Strategic Prioritization of Residential Buildings for Equitable and Sustainable Renovation

Abstract

The prioritization of energy renovations is critical to achieving sustainability goals and addressing socio-economic disparities in building stock. This study proposes a novel hybrid MultiCriteria Decision-Making framework for identifying and prioritizing residential buildings for energy efficiency upgrades. By integrating granular building-level data, such as energy performance and construction year, with socio-economic indicators like energy poverty and municipal income, the framework provides a comprehensive and equitable approach. Using Python for data integration and analysis, the methodology applies weighted factors to calculate the Building Priority Factor and the Municipal Energy Poverty Factor. A prioritization analysis for Slovenia demonstrates significant regional disparities in energy savings potential and renovation priorities, with some regions emerging as high-priority targets due to their aging infrastructure and elevated energy poverty levels. Conversely, densely populated urban regions with larger cities show lower prioritization needs. The proposed framework addresses limitations in existing methods by incorporating socio-economic and spatial data, enabling a dynamic and scalable approach to financial incentives. This approach aligns with the Energy Performance of Buildings Directive, providing actionable insights for national renovation plans. The findings highlight the importance of targeted, regionally tailored interventions to maximize energy savings, reduce inequities, and support sustainable development goals.

1. Introduction

1.1. The Energy Inefficiency of Residential Buildings

A significant portion of Europe’s building stock faces critical energy inefficiency challenges. According to the Buildings Performance Institute Europe, more than 40% of the European building stock was constructed before the 1960s [1], predating modern energy efficiency requirements. These buildings contribute substantially to global greenhouse gas (GHG) emissions, waste generation, and the depletion of raw materials. Approximately 75% of European buildings are energy inefficient, accounting for 40% of the EU’s total energy consumption and 36% of its CO₂ emissions [2]. At a time when climate change is a pressing global concern, the energy performance of buildings has emerged as a critical focus area for achieving sustainability targets.

The international commitment to mitigating climate change, particularly through the Paris Agreement [3], underscores the urgency of reducing carbon emissions. The agreement aims to limit the global temperature rise to 1.5 °C above pre-industrial levels, which necessitates a 50% reduction in global carbon emissions by 2030 and achieving climate neutrality by 2050. In line with this goal, the European Union has launched the ’Renovation Wave’, an ambitious initiative aimed at doubling the renovation rate of buildings to address their significant role in energy consumption and emissions [4]. This strategy prioritizes comprehensive interventions, including the revision of the Energy Efficiency and Energy Performance of Buildings Directives (EPBD) to introduce mandatory minimum energy performance standards, strengthen support for building renovation at all levels of public administration, and enhance incentives for both private and public stakeholders. Additionally, the EU aims to ensure well-targeted funding, expand access to technical assistance, and promote integrated solutions for smart buildings, renewable energy integration, and sustainable construction.

By leveraging renovation as a tool to combat energy poverty, promote healthy housing, and decarbonize heating, hot water, and cooling systems—responsible for 80% of residential energy consumption—the EU’s policies mark a pivotal step towards achieving its climate and sustainability goals [5]. These policies include ensuring that all new buildings are zero-emission by 2030, with public authority buildings achieving this standard by 2028. Member States are also tasked with implementing life-cycle global warming potential assessments for buildings, addressing emissions from construction materials to end-of-life disposal.

Residential buildings, in particular, are subject to specific measures to reduce primary energy consumption by at least 16% by 2030 and 20–22% by 2035. Similarly, the revised EPBD mandates the renovation of the 16% worst-performing non-residential buildings by 2030 and the 26% worst-performing by 2033. To harmonize energy performance standards across Member States, the EPBD introduces a classification scale from A to G, where A corresponds to zero-emission buildings (ZEB) and G represents the 15% worst-performing buildings. Member States must ensure even distribution across classes B to F and implement stricter requirements for energy performance certifications (EPCs), including shorter validity periods for lower-performing classes.

Renovation strategies emphasize the worst-performing buildings, aiming to trigger higher renovation rates and deeper energy efficiency improvements. For residential buildings, Member States must ensure that the lowest-performing properties reach at least energy class F by 2030 and class E by 2033. These efforts target not only decarbonization but also broader socio-economic benefits, such as reducing energy poverty and improving the quality of life for residents.

Despite these ambitious targets, the limited resources available for renovation subsidies, such as financial resources (e.g., national budgets, EU funding programs like the Resilience and Recovery Fund) and technical resources (e.g., skilled labor, technical expertise, and project development support), present a significant challenge. Addressing the energy inefficiency of residential buildings requires strategic allocation of funds to maximize the impact of renovations. This necessitates a prioritization framework that identifies buildings with the highest potential for energy savings, GHG emission reductions, and social benefits. By focusing on the worst-performing classes and integrating socio-economic considerations, such a framework can help Member States meet their renovation goals while ensuring equity and sustainability.

1.2. Objective

The energy inefficiency of residential buildings poses a significant challenge for achieving sustainability goals, particularly in light of limited resources for needed renovation financing grant components. This article aims to address this challenge by developing a hybrid MultiCriteria Decision-Making (MCDM) framework for identifying priority renovation groups of residential buildings in Slovenia. The proposed framework integrates building-specific characteristics with broader municipal-level factors, providing a holistic approach to targeting renovation efforts.

By considering the dual dimensions of building performance and municipal socio-economic and structural factors, this hybrid MCDM framework seeks to identify the buildings and locations with the highest potential for energy efficiency improvements. Building-level characteristics, such as energy performance (e.g., kWh/m²) and construction age, offer insight into the technical and structural renovation needs of the building stock. Municipal-level factors, including average income, population density, and the prevalence of energy poverty, add a socio-economic perspective, ensuring that priority interventions align with financial support needs and equity considerations at the regional level.

The objective is to create a decision-making tool that enables policymakers to strategically design targeted renovation programs and allocate respective resources. This approach not only maximizes the impact of limited renovation budgets but also supports Slovenia’s alignment with EU directives on energy efficiency, such as the EPBD. The focus on priority groups facilitates compliance with requirements for upgrading the worst-performing building stock while addressing energy poverty and promoting social and economic benefits.

Ultimately, the methodological framework developed in this article aims to

• Prioritize renovation efforts: by identifying the buildings with the highest potential for energy savings and GHG emission reductions.
• Inform policy decisions: by integrating municipal characteristics to ensure equitable and effective distribution of renovation subsidies.
• Support national and EU goals: by contributing to Slovenia’s progress toward climate neutrality and the energy transition.

This framework represents a scalable and adaptable approach that can be applied to other countries or regions facing similar challenges, providing a valuable tool for policymakers navigating the complexities of energy renovation priorities.

1.3. Relevance: Supporting the Design of National Policies and Targeted Financial Support for Energy-Efficient Renovations

The residential building sector plays a pivotal role in addressing climate change, reducing energy consumption, and alleviating energy poverty. In Slovenia, as in other European Union Member States, achieving these goals requires carefully designed national policies and renovation financial incentives that prioritize energy efficiency while ensuring equitable resource distribution. This paper’s framework provides a vital tool for supporting such efforts and addressing key challenges in renovation policy design and implementation.

1. Aligning with National and EU Policy Goals

The hybrid MCDM framework directly supports Slovenia’s commitment to achieving the objectives outlined in the EPBD and broader climate policies. Specifically, the EPBD mandates are as follows:

By identifying priority groups of residential buildings, the framework facilitates targeted actions to meet these ambitious goals. It ensures that renovation efforts focus on the areas with the highest potential for energy savings and emission reductions, thereby maximizing the impact of national renovation programs.

Moreover, the hybrid MCDM framework also partially aligns with recent recommendations to incorporate multiple benefits of energy efficiency into the EPBD’s cost-optimal methodology [6]. By considering factors such as improved indoor environmental quality, enhanced occupant health, and increased property values, the framework not only addresses energy performance but also promotes broader socio-economic advantages. This comprehensive approach ensures that renovation strategies are both cost-effective and beneficial to society, supporting Slovenia’s sustainable development objectives.

2. Enhancing Resource Efficiency

Public resources are limited, requiring strategic allocation to ensure doing more with less, and bringing in private investment. The proposed framework provides a systematic approach to prioritize funding based on key criteria:

• Building characteristics (e.g., energy performance, age, and renovation potential).
• Municipal factors (e.g., income levels, energy poverty prevalence, and population density).

This targeted approach prevents the misallocation of resources and ensures that funds are directed to areas where they are most needed and most effective.

3. Addressing Energy Poverty

Energy poverty remains a pressing issue in Slovenia, particularly in rural and economically disadvantaged municipalities and regions. By incorporating socio-economic factors into the prioritization framework, this methodology supports the dual objective of improving energy efficiency and alleviating energy poverty. Targeted grants for low-income households and municipalities with high energy poverty rates not only address equity concerns but also contribute to broader social and economic benefits.

4. Supporting Long-Term Planning

The framework’s integration of building characteristics and municipal factors provides policymakers with a decision-support tool for long-term renovation planning. This data-driven approach allows for

• monitoring progress toward national and EU renovation targets;
• adjusting subsidy programs based on evolving energy performance and socio-economic conditions;
• ensuring compliance with EU requirements, such as the introduction of harmonized energy performance classes.

5. Facilitating Stakeholder Engagement

The framework can also enhance collaboration between national governments, local authorities, financial institutions, and building owners. By clearly identifying priority renovation potential and financial needs, it helps stakeholders understand the rationale behind financial incentives distribution and builds support for renovation programs.

This framework offers a robust foundation for designing national policies and targeted grants and subsidies that promote energy-efficient renovations. Furthermore, its adaptability makes it a valuable tool for other countries facing similar challenges in the residential building sector.

1.4. Structure of the Paper

The paper begins with an introduction that outlines the urgency of addressing energy inefficiency in residential buildings, the challenges of limited renovation financial resources, and the objective to develop a framework for identifying priority groups of buildings at the municipal and regional level. The introduction also highlights the study’s relevance in supporting national policies and aligning with EU directives.

Following the introduction, the literature review examines existing studies related to energy efficiency in residential buildings, prioritization approaches, and the integration of socio-economic factors. This section identifies gaps in current methodologies and positions the contribution of this article within the broader research context.

The methodology section describes the proposed framework for prioritizing residential buildings, focusing on building-specific characteristics and municipal-level factors, including income levels and energy poverty. Details are provided on data sources, normalization techniques, weighting methods, and the allocation of regional energy poverty data to municipalities.

The results section presents findings from the application of the hybrid MCDM framework to Slovenia’s residential building stock, identifying priority groups of buildings at both municipal and regional levels. These groups are classified based on building type, construction period, and geographic location, ensuring a structured approach to prioritizing energy renovations. Visualizations such as maps and graphs illustrate key patterns, with a specific focus on differences between urban and rural areas and their implications for renovation strategies.

