Optimal building retrofit pathways considering stock dynamics and climate change impacts

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Article

Reference

Optimal building retrofit pathways considering stock dynamics and climate change impacts

STREICHER, Kai Nino, et al.

Abstract

Deep energy retrofit across the European building stock would require decades during which boundary condi-tions will change. This study identifies a range of retrofit pathways, using a dynamic stock model, a bottom-up energy model and an optimization model for different climate scenarios. We consider 1.1 million different retrofit options in the Swiss residential building stock for different economic/environmental objectives until 2060. Despite the replacement of old by new buildings, energy demand and greenhouse gas (GHG) emissions in the reference scenario without deep energy retrofitting are likely to decrease by only about 25%, while ac-counting for investments of 2–3 billion CHF/a. Partial energy retrofitting or an investment-minimized pathway are neither cost-effective nor sufficient to get close to the net zero targets. In contrast, the highest GHG-saving pathway leads to very high emission reduction of 90%, but requires investment cost of 9 billion CHF/a, which leads to specific cost of 180 CHF/t CO2eq. The cost-optimal pathway shows moderate trade-offs for in-vestment cost and could reach GHG savings of [...]

STREICHER, Kai Nino, et al . Optimal building retrofit pathways considering stock dynamics and climate change impacts. Energy Policy , 2021, vol. 152, no. 112220

DOI : 10.1016/j.enpol.2021.112220

Available at:

http://archive-ouverte.unige.ch/unige:154417

Disclaimer: layout of this document may differ from the published version.

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Energy Policy 152 (2021) 112220

Available online 2 March 2021

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Optimal building retrofit pathways considering stock dynamics and climate change impacts

Kai Nino Streicher

^a^,^*

, Matthias Berger

^b

, Evangelos Panos

^c

, Kapil Narula

^a

, Martin Christoph Soini

^a

, Martin K. Patel

^a

aChair for Energy Efficiency, Institute for Environmental Sciences and Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, Switzerland

bCompetence Centre for Thermal Energy Storage, School of Engineering and Architecture, Lucerne University of Applied Sciences and Arts, Technikumstrasse 21, 6048, Horw, Switzerland

cPaul Scherrer Institute, Energy Economics Group, Villigen, 5232, Switzerland

A R T I C L E I N F O Keywords:

Deep energy retrofit pathways Energy efficiency

Dynamic building stock model Indicator-based optimization Climate change scenarios

A B S T R A C T

Deep energy retrofit across the European building stock would require decades during which boundary conditions will change. This study identifies a range of retrofit pathways, using a dynamic stock model, a bottom-up energy model and an optimization model for different climate scenarios. We consider 1.1 million different retrofit options in the Swiss residential building stock for different economic/environmental objectives until 2060. Despite the replacement of old by new buildings, energy demand and greenhouse gas (GHG) emissions in the reference scenario without deep energy retrofitting are likely to decrease by only about 25%, while accounting for investments of 2–3 billion CHF/a. Partial energy retrofitting or an investment-minimized pathway are neither cost-effective nor sufficient to get close to the net zero targets. In contrast, the highest GHG-saving pathway leads to very high emission reduction of 90%, but requires investment cost of 9 billion CHF/a, which leads to specific cost of 180 CHF/t CO2eq. The cost-optimal pathway shows moderate trade-offs for investment cost and could reach GHG savings of 77% with specific cost of −140 CHF/t CO2eq. Hence, early and deep energy retrofit is cost-effective and allows deep GHG emission reductions by making full use of the synergies between GHG and cost savings.

1. Introduction

The European building stock holds a large potential for energy and emission savings through large-scale implementation of deep energy retrofit measures (European Commission, 2016a; IEA, 2016). Yet, the actual annual rate of retrofitting to varying degrees of energy efficiency is only reaching about 1%, which is insufficient to reach substantial emission reductions (Castellazzi et al., 2019). Since the high upfront capital cost represents a major barrier to deep energy retrofitting it is important to study the long term economic benefits for building owners in order to understand whether and if so, to which extent they allow to counterbalance the high initial investment cost required for deep energy retrofit (Lohse and Zhivov, 2019),. Several studies have estimated the techno-economic energy efficiency and greenhouse gas (GHG) emissions reduction potential of large-scale energy retrofit measures in different national building stocks (Amstalden et al., 2007; Jakob, 2006; Mata et al, 2010, 2018; Polly et al., 2011). This also includes our previous

studies on the deep-energy retrofit potential in the Swiss residential building stock (Streicher et al., 2017, 2019a, 2020). However, these sector-wide models are estimating the current potential of retrofitting without accounting for the actual timing of the large-scale retrofit actions. Deep energy retrofitting of an entire stock would require at least several decades, which implies that underlying parameters, such as the structure of the building stock, or boundary conditions might change over time. For example, the need for refurbishment (defined in this article as heating replacement and non-energy measures to ensure functionality) depends on the age of their respective building elements (N¨ageli et al., 2019). In order to account for the evolution of these aspects and to incorporate the temporal resolution of building stock retrofitting, a dynamic scenario-based modelling approach is required.

1.1. Deep energy retrofit scenarios

Scenarios are commonly used to explore possible alternative future

* Corresponding author.

E-mail address: [email protected] (K.N. Streicher).

Contents lists available at ScienceDirect

Energy Policy

journal homepage: http://www.elsevier.com/locate/enpol

https://doi.org/10.1016/j.enpol.2021.112220

Received 27 July 2020; Received in revised form 3 February 2021; Accepted 20 February 2021

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states and to estimate their impacts for a wide range of parameters (IEA, 2004; Schwartz, 1996; Trieb et al., 2006). In the case of deep energy retrofitting, scenario approaches allow to estimate the potential energy savings in a dynamically changing context, which provides valuable insights for policy analysis (N¨ageli et al., 2020). It is important to not just present a future state (e.g., expected energy demand in 2050), but to account for their evolution of cumulative impacts involved in reaching this state, which will differ between early action, steady implementation and late action (IEA, 2013). This is done by incorporating step-by-step decisions into the modelling process, which help to identify lock-in effects and non-consistent model decisions (Vogt-Schilb et al., 2013). For this purpose, bottom-up models, are applied to assess the impact of certain retrofit measures among groups of (archetype) buildings (Swan and Ugursal, 2009). Bottom-up scenario models have been applied to case studies of single houses (Gabrielli and Ruggeri, 2019; Shen et al., 2019), to cities or regions (Corrado and Ballarini, 2016; N¨ageli et al., 2019; Yazdanie et al., 2017) as well as to the national scale. A selection of bottom-up building energy retrofit scenarios on a national scale is presented in Table 1. In general, the studies can be classified by their method to model stock dynamics, energy demand, retrofit choices as well as the considered indicators (Sartori et al., 2016).

