The wind power potential is derived from the study Winkelmeier et al. (2019), in which the technical wind power potential was assessed at county level (NUTS-3), which requires downscaling to municipal level. This was done pro rata for the available agricultural areas on municipal and county level, respectively. The potential assessment itself was considering these main aspects: Based on planned land use (area reserved for settlements and infrastructure), protected areas (national parks, nature reserves, and Natura 2000), unsuitable terrain conditions and land cover, available wind resources and finally technology and costs of installations. Depending on the wind conditions and achievable yield, locations in Austria were classified into three quality classes: A, B, and C. According to the study, Austria is assumed to have a wind power potential of 32,939 MW, which is divided down to the county level. The wind power potential for quality class A is also separately available at the county level and amounts to 10,209 MW. The potentials for quality classes B (8,214 MW) and C (14,516 MW) are available only as national aggregates. Therefore, the total potential has been separated into quality class A and quality classes B + C in aggregated and calculated on county level. For municipal level, a specific wind potential in MW/m² is calculated using the agricultural area as spatial variable. The municipal potential (Equation 6) calculated by multiplying the specific wind power potential of the county with the municipal agricultural areas, where the different quality classes refer to different full-load hours t_f. Table 4 summarizes the information on the quality classes and the expected annual potential. On the basis of the calculations, the annual generation potential of wind power plants in Austria is 69.7 TWh.

To determine the remaining potential at the municipality level after existing installations, it was assumed that 3,018 MW of currently installed wind power capacity are located exclusively in quality class A sites. This means that the entire potential of quality class B + C is remaining as untapped potential and only roughly two thirds of the quality class A potential remain unclaimed. With this remaining potential, a specific remaining wind power potential is calculated for each county based on the county's agricultural area, following a similar approach as for the total potential. The remaining wind power potential is then scaled up for each municipality based on its agricultural area.

Data on the technically economic hydropower potential were obtained from Fuchs (2018, p. 12), which defines the potential as the technical-economic hydropower potential that can be reasonably realized under the given technical and economic conditions. For the assessment of the potential, sub-areas were classified with reference to their suitability for hydropower utilization. The study gives a value for Austria's hydropower potential of 56.1 TWh. This data on hydropower potential is available at county level. Municipal potentials were derived by Equation (7), using the total areas of municipalities and counties for allocation.

The determination of the remaining potential was obtained by subtracting the installed power plant capacity from the total technical-economic potential, resulting in a remaining potential of 16,000 GWh (Fuchs, 2018, p. 35). The estimation of the municipal potentials is analogously scaled to the total potential by the share of municipal to county land area.

The PV potential was estimated using a study Fechner (2020) with a focus on identifying suitable areas for PV systems within various space use categories such as buildings, including commercial and industrial areas, transportation and military areas, as well as agricultural and forestry areas. The objective of the study was to determine the potential of these areas for generating energy through photovoltaic systems and to evaluate the feasibility of implementing this potential by 2030. It analyzed various aspects, ranging from theoretical/physical considerations to technical, economic, ecological, and social factors, to identify the barriers that need to be overcome during the implementation of the PV potential. These barriers could include technological limitations, economic constraints, environmental considerations, and social acceptance. Table 5 shows the potential of PV from the physical-theoretical to the economic/social potential based on the study (Fechner, 2020). However, for this paper, the assessment of the potential is limited to the building sector only.

This results in an estimated PV potential of 3.5 TWh/a when excluding façade utilization. However, to meet the national energy and climate targets, specified in the Renewable Expansion Act [Nationalrat, 2022, §4 (4)] to an annual PV generation of 11 TWh/a, additional technically feasible potential must be utilized. Excluding façade areas, the resulting annual 11.4 TWh/a would be sufficient to cover the national target. The municipal allocation of this target was carried out according to the share of roof areas in each category, which were obtained from the Austrian national building stock register count in 2011 (Statistics Austria, 2022) by approximating the roof area to the gross floor areas over the number of floors in each category. Equations (8) and (9) show how the total potential is determined using specific PV yield values and roof area.

