https://orcid.org/0000-0002-4598-8484
Figures
Abstract
Decarbonising the building stock relies on the strategies of efficiency, sufficiency and consistency. Building stock energy scenarios (BSES) help evaluate the effect of these measures by modelling the existing building stock and using appropriate inputs, but must also account for boundary conditions, such as the structure of the energy system. In renewable energy (RE)-based systems, high summer generation contrasts with winter building stock demand, creating a seasonal gap. This study presents two BSES for Austria: a BAU scenario with standard decarbonisation measures (HVAC change and renovation rates) and a BEST scenario with more ambitious rates. Three energy system configurations are considered: (A) a demand-independent energy system based on current data of the Austrian electricity generation, (B) a RE-based generation system in terms of net-annual balance with the energy demand but connected with surrounding countries, and (C) an autarkic RE-based system with seasonal storage (based on green hydrogen). The key performance indicators (KPIs) used to assess the decarbonisation of a system are the equivalent CO2 emissions, the load cover factor (LCF) and the required PV generation to reach energy autarky. The results show that the assumption of the energy system structure has a strong impact on the effectiveness of different measures. Hence, the choice of the KPIs is sensitive with respect to the boundary conditions. A building stock within a RE-based domestic energy system relying on energy imports to cover the winter gap cannot be considered fully decarbonised, if the import electricity mix is not known. On the other hand, an autarkic system is not feasible if the domestic demand exceeds the RE potential. The RE mix of the generation system, along with the load characteristics, has an impact on the winter gap magnitude, consequently influencing the energy imports or the seasonal storage requirements.
Citation: Tosatto A, Ochs F, Venturi E, Magni M, Beck S (2026) Energy scenarios for the decarbonisation of the building stock in the context of the energy system transformation. PLOS Clim 5(3): e0000842. https://doi.org/10.1371/journal.pclm.0000842
Editor: Federico d'Amore, University of Padova, ITALY
Received: October 6, 2025; Accepted: January 29, 2026; Published: March 4, 2026
Copyright: © 2026 Tosatto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper.
Funding: This research was carried within the framework of the INTEGRATE project (Austria’s path to climate neutrality: identifying a cross-sector integrated framework and incentive design, distributional and budgetary implications, ACRP 14/2022 KR21KB0K00001) financed by the Austrian “Klima-und Energiefonds”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. AT received partial salary support from the INTEGRATE project, which funded her PhD.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
The rise in atmospheric greenhouse gas (GHG) concentrations and the resulting global warming have prompted nations, international organisations, and policy makers to implement measures to reduce anthropogenic emissions and support the transition to a low-carbon energy system [1]. Currently, the European Union (EU) has agreed to increase the net reduction of GHG emissions from the previous 40% to at least 55% by 2030, compared to 1990 levels [2].
The assessment of GHG emissions related to building stock operation is a critical aspect. The EU Emissions Trading System (ETS) has been established as a carbon market to monitor and reduce GHG emissions in specific sectors (electricity and heat generation and intensive industrial manufacturing) [3], but does not yet cover other sectors responsible for GHG emissions, such as building level emissions related to heat generation from fossil fuels and biomass. As highlighted in [4], the electricity demand of buildings, which has been increasing in the last decade, within the current ETS allocation is not considered as part of the direct emissions of the building sector. The energy transition with the gradual phase out of fossil-based systems and the increase of electric systems is shifting the GHG emission from the building sector to the energy sector, thus making more difficult a proper linkage between decarbonisation measures applied on the building level and effective decarbonisation of the energy supply.
1.1. Strategies for the decarbonisation of the building stock
In the pursuit of decarbonisation for sustainable development, the three strategies of efficiency, sufficiency and consistency [5] can be applied. However, these three strategies are not unambiguous in the case of the building sector, as the concepts and the related measures often overlap. In the present work, this distinction is applied:
- Efficiency. Implementing efficiency measures involves reducing the energy demand through technological improvements. Thermal renovation (TR) of the existing building envelopes falls into this category. Improving the efficiency of HVAC systems also plays an important role in minimising energy usage [6,7].
- Sufficiency. Sufficiency measures involve the reduction of the energy demand through changes in occupant behaviour [8,9]. Sufficiency measures to control the energy consumption and avoid waste have fundamental impacts beyond the building sector but are mainly dependent on the population perspectives and need a regulatory framework to support them [10,11], therefore will not be considered in the present work.
- Consistency. This strategy focuses on the introduction of sustainable energy generation processes, with a key focus on shifting from fossil fuel-based energy systems to those relying on renewable energy (RE)-based technologies.
Another approach for the definition of decarbonisation strategies uses the concepts of avoid-shift-improve [4], which replicate the previously mentioned efficiency, sufficiency and consistency, with some overlap.
1.2. Consistency challenges in the decarbonisation
Technologies available to replace fossil-boilers at the building level are biomass-boilers, heat pumps (HPs) and district heating (DH). However, RE sources need to be applied to decarbonise electricity generation and DH, to ensure a system-wide coherence in decarbonisation efforts. In this sense, RE integration measures at the building level do not necessarily lead to the decarbonisation at energy system level, highlighting the need for consistent alignment across all sectors.
Biomass has historically been used for space heating (SH) and domestic hot water (DHW) preparation, but its availability is limited and it is required in many industrial processes (e.g., high temperature heat provision in the industry, paper industry), in construction and in the power sector. Therefore, its use needs to be minimised in the buildings, as it is too valuable to be burned for low-temperature applications [12]. As shown in few studies [13,14], biomass can be used as fuel for RE-based combined heat and power (CHP) plants and is therefore a key for the decarbonisation of DH. About the electrification of building heating via HPs, the authors in [15] observe that this technology is a key component in the decarbonisation of heat generation at building- and district- level, if RE-based electricity is employed. Blue and green hydrogen can support building-heating decarbonisation, but their limited availability and high cost make them less suitable than electrification via HPs [16]. Instead, they are more appropriately reserved for decarbonising energy-intensive industry and mobility [17,18]. Geothermal energy is also a strong candidate to support the energy transition both for power [19] and heat generation [20], due to its continuous and weather-independent output, although its deployment remains highly location-dependent.