In terms of the building stock dynamic, the list in Table 1 shows that some studies completely omit the building stock dynamics (denoted as

“n/a”) or only rely on exogenous and constant rates of demolition and new construction, which often do not distinguish by building types or construction period. However, the actual dynamics of the building stock are linked to the existence of different building types and construction periods that feature very different states of aging and functionality loss (Sandberg et al., 2016). Moreover, previous research has shown that the size of the building stock is related to the population size and the economic wealth (usually measured in Gross Domestic Product – GDP) (Sartori et al., 2016). A comprehensive building stock simulation should therefore be based on a Material Flow Analysis (MFA), that balances dynamically the demand for living space with the number of demolished (or discontinued) buildings by archetype, in order to determine the amount of new constructions (Sartori et al, 2009, 2016).

In terms of energy demand estimation, accounting models are based on the evolution of the entire building stock and the energy demand is therefore estimated as a function of the share of buildings belonging to a

certain performance class (Mundaca et al., 2010). This approach can represent well long-term trends of the entire stock, but it does not distinguish specific building characteristics or physical properties of the building elements and the different energy efficiency measures (Swan and Ugursal, 2009). In contrast, detailed simulation models consider single or representative (archetype) buildings down to their building elements. Hence, energy retrofit measures can be applied and simulated directly for each archetype or at the level of building elements. This might also entail the synchronization of retrofitting with natural renewal cycles per element (N¨ageli et al., 2019) or the assessment of costs based on the age of the building element (Streicher et al., 2020).

As for the retrofit decision, the majority of the studies in Table 1 are using either exogenously defined scenarios or they rely on retrofit rates (activity or stock driven) for a simulation of the retrofit in the stock.

However, in order to estimate the techno-economic potential of retrofit options and to establish dynamic retrofit pathways, a higher flexibility of retrofit choices is required (N¨ageli et al., 2020). This could entail an agent-based modelling approach or the application of an optimization algorithm to dynamically develop retrofit scenarios. Optimization models offer the advantage that they provide an assessment of trade-offs between different interests (expressed as objective functions), which can offer valuable information for policy making. Hence, these models can estimate the full impact of retrofit pathways in order to identify possible transition strategies (Vogt-Schilb et al., 2013). For example, strategies for the ideal timing of retrofitting may ensure consistency with demolition years as well as with the timing of natural refurbishment cycles (N¨ageli et al., 2019).

In terms of indicators, only a third of the studies in Table 1 are actually considering economic aspects. However, none of these studies presents the evolution of cost-effectiveness, considering the cumulative sum of the annual cash flows over the scenario period in order to arrive at the Net Present Value (NPV) of the investment (Blok and Nieuwlaar, 2017). This approach can be combined with different economic assessment approaches representing diverse stakeholder perspectives (Pielli, 2008), resulting in a more elaborate analysis. The lack of these aspects in Table 1 represents a research gap related to the economic potential of large-scale retrofit measures.

Moreover, even if climate change-driven effects become more pronounced only in the second half of the century, it might be beneficial Table 1

Selection of bottom-up energy retrofit scenario studies on nation-wide building retrofit scenarios and their scope (based on our own classification). If a specific aspect is not included in the respective study it is marked as not available (n/a). (MFA =Material Flow Analysis).

Source Country Stock

dynamics Energy

demand Retrofit

decision Technical/environmental

indicators Economic

indicators Climate change (Dascalaki et al., 2016) Greece rates simulated rates (activity) Final, Primary energy, GHG n/a Fixed

(Sartori et al., 2009) Norway rates accounting rates (activity) Final energy n/a Fixed

(García Kerdan et al.,

2017) UK rates simulated exogenous Exergy n/a Fixed

(Sandberg et al., 2017) Norway MFA simulated rates (stock) Final energy n/a 14% HDD reduction until

2050 (RCP 4.5) (Dineen and O ´

Gallachoir, 2017) ´ Ireland rates simulated exogenous Primary energy n/a Fixed

(Diefenbach et al., 2016) Germany MFA simulated rates (activity) Final, GHG n/a 7% HDD reduction until 2050

(Schimschar et al., 2011) Germany rates accounting rates (activity) Final, Primary, GHG n/a Fixed (Holm and Spengard,

2015) Germany n/a simulated rates (activity) Final n/a Fixed

(PWC, 2015) Germany rates accounting rates (activity) Final, Primary, GHG Cost difference Fixed (Henning and Palzer,

2013) Germany n/a simulated optimization Final, Primary, GHG Annual cost Fixed

(DENA, 2017) Germany MFA simulated rates (activity) Final, GHG Cost difference Fixed

(Nitsch et al., 2012) Germany rates accounting rates (activity) Final, Primary, GHG Annual cost Fixed

(Siller et al., 2007) Switzerland MFA simulated rates (activity) Final, GHG n/a Fixed

(Kirchner et al., 2012) Switzerland MFA simulation rates (activity) Final, Primary, GHG Cost difference 15% until 2050 (linear increase)

(econcept, 2017) Switzerland MFA accounting rates (activity) Final, GHG Annual costs 15% until 2050 (linear increase)

(N¨ageli et al., 2020) Switzerland MFA simulated agent-based Final, GHG Annual costs Fixed

(Narula et al., 2019) Switzerland n/a accounting rates (activity) Final, GHG n/a Fixed

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that investment decision for retrofit anticipate this change as early as possible, thereby considering differences within a region or a country, since climate change will not affect all locations to the same extent (Berger and Worlitschek, 2019). However, among the studies of Table 1, only four studies are actually accounting for future climate change, with three of them using an oversimplified percentage reduction of the expected number of Heating Degree Days (HDD) without accounting for regional differences.

In light of the above limitations, we propose a modelling framework that consists of a dynamic building stock model which accounts for the probability of refurbishment by building element and is combined with a bottom-up energy simulation model (element-based building physics) that incorporates a high number of building archetypes and regional differences such as climate change variations. Additionally, an optimization model allows a flexible choice of retrofit options together with an assessment of trade-offs between different objectives: final and primary energy as well as GHG savings for the technical and environmental assessment; discounted investment cost and NPV for the economic evaluation considering different economic assessment approaches. This provides a comprehensive understanding of the cumulated impacts of different retrofit pathways and strategies which can be used to identify technical and economic baselines.

