
Embodied new target
Goal year
3.1.3. Results. The final block, as illustrated in Figure 1, produces the results by cumulating, over each
year, emissions stemming from the stock for each possible combination of the variable parameters
(348 480 combinations in total). Results are subdivided into operational cumulative emissions,
embodied cumulative emissions, and total cumulative emissions. This subdivision gives a better
understanding of the influence of each parameter on final emissions over time. The final output of the
Python model is a csv file compiling all combinations of variable parameters and respective results.
3.2. Variable and dynamic parameters
Variable parameters are characterized in this work as the clue elements defining long-term strategies at
the stock level. Their variability stands in the possible “target” value to be achieved in the chosen goal
year. Each variable parameter in the model has predefined sets of values it can reach in the goal year as
well as initial values as presented in Table 3 and graphically shown in Figure 2. The list of possible
values was limited for computational reasons but can easily be changed, increased, or decreased in the
Python model to generate different scenarios. The goal year definition was kept separate as it has a
different level of relation compared to the other parameters. The choice of the goal year complements
the other target choices by defining the length of the x-axis and not the y-axis in Figure 2. For all
parameters, the choice of minimal, maximal, and steps of possible values was chosen to represent both
acceptable and extreme scenarios. The renovation rate was assumed to remain constant or increase
according to Swiss and European commitments [18,19] and 10% is considered a drastic value.
Table 3. Variable parameters definition.
Initial value (2018)
List of possible values
Renovation rate
1% [20]
[1%, 3%, 5%, 10%]
Operational emissions of new
buildings (in kgCO2-eq/m
2
.year)
3.5
[0 to 10]
Operational emissions of renovated
buildings (in kgCO2-eq/m
2
.year)
5.8
[0 to 10]
Embodied emissions of new buildings
(in kgCO2-eq/m
2
)
696
[-540 to 1140] steps of 120
Embodied emissions of renovations
(in kgCO2-eq/m
2
)
440
[-300 to - 600] steps of 60
Goal year
/
[2040, 2045, 2050]
3.2.1. Dynamic evolution over time of parameters. The dynamic aspect applied in this work refers to
the non-static behaviour of the parameters in the model. The variable parameters presented in the
previous chapter are characterized by an initial value and a possible target (variable) to be reached in a
certain year (goal year). The assumption is here made that each parameter evolves linearly from its
starting point till its target value in the time frame defined by the goal year chosen as presented in Figure
2.
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Figure 2. Linear evolution of variable parameters.
3.3. Graphical representation
Finally, the model output feeds a parallel coordinate graph [21], outlining all variable parameters and
related outcomes in terms of cumulative emissions in Mt.CO2eq. The graph is also created in python,
using the pandas and plotly libraries [22,23]. The graph is interactive, and strategies can be explored to
compare results with defined limited carbon budgets. The same graph can be used as well to test the
sensitivity of the different parameters by choosing the cumulative emissions goal and visualizing instead
the span of possible combinations.
3.3.1. Climate goals reference. The final graphical tool makes a reference to temperature limit targets
as seen in Figure 3 (coloured scale bar on the right). Those are derived by calculating the limited carbon
budgets (to be spent during the time frame of the study) for the operation and construction of buildings
in Switzerland. The methodology used to derive a 1.5°C and a 2°C budget is presented in previous work
[12], where global carbon budgets are first allocated to Switzerland with an equal per capita method and
then further distributed to the relevant sectors with a grandfathering method, considering future
estimations of negative emissions technologies. Furthermore, the Swiss climate strategy goal was added
as reference, taken from the Energy Perspective 2050+ [24].
4. Results
This section presents the main outcomes of this work by showing first, results in a business-as-usual
scenario, secondly a commonly accepted strategy of increased renovation rate and finally the sensitivity
of parameters to achieve specific climate goals. The final output of this research, the interactive graph,
is meant to be used to explore different solutions and this can be done only by actively using the said
graph on https://github.com/YasminePriore/Exploring-long-term-building-stock-strategies. The results
presented here are just part of the possible conclusions and outcomes of this output.
