At the European level, the Renewable Energy Directive (RED II) has regulated and provided a tool for European citizens to create innovative social models [1]. As emphasized in the Directive, Energy Communities (ECs) produce added value in terms of "acceptance of renewable energies” and stimulate investments on the ground: the overall contribution of these initiatives is demonstrated by their active participation in the ecological transition process [2]. As part of the European Union's (EU) strategy, the Commission realized the value of ECs for the achievement of national targets: it was therefore decided to support them by establishing a favorable regulatory framework, as specified in Recital 70 of RED II. The Directive provided definitions, rights and obligations for Renewable Energy Communities (RECs) and required EU Member States to create frameworks for their development [3]. In particular, the definitions of collective self-consumption and RECs were detailed in Articles 21 and 22, respectively. The latter stipulated that RECs have the right to "produce, consume, store and sell renewable energy" (subsection 2 letter a) and then "exchange, within the same community, the renewable energy produced". For example, photovoltaic (PV) producers effectively act as dual consumers and producers of electricity, allowing them to not only reduce their energy expenses but also generate additional income by selling surplus energy. These advantages are driving the adoption of prosumership within energy communities and are expected to become more deeply integrated, involving a diverse array of participants.

European countries RECs regulation comparison in [3], the authors provide a graphical overview in Figure 2 that aims to provide an assessment of the degree of maturity of transposition processes evaluating a series of indices provided by RESCoop (REScoop.eu). The development of support schemes for Renewable Energy Communities (RECs) across Europe varies significantly, with Austria, France, and Germany leading in terms of structured policy frameworks. In Austria, RECs are actively considered in the design of eligibility criteria for renewable energy incentives, ensuring that community-led projects can access financial and regulatory support. Similarly, France integrates RECs into its broader energy transition strategy, providing preferential conditions for local energy communities within its support mechanisms. Germany, known for its strong community energy sector, has tailored policies that facilitate REC participation in the energy market by ensuring access to grid infrastructure and financial incentives. Belgium also shows progress, with specific support mechanisms for community-led renewable production. In contrast, other European countries are still in the early stages of REC integration, often lacking targeted measures. This disparity underscores the need for a more coordinated European approach to strengthen the role of RECs in the energy transition. In [4], Fina et al. discuss the difficulties and positive aspects encountered in Austria in the transposition process of the EU Directive and point out that only a multidisciplinary team can effectively help in the drafting of a national regulatory framework for RECs.

Notably, Italy remains the only European country that considers a support scheme where incentives vary based on both the size of the renewable energy installation and the geographical location. This distinctive approach reflects an effort to tailor financial support to local energy conditions, potentially serving as a model for other nations aiming to enhance REC participation in diverse regional contexts.

In order to enable the reader to better understand the following sections, a brief description of the structure of the Italian grid, the functioning of the electricity market and the main actors involved in it is given below.

The Italian electricity grid is organized into two main systems: the high-voltage transmission grid and the medium-to-low voltage distribution grid. The high-voltage transmission grid operates at voltage levels of 380 kV, 220 kV, and 150 kV and covers long-distance electricity transport across the country. It is managed by Terna, the national Transmission System Operator (TSO), which ensures the efficient flow of electricity and stability of the system. Within this system, primary cabins play a critical role by transforming high voltage into medium voltage (10-30 kV) for regional distribution. The development and maintenance of this grid is overseed by Terna, as outlined in its Grid Development Plan, which includes strategic projects to integrate renewable energy and enhance energy security (Terna - Grid Development Plan).

The medium-to-low voltage distribution grid is responsible for delivering electricity locally. It operates at medium voltage (10-30 kV) for regional networks, which is then reduced to low voltage (230-400 V) for final delivery to end users such as homes, businesses, and small industries. This part of the grid is managed by Distribution System Operators (DSOs), including Enel and other regional providers. Secondary cabins are key components of this system, as they transform medium voltage to low voltage for direct supply to consumers. This structured and hierarchical organization ensures the seamless flow of electricity from generation sources to consumption points while maintaining efficiency and reliability (Terna - Italian Grid Code).

