Carbon neutrality in the residential sector: A general toolbox and the case of Germany

Hornykewycz, Anna; Kapeller, Jakob; Weber, Jan David; Schütz, Bernhard; Cserjan, Lukas Working Paper Carbon neutrality in the residential sector: A general toolbox and the case of Germany ifso working paper, No. 41 Provided in Cooperation with: University of Duisburg-Essen, Institute for Socioeconomics (ifso) Suggested Citation: Hornykewycz, Anna; Kapeller, Jakob; Weber, Jan David; Schütz, Bernhard; Cserjan, Lukas (2024) : Carbon neutrality in the residential sector: A general toolbox and the case of Germany, ifso working paper, No. 41, University of Duisburg-Essen, Institute for Socio-Economics (ifso), Duisburg This Version is available at: https://hdl.handle.net/10419/305298 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ uni-due.de/soziooekonomie/wp ifso working paper Anna Hornykewycz Jakob Kapeller Jan David Weber Bernhard Schütz Lukas Cserjan Carbon Neutrality in the Residential Sector: A General Toolbox and the Case of Germany 2024 no.41 CARBON NEUTRALITY IN THE RESIDENTIAL SECTOR: A GENERAL TOOLBOX AND THE CASE OF GERMANY ∗ Anna Hornykewycz† Jakob Kapeller‡ Jan David Weber§ Bernhard Schütz¶ Lukas Cserjan∥ October 29, 2024 ABSTRACT This paper presents a general framework for estimating the renovation and investment requirements associated with a green transformation of the residential sector that effectively reduces the net emissions of the residential sector (close) to zero. The framework takes ecological and distributional considerations into account and aims to provide concrete outcomes suitable to inform policy-making, while being as parsimonious as possible on the side of data requirements. All key steps associated with this framework are compiled in an openly accessible toolbox that can be adapted to different country-specific contexts. This paper takes the German case as an example to illustrate the main assumptions, data requirements, and outcomes that can be derived from this toolbox. Keywords socio-ecological transformation, residential sector, net zero, just transition, sustainable infrastructure ∗ We thank Sophie Hieselmayr, Laura Porak, Ulrike Röhr, Immanuel Stiess, Florian Wagner, and Rafael Wildauer for their input and support in the course of this project. Financial support from Dezernat Zukunft is gratefully acknowledged. JK, BS, and JDW also received financial support from the Hans Böckler Foundation under grant number 2021-544-2. This paper uses data from the Eurosystem Household Finance and Consumption Survey. †Institute for Comprehensive Analysis of the Economy (ICAE), Johannes Kepler University Linz, anna.hornyk[email protected] ‡ Institute for Socio-Economics, University Duisburg-Essen and Institute for Comprehensive Analysis of the Economy (ICAE), Johannes Kepler University Linz, jakob[email protected] §Institute for Socio-Economics, University Duisburg-Essen, [email protected] ¶ The Vienna Institute for International Economic Studies and Institute for Socio-Economics, University Duisburg-Essen, [email protected] ∥Institute for Comprehensive Analysis of the Economy (ICAE), Johannes Kepler University Linz, [email protected] WORKING PAPER Introduction Attaining carbon neutrality in the residential building sector is essential for successfully transforming modern economies to operate within the limits of planetary boundaries. This overall requirement gives rise to the more specific task of devising programs that facilitate such a transformation on domestic levels. We present a general toolbox that considers several key dimensions of interest suitable to assess, evaluate, or design such domestic programs. These dimensions include (1) the technological requirements for achieving a successful transformation, (2) the scale of effort required for implementing these technologies, (3) the investment costs associated with such an effort, (4) the economic impact of these investments, and (5) the distribution of the investment costs between the private and the public sector as well as within the private sector. The toolbox presented here explicitly incorporates the notion of a ‘just transition’ in the analysis by considering the impact of domestic action programs on distributional aspects. The toolbox is available in open-access form and can be adapted to the situation in different countries 1 . Due to its modular structure, different components of the toolbox can be applied in isolation. We keep the data requirements of our framework as parsimonious as possible to ensure broad applicability. In this paper, we apply our toolbox to the case of Germany to illustrate its core functionalities and analytical potential. The German building sector has, on average, suboptimal isolation standards and still makes heavy use of fossil energy sources. Since Germany committed to achieving climate neutrality by the year 2050, it represents a challenging and important case of application. A key motivation for developing our toolbox is the observation that no standardized procedure for calculating renovation requirements has been developed, although a plethora of studies on the ecological transformation of the German residential sector already exists (BCG, 2021; BMWi, 2015; Bürger et al.,2021; dena and geea, 2017; ifeu et al.,2018; Prognos et al.,2021; Repenning et al.,2018; Thomas et al.,2022, e.g.). Most of these studies lack transparency and replicability regarding the renovation requirements