To estimate the overall potential at the municipal level, three different scenarios are provided: (i) The potential based on the original study (Fechner, 2020), (ii) the potential with increased specific yields (kWh/m²_roof), (iii) specific utilization of pitched roofs [single-family houses (SFH)], flat roofs [multi-family houses (MFH)], and industrial roofs, as well as specific PV yield in kWh/m²_PV. Potential (i) on municipality level is calculated via the determined roof area. The survey results from the consideration of east-west oriented roofs, which were not used for occupancy in the potential study. To compensate for the resulting inadequate specific yield values, a surcharge factor of 1.67 (due to five instead of three orientations) and a discount factor of 0.93 (due to the reduced solar irradiation for east and west orientations compared to the south-facing modules) were considered in scenario (ii). The municipal potential is determined using the determined roof area and specified yields as well as percentages for PV occupancy for steep, flat, and industrial building roofs (Equation 10). Default values for specific yield values is 170 kWh/m²_PV, as well as 35% for steep, 55% for flat and 70% for industrial building roofs, leading to scenario (iii), which was used for demonstration purposes.

Table 6 summarizes the collected roof area, specific yield values, and the sum of the options for the total PV potential in Austria.

The estimation of the remaining potential involved leveraging data on subsidized installations at the municipality level, obtained from Statistics Austria (2022). This data provided information on the exploited installed capacity of subsidized projects within each municipality. The total installed capacity of these projects amounted to 1.78 GW_p. To contextualize this information, it was compared to the total installed capacity in Austria, which was reported to be 2.5 GW_p (Wien Energie GmbH, 2021). The ratio of the sum of subsidized installations to the total installed capacity, was used to allocate the proportion of the total capacity attributable to each municipality. This allows for an assessment of the distribution of installed capacity among municipalities and facilitates the estimation of the remaining potential for further development in those areas.

For the ST potential the same roof areas were used as in the PV case, but there is competition for space between the two technologies. It is assumed that the heat and domestic hot water demand in industrial buildings is the highest, which is why ST installations are mainly expected on industrial roofs. When specifying the utilization rates of these roof areas, the PV potential is correspondingly reduced (Equation 11).

The total ST potential for Austria amounts to 156.4 TWh of thermal energy per year. The current energy production of ST systems in Austria was obtained through statistical data (Statistics Austria, 2022). As the available data was at the states level, the energy production quantities were scaled down to the municipality level based on the ratio of municipality roof area to state roof area.

The biomass potential was estimated based on forested Areas. According to Abart-Heriszt et al. (2020), Austria has a total forest area of 4.88 million hectares. The average annual growth is around 8.9 solid cubic meters (scm) per hectare (Federal Ministry of Agriculture Forestry, Regions and Water Management, 2019, p. 8). Solid cubic meters are converted to stere (st) with a factor from literature of ~1.8 st/scm. With an average calorific value of 1,500 kWh/st, the maximum potential, assuming the utilization of the entire annual growth, amounts to 93.85 TWh/a:

The current biomass utilization is estimated based on the forest area of each municipality. For the maximum potential, 100% of the annual growth is allocated, while for the current utilization, it is assumed that 90% of the annual growth is harvested and only 30% of that harvested biomass is used for combustion (Equation 12):

According to this study (Stanzer et al., 2010, p. 131), the geothermal heat potential in Austria is estimated to be 35,506 GWh per annum and available at county level. This potential correlates with the heat demand, which was derived as follows: (i) The number of buildings for each type, that the study uses (i.e., four building types and six construction periods, resulting in 24 distinct types) was determined on a county level, (ii) determination of the residential or usable areas for all building types per county, (iii) for each building type, a heating energy indicator from literature is assigned according to the construction periods and building types, (iv) the indicator for the heat demand for domestic hot water was assumed to be 3,600 kWh per household. In addition, a minimum efficiency requirement was introduced that ruled out heat pump systems for space heating for building types constructed before 1981. Finally, the municipal potentials are allocated from county level based on the total municipal and county areas (Equation 13).

The data on current energy production from near-surface geothermal energy was obtained from Statistics Austria (2022) at the county level (NUTS-3). The energy production at the municipality level was scaled based on the ratio of municipality area to county land area. This approach allowed for a more detailed analysis of near-surface geothermal energy production, considering the specific characteristics and sizes of municipalities within each region. By scaling down the regional data to the municipality level, a more accurate picture of near-surface geothermal energy generation across Austria was achieved. The remaining potential for the municipalities, as well as the overall remaining potential for the country, is determined by calculating the difference between the total potential and the already developed potential.