The discussion evaluates the proposed MCDM framework in comparison with existing approaches, emphasizing its strengths, limitations, and implications for renovation policy and practice. Limitations related to data availability and opportunities for future research are also explored. The article concludes with a summary of key findings, practical contributions to policy design and financial incentives allocation, and recommendations for future research and implementation. This structure provides a systematic exploration of the methodology and its relevance to Slovenia’s energy renovation challenges.

2. Literature Review

2.1. Key Challenges in Improving Energy Performance

2.1.1. Aging Building Stock

A significant proportion of residential buildings in Europe were constructed before modern energy performance standards were established, particularly those built before the 1960s. These aging structures often lack adequate insulation, airtightness, and energy-efficient heating systems, leading to high energy consumption and thermal discomfort for occupants. Renovating such buildings is particularly challenging due to the need for extensive upgrades and compatibility issues with older materials, making the process both technically demanding and costly.

A significant portion of older structures were developed without consideration for energy efficiency, as fossil energy resources were abundant and inexpensive during their construction periods. As a result, they exhibit high thermal losses and require significant energy inputs to maintain indoor comfort, making them some of the largest contributors to energy consumption and GHG emissions in the built environment [7].

The aging building stock presents technical and financial challenges for energy efficiency improvements. Renovation of these structures is often complex due to their outdated construction materials, which can limit compatibility with modern energy-efficient systems. Furthermore, many older buildings hold cultural or historical value, requiring sensitive renovation approaches to preserve architectural heritage while achieving energy efficiency goals [8,9]. Financially, deep renovations of these buildings involve substantial upfront costs, creating barriers for both homeowners and governments aiming to meet energy performance targets. Addressing these challenges requires targeted policies, financial incentives, and technological solutions to support the large-scale renovation of aging buildings [10,11].

2.1.2. Financial Constraints

During the economic boom of the 1960s, Europe experienced a significant surge in residential construction, coinciding with the introduction of the first thermal building codes to address energy efficiency [12]. Despite these early efforts, much of the building stock today remains inefficient, reflecting outdated preferences, limited technologies, and opportunities of the past. As these buildings are expected to remain in use through 2050, their transformation is critical to achieving the goals of the Paris Agreement, reducing CO₂ emissions, and accommodating evolving user needs. However, despite ambitious climate policies at the EU level and comprehensive national frameworks, renovation rates remain insufficient to meet the target of climate neutrality by 2050 [13].

The renovation challenge is multifaceted, but financial barriers are among the most significant. Deep energy renovations, particularly for older, poorly insulated homes, require substantial upfront investments, which can reach as high as €200,000 for detached houses in countries like Belgium [14]. Studies highlight that financial constraints, especially among younger homeowners, are a primary obstacle to renovation uptake [1,8,9,10]. While energy simulations often demonstrate the long-term cost-effectiveness of renovation measures, they frequently neglect the short-term financial barriers, such as inadequate access to capital or high costs of financing [15,16,17].

Reviews of multifamily buildings in temperate climates emphasize cost-effectiveness but seldom address the inability of homeowners to secure funding [18]. The result is a persistent gap between theoretical renovation feasibility and practical implementation, particularly for renovations aiming to achieve nZEB standards. Sebi et al. [14] examine the financial barriers to energy-efficient renovations, particularly in the French residential sector. Their study highlights that while deep renovations are essential for achieving nearly Zero-Energy Building (nZEB) standards, the upfront investment costs remain a major deterrent for homeowners, even when long-term energy savings make the renovations cost-effective. However, their research primarily focuses on high-level financial constraints and does not provide a detailed quantitative assessment of how these constraints vary across different household income groups or regional policies.

Similarly, Almeida and Ferreira [19] propose a methodology for evaluating energy renovation measures using case studies across Austria, Czechia, Portugal, Denmark, Spain, and Sweden. Their approach applies cross-country comparisons to identify best practices in renovation policies, financial incentives, and technical solutions. While their study provides valuable insights into renovation feasibility across different regulatory environments, it primarily relies on macro-level case studies, limiting its applicability for granular, building-level prioritization strategies.

Surveys have also consistently identified financial limitations as a common reason homeowners delay or avoid renovations [20]. These barriers underscore the urgent need for holistic strategies, including robust government policies, targeted subsidies, grants, and accessible financial instruments, to overcome market and non-market obstacles [21,22]. Without addressing these issues comprehensively, achieving the widespread transformation of Europe’s building stock will remain an elusive goal.

The challenges of insufficient renovation rates and financial barriers highlight the need for a hybrid approach that integrates technical, financial, and socio-economic factors. Unlike existing methods focused solely on energy performance or cost-effectiveness, the proposed framework combines granular building-level data with municipal and regional insights to prioritize renovations based on both energy savings potential and socio-economic need. This approach addresses key barriers, ensures equitable resource allocation, and accelerates renovation rates for a sustainable and inclusive energy transition.

2.1.3. Energy Poverty

Energy poverty, defined as the inability of households to afford adequate heating, cooling, or lighting, remains a significant barrier to improving energy performance in the residential sector. This phenomenon creates a cyclical problem where high energy bills, combined with inefficient building stock, exacerbate financial stress, leaving vulnerable households unable to invest in energy-saving measures. Addressing energy poverty requires policymakers to balance short-term relief efforts with long-term strategies that align with broader climate and energy efficiency goals [23,24,25,26].

One of the primary causes of energy poverty in developed countries is the low energy efficiency of housing. Studies have shown that energy-poor households often reside in energy-inefficient dwellings with outdated or inadequate heating systems, making it challenging to achieve indoor thermal comfort [23,27,28]. Barrella et al. highlight the concept of “hidden energy poverty,” where low-income households consume far less energy than needed, living in thermal discomfort due to financial constraints [24]. This dual issue—social and environmental—underscores the limitations of merely subsidizing energy bills. While such subsidies provide immediate relief, they may inadvertently increase the carbon footprint of the residential sector unless paired with energy efficiency interventions [23,25].

To address energy poverty effectively, sustainable housing choices and targeted efficiency measures must be prioritized too. Thermal modernization, as advocated by EU officials and supported by national governments, should be complemented by shifts in housing preferences. Single-family homes, while appealing to many, pose additional challenges related to heating, cooling, and maintenance, which exacerbate energy affordability issues.

Energy efficiency interventions for vulnerable households typically fall into two categories: active and passive measures [25]. Active measures involve upgrading building systems, such as replacing outdated HVAC equipment with more efficient alternatives. Passive measures, on the other hand, focus on improving the thermal insulation of buildings by renovating windows, walls, and other elements of the thermal enclosure. Both approaches are critical for reducing energy consumption, enhancing indoor comfort, and alleviating energy poverty [23,25].

Addressing energy poverty requires an integrated approach that combines financial incentives with deep renovations of inefficient buildings. By doing so, policymakers can tackle both the immediate social needs of vulnerable households and the long-term environmental goals of reducing emissions and primary energy consumption. This dual focus ensures that efforts to alleviate energy poverty contribute to sustainable energy transitions.

2.1.4. Disparities Between Urban and Rural Areas

Urban areas benefit from greater access to funding, infrastructure, and technical expertise, which accelerates the adoption of energy-efficient measures. In contrast, rural areas face unique challenges, such as older and more dispersed building stocks, limited availability of skilled professionals, and higher logistical costs for renovation projects. These disparities contribute to unequal progress in achieving energy performance goals, emphasizing the need for targeted policies to bridge the urban-rural divide [26].

In addition to structural and logistical challenges, differences in energy consumption behavior between urban and rural dwellers further complicate energy efficiency initiatives. Rural households often have higher energy demands due to traditional heating and cooking methods, while urban households are more likely to adopt energy-intensive appliances such as air conditioning [27]. Addressing these behavioral and structural disparities is crucial for designing effective energy conservation policies that ensure equitable and sustainable energy efficiency improvements across regions.

2.1.5. Balancing Environmental Impact

While improving energy performance is crucial for reducing GHG emissions, the renovation process itself can have environmental impacts. The production, transportation, and disposal of construction materials generate emissions and waste, potentially offsetting some of the environmental benefits of renovations. A life-cycle approach that considers the environmental impact of materials and processes is essential to ensure that energy-efficient renovations contribute positively to long-term sustainability goals.

Sustainability in buildings encompasses multiple dimensions, including environmental, economic, social, ecological, technical, and technological aspects [28]. Green and sustainable buildings play a crucial role in mitigating the adverse impacts of buildings on the environment, economy, and society [29]. Achieving sustainability in buildings involves reducing GHG emissions through strategies such as minimizing embodied energy, utilizing natural resources efficiently, recycling materials, ensuring building safety, and meeting IEQ requirements [30]. Sustainable buildings, often referred to as green buildings, are better equipped than conventional ones to reduce GHG emissions and contribute to achieving commitments like Net-Zero Carbon Buildings [31].

Energy efficiency emerges as a foundational concept for realizing sustainable, green, low-energy, ultra-low-energy, and zero-energy buildings [32]. It is widely regarded as a key factor in the development of green and sustainable buildings, as it directly addresses the environmental, economic, and social impacts of the building sector [33]. By improving energy performance in both new and existing buildings, energy efficiency offers a fast-track solution for minimizing these impacts [34].

Achieving high energy efficiency in buildings requires the implementation of advanced methods and technologies. These include energy performance benchmarking [35], energy-saving measures [36], integration of renewable energy systems like building-integrated photovoltaic façades [37], innovative energy concepts, and resource-efficient materials [38]. Additional approaches such as energy modeling [39], overall thermal transfer value calculations [40], and multiobjective search for optimization further enhance energy efficiency. Moreover, strategies like integrating wind turbines in tall buildings, using high-performance building envelopes [41], and installing efficient light fixtures and photovoltaic systems exemplify the innovative practices essential for reducing environmental impacts and achieving sustainability in the built environment.

2.2. Approaches to Identifying Buildings for Energy Renovation: Limitations and Opportunities

The identification of buildings requiring energy renovations typically relies on three main approaches: constructed year-based identification, building energy benchmarking, and energy auditing [42]. Constructed year-based methods, widely used in many countries, select buildings for renovating based on their age, assuming that energy performance declines over time. For instance, Bardazzi and Pazienza [43] highlight that several countries prioritize older buildings for renovation subsidies, as these structures often exhibit lower energy performance. However, as Aksoezen et al. [44] demonstrate, there is often no direct correlation between a building’s age and its actual energy consumption, since renovations and maintenance can significantly alter energy performance over time. This limitation underscores the risk of misallocating resources when relying solely on age as a criterion.

Building energy benchmarking compares a building’s energy consumption to a reference benchmark, effectively identifying underperforming buildings. This method is widely used in regions with established data infrastructure, enabling more targeted renovation efforts. However, benchmarking requires extensive data collection and normalization to account for variables such as weather, building geometry, and occupant behavior, making it resource-intensive and less feasible in regions with limited data availability [42].

