ENECO₂Calc—A Modeling Tool for the Investigation of Energy Transition Paths toward Climate Neutrality within Municipalities

Abstract

The paper focuses on developing an energy-modeling tool called ENECO₂Calc, which allows the determination of current ecologic and economic footprints based on calculating the final energy demand within several sectors for municipalities. Furthermore, different energy transition paths until 2050 can be investigated and compared to the business-as-usual reference scenario. ENECO₂Calc is the first municipality-based energy-modeling tool that allows the development of meaningful scenarios until 2050 by considering climate policy goals and RES potentials, and it involves the mobility emission forecast tool “PROVEM”. ENECO₂Calc is exclusively based on consistent statistical datasets. Additionally, the energy-modeling process was demonstrated as exemplary for the Austrian municipality St. Margareten im Rosental. For the selected municipality, three different scenarios were investigated. It could be concluded that a mix of decentral RES technologies and central cogeneration units in the heat sector, a mix of solar PV and cogeneration units in the electricity sector, and the use of synthetic biofuels coupled with a higher share of electrification in the fuel sector seemed to be most promising in the considered region. ENECO₂Calc is a helpful energy-modeling tool toward climate neutrality to support municipalities in developing appropriate economic and ecological footprint strategies.

1. Introduction

In the year 2018, the EU-27 emitted 3764 million tons of greenhouse gas emissions [1]. Within the same period, Austria was responsible for 79 million tons of carbon dioxide equivalent (CO₂e), which amounted to a share of 2% compared to the total emissions of the EU-27 [2]. The Paris Agreement [3] set out a global framework to reduce climate change by limiting global warming to well below 2 °C compared to the pre-industrial level. The European Union responded to the Paris Agreement with the 2030 Climate and Energy Framework [4], implemented by the Renewable Energy Directive (RED II) [5]. The key targets are reducing greenhouse gas emissions, raising the share of renewable energy carriers, and improving energy efficiency. RED II [5] and the national implementation in the Austrian Renewable Energies Act [6] allow the establishment of renewable energy communities (RECs) and citizen energy communities (CECs). The COVID-19 epidemic destabilized the world economy and energy markets. Model calculations have shown that coal and crude oil prices are interconnected [7,8]. Subsequently, it is essential to raise the cost security of supply by increasing the share of renewable energy carriers. To increase energy autonomy, RECs and CECs can be established to enable local initiatives by citizens, communities, and businesses. RECs are defined as legal entities [5] with natural persons, small- or medium-sized enterprises, or local authorities as shareholders or members [5]. The primary purpose of RECs is to ensure environmental, economic, or social community benefits for the shareholders and members, as well as for the respective local areas, rather than financial profits [5]. RECs are allowed to produce, consume, store, and sell products based on renewable energies [9,10]. CECs are open for any legal entity and are technology-neutral [11]. CECs are independent of location. In addition to REC activities, CECs are also allowed to distribute, supply, and aggregate electricity, as well as provide energy services [9,10]. This sharing-economy approach should raise public acceptance through active participation and mobilize private capital for the energy transition process. Further effects are the economic strengthening of the domestic economy, the growing independence of energy providers, and the diversification of actors within the energy market. RECs and CECs are supposed to relieve the existing grid load by local balance of supply and demand [9,10,12]. For the successful implementation of energy communities within the national energy market, assurances of ecologic, economic, and social advantages for members and shareholders of CECs and RECs, as well as a cost-oriented network charge system, are essential [9,10]. Further details regarding the designing, potential, economic and ecologic viability, regulatory challenges, and opportunities of energy communities, as well as blockchain-based trading with RECs and CECs, can be found in [13,14,15,16,17,18,19,20,21].

In addition to RED II, the European Green Deal [22] shall generate further efforts regarding climate protection. The core of the European Green Deal is the European Climate Law [23]. This law shall be the basis for climate neutrality until 2050 in the European Union. The CO₂e emissions in the EU-27 are mainly caused by energy supply, industry, transport, residential sources, agriculture, waste, shipping, and aviation. Therefore, the broad range of CO₂ emitters requires a complete transition and global energy system optimization. The global optimum toward climate neutrality in the energy system can only be a mix of centralized and decentralized RES-based energy systems because of different requirements [2], from private consumers in households up to heavy industry. More than 50% of European and Austrian greenhouse gas emissions refer to decentral emissions within municipalities, such as heat and electricity consumption, passenger and freight transport, or energy use within agriculture and forestry [2]. Therefore, the energy transition process toward climate neutrality on the decentralized regional level can only work effectively by considering the specific requirements of the different municipalities. To conclude, an interdisciplinary approach between energy transition and spatial planning with a focus on environmental, economic, and social impacts is crucial to reach the notified climate goals [24,25]. Municipality-based energy models can support this interdisciplinary approach.

Numerous researchers, institutions, and companies have developed energy models with a wide range of different applications. The energy models can be classified according to [26] in econometric, macro-economic, economic equilibrium, optimization, simulation, spreadsheet, backcasting, and multicriteria models. Based on this classification scheme, it is evident that energy modeling is always a tradeoff between spatial-, temporal-, and content-related resolution and, further, between energy security, energy equity, and environmental sustainability, called the energy trilemma [27,28,29]. Many different energy models are available that differ, among other things, in terms of geographical and sectoral coverage [30]. Energy models used for national energy-system-modeling approaches are, for example, EnergyPLAN from Aalborg University [31], MARKAL/TIMES from IEA-ETSAP [32], PRIMES from NTUA [33], or MESSAGE from IIASA [34]. Modeling approaches regarding district heat and electricity grid simulations can be investigated, for example, with EDisOn [35] or the Hotmaps dispatch model [36], both developed by the EEG at TU Wien. Electricity market simulation can be conducted with ELMOD [37] or Green-X [35]. Energy modeling on the municipality level has to be applied to consider the necessary interdisciplinary approach between energy transition and spatial planning. For this approach, energy-modeling software tools such as HOMER [38] or TRNSYS18 [39] can be used. Most community-based holistic energy models require a significant amount of input data. Content and spatial relations have to be implemented manually [28,40]. The ELAS calculator [41], published in 2011, was the first holistic energy model to determine an Austrian municipality’s energy-related ecologic, economic, and social impact mainly using statistical data from publicly accessible sources or predefined surveys [41]. In 2019, the Energy Mosaic Austria [28] was published, representing an ELAS-based nationwide energy and greenhouse gas inventory on the municipal level in high spatial and contextual resolution. Detailed information regarding energy models can be found in [27,30,42,43,44,45].

Summing up, there are a lot of energy models for different applications available. Nevertheless, no local energy model exists that supports municipalities within their energy transition processes. For the development of such a tool, it is important to create a user-friendly support instrument that can handle present and predictive investigations. In addition to calculating the current energy demand within the considered region based on generally available statistical datasets, the energy-modeling tool should be able to analyze different energy transition scenarios toward climate neutrality. For this, a vast database for different renewable-energy-source (RES) technologies with their mass and energy balances, as well as ecologic and economic footprints, has to be available. Furthermore, these technology-specific values should also be available for future energy transition scenarios. Additionally, the energy-modeling tool should be able to consider further implications on the future energy balance of the municipality, such as decrease in the heat demand through renovation or increase in the electricity demand through digitalization or electrification. Finally, each region’s future energy transition scenarios should be based on local RES potentials and national climate policy goals. The results of the future scenarios should be compared with the present base scenario and visualized.

All these thoughts are gathered in the novel holistic energy model called ENECO₂Calc, which is first introduced in this work. ENECO₂Calc is based on the determination of a final energy demand using consistent statistical datasets [46,47] regarding the heat, electricity, and fuel of a selected municipality in Austria. Based on the final energy demand, a holistic, energy-related ecologic and economic footprint can be determined. Furthermore, different energy transition scenarios can be investigated in the municipality’s energy production, distribution, and infrastructure sectors until 2050. The involvement of the emission forecast tool “PROVEM” [48] allows the development of different scenarios regarding energy transition in the mobility sector. Thus, the methodology can be applied to reach the global optimum of an energy system considering the regional specificities, e.g., individual municipalities. Additionally, the decentral potentials of renewable energy technologies from RegioEnergy [49] and calculations based on consistent statistical datasets [46,47] support the development of realistic energy transition scenarios. Moreover, a wide range of different renewable energy technologies is integrated within the energy model. Additionally, ENECO₂Calc enables the integration of RECs and CECs, considering the related benefits of energy communities. The holistic approach of ENECO₂Calc enables the comparison of different energy transition paths regarding ecologic and economic footprints in terms of energy production, distribution, and infrastructure until the year 2050. Furthermore, the visualization of related energy flows within a municipality is possible. Therefore, a holistic economic and ecologic footprint approach can be created, with suggestions for CO₂ taxes and regional subsidies. Finally, the integration of renewable and citizen energy communities according to RED II [5] and the electricity market directive [11] within regions and municipalities is conducted. It offers excellent potential for the energy transition process on the local level.