The large-scale integration of RE-based technologies to meet the energy demand of the building sector faces two main challenges. First, SH final energy demand exhibits a strong seasonal pattern. The widespread integration of HPs at building and district levels would lead to a significant increase in electricity demand during the winter months [21]. As RE sources also exhibit strong seasonal and daily fluctuations, maintaining supply stability in decarbonised systems requires adequate storage and grid flexibility to cover the ‘winter gap’ [22–24]. Dunkelflaute periods –extended episodes in which little or no energy can be generated with wind or solar power– are critical for energy supply stability. Secondly, the limited potential of RE sources constraints how much they can realistically be used. In a non-decarbonised electricity system, replacing fossil fuels with electric systems in buildings or districts simply shifts the CO2 emissions to the energy sector. In fact, electrification without parallel efficiency measures to reduce or contain the electricity demand can even hinder decarbonisation if not enough RE sources for electricity generation are available [25].
1.3. Policies for the decarbonisation of the building stock
Target-oriented policies, such as economic instruments, bans and standards and behavioural interventions, can be implemented to support the application of the decarbonisation strategies in the building sector [26]. Economic instruments (e.g., subsidies) can be helpful in situations of low market and technological risk, when a technology is available, as in case of energy efficient appliances or TR of building envelopes. For the case of Austria investigated in [27], the authors remark that the higher public subsidies had an important role in promoting energy savings in the building stock between 2000 and 2017, but the increase in wealth and of population offset much of the achieved reductions. High subsidies may also lead to price inflation of construction materials and heating systems, as seen in Italy with the Superbonus programme [28] for energy-efficient upgrades in buildings [29]. Carbon pricing is another useful economic instrument, but as observed in [30] in a simulated case study of the German building stock, its application can have a negative impact on low-income tenants due to the diversity of buildings.
Bans are one of the most far-reaching forms of public market intervention. An example is the EU ban of refrigerants using hydrofluorocarbons [31], which has pushed HP manufacturers toward low global warming potential alternatives. Several countries have also banned oil and gas boilers in new buildings as a form of consistency measure —e.g., Austria (since 2020) and Denmark (since 2013)—with others implementing similar measures [32]. Standards, by contrast, allow more flexibility. At the EU- level, the Energy Performance of Buildings Directive (EPBD) recast [33] provides a regulatory framework to support the TR of low-performance buildings, updating the previous EPBD of 2010. In Austria, the guideline OIB-6:2023 [34] enforces a limit on the heating demand (HD) of buildings, mandating high-efficiency, non-fossil systems in deep renovations and new buildings.
Behavioural interventions, as sufficiency measures (e.g., lowering room temperature, reducing DHW use, downsizing dwellings, saving electricity), are highly context dependent and can have long-lasting effects if they turn into habits or social norms [35]. The present study however focuses on the technology availability and sets a baseline for the application of efficiency and consistency measures that can be effectively supported by subsidies, bans and standards.
1.4. Building stock scenarios and research questions
Understanding the potential impact of the different decarbonisation measures is necessary to select policies that can actively support the energy transition of the building stock. Building stock energy scenarios (BSESs) arise as an important source of information to investigate the long-term effects of these measures (forecasting) or to identify and quantify what kind of interventions are desirable to reach a specific target (backcasting) [36].
1.4.1. System boundaries.
In the implementation of BSESs, the selection of the system level requires the definition of the system boundaries that are applied in the model and the eventual interaction between different sectors (buildings, industry, transport, power). Several energy scenarios have been implemented to study the possible evolution of the building stock in terms of energy consumption at national [37–39], sub-national [40,41] and city or district [42] levels. Other authors focus on a limited cluster of buildings, representative of a specific region, in order to assess the effect of specific decarbonisation measures applied on building archetypes in terms of energy flexibility [43] and CO2 emissions [44].
Modelling parameters (e.g., TR rates, building quality, HVAC type) are used to describe the building stock evolution. Building renovation quality is usually defined as an exogenous parameter, but in optimisation studies it can be adjusted depending on factors such as costs and treated as endogenous (target-oriented) [42]. Efficiency measures affect the size of generation and storage systems [45], hence the electricity mix and the winter gap in cold climates [46] with effects on the final electricity prices, but the propensity of the users to implement them are also linked to the costs and to the energy prices [47].
As a consequence, the cross-sectoral coupling between building and energy-sector affects several parameters describing the evolution of both. When the focus is the building stock (as in a BSES), the energy system is usually treated as a boundary condition in terms of electricity prices, energy availability constraints and energy mix (i.e., equivalent CO2 factors). Depending on the modelling level, these boundary conditions may be exogenous at a lower level (e.g., city/district), and endogenous at a higher level (e.g., national) where the coupling effect is stronger. As highlighted in [48] for the modelling of hard-to-abate sectors like industry and transport, scenarios based on exogenous information for a specific technology facilitate the assessment of cause-effect relationship in evaluating decarbonisation targets, but at the cost of sacrificing the existing interactions between sectors that could otherwise be included as endogenous information.
The concept of positive energy districts (PEDs) allows to analyse in detail the performance of the buildings and their interaction with other elements such as e-mobility, local RE generation and energy storage, thus defining the influence of the energy flows between different sectors on a lower level [49]. This type of analysis requires a holistic approach that includes building physics and HVAC engineering in order to assess the role of the evolution of the building stock energy demand on the overall decarbonisation potential of the district. However, at higher levels (i.e., regional, national) investigating this mutual influence requires the definition of a proper methodology to apply simplifications due to increased complexity. At regional and national scales, cross-border energy exchange is further shaped by local demand, RE potential and installations, prices, and transmission-capacity expansion [50], thus adding additional complexity to the model and to the definition of its boundary conditions.