Energy auditing, conducted by qualified professionals, provides precise insights into a building’s energy performance through on-site assessments and simulations. While highly accurate, this method is time-consuming and costly, limiting its scalability for large-scale applications. Aksoezen et al. [44] further point out that even energy audits may fail to capture the broader patterns necessary for prioritizing renovations at a national scale.

These traditional approaches predominantly focus on individual building-level analyses, relying on parameters such as age, benchmarking scores, and detailed audits. However, they often fail to address the broader need for identifying groups of buildings requiring renovation on a national scale, incorporating factors such as regional characteristics in building stock age distributions and socio-economic contexts. This gap underscores the importance of scalable and comprehensive frameworks, like the hybrid MCDM approach proposed in this paper, which integrates energy performance, structural resilience, socio-economic factors, and regional data. By addressing these limitations, the proposed methodology provides a more holistic and effective tool for prioritizing energy renovations, aligning with national energy efficiency and sustainability goals.

Sankelo et al. [45] emphasize the use of bottom-up modeling to prioritize renovation by addressing building heterogeneity and targeting thermal inefficiencies. Their approach optimizes financial resource allocation through refined datasets and probabilistic record linkage, focusing on areas with the highest energy-saving potential. Similarly, Mucedero and Monteiro [46] propose a framework integrating seismic risk, energy performance, and socio-economic vulnerability to prioritize building renovations, but their reliance on broad datasets and static indicator weighting limits adaptability and depth.

Karmellos [47] focuses on optimizing energy efficiency in residential and small commercial buildings using primary energy consumption and initial investment costs as criteria. The methodology, based on a MultiObjective Mixed-Integer Non-Linear Problem and the weighted sum method, is implemented through a MATLAB v. 23 tool, offering practicality and scalability. While the study demonstrates adaptability across climates with case studies in Edinburgh and Athens, its focus on technical and cost-based factors limits the integration of socio-economic considerations like energy poverty, highlighting the need for broader frameworks like the hybrid MCDM approach proposed in this paper.

Shim et al. [42] propose a methodology for identifying buildings requiring renovation using the Energy Signature (ES) approach, which assesses energy inefficiencies through parameters such as balance-point temperature and heating sensitivity. This method requires minimal input data, is scalable for large-scale applications, and provides intuitive results, making it accessible to stakeholders. While the approach effectively diagnoses energy inefficiencies caused by poor building performance or occupant behavior, it does not explicitly account for socio-economic factors like energy poverty. Carbonara and Tiberi [48] propose a methodology for prioritizing energy efficiency measures in historical buildings through localized thermal zone interventions. Using TRNSYS dynamic simulations, they identify targeted improvements like thermal insulation and window replacements while preserving architectural integrity. Their study emphasizes resource optimization and energy savings in heritage contexts but focuses on thermal performance in a single case study. Unlike the hybrid MCDM framework proposed here, their approach lacks socio-economic integration and dynamic multicriteria weighting, limiting its applicability to comprehensive renovation strategies.

3. Methodology

The proposed methodology follows a structured, data-driven approach to prioritizing residential buildings for energy efficiency renovations. It integrates a hybrid bottom-up and top-down analysis, combining detailed building-level data with municipal and regional socio-economic indicators to ensure an equitable and efficient prioritization framework (Figure 1). The process consists of four key stages: Scope Definition, Processing Steps, Integration, and Results and Application.

The scope of the analysis encompasses 888,484 households across 212 municipalities in 12 regions. This broad dataset enables a comprehensive evaluation of renovation needs, accounting for variations in building stock, energy performance, and socio-economic conditions.

Slovenia is divided into 12 statistical regions, each with distinct socio-economic and energy performance characteristics. These regions, identified with unique numerical IDs for consistency throughout the analysis, include Pomurska (1), Podravska (2), Koroška (3), Savinjska (4), Zasavska (5), Posavska (6), Jugovzhodna Slovenija (7), Osrednjeslovenska (8), Gorenjska (9), Primorsko-notranjska (10), Goriška (11), and Obalno-kraška (12). These regional identifiers are used consistently across datasets and analyses in this study to ensure comparability and clarity when presenting results related to energy efficiency, energy poverty, and renovation prioritization.

The processing steps involve two parallel assessments. The bottom-up analysis focuses on building classification, distinguishing structures by type and construction period, alongside an evaluation of energy efficiency and potential savings based on renovation status. A Building Priority Factor (BPF) is calculated by weighting energy consumption, emissions, and savings potential. Concurrently, the top-down analysis estimates municipal energy poverty levels using indicators such as income index, building age distribution, and population density. This results in a Municipal Energy Poverty Factor (MEPF), which accounts for socio-economic vulnerabilities.

This comprehensive framework ensures a balanced prioritization approach that aligns technical building performance with socio-economic needs, thereby optimizing the impact of renovation investments.

The integration phase merges these two analytical layers, computing a Final Priority Factor (FPF) as the product of BPF and MEPF. This factor is normalized across all buildings, allowing for a structured ranking that informs regional and municipal prioritization efforts.

Finally, in the results and application stage, buildings are classified into priority categories (high, medium, low), visualized through priority maps and grouped rankings. The outcomes directly inform policy recommendations, supporting data-driven subsidy allocation and national renovation planning in alignment with EPBD goals.

3.1. Data Sources

The case analysis for Slovenia relies on multiple datasets to ensure a comprehensive assessment of building energy efficiency and socio-economic factors. The data sources and their relevance to the study are described below:

3.1.1. Building Classification

Building-specific data, including energy efficiency and construction year, are sourced from the Geodetic Administration of the Republic of Slovenia (GURS) and public datasets from the Slovenian Eco Fund.

• Energy efficiency of buildings calculation is based on data that derive from the GURS database and are supplemented by publicly available data from Eco Fund, which provides insights into energy consumption and renovation potential at the individual building level for each household (Figure 2a).
• Building age is extracted from the GURS dataset, which records the construction year for each property. This granularity ensures that the analysis captures the structural characteristics of buildings across the Slovenian building stock.

The GURS database offers a unique advantage as it provides comprehensive data at the level of individual buildings, enabling detailed and location-specific analyses. For this study, residential buildings were classified into two main categories based on their structural characteristics:

• Single-Family Houses (SFH): Detached or semi-detached houses designed for occupancy by a single household or containing two separate housing units.
• MultiFamily Houses (MFH): Apartment buildings or other multiunit dwellings with three or more housing units.

This classification was derived from the Geodetic Administration of the Republic of Slovenia (GURS) database, which provides detailed property records, including building size, type, and construction period. The distinction between these categories is critical, as different building types have varying renovation needs, energy consumption patterns, and technical feasibility for energy efficiency improvements.

The analysis applied different weighting factors to each building type based on their respective renovation potential, ensuring that priority assessments account for structural differences. By distinguishing these building types, the prioritization framework effectively identifies the most suitable renovation strategies for each category.

3.1.2. Municipal and Regional Socio-Economic Data

Socio-economic data at the municipal and regional level are sourced from the Statistical Office of the Republic of Slovenia (SURS). These datasets include:

• Municipal income index: This metric reflects the relative economic capacity of municipalities, essential for understanding disparities in energy affordability (Figure 2b).
• Regional energy poverty shares: These values represent the proportion of households experiencing energy poverty within each region, serving as a critical input for assessing socio-economic vulnerabilities.
• Population density: This data highlights the structural and logistical challenges faced by municipalities with varying settlement patterns, enabling equitable resource allocation.

SURS datasets provide aggregated data at municipal and regional levels, complementing the granular building-level data from GURS. Together, these sources enable a holistic analysis that integrates physical building characteristics with broader socio-economic, municipal, and regional contexts.

3.1.3. Data Integration Process

The data integration process was essential for constructing a comprehensive framework to prioritize residential buildings for energy renovations. It combined detailed building-level data with aggregated socio-economic and regional information, enabling a holistic analysis of energy inefficiency and socio-economic vulnerability. All data integration and analysis steps were executed using Python, leveraging its robust capabilities for data processing, merging, and validation.

Building-level data sourced from the GURS and Eco fund provided detailed insights into energy performance and structural characteristics. Attributes such as construction year and floor area, extracted from the GURS database, were crucial for assessing energy efficiency and renovation potential. This granularity allowed for an in-depth evaluation of energy inefficiencies specific to individual buildings.

Municipal and regional socio-economic data from the SURS complemented the building-level dataset. By incorporating the municipal income index, regional energy poverty shares, and population density variables, the analysis captured broader contextual disparities, ensuring an equitable allocation of resources across different regions.

The integration process began with data standardization, aligning geographic identifiers and ensuring consistency in measurement units. Missing data points were addressed using imputation techniques in Python to maintain dataset completeness. Regional energy poverty shares were allocated to municipalities through a weighted approach that considered income disparities, the proportion of older buildings, and population density. This ensured that socio-economic challenges unique to each municipality were accurately represented in the analysis.

The final integrated dataset comprised building-specific attributes, energy performance metrics, and socio-economic indicators. Each building entry included its physical characteristics, energy inefficiency metrics, and socio-economic context, facilitating the calculation of the BPF and its integration with the MEPF. This unified approach enabled the prioritization of buildings based on both technical inefficiencies and socio-economic vulnerabilities.

To ensure accuracy and reliability, the integrated dataset underwent rigorous validation. Cross-referencing between datasets resolved inconsistencies, and sensitivity analyses evaluated the robustness of the weighted allocation of regional energy poverty shares. Python’s tools for debugging and testing ensured the accuracy of calculations, enhancing the credibility of the final dataset.

This integration process resulted in a robust and comprehensive dataset that serves as a strong foundation for the prioritization framework. By merging granular building-level data with socio-economic factors, the methodology enables a data-driven and equitable approach to identifying priority buildings for energy renovations. This approach supports the dual objectives of improving energy efficiency and addressing socio-economic disparities.

3.2. Calculation of Priority Indicators

3.2.1. Building Age Periods

Buildings were classified into distinct age periods based on their year of construction, drawing from established typologies such as the TABULA project [49]. These periods reflect historical trends in construction practices, material use, and energy efficiency standards, providing critical insights into the energy performance and renovation potential of the building stock. The periods considered in this analysis are: before 1945, 1946–1970, 1971–1980, 1981–2002, 2003–2008, and 2009 to today.

The classification highlights the correlation between building age and energy inefficiency. Older buildings, particularly those constructed before 1945 and between 1946 and 1970, exhibit the greatest potential for energy savings. These structures are characterized by outdated construction methods, lack of insulation, and inefficient heating systems, which significantly increase their energy demand. Buildings constructed between 1971 and 1980 also display notable inefficiencies, as they were built during a transitional phase where awareness of energy performance was growing but regulations were still insufficient.

The pre-1945 category encompasses buildings with diverse architectural styles and construction methods, some of which have historical significance. While this study focuses on energy efficiency potential, it should be acknowledged that historic buildings may require tailored renovation approaches due to preservation constraints. The classification prioritizes thermal inefficiencies rather than heritage value, but future research could refine this by distinguishing sub-periods within pre-1945 structures to account for heritage considerations.