Energy transition paths regarding climate neutrality in the whole world look different. Every region and municipality is faced with different energy consumption distributions and industry sectors [28]. Furthermore, every region and municipality has different possibilities for establishing RES technologies. Therefore, the need for a regional-based energy-modeling tool for planning regional energy transitions becomes evident to find appropriate energy transition scenarios for every region. This gap can be closed by the development of ENECO₂Calc. In the first development stage, which is the objective of this study, ENECO₂Calc is built to deal with all the municipalities (2095 in total) in Austria [28]. Therefore, in the present stage, the methodology is focused on dealing with consistent statistical datasets from Austrian municipalities. In the future, the methodology can be extended to also deal with statistical datasets from the municipalities of other countries. The holistic approach of ENECO₂Calc is reached through an interdisciplinary approach between the research areas of energy systems and thermodynamics, chemical engineering, and powertrains and automotive technology. Moreover, ENECO₂Calc is user-friendly and expandable, and it can support the regional energy transition process.

First of all, the present paper describes the methodology of ENECO₂Calc and discusses the following sections:

• General purpose and approach of the novel tool;
• Methodology for the calculation of the final energy demands of municipalities;
• Methodology for the distribution of the final energy demands and energy capacities;
• Methodology for the monthly discretization of the final energy demand and supply;
• Ecologic and economic evaluation framework;
• Development framework regarding energy transition scenarios.

Furthermore, ENECO₂Calc is applied to build up energy transition scenarios for St. Margareten im Rosental, a municipality in Carinthia. Therein, the present energy demand and the ecologic and economic footprint of the raised municipality are shown. Additionally, the possible energy transition scenarios and the predictions of the future energy demand, as well as the ecologic and economic footprint, are visualized.

2. Concept and Methodology of ENECO₂Calc

For answering the research questions, the systematic approach of ENECO₂Calc is explained. The methodology ranged from calculating the final energy demand based on consistent statistical datasets to developing energy transition scenarios until 2050.

2.1. General Purpose and Approach of ENECO₂Calc

ENECO₂Calc is the first energy-modeling tool for developing energy transition scenarios for the defossilization of municipalities. In the first evolution stage, which is the objective of this study, ENECO₂Calc is able to develop energy transition paths for all the Austrian municipalities. ENECO₂Calc helps to estimate a municipality’s ecologic and economic footprint after integrating new energy technologies, with a particular focus on RECs and CECs. ENECO₂Calc is an artificial word to express the connection of the ENergetic, ECOlogic, and ECOnomic footprint. In Figure 1, the methodology of ENECO₂Calc is illustrated. ENECO₂Calc delivers, based on consistent statistical datasets for a selected municipality in Austria, the final annual energy demand in terms of space heat, hot water, electricity, and fuels. Afterward, this final annual energy demand is distributed regarding energy technologies and energy capacities. The monthly discretization of energy demand and supply leads to the monthly surplus energy being fed into the Austrian grid, and the monthly deficit energy obtained from the national energy system.

A broad database of emission and cost factors allows the economic and ecologic footprint to be divided into energy production, energy distribution, and infrastructure. The scenario development until 2050 is based on predicted national and regional potentials and energy system targets to reach the energy transition concerning legislative targets within the next three decades. ENECO₂Calc is based on a variety of MS Excel spreadsheets with around 6500 specific and absolute preset values based on studies, as well as around 1000 user-based set values to define the municipality. These parameters can be obtained from consistent statistical datasets, such as Statistik Austria or open-source databases. The modular methodology allows the implementation of statistical datasets and, thus, the extension of ENECO₂Calc to analyze further countries.

2.2. Calculation of Final Energy Demand of Municipalities

The first step within ENECO₂Calc is the calculation of the final energy demand of a selected municipality. Determining the final annual energy demands of municipalities in Austria per year are based on a mixture of bottom-up and top-down approaches [26]. The reasons are different requirements and dependencies in various sectors. In Figure 2, the methodology with the essential input data is illustrated. All the visualized input data are based on consistent statistical datasets or open-source databases to ensure a widely applicable municipality-based energy-modeling tool. For calculating the final energy demand of a selected municipality in Austria, the space heat, hot water, electricity, and fuel demand are calculated. The demands of these energy carriers are determined in the residential building, agriculture and forestry, tourism, industry, public services, and mobility sectors. The space heat demand is calculated in all the mentioned sectors except the mobility sector. The hot water demand is estimated for the residential building, tourism, industry, and public services sectors. The space heat and hot water demands within the industry sector are calculated together as the total heat demand. The electricity demand is determined in all the industries. The fuel demand for tractors is assigned to the agriculture and forestry sector according to Austrian sectoral CO₂e distribution [2]. The fuel demand due to passenger cars, light commercial vehicles (LCV), medium commercial vehicles (MCV), and heavy commercial vehicles (HCV) is considered in the mobility sector. Within the calculation of the fuel demand, the vehicle stock and consumption rates of different technologies are fed from PROVEM [48]. The integration of the institute-owned emission forecast tool PROVEM into ENECO₂Calc enables the determination of vehicle fleets and fuel consumptions in the considered municipality.

The final energy demand in the residential building sector is based on the inhabitant structure, building period, commuter balance, population, and specific energy demands, which could be defined as a classic bottom-up approach. The public services sector is considered similarly to the residential building sector based on a bottom-up approach related to the usable area. Exemptions are hospitals and sewage sludge plants. The bottom-up approach within the sewage treatment sector refers to the population equivalent, the design basis for sewage treatment plants. The use of hospital bed numbers calculates the energy demand for hospitals. A mixed approach calculates the energy demand of the agriculture and forestry sector. Therefore, the heat and electricity demand is proportional to the agricultural and forestry land distribution, animal population, and the number of animal owners for the municipality in the dedicated state. The fuel demand is determined by the land distribution of the municipality and specific fuel demands per agricultural land. The energy demand within the tourism sector is calculated using a bottom-up approach based on tourism building stock and the number of seasonal guest beds, with related specific energy demands. The primary and secondary industry sectors, such as the manufacturing goods or construction sectors, are based on a top-down approach according to different frameworks for the related state. The tertiary industry sectors, such as trading enterprises, are based on a bottom-up approach.

The calculation of the final energy demand within the mobility sector is determined using a top-down approach. Therefore, the determination of the vehicle stock of passenger cars is based on comparing the population of the dedicated federal state and the municipality. The calculation of the vehicle stocks of LCV, MCV, and HCV are based on the number of enterprises. This approach is supported by implementing PROVEM [48], a forecasting tool for emissions within the mobility sector. Therefore, an extensive database on Austrian vehicle stock and consumption numbers is accessible. After the calculation of the final energy demand, a validation of the results is possible. Therein, a comparison of the calculated values with results from EMA [28] or other specific literature could be made.

Another way to feed ENECO₂Calc with final energy demand numbers is to import analysis results from EMA [28], which provides data with similar sectoral distributions for every municipality in Austria.

Summing up, the determination of the final energy demand is only based on consistent statistical datasets, which are structured in the same way for all the municipalities in Austria. Therefore, ENECO₂Calc is able to determine the final energy demand of all 2095 municipalities in Austria in the current development phase. The modular structure of ENECO₂Calc allows an easy adaption of the methodology to deal in the future with municipality-based statistical datasets from other countries.

2.3. Distribution of Final Energy Demand and Energy Capacities

Based on the final annual energy demand, the space heat, hot water, electricity, and fuel demands are determined via a distribution regarding energy supply technologies within the selected region. Therefore, several energy supply technologies are implemented for decentral and central heat and electricity production, as well as several mobility options. In Table 1, an overview of the energy supply technologies and mobility options is given. The assumed energy source mix, technology, plant size, and annual average efficiency factors are listed. Most decentral heat production technologies are based on solid or liquid energy sources converted in a boiler to heat, as shown in [50]. A technology distribution of 50% flat plate and 50% vacuum tube collector for solar thermal systems is assumed. Ambient heat systems are mainly based on air heat according to the Austrian market distribution [51]. Combined heat and power plants, such as fossil fuel or waste-based plants, are supra-regional supply technologies and, therefore, influence the Austrian electricity mix due to feeding into the Austrian grid. Biomass, biogas, or cogeneration plants are considered energy supply technologies for regional application within municipalities. According to the Austrian energy mix, hydropower plants are allocated by 75% run-off river plants and 25% pump storage plants [52]. In ENECO₂Calc, a lot of mobility options for the distribution of the final fuel demand are available. The mobility options are classified as internal combustion engine technologies, gas engines based on compressed natural gas, plug-in hybrid electric vehicles, battery electric vehicles, and fuel-cell electric vehicles. The use of diesel and gasoline is assumed with the Austrian biofuel share. For diesel, a biofuel share of 5.9% FAME [53] and, for gasoline, a biofuel share of 3.1% bioethanol [53] is considered, based on the European production mix. Further mobility options for internal combustion engines are the application of bioethanol or FAME. The related adaption of internal combustion engines for this approach is currently not considered. Further options are the use of biomass-to-liquid (BtL) or power-to-liquid (PtL) biofuels. In addition to fossil natural gas, synthetic natural gas (SNG) options based on biogas or biomass gasification plants are listed for gas engines. Typically, demand distributions for plug-in hybrid electric vehicles, according to [54], are assumed. The Austrian electricity mix or RES technologies, such as wind power, water power, solar PV, and biomass as fuel energy sources, can be considered for battery electric vehicles. Fuel-cell electric vehicles can be powered by biomass-based or electricity-based hydrogen.

