In order to meet the targets of the Paris Agreement, a rapid and far-reaching phase-out of fossil fuels is required. Against this background, Austria aims to cover 100% of its electricity generation from renewable energy sources by 2030. To meet this target in a standard scenario of final electricity demand, renewable electricity generation needs to be expanded by 27 TWh compared to 2019 (11 TWh PV, 10 TWh wind power, 5 TWh hydropower, and 1 TWh biomass). With respect to the ambitious expansion target for PV, the contribution of households, i.e., prosumers, will be of crucial importance.
Depending on their background (most notably, type of building, income), the ability of different household groups to participate in the electricity market as prosumers—and hence, the possible distributive impacts of the electricity transformation—will vary substantially. Prosumers reduce the consumption of electricity from the grid and can thereby realize cost savings, increasing their consumption opportunities for other (non-energy) goods.
This paper investigates the economic and distributive impact of increasing household PV electricity generation in Austria until 2030. For this purpose, the household module of the macroeconomic model DYNK is expanded, differentiating the degree to which households engage in “prosumer” activities. A set of PV support policy scenarios is then defined to simulate the increase in the number of prosumers as well as the distributive impacts on the different household types and the macroeconomic impacts with the expanded model. The simulation results show a small but positive effect of increased investment in residential photovoltaic systems on the GDP. With respect to the distributive effects, the design of the support scheme is essential. Targeting support for low-income households also has positive impacts on GDP growth.
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1 Introduction
For meeting the targets of the Paris Agreement, a rapid and far-reaching phase-out of fossil fuels is required. Against this background, Austria aims at covering 100% of its electricity generation from renewable energy sources by 2030. To meet this target in a standard scenario of final electricity demand, the Austrian Renewable Energy Expansion Act (Erneuerbaren-Ausbau-Gesetz, EAG, [1]) stipulates that renewable electricity generation shall be expanded by 27 TWh compared to 2019 levels (11 TWh PV, 10 TWh wind power, 5 TWh hydro power and 1 TWh biomass). With respect to the ambitious expansion target for PV, the contribution of households, i.e., prosumers, will be of crucial importance.
Depending on their background, the ability of different household groups to participate in the electricity market as prosumers—and hence the possible distributive impacts of the electricity transformation—will vary substantially. Prosumers reduce consumption of electricity from the grid and can spend potential savings in non-energy consumption, being then better off in terms of non-energy consumption than other households.
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For Austria, to the best of our knowledge, only one study on the drivers of residential PV adoption is available. Braito et al. [2] focus on differences in household PV investment between Austria and Italy, using survey data. For Austria, they find that environmental protection was the most dominant motive for adopting residential PV systems followed by a reduction of the dependence on fossil fuels. By contrast, the most important motive for refraining from investing in a PV system was that it was regarded a bad investment. For Germany, Jacksohn et al. [3], using data from the German Socio-Economic Panel, show that economic factors—i.e., costs and revenues—are most relevant in households’ investments in PV (and solar thermal) systems. Particularly investment costs can explain investment since they are more certain than potential future revenues. Sociodemographic and housing characteristics also have a significant impact on the investment in household PV installations: For instance, households living in detached houses or rural areas are found to more likely invest, as are households with higher income. By contrast, investment probability decreases with the age of the household head and is lower if the household head is female. Finally, the analysis finds only little relevance of environmental preference and personality traits. Based on an online survey, Korcay et al. [4] find that German homeowners have a high basic willingness to adopt a PV system, but have a low willingness to pay, confirming the decisive role of costs in PV investments. Moreover, their analysis shows that purchase intentions strongly depend on subjective norms as well as on the attitude towards PV.
This paper investigates the economic and distributive impact of increasing household PV electricity generation in Austria until 2030. For this purpose, the household module of the macroeconomic model DYNK is expanded, differentiating the degree to which households engage in prosumer activities. A set of PV support policy scenarios to increase prosumer activities is then defined to simulate the distributive impacts on the different household types and macroeconomy with the expanded model.
The paper is structured as follows: Sect. 2 provides a short description of the macroeconomic model DYNK, focussing on the expansion of the household module with respect to prosumer activities. Sect. 3 presents the policy scenarios, followed by the description of simulation results in Sect. 4. The final section concludes.