More recent periods, such as 1981–2002, reflect the gradual introduction of stricter energy regulations, leading to moderate improvements in energy performance. However, these buildings still lag behind contemporary standards, particularly in terms of insulation and heating efficiency. Buildings from 2003 to 2008 were constructed under significantly improved regulations and exhibit relatively better energy performance, while those built after 2009 generally adhere to nZEB standards, offering limited opportunities for major energy savings.

To account for these variations in renovation potential, weights were assigned to each building age period during the prioritization process. These weights reflect the relative importance of addressing inefficiencies and achieving energy savings for each period. While this section focuses on the classification and characteristics of building periods, the methodology for assigning weights and incorporating them into the prioritization framework is detailed in subsequent sections.

This classification not only enables a targeted approach to identifying high-priority buildings but also aligns Slovenia’s building stock analysis with broader European standards. By linking building age to renovation potential, this framework provides a robust basis for tailoring renovation strategies to the specific needs of each age period.

3.2.2. Bottom-Up Modeling of Energy Efficiency

The bottom-up modeling approach to building energy efficiency emphasizes the detailed assessment of individual buildings, integrating both their current energy performance and their renovation potential and is based on [50,51]. This method allows for a highly granular evaluation, capturing variations across building types, construction periods, and renovation histories. The inclusion of data on each building’s current energy status ensures that the analysis accurately reflects the reduced renovation potential for buildings that have already undergone energy efficiency improvements.

Key to this methodology is the differentiation between buildings based on their renovation status. Buildings that have not undergone any energy efficiency interventions are identified as having the highest potential for energy savings, as they typically lack modern insulation, efficient heating systems, or other energy-saving measures. Conversely, buildings that have undergone partial or complete renovations are assessed with reduced potential for energy savings, as their energy performance has already been enhanced.

This modeling framework leverages data from sources like the GURS and Eco Fund, which provide detailed records of building attributes, including age, floor area, and renovation status. These attributes are combined with energy performance indicators to estimate both current energy demand and achievable savings through further renovations. Buildings are categorized based on their potential savings, considering:

• Pre-renovation energy efficiency: Buildings constructed under older standards generally exhibit higher baseline energy consumption, offering significant opportunities for savings.
• Post-renovation status: Renovated buildings show lower baseline energy consumption, with smaller margins for improvement.

By incorporating this dynamic assessment, the model ensures that renovation strategies are both efficient and equitable, prioritizing buildings with the highest potential impact.

The integration of current building status into energy efficiency modeling provides a more realistic estimation of renovation potential, ensuring that policy decisions are grounded in an accurate understanding of the building stock. This nuanced approach enhances the reliability of energy performance projections and supports data-driven strategies for achieving national, regional, and municipal energy goals.

3.2.3. Socio-Economic Factors

Energy poverty is a critical socio-economic factor that significantly impacts the prioritization of residential buildings for energy renovation. However, in many cases, data on energy poverty is only available at a regional level, making it challenging to assess its distribution at the municipal or building level. To address this limitation, a methodological framework is proposed to estimate energy poverty at the municipal level, ensuring a more accurate and equitable basis for prioritization. This section outlines that methodology and its rationale.

Energy poverty is not uniformly distributed within a region. Municipalities vary significantly in their socio-economic and structural characteristics, leading to differences in the prevalence and severity of energy poverty. A regional average may mask these intra-regional disparities and result in misallocation of financial resources. For example:

• Income disparities: Wealthier municipalities are likely to experience lower levels of energy poverty compared to economically disadvantaged areas.
• Housing stock characteristics: Municipalities with older or poorly insulated buildings tend to have higher energy poverty rates.
• Rural versus urban differences: Rural municipalities often face higher energy costs due to lower population densities and limited access to infrastructure, exacerbating energy poverty.

These variations highlight the need for a methodology that translates regional energy poverty data into more granular municipal estimates.

Data-Driven Approach for Estimating Energy Poverty at Municipal Level

To estimate energy poverty at the municipal level, the following data are utilized:

• Regional energy poverty share (%): The proportion of households experiencing energy poverty within the entire region.

Municipal socio-economic and structural data:

a.

Average household income: Reflecting economic conditions and vulnerability.

b.

Percentage of older buildings: Capturing the energy efficiency of the local housing stock.

c.

Population density: Indicating urban versus rural characteristics and infrastructure accessibility.

Rationale for Weighted Distribution

This methodology accounts for the underlying factors that influence energy poverty at the municipal level, addressing several limitations of a simple proportional allocation based on household numbers:

• Capturing income disparities: Municipalities with lower incomes are more likely to experience energy poverty, making income a critical factor.
• Reflecting housing stock conditions: The age and efficiency of buildings directly impact energy consumption and costs.
• Accounting for rural challenges: Rural municipalities often face unique challenges, including higher energy costs and fewer energy-saving options.
• Reducing financial resource misallocation: By considering these factors, the methodology ensures that interventions are directed to areas where they are most needed.

3.2.4. Geographic Factors: Population Density

Population density serves as a critical geographic factor influencing the prioritization of energy renovations. Regions with lower population densities, typically rural areas, face distinct challenges compared to urbanized regions. These challenges include higher logistical costs for material and workforce mobilization, limited access to skilled professionals, and reduced economies of scale in renovation projects. Conversely, urban areas benefit from concentrated resources, infrastructure, and expertise, which facilitate the implementation of energy efficiency measures.

By incorporating population density into the prioritization framework, the analysis accounts for these spatial disparities. Lower-density municipalities are assigned higher weights to ensure that renovation efforts address the unique barriers faced by rural areas. This approach promotes equitable resource allocation, ensuring that both urban and rural regions can progress toward national energy efficiency goals.

3.3. MCDM Model with Integration of Spatial and Building Data

The prioritization framework developed in this study utilizes a hybrid MCDM model that integrates granular building-level data with socio-economic and spatial factors. This hybrid approach enables the evaluation of energy renovation priorities across a large and diverse building stock while accounting for both physical inefficiencies and regional disparities. The flexibility and scalability of the MCDM framework make it particularly suitable for addressing complex decision-making problems in energy policy and urban as well as rural planning.

The core principle of the MCDM model lies in its ability to combine data from multiple domains into a unified framework. Building-level data provide a detailed understanding of energy performance, renovation potential, and structural characteristics. These granular inputs are critical for capturing variations in energy efficiency at the level of individual buildings. Complementing this, socio-economic and spatial data from the SURS offer insights into disparities in energy poverty, income levels, and population densities across municipalities and regions. Together, these datasets create a holistic picture of the challenges and opportunities for energy renovations.

The MCDM model applies a systematic process for integrating these diverse datasets. Building-level data, including energy consumption, potential savings, and GHG emissions, are normalized and aggregated into a Building Priority Factor. This factor reflects the intrinsic energy inefficiency of each building. Concurrently, municipal-level factors, such as income disparities, building age distributions, and population densities, are combined into a MEPF. These two components are integrated to compute a Final Priority Factor for each building, ensuring that both energy inefficiencies and socio-economic vulnerabilities are represented in the prioritization.

The framework is distinguished by its incorporation of spatial and demographic variability. By including regional and municipal factors in the prioritization process, the model ensures that renovation efforts address socio-economic disparities and promote equitable resource allocation. Moreover, the use of Python for data integration and analysis facilitates the processing of large datasets and the implementation of advanced weighting and normalization techniques, ensuring the reliability and robustness of the results.

In addition to its methodological strengths, the MCDM framework provides actionable insights for policymakers. The outputs of the model enable the identification of high-priority buildings and regions, supporting the design of targeted subsidy programs and energy policies. This MCDM model demonstrates the importance of integrating diverse datasets to address the multifaceted challenges of energy renovation. The subsequent section presents the detailed methodological steps, equations, and weighting approaches used to operationalize the framework.

3.4. Comprehensive Methodology for Prioritization Framework

The methodology provides a robust, scientific approach to prioritizing residential buildings for energy renovations. It integrates critical factors such as energy efficiency, potential energy savings, GHG emission reductions, and socio-economic considerations to identify buildings and regions with the highest impact potential. These parameters represent key pillars of sustainability, particularly in the built environment, where energy efficiency improvements contribute to carbon reduction, social equity, and long-term environmental benefits.

While material recycling and indoor environmental quality (IEQ) are also relevant to sustainable renovations, this study focuses on energy-related parameters due to their direct alignment with renovation policy objectives and the Energy Performance of Buildings Directive (EPBD).

Material recycling, which involves considerations such as embodied carbon and construction waste, plays an increasingly important role in circular economy approaches. However, due to limited large-scale life-cycle data, these factors were not integrated into the current methodology. Future adaptations of this framework could incorporate life-cycle assessments (LCA) to address embodied carbon reductions and material efficiency.

Similarly, IEQ factors such as air quality, ventilation, and thermal comfort significantly impact occupant well-being but typically require on-site measurements or advanced simulation models. As this study focuses on large-scale, data-driven prioritization, these aspects were not included in the current framework but could be incorporated into future refinements.

By maintaining a sustainability-driven approach to energy renovations, this methodology provides a foundation that can be expanded in future research to include additional environmental and social considerations as data and policy frameworks evolve.

3.4.1. Incorporating Energy Efficiency and Emissions

The first step involves evaluating the energy and environmental performance of individual buildings. This ensures that the prioritization framework targets buildings that are the most energy-inefficient and contribute significantly to GHG emissions.

Inputs

The methodology considers the following input parameters:

• Energy use (Q_h,nd,an, kWh/(m²an)): A measure of the current energy inefficiency of a building, normalized to its floor area.
• Potential energy savings (S_potential, kWh/m²): An estimate of the potential energy savings achievable after renovation, derived from the building’s initial condition and potential upgrades.
• GHG emissions (E_GHG, kg CO₂/m²): The current carbon dioxide equivalent emissions associated with the building’s energy use per square meter.
• Building age factor (B_age): A scalar representing the likelihood of suboptimal energy performance due to outdated construction standards.

Calculations

• Building Priority Factor (BPF)—Data-Driven Weighting

Unlike traditional approaches that assume equal parameter influence or rely on subjective expert weighting (e.g., Analytic Hierarchy Process or pairwise comparisons), this study employs a data-driven weighting approach where weights are dynamically derived from actual building and municipal characteristics.

The BPF, as defined in Equation (1), is calculated as:

$$BPF=(F_{energy}+F_{emissions}+F_{savings})\times B_{age}$$

(1)

where:

• $F_{energy}, F_{emissions} \mathrm{a}\mathrm{n}\mathrm{d}{ F}_{savings}$ are computed directly from empirical building data
  • $F_{energy}={\displaystyle \frac{Q_{h,nd,an}}{\mathrm{m}\mathrm{a}\mathrm{x}\left(Q_{h,nd,an}\right)}}$, the normalized energy consumption factor
  • $F_{emissions}={\displaystyle \frac{E_{GHG}}{\mathrm{m}\mathrm{a}\mathrm{x}\left(E_{GHG}\right)}}$, the normalized emissions factor
  • $F_{savings}={\displaystyle \frac{S_{potential}}{\mathrm{m}\mathrm{a}\mathrm{x}\left(S_{potential}\right)}}$, the normalized savings factor
• $B_{age}$ introduces a weight for building age, prioritizing older, less efficient buildings.