2.4. Monthly Discretization of Final Energy Demand and Supply

The monthly discretization of the calculated and distributed final energy demand and supply is the next step within the ENECO₂Calc methodology. Therefore, the heat demand, which represents the sum of hot water and space heat, is discretized according to the monthly reference load profiles used within the Hotmaps dispatch model [36,55]. The electricity demand is discretized according to Austria’s average monthly electricity demands from 2017 to 2020 [56]. The fuel demand and supply are not discretized because of equal distribution over the year. Therefore, it is assumed that a model region’s fuel demand and supply are uniformly distributed over a year. The same assumption is applied for non-volatile energy supply technologies, such as biomass-based cogeneration plants. For the monthly discretization of volatile electricity supply technologies, such as solar, wind, and hydro, monthly distributions [57] are used based on mean values from 1986 to 2016. Therefore, the discretization of the heat demand enables the definition of possible central heat production technologies. The discretization of the electricity demand and supply delivers a deficit or surplus of electricity, which has to be imported from or exported to the Austrian electricity grid.

The energy distribution and discretization enable the visualization of energy flows in and connected with a selected municipality. Therefore, a direct link between the MS-Excel-based ENECO₂Calc and e!Sankey [58], a tool for visualizing energy flows, is used. The connection with ENECO₂Calc allows the visualization of all the energy flows connected to a municipality.

Table 1. Overview of heat and electricity supply technologies and mobility options within ENECO₂Calc.

Energy Supply Technology	Energy Source	Technology Specification	Unit Size	Annual Efficiency * [kWhOutput/kWhIntput]	Literature
Heat Production Technologies
Coal boiler	coal	boiler	<15 kWth (decentral)	80%th	[59,60]
Oil boiler	oil	boiler		85%th	[59,60,61]
Gas-fired boiler	gas	boiler		90%th	[59,60,61]
Electricity heater	electricity (AT mix)	heater		100%th **	[59]
Wood-fired boiler	firewood	boiler		85%th	[53,59,60,61]
Wood pellet boiler	wood pellets	boiler		90%th	[53,59,60,61]
Wood chip boiler	wood chips	boiler		90%th	[53,59,60,61]
Solar thermal system	solar power	50% flat plate 50% vacuum tube collector		50%th	[62]
Ambient heat system	70% air/25% ground/ 5% water heat	heat pump		2.8 (COP)	[51,59,62,63]
Combined heat and power production technologies
Coal-fired power plant	coal	steam power plant	50–500 MWel	48%th/42%el	[35,63,64,65,66]
Oil-fired power plant	heating oil	steam power plant	0.05–1 GWel	45%th/45%el	[35,63,64,65]
Gas-fired power plant	gas	gas turbine plant	0.05–1 GWel	47%th/45%el	[35,63,64,65,66]
Combined-cycle gas power plant	gas	gas turbine and steam power plant	0.05–1 GWel	35%th/60%el	[35,63,64,65,66]
Waste incineration plant	waste	steam power plant	5–30 MWel	67%th/23%el	[67,68]
Biomass power plant	33% wood residue 33% industrial wood residue 33% wood pellets	steam power plant	2–20 MWel	65%th/25%el	[35,63,64,65]
Biogas plant	33% energy crops 33% manure 33% waste	anaerobic digestion plant	0.1–8 MWel	45%th/30%el	[35,63,64,65]
Cogeneration power plant	50% wood pellets 50% wood chips	air gasification and gas engine	0.05–5 MWel	55%th/25%el	[35,63,64,65]
Electricity production technologies
Rooftop PV system	solar power	crystalline silicon solar cells (rooftop)	<15 kWpel (decentral)	18%el	[35,62,63,64,66]
Wind turbine	wind power	onshore	>500 kWel	45%el	[35,62,63,64,66]
Hydropower plant	water power	75% run-off river 25% pump storage	250 kWel– 250 MWel	85%el	[35,62,63,64,66]
Ground-mounted PV system	solar power	crystalline silicon solar cells (ground-mounted)	>500 kWpel (central)	18%el	[35,62,63,64,66]
Mobility options
ICE—Diesel	5.9% FAME (EU mix) 94.1% fossil diesel	internal combustion engine	-	-	[53,69,70,71,72,73,74,75,76,77]
ICE—Biodiesel	FAME (EU mix)	internal combustion engine			[60,69,70,71,72,73,74,75,76,77,78,79]
ICE—FT Diesel (BtL)	wood-based diesel (DFB)	internal combustion engine			[60,70,73,76,78,80,81]
ICE—PtL Diesel	hydrogen (EU mix) and CO2 (EU mix)-based diesel	internal combustion engine			[78,80,81,82]
ICE—Gasoline	3.1% bioethanol (EU mix) 96.9% fossil gasoline	internal combustion engine			[53,70,71,72,73,74,75,76,77]
ICE—Bioethanol	bioethanol (EU mix)	internal combustion engine			[53,70,71,72,73,74,75,76,77,78,79,83]
ICE—FT Gasoline (BtL)	wood-based gasoline (DFB)	internal combustion engine			[60,70,73,76,80,81]
ICE—PtL Gasoline	hydrogen (EU mix) and CO2 (EU mix)-based gasoline	internal combustion engine			[80,81,82]
CNG—Fossil	natural gas	compressed natural gas			[74,76,81,84]
CNG—SNG from Biogas	SNG from biogas plant (AT mix)	compressed natural gas			[63,70,72,73,74,76,78,79,80,81,84]
CNG—SNG from Biomass	SNG from wood (DFB)	compressed natural gas			[60,73,80]
PHEV—Diesel	63% electricity (AT mix) 2.2% FAME (EU mix) 34.8% fossil diesel	plug-in hybrid electric vehicle			[74,76,85]
PHEV—Gasoline	63% electricity (AT mix) 1.1% bioethanol (EU mix) 35.9% fossil gasoline	plug-in-hybrid electric vehicle			[74,76]
BEV—AT mix	electricity (AT mix)	battery electric vehicle			[53,76,77,81,82,85]
BEV—Mix RES technologies	electricity (mix RES)	battery electric vehicle			[53,76,82,85]
FCEV—H2 electrolysis	hydrogen from electricity (mix RES) (electrolysis)	fuel-cell electric vehicle			[70,73,74,76,80,81,82,86,87]
FCEV—H2 from Biomass	hydrogen from wood (DFB)	fuel-cell electric vehicle			[70,73,80,87]

* Assumed parameter for the year 2020 (present)/annual COP for ambient heat system; ** Assumption: no losses due to the conversion from electricity to heat.

Table 1. Overview of heat and electricity supply technologies and mobility options within ENECO₂Calc.

Energy Supply Technology	Energy Source	Technology Specification	Unit Size	Annual Efficiency * [kWhOutput/kWhIntput]	Literature
Heat Production Technologies
Coal boiler	coal	boiler	<15 kWth (decentral)	80%th	[59,60]
Oil boiler	oil	boiler		85%th	[59,60,61]
Gas-fired boiler	gas	boiler		90%th	[59,60,61]
Electricity heater	electricity (AT mix)	heater		100%th **	[59]
Wood-fired boiler	firewood	boiler		85%th	[53,59,60,61]
Wood pellet boiler	wood pellets	boiler		90%th	[53,59,60,61]
Wood chip boiler	wood chips	boiler		90%th	[53,59,60,61]
Solar thermal system	solar power	50% flat plate 50% vacuum tube collector		50%th	[62]
Ambient heat system	70% air/25% ground/ 5% water heat	heat pump		2.8 (COP)	[51,59,62,63]
Combined heat and power production technologies
Coal-fired power plant	coal	steam power plant	50–500 MWel	48%th/42%el	[35,63,64,65,66]
Oil-fired power plant	heating oil	steam power plant	0.05–1 GWel	45%th/45%el	[35,63,64,65]
Gas-fired power plant	gas	gas turbine plant	0.05–1 GWel	47%th/45%el	[35,63,64,65,66]
Combined-cycle gas power plant	gas	gas turbine and steam power plant	0.05–1 GWel	35%th/60%el	[35,63,64,65,66]
Waste incineration plant	waste	steam power plant	5–30 MWel	67%th/23%el	[67,68]
Biomass power plant	33% wood residue 33% industrial wood residue 33% wood pellets	steam power plant	2–20 MWel	65%th/25%el	[35,63,64,65]
Biogas plant	33% energy crops 33% manure 33% waste	anaerobic digestion plant	0.1–8 MWel	45%th/30%el	[35,63,64,65]
Cogeneration power plant	50% wood pellets 50% wood chips	air gasification and gas engine	0.05–5 MWel	55%th/25%el	[35,63,64,65]
Electricity production technologies
Rooftop PV system	solar power	crystalline silicon solar cells (rooftop)	<15 kWpel (decentral)	18%el	[35,62,63,64,66]
Wind turbine	wind power	onshore	>500 kWel	45%el	[35,62,63,64,66]
Hydropower plant	water power	75% run-off river 25% pump storage	250 kWel– 250 MWel	85%el	[35,62,63,64,66]
Ground-mounted PV system	solar power	crystalline silicon solar cells (ground-mounted)	>500 kWpel (central)	18%el	[35,62,63,64,66]
Mobility options
ICE—Diesel	5.9% FAME (EU mix) 94.1% fossil diesel	internal combustion engine	-	-	[53,69,70,71,72,73,74,75,76,77]
ICE—Biodiesel	FAME (EU mix)	internal combustion engine			[60,69,70,71,72,73,74,75,76,77,78,79]
ICE—FT Diesel (BtL)	wood-based diesel (DFB)	internal combustion engine			[60,70,73,76,78,80,81]
ICE—PtL Diesel	hydrogen (EU mix) and CO2 (EU mix)-based diesel	internal combustion engine			[78,80,81,82]
ICE—Gasoline	3.1% bioethanol (EU mix) 96.9% fossil gasoline	internal combustion engine			[53,70,71,72,73,74,75,76,77]
ICE—Bioethanol	bioethanol (EU mix)	internal combustion engine			[53,70,71,72,73,74,75,76,77,78,79,83]
ICE—FT Gasoline (BtL)	wood-based gasoline (DFB)	internal combustion engine			[60,70,73,76,80,81]
ICE—PtL Gasoline	hydrogen (EU mix) and CO2 (EU mix)-based gasoline	internal combustion engine			[80,81,82]
CNG—Fossil	natural gas	compressed natural gas			[74,76,81,84]
CNG—SNG from Biogas	SNG from biogas plant (AT mix)	compressed natural gas			[63,70,72,73,74,76,78,79,80,81,84]
CNG—SNG from Biomass	SNG from wood (DFB)	compressed natural gas			[60,73,80]
PHEV—Diesel	63% electricity (AT mix) 2.2% FAME (EU mix) 34.8% fossil diesel	plug-in hybrid electric vehicle			[74,76,85]
PHEV—Gasoline	63% electricity (AT mix) 1.1% bioethanol (EU mix) 35.9% fossil gasoline	plug-in-hybrid electric vehicle			[74,76]
BEV—AT mix	electricity (AT mix)	battery electric vehicle			[53,76,77,81,82,85]
BEV—Mix RES technologies	electricity (mix RES)	battery electric vehicle			[53,76,82,85]
FCEV—H2 electrolysis	hydrogen from electricity (mix RES) (electrolysis)	fuel-cell electric vehicle			[70,73,74,76,80,81,82,86,87]
FCEV—H2 from Biomass	hydrogen from wood (DFB)	fuel-cell electric vehicle			[70,73,80,87]