as well as consistent definitions of key concepts like the renovation rate. In this respect, we follow the example of Ermgassen et al. (2022) to model domestic transformation pathways for the residential sector in way that is replicable and can potentially be transferred to other countries. In addition, a consistent blind spot of existing studies is that the socio-economic impact of the proposed policy measures is not analyzed. In this paper, we bring more clarity into technical requirements as well as economic consequences associated with a socio-ecological transition of the residential sector in a transparent and reproducible way. Results To reach the goal of climate neutrality by the year 2050, Germany set emission targets for all relevant sectors, including the residential sector (Section 4 of the Federal Climate Change Act (Klimaschutzgesetz, KSG)). The direct carbon dioxide (CO 2 )-equivalents attributed to the German residential sector are estimated to account for 14% of overall emissions in Germany, while this share increases to at least 25% when also considering indirect emissions (Thomas et al.,2022). 2 The German residential sector consists of 19,4 million buildings. The majority of these are single or two-family houses (83%), while the remaining buildings are apartment buildings containing an average of seven flats (dena, 2021). Information on the quality of insulation and the type of heating system is collected in energy certificates (Sections 79-88 of the Buildings Energy Act (Gebäudeenergiegesetz, GEG)) that contain information on the year of construction, the last renovation, the heating system, and energy requirements. These data indicate that German heating systems are still mostly based on fossil energy sources as nearly 70% of all heating energy is provided by decentralized oil and gas heaters. In contrast, only 17% of all energy devoted to heating draws on renewable energy sources (BMWi, 2022; dena, 2021). Against this backdrop, we first elaborate on the technical requirements before moving on to economic requirements and the consequences of the socio-ecological transition. 1The source code of the model is available via GitHub 2 Direct emissions entail all emissions that are directly produced within the residential sector (e.g. through gas-based heating systems), whereas indirect emissions include all emissions that are consumed in the residential sector(e.g. electricity-based heating systems). 2 WORKING PAPER Technical Requirements: Reduction and Decarbonization of Energy Use Existing studies on the transformation of the German residential sector show a strong consensus on suitable technical strategies to achieve the climate goals: Reducing carbon emissions can be achieved by a combination of renovations dedicated to improve energy efficiency and the replacement of fossil-based decentralized heating systems. The latter typically relies on heating pumps, that use emission-free ambient heat as well as the expansion of district heating. 3 While following this line of argument, we also take into account that replacing fossil fuels in the residential sector reduces direct emissions, but may increase indirect emissions as heating pumps require electricity to operate. To capture this potential outsourcing of heating-related emissions from the residential sector to the energy sector, we focus on the emissions intensity of the German residential sector. This more inclusive measure also captures all indirect emissions induced by heating and thereby allows for a holistic assessment of how changes in the residential sector impact net emissions created. Renovation Rate Two different concepts have been dubbed ‘renovation rate’: First, the share of buildings that are subjected to some form of renovation and, second, the share of square meters that are subjected to a ‘full renovation’ (where partial renovations are aggregated into full renovations). As these concepts differ substantially, we try to achieve clarity in our analysis by defining the renovation rate as the share of buildings that undergo some form of energy-efficient renovation, whereas we will call the share of fully renovated living area the full renovation equivalent. Currently, the full renovation equivalent in Germany is approximately 1.15% (own calculation based on Prognos et al., 2021). Past studies typically recommend increasing the full renovation equivalent to 2%. We show that this will not be sufficient to reach the climate goals. Based on our assumptions, it is necessary to increase the full renovation equivalent to at least 2.4%, which corresponds to a renovation rate of 3% per year. In other words, the analysis facilitated by our toolbox suggests that prior studies have underestimated the necessary renovation rate. To complement and contextualize this main result, we additionally show that an increased renovation rate must be paired with a decarbonization strategy for external energy sources and a prioritization strategy that puts the renovation of badly insulated buildings first. These results are summarized in Figure 1, Panels (A) and (B), which show how different assumptions impact the speed and intensity of emission reductions and plot these reductions relative to the official climate goals. Prioritization There is a significant difference between renovating buildings in a random order and prioritizing the renovation of the worst-performing buildings. Both renovation strategies can, in the long-run, achieve