2.

Normalization Across All Buildings

To ensure comparability across the dataset, the BPF for each building is normalized relative to the total BPF values of all buildings:

$$F_{normalized}={\displaystyle \frac{BPF}{\sum BPF}}$$

(2)

where:

• $F_{normalized}$ is the normalized priority factor for an individual building
• $\sum BPF$ is the sum of all BPF values in the dataset

3.4.2. Integrating Socio-Economic Aspect

To ensure the prioritization framework addresses socio-economic disparities, socio-economic factors are integrated by distributing energy poverty across municipalities in a region. This step introduces a MEPF, which reflects the socio-economic and structural conditions of each municipality. The MEPF is further incorporated into the overall prioritization process to ensure equitable targeting of renovation efforts.

The regional energy poverty share (EPS_region) is a measure of the proportion of energy-poor households in a given region. For instance, if 8.3% of households in the Podravje region are classified as energy-poor, this share is allocated across the municipalities within the region based on their socio-economic and structural characteristics.

Three key factors are calculated for each municipality to capture socio-economic and structural differences:

• Income Factor (F_income)

The income factor reflects the economic capacity of households in a municipality relative to the regional average:

$$F_{income}={\displaystyle \frac{Average Regional Income}{Average Municipal Income}}$$

(3)

Municipalities with lower average incomes receive a higher weight, indicating greater vulnerability.

2.

Building Age Factor (F_age)

Municipalities with a higher share of older, less energy-efficient buildings are assigned a higher weight. This accounts for the share of older buildings within a municipality and is different per municipality and per building period.

$$F_{age}=Percentage of older buildings in the municipality$$

(4)

3.

Population Density Factor (F_density)

This addresses logistical and infrastructural challenges in sparsely populated municipalities. Lower population density municipalities (often rural) are assigned a higher weight, as they face higher energy costs and less access to efficient infrastructure.

$$F_{density}={\displaystyle \frac{Average Regional Population Density}{Municipal Population Density}}$$

(5)

4.

Municipal-Level Weighting: Integrating Socio-Economic Factors

At the municipal level, a similar data-driven approach is applied to determine (W_municipal), the weight assigned to each municipality in the final prioritization framework

$$W_{municipal}=F_{income}\times F_{age}\times F_{density}$$

(6)

Each of these factors is computed systematically based on municipal characteristics rather than assigned manually or arbitrarily.

5.

Normalize Municipal Weights

The municipal weights are normalized to ensure that their sum equals 1 across all municipalities in the region:

$$W_{normalized}={\displaystyle \frac{W_{municipal}}{\sum W_{municipal}}}$$

(7)

where:

• $W_{municipal}$ is the normalized weight for each municipality
• $\sum W_{municipal}$ is the total weight of all municipalities in the region

6.

Distribution of the Regional Energy Poverty Share

Distributing the EPS_region involves allocating the total energy poverty burden of a region across its municipalities based on their normalized weights. This step ensures that municipalities with greater socio-economic challenges, older building stocks, or sparse populations are given priority in energy renovation efforts.

The objective of this step is to create an equitable allocation of the regional energy poverty share to municipalities, reflecting their relative need and structural challenges. This allocation forms the MEPF, which is integrated into the final prioritization framework.

The normalized weights are used to allocate the regional energy poverty share to each municipality:

$${EPS}_{municipality}={EPS}_{region}\times W_{Normalized}$$

(8)

where:

• ${EPS}_{municipality}$ is the energy poverty share of the municipality
• ${EPS}_{region}$ is the energy poverty share of the region

3.4.3. Combining Building and Municipal Factors

The third step integrates building-level and municipal-level data into a unified prioritization framework. This approach ensures that both the physical characteristics of buildings and the socio-economic conditions of their locations are considered, allowing for a holistic prioritization of energy renovation efforts.

• Final Priority Factor Formula

The Final Priority Factor (FPF_building) for each building is calculated by combining the BPF, which encapsulates the energy and environmental aspects of the building, and the MEPF, which represents the socio-economic and structural conditions of the municipality where the building is located. The formula is expressed as:

$${FPF}_{building}={BPF}_{building}\times {MEPF}_{municipality}$$

(9)

where:

• ${FPF}_{building}$ is the final priority factor for the building
• ${BPF}_{building}$ is the building priority factor, as calculated
• ${MEPF}_{municipality}$ is the municipal energy poverty factor, as calculated

This formula combines the localized socio-economic context with the intrinsic characteristics of individual buildings to produce a comprehensive prioritization score.

2.

Normalize Final Priority Factors

To ensure comparability and facilitate ranking across all buildings in the dataset, the Final Priority Factor for each building is normalized. Normalization ensures that all FPF_building values are expressed as a fraction of the total priority factors in the dataset. The normalized formula is given as

$${FPF}_{normalized}={\displaystyle \frac{{FPF}_{building}}{\sum {FPF}_{building}}}$$

(10)

where:

• ${FPF}_{building}$ is the normalized final priority factor
• $\sum {FPF}_{building}$ is the sum of all final priority factors across the dataset

3.5. Statistical Measures in Regression Analysis

To ensure a systematic, data-driven approach to prioritizing energy renovations, a regression model was employed to analyze the relationship between key regional factors and the Regional Final Priority Factor (RFPF). The purpose of this analysis is not to validate RFPF as an independent variable but rather to examine the relative influence of different socio-economic and building-related characteristics on the final prioritization scores. This approach provides additional insights into how various parameters contribute to renovation prioritization and helps assess the internal consistency of the framework.

Unlike traditional ranking systems, which may rely on subjective assessments or oversimplified weights, this statistical approach enhances transparency, accuracy, and replicability, ensuring that prioritization decisions are quantifiable and data-driven.

The regression model evaluates the impact of average energy poverty, building age, income, and population density on RFPF. While these parameters were already incorporated into the prioritization framework, the regression analysis serves as a secondary assessment to

• Identify which factors have the strongest statistical influence on the final priority rankings.
• Assess whether the weighting of variables in the prioritization framework aligns with observed regional trends.
• Detect potential over-weighting or under-weighting of specific criteria, ensuring that no single factor dominates the final prioritization rankings disproportionately.

To assess the reliability and significance of these relationships, the following statistical measures were used:

• Coefficient (Coef): The coefficient quantifies the direction and magnitude of each independent variable’s effect on RFPF. A positive coefficient suggests that an increase in the independent variable leads to a higher priority for renovations, whereas a negative coefficient implies a lower prioritization need. This is particularly important in identifying whether socio-economic challenges or structural inefficiencies drive the need for renovations.
• Standard Error (Std Error): This metric measures the variability in the estimated coefficient. Lower standard errors indicate more precise and reliable estimates, while higher values suggest greater uncertainty. In the context of policymaking, ensuring the stability of coefficient estimates helps avoid the misallocation of resources based on statistical noise rather than meaningful trends.
• t-Statistic (t): The t-statistic is a measure of how strongly an independent variable affects the dependent variable. Higher absolute values indicate stronger relationships between a factor and the need for renovation prioritization. This helps to distinguish significant drivers of renovation needs from those with minimal influence.
• p-Value (p): The p-value determines whether a given variable significantly contributes to the model. A value below conventional thresholds (e.g., 0.05 or 0.1) indicates strong evidence that the factor plays a critical role in determining renovation priorities. If a variable has a high p-value, it suggests that its relationship with RFPF may be due to random variation rather than a true underlying effect.
• Confidence Interval (0.025–0.975): A 95% confidence interval provides a range in which the true effect size of each variable is likely to fall. If the interval does not include zero, it suggests that the factor has a meaningful impact on regional prioritization. This is particularly useful in avoiding false positives, ensuring that only the most reliable indicators are used for policymaking.

By conducting this statistical assessment, we provide additional transparency and interpretability to the framework, allowing policymakers to understand which socio-economic and energy performance parameters drive prioritization outcomes most strongly.

Additionally, the regression was conducted using Ordinary Least Squares (OLS) to ensure a robust and interpretable relationship between regional characteristics and prioritization rankings. The analysis was performed on previously normalized data, ensuring comparability across factors without introducing bias from differing numerical scales.

While the regression analysis does not serve as an external validation of RFPF, it provides valuable insights into how different factors contribute to prioritization rankings and highlights potential areas for refinement in future iterations of the framework.

3.6. Implementation of the Computational Framework

To efficiently process the large-scale dataset used in this study, we developed a Python-based computational framework to automate the analysis of 888,484 households, as shown in Figure 1. This framework was essential for handling multisource data, performing complex calculations, and ensuring consistency in the prioritization process across all regions. The Python script was used for the following key tasks:

• Data Preprocessing and Integration:

  a.

  Merging datasets from multiple sources, including building registries, socio-economic databases, and energy performance records.

  b.

  Handling missing or inconsistent data values to improve data quality.

• Computation of Key Parameters:

  a.

  Energy need for heating was calculated based on thermal envelope characteristics (insulation, glazing, air tightness), independent of the heating system used.

  b.

  GHG emissions were determined by incorporating information on technical heating systems, distinguishing between fuel oil, biomass, heat pumps, and other energy sources. This ensured that emissions were calculated based on real energy consumption patterns, avoiding redundancy with energy need calculations.

  c.

  Socio-economic indicators, such as energy poverty and household income, were processed separately to avoid collinearity while still allowing for a comprehensive assessment of renovation priorities.

• MultiCriteria Decision-Making (MCDM) Model Execution:

  a.

  The prioritization framework implemented a hybrid weighting approach, adjusting criteria dynamically based on regional energy and social contexts.

  b.

  Scenario analysis and sensitivity testing were performed to ensure robust ranking of renovation priorities.

• Visualization and Results Interpretation:

  a.

  Python was used to generate maps and rankings, facilitating the identification of priority regions for renovation.

  b.

  Comparative analysis across weighting schemes allowed for cross-validation of prioritization results.

This computational approach was chosen due to its efficiency, scalability, and automation capabilities. Given the size and complexity of the dataset, Python provided a flexible solution that would not have been feasible with conventional spreadsheet-based tools. By automating data processing and analysis, this framework ensures that renovation priorities can be updated dynamically in response to policy changes, new data availability, or evolving climate targets.

4. Results

The results of the analysis provide a comprehensive prioritization framework for energy renovations at both the local, regional, and national levels. This framework identifies priority groups of buildings and municipalities, offering actionable insights to guide targeted renovations policy, programs, and efficient financial incentives allocation. The results are structured to enable policymakers to address the diverse energy efficiency challenges across Slovenia while aligning with national energy performance climate goals.