Several studies considered the evolution of the energy system, considering the building sector as boundary condition. In [51] a gap is identified in the analysis of the energy transition of multi-sector configurations and a cross-sectoral investigation is conducted considering the power, heat, transport and desalination sectors using the case of Chile, but the authors do not consider the building stock. For the case of Europe, in [52] the authors compare 30 nodes (for the 30 countries) isolated and interconnected considering the power, heat and transport sectors under different sector-coupled variations, but considered the building stock only in terms of heat demand without a detailed representation. In [53], the authors investigate the impact of heat demand electrification in the Italian civil sector on the national energy system, assuming a large installed capacity of RE generation systems.
1.4.2. Scenario scope and KPIs definition.
The research question determines the choice of the methodology, which can be an optimisation or a simulation. Reference [54] distinguishes between these two approaches in the context of the energy system. Optimisation models aim to find the optimal solution to a specific question (e.g., minimising costs, energy consumption); the wide number of degrees of freedom requires an endogenous selection of the most optimal combination to achieve a specific scope. For example, [42] applies an optimisation model at the city level to assess the gap between the cost-optimal performance of the analysed building stock and nearly zero energy building (nZEB) targets. Simulation-based energy models are descriptive and are meant to provide a direct result following specific exogenously defined inputs and boundary conditions. This approach is applied in [37] for the study of the Norwegian building sector, which investigates how the use of energy efficiency measures and the replacement of fossil-based systems can contribute in the reduction of the final energy demand. The work presented in [55] simulates the development of the city of Innsbruck (Austria), starting with a detailed baseline of the residential and non-residential building stock and applying a hybrid bottom-up approach to evaluate the development of the energy consumption considering possible building renovation paths and intensities and the contribution of different energy carriers. In [41], energy consumption evolution of Wallonia’s building stock is studied under varying renovation rates and different degree days scenarios. A broader study of the development of the building stock energy demand in China is proposed in [56], exploring different scenarios based on existing trends and policies in terms of building energy conservation, fossil heat source planning, occupancy behaviour, electrification and biomass use.
In these studies, the energy sector and the cross-sectoral implications are backgrounded or treated as fixed exogenous boundary conditions. However, ignoring cross-sectoral effects, as discussed previously, risks undermining result robustness when energy system boundaries conflict with building stock evolution. BSES results are measured through key performance indicators (KPIs) such as CO2 emissions, final energy demand, primary energy demand and size of RE and storage installation [45], as well as investment and operation costs.
The use of equivalent CO2 emissions as a decarbonisation metric requires careful consideration. As noted in [57], in fossil-based energy generation systems there is a strong connection between emissions and primary energy demand, but this link is less clear in energy systems transitioning towards RE, where electricity mixes vary by country and depend on local policies and investments. A similar point applies to DH, as the type of grid and generation characteristics vary by city. In the assessment of the CO2 emissions for the building sector, the role of sector coupling expressed in section 1.4.1 is relevant, as CO2 emission factors for electricity and DH depend on the rate of decarbonisation of the power sector, for both nationally generated and imported electricity. As pointed out in [44] and [57], under a fully decarbonised energy system, operational CO2 emissions cannot distinguish between different building-level efficiency efforts.
Clear and consistent system boundaries are essential for the definition of the GHG intensity of energy carriers and generation technologies. Unlike fossil plants, RE-based technologies have near zero operation emissions, as they employ carbon-free resources (i.e., wind, solar, water) or in the case of biomass the emissions are counted as biogenic (i.e., absorbed during plant growth). However, life-cycle assessments must include upstream emissions [58]. For RE-based technologies like wind farms and PV these are related to the manufacturing processes [59]. Upstream emissions for biomass include harvesting, land-use change (e.g., from agriculture to energy purpose), transport, material processing, but additional negative impacts on the biodiversity are more difficult to quantify and vary across different countries [60,61]. In case of fossil fuels, upstream emissions related to extraction/ mining, processing, transport, but also leakages are relevant contributors to the overall life-cycle GHG emissions [62]. At the building stock level, the indirect emissions associated with the manufacturing of building materials (‘grey energy’ or ‘embodied’ emissions) need also to be accounted in life-cycle assessments [63], but are not the focus of the present study.
KPIs sensitive to BSES variations must reflect cross-sectoral implications. Since this work focuses on building stock efficiency measures for decarbonisation, national and international energy system modelling is out of scope. Instead, the energy system serves as a boundary condition, with a case matrix used for sensitivity analysis to show its influence on the BSES results.
Different BSESs can also be compared considering the operation costs (e.g., national electricity price, imported electricity) and the investment costs (e.g., for TR and new HVAC systems, additional RE installation). However, this evaluation is not included in the present work, which focuses on the energy aspects, as it requires a detailed definition of the specific costs and possible subsidies, which depend on the geographical area.
2. Aim of the work
The goal is to assess the strong interdependency between the building stock and the energy system (with a focus on electricity generation), and demonstrate how the ambition level of efficiency measures and rates in the building stock can support the decarbonisation of the energy system and influence the required RE generation expansion.
The evolution of the energy demand of the building sector is analysed through the investigation of two possible development paths for the Austrian building stock until 2050, both aiming at the phase out of fossil-based systems. The two BSESs are defined and implemented in a spreadsheet-based tool which describes the building stock development in terms of gross floor area (GFA) and final energy demand on an annual basis: one scenario considers low-ambition efficiency measures (BAU), i.e., based on current policy guides, and one high-ambition measures (BEST). The electric load curve of the resulting building stock in year 2050 is derived from a lumped-mass building simulation with hourly resolution, based on the average building quality of the year 2050 resulting from the two BSESs and the Austrian climate.