4.1. Business as usual
The business-as-usual scenario represents a path till 2050 that keeps current targets and practices
unchanged in this time span. In this case the renovation rate stays constant at 1% until 2050 and operation
of new and renovated buildings as well as their embodied emissions keep current values, as presented
in Table 3. As shown in Figure 3 this scenario exceeds the 2°C budget reaching a 2.1°C temperature
limit. Total cumulative emissions are here clearly driven by cumulative operational emissions. These
results are explained by the relatively low renovation rate, resulting in a high number of existing
buildings that keep a high operational impact until 2050.
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Figure 3. Illustration of cumulative emissions results for the Business-as-usual scenario.
4.2. Renovation+ scenario
Following up on the previous results, the renovation+ scenario follows a commonly agreed pathway
where renovation rate is gradually increased to reduce the operational impact of the existing stock. In
this case, presented in Figure 4, renovation rate linearly reaches 10% in 2050. However, it must be noted
that this rate influences only the existing stock of 2018 and by circa 2040 all buildings are renovated;
thus, renovation works stop. All other parameters are kept the same as in the BAU scenario. As shown
in Figure 4, although cumulative operational emissions are strongly reduced by this measure, embodied
emissions increase due to the strong renovation activities, resulting in the same total cumulative
emissions in 2050 as in the BAU scenario. This result clearly demonstrates that just renovating more,
without considering the carbon content of the materials, will not help the end result for our climate
change goals.
Figure 4. Illustration of cumulative emissions results for the Renovation+ scenario.
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4.3. Sensitivity to reach climate goals
As mentioned before, the interactive graph can also be used to test the sensitivity of the parameters in
reaching specific climate goals. Previously presented scenarios were constraining the variable
parameters to explicit values in order to get results. In the following sections, instead, results are
constrained to a precise goal to visualize the range of possible parameters.
4.3.1. 1.5°C limited budget. Figure 5 represents the possible range of values to achieve a 1.5°C limited
budget and in Table 4 the minimal and maximum values possible to achieve this goal are listed. It must
be noted that each line of the graph represents a specific scenario and that not every combination of
values in the range will reach the 1.5°C goal.
Figure 5. Illustration of variable parameters’ range to comply with a 1.5°C budget.
Although operation of new and renovated buildings can span through all possible values, they are not
compatible with all possible values of embodied targets and renovation rate. What is most evident in
Figure 5 are the excluded values such as 2050 as a goal year or 1% renovation rate and the very limited
embodied targets. These values are, in no scenario, a possibility to achieve a 1.5°C target. One can also
immediately visualize the high sensitivity of the embodied targets, where the only possible values are
negative targets to be achieved to compensate emissions.
Table 4. Range of possible values for a 1.5°C goal.
Goal year
Operation
new
Operation
ren
Embodied
ren
Embodied
new
Renovation
rate
Max
2040
10
10
-120
-180
10%
Min
2040
0
0
-300
-540
3%
4.3.2. Swiss climate strategy. Achieving the goals set by the Swiss climate strategy will leave more
freedom in terms of long-term strategies. Important to notice in Figure 6 is the high sensitivity of the
embodied targets. Although relatively high embodied targets are possible, they are only compatible if
all other parameters are drastically decreased (grey lines starting from high embodied targets). From
the result part of the graph one can also notice how the reduction of cumulative embodied emissions is
effectively essential in achieving the goal.
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Figure 6. Illustration of variable parameters’ range to comply with the Swiss climate strategy.
Table 5. Range of possible values for the Swiss climate strategy.
Goal year
Operation
new
Operation
ren
Embodied
ren
Embodied
new
Renovation
rate
Max
2050
10
10
420
780
10%
Min
2040
0
0
-300
-540
1%
5. Discussion
The model used in this paper was simplified, to reduce the level of complexity, to six main variables
and dynamic parameters, defined as the most impactful long-term strategies on cumulative emissions.
Further parameters could be implemented to increase the level of detail such as a more precise
renovation rate of heating systems or the operational-embodied trade-off of renewable production on
site or, again, the dynamic emission factors for electricity production. The top-down approach applied
to the whole Swiss building stock used in this work has a main limitation of feasibility/representability
of the strategies on single building solutions. The building stock is here generalized, and strategies are
applied to every square meter without distinction of real design feasibility. This low level of detail is
useful to understand overall dynamics in the stock but fails to propose concrete design solutions.