climate neutrality. However, to achieve conformity with the climate targets over time, a prioritization of the worst-performing buildings is necessary (see Figure 1, Panel (C)). Decarbonization The need to decarbonize external energy sources applies to the energy sector, which provides the main energy source for heating pumps, as well as to the provision of district heating. In this context, our toolbox allows for mapping the relative contribution achieved by decarbonizing these heterogeneous energy sources as illustrated in Figure 1, Panel (D). For doing so, we employ the notion of emission intensity in the residential sector as defined in section . This measure will be closely aligned with conventional estimates based on direct emissions if all external energy sources are truly decarbonized (see Figure 1, Panel (E)), while differences will emerge as soon as outsourcing to other sectors occurs (as already observed in Figure 1, Panels (B) and (D)). Financial requirements, economic impact and distributional aspects Financial Requirements Currently, the annual sum of expenditures for energy-efficient renovations in Germany amounts to roughly 58 bn C. According to our calculations, implementing energy-efficient renovations as described above would induce additional costs of about 58 bn C per year. The total investment until 2050 sums up to 3.1 trillion C (in 2023 prices). Prioritizing the renovation of the worst-performing buildings implies that an over-proportional share of total costs occurs in the early years. In the first year of the policy measure, additional costs of 81 bn C are anticipated, 3 However, at the moment district heating in Germany still relies heavily on fossil fuels as only 12% are provided through renewable sources such as biomass, organic waste, geoand solar thermal energy and waste incineration (BDEW, 2021). 3 WORKING PAPER climate goals renovation rate of 1.5% renovation rate of 2% renovation rate of 3% 2025 2030 2035 2040 2045 2050 2055 0% 20% 40% 60% 80% 100% (A)Exchange of Heating Systems and Renovation climate goals renovation rate of 2.5% renovation rate of 3% renovation rate of 2.5%+decarb renovation rate of 3%+decarb 2025 2030 2035 2040 2045 2050 2055 0% 20% 40% 60% 80% 100% (B)Exchange, Renovation and Decarbonzation climate goals renovation rate of 3%+decarb (no prio) renovation rate of 3%+decarb 2025 2030 2035 2040 2045 2050 2055 0% 20% 40% 60% 80% 100% (C)The impact of priorization climate goals renovation rate of 3% (no decarb) renovation rate of 3%+decarb energy renovation rate of 3%+decarb all 2025 2030 2035 2040 2045 2050 2055 0% 20% 40% 60% 80% 100% (D)Details on decarbonization climate goals renovation rate of 3%: direct emissions only renovation rate of 3%: emission intensity 2025 2030 2035 2040 2045 2050 2055 0% 20% 40% 60% 80% 100% (E)Emission intensity vs. direct emissions cost in %of GDP (prioritized) cost in %of GDP (randomized) 2025 2030 2035 2040 2045 2050 0.0% 0.5% 1.0% 1.5% 2.0% (F)Transition costs in %of GDP Figure 1: Emission reductions in the residential sector: Panels (A)-(E) show expected emission reductions under different scenarios, while Panel (F) shows expected (additional) costs arising in a full-decarbonization scenario with a 3% renovation rate. 4 WORKING PAPER which amounts to about 1.9% of GDP, which over time decreases to 0.3% (Figure 1, Panel (F)). 4 As discussed below, we propose combining targeted public subsidies with regulatory means to meet these requirements. Regulatory means are thereby relevant for an effective prioritization of renovations, i.e. for renovating the worst-performing buildings first, but also to ensure that owners, who do not qualify for a subsidy, cannot delay renovation measures. Economic Impact These additional investments in the residential sector have direct economic consequences, which are explored by making use of an input-output model. We find a – comparatively low – GDP multiplier of 1.16, which implies that every C invested in transformation measures will increase GDP by 1.16 C. This result is due to the low pre-production intensity of the affected sectors and the fact that many intermediate goods required for such an investment initiative need to be imported from abroad5. Our toolbox can be used to assess potential capacity constraints. We find that initiating the suggested transformation will increase employment in the construction sector by approximately 274,000 workers. In the early years, this demand can range up to a maximum of 377,000 workers. Long-term estimates of labor market development in the German construction sector argue that this additional labor demand could be met through a decline in new construction projects (Dorffmeister, 2020). Zika et al. (2022) even points to an endogenous decrease in labor demand in the construction industry by 60,000 by 2030 and by 220,000 by 2040 – a trend that could potentially be further intensified by recent ECB interest rate hikes. This leaves a substantial pool of labor in the new construction sector that can be utilized for increased renovations of existing buildings, especially when taking into account the possibility of reallocating the existing workforce from the construction of new buildings toward renovation. Such a reallocation is also advisable when taking into account that the construction of new building contributes significantly to net emission output (see Drewniok et al.,2023a,b). Distributional aspects The distribution of