4.1. Building Stock Analysis

The analysis of building stock across Slovenian regions provides critical insights into the total floor area and the share of buildings constructed before 1980. These metrics are essential for understanding the age and energy efficiency potential of the existing building stock and identifying regions where interventions are most needed.

Figure 3a provides an overview of the total floor area across different regions, allowing for the identification of regions that include major urban centers. The region identifiers are introduced at the beginning of the Methodology section for reference.

Regions with the largest total floor areas, such as Osrednjeslovenska and Podravska, highlight distinct challenges. The Osrednjeslovenska region has a total floor area of 18.3 million m², the highest among all regions, with 58% of its buildings constructed before 1980. Similarly, the Podravska region has a substantial total floor area of 11.9 million m², with 57% of its buildings being older (Figure 3b). While these regions have extensive building stocks, the relatively moderate share of older buildings indicates a need for widespread but less intensive renovation measures to improve energy efficiency.

In contrast, regions such as Goriška, Primorsko-notranjska, and Zasavska stand out for their high proportions of older buildings, with 68%, 65%, and 67% of their stock constructed before 1980, respectively. These regions, despite having smaller total floor areas, present a concentrated need for energy renovation efforts to address inefficiencies in aging infrastructure. Their high share of older buildings signals a pressing need for targeted interventions to meet energy efficiency goals.

Regions like Jugovzhodna Slovenija exhibit a different profile, with a total floor area of 4.7 million m² and a relatively low share of pre-1980 buildings (56%). This suggests that while the region may benefit from energy efficiency measures, it faces less immediate urgency compared to regions with higher shares of older buildings.

This analysis underscores the need for a tailored approach to energy renovation policy. Regions with large building stocks, such as the Osrednjeslovenska region, require strategies focused on scalable, incremental improvements. On the other hand, regions with a high proportion of older buildings, such as Goriška and Zasavska, demand more intensive renovation programs to address significant energy inefficiencies.

Policy recommendations emerging from this analysis emphasize the importance of prioritizing regions based on both the scale of their building stock and the proportion of aging inefficient infrastructure. Regions with extensive but moderately aged building stocks should focus on broad, systematic upgrades, while those with concentrated shares of older buildings need targeted investments to address their more critical challenges.

While this section analyzes individual building stock characteristics such as age and construction period, the final prioritization of renovation efforts is determined using a combined index that integrates both micro- and macro-level information. Micro-level parameters include energy efficiency (energy need for heating), GHG emissions, and building stock age, while macro-level factors incorporate socio-economic conditions such as household income levels, energy poverty share, and regional economic disparities. This multiscalar approach ensures that the prioritization is not solely based on technical building characteristics but also considers broader socio-economic constraints that influence renovation feasibility.

4.2. Regions Specific Energy Savings Potential

The analysis of specific energy savings potential, calculated in megawatt-hours per household (MWh/household), highlights significant regional variations. These findings provide valuable insights into how energy efficiency interventions can be tailored to maximize savings across different regions (

Table 1. Specific energy savings per household in Slovenian regions.

Region	Energy Savings		Number of Households	Energy Savings
	Single-Family Houses	Multi Family Houses
Unit	GWh/a	GWh/a	-	MWh/househ.
Gorenjska	358.6	56.3	80,694	5.141
Goriška	260.4	35.0	47,182	6.261
Jugovzhodna Slovenija	295.1	26.0	56,223	5.711
Koroška	116.2	20.3	29,744	4.586
Obalno-kraška	198.5	67.7	49,313	5.399
Osrednjeslovenska	741.8	237.3	225,390	4.344
Podravska	560.6	96.7	141,388	4.649
Pomurska	265.7	18.6	45,544	6.241
Posavska	159.4	12.6	30,842	5.575
Primorsko-notranjska	106.9	14.7	21,210	5.732
Savinjska	443.9	79.1	108,464	4.822
Zasavska	80.2	22.0	23,788	4.295

).

The results show that the Goriška and Pomurska regions stand out as the regions with the highest specific energy savings technical potential, achieving 6.26 MWh/household and 6.24 MWh/household, respectively. These high values suggest that targeted energy efficiency measures in these regions could result in substantial energy savings, making them prime candidates for focused investments and financial support.

Regions such as Primorsko-notranjska and Jugovzhodna Slovenija also exhibit significant energy savings potential, with values of 5.73 MWh/household and 5.71 MWh/household, respectively (Figure 4). These regions could benefit from moderate prioritization to leverage their savings potential effectively.

On the other end of the spectrum, regions like Zasavska (4.29 MWh/household) and Osrednjeslovenska (4.34 MWh/household) show the lowest specific energy savings potential. These findings suggest that while energy efficiency measures in these areas may still be beneficial, the potential return on investment might be comparatively lower. Nevertheless, strategic measures in these regions could still play a role in achieving broader energy efficiency goals.

Other regions, including Gorenjska, Obalno-kraška, and Posavska, exhibit moderate savings potential, ranging from 5.14 to 5.57 MWh/household. These results indicate the need for balanced strategies that consider both regional priorities and the scale of potential energy savings.

Overall, this analysis underscores the importance of adopting a regionally differentiated approach to energy efficiency interventions. Regions with higher specific energy savings potential, such as Goriška and Pomurska, should be prioritized for immediate and intensive measures. Conversely, areas with lower potential may benefit from supplementary or incremental improvements to complement broader energy policy objectives. This tailored approach ensures that resources are allocated efficiently, maximizing energy savings while addressing regional disparities.

The evaluation of energy-saving potential in this section provides important insights into regional differences in renovation needs. However, it is important to note that the final prioritization of energy renovations does not rely on energy performance parameters alone but rather on a comprehensive index that integrates both technical (micro-level) and socio-economic (macro-level) information. By combining these factors, the methodology ensures that prioritization is not only driven by theoretical energy efficiency improvements but also considers financial accessibility, social equity, and regional renovation feasibility.

4.3. Regions Renovation Prioritization Analysis

The analysis aggregated the FPF_building across municipalities and regions to classify them into priority categories. Regions were categorized into four groups—very high, high, medium, and low priority—based on quantiles of the regional average FPF. This categorization highlights regional disparities and provides a foundation for equitable resource allocation.

Regions with a higher share of older buildings, elevated levels of energy poverty, and lower population densities were consistently classified as higher priorities. These findings emphasize the need to direct resources toward regions that face the greatest challenges in achieving energy efficiency improvements. Conversely, regions with newer building stock and higher income levels were generally classified as lower priority, requiring fewer interventions.

The results reveal that regions such as Obalno-kraška, Koroška, and the Primorsko-notranjska region consistently fall into the “Very High” priority category (Figure 5). These regions share common characteristics, including a high prevalence of older buildings, elevated levels of energy poverty, and, in some cases, lower population densities. The concentration of these factors indicates significant challenges in achieving energy efficiency improvements, making them critical targets for financial resource allocation.

In contrast, regions like Gorenjska and Osrednjeslovenska are classified as “Low” priority, attributed to their newer building stock, higher income levels, and generally lower energy poverty. These regions face fewer barriers to achieving energy efficiency, which explains their lower prioritization in the FPF analysis.

Regions such as Pomurska, Posavska, and Savinjska exhibit mixed characteristics, with varying priorities depending on building types. For instance, Pomurska demonstrates “Very High” priority for one building type, reflecting localized challenges, while Savinjska is classified as “High” for some factors but “Medium” for others. These intermediate classifications highlight the importance of tailoring interventions to specific regional contexts.

The analysis underscores that energy poverty and building age are the dominant factors influencing the prioritization. Regions with higher shares of older buildings and elevated energy poverty levels require urgent attention. This is further supported by the regression results, where the coefficient for energy poverty was found to have the largest and most statistically significant impact on the Final Priority Factor.

Conversely, regions with newer building stock and higher income levels were consistently classified as lower priority. These findings suggest that such regions may require less intensive interventions and can focus on maintaining existing energy efficiency standards rather than extensive renovation or upgrades.

This regional prioritization provides actionable insights for policymakers and stakeholders. By directing resources toward regions with “Very High” priority, decision-makers can address the most critical needs and ensure equitable progress toward national energy efficiency goals. Simultaneously, regions with lower priorities can focus on incremental improvements or maintain existing standards, optimizing the allocation of resources across the country.

4.4. Regions’ Renovation Prioritization Through Regression Analysis

A regression model was used to explore and quantify the relationship between a dependent variable and one or more independent variables. In this analysis, the goal was to understand how factors such as average energy poverty, building age, income, and population density influence the RFPF, a measure of regional prioritization.

RFPF is a composite metric designed to assess and compare the urgency of energy renovations at the regional level. It is derived by aggregating the FPF of individual buildings within each region, providing a broader perspective on renovation needs. The RFPF integrates key determinants such as building energy efficiency, potential energy savings, socio-economic indicators, and energy poverty levels. By normalizing these factors across regions, the RFPF enables a systematic comparison, highlighting areas with the greatest need for intervention. This metric supports policymakers in allocating renovation subsidies more effectively, ensuring that resources are directed toward regions where energy inefficiencies and socio-economic vulnerabilities are most pronounced.

The data preparation phase involves organizing information for each region, including variables that reflect socio-economic conditions and infrastructure characteristics. Average energy poverty indicates the proportion of the population with limited access to affordable energy, while average building age serves as a proxy for the quality and energy efficiency of infrastructure. Average income reflects economic conditions, and population density offers context about urbanization and its potential impacts.

To ensure consistency and comparability, all variables were scaled using Min-Max normalization, transforming their values to a range between 0 and 1. This step was essential to mitigate the impact of differing units and scales, allowing the regression model to interpret each variable’s influence more effectively.

The regression model was constructed using the Ordinary Least Squares (OLS) method, a statistical approach that minimizes the differences between observed and predicted values. This method estimates the relationships between the dependent variable and the predictors through a linear equation. The equation includes an intercept, representing baseline effects, and coefficients for each predictor, which quantify the impact of the variables. A positive coefficient indicates that an increase in the variable is associated with a higher Final Priority Factor, while a negative coefficient suggests the opposite.

Key metrics such as R-squared and Adjusted R-squared were used to evaluate the model’s fit, showing how well the independent variables explain variations in the dependent variable. Additionally, p-values for each coefficient were assessed to determine the statistical significance of the predictors.

To better understand the importance of each factor, the coefficients are ranked by magnitude, highlighting the variables with the greatest impact. This process not only aids in identifying key drivers but also provides a basis for informed decision-making and policy recommendations.

The analysis incorporates both regional and building-level factors. While some variables, such as energy poverty and municipal socio-economic indicators, are initially analyzed at the regional level, they are subsequently distributed to municipalities and individual buildings using weighted normalization techniques. This ensures that the analysis captures both macro-level regional disparities and micro-level building-specific characteristics. The final prioritization framework is applied across all regions, allowing for a comparative assessment of renovation needs while maintaining consistency in factor normalization.