* Assumed parameter for the year 2020 (present)/annual COP for ambient heat system; ** Assumption: no losses due to the conversion from electricity to heat.

2.5. Ecologic and Economic Evaluation

For the definition of a municipality’s ecologic footprint, the direct and indirect emissions of the energy production, energy distribution, and infrastructure are calculated. Therein, the direct and indirect emissions due to the heat and electricity supply are considered through emission factors according to

Table 2. Ecologic and economic factors of supply technologies within ENECO₂Calc for the year 2020.

Energy Supply Technologies	Indirect CO2e Emissions * [kg CO2e/kWhoutput]		Direct CO2e Emissions * [kg CO2e/kWhoutput]		Levelized Production Costs * [€/kWhoutput]		Literature
Heat Production Technologies
Coal boiler	0.060		0.415		0.161 ***		[59,60,63,90]
Oil boiler	0.061		0.279		0.157		[53,59,60,63,91,92]
Gas-fired boiler	0.077		0.215		0.153		[53,59,60,63,93,94]
Electricity heater	0.258				0.226		[53,93]
Wood-fired boiler	0.016		0.007		0.129		[53,59,60,63,95,96]
Wood pellet boiler	0.030		0.007		0.147		[53,59,60,63,95,97]
Wood chip boiler	0.016		0.007		0.123		[53,59,60,63,95,96]
Solar thermal system	0.020		0.000		0.134		[53,59,63,94]
Ambient heat system	0.112		0.000		0.217		[63,65,94,97]
Combined heat and power production technologies
Output type	Heat	Elec.	Heat	Elec.	Heat	Elec.
Coal-fired power plant	0.045	0.089	0.307	0.613	0.060	0.073	[63,65,66,90,94]
Oil-fired power plant	0.066	0.132	0.268	0.536	-	0.237	[53,63,91,94]
Gas-fired power plant	0.053	0.107	0.150	0.301	0.042	0.110	[35,53,63,65,66,94]
Combined-cycle gas power plant	0.046	0.091	0.129	0.258	0.042	0.076	[35,53,60,63,66]
Waste incineration plant	0.153 (heat)/0.306 (elec.) **				0.041	0.082	[65,68,94]
Biomass power plant	0.018	0.036	0.005	0.011	0.053	0.107	[35,53,59,63,94]
Biogas plant	0.069	0.139	0.049	0.098	0.058	0.138	[35,53,62,63,66,98]
Cogeneration power plant	0.019	0.038	0.005	0.010	0.084	0.159	[77]
Electricity production technologies
Rooftop PV system	0.062		0.000		0.106		[35,53,63,66,92,93]
Wind turbine	0.010		0.000		0.063		[35,53,63,66,93]
Hydropower plant	0.007		0.000		0.056		[35,53,63]
Ground-mounted PV system	0.062		0.000		0.077		[35,53,63,66]
Fuel production technologies/Mobility options
ICE—Diesel	0.054		0.289		0.058		[60,73,81,89]
ICE—Biodiesel	0.054		0.159		0.072		[60,70,73,80,89]
ICE—FT Diesel (BtL)	0.054		0.052		0.117		[60,73,80,89]
ICE—PtL Diesel	0.054		0.123		0.410		[81,82]
ICE—Gasoline	0.047		0.337		0.059		[60,81,89]
ICE—Bioethanol	0.047		0.256		0.074		[60,70,73,80,89]
ICE—FT Gasoline (BtL)	0.047		0.052		0.117		[60,73,80,89]
ICE—PtL Gasoline	0.047		0.106		0.410		[81,82]
CNG—Fossil	0.044		0.254		0.032		[81,89,93]
CNG—SNG from Biogas	0.044		0.062		0.080		[60,70,73,89,93]
CNG—SNG from Biomass	0.044		0.045		0.072		[60,73,89,93]
PHEV—Diesel	0.110		0.279		0.066		[54,60,73,77,81,89,93]
PHEV—Gasoline	0.101		0.313		0.066		[54,60,77,81,89,93]
BEV—AT mix	0.274		0.327		0.070		[53,77,89,93]
BEV—Mix RES technologies	0.274		0.020		0.100		[35,53,66,89,93]
FCEV—H2 electrolysis	0.144		0.050		0.125		[73,87,89,99]
FCEV—H2 from Biomass	0.144		0.046		0.082		[73,87,89,99]

* Assumed parameter for the year 2020 (present); ** emissions not separated into indirect and direct emissions; *** investment and O&M costs assumed to be equal to wood-fired boiler.

. The implemented emission factors are mean values from different sources, as listed in Table 2. The same applies to the economic evaluation based on levelized production cost factors. All the economic factors were scaled to 2020 by applying the Austrian consumer price index (CPI) [88]. Emissions due to operation are considered within the direct emissions. Indirect emissions are linked to the preceding chain, such as preparation and transport of fuel, plant manufacturing, and auxiliary electrical energy [63]. The ecologic footprint of the mobility sector is determined by applying ecologic factors according to Table 2. The lifecycle approach to vehicles is implemented to generate the ecological footprint of the mobility options. Therefore, the direct emissions of the mobility sector contain the direct and indirect emissions of the fuel production process and the direct emissions within the conversion of fuel or energy in vehicles. The indirect fuel emissions are linked to the emissions of the vehicle-manufacturing process [89]. Therefore, within the ecologic footprint, all greenhouse gas emissions due to energy production are considered. By applying grid loss factors for heat and electricity, the energy distribution process considers the ecologic footprint. The emissions due to the construction of district heating systems, grids, filling stations, and the renovation of buildings are currently not considered.

For the determination of the economic footprint of a municipality, fuel, operation and maintenance (O&M), and investment costs are considered in terms of energy production, energy distribution, and infrastructure. The energy production costs for energy supply within the model region are calculated for the heat, electricity, and fuel supply technologies by applying the levelized production cost factors from Table 2. These levelized production costs are split into investment, O&M, and fuel costs.

ENECO₂Calc represents the first municipality-based modeling tool that enables the simultaneous determination of economic and ecologic footprints exclusively based on statistical datasets. In the current development phase, ENECO₂Calc is built to deal with all Austrian municipalities. Therefore, the ecologic and economic factors are based on Austrian sources. In the future, the tool could be extended to analyze further countries.

The levelized energy production costs are raised by costs for the monthly-based deficit electricity. The Austrian electricity mix covers the deficit electricity by applying energy price mean values from 2020 [100]. Further natural gas costs are calculated, similar to the costs for the Austrian electricity mix, by applying energy price mean values from 2020 [101].