We focus our study on the Swiss residential building stock until 2060, for which the technical energy and GHG emission reduction potential amounts to 60–80% (Narula et al., 2019). In its Energy Strategy 2050, Switzerland has set itself ambitious targets to increase the use of renewable energy and energy efficiency, while at the same time phasing out of nuclear power (BFE, 2013). As part of this strategy, the building stock’s final energy demand per m²of floor area needs to be reduced by 60% from 2020 until 2050. The Swiss federal council is currently working on a revision of their CO₂law, which would allow to reach net zero emissions from the building stock in 2050 (BFE, 2020). In light of these ambitious targets, a comprehensive analysis on the potential and trade-off of different retrofit pathways and strategies seems crucial.

1.2. Aims and objectives

The objective of this study is to prepare an explorative scenario analysis of large-scale deep energy retrofitting to identify possible retrofit pathways for the Swiss residential building stock. We aim to account for the cumulated impact of the retrofit pathways over the entire time horizon (expressed as average annual impacts), focusing on retrofitting of the existing building stock.

In this paper, the following research questions are addressed: 1) How will the Swiss building stock evolve in the future? 2) How would this affect the expected energy demand and refurbishment cycles in the future? 3) What are the expected trade-offs between different retrofit objectives and their related pathways? 4) How do changes to the stock, climate and socio-economic parameters influence the choices of retrofit measures in the future? Ultimately, the paper aims to assess cumulative impacts of retrofit pathways to establish technical and economic baselines. Based on an assessment of different retrofit strategies the possible implications for policy making are discussed.

2. Methods

This study extends our previous research on the thermal performance of building elements (Streicher et al., 2018), which allowed to represent the technical characteristics of the current building stock as a basis for modelling refurbishment or retrofit actions. Furthermore, this study builds upon the representation of the stock in the Swiss Residential Building Stock Energy (SwissRes) model (Streicher et al., 2019b) and the development and application of different approaches for evaluating the cost-effectiveness of retrofit packages (Streicher et al., 2020). In all these studies, we have used a set of building archetypes that allow to represent the Swiss residential building stock by means of a bottom-up model

based on individual building elements (for more details see Table A1 in the Supplementary Material). The different archetype buildings are represented by their respective energy reference areas (ERA), which add up to the total heated surfaces by group of buildings. One important specificity of the present study is that we consider four retrofit periods from 2020 to 2060, while still accounting for subsequent impacts until 2090.

Fig. 1 shows the general model framework for the development of the deep-energy retrofit pathways, which can be divided into three main steps: 1) the dynamic development of the ERA of the building stock and the related refurbishment probability for each archetype and building element is estimated with the stock model, 2) based on the climate and socio-economic data, the impacts and related cash flows for each archetype and all possible combinations of retrofit options are calculated in the cash flow model, 3) given the input matrix of possible retrofit options and archetypes, an optimization algorithm is selecting the retrofit pathway that leads to the best performance for a given objective function (e.g. least-cost). A detailed description of the different models is provided in section B of the Supplementary Material.

The resulting retrofit pathways are expressed as the share of ERA per archetype assigned to a certain retrofit option (see Fig. B1 in Supple- mentary Material). These retrofit options entail the choice among pre- defined retrofit packages based on our previous study (Streicher et al., 2020), consisting of four deep energy retrofit packages (i.e., Sys1 to Sys4), a deep energy package which does not affect the outside appearance of the building (Sys5), one passive house package (SysP), only envelope retrofit (Envelope) and one package entailing only a replacement of the existing heating system with a heat pump (HP). To account for protected buildings, we assume that all archetypes constructed before 1945 can only be retrofitted with Sys5 or the HP option.

This is followed by the decision of either retrofitting according to the natural refurbishment cycles (i.e. retrofitting building element by building element as each of it reaches the end of its economic lifetime), referred to as “stepwise”, or implementing the complete retrofitting package in one go within a decade, referred to as “complete”. The optimizer can also choose to follow the reference scenario (REF), where a non-energy related refurbishment of the envelope and a renewal of the existing heating system is assumed.

This framework is applied independently for different boundary conditions, which consist of a combination of climate change scenarios and the economic assessment approaches. For the climate scenarios, we choose three different Representative Concentration Pathways (RCP) to account for differences in climate change intensity and spatial climate patterns (CH 2018, 2018; CH 2018 Project Team, 2018). Additionally, we adopt the three distinct economic assessment approaches from our previous study (Streicher et al., 2020): 1) full approach (FULL), which accounts for full investment cost and energy savings, 2) improvement approach (IMP), which considers only the cost of energy efficiency improvement and the related energy savings (i.e. excluding anyway occurring refurbishment cost) and 3) depreciation approach (DEP), which is in line with the IMP approach but additionally assigns a re- sidual value to each building element based on its age. These three approaches are not just different calculation methods, but also imply different mind-sets or strategies that apply to different stakeholders (for more details see Table B1 in the Supplementary Material).

In order to model the evolution of the building stock, we apply a dynamic material flow analysis (MFA) of additions to and removal from the building stock (inflows and outflows of buildings) as a function of the projected housing demand, based on a statistical analysis of pre- existing building stock data by archetype (Fig. B2 in Supplementary Material). In order to better understand the implications of the timing of retrofit decisions for buildings with lower life expectancy, the decreasing building stock is furthermore divided into demolition periods (Fig. 2). Based on the ERA projections for the existing building stock (blue line) and their related reference point (orange dot) for each scenario period, the share of ERA for each demolition band can be

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determined as the difference between two consecutive reference points.

Following the European Commission’s recommendation of a time horizon of 30 years as for the assessment of building retrofit (European Commission, 2016b), we define as rule that a building is eligible for energy retrofitting only more than 30 years before reaching the projected end of life. This constraint is represented in Fig. 2 by the bright colours (energy retrofitting possible) while this is not the case for the last three decades (light and dark grey). During the last 10 years of a building (dark grey), losses in functionality are more tolerable and therefore no refurbishment is to be expected.