The regression analysis demonstrated a strong model fit, with an R-squared value of 0.912, indicating that 91.2% of the variability in the FPF is explained by the independent variables. The adjusted R-squared of 0.861 further supports the model’s robustness. The F-statistic of 18.07 (p = 0.000857) confirms the overall statistical significance.

Among the predictors, Average Energy Poverty emerged as the most influential variable, with a coefficient of 2.6761 and a highly significant p-value (p < 0.001) (

Table 2. Regression Analysis Summary.

Factors	Coef	Std Error	t	p	0.025	0.975
const	0.8709	0.456	1.909	0.098	−0.208	1.949
Avg_Energy_Poverty	2.6761	0.379	7.064	0.000	1.780	3.572
Avg_Income	−0.0842	0.533	−0.158	0.879	−1.345	1.176
Avg_Building_Age	−0.6850	0.483	−1.417	0.199	−1.828	0.458
Avg_Pop_Density	0.9285	0.523	1.777	0.119	−0.307	2.164

). This suggests that higher levels of energy poverty strongly correlate with an increased FPF, emphasizing the importance of addressing energy accessibility in regional prioritization efforts. Average Population Density displayed a positive coefficient (0.9285), indicating a potential association with urban areas, though its p-value (0.119) suggests the relationship is not statistically robust. Average Building Age showed a negative but non-significant influence (−0.6850, p = 0.199), while Average Income exhibited negligible impact (−0.0842, p = 0.879).

To evaluate the validity and reliability of the regression model, diagnostic plots were generated to assess key assumptions (Figure 6):

• Q-Q Plot of Residuals: The Q-Q plot demonstrates that the residuals are approximately normally distributed. The majority of the sample quantiles (blue points) align closely with the theoretical quantiles (red line). This supports the assumption of normality, which is critical for valid hypothesis testing and reliable p-values for the regression coefficients. Minor deviations at the tails are present but are not severe enough to invalidate the model.
• Residuals vs. Fitted Values Plot: This plot evaluates the linearity and homoscedasticity assumptions of the model. The residuals appear randomly scattered around the horizontal red dotted line at zero, indicating no systematic patterns or biases in the model. The absence of a funnel-shaped pattern suggests that the variance of residuals is constant across fitted values, fulfilling the homoscedasticity assumption. While a few points with larger residuals exist, they do not significantly affect the overall model validity.

These diagnostic plots provide strong evidence that the regression model assumptions are satisfied, supporting the robustness and reliability of the results.

Variance Inflation Factor (VIF) values for all predictors were below 2, indicating minimal multicollinearity. Residuals were approximately normally distributed, as evidenced by the Q-Q plot, and the Durbin-Watson statistic of 1.632 showed no severe autocorrelation.

The analysis confirms that Average Energy Poverty is the primary determinant of the FPF as seen in Figure 7. Addressing energy poverty should be prioritized in policy design and resource allocation for regional improvement. While other variables such as Average Population Density, Building Age, and Income play a lesser role, their contributions could be explored further in future studies. Enhancing the model by addressing potential non-linear relationships and incorporating additional contextual factors would provide a more comprehensive understanding of regional prioritization dynamics.

In summary, the analysis reveals that energy poverty is the most significant determinant of the FPF, while the other variables play minor or statistically insignificant roles. These findings suggest that efforts to address energy poverty should be prioritized when allocating resources or designing policies for regional improvement. The model provides a robust foundation for understanding these relationships, but further investigation into multicollinearity and additional contextual factors is recommended for a more comprehensive analysis.

5. Discussion

5.1. Implications of Regional Disparities in Energy Efficiency Potential

The findings from the analysis reveal significant regional disparities in building stock characteristics, specific energy savings potential, and priority classifications based on the FPF. These disparities underline the necessity of a regionally tailored approach to energy efficiency interventions.

Regions with extensive building stocks, such as Osrednjeslovenska and Podravska, highlight the need for scalable and systematic improvements. Although these regions have a relatively moderate share of older buildings, the large total floor areas suggest that even incremental improvements could yield substantial energy savings. In contrast, regions like Goriška, Primorsko-notranjska, and Zasavska, with their high proportions of older buildings, face greater challenges in achieving energy efficiency goals. Targeted renovation programs in these regions are critical to addressing aging infrastructure and maximizing the impact of available resources.

The analysis of specific energy savings potential provides further evidence to guide regional prioritization. Regions such as Goriška and Pomurska, with the highest megawatt-hour savings per household, emerge as prime candidates for intensive investment. These regions have the greatest potential for impactful interventions, making them crucial to achieving national energy savings targets. Conversely, regions with lower savings potential, such as Osrednjeslovenska and Zasavska, may not require the same level of investment but can still benefit from incremental upgrades and maintenance programs to enhance efficiency over time.

The FPF-based prioritization reinforces the importance of addressing energy poverty and building age disparities. Regions consistently classified as “Very High” priority category, including Obalno-kraška, Koroška, and Primorsko-notranjska, share common challenges, such as older building stock, elevated energy poverty levels, and, in some cases, lower population densities. Policymakers should focus on directing resources to these regions to address the most pressing energy inefficiencies and improve equity in resource allocation.

In regions where energy poverty is less significant, the challenges to achieving energy efficiency are not necessarily fewer but rather different, often related to market dynamics, homeowner incentives, or financial return on investment. While direct financial support may be less critical, alternative intervention strategies—such as facilitating private capital investment, improving financing mechanisms, or offering tax incentives—could play a more prominent role. Future policy frameworks should consider differentiated approaches, ensuring that regions with lower energy poverty still receive the necessary support to overcome barriers specific to their context.

The regression analysis further underscores the critical role of energy poverty in determining regional priorities. The strong and statistically significant influence of energy poverty on the FPF highlights its centrality to energy efficiency policymaking. Interventions aimed at alleviating energy poverty, such as subsidized renovation or support for vulnerable households, can have a transformative impact on regional energy outcomes.

These findings underscore the importance of a nuanced and data-driven approach to energy efficiency policy. A one-size-fits-all strategy is unlikely to succeed given the diverse challenges and opportunities across regions. By aligning interventions with regional characteristics—whether through widespread upgrades in regions with large building stocks or focused renovation in areas with aging infrastructure—policymakers can ensure the efficient use of resources while addressing inequities. Future research should explore additional contextual factors, such as the socio-economic composition of households and regional climate conditions, to further refine prioritization strategies.

5.2. Comparison with Existing Approaches

The proposed framework represents a significant advancement over existing energy renovation prioritization strategies by integrating building-level data, socio-economic indicators, and regional characteristics into a unified and scalable methodology. This comparison highlights key improvements and complementary aspects of the framework relative to established approaches in the literature.

• Granularity of Data Integration: Unlike traditional methods that often rely on archetype-based or top-down modeling, the proposed framework incorporates highly detailed building-level data, such as energy performance, construction year, and floor area. This granularity allows for tailored prioritization of individual buildings, addressing the limitations of oversimplified models that fail to capture the diversity of building stock. By integrating municipal and regional data, such as energy poverty indices and population density, the framework bridges the gap between localized building-specific insights and broader policy objectives.
• Socio-Economic Considerations: Existing approaches often emphasize technical or economic factors while overlooking socio-economic disparities. The proposed methodology explicitly incorporates socio-economic factors through the calculation of a MEPF. This inclusion ensures that the framework prioritizes buildings in municipalities with greater vulnerabilities, such as lower income levels, higher energy poverty, and sparse populations, which are frequently underserved in energy renovation programs.
• Dynamic Weighting and Adaptability: The use of weighted factors for prioritization enables dynamic adaptability to regional and national policy goals. While some existing models use fixed criteria, the proposed framework allows for the adjustment of weights for key factors, such as energy consumption, GHG emissions, and socio-economic indices. This flexibility ensures that the framework can adapt to evolving priorities, such as those set by the EPBD or national sustainability goals.
• Equity and Spatial Distribution: The integration of spatial data ensures that the framework accounts for geographic disparities in renovation potential and logistical challenges. Existing models often fail to address the unique barriers faced by rural or low-density areas, whereas the proposed framework explicitly adjusts priorities based on regional population density and energy poverty. This approach promotes equitable distribution of renovation resources and aligns with principles of social equity in sustainability transitions.
• Complementing Policy Tools: The proposed framework complements existing policy tools by providing actionable insights for the preparation of National Renovation Plans, as required under the EPBD. Its comprehensive prioritization approach supports decision-makers in identifying high-priority regions and buildings for targeted interventions. Additionally, by aligning with both technical and socio-economic objectives, the framework enhances the effectiveness of financial incentives allocation and programs, and maximizes energy savings at a national scale.
• Scalability and Replicability: Unlike many region-specific approaches, the proposed methodology is designed to be scalable and replicable across different geographic contexts. By leveraging Python for data integration and analysis, the framework ensures transparency, efficiency, and reproducibility, making it adaptable for use in other EU member states or regions with diverse building stock characteristics.

5.2.1. Generalizability of the Framework and Adaptation to Data-Limited Contexts

While the proposed framework demonstrates strong applicability in the studied region due to the availability of comprehensive government data, its generalizability to other regions depends on data availability and quality. In some countries and municipalities, detailed building-level or socio-economic datasets may be incomplete or unavailable. To address this, we introduce a data replacement and adaptation strategy that enables application in diverse geographic and economic contexts:

• Comparison of Accuracy and Data Availability Across Regions:
  • The accuracy of the framework is highest when high-resolution building and socio-economic datasets are available.
  • In regions with limited datasets, the accuracy of prioritization may be lower, but strategic modifications can enhance its applicability.
• Adaptability to Data-Limited Contexts:
  • Building performance data: If building-level energy performance data is unavailable, estimates can be derived from remote sensing, archetype modeling, or satellite imagery-based thermal analysis.
  • Socio-economic indicators: If municipal-level energy poverty data is not available, proxy indicators such as GDP per capita, unemployment rates, or access to energy subsidies can be used.
  • Regional weighting adjustments: In the absence of direct energy use data, statistical models using climate zone classifications and economic profiles can be applied to infer expected consumption levels.
• Proposed Data Replacement Plan for Regions with Limited Data:
  • Utilize regional energy consumption models where direct heating demand data is unavailable.
  • Apply machine learning techniques to predict missing building stock parameters based on available partial datasets.
  • Use national census data and household expenditure surveys as alternative sources for socio-economic indicators.

5.2.2. Future Adaptation for Expanding EPBD Compliance

The proposed methodology aligns with the new EPBD Directive (EU) 2024/1275, particularly in its focus on building prioritization for energy renovations. While the current framework emphasizes energy efficiency and energy poverty reduction, the EPBD’s expanded focus on whole-life carbon emissions (including Global Warming Potential) presents an opportunity for future refinement.

• Integration of Whole-Life Carbon Data: Future enhancements to the framework could incorporate embodied carbon assessments in addition to operational energy efficiency metrics.
• Incorporation of Life-cycle Analysis (LCA): Developing a methodology for integrating embodied emissions from building materials and renovations would align the framework with Zero-Emission Building (ZEB) objectives.
• Hybrid Prioritization Models: Expanding the model to include both energy savings and total carbon impact would enable a more holistic sustainability-driven prioritization framework.