In the case of buildings, a reduction factor represents energy savings for energy efficiency measures such as renovation and optimization of heating and hot water systems. This was operationalized using two parameters readily available in municipal statistical data: the year of construction and a rough building type (single-family houses and multi-family or multi-storey houses from TABULA) for each built area. The TABULA (Typology Approach for Building Stock Energy Assessment) Webtool features a set of energy performance indicators (energy costs, CO₂ emissions, heat demand, primary energy demand) and provides online calculations of exemplary buildings, showcasing their energy characteristics and the potential energy savings through the implementation of renovation measures. The package of measures includes the modernization of the thermal envelope and the heating supply system (Institute for Housing and Environment, 2017). The national building typology in TABULA is defined by a “Building Type Matrix,” with four main building typologies defined: single-family houses, terraced houses, multi-family houses, and apartment blocks.

The rows of the matrix correspond to different construction year classes, which align with the years available in Energiemosaik. This alignment allows for a seamless combination of data between TABULA and Energiemosaik, facilitating comprehensive analyses and assessments of the building sector's energy characteristics.

To obtain the reduction factor, the following approach was employed: Data on energy consumption before and after renovation for each of the four building size classes [SFH, terraced houses (TH), MFH, and apartment blocks (AB)] was collected. The difference between the energy consumption before and after the renovation was then determined and referred to as the energy-saving potential, and the corresponding fraction as the Reduction factor.

Subsequently, the four building size classes were mapped onto the two available categories for energy consumption from Energiemosaik (Abart-Heriszt et al., 2020): “single-family houses and terraced houses” and “multi-family houses and apartment blocks” by using the average reduction for each category. The projected energy demand of municipalities in the household sector is then determined by multiplying the individual reduction factors with the respective final energy consumption. Table 8 displays the reduction rates for consumption sourced from the TABULA website, alongside the corresponding calculated relative reduction factors. Additionally, it provides the net floor areas for the buildings based on data extracted from Energiemosaik.

The Equation (14) represents the total energy demand for the household sector. It is calculated by sum the energy demand for each building category (a) across all construction periods (t).

In the case of sectoral services, the focus is primarily on renovating the buildings themselves, while the optimization of processes related to the specific services is not considered. To determine the reduction factor for this sector, the average value of the housing category was calculated and used as the reduction factor for services (Equation 15).

The end energy consumption of the sector services is then multiplied by reduction factors. The results obtained in this way lead to the future final energy demand (Equation 16).

To estimate the energy saving potential of daily mobility, the spreadsheet tool described in Mair am Tinkhof (2020) was used. The tool provides calculation of various indicators related to everyday mobility in settlements and neighborhoods, considering planned mobility concepts and local conditions and offers diverse options for influencing the calculation of everyday mobility. To develop a comprehensive methodology with a broad applicability, it was decided that in future scenarios all passenger cars will be electric vehicles charged with green electricity. Based on these assumptions, the current and future GHG emissions and the primary energy demands were calculated. The ratio between the two was used as reduction factor f_tm for the transport and mobility sector. The energy consumption data from Energiemosaik is multiplied by the f_tm to calculate the energy demand of the transport and mobility sector in the future (Equation 17). This gives an estimate of the achievable energy demand reduction in the sector based on the assumed improvements in energy efficiency and the transition to electric vehicles charged with green electricity.

In the industry and production sector, energy savings were assumed to be obtained through process optimization and automation, as well as the use of energy-efficient equipment and lighting. Technological improvements and automation offer a straightforward and effective path to enhancing energy efficiency. According to Dehli (2020), meaningful opportunities for increasing energy efficiency include:

An overview of end energy consumption in different application areas within the industrial sector is also provided in Dehli (2020): It clearly illustrates that the most significant application areas are process heat, space heating, and domestic hot water, which together account for over 70% of the total end energy consumption. Mechanical energy comes second, constituting 23% of the overall consumption. The underlying data for this analysis again is sourced from Energiemosaik (Abart-Heriszt et al., 2020). By weighting the shares of application areas and their corresponding saving potentials, an overall f_ip is derived. To obtain the projected energy demand of the sector, the end energy consumptions from Energiemosaik data are multiplied by this f_ip (Equation 18).