This is combined with an estimation of refurbishment cycles and subsequently the probability distributions for refurbishment at a certain point in time for each building archetype and each building element it is composed of (Fig. B4 in the Supplementary Material). This serves as basis for estimating the cash flows and impacts of all possible retrofit options for each archetype building following the techno-economic assessment method developed in our previous study (Streicher et al., 2020). The combination of the different retrofit options results in a matrix of approximately 1.1 million possibilities for each set of boundary conditions, which is then used to identify the optimal retrofitting pathways.

For this purpose, the optimizer assigns the optimized shares of ERA by archetype to the different retrofit options, based on different objective functions, ranging from a maximization of the NPV or the minimization of investment to maximizing savings of final energy, primary energy or GHG emissions (see Table 2).

3. Results

In this section, we present the results for the building stock in the REF case (no retrofit measures taken) as well as the impacts and trade-offs of the identified retrofit pathways according to the results of the cash flow calculations and linear optimization. While the energy demand show some differences between the three RCP scenarios, their influence on the results is usually well below 10%. Consequently, we present only the

mid scenario RCP 4.5 in the main part of this study, while results for the other two RCP scenarios are provided in the Supplementary Material and are referenced in the text accordingly. Due to the very high uncer- tainty of impacts beyond the retrofit activities of 2060, values for the time span of 2060–2090 are presented as indicative model results (with a slight blur in the respective graphs).

3.1. Building stock development

Fig. 3 shows the projected development of the ERA for the Swiss building stock, indicating an increase from around 480 million m²in 2020 to 620 million m²in 2060 and up to 670 million m²in 2090. A pronounced transition from old to new buildings is anticipated in particular after 2060, when over half of older buildings are demolished.

The largest shift is expected for MFHs, in particular from 2070 onwards, Fig. 1. Overview of basic input data and model steps allowing to identify optimal retrofit pathways, applied for each archetype and time step. (GDP =Gross-domestic-product, ERA =Energy reference area, NPV =Net present value).

Fig. 2. Concept of “demolition bands” for modelling consequences of retrofit timing in the declining building stock. The blue line represents the projected development with the orange dots marking the related reference points per scenario period. The difference between two consecutive points defines the share of ERA for a certain demolition band. Light grey areas mark a constraint for energy retrofitting and dark grey areas for refurbishment before the end of life of a certain demolition band. (ERA =Energy Reference Area, MFH =Multifamily house, SFH =Single family house). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Table 2

Definition of retrofit pathways and their respective saving potential, indicator and optimizer objective. (NPV =Net Present Value).

Category Pathway/

Potential Indicator Optimizer objective economic investment-

minimized discounted

investment Minimize the cumulated sum of discounted investment.

cost-optimal NPV Maximize the NPV.

environmental GHG-optimal GHG

emissions Minimize cumulated GHG emissions. (Maximize savings)

final-energy-

optimal final energy Minimize cumulated final energy usage. (Maximize savings)

primary-

energy-optimal primary

energy Minimize cumulated primary energy usage.

(Maximize savings) A detailed description of the applied methods and the related input data is provided in section B in the Supplementary Material.

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when the ERA of new buildings will exceed the ERA of the subset of today’s MFH buildings that still exist by then. In the case of SFHs, the results indicate a more moderate transition, with a stable level of ERA over the whole time period. The decrease of the existing stock can then be translated to the respective demolition periods per archetype (Fig. 2 for the existing building stock, i.e. excl. new buildings). For SFHs, the share of ERA per demolition period is equally distributed until 2080, whereas a rather large share of MFHs will be demolished between 2060 and 2080.

Based on these ERA projections the impacts of the REF case (assuming no retrofit activity) can be estimated. As Fig. 4 shows, the gradual replacement by new buildings and the warmer climate results in a decrease of final and primary energy demand as well as of GHG emissions, however by only around 25% in 2060 compared to 2020 (Fig. 3). The overwhelming share of the GHG emissions is caused by the existing (non-retrofitted) building stock, whereas new buildings feature

comparatively higher shares of final energy and especially of primary energy. These results clearly indicate the importance of deep energy retrofit in the existing building stock if major reductions of impacts are targeted. The projected investment cost for refurbishment of the existing building stock show a peak of around 5.8 billion CHF/a in the period of 2040–50 and then decline to 1.8 billion CHF/a. Fig. C2 in the Supple- mentary Material shows these investment costs differentiated by building element. The peak in period 2040–50 for the REF scenario can be explained by the high probability of refurbishment of buildings constructed between 2010 and 2020. The differences between the three RCP climate scenarios are only visible after 2060 and even then stay below 10% between the most extreme scenarios RCP 2.6 and RCP 8.5 (see Fig. C1 in the Supplementary Material).

Given the demolition periods per archetype (Fig. 2) and the related impacts (Fig. 4), the results can provide an understanding of the required time needed for retrofitting the existing building stock to Fig. 3. Estimated dynamic evolution of the Energy Reference Area (ERA) in the Swiss residential building stock by building type and construction period. (ERA = Energy reference area, MFH =Multifamily house, SFH =Single family house).

Fig. 4.Evolution of investment cost, greenhouse gas (GHG) emissions final and primary energy demand in the reference scenario (REF) for the RCP 4.5 climate scenario. The y-axis shows the relative changes in comparison to the total value of the respective unit in 2020, while the labels represent the absolute value. Results are shown for the climate scenario RCP 4.5 and by building type and construction period. (MFH =Multifamily house, SFH =Single family house).

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reduce cumulated impacts. Focusing on GHG emissions, Fig. 5 shows the aggregated contribution of each demolition period by archetype category, construction period and building type. Remarkably, nearly 50% of the total cumulated impacts are found to be released by buildings that might be demolished before the end of the scenario time horizon of 2090. Moreover, according to the 30 years minimum time horizon re- striction, there are only three decades left to actually address these buildings. This leads to the important insight that, for realizing significant GHG emission abatement, it is not sufficient to simply wait for the renewal of the building stock and/or to focus energy retrofitting only on buildings that will last at least until the end of the time horizon (green band in Fig. 5). Among the buildings that are to be demolished before 2080, it is crucial to address in particular MFHs constructed before 1980, which account for around one third of the total cumulated GHG impacts.

Furthermore, buildings to be demolished before 2050 are unlikely to undergo energy retrofitting. As a consequence, roughly 2.5 Mt of CO2

emissions for MFHs and 5.5 Mt for SFHs, jointly accounting for approximately 6% of the total emissions of the building stock until 2060, are not preventable in the future by any of the foreseen retrofit strategies. Since these buildings would be demolished by 2050, this would not necessarily impede the achievement of the Swiss net zero GHG target to reduce emissions down to net zero, but it shows the importance of cumulated impacts and how the application of the demolition period (in Fig. 5) allows to gain deeper insights into the actual timing of retrofit actions and the related reduction of cumulated impacts.