The Italian electricity market is structured to ensure efficiency and competitiveness while integrating renewable energy sources. It operates through several market segments, with the Day-Ahead Market (DAM), playing a crucial role. The DAM allows electricity producers and consumers to submit bids for the next day, facilitating the determination of energy prices based on supply and demand. Italy is divided into several market zones, reflecting geographical constraints and grid congestion, which can lead to price differentiation among regions.

The Energy services Management authority (GSE) plays a pivotal role in promoting and managing incentives for renewable energy producers. The GSE funds these incentives through revenues generated from the sale of electricity on the market and fees collected via the system charges included in consumer electricity bills. The incentives managed by GSE include feed-in tariffs and incentives for self-consumption in Renewable Energy Communities (CERs). The GSE also administers the so-called Ritiro Dedicato (RID) scheme, allowing producers to sell their electricity directly to the GSE at regulated market prices.

The Regulatory Authority for Energy, Networks, and Environment (ARERA) is the regulatory authority overseeing the electricity market to ensure transparency, competition, and consumer protection. ARERA defines the tariffs for electricity distribution and transmission, establishes rules for the integration of renewable energy, and supervises market operations to prevent monopolistic behaviors. Additionally, ARERA sets the criteria for network access and system balancing, ensuring that both traditional and renewable energy producers can operate efficiently within the market. Through its regulatory framework, ARERA fosters a fair and sustainable energy transition aligned with European Union directives.

Together, the DAM, GSE, and ARERA form the backbone of Italy's electricity market, balancing economic efficiency, sustainability, and grid stability while promoting the development of renewable energy sources.

The Ministry of Environment and Energy Security (MASE), with the transposition of European directives in 2020 through Decree Milleproroghe 162/2019, later converted to n. 8/2020 on February 28, 2020, ARERA Resolution 318/2020, and the Ministerial Decree of September 16, 2020 (from the Ministry of Economic Development (MISE)), the regulatory framework and mechanisms for incentivizing Renewables Energy Communities (RECs) were defined.

Since late 2019, the national implementation process of the two EU directives has provided momentum for the development of state support for the Community Energy sector in Italy. As a first step towards national implementation of the EU directive, Article 42-bis of Law 8/2020 provided an experimental formal definition within the Italian regulatory framework, thus initiating the pilot phase.

The requirements and constraints for the establishment of configurations have been defined, specifying that the renewable energy plant serving the community may have a nominal power limit of 200 kW_P, and providing a narrow definition of "locality", allowing only members connected to the same low-voltage distribution cabin, also known as a secondary cabin. In August 2020, ARERA defined the support incentive regime. RECs were entitled to a direct incentive for each kWh of self-consumed electricity (110 EUR/MWh) and partial reimbursement of transport tariffs reflecting costs (10 EUR/MWh). The total incentive was defined to amount to 120 EUR/MWh, to be granted only for kWh of electricity consumed simultaneously with production.

Three types of incentives have been determined, which fall within the regulatory framework:

• shared electricity feed-in-tariff under the Ministerial Decree DM MASE no.414/2023;

• valorization of self-consumed electricity by returning the tariff components as provided for in ARERA Resolution 727/2022/R/eel;

• withdrawal of electricity fed into the grid by the GSE.

For each kWh of incentivized electricity, the GSE pays, for a period of twenty years, a unit fee, defined as a premium rate. For each kWh of self-consumed electricity, the GSE recognizes, again for a period of twenty years, a unit fee, defined as an enhancement contribution, relating to the transmission tariff. Shared energy is defined as the minimum, in each hourly period, "between the electricity injected for sharing purposes and the electricity withdrawn for sharing purposes". This structured mechanism is meant to incentivize consumer behaviors that maximize local consumption of electricity and minimize export to the grid. Any electricity not self-consumed within the REC is sold to the grid at zonal prices. The incentive flows are visualized in Figure 1.