A reduction factor for the agriculture and forestry sector was not considered as it constitutes only a minor share of 2.3% in the total end energy demand and does not significantly impact the overall analysis. This can be addressed in future research by defining the desired allocation function and spatial variable as necessary.

The following Figure 3 visually depicts the expansion targets for renewable energies across municipalities left in GWh per annum and right in MWh per capita per annum. Intensity of color indicates the magnitude of required expansion for each municipality. Darker shades indicate a higher obligation for the municipality to augment their renewable energy capacity, suggesting the presence of greater potential. This is aimed to discern the distribution of expansion goals, guiding municipalities toward targeted efforts in harnessing their specific renewable energy potential and fostering sustainable energy development. It can be seen that installation targets of wind power is higher toward the eastern and central parts of the country, whereas the mountainous west generally has lower potential and subsequent targets, with notable exceptions. There is no notable change in distribution between absolute and relative targets per capita. The distribution of expansion targets across municipalities shows that about 11% of the expansion target is distributed among 1,260 municipalities, each having a target of <10 GWh, or roughly two modern wind turbines. But 30% of the expansion target is achieved by only sixty municipalities, which hold goals exceeding 100 GWh based on their high wind potential. The hydropower expansion targets mainly highlight the remaining potentials of the western region. The illustration of the distribution of hydropower expansion targets among municipalities shows that 11% of municipalities lack significant hydropower potential, and another 37% of municipalities only account for 6% of the national target. In contrast, 30% of the expansion target is achieved by only sixty-six municipalities with goals exceeding 100 GWh/a each. The Photovoltaic and Solar Thermal heatmaps highlight the need for action in larger cities and urban areas.

Note that in this context, the PV and Solar thermal potential is not solely dependent on solar radiation; rather, it relies on available roof areas for photovoltaic installations and industrial rooftops for solar thermal installations. Evidently, the municipalities with the greatest potentials are primarily urban centers such as Linz, Salzburg, and districts within Vienna, which is consistent with their larger built environments and roof areas. A comparison with the heatmap per capita shows a different perspective, where the obligations of urban areas is in fact much smaller when looking at the per capita targets than for rural settlements. The expansion target for biomass can be located in the forested middle of the country and extends to the southern and western regions. The distribution per capita shows a slight deviation, due to the varying population density of these rural and mountainous regions. Note that the potential refers to biomass use of wood only, which means that the potential depends on the size of the municipality's forest area. As such, seventeen municipalities have no biomass potential, and another 711 municipalities have such a low potential that their cumulative contribution would be only 4% of the target. However, due to the mostly large forest areas in middle, south, and west of Austria, about half of the expansion target could be reached by 222 municipalities. In the case of ambient heat, the larger cities are again highlighted due to the assumption of predominant use for space heating and cooling. The specific per capita breakdown on the right again contrasts the high absolute targets with low relatives.

Finally, the reduction targets for energy consumption for buildings and mobility sectors are shown with color intensity corresponding to the magnitude of required energy savings for each municipality. In both building and mobility sectors, larger municipalities can be seen to have a greater potential for energy savings due to higher building and traffic density. The reduction potentials per capita unsurprisingly show the opposite trend.

The aggregate of the different renewable expansion targets and sectoral reduction targets are presented as an integrated energy balance target for all Austrian municipalities in Figure 4 in GWh per annum and in MWh per Capita per annum. The colors represent the following: Municipalities marked in red have negative energy balance targets, indicating either a limited potential for renewable energy sources, high energy consumption with minimal saving potential or a combination of both. Naturally, their contribution to the national target will be lower. On the other hand, green-shaded municipalities have positive energy balance targets, indicating higher potential and subsequent targets for renewable energy expansion than their achievable energy demand requiring significant expansion to help offset the deficits of the red-marked regions. Yellow-colored municipalities fall somewhere in between, indicating a more balanced position with regards to their renewable energy potential and consumption levels. This visually informative representation assists in identifying and addressing disparities, promoting a more equitable distribution of efforts to achieve sustainable energy objectives across all municipalities.