In summary, despite a major transition from the existing building stock to new buildings and a warmer climate, the REF scenario shows only a reduction of GHG emissions by 25% until 2060. This clearly points to the importance of early retrofitting (starting as soon as possible), in order to prevent high impacts from the existing building stock.

3.2. Retrofit pathways

Retrofit pathways maximizing impact reduction in terms of energy use and GHG emissions fully exploit the retrofit learning rate over the scenario time period (Fig. C3 in the Supplementary Material). In comparison, the cost-optimal pathway (maximization of NPV) follows the same strategy in the first period, while slowing down after 2045. This is an important insight, showing that energy retrofitting should be

implemented as early and as quickly as possible even from an economic point of view, despite the reduction of future investment cost as a consequence of discounting.

The different retrofit levels and the specific retrofit choices lead to different impacts over the scenario period. Fig. 6 shows the evolution for the five indicators and the three economic approaches for the RCP 4.5 climate scenario. For better comparability, the REF case is added as a dotted line. As by definition, the investment cost increase from IMP to DEP to FULL, which is true across all objectives (for more details see (Streicher et al., 2020)). Except for the DEP approach, the investment cost of the cost-optimal pathway (NPV) follows basically the trajectory of the investment-minimized pathway, which in turn is very close to the REF for FULL cost. Investment cost for the objectives of energy and GHG savings are significantly higher (up to 100% for FULL, for DEP and IMP even more in %-terms), peaking around 9 billion CHF/a in the 2040s for FULL cost approach.

The ranking of the NPV by economic approach is in line with our previous results (Streicher et al., 2020), with IMP leading to the economically most attractive pathways with up to 2.5 billion CHF/a of benefits (positive NPV) for the whole building stock, followed by DEP with little less than 1.5 CHF/a. When accounting for the FULL investment costs of retrofit, these cannot be compensated by only the related cost savings, explaining the very low (negative) numbers for the NPV in the range of − 7.5 to − 12.5 billion CHF/a. Again, the trajectories resulting in lowest investment cost and the highest NPV are very close, with all the saving pathways leading to very similar results that are up to 5 billion CHF/a lower than the results for the highest NPV pathway.

All trajectories for the environmental objectives (minimal GHG emissions, final and primary energy) show a similar pattern, with the investment-minimized pathway close to the REF case, and the cost- optimal pathway always in between the pathways representing environmental objectives on the one hand and the investment-minimized pathway on the other. Since the IMP approach by definition only allows a stepwise retrofit procedure, the environmental impacts in 2060 are up to 40% higher compared to FULL and DEP. This indicates, that a strategy following the natural refurbishment cycles might not be sufficient if a maximum reduction of impacts are targeted. Among the environmental impacts, the highest relative impact reduction between 2020 and 2060 is achieved for GHG emissions with − 90% leading to an emissions level of 1.2 Mt CO2/a, while the highest savings for final and

Fig. 5. Cumulated impacts for GHG emissions in the REF scenario by construction period and building type as well as per projected demolition period. Blocks marked with a red border, represent more than 2% of total cumulated impact from 2020 to 2090. Results are shown for the climate scenario RCP 4.5. (MFH =Multifamily house, SFH =Single family house). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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primary energy are only in the range of 60% leading to energy demand levels of 24.0 TWh/a and 28.4 TWh/a respectively (based on the DEP and FULL approach). If instead of the environmental objective, the cost- optimal pathway with highest environmental impact reduction is

chosen, the savings decrease to 77%, 42% and 46% respectively for GHG, final and primary energy demand in the DEP approach. For GHG emissions, arguably the most important environmental objective among the three studied, the low level of impacts achieved in 2060 for NPV as Fig. 6.Evolution of final and primary energy demand, greenhouse gas (GHG) emissions and investment costs of energy retrofit pathways by objective and economic approach. Results are shown for the climate scenario RCP 4.5 and compared to the REF scenario (dotted line). (FULL =Full cost approach, DEP =Depreciation cost approach, IMP =Improvement cost approach).

Fig. 7. Indicator performance and trade-off between different objectives by approach. The numbers on top and bottom of each bar show the potential range of this indicator in average annual values. Coloured curves represent the various objectives (5) for which their performance according to the five criteria is reported in the columns. Absolute values of Investment and NPV are given in and environmental impacts are expressed as saving potentials. Results are shown for the climate scenario RCP 4.5. (FULL =Full cost approach, DEP =Depreciation cost approach, IMP =Improvement cost approach).

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objective indicates a good compromise between economic and environmental performance.

In order to better visualize the trade-offs between the pathways across the various indicators, Fig. 7 displays a relative performance indicator representing the range between the best and worst value, as achieved by the optimization algorithm. The results show a similar pattern across all economic assessment approaches (FULL; DEP, IMP), with high trade-offs between the environmental objectives and the investment objective and mostly moderate trade-offs for the NPV objective.

In more detail, the results indicate very marginal trade-offs (within

− 10% in indicator performance) across the different environmental objectives (final and primary energy use, GHG emissions). For the economic indicators (NPV and Investment) the pattern of trade-offs is similar but the extent differs: for cost-optimal pathways (NPV), we find a decrease of environmental performance by 25–50%, while for investment-minimized pathways, the environmental performance de- creases by almost 90%. In contrast, the investment-minimized pathway performs up to 20% better in terms of NPV compared to the environ- mentally optimized pathways, but then again shows to a major trade- offs between − 75 and − 95% for environmental performance. Given these results, there seems to be an irreconcilable trade-off between strategies aiming for low investment costs compared to high savings objectives.

As already identified in Fig. 6, the cost-optimal pathway (maximizing the NPV) represents a good compromise between economic and environmental (GHG) performance, with trade-offs in the range of

− 25% to − 50% across all indicators in the DEP approach. This is in particular true if GHG savings are considered important, since the gap of the cost-optimal pathways to the maximum reachable CO2 emission reduction is always below 25%. The relative trade-offs identified in this study are only marginally affected by the choice of the RCP climate scenario, as Fig. C4 in the Supplementary Material illustrates.