The influence of the decarbonisation measures applied on the building stock on the chosen KPIs is investigated under the framework of different energy system boundary conditions. Three types of interaction between the energy system and the building sector are therefore considered for the specific case of electric energy: (A) a demand-independent energy system based on the current Austrian electricity mix, (B) an annual net balanced RE-based electricity supply with possibility of energy import-export with surrounding countries and (C) an autarkic RE-based energy system with seasonal storage.
The balance between energy supply and demand is addressed through a monthly evaluation, that allows to account for the seasonal pattern of RE generation and building energy demand. The KPIs considered to compare the results for the different scenarios are the equivalent CO2 emissions related to the plants operation and the generation requirements in terms of PV surplus and seasonal storage. Fig 1 presents a general overview of the main steps followed in this work in the implementation of the BSES, the definition of its boundary conditions and the results analysis.
3. Methods
The Austrian BSES presented here is based on the study conducted within the INTEGRATE project (Austria’s path to climate neutrality: identifying a cross-sector integrated framework and incentive design, distributional and budgetary implications) and presented in [4], for the period 2020–2050. The study considers three different energy system boundary conditions for the two investigated BSESs. The variants resulting from the combination of energy system (A, B, C), electricity mix and building stock development (BAU, BEST) are shown in Fig 2. A detailed definition of the different variants is presented in the following paragraphs.
3.1. Scenario control volume, constraints and boundary conditions
The scenario considers the Austrian building stock at a national level, without taking into account the specific requirements of different regions. The data are aggregated at a national level. Fig 3 shows a simplified representation of the interconnections of the investigated national building stock with other sectors from the point of view of the energy transfer for different levels of spatial resolution.
3.1.1. Energy system boundary and boundary conditions.
The structure of the energy system is a fundamental boundary condition to assess the decarbonisation potential of the building stock. In this study the energy system is considered in terms of availability of the energy carriers that support the building stock operation, which in future decarbonised building stock and energy system are electricity, DH and biomass. Three cases are distinguished in this work to model the interaction between building sector and energy system as shown by the graphical representation in Fig 4.
Independent energy system decoupled from load (case A). In this case the energy system is considered independently from the building stock energy demand, and it is assumed that it can meet the entire building stock demand at any given time. The electricity mix is an exogenous parameter, defined as boundary condition in terms of monthly equivalent CO2 conversion factors ().
Decarbonised energy system in net-annual balance with load (case B). Case B assumes a fully RE-based energy system in terms of an annual net balance with the demand of the building stock, disregarding seasonal and short-period fluctuations. That is, over a one-year balance period, the energy demand of the building stock equals the domestic RE generation. The electricity grids of Austria and neighbouring countries are assumed to be ideally interconnected, allowing energy to be exchanged freely in response to temporal mismatches between generation and demand (‘grid as a storage’). The energy system of the surrounding countries is considered as a single node exogenously to the building stock model.
RE-based autarkic energy system (case C). In case C, an autarkic energy system based on RE is assumed to be in balance with the demand of the building stock, i.e., no import/export of energy is allowed. In order to cover the winter gap, the generation surplus available in summer is stored in a seasonal storage and the additional generation to offset the winter gap is evaluated depending on the electricity mix (i.e., share of hydro, wind, PV, biomass).
3.1.2. Energy mix and RE potential.
The current energy sources for electricity generation in Austria in 2022 and the equivalent CO2 conversion factors for the Austrian electricity mix are presented in Fig 5 [64,65]. The seasonal variation is linked to the increased availability of hydropower and PV in the summer months. These conversion factors are used for the evaluation of the CO2 emissions in case A.
Considering the expected increase in electricity demand due to the electrification of the building sector, the generation potential of electricity from RE sources in Austria (shown in Table 1) should be taken into account, as it represents a constraint on the effective national decarbonisation potential of electricity generation.
In this study, four technologies are considered as main drivers for a decarbonised electricity generation (energy systems B and C): biomass (in CHP systems), run-of-river (ROR) hydropower, wind and sun (i.e., PV). This is in line with the Austrian Nationaler Energie- und Klimaplan (NEKP), which is the official pathway for the future Austrian energy system [72].
Monthly averaged electricity generation profiles are used to account for seasonal fluctuations of the RE sources, based on historical data for wind (2000–2020) and for ROR hydropower and solar global radiation (2010–2020), as provided in [73] and illustrated in Fig 6. For electricity from biomass, a constant annual base generation is assumed. Wind energy generation is distributed more evenly throughout the year, with slightly higher levels in autumn, winter and spring. In contrast, solar (PV) and ROR hydropower show higher levels of generation in the summer months. These profiles are used to describe the RE generation in the year 2050: changes related to the effects of climate change are not considered in this study. The effect of the inclination and orientation of PV panels on PV yield is also disregarded.
To account for the different seasonal distribution of RE sources in the 100% RE-based energy systems B and C (autarkic), two scenarios for the Austrian electricity mix are considered following the approach suggested in [74] (Table 2):
- a “30_30” mix with an equal share of wind and PV generation (in terms of yearly electricity demand coverage)
- and a “45_15” mix with a higher yearly share provided by PV.
The CO2 conversion factor for electricity generation from these sources is assumed = 0 g/kWh (carbon neutral), since only emissions from the operational stage are considered. The RE generation required to cover the electric load of other sectors (i.e., transports, industry) is not considered. The required RE surplus generation in the energy system case C is evaluated in terms of PV energy, as this is the technology with the largest expansion potential in Austria according to [64].