residential property in most countries is highly uneven as large shares of residential property are held by households at the upper end of the wealth distribution (OECD, 2022). Hence, funding the transformation with a watering-can principle might be effective in technical terms, but it would redistribute taxpayer money from less wealthy individuals toward – already comparably wealthy – residential real estate owners. This would further speed up observed secular trends towards increasing inequality (Frick et al.,2012) and is also likely to reduce public support for such measures (Dabla-Norris et al.,2023). According to HFCS data, the wealthiest 10% of the German population own 48% of residential wealth while the least wealthy 50% only own 3% of real estate property. These observations indicate that possible subsidies should ensure to not intensify wealth concentration. Energy-efficient renovation measures on residential buildings lead to an increase in the value of the affected properties. If the properties were owned by private individuals, a subsidy would imply subsidizing private wealth. To avoid such a constellation, we assess the economic capacity of subsidized households based on private wealth and suggest to incorporate this into the subsidy decision. We argue that it is reasonable to link the extent of public subsidy to the (net) wealth position of subsidized households to (a) guarantee support to those who cannot afford the necessary renovation efforts, and (b) avoid using general tax revenues to subsidize the wealth of the richest households. Specifically, we propose that a given segment of the least wealthy households receives funding for the full cost of the necessary renovations, whereas some upper segment of the wealthiest households has to bear the full cost of the renovations – for households falling between these wealth extremes, the subsidy rate can be determined through linear interpolation. 6 According to our calculations, the German government would cover approximately 26% of the financing needs for buildings owned by private households (which corresponds to 21% of total costs when also taking institutional ownerships into account). This amounts to average public costs of around 24 bn C annually. 4 In this context, we assume a real annual GDP growth rate of 1% and an inflation rate in the construction sector that corresponds to the overall inflation rate. 5 Most imports are generated in the following sectors: Specialised construction works,Rubber and plastics products,Chemicals and chemical products,Ceramic products, processed stone and clay,Coke and refined petroleum products,Machinery,Glass and Glassware, and Electrical Equipment. 6 In our baseline application we employ threshold values of 65% (to demarcate the poorer segment that is fully subsidized) and 90% (to demarcate the richer households, which should receive no public funds), but these numbers could be adapted to local circumstances. The implementation of such a measure could be based on self-declarations of households, subject to random audits, in a rapid, efficient, and data protection-friendly manner. 5 WORKING PAPER For institutional owners, we suggest considering discounted costs instead of full costs and restricting the use of subsidies to those renovation costs that cannot be amortized within thirty years. The main reason for this assumption is that institutional owners typically have a longer planning horizon and benefit directly from the impact of renovations on balance sheets, which often leads to an increase in equity that compensates a significant fraction of the investment costs even before cost-reductions are realized. Discussion This paper is concerned with the question of how to conceptualize a trajectory towards climate neutrality in the residential sector. Using our toolbox, we find that this trajectory differs significantly from the estimates given in existing studies. The full renovation equivalent necessary to reach German climate goals is at least 2.4% – higher than previously expected. Additionally, we find that, to stay in line with the climate targets, worst-performing buildings need to be renovated first. The required high renovation rate and prioritization imply a need for regulatory measures. A second major finding is related to sector linkages. Typically, the focus on a single sector takes direct emissions as a natural benchmark, which overlooks the potential outsourcing of direct to indirect emissions. By focusing on emission intensity as a key concept, we take implications for the energy provision into account to provide an integrated assessment demonstrating the need to decarbonize external energy sources. Our estimates of the investment required to reach necessary renovation rates are also higher than in previous studies. This is partly due to the higher renovation rate and partly to the fact that existing studies usually employ a net present value method when calculating investment costs.We argue that this approach is not suitable for private owners since, other than institutional owners, they are faced with a more short-term planning horizon. Also, this setup leads to a high variation in existing estimates of the required investment and has contributed to confusion on what costs to expect as well as on what economic impact the investment might have. According to our calculations, an additional yearly investment of 58 bn C is needed (which, on average is below 1.3% of Germany’s