The energy distribution costs include the transportation or grid costs for the provision of heat, electricity, and fuels except for the transportation costs of liquid fuels to filling stations, which are already covered by the levelized fuel production costs. The hydrogen distribution costs to filling stations are added by applying a fixed share compared to the total provision cost rate according to [86]. The distribution costs for central district heat systems are calculated by assuming a total length of the district heating system under the consideration of an investment cost factor [102]. Other costs for the district heating system, such as consumer stations, are considered by applying a fixed cost ratio between the grid investment costs and the total costs for the district heating system. Distribution costs for electricity and gas are calculated by applying grid cost factors according to [100,101]. The assumption is that only 50% of the electricity grid costs are offset for RECs. This is based on the claims of several experts in this field [9,10] regarding cost-orientated grid charges. The reduced cost factor of 50% grid costs for RECs is the result of comparing grid cost factors within the different grid levels in Austria. Therefore, the grid costs for level 5 to level 7, which are for low- and medium-voltage grid systems, are about half of the grid charges of the superordinate levels [103].

For the infrastructure costs, different sources are applied. The filling station costs are based on [85] and [104]. The vehicle costs for passenger and commercial vehicles (LCV, MCV, and HCV) are based on consumer prices without taxes according to [53]. The costs for conventional diesel-powered tractors were determined by comparing manufacture sale prices. The costs of alternative-powered tractors are assumed to be proportional to HCV prices. Further infrastructure costs are considered due to the renovation of buildings in the heating sector. For this, cost factors per square meter usable area of buildings [59] are implemented.

Additionally, revenues and ecological taxes are considered for the calculation of the economic footprint of a municipality. Revenues are considered due to the month-based surplus electricity that is exported to the Austrian electricity grid [105]. Therein, market value factors according to [35] are implemented to consider the fluctuating seasonal electricity market conditions. Finally, ecological taxes, such as CO₂ emission certificate costs for the production of the Austrian electricity mix [106] and mineral oil tax for diesel, gasoline, and heating oil [91,107], as well as coal tax [90] for energy production, are considered.

Several assumptions are made for the economic and ecologic footprint prediction until the year 2050, which is the basis for building future scenarios (see Section 2.6). To determine future direct emission factors and indirect preceding chain factors within the years 2030, 2040, and 2050, an efficiency increase of 0.35%/year is assumed for renewable-based heat and electricity supply technologies [92], which results in lower emission factors. For the indirect emissions, due to electrical auxiliary energy use, the factors are based on the electricity mix of the model region within the year. For fossil-based energy technologies, no efficiency increase was assumed. Predicting future direct and indirect emission factors within the mobility sector is based on [89]. Investment, O&M, and fuel cost parameter assumptions are the basis of the implemented economic factors. The investment costs are scaled to 2050 by applying technological learning rates [65,108,109,110]. The O&M costs until the year 2050 are predicted by applying the CPI [88]. For this assumption, the yearly mean CPI value in 1999–2019 was extrapolated to 2050. Further, for renewable-based energy supply technologies, the prediction of the O&M costs relies on the mean CPI value reduced by the above-mentioned yearly efficiency increase of 0.35%. The applied scaling method for the fuel costs is very similar to the scaling of the O&M costs. Instead of the CPI, the energy price index (EPI) [97] is used. The cost factors for the distribution costs are scaled by applying the CPI. The building renovation cost factors are scaled according to learning rates from [98]. Finally, energy supply technologies’ annual future investment costs are calculated by the total investment costs for the analyzed period, divided by the technology lifetime [35,59,102]. The lifetime factors of vehicle stock are implemented according to [99]. The predictions for other infrastructure investment cost factors, such as vehicles or filling stations, are based on studies from the literature.

Finally, the ecologic footprint can be validated with specific literature according to the climate model regions [47] or EMA [28] within ENECO₂Calc. For the validation of the economic footprint, no database on the municipality level is currently available.

2.6. Scenario Development Regarding Energy Transition

Using the knowledge of the current energy carrier distribution and the methodology explained beforehand, it is possible to determine the overall energy demand and the economic and ecologic footprint of a specific Austrian municipality of interest for 2020 using ENECO₂Calc. Based on the 2020 data, different scenarios for possible energy transitions until 2050 for a given municipality could be investigated, whereby three major frameworks are considered for the scenario development. First and foremost, the European and national climate policy goals needed to be reached. Second, realizable potentials of RECs within the municipality and CECs regarding biomass-based technologies are considered. Third, future heat, electricity, and fuel demands until 2050 are predicted.

With the European Green Deal [22] in combination with the European Climate Law [23], the EU has set a legal framework to reach climate neutrality by 2050. To meet these climate ambitions, the European Commission recently adopted a package of comprehensive and interconnected proposals for the next decade called “Fit for 55” [111] to ensure the necessary acceleration and to tackle a reduction of greenhouse gas emissions by at least 55% by 2030 compared to the year 1990. Furthermore, the share of renewable energy carriers in the EU should reach 32% by 2030 [5]. Austria has even more vital ambitions and wants to reach climate neutrality by 2040 [112], with a share of renewable energy carries between 46% and 50% by 2030 [113].

Raising the share of RECs based on biomass and other renewable technologies within municipalities can help reach the set targets. Therefore, in this work, potentials for RECs and CECs were defined as reduced technical potentials [114,115] and comprised today´s possible proceeds of current “state-of-the-art” energy conversion technologies by considering the possible competition for land between energetic and non-energetic forms of use, as well as competition between different forms of renewable energy technologies. Moreover, and especially crucial in terms of biomass-based energy carriers from forestry biomass, a reduced technical potential in this work always implied sustainable forest management. Different works have investigated the potential to substitute fossil gas and fuels over the coming years by converting biomass from woody, agricultural, and other biogenic residues, as well as waste, into synthetic natural gas (SNG), synthetic Fischer–Tropsch (FT) fuels, or hydrogen (H₂) [73,116,117,118,119]. Based on these literature studies, Figure 3 shows a comparison between the Austrian annual energy demand of 2020 and biomass-based RES potential for 2050. The potential of biomass-to-gas (BtG) in 2050 to produce SNG could come close to one-half of the annual gas demand in 2020 if SNG were produced from the biomass potential. BtG in terms of hydrogen (H₂) and biomass-to-liquid (BtL) to produce Fischer–Tropsch fuels had a potential share in 2050 of nearly one-third of the 2020 annual energy demand in gasoline and diesel if H₂ or FT fuels would be produced from the biomass potential.

Furthermore, for the definition of potentials of RES technologies within the selected Austrian municipality, average reduced technical potential data from REGIO Energy [49,115] were considered for solar PV, solar thermal heat, wind power, and heat pump technologies. They were scaled down from the district level to the municipality level. Despite REGIO Energy being a study from 2010 defining potentials for all Austrian municipalities, it was assumed that potentials concerning the mentioned RES technologies would not change significantly up to 2050. According to [115], the most hydropower potential was based on revitalizing existing hydropower plants. For the defossilization of the Austrian electricity mix, a hydropower potential of 5 TWh was predicted [115]. This potential was about 10% of the existing hydropower supply capacity. Therefore, a hydropower potential of 10% for the selected municipality compared to the existing hydropower plant capacity in the region was foreseen. The biomass-based RES potentials results from our own investigations. The potential of biomass within the municipality could be categorized into two groups: forestry and agriculture biomass.

For the forestry biomass, two different potentials were defined. These were the potential of unused forest area in 2020 and unused growth within harvested forest area in 2020. Just a share of the overall forest area of a municipality was considered as the harvested forest area. Depending on the municipality, these shares could vary significantly for different reasons, for example, challenging topographies in mountainous regions, making harvest technically or economically unreasonable. By comparing data for the share of the harvested forest area for the selected municipality with those of directly adjacent municipalities with similar topographies [46], forestry biomass potentials for the selected municipality could be raised. Based on data from [120], the potential of unused forest growth was defined as the difference between the annual growth within the harvested forest area and the current annual harvested amount within the forest. It was assumed that sustainable forest management could be fulfilled when, on average, the annual harvest did not overcome the annual growth, which meant that the forestry mass would not change over the years. Based on these potentials, it was possible to define the potential surplus amount of wood from harvested forests that could be generated for 2020. Furthermore, for the wood potential in 2050, the following scenarios were considered. According to [73], for the last 30 years, the share of hardwood has increased, while the share of softwood has constantly decreased within Austrian forests and the species being harvested. Hardwood is mainly used as energy wood, while softwood is primarily used in the sawmill and general industries [73]. This trend should lead to an increase of-energy wood in the coming years and is being pursued until 2050. Wheat straw and corn cobs as agriculture residue, as well as miscanthus and short-rotation wood (e.g., willow and poplar), as energy plants have been investigated for their agriculturally potentials. For the potential in 2020, data from [46] for cultivation areas of the investigated species were considered. For the potential in 2050, different scenarios were applied. Average change rates from [121,122] for wheat and corn cultivation areas in 2010 to 2020 being pursued before 2050 were assumed for corresponding areas. Furthermore, change rates for the general agricultural area in the municipality until 2050 and potential shares of miscanthus and short-rotation wood in 2050 according to scenario A (base scenario) in [60] were applied. Based on the corresponding areas of different species, annual harvest yields for wheat straw according to [123,124], as well as miscanthus, short-rotation wood, and corn cobs according to [125], could be calculated. To maintain sustainable use of wheat straw, only one-third was considered for energy conversion, according to [121]. For corn cobs, miscanthus, and short-rotation wood, no restrictions had to be considered.