Figure 1.

Incentive flow visualization

Upon initial analysis, it emerged that the regulatory framework introduced by Law 8/2020 was intended by the Italian legislature as a first step towards a more comprehensive and extensive implementation of the EU directive.

With the publication of Legislative Decree 199/2021 [24], there is an update of the criteria related to the configuration of RECs.

With the final regulatory framework thus consolidated, Community Energy Resources fall within the definition of "Distributed Self-Consumption Configurations", which has been based on a virtual model that allows participation even for those who do not have a plant connected to their own supply. The goal is to promote access to renewable energy production for a larger number of users, encouraging investments in the territories. The nominal power for each individual plant included in an energy community was increased from a scale of 200 kW_P to 1 MW and the geographical boundaries were also expanded from the secondary cabin to the primary cabin, identified as a requirement for accessing incentives.

In 2023 and 2024, the GSE undertook a comprehensive process to delineate the land coverage and user base of primary cabins within Italy's electricity grid. This initiative, based on data provided by Distribution System Operators (DSOs), aimed to enhance transparency and support the development of energy communities. The process included a period of public consultation, during which feedback was solicited to refine and adjust the boundaries of these areas. The finalized coverage maps and related information are now accessible on the GSE's official website, providing valuable resources for stakeholders involved in energy planning and community initiatives.

To grasp the evolution of the regulatory system from the experimental phase to the final one the regulations on energy communities outlined above are compared in Table 1.

In this section, the modeling framework adopted for the policy analysis is introduced. The proposed method is also depicted in Figure 2 to give a better overview of the process.

Figure 2.

Method overview

The process began with the collection of key input data, including the regulatory framework from MASE, load profiles from ARERA, PV production simulations from Renewable Ninja, and electricity market prices from the Energy Markets Management authority (GME). All this input data has been further justified in the dedicated chapter. Using this data, the REC simulation was conducted, applying the incentive scheme rules and formulas to model scenarios for three Italian cities (Milan, Rome, Catania) and evaluating collective PV plant configurations of 100, 500, and 1000 kW_P. The different locations and sizes were chosen so that all variables in the formula for calculating the incentives would vary. The simulation progresses into the research scope, where an analysis is performed to compare old and new regulatory decrees to assess the feasibility and performance of RECs in both regulatory frameworks. Finally, in the results presentation, the findings are used for community optimization, identifying the best operational strategies, and conducting a policy implication analysis to provide insights for improving future regulations.

The data collection process was conducted using Excel as the primary tool for organizing and analyzing inputs and outputs.

1. Initial Configuration Optimization: the first step involved optimizing the configurations of photovoltaic systems to align with the study's objectives. This included identifying parameters such as plant size, location, and market pricing, ensuring that the selected configurations were realistic and representative of diverse scenarios.

2. Scenario analysis: the objective of the study was to compare cash flow calculations across three scenarios, based on different photovoltaic system sizes: 100 kW, 500 kW, 1 MW. For each scenario, the analysis incorporated data from three distinct macro-regions. Zonal market prices (PZ) for each region were derived from official market data to provide accurate input for financial modeling. After analyzing the scenarios for the three system sizes, the study further evaluated policy implications.

3. Data synthesis and comparison: the collected data was synthesized in Excel, allowing for a clear comparison of cash flows, operating costs, and benefits across different scenarios. This step facilitated the evaluation of both technical and economic aspects of the proposed solutions.

In this section, the sources where datasets for the simulations were extracted, the made assumptions and the established boundaries are presented. To make the discussion clearer, the assumptions made in this model have been broken down as follows:

• Mathematical model for incentive calculation;

• Geographical variables;

• Irradiance and PV production;

• Electricity market and zonal prices;

• Consumption data.