2 Modelling framework
The simulations are conducted with DYNK (Dynamic New Keynesian), a macroeconomic model with input-output linkages covering the economic interdependencies between multiple sectors in a single region and integrates a variety of module blocks (see Fig. 1).
Fig. 1
Schematic structure of DYNK. (Source: Own illustration)
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2.1 General model features
The DYNK model describes the interlinkages between 74 NACE1 industries as well as the consumption of five household income groups (quintiles) by 47 consumption categories (COICOP2) in the Austrian economy.
The modelling approach bears some similarities with DSGE (Dynamic Stochastic General Equilibrium) models, as it explicitly describes an adjustment path towards a long-run equilibrium. This feature of dynamic adjustment towards equilibrium is most developed in the consumption block and in the macroeconomic closure via a fixed short- and long-term path for the public deficit. The term “New Keynesian” refers to the existence of a long-run full employment equilibrium, which will not be reached in the short run due to institutional rigidities. These rigidities include liquidity constraints for consumers (deviation from the permanent income hypothesis), wage bargaining (deviation from the competitive labour market) and an imperfect capital market. Depending on the magnitude of the distance to the long-run equilibrium, the reaction of macroeconomic aggregates to policy shocks can differ substantially.
DYNK is an input-output model in the sense that it is demand-driven, as all what is demanded is produced. The price block in DYNK is similarly elaborated as in a CGE (Computable General Equilibrium) model, with user-specific prices and a proper account of margins, taxes less subsidies, and import shares that are different for each user. Besides the price block, also other parts of the DYNK model, in particular the labour market block, have similar specifications as a dual CGE model (see for example Conrad and Schmidt [5] or Löfgren et al. [6]). The dual model is based on price and cost functions instead of production functions and therefore these models in a certain sense are also “demand-driven”, especially if constant returns to scale do not allow for price setting on the supply side.
For more comprehensive descriptions of the DYNK model, see for instance Kratena and Sommer [7], Sommer and Kratena [8] or Kirchner et al. [9].
2.2 Integrating prosumer activities in the household module
For the analysis of prosumer activities, both the demand and supply side of DYNK are expanded. On the supply side, the cost structure of the input-output sector “energy” (NACE sector D35), which comprises the generation and distribution of electricity, natural gas and district heat, has been disaggregated into natural gas supply/distribution (D35B), district heat supply/distribution (D35C) and the sector that generates and provides public electricity (D35A) in a special evaluation by Statistics Austria. The electricity sector has then been further disaggregated into subsectors comprising 10 electricity generation technologies (such as gas power plants, PV, wind and hydro) as well as electricity trade and distribution (i.e. grid operation).
On the demand side, the module of the private households has been expanded. The module on households in DYNK comprises investment behaviour in durable commodities, such as own houses, vehicles and electric appliances. We expand this approach by a new type of durables—“energy supply and storage appliances”—allowing to simulate the economic impacts and the increasing role of prosumers in the electricity system and assessing impacts of these developments on different household groups.
A special evaluation of the microcensus “Energy Consumption of Private Households” by Statistics Austria [10] shows that whether or not a household commands over a PV system depends mainly on two factors: (1) the type of building (single-family house, SFH, vs. multi-family house, MFH) and (2) the income level. Households with higher incomes are more likely to have a PV system installed than low-income households. The building type is even more decisive: Irrespective of the household income level, in multi-family houses the proportion of households with a PV system is significantly lower than in single-family or two-family houses. This reflects i.a. existing legal provisions (especially the requirement of majority voting for the installation of a PV system on the roof or façade of a multi-family building). For modelling prosumer activity, the household sector in DYNK was therefore split up into ten types, differentiating between single-/double and multi-family houses on the one hand and five household income groups (quintiles) on the other hand. The corresponding consumption and income data for the ten household types was derived from Statistics Austria’s Household Budget Survey.