In absence of an adequate supply of directly available RE in energy system case B, electricity is imported from neighbouring countries. The evolution of the equivalent CO2 conversion factors for Austria and for the neighbouring countries in the last decades is presented in Fig 7. Following the integration of gas in favour of coal and the increased share of RE, the EU electricity mix has become less carbon intense.
To assess the impact of the development of the energy system of the neighbouring countries, a simplified sensitivity analysis is conducted considering two extreme cases for the import electricity mix:
- ‘Mix’ scenario assumes a decarbonised import mix aligned with the 2050 projections in Fig 7 (
= 85 g/kWh).
- ‘Gas’-based scenario represents a worst case, where import mix is gas-based (
=230 g/kWh).
Most likely, the future European electricity mix fall between these two extremes. Substantial investments in wind energy in northern countries (e.g., Germany) may result in high wind power availability in winter, potentially leading to large RE-based electricity availability at low electricity prices. However, in the event of several days of Dunkelflaute affecting wide regions [75], electricity generation would require backup from gas-fired CHP plants or long-term storage solutions. As a result, CO2 conversion factors would fluctuate on an hourly or daily basis depending on weather conditions and corresponding demand across interconnected countries. Scope of this work is not to model the electricity generation and demand of European countries, but to assess the influence of boundary conditions on the decarbonisation of the building stock of one country/region. Therefore, a sensitivity analysis considering the best- and worst-case scenarios is presented to retain control over input assumptions and boundary conditions and to derive clear cause-effect relationships.
The emissions related to the DH generation need also to be considered as this is one of the main technologies for the replacement of building-level fossil systems. The current DH generation in Austria is mainly supported by biomass and fossil fuels, as presented in Fig 8 (left). The DH mix and generation type (e.g., CHP vs. heating plants) is very individual and depends on local regional and urban conditions, therefore the equivalent CO2 conversion factors also vary depending on both the location and (in presence of RE) the season [77]. Based on the recent historical data available from [64], the equivalent CO2 conversion factor for the Austrian DH grids (between 2005 and 2022) are derived and presented in Fig 8 (right).
For the three investigated energy system cases, three scenarios for the DH are identified for the year 2050 and are presented in Table 3.
Suitable types of seasonal energy storage applicable for the building sector are large-scale water-based thermal energy storage within DH systems for the thermal energy, and power-to-gas-to-power (PtGtP) and pumped-storage hydropower for the electrical energy [78], the latter however being restricted to locations with specific geographical conditions. In this work only the electric energy demand is considered, therefore a green hydrogen-based electrical energy storage is assumed to be able to cover the seasonal mismatch in the investigated scenarios. A round-trip efficiency, in terms of energy conversion and storage, , of 31.5% is assumed, considering conversion efficiencies of 70% and 50% for the electrolysers and fuel cells, respectively, and a hydrogen storage efficiency of 90% [79]. The seasonal storage is sized for the energy system case C, taking into account the total summer PV surplus required to cover the winter electricity gap represented by the electricity demand on the building level and the electricity demand of HPs in DH systems.
3.2. Building stock baseline
The investigated building stock is divided into different building categories, and within each category, the buildings are further clustered according to their construction period, based on the data available from [80]. Residential buildings are categorised in single family houses (SFH), small- and large multifamily houses (sMFH and lMFH), while non-residential buildings are categorised into public buildings and hotels. An overview of the investigated building stock used as baseline is presented in Table 4. More details are provided in S1 Appendix.
The data concerning the specific energy demand for SH, DHW and household electricity are available from Austrian databases [81] and from previous studies [82]. A detailed description of the building stock baseline defined for this scenario is presented in [4] and [83], while [84] provides an exhaustive description of the modelling process of the implemented spreadsheet-based tool. The baseline SH, DHW and electricity demands for the different building categories at the beginning of the scenario are presented in Tables 5–7.
The energy carrier partition between the different building typologies and construction periods in the baseline building stock was not directly available, therefore the authors defined them according to existing studies on the Austrian building stock situation [85]. The total energy demand per energy carrier according to the final use (whether SH or DHW) for the base year (2018) is derived from the data available in [86] and is presented in Table 8. In order to account for non-occupied buildings and non-heated spaces, calibration factors are used on the existing building stock to match the energy demand available from [86]: 0.82 for SH, 0.75 for DHW preparation and 0.65 for electrical energy. Considering 2018 as base year, an extrapolation is required for the period 2018–2020 (effective start of the scenario): with sufficiently good approximation, a constant energy consumption in the building sector can be assumed in these years (i.e., a business-as-usual development with same energy carriers as 2018 and unchanged building stock).
3.3. Scenario variables and parameters
The assumptions for the different scenarios for the building stock development towards decarbonisation follow the strategies of efficiency and consistency (Section 1.1). These assumptions are introduced as inputs for the two BSESs (BAU and BEST) and are listed in Table 9, where they are categorised into measures related to the evolution of the building stock energy demand (independent of the decarbonisation targets) and related to the decarbonisation strategies.
3.3.1. Building stock scenario implementation and assumptions.
The implemented spreadsheet-based BSES tool presented in [84] evaluates for every year and for every building period (bp) the demolished buildings (), newly constructed buildings (
) and renovated buildings (
) and buildings with HVAC change (
) in terms of GFA, according to Equations (1) to (8).
The size of GFA hypothetically available for demolition for each building period () is determined each year as the sum of the existing never-renovated GFA (
) exceeding an age threshold (
), and the GFA that has previously undergone a TR (
) or an HVAC system change (
), provided that the time elapsed since the intervention exceeds a defined threshold (
or
). This formulation is expressed in Equation (1). The GFA effectively demolished (
, Equation (2)) is then defined as the minimum between
and the demolition share of the existing building stock, calculated as
.
The total GFA at the beginning of every year (per building period, ) is then defined from the difference between the existing GFA of the previous year and the demolished one, as from Equation (3).