In summary, the results of this section confirm the importance of early and fast retrofit activity (at 2% p. a. at least until 2045), not only for high GHG emission abatement but also for economic performance (NPV). For this high retrofit level, the results show that it is possible to reach very high reductions for GHG emissions of up to − 90% in 2060. In order to reach these high emission reductions, very high investment cost of up to 9 billion CHF/a (in the FULL approach) are required (Fig. 6), leading to a very high trade-off between a retrofit pathway aiming for high savings and low investments. In contrast, cost-optimal pathways aiming for the maximum NPV are actually showing more moderate trade-offs (− 25% to − 50%) both for the investment costs and the reduction of environmental impacts.

4. Discussion

In this part, the results and implications of the previous section are discussed in more detail and put into the context of Switzerland’s energy and climate policy agenda. This entails an evaluation of the retrofit pathways by comparison to related Swiss studies and a more detailed analysis of possible retrofit strategies and policy interventions for the building stock. In addition, this section discusses the limitations of this study and recommendations for further research.

4.1. Evaluation of retrofit pathways

As mentioned in the introduction, Switzerland has set itself ambitious goals to reduce GHG emissions to net zero and the specific final energy demand by 60% from 2020 to 2060 (BFE, 2020). In the light of these targets and our results from Figs. 4, Figs. 5 and 6, we conclude that the following two strategies are not suitable: 1) REF case with long term transition from old to new buildings due to natural demolition cycles without additional retrofit, 2) REF case and retrofitting only the part of the existing building stock that would remain after 2080.

Based on the detailed comparison with other studies and the Swiss targets in section 4.1 in the Supplementary Material, we can summarize that the DEP approach provides a balanced assessment, which aligns very well both with today’s stakeholder preferences as well as with environmental concerns. We therefore focus the remaining discussion of retrofit strategies on the DEP approach combined with the midrange RCP 4.5 climate scenario. The comparison also shows that our envi- ronmentally optimized pathways (Table 2) represent well the targets of Swiss climate policy and related high saving scenarios. GHG emissions reduction has been regarded as one of the primary objectives of energy and climate policy (Diefenbach et al., 2014; Stein et al., 2016) and our GHG-optimal pathway in the DEP approach can therefore serve as good indication of the technical potential of deep energy retrofitting, with specific cost of GHG reduction of 182 CHF/t CO2eq leading to a reduction of 89% in 2050, which is 11% short of the net zero GHG target. The cost-optimal pathway on the other hand misses the Swiss targets of 2050 by roughly 30% but fits well to intermediate scenarios in terms of energy savings and GHG emission reduction, while at the same time representing the most cost-effective pathway with specific cost in the range of

− 140 CHF/t CO2eq. This pathway can hence be interpreted as the cost-optimal saving potential of retrofitting the building stock. It should be noted, that this potential of roughly 70% of GHG emission reduction is significantly higher than the economic potential of energy retrofitting established in our previous study, which was around 15% for the DEP approach (Streicher et al., 2020). The reason is that our previous study was based on the most cost-effective retrofit option per archetype in terms of levelized cost (calculated as the negative NPV divided by the discounted energy or GHG savings). As mentioned in section 2, the resulting ratio can lead to suboptimal choices in specific cases, where the NPV is similar (positive) but lower savings lead to lower and therefore more favourable levelized cost. Finally, the minimal investment pathway is not a very advisable strategy based on the trade-offs (see Fig. 7) but in view of other studies it could be interpreted as the actual BAU scenario rather than our REF case (see Fig. 6).

4.2. Retrofit strategies and policy interventions for the building stock transformation

To gain more insights into the underlying structure and the choices of the GHG-optimal and the cost-optimal pathways, Fig. 8 provides a very detailed overview of the optimizer results down to the archetype and retrofit options level. The length of each horizontal bar shows the assigned ERA to either refurbishment (white bar section) or retrofitting with colour-coding for the retrofit period. In order to achieve highest cumulated GHG savings (GHG-optimal pathway), the recommended retrofit strategy is relatively straightforward: early and complete retrofit with the highest energy standard, namely passive houses (SysP) or Sys5 (if protected building), beginning with older buildings with fossil fuel- based heating systems. This means that, starting in the 2020s, when the retrofit rate is slowly progressing, deep energy retrofit should be focused on buildings with an oil boiler constructed before 1980 with main focus on the construction period 1950–1970, with a slight pref- erence for SFHs in rural areas. This implies prioritizing buildings with the highest specific demand of around 160–200 kWh/m²/year (representing roughly 17% of the total final energy demand of the stock (Streicher et al., 2019b)) and with relatively short remaining lifetime (demolition period 2050). One decade later in 2030, when the retrofit activity has increased to a level close to the maximum rate, a peak of passive house equivalent retrofits should start for buildings with oil or gas heating system, now shifting towards more urban and suburban MFHs. Priority needs to be given again to the buildings with short expected lifetime (demolition period 2060), while also retrofitting the longest lasting buildings in order to maximize the cumulated achievable savings. In the last two decades, passive house retrofitting should then shift towards more recent buildings (1980–2020) with mainly fossil based heating systems, again with some priority for MFHs in urban and

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suburban areas. Due to the constrained retrofit rate, not all buildings can be retrofitted within the time horizon, which makes it particularly interesting to pinpoint to the buildings that are excluded from retrofit, since these buildings do not represent a top priority for GHG abatement.

According to Fig. 8 this concerns in particular buildings that are heated with wood or a heat pump (HP), which applies mainly to very old or the most recent buildings. This is an interesting point when it comes to tailoring specific policies for GHG reduction in the building stock.

The general strategy towards highest GHG savings itself is not very surprising, following the main principle of saving as much as early as possible (Diefenbach et al., 2016; Stein et al., 2016). If instead the cost-optimal pathway is chosen, the distribution in Fig. 8 shows a clearly more diverse picture with a wide range of retrofit packages applied mainly in a stepwise procedure. The cost-optimal pathway shows a relatively high share of buildings that do not undergo an energy retrofit (REF) including those which are demolished in due time. As expected, this pathway focusses on the “low-hanging fruits” (Gabrielli and Rug- geri, 2019), by giving priority to buildings that remain in use for the longest possible time. Interestingly enough though, even if cost-benefit is top priority, it is still recommended to start retrofit as early as possible, with comparably high retrofit activity in 2020, 2030 and 2040, while ensuring the highest possible energy standard in this early stage in order to maximize cumulated energy savings and the associated cost savings. However, the cost-optimal pathway differs quite a lot from the GHG-optimal pathway. While the latter gives higher priority to old SFHs in rural areas, retrofitting in the cost-optimal pathway starts mainly with

MFHs constructed in the 1990s and 1970s, while prioritizing the longest lasting buildings (demolition period 2070–2090). In the next decade, the cost-optimal pathway requires retrofit activity across the entire remaining building stock, with an increasing number of retrofits that only replace the existing fossil based heating system with a HP without further improvement of the envelope, hence resulting in relatively limited energy savings. In the last two decades, retrofitting in the cost-optimal pathway shifts towards the most recently constructed buildings, while prioritizing MFHs with oil and gas heating systems.