The investment decisions of the different household groups were added to the consumption block and are modelled as follows. The highest household income group (quintile 5) behaves according to the Permanent Income Hypothesis (PIH) and does not face liquidity constraints [11]. Its stock of photovoltaic (including storage3) appliances in period t, KPV,t, is optimal and grows by taking into account the relationship (β) between the price of electricity (pel,t) and the interest rate rt plus all shocks e from previous periods t and the adjustment to these shocks with parameter α:
For all other household income groups this equation of PIH consumers describes the development of some optimal stock \({K}_{PV{,}t}^{*}\)to which these households attempt to adjust, given their liquidity constraints. Due to these constraints, the development of real disposable income ∆log(YDt/Pt) drives their investment in photovoltaic (including storage) appliances. The subsidy offsets the liquidity constraints completely at a subsidy rate (tr) of 1, so that we get for the investment function of households in quintile 1 to 4:
The data set, which is a special evaluation by Statistics Austria of a large cross-section data set, did not allow for an econometric estimation of the parameters β, μ and γ. Therefore, we calibrated β according to the assumption of rational behaviour where any increase in electricity prices is fully compensated by additional consumer capacity leaving the costs of energy services unchanged. The parameters μ and γ were calibrated to reproduce the allocation of PV investments across household groups in the base year.
The new household structure has then been integrated into the adapted DYNK model and the household variables (income, consumption structures) have been linked to relevant variables of the new disaggregated electricity sector in the adapted DYNK.
3 Policy scenarios
With respect to prosumer activities, we focus on three different scenarios:
1.
Baseline Prosumer Scenario (BS):
In this scenario, the absence of subsidies for incentivising household PV investments is assumed. The prosumers are only compensated via a reduction in consumption of electricity from the grid and—given strong separability between energy and non-energy consumption—spend this saving in non-energy consumption and are better off in terms of non-energy consumption than other households.
2.
Prosumer Support Scenario 1 (PS1):
In this scenario, a common investment subsidy (30%) is granted to all households for prosumer investment. Prosumer households are better off than in the baseline, also relative to other households.
3.
Prosumer Support Scenario 2 (PS2):
In this scenario, an income-dependent investment subsidy is granted to households for prosumer investment. The overall support level is fixed at the level of PS1. Households from the two highest income quintiles will not receive any investment support; households from the third income quintile will receive a subsidy of 30%; and the remaining support budget will be distributed to the two lowest income quintiles.
The three scenarios are integrated into a common baseline scenario regarding the development of the Austrian electricity system—integrated in the Sustainable Transition Scenario of the TYNDP 2018 [12]—simulated with the model ATLANTIS [13, 14]. In this scenario, the target of 100% renewable electricity generation in Austria by 2030—excluding control and balancing energy—is met. The different energy sources contribute to the achievement of the overall target according to the sub-targets defined by the Austrian government (11 TWh PV, 10 TWh wind power, 5 TWh hydro power and 1 TWh biomass). Accordingly, we assume that higher investment in household PV systems will result in lower investment in (larger scale) business PV plants. Given the development of electricity demand and supply in the scenario, the electricity price for households in all scenarios will rise from 17 ct/kWh in the base year 2017 to 38 ct/kWh in 2030. This reflects price formation according to the merit order with a rising gas price and the increasing role of gas for providing control and balancing energy in an electricity system predominantly based on renewable energy sources.
For the residential PV systems, constant average investment costs of 1300 € per kWp [15] are assumed for the whole simulation period (2017 to 2030). With respect to the specific electricity output, 1000 kWh per kWp are assumed.
4 Simulation results
The following sections summarise the results of the model simulations in terms of the effects on the adoption of residential PV systems as well as the macroeconomic and distributive effects. Even though the expansion of residential PV systems requires investments of several hundred million Euros the effects are relatively small since (i) the investments occur over a period of 14 years, (ii) the effects and investments are compared to a baseline where PV investments also take place due to rising electricity prices. Moreover, the economic effects are small as they are presented on the household level, and as net effects4.
4.1 Adoption of residential PV systems
As a result of rising electricity prices, household PV investments of the highest income quintile will accelerate compared to past investment trends. In the baseline scenario (BS), the total additional capacity installed between 2017 and 2030 amounts to 1.7 GWp (see Table 1). In the prosumer scenarios PS1 and PS2, subsidies lead to higher investment (1.9 and 2.1 GWp respectively) and in turn higher residential PV electricity generation. Scenario PS2 that favours households with lower income achieves a higher expansion of PV using the same support volume as scenario PS1 that grants a subsidy of 30% to all households. This reflects the fact that the subsidy increases the investments of low- and medium-income households that are facing budget constraints, while it rather constitutes a deadweight effect for the highest income quintile.