The maximum GFA hypothetically available for thermal renovation (, Equation (4)) is defined annually as the sum of the existing never-renovated GFA exceeding a minimum age threshold (
) and the GFA that has previously undergone a TR or an HVAC system change, provided that a minimum time has elapsed since the intervention and that the current HD exceeds the HD achievable after TR (
). The GFA effectively renovated every year (
, Equation (5)) is defined as the minimum between this available GFA (
) and the share of GFA to be renovated, calculated from the TR rate (
).
The maximum GFA hypothetically available for HVAC change (, Equation (6)) is defined each year as the sum of the existing never-renovated GFA exceeding a certain age, the GFA that underwent a renovation or an HVAC change, provided that a minimum time has elapsed since the intervention. The GFA which effectively has a HVAC change every year (
, Equation (7)) is then defined as the minimum between
and the share of GFA calculated with a HVAC change rate excluding the share which in the same year is undergoing a TR (
).
In each year of the scenario study, the newly built GFA () is evaluated considering the amount of area demolished and the additional increase of the existing GFA required by population increase.
Table 10 presents the parameters applied for the two investigated BSESs, based on the categorisation suggested in Table 9. Sufficiency measures are not considered as they do not have a regulating framework and are strongly dependent on social and education aspects which are not the focus of this work.
The maximum contribution of each energy carrier for the decarbonisation of the SH and DHW preparation in buildings is constrained by the potential of the corresponding RE generation. For the buildings undergoing an HVAC change, the distribution of the energy carriers presented in Table 11 is assumed to be the same for both BAU and BEST scenarios. Considering the transition to RE sources, HPs and DH are assumed to cover the largest share. It is assumed that HPs will be the most common system in rural SFH, while DH will play a major role in MFH. Meanwhile, biomass boilers will be mostly employed in renovated SFH, rather than in new buildings, whose better envelope quality will make the application of HPs more effective. In absolute terms, biomass use will remain at or below the base year value.
The yearly energy demand per energy carrier per building category is then defined from the calculated GFA of new, thermally renovated or with HVAC change buildings, the respective yearly HD according to Tables 5–7 (for existing, non-renovated buildings) and Table 10 (for new buildings and buildings which undergo a TR), and the energy carriers shares from Table 8 and Table 11, considering the generation efficiency of each system [83].
3.3.2. Load curves.
From the annual energy demand derived from the two BSESs, the electric load curves of the investigated building types are then determined for the year 2050 using a 1 zone lumped-mass-building simulation with the reference Austrian climate. The dynamic building model is implemented in MATLAB, and the resulting system of differential equations is solved using built-in ordinary differential equation solvers (ode23) on a sub-hourly time step as described in [46].
In order to simplify the analysis, a reference building is considered for each building category; this building has an average quality level for its category in the year 2050. The aggregated load curves of the two BSESs (BAU and BEST) for the year 2050 are derived considering the specific share of each building category and the number of buildings with specific HVAC system (HP, DE, Biomass or DH).
The electric load curve represents the total electricity demand of the building stock. It considers the electricity required for appliances and auxiliaries for all the buildings, and the electricity for SH and DHW preparation in buildings with direct electric (DE) system or HP, as from Equation (9). In the present analysis, the electricity demand for space cooling is not considered. The results are then aggregated on a monthly level.
The peak load is an indicator of the required grid extension (and storage capacity) as it defines the most demanding condition the energy generation/storage mix needs to face [46]. This needs to be defined on an hourly or daily basis, as monthly aggregations smooth out the peaks and fails to capture these critical high-demand moments accurately.
3.4. KPIs and scenario results
The following KPIs are used to evaluate the results of the BSESs and assess the relevance of the interactions between the building stock and the energy system:
- Winter gap and load cover factor (LCF). The winter gap quantifies the mismatch between the building stock electricity demand and RE generation during winter. It is defined as the cumulative positive difference between the building stock load and the RE generation, according to Equation (10).
In a yearly-balanced energy system (case B), the electric energy required to cover the winter gap is imported from neighbouring countries. The LCF (the share of energy covered by domestic supply) is defined according to Equation (11) [46].
- Equivalent CO2 emissions for the electric energy generation. For the energy system case A, which considers an electricity generation decoupled from the actual demand, the emissions are calculated from the current Austrian electricity mix (Fig 5b), according to Equation (12).
In the energy system case B, although the yearly-balanced RE-generation can cover part of the electricity demand, the emissions imported electricity mix are taken into account (Fig 7).
In energy system C, the entire electricity generation is based on RE.
- The CO2 emissions related to the DH generation are defined by Equation (14) for energy system A.
For energy system B, the DH generation is assumed to be mainly covered by biomass-based generation (=0 g/kWh), up to a maximum limit EDH,bio,max = 12 TWh/a, while the rest is covered by gas-based systems (see Table 3).
In energy system C, the DH generation is assumed to be entirely decarbonised, as it comes from biomass and HPs powered by RE-based electricity.
- Seasonal storage requirements and RE surplus. In the case with autarkic energy system (case C), the PV surplus required to reach the autarky through the generation of green-hydrogen, and the size of the hydrogen storage are defined according to Equations (17) and (18) [45,46].
4. Results
4.1. Building stock energy demand
The effect of the decarbonisation strategies applied at building level can be seen in the development of the energy demand of both BAU and BEST BSESs, through the gradual relative increase of RE carriers such as biomass and electricity for HPs. Fig 9 shows the overview of the total final energy demand for both BSESs in the base year (2018) and in 2030, 2040 and 2050 (the final year for which the evaluation was conducted). While a direct comparison between different energy carriers is not usually recommended due to their reliance on different primary energy factors, it is helpful in this instance as the focus is on the final demand for the building stock.