GDP). We further study the economic impact of this investment in an input-output framework. Due to a high dependency on imports (30% of initial investments), the multiplier of 1.16 is relatively low. While material bottlenecks are not expected, the investment strategy will generate an average of 274,000 new jobs in the construction sector. A drawback of the input-output method is that it cannot account for possible economies of scale effects arising from such investment. Such effects would reduce overall cost, import-dependency and help to avoid labor shortages. The results of our analysis have two important policy implications. First, creating an innovation-friendly environment in Germany could both help to reduce the import dependency and foster scale effects, thereby reducing costs and the required workforce. Second, measures to avoid short-term labor shortages could support the transformation. Finally, we provide a financing model that, in the sense of a ‘just transition’ takes the highly unequal distribution of wealth into account. We propose that the state fully subsidizes renovation measures for the least wealthy households, while the wealthiest should carry the entire costs themselves. Households between these two groups could be subsidized according to a linear function. Providing subsidies conditional on household wealth, (a) can increase support for the necessary renovation efforts, and (b) avoids subsidizing the richest households’ wealth. We develop a scenario for illustrative purposes in which the lowest 65% in the wealth distribution are fully subsidized whereas the highest 10% are not subsidized at all.7In this scenario, the state would carry 26% of overall renovation costs. A financing model fit for a socio-ecological transformation has to also take into account the situation of tenants. In Germany, the share of households who live in rental units is above the EU average. It is, therefore, especially important that renovation costs are not passed on by owners (who are already subsidized according to their wealth) to the tenants. According to the ‘landlord/tenant dilemma’, energy-efficient renovations benefit both parties. While tenants benefit from renovations with lower energy costs and higher living comfort, landlords increase the economic value of their properties. It is therefore not immediately clear who should bear the costs of energy-efficient renovation (Ástmarsson et al.,2013). While it is generally allowed in Germany to pass on renovation costs to tenants via increased rents, it remains controversial whether and to what extent such rent increases can be justified in the long term by lower energy 7These thresholds can be adapted to local circumstances. 6 WORKING PAPER Figure 2: This graph shows the steps necessary to derive reliable results and viable policies on the transformation of the residential sector. All these steps can be also applied in isolation. costs for tenants (see, e.g. Enseling and Hinz, 2006; Galvin and Sunikka-Blank, 2012). Against this backdrop, additional regulatory measures seem required for the German case to ensure that transition costs are not passed on to renters. Method In this section, we present our toolbox that can be used to compute different transformation scenarios for any country of interest. Figure 2summarizes the main steps of the underlying procedure to derive the necessary renovation rate (steps 1 - 4), associated costs (step 5), the overall economic impact (step 6), and related policy measures that are both considerate of distributional factors and fit to conduct the transformation (step 7). The toolbox makes use of a variety of macro-, meso-, and micro-data. An overview of the data necessary to replicate our study for other countries can be found in Table 1. Getting the Sample Right In our toolbox, we assume the availability of microdata on the residential sector. As available data might not be representative of core dimensions of interest, we employ a two-dimensional stratification approach that replicates the known aggregate distribution of (1) housing types and (2) energy efficiency classes. By doing so, we provide a useful starting point to avoid biased results due to selection problems. In addition, the toolbox allows the extraction of aggregate properties from the stratified sample that can be compared with available macro-information, such as the average energy efficiency per square meter and year, to verify the validity of the stratified sample. For the German case, we use the RWI-GEO-RED Real Estate Data, a dataset that provides information on the German real estate sector. It consists of raw data from the real estate platform immoscout24.de on which owners of residential properties provide excessive information to potential tenants and buyers, including data on heating facilities and energy requirements. The RWI dataset is, however, not representative of Germany as older single or two-family houses are less likely to be rented out or sold. Since these buildings tend to have a low energy efficiency class, this leads to an underrepresentation of low energy efficiency buildings in the RWI sample. We counter this shortcoming by applying step 1 of our procedure as described above. This method reproduces quite accurately aggregate information on the distribution of efficiency classes dena and Krieger, 2019. Plausibility checks support the thesis that our stratified data is representative of German residential buildings. 7