Next, Figure 4 show the distribution of municipalities based on the size of their absolute target balance. They illustrate that many municipalities in fact have a target balance in the range between −200 and 200 GWh/a, as well as the quantified difference between negatively balanced urban areas and positively balanced hinterland. The balance target provides an important quantitative threshold—how to reach it is a different story, which should take into consideration local contexts and political decisions. Therefore, apart from the detailed targets presented before, it is also useful to group the results into concrete needs for action: Figure 4 also depict these needs as electrical and thermal actions, respectively, represented on the left in GWh per annum and on the right in MWh per capita per annum. Values below zero indicate that the municipality has already sufficiently exploited its potential for renewable energies and/or exhibits low energy consumption. Conversely, higher positive values indicate the additional energy capacity required for the municipality to meet its targets. Crucially, this can be used to quantify other forms of renewable expansion or savings than what the initial allocation yielded. For example, a municipality could opt to realize their electrical need for action resulting from a high wind potential with increased PV installation and additional demand reductions beyond the initially targeted thresholds instead.

The results can also be aggregated on an intermediary level of geographic granularity. This section presents the resulting renewable exploitation and sectoral saving targets aggregated by federal states. Figure 5 shows the resulting energy balances on the level of Austrian federal states (NUTS-3) from left to right: Demand Status-Quo (2019), Demand Targets, RES Expansion targets and Status-Quo (2019). The excess RES potential not required for expansion is depicted in green, the “reduction gap” between current energy demand and target levels in red on top of the demand target column, and the “expansion gap” between current RES and target levels in gray in top of the RES Status Quo column. According to the allocation approach, a federal state is expected to contribute more to the overall target if it has a greater potential from a particular renewable energy source. For instance, the third state from the left—“Burgenland”, a western state with high wind potential—is projected to generate significantly more energy from wind power, the alpine eastern Tyrol more energy from hydropower, and the forested state of Styria in the south more energy from biomass. This allocation strategy aims to capitalize on the specific strengths and resources of each region, optimizing the use of renewable energy sources according to their availability.

The figure illustrates current energy consumption and corresponding energy demand targets for individual federal states. The approach is characterized by a clear allocation strategy. It considers the particularities of each federal state, such as varying proportions of buildings in need of renovation, and higher energy consumption in the transport and mobility sector. Consequently, the targets are adjusted to address these specific circumstances. This customized allocation method recognizes the heterogeneity of energy consumption patterns and building stocks across different regions of Austria. By setting tailored targets for each federal state based on their unique characteristics, the energy efficiency measures can be more precisely targeted to address the specific requirements and challenges of each area. The allocation of targets in this manner ensures an equitable and balanced contribution from all parts of the country toward achieving the overarching national objectives in energy efficiency and sustainability.

The figure also presents a comparison between the target final energy demand and the expansion target for renewable energies across different federal states. The graph highlights the varying degrees of individual agency or discretion that each federal state possesses. Notably, states like Lower Austria and Styria exhibit significant untapped potential for renewable energy expansion. Conversely, federal states such as Vienna or Upper Austria, which possess renewable energy expansion potentials lower than their current end energy consumption, should complement expansion efforts with energy efficiency measures, including building renovations and energy-saving initiatives.

The following presents the resulting energy balance component targets for three example municipalities of decreasing size: Steyr (38 thousand inhabitants, motor industry), Hollabrunn (twelve thousand inhabitants), and Gols (four thousand inhabitants, high wind power installation). The left side of the balance figure show energy demand and consumption, the right-side renewable energy sources.

Figure 6 illustrates the results in a comparison. The first and largest municipality of Steyr exemplifies a pronounced industrial sector, which in this allocation scenario has no saving targets (16.9 kWh/Person/a). The focus of the need for action is split into one third energy savings (−8.3 kWh/Pers/a) and two thirds on renewable expansion of 16.2 kWh/Pers/a. The diagram shows that PV, ST, and ambient heat are the largest untapped potentials.

In contrast, the central bar chart shows the smaller municipality of Hollabrunn. Here, the industrial sector is insignificant, and the need for action is instead constituted predominantly by the need for expansion of renewables. The bottom bars show the nominal energy balance of the smallest municipality, which is already significantly positive due to the extensive development of wind power by 2019, as depicted in the first two bars of the Status Quo. Nevertheless, the potential-dependent allocation process still indicates a further need for action in Gols, both in terms of renewable expansion and energy savings of −9.2 and 8.8 kWh/Pers/a respectively.