Mathematical model for incentive calculation

Given the complexity that has arisen in terms of the incentive calculation, the formulas governing this incentive were included in the final decree [24]. These formulas have been set out below in order to make the variables involved and the constraints imposed clear.

Firstly, the premium tariff has been calculated according to the size of the power plant. There were therefore three different classes, as represented in Table 2. For each of the classes, a formula was defined with which the Reward Incentive Tariff (TIP) was calculated, consisting of a fixed part and one that varies according to the grid sale price of the energy. The variable component is a function of the zonal price increasing when the market price decreases. Finally, a ceiling was imposed on the tariff calculated in this way, varying between 100 and 120 EUR/MWh depending on the size of the community plant.

Table 2.

Feed in Tariff (FiT) calculation

Size	TIP	Maximum FiT
PV plant > 600 kW	60 + max (0; 180 – PZ)	100 EUR/MWh
PV plant > 200 kW & ≤600 kW	70 + max (0; 180 – PZ)	110 EUR/MWh
PV plant ≤ 200 kW	80 + max (0; 180 – PZ)	120 EUR/MWh

Following the formulas described in Table 2, the (FiT) varies with the zonal price (PZ) as displayed in Figure 3. When compared with the FiT of the experimental regulation (110 EUR/MWh fixed), it can be seen immediately that:

• If the energy selling price remains under 140 EUR/MWh small-size plants (less than 200 kW) are economically advantaged by the final decree. Medium-size plants experience no change in incentive while large-size plants experience a decrease (from 110 to 100 EUR/ MWh;

• If the selling price of energy exceeds 150 EUR/MWh all configurations will see a decreased incentive compared to the experimental case;

• If PZ is between 140 and 150 EUR/MWh, the only configurations that see an increased economic return are those formed by small-scale plants.

Figure 3.

Feed in tariff visualization for different plant size

For photovoltaic installations, a correction factor has been also applied to the premium tariff a value of +4 EUR/MWh must be added for the Central Regions and +10 EUR/MWh for Northern Regions as depicted in Table 3. This correction factor was intended to balance the different hours of sunshine that differentiate the various regions of the Italian peninsula.

Table 3.

Correctional coefficient according to REC geographical location.

Location	Premium	Unit
North of Italy	+10	EUR/MWh
Centre of Italy	+4	EUR/MWh
South of Italy and islands	+0	EUR/MWh

The entire premium incentive described above was calculated on the basis of the energy shared within the community. The shared energy is being calculated by the following formula:

EACI,h=min(Einjected, h;Eabsorbed, h) (1)

where are: E_{ACI, h} - shared energy [kWh], E_njected,h - injected energy into the grid [kWh], and E_absorbed,h - absorbed energy from the grid [kWh].

The variable premium tariff is linked to the PZ and therefore calculated on an hourly basis. The equation described its operation as follows:

TIPh={min[(CAP;TPbase +max(0;180−PZ)]+FCzonal } (2)

where are: CAP - maximum premium tariff varies considering the Power peak of the PV plant [EUR/kWp], TP_base = fixed tariff, varies considering the Power peak of the PV plant [EUR/kWp], and FC_zonal = zonal price correction factor

Irradiance and PV production

Solar irradiance across Italy exhibits significant regional variation, with southern areas receiving the highest levels due to factors such as lower latitude, milder climate, and longer daylight hours. In contrast, the northern regions, particularly the Alps and the Po Valley, experience lower solar radiation, especially during the winter months, owing to a combination of geographical factors including latitude, altitude, and climate. Additional local variables, such as weather patterns, altitude, and proximity to coastal areas, further influence the amount of solar radiation received in specific regions.

The data presented in Table 4 represents the average annual solar irradiance (Global Horizontal Irradiance (GHI)) from 2006 to 2022, as provided by the Department of Energy Technologies and Renewable Sources of the National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA). These data were derived using meteorological stations and simulation models.