Nevertheless, results can help policy makers to set high ranked national strategies to decarbonize the
building sector without compromising the design intelligence required at a higher level of detail.
Furthermore, the range in which each parameter was allowed to be explored was defined and limited
for computational reasons. Minimal and maximal values are meant to represent a realistic span in which
each parameter could fall in the design of buildings but do not claim to be exhaustive. The limits chosen
have an influence, not on the overall final result but mainly on the amplitude of possible results,
especially in the comparison between the amplitude of cumulative operational emissions and cumulative
embodied emissions. This contrast in the presented range of results should not be considered as an
absolute reality but is dependent on the values the model is exploring. The flexibility to change these
limits in the model is open and further investigations are possible.
Important to discuss is the fact that operational emissions, both for new and renovated buildings,
were limited to 0kgCO2eq/m2 as a best solution in contrast to embodied emissions that reach negative
values. This choice was made by considering negative emissions as a true sequestration effect and not a
balance in limited system boundaries. It could be argued that producing more energy on site than the
building’s demand would result in negative operational emissions for that specific building, but should
this effect be accounted for on cumulative building stock emissions? On the other hand, the
implementation of materials with a carbon capture potential (ex: fast growing biobased materials) was
deemed as a possible contribution to reduce cumulative stock’s emissions. Current building calculation
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methods in Switzerland [25] do not consider biogenic materials as potential negative emissions, so there
are no possibilities of producing a carbon negative building, although other methods would allow it [26].
The model and the tool are built on easily accessible building stock data and can, in a way, be adapted
to different building stocks (in other countries for instance). The simplistic nature of the model and the
relatively few inputs make it a very adaptable tool.
The next step would be to make the tool available in an online format, allowing its access to the
responsible entities for long-term strategies of decarbonization of buildings. Future works are envisioned
to investigate the feasibility of single strategies on detailed archetypes of buildings to prove the viability
of the targets.
6. Conclusions
The main objective of this contribution was to investigate and visually represent the influence of
building stock parameters on cumulative greenhouse gas emissions until 2050. An initial static model,
presented in previous work, was enhanced by the dynamic evolution of the parameters over time
allowing a gradual improvement of the building stock based on set long-term goals. Six parameters have
been identified and scenarios for each one of them are defined to explore multiple combinations of them.
The initial business-as-usual state of the building stock is identified with average current values.
Results highlight the urgent need to change the way we build and operate buildings, demonstrating
that by continuing with a business-as-usual scenario we would surpass a 2°C budget, far from the goals
set nationally and internationally. Another important result presented in this paper is the importance of
accounting for interactions between different strategies as, for example, increased renovation rate
without decreasing embodied impact of said renovations. Cumulative emissions until 2050 in such a
scenario would result in the same 2.1°C budget as in the BAU scenario. Although renovating the existing
stock remains essential to reach challenging goals, the link with the materials’ impact we put into these
renovations plays an essential role. Furthermore, 2050 seems already a very challenging target but it is
not enough to reach a 1.5°C limit in temperature.
Finally, the visually attractive final visualization is a promising solution to easily explore different
scenarios and combinations of targets and strategies. The graph further allows a simple way to test the
sensitivity of each parameter in achieving set goals. At the policy making level there is a need to easily
understand interactions of parameters without analysing complex construction details and high-level
exploration tools can fill this gap.
Acknowledgments
The authors would like to thank the collaborators (Radu Florinel, Jonathan Parrat, Julie Runser –
Transform Institute at the University of Applied Science of Western Switzerland) and partners (Emilie
Nault – CSD Ingénieurs; Igor Andersen – Urbaplan; Philippe Jemmely – Bluefactory SA; Werner Halter
– Climate Services; Francois Guisan – One Planet Living) of the research project SETUP PRO for the
constructive discussions held around the topics presented in this article. Financial support is gratefully
acknowledged from the HEIA-FR Smart Living Lab research program.
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