Table 1
Development of residential PV generation, subsidies and investment by scenario
BS
PS1
PS2
Additional electricity generation
TWh
1.7
1.9
2.1
Subsidies (cumulative)
m €
–
788
791
Investment in PV (cumulative)
m €
2329
2627
2966
Subsidies/kWp
€
–
420
373
4.2 Macroeconomic effects
Fig. 2 shows the average changes in real GDP in the period 2017 to 2030 in the two prosumer scenarios compared to the baseline by GDP component. Overall, the impacts of the investment in residential PV systems are very moderate, but positive in both prosumer scenarios. The highest positive contribution to GDP stems from private consumption, which includes household PV investment and the effects from shifting expenditure on electricity that is now provided by the PV systems to other consumption categories. The positive effect of the rise in private consumption is partly compensated by declining public consumption5 and a net increase in imports. The import effect is mainly related to the import shares of consumed commodities as well as PV investment.
Fig. 2
Change in GDP by prosumer scenario
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4.3 Distributive effects
The distributive impacts of the two policy scenarios compared to the baseline scenario assuming no subsidies for residential PV systems are shown in Fig. 3. In PS1, with a common subsidy of 30% eligible to all households, household consumption rises over the income quintiles, i.e. households with higher incomes benefit more strongly than low income households. In PS2, gains compared to the baseline show for low- and medium-income households. Overall, in PS2 compared to PS1 disposable income increases for all income quintiles due to the macroeconomic feedback effects. However, due to the low level of investment compared to GDP, this effect is very small.
Fig. 3
Change in average annual household consumption by scenario (2017–2030)
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5 Conclusions
Due to the war in Ukraine—and its impact on the gas market—the challenges for the transformation of the European energy system have been instantly amplified. Against this background, as part of the “REPowerEU” initiative [16] in May 2022, the Commission developed options for an accelerated phase-out of (Russian) natural gas, which had previously been regarded as a key bridging technology for achieving the climate targets. In addition to a higher degree of diversification of gas supply, central aspects of the proposal include a more rapid electrification and simultaneous substitution of fossil fuels by renewable energy sources (especially wind and solar energy) and heat pumps, as well as increases in energy efficiency.
Due to the increase in gas prices, electricity prices have risen significantly in Austria and other EU Member States. Households that own a PV system are significantly less affected by these price changes or might even be not affected at all in case they can fully cover their electricity consumption with their PV system. For Austria, the data show that the ability of different household groups to participate in the electricity market as prosumers varies substantially, and depends particularly on the characteristics of the dwelling and on the household income. To ensure a fair participation of all groups of households in the energy transition, it will therefore be essential to reduce both legal barriers for the installation of PV systems on multi-family buildings and provide targeted support to low-income households to ensure broad affordability of residential PV systems. Enhancing citizen participation in renewable energy communities can also contribute to achieving a more equitable transformation of the electricity system.
Acknowledgements
This research was conducted in the START2030 project that was funded by the Austrian Climate and Energy Fund and carried out in the Austrian Climate Research Program (ACRP). We are grateful for excellent research assistance provided by Eva Wretschitsch and Katharina Köberl.
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Nomenclature Statistique des Activités Économiques dans la Communauté Européenne (Statistical Classification of Economic Activities in the European Community).
10% of new PV installations are assumed to be combined with battery storage units assuming a battery capacity of 1 kWh battery per 1 kWp and price of 1000 € per kWh.
The net effect is moderate if counteracting impacts are simulated, e.g., the increase of consumption of non-energy goods equals the decrease of energy costs.
Higher consumption demand increases labour demand. In turn, wages increase which results in an increase in prices. Since the GDP components are represented in real terms (i.e. constant prices) this price level increase translates into the slightly negative impact on public consumption. In nominal terms public consumption is fixed exogenously in DYNK and does not differ from the baseline development.