Regions such as Sicily, Sardinia, and Calabria exhibit notably higher solar irradiance compared to other Italian regions, with Sicily being the standout. The patterns observed in the data reflect how the zonal price variable introduced by the GSE aligns with increased solar radiation, which varies according to latitude. On average, Sicily benefits from higher levels of solar irradiance, a result of its lower altitude, mild climate, and reduced pollution levels. These factors contribute to a more stable irradiation profile, with lower seasonal variation and reduced cloud cover, compared to the northern regions of Italy. Sicily receives between 4.5 and 5.5 kWh/m²/day of solar radiation, with summer peaks reaching up to 6.0 kWh/m²/day, while northern regions typically receive 3.5 to 4.5 kWh/m²/day. In regions characterized by heavy cloud cover or air pollution, these values can be further reduced.

It was important to note that using regional averages introduced a margin of error of considerable weight, particularly in areas where local conditions vary significantly. Any analysis that wants to go in further in granularity has to take this into account. While ENEA provides more granular data at the provincial level, reducing this margin of error does not eliminate it entirely. However, the regional averages offer valuable insights into how the North-Central-South division in the zonal price variable benefits some regions more than others. For example, the average annual irradiation in Liguria is very similar to that of Marche and slightly lower than that of Molise, despite the additional +10 EUR/MWh incentive offered to regions in the south.

When examining solar irradiance on a seasonal basis, the data reveals a similar overall pattern, as shown in Figure 4, though with a more pronounced gap during the summer months. This seasonal variation is particularly relevant for energy communities that are active primarily during certain periods of the year. The increased solar radiation in summer aligns with higher energy demand, making the coupling of energy production and consumption more favorable in southern regions. In these areas, local energy production can more easily meet the demand, enhancing self-consumption of renewable energy. However, this coupling is highly contingent upon the ability to match peak production periods with local consumption, and this challenge is exacerbated by the lack of additional incentives in the final decree.

Figure 4.

Monthly global solar radiation on the ground on a horizontal plane (Average 2006-2022) by region. Data elaborated from the “Italian Atlas of Solar Radiation” developed by ENEA [25]. The color coding follows the scheme used by TERNA in Figure 1 of Annex 1 of the Technical Operating Provisions [26], based on the areas of the Italian electricity grid, which correspond to the Market Zones, as specified in the map presented and outlined in Table 4

For the simulations conducted in this study, it was essential to analyze local hourly production data to accurately evaluate the performance of energy communities. To achieve this, the Renewable Ninja platform [27] was utilized, offering high-precision forecasts of solar and wind energy generation. The parameters chosen for generating production curves are outlined as follows: the dataset used was MERRA-2 (global) [28], selected for its comprehensive global coverage and high data availability, with the year 2019 chosen as the most recent complete dataset. Latitude and longitude were manually input based on the location of the case study. The system's capacity was also manually input, representing the maximum AC electrical power it could generate. A standard system loss of 10%, derived from the literature, was applied. No tracking system was implemented, and the tilt and azimuth angles were optimized for the specific location of the case study to maximize performance.

Electricity market and zonal prices

In Italy, the electricity market is structured into multiple market zones, a strategic approach designed to optimize the management of electricity production, distribution, and consumption across the country. The division into market zones is intended to address the regional disparities in electricity supply and demand, as well as the varying availability of renewable energy sources such as solar and wind. Each zone reflects the local energy dynamics, and GSE applies regional price variations based on the energy production and consumption characteristics unique to each zone.

The zonal prices through which the incentive for REC is calculated in turn follow the national unique price (PUN). In Figure 5, the PUN is graphed from 2015 to 2024. The figure that stands out conspicuously is the anomaly of the pandemic period. From the end of 2021 and throughout 2022 (green line and brown line), the PUN experienced an unprecedented increase.

Figure 5.