The total energy demand includes the energy required for building operation (SH, DHW preparation and electricity for appliances and operation). In the BAU scenario a higher electricity demand is observed in 2050 (33.4 TWh/a, + 42% with respect to the base year), due to the electrification with HPs in non-renovated buildings and buildings with low-ambition renovation quality. The high-quality TR applied in the BEST scenarios allows a significant decrease of the energy demand for SH (-40% with respect to the BAU scenario in 2050). In the BEST scenario, the lower demand of new and renovated buildings allows to reduce the total electricity demand in 2050 (21.8 TWh/a, -7% with respect to the base year). In the BAU scenario in 2050 there is still a minor share of fossil-based systems which could not be phased-out with the imposed parameters.
The electric load for appliances and auxiliaries is independent from the installed heating system and is therefore assumed to be equal for all the buildings within a specific category. The electric load for SH and DHW preparation is only considered for the buildings equipped with HP or DE system. Table 12 presents a summary of the average building for each investigated building category resulting from the two BSESs used to calculate the load curves in 2050.
The aggregated daily and monthly electric load curves for the two BSESs, based on the total yearly electricity demand shown in Fig 9, are presented in Fig 10. The seasonal pattern is visible both from the daily and the monthly representation, but it is less pronounced in the BEST scenario, where the efficiency measures lead to a lower electricity demand in particular in the winter months compared to the BAU scenario (e.g., -40% electricity demand in January, compared to -34% on annual basis).
An additional key insight provided by the load curves is the impact of these measures on peak electricity demand, particularly during the winter period. The difference in daily peak load between the two scenarios is notably more pronounced than what a monthly average analysis would suggest. On the most demanding day of the year (January 4th, based on the selected climate data), the BAU scenario requires 0.22 TWh/day, while the BEST scenario requires only 0.127 TWh/day—a reduction of 42%.
4.2. Energy system
The final electric energy demand of the two investigated BSESs is evaluated considering different boundary conditions for the electricity generation (cases A, B and C).
In case A, the energy generation is assumed decoupled from the energy demand.
In the case of energy system B, the generation profiles derived from the electricity mixes of Table 2 are applied to the two BSESs, with results presented in Fig 11. The seasonal mismatch (i.e., the winter gap) between RE-based electricity generation and electric load of the buildings is influenced by the performance of the building stock (depending on the efficiency measures implemented) and the characteristics of the RE-generation mix. The BEST scenario exhibits a less pronounced seasonal consumption pattern than the BAU scenario. This enables a higher share of the load to be met by RE generation (on a monthly basis).
Top left: BAU_B_30_30,top right: BAU_B_45_15, bottom left: BEST_B_30_30, bottom right: BEST_B_45_15.
The resulting monthly-based winter gap of the four investigated subcases is presented in Fig 12. In January, a low-efficient building stock based on a PV-dominant electricity mix (i.e., 45_15) requires an additional amount of energy which is 40% higher than a high-efficient building stock with the same electricity mix, and 49% higher than a highly efficient building stock based on an electricity mix with an electricity generation with higher shares of wind power (i.e., 30_30).
In an ideally interconnected European grid, the surplus electricity generation in summer is exported to the neighbouring countries and compensated by equivalent imports in winter. However, if neighbouring countries are also expected to experience a similar mismatch between RE generation and demand, it cannot be assumed that the winter gap in Austria could be compensated by RE-based electricity imports. Therefore, the net annual balance alone is insufficient to account for this seasonal discrepancy.
In the autarkic system (case C), energy import and export are not permitted. Seasonal electricity storage is included to bridge the winter gap with the summer surplus RE generation, which in the presented analysis is provided by PV. The results are presented for the two investigated BSESs and electricity mixes in Fig 13. In this case, the electric load of DH (driven by large-scale HPs) is also included, but only when biomass is not sufficient to meet the DH heating load. This situation arises in the BAU scenario, where the efficiency measures are not sufficient to reduce the DH energy requirement enough to be fully covered by biomass. Accordingly, the PV surplus in this case also accounts for the additional energy needed for DH.
Top left: BAU_C_30_30, top right: BAU_C_45_15, bottom left: BEST_C_30_30, bottom right: BEST_C_45_15.
The role of the energy mix is particularly relevant in the BAU BSES, where on a monthly basis with the analysed climate, the RE generation in June is 17% higher than the electric load demand in January in the 30_30 generation mix, and 30% higher in the 45_15 mix.
The decarbonisation potential of the investigated BSESs is assessed based on equivalent CO2 emissions for the three energy system cases, as shown in Fig 14. The total emissions correspond to the 2050 building stock energy demand shown in Fig 9, while the emissions for the initial year (2020) are provided as reference. With the electrification of the heating and the resulting increase in the electricity consumption required by HPs, the ‘non-ETS’ emissions decrease under the BAU scenario and can be eliminated in the BEST scenario.
The impact of consistency measures becomes evident when comparing the three energy system cases. In case A, where the decarbonisation effort relies solely on the building sector, the reduction in CO2 emissions compared to 2020 is only 33% in the BAU scenario (where an increase of ETS emissions is expected) and 67% in the BEST scenario.
As pointed out with the winter gap analysis, in case B part of the winter demand must be met by electricity imports. A sensitivity analysis is conducted considering one case with electricity imports from a partially decarbonised electricity mix (‘mix’ import) and a worst-case scenario where all neighbouring countries face a similar winter gap (‘gas’ import). As a result, a low-ambition building stock within a non-fully decarbonised European network (BAU_B_30_30_gas and BAU_B_45_15_gas), will have equivalent CO2 emissions up to 46% higher than a case when the surrounding countries will also implement ambitious efficiency and consistency measures that reduce the power gap in the winter period (BAU_B_30_30_mix and BAU_B_45_15_mix). With a fully decarbonised RE-generation (case C), the operational CO2 emissions are not sensitive to the efficiency measures, as electricity emissions are reduced to 0 in both scenarios.