Similar to the GHG-optimized pathway, buildings with wood-based heating systems or HPs are left mostly untouched.

In view of this comparison, we recommend to fully utilize the synergies between the two pathways, resulting in maximized economic as well as environmental benefits. According to Fig. 8, this would imply to increase the current retrofit rate of 1% in the stock as soon as possible, in order to ensure the earliest retrofit as possible and therefore maximize the GHG and cost savings. This is in line with the recommendations of other studies prepared for different European countries (Stein et al., 2016). According to Fig. 8 this concerns in particular long lasting buildings which are currently equipped with a fossil fuel-based (especially oil) heating system. Pathways which do not only concern the environment, but at the same time also offer an economic advantage for the owner are more likely to be adopted by different stakeholders. As Fig. 8 shows, it is crucial to offer a wider range of different retrofit choices to building owners.

Next to a distinction between the different archetype categories and Fig. 8. Choice of retrofit options among archetypes for the cost-optimal (max. NPV) and the GHG-optimal (max. savings) retrofit pathway. Results are provided by each archetype category (i.e., construction period, building type, typology, heating system, demolition period) and retrofit option category (i.e., package, procedure, period) and their respective classes (e.g., rural, suburban, rural for the typology category). The length of each horizontal bar represents the total amount of ERA either refurbished (white) or retrofitted (marked by a colour gradient for the retrofit periods). Results are shown for the DEP approach in the RCP 4.5 climate scenario. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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classes, the bottom-up SwissRes model, combined with the RCP climate scenarios, allows to gain a better understanding of the geospatial distribution of retrofit activities for the different objectives. Fig. 9 shows the relative retrofit activity by municipality. The optimization runs indicate that in order to achieve the highest GHG savings it will be crucial to start with high retrofitting levels in the more remote and mountainous areas in the north or south of Switzerland. This clearly indicates that for high GHG savings, the focus should be put on buildings with a high specific energy demand, mainly due to the colder climate (Streicher et al., 2019b). Only from 2040 onwards, the major retrofit activities shift to lower elevations and/or warmer climates, with still a larger specific retrofit activity for rural areas. In the case of the cost-optimal pathway, retrofit activities start more slowly but also in the remote mountainous areas, mainly in the south of Switzerland. From 2030 on, the retrofitting is extended across the entire country, with a strong focus on the north-west around 2030 and with relatively low retrofit activity in the last decade of the scenario (2050). According to Fig. 9 increased levels of retrofitting in rural areas in the west and east of Switzerland would be both beneficial for GHG abatement and in economic terms.

Given the fact that the cost-optimal pathway would not be sufficient to reach the Swiss goals of net zero GHG emissions, we discuss in the following which categories of policy measures could enhance the retrofit potential in the residential building stock. We summarize here the main results of the sensitivity analysis of our model for the most influential parameters. The full sensitivity analysis can be found in Section 4.2 in the Supplementary Material D. Based on our results, the identified potential GHG emission reduction of the GHG-optimal pathway is close to the theoretical saving potential as even an increase of the retrofit ca- pacity would only marginally increase the potential for 2050. Any further decrease to net zero GHG emissions would therefore require changes and political efforts in other sectors. This could for instance entail a switch to renewable sources of heating such as solar, biomass based, geothermal or waste heat recovery from incineration and industry.

In order to mobilize the cost-optimal GHG abatement potential, it would be necessary that the economic rationale (in our analysis represented by NPV, i.e. cost minimization) is widely adopted and that the barrier of high upfront investment costs is overcome (we showed that minimization of investment costs leads to trajectories that are far from

goal achievement). This in particular concerns buildings with a relatively short expected remaining lifetime since their retrofit is economically unattractive while they continues to contribute importantly to the overall impact (see Fig. 5). Earlier demolition and rebuilding of these buildings would lead to a faster switch towards low energy buildings, but the high impact of construction makes this a controversial decision as discussed in section 4.3. Therefore, in the best case, adaptation of market conditions or more specific incentives should increase the economic potential and subsequently ensure better economic conditions for building archetypes and retrofit options that are crucial for high cumulated emission reductions. Suitable economic and regulatory policy measures will need to be devised for this purpose. In addition, R&D and demonstration of innovative and highly efficient retrofit technologies and practices are urgently required.

4.3. Limitations & further research

Uncertainties related to input data, the energy simulations and the economic parameters were presented and discussed in our previous studies (Streicher et al, 2018, 2019b, 2020). Here, we focus on the scenario aspects and the related limitations concerning this study.

Firstly, we reiterate that this paper analyses the isolated impacts and potentials of the residential building stock and does not include any sector coupling or feedback effects between the different steps within a pathway. In reality, a large-scale retrofit of the entire stock would have impacts on other sectors such as the energy supply and industry and this could in turn have an influence on the availability and preferences of retrofit options. Our results have shown no significant differences between the RCP scenarios when it comes to the choices of retrofit options, if however cooling applications would be considered, this would lead to clearly higher differences for the potential of actual energy savings (Berger and Worlitschek, 2019). Sector coupling could therefore lead to more synergies and in turn higher saving potentials for energy and GHG emissions. However, a comprehensive estimation of this cross-sectoral effects usually requires a full energy system model on the national level (Kirchner et al., 2012), which is outside the scope of this study.

Secondly, the creation of the pathways in this study is based solely on technical and economic properties from an energy system perspective.

Even though we included a social constraint to avoid visible changes to the appearance of historical buildings, our retrofit pathways cannot

Fig. 9. Geospatial distribution of the specific retrofit activity by commune for the highest GHG savings and the cost-optimal pathway. Results are provided as the share of retrofit activity in relation to the total ERA per commune.