Annual trend of the Italian national price of electricity. Data elaborated from the “Results” section, PUN, of GME [29]

The year 2023, in blue, readjusted to more stable values, while remaining at a much higher average than the pre-pandemic situation. It was interesting to note how the energy price trend of 2024 is generally very similar to the average of the years 2015-2023, providing a relatively small margin of difference if used in an economic sustainability study for a project investment concerning RECs

For this reason, in the results analysis, a comparison of the REC incentives calculated with an average PZ (black line) and with a PZ referring to 2022 has been studied, in the knowledge that pandemic or geopolitical events such as those that occurred can always recur in the future. Following the formula for small-scale plants described in Table 2, Figure 6 shows the development during the pandemic period of the average incentive per RECs. The incentive varies between 80 and 120 EUR/MWh, reaching a minimum when the PZ exceeds 180 EUR/MWh. This situation has occurred in the last months of 2021 and several months of 2022. At the beginning of 2023, the PUN began to fall back to average values, and the incentive rose again towards 120 EUR/MWh.

Figure 6.

RECs incentive variation between 2021 and 2024 for small-scale plants using. Data elaborated from the “Results” section, PUN, of GME [29]

In Figure 7, the average hourly zonal price calculated from 2015 to 2023 is presented for all the different market zones. The values and trends throughout the year are similar across all zones, with a peak during the summer months. However, Sicily shows deviations both in the spring and summer periods, with a peak in August. Meanwhile, Sardinia maintains a lower price during the summer. The relative differences in PZ confirm the use of the PUN in the analysis.

Figure 7.

Average Zonal Price variations for different market zones, between the years 2015 and 2023. Data elaborated from the “Results” section, Zonal Prices, of GME [29]. The color coding follows the scheme used by TERNA in Figure 1 of Annex 1 of the Technical Operating Provisions [26], based on the areas of the Italian electricity grid, which correspond to the Market Zones, as specified in the map presented and outlined in Table 4

The results are presented for three different geographical areas in the following order:

1. A breakdown of the three national incentives, using both average prices from 2015 to 2023 and prices from 2022 under the high price scenario;

2. An evaluation of the percentage of energy sharing, focusing on strategies to maximize incentives;

3. A comparison between the old and new Italian REC Decree, examining changes in the regulatory framework introduced by the new decree relative to the previous one.

This comparison leveraged data from earlier calculation steps to highlight key differences and their impact on ECs, aiming to evaluate how the updated decree and the yearly variations of the energy market may influence economic and operational outcomes while aligning with policy goals.

A size-site combination (Milan-100 kW plant) has been depicted in Figure 8 in order to include in the discussion the case where the REC is the owner of a community plant and therefore receives both the TIP and the incentives for the sale and distribution of energy to the grid (RID and ARERA tariff). The same configuration is considered in the case of average hourly zonal prices and extreme zonal prices, i.e. relative to the pandemic period (2022). If in 2022, as the zonal price increases, TIP decreases, it should also be noted that the total resulting from the sum of the three incentives increases from 34.152 EUR to 61.032 EUR. Being the owner of the community plant therefore means ensuring resilience in the case of unforeseeable events.

Figure 8.

Comparison between total REC remuneration (TIP + RID + ARERA incentives) in Milan and for a 100 kW PV plant

In Table 7 can be observed that, in the event of a significant increase in energy prices, the percentage of the RID contribution to the total rises more sharply for larger-scale installations. This highlights that, despite the new incentive system being designed to balance differences in local photovoltaic potential and account for energy price fluctuations - favoring smaller-scale installations - those in the 600 to 1000 kW_P range demonstrate greater resilience to increases in the PUN Such resilience could be an advantage in the current context, where twenty-year energy price projections do not provide sufficient certainty for stable assessments.