5. Discussion
In this study, the influence of the system boundary and the boundary conditions of BSESs is investigated regarding the development of the (net) electric energy demand and the CO2 emissions. The assumptions for the decarbonisation of the energy system, which is a boundary condition for the BSES but which is also influenced by the development of the building stock, are investigated considering 14 variants (2 ambition levels regarding the building-level decarbonisation, 3 energy system cases, 2 national electricity mixes in the RE-based energy systems, 2 import electricity mixes, see Fig 2). An overview of the resulting KPIs for the investigated variants is presented in Table 13.
The results demonstrate the need to consider the evolution of national energy systems and the strong cross-sectoral implications when assessing the decarbonisation potential of the building stock. In the case of an energy system based on the current electricity mix (case A), the electrification associated with installing HPs to replace fossil-based systems would even increase the CO2 emissions related to electricity generation. Compared to the initial year, this would result in an increase of 80% in the BAU scenario and 15% in the BEST scenario (see also Fig 14).
A significant challenge of electrification of SH (and DHW preparation) is the seasonal mismatch between RE availability and energy demand. An annual CO2 conversion factor that aggregates the electricity mix over the whole year is inadequate for accounting for this seasonal variation, resulting in an underestimation of CO2 emissions. In the analysed variants, applying an annual equivalent CO2 conversion factor compared to monthly-based conversion factors underestimates emissions by 23% in the BAU_A scenario, where the seasonal gap is more pronounced, and by 20% in the BEST_A scenario.
In energy system B, the outcomes of efficiency measures are highly sensitive to system-level boundary conditions. Assuming fully decarbonised imported electricity can artificially mask the impact of building-level inefficiencies, leading to misleadingly optimistic results. Within energy system B, the equivalent CO2 emissions (that are only related to imported electricity) can be up to 2.5 times higher if the imported electricity mix is gas-based rather than based on a decarbonised mix (‘mix’).
The role of cross-sectoral integration is highlighted in case C, where the national RE generation must balance the electricity demand while accounting for the seasonal mismatch. With 31.3 TWh/a, the total PV generation required by the scenario BAU_C_45_15 is the worst case. However, this scenario excludes electricity demand from the transport and industrial sectors, indicating that potentially limited PV generation capacity would remain available to satisfy their energy needs under these autarkic conditions in the building sector.
6. Conclusions
Decarbonising the building sector is a key part of the energy transition, and the strategies to achieve it are closely interconnected. The implementation of BSESs allows to assess and quantify the impact and the potential of these strategies, applied with measures specific to the building stock, while taking into account relevant energy system boundary conditions.
The case of Austria is here presented. Two BSESs are investigated for the evolution of the energy demand of the Austrian building stock between 2020 and 2050: a BAU scenario with standard ambition level and intensity of the efficiency measures and a BEST scenario with higher ambition level. The tool allows to assess the evolution of the building stock annual final energy demand per energy carrier for SH, DWH preparation as well as electricity for appliances and operation. The electric load curves for the two Austrian BSESs are defined through a lumped-mass building simulation with hourly resolution, based on the average building quality of the year 2050 resulting from the scenario tool. The final energy demand for the year 2050 is evaluated in the broader context of the transition of the Austrian electricity generation system considering three scenarios for the electricity generation system: (A) a demand-independent energy system based on the current Austrian electricity mix, (B) a 100% RE-based system in net annual balance with the domestic demand and interconnected with surrounding countries, and (C) an autarkic 100% RE-based system with seasonal storage. A monthly resolution is considered in the balance between energy generation and demand, in order to capture the challenge associated to the seasonal pattern.
The results of the study show that electrification linked to a massive integration of HPs can significantly increase the electricity demand in buildings if the phase-out of fossil-based systems is not supported by strong efficiency measures, leading to a substantial winter-gap between the energy demand and RE generation availability in fully decarbonised energy systems.
The choice of system boundary conditions therefore critically influences the perceived effectiveness of decarbonisation measures. In case B, assuming a fully decarbonised electricity import mix can mask the impact of low efficiency measures in the building stock (BAU). In autarkic scenarios (case C), where the energy system is sized according to the building stock energy demand, feedback on the effective availability of RE is necessary to formulate consistent assumptions for the BSES input, such as TR quality and rate.
In RE-based scenarios, the use of CO2 emissions as KPI may lead to an inconsistent rating of the two investigated BSESs if the future imported electricity mix is not known. The range of CO2 emissions from the sensitivity analysis on the electricity mix (domestic and imported) shows that the CO2 emission balance does not necessarily correspond to the building stock energy balance. Winter gap, RE-generation requirements and storage capacity are therefore recommended KPIs to support the ranking of decarbonised systems.
In the present study, the connection between energy system and building stock has been considered; however, a broader analysis should be conducted in the future to include other sectors - such as transport and industry- that are undergoing a strong electrification process (e.g., E-mobility) and play a key role in the sector coupling (e.g., industry and DH). This extension would also capture the important impact of RE integration on transmission infrastructure, particularly regarding power fluctuations during peak production periods.
Although this work addresses only operational emissions associated to energy generation, future extensions to consider life-cycle emissions will require particular attention to the definition of specific boundaries and control volume for each technology. Upstream processes for RE technologies, such as manufacturing of PV panels and wind turbines, are typically allocated differently than the extraction of fossil fuels, potentially affecting the consistency and interpretation of results.
An additional element that was not considered in this study, but which is of relevant importance, is related to the costs of the energy transition, at both the building and system levels. These costs are however specific to the individual regions and are related to local subsidies and regulations and require a deeper analysis, which was not the scope of this work.
Supporting information
S1 Appendix. Baseline used to build the building stock scenarios (residential and non-residential buildings).
https://doi.org/10.1371/journal.pclm.0000842.s001
(DOCX)
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