The following methodology was implemented to predict the future heat, electricity, and fuel demands for 2050 in the analyzed Austrian municipality. Based on [59], it was possible to predict the future energy demand for space heating and warm water supply based on the assumed renovation rate for the housing sector. The Austrian annual change rate from the last 20 years [122] was pursued until 2050 and applied to the municipality for the future electricity demand. Regarding the future fuel demand, it was assumed that the number of vehicles would not change in the given Austrian municipality until 2050, although efficiency enhancement was considered.

After developing scenarios, the energy flows for selected scenarios can be visualized within an energy flow diagram. Furthermore, a sensitivity analysis can be developed and visualized by adjusting ecologic and economic factors.

With regard to the present energy crisis related to the COVID-19 pandemic [7,8] and other disruptive events, it is essential to investigate possible negative and positive effects related to the economic and ecologic key parameters. The applied data are mostly from 2019 and 2020. Consequently, the effects of COVID-19 are considered depending on available data for the status quo of 2020.

Comparing the different scenarios with the status quo enables the recommendation of an energy transition path for the considered municipality regarding the regional conditions and frameworks. As a result, recommendations for applicable CO₂ taxes and subsidies could be made.

Summing up, ENECO₂Calc is a municipality-based energy-modeling tool, which is exclusively based on statistical datasets and open-source databases. ENECO₂Calc is the first municipality-based energy-modeling tool that allows the combined determination of the final energy demand, CO₂ footprint, and energy costs for all Austrian municipalities. The novel implementation of the mobility emission forecast tool “PROVEM” enables the determination of vehicle fleets and fuel consumption on the municipality level. Furthermore, the integration of PROVEM facilitates the consideration of a vast amount of mobility options and the development of realistic energy transition scenarios by 2050 due to modeling the future Austrian vehicle fleet. Thus, ENECO₂Calc delivers a novel approach to investigate municipality-based energy transition scenarios to support municipalities toward the way to climate neutrality.

3. Results and Discussion

In the first evolution stage, which is the objective of this study, ENECO₂Calc is able to develop energy transition paths for all Austrian municipalities. To show the extensive possibilities, ENECO₂Calc was applied to develop several transition paths of a selected municipality in the following chapter. For that, St. Margareten im Rosental, located within Carinthia near Klagenfurt in Austria, was chosen. The municipality area is about 43.98 km² and is characterized mainly by forest area. At the beginning of 2020, 1084 inhabitants [46] were living in St. Margareten im Rosental. The industry sector consisted primarily of tertiary service enterprises, such as gastronomy and other service-orientated SMEs. For the primary and secondary industry sectors, only the mechanical engineering sector was relevant [77].

Subsequently, the present final energy demand results and the investigated scenarios for the year 2050 developed with ENECO₂Calc are shown. The scenarios show possible ways to reach climate neutrality within St. Margareten im Rosental within the next three decades.

3.1. Present Annual Energy Demand and Ecologic and Economic Footprint

The determination of the current final annual energy demand was the first step in the ENECO₂Calc methodology, as mentioned in Section 2.2. In Figure 4, the final annual energy demand of St. Margareten im Rosental divided into defined sectors is shown. In the considered municipality, the final energy demand within the year 2020 was about 22.7 GWh. Figure 4 shows that the residential building sector with space heat demand and the mobility sector with fuel demand are the biggest energy consumers. The industry and residential building sectors mainly determined the electricity demand. In addition, the fuel and space heat demands within the agriculture and forestry sectors are relevant. The validation of the calculated final energy demand results within ENECO₂Calc can be seen in

Table 3. Validation of present annual energy demand and CO₂e emissions of St. Margareten im Rosental for the year 2020 with the literature data.

Validation of Results	ENECO2Calc	Austrian Heat Map [126]	EMA Austria [28]	Carnica Rosental Study *** [127]
Final heat demand [GWhth/a]	11.69	10.75 (−8%) *	11.21 (−4%) *	11.70 (+0.1%) *
Final electricity demand [GWhel/a]	3.63	-	4.23 (+17%) *	3.92 (+8%) *
Final fuel demand [GWh/a]	7.34	-	9.46 (+29%) *	5.45 ** (−26%) *
Total CO2e emissions [M kg CO2e/a]	5.12	-	5.71 (+11%) *	4.62 (−10%) *

* Compared to ENECO₂Calc result; ** without freight traffic; *** heat demand scaled by the total floor area of buildings/electricity and fuel demand scaled by inhabitants.

. The comparison of the calculations with the literature results showed a deviation of no more than ±29%.

In Figure 5, the energy flow diagram of St. Margareten im Rosental regarding the current annual energy demand is visualized. Therein, all the energy flows that are connected with the municipality are shown. The primary energy sources, listed as sources from the municipality, the state (Austria), or abroad, can be seen on the left. The energy conversion processes within abroad, Austria, and municipality categories are listed from left to right after the primary energy sources. The final energy demands distributed to the residential building, agriculture and forestry, tourism, industry, public services, and mobility sectors are visualized on the right side. The visualization of the energy flows associated with the municipality showed that, within the year 2020, around 30% of the Austrian electricity mix, which was imported into the municipality, was based on imports from abroad. Therefore, significant amounts of oil- and coal-based electricity were imported. The decentral heat demand was covered mainly through biomass-based and oil-based heating boilers. Furthermore, a small district heating grid driven by a cogeneration unit was in use. Other decentral heat generation technologies within the municipality were heat pump systems and electric heaters. The present heat and electricity demand distribution of 2020 is also shown in Figure 7 (reference case in the year 2020–2020 RF). The electricity demand within the present reference case was mainly based on imports from the Austrian electricity mix. In addition to the cogeneration unit, there were also some decentral rooftop PV systems and small hydropower plants. The current fuel demand was based three-quarters on diesel and one-quarter on gasoline. The biogenic shares of biodiesel within diesel and bioethanol within gasoline were based on the Austrian mix, with about 5.9% biodiesel and 3.1% bioethanol. Figure 8 (2020 RF) also visualizes the current fuel demand.

The annual CO₂e emissions and energy costs were calculated based on the previously described final energy demand. The present CO₂e emissions distributed to the heat, electricity, and fuel sectors are visualized in Figure 9 (2020 RF). The total annual CO₂e emissions of St. Margareten im Rosental were about 5.12 M kg CO₂e/a and can be seen in Figure 11 (2020 RF). Therein, it is highlighted that the most direct CO₂e emissions were caused by diesel and gasoline consumption in the mobility sector and electricity and oil consumption in the heating sector. Furthermore, electricity consumption was responsible for 15% of the total CO₂e emissions. The indirect emissions within the fuel sector, which were around 7% of the total emissions, were based on the manufacturing of vehicles. Additionally, the indirect emissions for transport and the extraction of heating oil and biomass, as well as indirect emissions caused by the Austrian electricity mix, were relevant for the ecologic footprint of the municipality.

The CO₂e emissions within the present reference case were also validated with the literature data in Table 3. Therein, it can be concluded that the ENECO₂Calc results were close to the literature data, with a deviation of ±11%. The calculated annual energy costs for St. Margareten are visualized sectorally distributed in Figure 10, and a total perspective is given in Figure 11 (2020 RF). The sectorally distributed annual energy costs in Figure 10 show that the most annual energy costs were based on fuel and O&M costs in the heating sector. Additionally, fuel costs and taxes of the mobility sector were relevant. The results of the energy costs could not be validated because of a lack of data from the literature.

3.2. Scenario Development for St. Margareten im Rosental in 2050

Subsequent energy transition scenarios for St. Margareten im Rosental were investigated and compared with the reference case. Different frameworks had to be considered to develop a broad range of scenarios, as mentioned in Section 2.6. These frameworks were the national climate targets, central and decentral potentials for RES technologies, and possible prediction methodologies regarding the municipality’s heat, electricity, and fuel demands. The heat, electricity, and fuel demand predictions for all the developed scenarios were applied. Therefore, the final heat demand was decreased by around 27% by 2050 compared to 2020, which assumed an ambitious renovation rate of 3% [59]. The final balanced annual electricity demand increased in all the scenarios by 31%, according to the trend of the past two decades of the total energy balance of Austria [122]. The fuel demand predictions were based on the assumption that the number of vehicles remains constant within the next three decades in the municipality. Therefore, the fuel demand decreased depending on the mobility option mix due to efficiency improvements. In addition to reaching the climate targets, which meant no fossil energy consumption by 2050, the explained central biomass potentials (see Section 2.6) and decentral RES technology potentials were the framework for the scenario development. The central biomass potentials showed several promising options to use biomass for the central production of synthetic natural gas, synthetic biofuels (FT fuels), or hydrogen. The decentral RES potentials for St. Margareten im Rosental are visualized in Figure 6. The reduced technical-biomass-based potential combined with forestry and agriculture potentials used in biomass CHP plants are plotted as a solid line. The dashed line shows the additional potential if it was assumed that the 2020 used wood in heating boilers in St. Margareten im Rosental could also be used in biomass CHP plants. According to REGIO Energy [49,114], solar thermal energy had the most significant potential and could cover nearly three times the 2020 thermal energy demand. The reduced technical potential of biomass CHP plants could cover up to half of the 2020 heat demand. Using the additional biomass potential, substituting existing biomass-based heating boilers could increase this share to over 80%. Solar PV showed the biggest electricity potential in 2050 and could reach over three times the electricity demand in 2020. The reduced technical potential of biomass CHP plants reached one-third of the electricity demand. The overall electricity demand could be covered entirely by using the additional wood from household heating boilers in biomass CHP plants. St. Margareten im Rosental already has a hydropower plant, and it was assumed that revitalization could increase the efficiency by 10%. Nevertheless, hydropower potential showed a small share of the overall electricity demand. According to [127], St. Margareten im Rosental show no development potential for wind energy.