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reflect the variety of choices or criteria that would be applied by individual owners, city planners or architects. For instance, certain buildings would face technical and non-technical barriers that make it impossible to perform any retrofit. Related studies have estimated this part of the building stock at about 5%, indicating that its inclusion would not influence much our results (Dineen and O Gallach´ ´oir, 2017). Nevertheless, the refurbishment and maintenance of a building or portfolio can imply a wide range of strategies and objectives, ranging from high building standards over customer satisfaction to economically optimized refurbishment cycles (Christen et al., 2016; Farahani et al., 2019). The objectives of the pathways could therefore be further extended by applying an agent-based modelling that can better represent the choices of individual actors or groups (N¨ageli et al., 2020). Furthermore, our study does not consider the indirect impact of retrofit actions on people actually living in the building. This concerns discomfort or even relo- cation during retrofitting as well as an assessment of the rent increase in MFHs and whether this could be kept at a socially acceptable level (Lang and Lanz, 2018). Future studies could hence compare the choices of our retrofit pathways with prevailing stakeholder preferences.

Thirdly, the scope of this study as well as the detailed analysis of thousands of archetypes made it necessary to apply certain simplifica- tions or assumptions concerning the technical aspects of retrofitting.

This implies that we do not consider alternative renewable heating systems (beyond the air-sourced HP) since their feasibility depends on the geospatial context and would therefore require a comprehensive analysis of the spatial and temporal resources per archetype building.

The same is true for use of centralized district heating networks, which is very sensitive to the estimated heat demand density (Chambers et al., 2019a). These networks are a promising alternative to individual heating solutions, since they provide synergy effects resulting in cost reduction and efficiency improvement (Chambers et al., 2019b; Murray et al., 2020). Here, emphasis should be put on developing programmes for retrofitting entire city districts at once (Stein et al., 2016).

Lastly, another major limitation of this study is that we only consider the direct emission impacts without accounting for grey energy and embodied emissions of the related retrofit actions and of demolition combined with rebuilding. Other studies have shown that the consid- eration of the life cycle impacts of measures concerning the envelope and the heating system still shows a net environmental benefit in the long term (Diefenbach et al., 2016). However, the demolition and rebuilding of an entire building would again lead to very high additional environmental impacts, that might not always be the most environmental friendly solution, very much depending on the local context (Pantini and Rigamonti, 2020). It is therefore not possible to draw a general conclusion whether early demolition of the existing building stock in Switzerland would actually be beneficial. The same is true for the related economic aspects, since demolishing and rebuilding is also associated with very high investment cost and temporal loss of living space. Further research should therefore include the demolishing and rebuilding as a “retrofit option” (with all its impacts) in order to give more precise recommendations when it comes to a fostered renewal of the building stock versus a deep energy retrofit of existing structures.

Finally, rather than optimizing pathways for selected economic or environmental parameters, future studies could merge different indicators in multi-objective optimization or present the Pareto frontiers of indicators representing conflicting targets (Rosso et al., 2020).

5. Conclusions and policy implications

This study investigated a range of deep energy retrofit pathways for the Swiss residential building stock, using a combination of a dynamic stock model, bottom-up energy model and optimization model for different climate scenarios. The results show that the building stock will continue to increase, with a pronounced transition from old to new buildings, in particular for multifamily houses. Despite this natural replacement of older and less energy efficient buildings by new and

efficient ones, the annual energy demand and greenhouse gas emissions can be expected to decrease only by a quarter until 2060, with high investment cost for refurbishment (non-energy related measures and identical replacement of heating system) of the aging building stock.

This will not be sufficient to even get close to the Swiss targets of 60%

energy demand reduction and net zero greenhouse gas emission target for 2050. The same is true for a somewhat more ambitious strategy that focuses on retrofitting only those buildings in the stock that are sup- posed to continue to exist at least until 2090, since this leads to very high cumulated impacts of non-retrofitted buildings, in particular in the first decades.

A large-scale retrofit of the entire existing building stock is therefore indispensable to reach the Swiss targets. This study identifies different retrofit pathways according to a combination of economic and environmental objectives that can represent different stakeholder interests or policy objectives. The results show that a strategy that aims for lowest investment cost would result in too low retrofit activity and would subsequently lead to low energy and greenhouse gas savings. In contrast, a strategy that aims for maximum savings would lead to a very high saving potential – independently, whether greenhouse gas emissions, final energy or primary energy is targeted - however with the drawback of very high investment cost. If instead the cost-optimal retrofit pathway is chosen (based on the maximization of the net present value), only moderate trade-offs between required investment cost and greenhouse gas savings are to be expected. In light of these results and compared to related scenarios of other Swiss studies, the maximum greenhouse gas saving pathway can be interpreted as the technical saving potential, while the cost-optimal pathway could be interpreted as the cost-optimal saving potential of deep energy retrofit in the building stock. As one key finding, both pathways point to the need to increase the level of retrofit activity as early and as fast as possible in order to benefit from the long- term gains. These conclusions have been found to be independent of the expected future changes in climate. However, the choices of retrofit options for different building groups differ from each other, with the maximum greenhouse gas saving pathway addressing first older and shorter lasting buildings, whereas the most economical pathways focus mainly on long lasting buildings across all construction periods.

Based on these conclusions, it is crucial to discourage refurbishment or light retrofit options based on the lowest investment cost and instead encourage early and deep retrofit. This would allow to foster the synergies between high energy and cost savings for long lasting and energy intensive buildings in the stock. Nevertheless existing barriers will need to be addressed and it will also be necessary to find solutions for those buildings with shorter lifetimes or higher specific investment cost that are causing high cumulated impacts. Since these buildings cannot benefit from an economically viable retrofitting under current circum- stances it seems crucial to enhance their economic potential by adaptation of suitable policies, since the economic advantage would also offer an additional incentive for building owners. According to this study, economic incentives for very energy efficient retrofit options as well as increased research and development of innovative and highly efficient technologies could play a major role. In addition, more research would be required to examine whether the early replacement of these buildings by new ones would be beneficial from an environmental or economic aspect, given the high impacts and cost of demolition and rebuilding. This should be coupled with a cost-benefit analysis accounting for macro-economic interactions between different sectors.

In summary, this study demonstrates the importance of accounting for pathways and their cumulated impacts across a wide range of building archetypes and retrofit options for the assessment of deep energy retrofit in the future. The retrofit pathways defined in this study could serve as techno-economic baselines for different political and strategic targets and as approach to assess the associated trade-offs, which can guide policy makers in the development and implementation of energy efficiency objectives and programmes.