The relationship between the percentage of shared energy and the resulting REC cash flows for three different sizes (100 kW, 500 kW, and 1 MW) in Rome is illustrated in Figure 9. With the increase of shared energy from 25% to 100%, a consistent rise in total cash flows across all plant sizes and locations is registered. Among the three different geographical zones, the highest cash flows are consistently exhibited by Catania, followed by Rome and Milan, reflecting regional differences in solar potential and market conditions. Overall, the substantial financial benefits of maximizing the percentage of shared energy, particularly for larger photovoltaic systems, are underscored by the results.

Figure 9.

Comparison between percentage of shared energy and total cash flows for Rome

The old Italian REC Decree (199/2021) and the new Decree (414/2023) are compared in Figure 10, highlighting the regional differences and overall changes in incentive structures and the reduction in the disparity between southern and northern regions under the new decree. Under the old decree, the tariff incentive for the South/North scenario showed a significant gap, with southern regions benefiting from much higher incentives, as evidenced by the 14% difference. In contrast, this gap has been considerably narrowed by the new decree, reducing the relative advantage of the South to less than 6%, particularly for scenarios like South/North and South/Centre. As shown, the analysis was again conducted for three different plant sizes, 100, 500 and 1000 kWp (due to the constraints explained in Table 2).

Figure 10.

Comparison between old and new Italian REC Decree

A broader trend is underlined: the general reduction in premium incentives for larger plants (1 MW), particularly in the southern region. This is especially evident in the South/South scenario, where the incentives under the new decree drop dramatically compared to the old decree, showing negative percentage changes for larger systems. For instance, the data for Catania clearly reflects this shift, with the highest reductions observed for large-scale plants, while smaller systems (100 kW) maintain relatively better performance. Two size-site combinations experience no change in the regulatory framework shift: the northern larger-scale plants and the central medium-size plants. Overall, the results suggest a shift toward a more balanced national incentive structure but at the cost of reduced financial support for southern regions, particularly for large-scale photovoltaic systems.

The present work employed a comparative methodology, to assess the impact of Italy's regulatory evolution for Renewable Energy Communities (RECs). By examining incentives under the previous Decree (199/2021) and the revised Decree (414/2023), the study analyzed changes in the distribution and magnitude of incentives across regions and plant sizes. Scenario simulations incorporated diverse variables, including zonal energy prices, plant capacities, and energy-sharing percentages, allowing a nuanced evaluation of both regional and national impacts. Historical price comparisons (2015–2023) and specific cases like pandemic-era energy prices provided additional insights into market-dependent variations in cash flows.

The results revealed significant shifts under the new decree, particularly in regional incentive equity. The historical advantage of southern regions, which previously benefited from 14% higher incentives due to superior solar resources, has been reduced to less than 6%. While this adjustment aims to promote national equity, it has led to a notable decrease in overall incentive levels for southern regions, particularly for larger photovoltaic systems (e.g., 1 MW installations in Catania). This reduction raises concerns about the strategic alignment of policies with regional renewable energy potential, potentially diminishing the attractiveness of large-scale solar investments in the South, where solar resources are abundant. In addition, the economic and quality-of-life gap that exists between North and South in the Italian peninsula should be leveled out, also benefiting the southern regions, which in this way risk instead being left behind or at best treated in the same way.

Scenario analyses further underscored the importance of shared energy within RECs, showing that higher percentages of shared energy correlate with increased cash flows. However, under high energy price conditions (e.g., during the pandemic), the incentive structure tended to favor individual prosumers, reducing the collective financial benefits for the community. This highlighted the resilience of energy communities that own their plants, which proved more capable of sustaining equitable benefits during market fluctuations.

The findings suggest that the new decree aligns with a vision of balanced, community-focused energy transitions, emphasizing smaller-scale installations and shared energy solutions. However, the reduction in southern incentives raises critical questions about the long-term regional development of renewable infrastructure in areas with the highest solar potential. These conclusions highlight the need for carefully calibrated policy tools that balance national equity objectives with the advantages of regional renewable resources. Future research could further explore the implications of such policies on the distribution and size of RECs, offering valuable guidance for other European nations designing their national incentive schemes.