Based on the central Austrian biomass and decentral RES potentials of St. Margareten im Rosental, different scenarios were investigated to substitute fossil energy carriers with renewables. In summary, the greatest potential within the municipality could be provided with solar PV, biomass-based technologies, and heat pumps. In the total Austrian view, there were also huge biomass potentials to substitute significant amounts of natural gas with SNG or diesel and gasoline with synthetic biomass-based fuels or hydrogen. These considerations led to the assumed scenarios for the energy transition process within St. Margareten im Rosental by 2050 shown in

Table 4. Overview of investigated scenarios for St. Margareten for the year 2050.

Development of Energy Transition Scenarios		Scenario 0 (Business-as-Usual—BAU)	Scenario 1 (Biomass-to-Mobility—BtM)	Scenario 2 (Biomass to Combined Heat and Power Plants—BtCHP)	Scenario 3 (Biomass-to-Gas—BtG)
Heating sector		no changes in energy distribution compared to the reference case (2020 RF)	coal and oil boilers were substituted by solar thermal collectors and heat pumps	thermal decentral fossil- and renewable-based boilers and electrical heaters were substituted by the expansion of the existing district heating system driven by several cogeneration units	thermal decentral fossil- and renewable-based boilers were substituted by solar thermal collectors and heat pumps
Electricity sector			AT mix was substituted mainly through solar PV and small amounts of hydropower due to the revitalization	AT mix was substituted uniformly by cogeneration units and solar PV	AT mix was defossilized by the use of synthetic natural gas instead of fossil natural gas in central gas power plants
Fuel sector *	focus scenario:		strong focus on synthetic fuels	strong focus on BEV	strong focus on FCEV
	cars and LCV:		ICE (54%) + BEV (36%) + PHEV (6%) + FCEV (4%)	ICE (23%) + BEV (61%) + PHEV (2%) + FCEV (14%)	ICE (33.5%) + BEV (36%) + PHEV (2.5%) + FCEV (28%)
	MCV:		ICE-D (100%)	BEV (100%)	FCEV (100%)
	HCV:		ICE-D (100%)	ICE-D (40%) + FCEV (60%)	FCEV (100%)
	tractors:		ICE-D (95%) + BEV (5%)	ICE-D (52%) + BEV (14%) + FCEV (34%)	ICE-D (18%) + BEV (21%) + FCEV (61%)
	ICE fuel:		BtL (33.5%) + PtL (66.5%)	PtL (100%)	PtL (100%)
	hydrogen:		H2 electrolysis (100%)	H2 electrolysis (100%)	H2 from Biomass (33.5%) + H2 electrolysis (66.5%)

* Scenarios are based on mobility distribution scenarios according to [48] (cars and LCV) and [54] (MCV and HCV); for tractors, our own estimations following the distribution of HCV according to [54] are implemented.

. Scenario 0 represents the business-as-usual (BAU) scenario. Therein, the same energy distribution from the present reference case (2020 RF) was assumed. Scenario 1 is called biomass-to-mobility (BtM). Therein, the biomass was used centrally to produce synthetic biofuels. As a consequence, a lot of ICE vehicles were still in the vehicle fleet. Furthermore, the heating sector was defossilized by solar thermal collectors and heat pumps and the electricity sector mainly by the expansion of solar PV. Scenario 2 is called biomass to combined heat and power plants (BtCHP), which meant that the biomass was used in the municipality to produce heat and electricity within cogeneration units. Therefore, the heating sector was defossilized by the construction of a huge district heating system. The electricity sector was supplied half through the electricity from cogeneration units and half through solar PV plants. Furthermore, a strong focus on electrification, especially BEV, was placed within this scenario. Scenario 3 is called biomass-to-gas (BtG), which aimed to defossilize the Austrian electricity mix by substituting natural gas with biomass-based SNG within gas-fired power plants. Furthermore, the biomass was also used for the central production of hydrogen. Therefore, within scenario 3, there was a strong focus on FCEV. The heating sector was defossilized by solar thermal collectors and heat pumps.

In summary, the developed scenarios represent the possible development paths of the energy system. Therefore, on the one hand, the defossilization of the energy system could be reached by a focus on decentralized energy production enabled through local initiatives by citizens, communities, and businesses, supported by authorities to increase energy autonomy. On the other hand, the defossilization of the energy system could be based on the centralized production of synthetic biofuels, natural gas, hydrogen, and renewable electricity to strengthen the national and global economies. The considered scenario frameworks related to the possible trends regarding distributed or global energy production are underpinned by the Ten-Year Network Development Plan from the European Network of Transmission System Operators for Gas and Electricity [128].

3.3. Prediction Energy Demand and Ecologic and Economic Footprint in 2050

The scenarios in Section 3.2 were the basis for predicting the energy demand, as well as ecologic and economic footprint, in 2050. First of all, the results regarding heat and electricity demand compared with the present reference case are shown in Figure 7. The comparison of heat demand showed that the assumed building renovation rate of 3% decreased the final annual heat demand by around one-third. Furthermore, the heat distribution of the BtCHP scenario was dominated by cogeneration units and the BtG scenario by ambient heat systems. Within the BtM scenario, only the fossil oil and coal boilers were substituted with solar thermal collectors and heat pumps. The final annual electricity demand distribution, which included the electricity demands in the heating and mobility sectors, showed that the electricity demand increased within all the scenarios due to the underlying assumptions (see Section 3.2). Therefore, comparing the BAU scenario with the present reference case (RF) visualized the assumed increase according to the electricity demand. Based on the assumption that all the monthly deficit energy had to be covered by the AT electricity mix, the total final annual electricity demand was highest within the BtM scenario, according to the highest share of volatile energy technologies. Within the BtG scenario, the high share of ambient heat systems led to increased electricity demand due to the high amounts of auxiliary electric energy. Within the BtCHP scenario, only the low share of heat pumps led to a slight increase in the final electricity demand.

In Figure 8, the comparison of the annual final fuel demand of the present reference case (2020 RF) with alternative future scenarios can be seen. Due to the assumption that the number of vehicles was constant over the next 30 years and an assumed efficiency increase in the mobility sector, the final fuel demand decreased in all the alternative scenarios compared to the reference case. By comparing the final fuel demand within BAU and RF, the assumed efficiency increase for ICE could be seen. Within the scenario of BtCHP, with the highest electrification share in the mobility sector, the final fuel demand was the lowest due to the high efficiency of the vehicle fleet. The final fuel demand in the BtM scenario was higher than in the other scenarios because of the high share of ICE. The BtG final fuel demand was also deficient because of the high efficiency of fuel-cell vehicles in this scenario.

The results regarding annual CO₂e emissions are visualized in Figure 9. Therein, it can be seen that the substitution of fossil fuels and electric heaters in the heating sector led to a huge reduction in CO₂e emissions. The CO₂e emissions in the heating sector of the BtM scenario were slightly higher than in the other alternative scenarios because of the electrical-based indirect emissions due to solar PV, which could also be seen in the electricity sector. In the BtG scenario, there were very low direct emissions in the heating sector because of the substitution of all thermal heating boilers. The electricity CO₂e emissions within the BtG scenario were the lowest because of the defossilization within the Austrian electricity mix through the use of biomass-based SNG in existing centralized gas-fired power plants instead of fossil natural gas. The substitution of fossil fuels with alternative technologies or synthetic biofuels showed the highest potential concerning CO₂e emission savings in the fuel sector. By comparing the indirect emissions of the alternative scenarios regarding fuel, it could be concluded that the higher indirect emissions of BEV and FCEV were balanced through the high efficiencies of the mobility systems. Therefore, the CO₂e emissions in the BtM scenario focused on synthetic biofuels caused more direct emissions due to the higher CO₂e footprint of PtL fuels than renewable-based electricity in BEV or H₂ in FCEV.

In Figure 10, the comparison of annual energy costs for the reference case and within the assumed scenarios is shown. In the heating sector, the renovation costs (infrastructure and investment costs) were much lower than the savings due to less heat demand. The heating costs in the BtCHP scenario were the lowest because of the central production of heat. The investment costs due to the expansion of the district heating grid (energy distribution and investment costs) were lower than the savings due to cheaper heat production costs. The heating costs within the BtG scenario were the highest because of the high O&M costs for heat pumps. The annual costs for the electricity sector were lower than the costs for the fuel and heating sectors. Within the BtM and BtCHP scenarios, the revenues due to surplus energy overtook the tax costs. The BtM scenario with huge amounts of solar PV cells had the lowest electricity costs because of the marginal fuel costs. The electricity costs in the BtCHP scenario were slightly higher than the others due to cogeneration units’ higher electricity production costs than solar PV. The BtG scenario regarded electricity costs in the range of the BtCHP scenario. The defossilization of the Austrian electricity mix increased the energy costs for electricity imports in the municipality. In the fuel sector, it was evident that the annual investment costs for vehicles overtook the fuel costs. The comparison of the scenarios showed that the higher costs of FCEV and BEV compared to ICE were meaningful. The lower fuel costs in the BtCHP scenario due to the high electrification rate balanced the higher investment costs of BEV. Compared with the BAU scenario, the higher fuel costs of synthetic biofuels in the BtM scenario could be balanced due to the reduced mineral oil tax exemption.

The total annual CO₂e emissions and energy costs can be seen in Figure 11. It can be seen that the lowest CO₂e emissions were reached in the BtG scenario due to the low electricity emissions. Therein, the total CO₂e emissions could be reduced by around 85% in comparison to the BAU scenario. The total energy costs were nearly the same in the BtM and BtCHP scenarios. By comparison, the higher electricity costs in the BtCHP scenario were balanced by the lower heat costs. The total energy costs within these two scenarios were about 12% lower than those within the BAU scenario. The reason was the much lower heat costs due to the ambitious renovation rate. The BtG scenario was in the BAU scenario range due to the fuel sector’s higher costs with a focus on FCEV. The reference case, which represented the status quo of 2020, caused more emissions than the BAU scenario. However, the energy costs were much lower due to the missing investment costs. The comparability is to be discussed.

It can be concluded that the fuel sector in St. Margareten im Rosental, due to the high number of vehicles, especially tractors, had the most impact on the total CO₂e emissions and energy costs. The fuel sector’s lowest emissions were calculated by applying a high share of FCEV (BtG scenario). BEV vehicles (BtCHP scenario) caused more indirect emissions than FCEV. ICE vehicles (BtM scenario) caused more direct emissions due to the conversion of synthetic fuels than BEV or FCEV. The total energy costs in the fuel sector were lowest within the ICE-based (BtM) and BEV-based (BtCHP) scenarios because of the lower investment costs in comparison to FCEV (BtG). The greatest CO₂e emission reduction in the electricity sector could be reached by the defossilization of the Austrian electricity mix by substituting natural gas with biomass-based synthetic natural gas within the BtG scenario. The decentral defossilization of the municipality electricity mix could be reached by expanding solar PV (BtM) or biomass-based cogeneration plants (BtCHP). Despite higher electricity demand, the electricity costs were lowest within the solar PV (BtM) scenario. The heat costs were lowest when implementing central heat production with cogeneration plants within the BtCHP scenario. The BtG scenario, which was based on heat pumps, was the most expensive one, but the emissions were lowest because of the elimination of direct emissions.

Therefore, through a comparison of the total emissions and energy costs within the different scenarios, it could be recommended that, within the fuel sector, a mix of high electrification due to BEV in passenger transport and synthetic biofuels in freight transport and agriculture, could be a good compromise between energy costs and CO₂e emissions. In the heat sector, the lowest energy costs could be reached by the expansion of cogeneration plants. Therefore, it could be concluded that the existing district heating system should be extended and combined with decentral heat pumps. In the electricity sector, cogeneration plants combined with solar PV could perfectly defossilize the electricity demand. The electrification would increase in the fuel and heating sectors due to the expansion of heat pumps. Increasing the CO₂e emission certificate price to a level of 100–150 €/tCO₂e would help support biomass-based technologies for electricity, gas, and biofuel production to achieve more market penetration. The market premium schemes in Austria to support electricity RES technologies could also help balance the additional costs in the energy transition process.

In summary, ENECO₂Calc in the present evolutionary stage is able to develop energy transition paths for all 2095 municipalities in Austria based on consistent statistical datasets. Furthermore, ENECO₂Calc is the first municipality-based energy-modeling tool that allows the combined determination of the final energy demand, CO₂ footprint, and energy costs. ENECO₂Calc delivers results for the status quo, which is based on the year 2020, and helps to develop appropriate energy transition scenarios until the year 2050. Additionally, ENECO₂Calc enables the investigation of a sensitivity analysis to quantify examples of the impact of changes in energy resource cost factors caused by crisis.

The exemplary investigation of the Austrian municipality of St. Margareten im Rosental delivered results for the final energy demand and CO₂ footprint, as well as energy costs based on 2020. The results for the final energy demand and CO₂ footprint were in line with several other studies from the literature. It is underlined that ENECO₂Calc is the first municipality-based energy-modeling tool that enables the determination of the economic footprint for the reference year 2020. Additionally, ENECO₂Calc is the first tool for determining the economic and ecologic footprint for several energy transition scenarios based on realistic frameworks, such as potential analysis, and climate goals for the year 2050. Finally, ENECO₂Calc is coupled with the energy flow diagram software e!Sankey to visualize all the energy flows connected to the municipality. The investigation of energy transition scenarios for St. Margareten im Rosental showed that a mix of decentral RES technologies and central cogeneration units in the heat sector, a mix of solar PV and cogeneration units in the electricity sector, and the use of synthetic biofuels coupled with a higher share of electrification in the fuel sector seemed to be most promising in the considered region.

The modular methodology also allows the extension of ENECO₂Calc to deal with statistical datasets from municipalities of other countries in the future.

4. Conclusions and Outlook

This publication’s scope was to set up and investigate an energy-modeling tool on the regional level that allows the prediction of energy transition scenarios for selected municipalities and projected the upcoming economic and ecologic footprint. Furthermore, the energy-modeling tool should allow the integration of energy communities within the municipality. With the development of ENECO₂Calc, it is possible to determine the current energy flows and the economic and ecologic footprint within a selected Austrian municipality. For this, a broad database of economic and ecologic factors of different energy supply technologies is provided with ENECO₂Calc. Furthermore, the final energy demand determination is exclusively based on consistent statistical datasets, such as Statistik Austria or other open-source databases. The distribution of the final energy demand and supply can be specified based on the present annual final energy demand. Additionally, a monthly discretization of the final energy demand and supply can cover seasonal differences in volatile energy supply technologies. As a result, the selected municipality’s present economic and ecologic footprint can be determined. ENECO₂Calc also enables the prediction of different energy transition paths on the municipality level. Therefore, the European and national climate policy goals, the determination of future decentral and central RES potentials, and the prediction of the future heat, electricity, and fuel demands until 2050 build the framework for developing meaningful scenarios. Furthermore, ENECO₂Calc enables the implementation of RECs and CECs within the energy model, which consideres the direct sale of final energy to the end-consumers on the levelized production cost level and savings concerning electricity network fees. The implementation of renewable energy technologies in ENECO₂Calc requires high-fidelity monthly distributed mass and energy balances enabled through the integration of digital twins.

The application of ENECO₂Calc within the Carinthian municipality of St. Margareten im Rosental proved the huge possibilities of the energy-modeling tool. The results showed that the final energy demand in St. Margareten im Rosental was mainly based on the residential building and mobility sectors. The validation of the calculated final energy demand was in line with several references. Due to the huge amount of forest area, the municipality was suitable for integrating different biomass-based supply technologies. For the development of energy transition scenarios, three different ways toward a fossil-free municipality were set up and compared. The BtG scenario, with the defossilization of the heat sector by integrating solar thermal collectors and heat pumps coupled with the defossilization of the Austrian electricity mix by the central production of synthetic natural gas, resulted in the lowest ecologic footprint but also implied the highest energy costs. The BtCHP scenario, primarily based on biomass-based cogeneration units, and the BtM scenario, primarily based on a mix of decentral RES technologies in the heat and solar PV in the electricity sector, predicted much lower energy transition costs but also slightly higher greenhouse gas emissions. In the mobility sector, defossilization by synthetic biofuels in the BtM scenario and electrification via BEV in the BtCHP scenario were the most promising options for an affordable and sustainable energy transition within St. Margareten im Rosental.

ENECO₂Calc provides the first municipality-based energy-modeling tool for determining the present economic footprint, as well as the final energy demand and CO₂ footprint. Additionally, ENECO₂Calc offers the novel possibility to provide several energy transition paths for a selected municipality regarding their ecologic and economic impacts until the year 2050 based on realistic frameworks, such as a potential analysis and climate goals. Further development of ENECO₂Calc should focus on different final energy calculation approaches in the industry sector. Due to the general top-down approach, bigger inaccuracies could arise in industrialized municipalities. Additionally, the tool could be expanded by the cooling demand. Moreover, further discretization of the final energy demand would enable the consideration of storage and flexibility demand. In addition, the energy-modeling tool could be extended by implementing other technologies or ecologic characterization categories, such as water consumption or ozone depletion. Furthermore, the determination of the ecologic and economic footprint could be extended by social impact, such as the regional added value. Additionally, the economic and ecologic factors could be extended by regional dependencies, such as the implementation of regional distributed solar irradiation or wind speed. The prediction of cost factors for energy resources, such as crude oil or biomass, could be combined with energy market models to implement possible dynamic effects due to crisis. Furthermore, the methodology could be extended to also deal with statistical datasets from other countries than Austria. Finally, the usability of ENECO₂Calc could be improved by developing automatic data import processes and a user interface. In conclusion, ENECO₂Calc can support municipalities in finding the best strategy regarding economic and ecological footprint toward climate neutrality. In the present development stage of ENECO₂Calc, the methodology is focused on dealing with consistent statistical datasets from all 2095 Austrian municipalities. In the future, the methodology can easily be extended to also deal with statistical datasets from the municipalities of other countries.