By addressing key limitations of existing approaches—such as the lack of socio-economic integration, limited granularity, and insufficient adaptability—the proposed framework establishes a robust foundation for energy renovation prioritization. Its ability to harmonize detailed building-specific data with broader socio-economic and geographic insights positions it as a valuable tool for policymakers aiming to achieve equitable and sustainable energy performance improvements.

5.3. Limitations

While the proposed framework offers significant advancements in the prioritization of energy renovations, several limitations must be acknowledged. These limitations stem from data availability, methodological assumptions, and inherent challenges in integrating diverse datasets.

One of the primary limitations is the reliance on aggregated data to address gaps in individual building-level information. While the GURS and Eco Fund provide detailed data on energy performance and building characteristics, certain key variables, such as the actual state of maintenance or detailed renovation history, are often missing. This limitation was mitigated by integrating regional and municipal datasets, such as average income levels and population density, to supplement and contextualize the building-level analysis. However, this approach introduces potential biases, as aggregated data may not fully capture the heterogeneity of individual buildings within a municipality or region. Future work could benefit from incorporating dynamic data sources, such as IoT-enabled sensors, or conducting targeted surveys to enrich the dataset.

The estimation of energy poverty shares at the municipal level represents another significant limitation. Due to the absence of detailed energy poverty data for individual municipalities, regional-level data were distributed using weighted factors, including income disparities, building age distributions, and population density. While this approach ensures a degree of contextual accuracy, it assumes uniform distributions of energy poverty within regions, which may not reflect local variations. For instance, municipalities with extreme income disparities or unique socio-economic profiles might be under- or overrepresented in the analysis. This limitation underscores the need for more granular energy poverty data at the municipal level, which could improve the precision of the prioritization framework.

Despite these limitations, the methodologies employed—such as data fusion and weighted factor distribution—serve to mitigate the impacts of data gaps and ensure a robust prioritization framework. However, the inclusion of more granular and dynamic data sources in future iterations would enhance the accuracy and reliability of the model, particularly for addressing socio-economic and energy performance disparities at the local level.

5.4. Scientific Rationale and Practical Implications

The integration of building-specific and municipal-level factors into a unified prioritization framework represents a critical advancement in targeting energy renovation efforts. This methodological approach captures the multidimensional nature of energy renovation priorities by combining the intrinsic inefficiencies of buildings with the socio-economic and structural conditions of their municipalities. The result is a comprehensive and equitable prioritization framework that supports evidence-based decision-making.

The inclusion of the MEPF ensures that municipalities with greater socio-economic challenges and structural barriers, such as lower incomes, older building stocks, and sparse populations, are adequately represented in the prioritization process. By combining the BPF with the MEPF, the framework addresses both equity and efficiency concerns. This dual focus ensures that resources are allocated not only to the most energy-inefficient buildings but also to areas with the greatest socio-economic need, thereby maximizing the societal impact of energy renovation programs.

Normalization of the FPF across all buildings further enhances the framework’s utility by enabling direct comparisons of priority levels across diverse contexts. This facilitates clear rankings, allowing policymakers to identify the most impactful projects within and across municipalities. Additionally, the flexibility of the framework allows for its adaptation to various geographical scales, from neighborhoods to regions, depending on data availability and policy objectives.

From a practical perspective, the methodology provides policymakers with actionable insights for designing targeted renovation programs. Buildings with higher normalized FPF can be prioritized for subsidies, technical support, or pilot projects. The framework also allows for scenario testing, where adjustments to input factors can simulate the effects of different policy objectives, such as emphasizing GHG emission reductions over energy savings.

Moreover, this integrated approach addresses critical socio-economic disparities, ensuring that vulnerable communities benefit from energy renovations. This contributes to broader social and environmental goals, such as reducing energy poverty, enhancing urban resilience, and achieving national climate targets.

5.5. Implications for Policy and Research

The proposed hybrid MCDM framework offers significant implications for both policy development and future research, particularly in addressing energy poverty and prioritizing energy renovations. By integrating municipal-level estimations of energy poverty into the framework, the approach provides a foundation for more targeted and equitable policy interventions.

From a policy perspective, the municipal-level estimation of energy poverty enables the fair and efficient allocation of resources. Municipalities with higher energy poverty shares can be prioritized for renovation financial incentives and support programs, ensuring that vulnerable populations receive the assistance they need to achieve adequate energy performance. This targeted allocation not only promotes social equity but also maximizes the impact of limited resources, aligning national renovation efforts with broader sustainability and climate goals.

Moreover, this methodology supports improved prioritization of energy efficiency upgrades. By identifying municipalities with the highest energy poverty indices, policymakers can design tailored interventions that address the unique socio-economic and logistical challenges of these areas. This ensures that policies are not only effective but also responsive to the needs of local communities.

For future research, there is a clear opportunity to refine the methodology by incorporating more granular data, such as household-level surveys or detailed energy consumption patterns. Leveraging machine learning models could further enhance the framework by identifying complex patterns and interactions between socio-economic and physical building characteristics. These advancements would improve the accuracy of energy poverty estimates and provide deeper insights into the drivers of energy inefficiencies.

By bridging the gap between regional data and localized policy implementation, the proposed approach enables a more nuanced understanding of energy poverty at the municipal level. This integration of granular socio-economic and energy performance data ensures that renovation strategies are both equitable and effective, setting a strong foundation for achieving national and EU-wide energy and sustainability targets.

5.6. Opportunities for Future Research

While the proposed framework provides a robust foundation for prioritizing energy renovations, there are several avenues for future research that could enhance its effectiveness and applicability. These opportunities include leveraging more granular data and adopting advanced computational techniques to refine the prioritization process.

One of the key challenges in the current framework is the reliance on regional-level energy poverty data, which is distributed across municipalities using weighted factors. Future research could focus on obtaining and integrating more granular data, such as household-level energy expenditure, heating and cooling habits, and access to energy-efficient technologies. Such data could be collected through targeted surveys, energy audits, or by leveraging smart meter data where available. This would enable a more accurate representation of energy poverty at the local level and improve the equity and precision of prioritization efforts. Granular data could also facilitate a better understanding of the interaction between energy poverty and other socio-economic factors, leading to more effective policy interventions.

Machine learning offers significant potential to enhance the prioritization framework by uncovering complex patterns and relationships in the data that traditional methods may overlook. For instance, supervised learning algorithms could be employed to predict the energy renovation potential of buildings based on historical renovation data and building characteristics. Unsupervised techniques, such as clustering, could identify groups of buildings or regions with similar renovation needs, streamlining the allocation of resources. Additionally, machine learning models could dynamically optimize the weighting of prioritization criteria, adapting to changing policy goals and data inputs. These approaches would not only improve the accuracy and adaptability of the framework but also enable real-time analysis and decision-making.

Future research opportunities, particularly the integration of more granular data and the adoption of machine learning techniques, hold the potential to significantly enhance the prioritization of energy renovations. By addressing current limitations and embracing advanced methodologies, these advancements could support more equitable, efficient, and scalable approaches to achieving energy efficiency and sustainability targets at both national and regional levels.

6. Conclusions

The developed hybrid MCDM framework represents a novel approach to prioritizing energy renovations by systematically integrating building-level and regional socio-economic factors. For the first time, this framework combines the BPF—which accounts for energy performance, potential savings, and emissions—with the MEPF to ensure an equitable and impact-driven allocation of renovation resources. Tested across Slovenian regions, the methodology provides a data-driven strategy for targeting renovations where they are most needed, aligning with both national and EU energy efficiency objectives. The results offer actionable insights, highlighting the critical role of building age, regional energy poverty levels, and population density in shaping renovation priorities. This comprehensive approach ensures that energy efficiency improvements contribute not only to climate goals but also to social equity and economic sustainability.

6.1. Key Findings

The analysis identified distinct priority groups of buildings and municipalities based on energy efficiency potential, socio-economic factors, and regional characteristics. Buildings constructed before 1970 consistently emerged as high-priority targets due to their older construction standards and significant energy inefficiencies. Regions such as Goriška, Primorsko-notranjska, and Zasavska were identified as having the highest energy savings potential, driven by their aging building stock and higher levels of energy poverty. Conversely, regions like Osrednjeslovenska, with more modern building stock and relatively lower energy poverty levels, fell into lower priority categories.

The findings underscore the need for a regionally tailored approach to energy renovations. Municipalities with high proportions of energy-poor households, older buildings, and low population densities require targeted interventions to maximize impact. By directing resources to these high-priority groups, national policies can address the dual objectives of improving energy efficiency and reducing socio-economic disparities. The integration of granular building data with regional socio-economic indicators provides policymakers with actionable insights for equitable and effective subsidy allocation.

6.2. Contribution

The proposed hybrid MCDM framework makes a significant contribution to energy renovation planning by bridging the gap between building-level and regional-level analyses. Unlike traditional approaches that focus solely on individual buildings or generalized benchmarks, this methodology integrates detailed energy performance metrics, socio-economic factors, and geographic disparities, ensuring a comprehensive and equitable prioritization of renovation efforts.

A key innovation of this approach is its dynamic weighting of criteria, which allows adaptability to evolving policy goals. This ensures that the framework remains aligned with national and EU-wide objectives, such as those outlined in the EPBD. By incorporating weighted prioritization criteria, the methodology optimizes decision-making, allowing policy interventions to reflect local needs and stakeholder objectives.

One of the most critical contributions is the framework’s role in equitable subsidy distribution. By integrating the MEPF, the model ensures that vulnerable populations and underprivileged regions receive appropriate financial support. This enhances the social equity of energy renovation programs, making them more inclusive and impactful, while also improving overall energy performance at a national scale.

This methodology is particularly relevant for National Renovation Plans, as it facilitates targeted interventions to improve energy efficiency, reduce GHG emissions, and enhance energy affordability. By systematically identifying priority buildings based on energy inefficiency and socio-economic need, the framework ensures that renovation efforts maximize impact and cost-effectiveness.

Beyond its technical merits, the framework advances sustainability by integrating energy efficiency improvements with socio-economic considerations. Energy efficiency serves as a foundation for green, low-energy, ultra-low-energy, and nZEBs. Additionally, by optimizing energy consumption, promoting material recycling, and enhancing indoor environmental quality (IEQ), the methodology supports a holistic approach to sustainable renovations.

By bridging the gap between data-driven prioritization and real-world policy implementation, this scalable and adaptive framework provides policymakers with actionable insights for designing equitable and impactful renovation strategies. Ultimately, it contributes to long-term carbon neutrality, social inclusion, and improved living conditions, making it a robust tool for achieving both national and EU-wide climate objectives.

6.3. Recommendations

To translate the findings into actionable policies, the following recommendations are proposed:

• Targeted Incentives for High-Priority Groups: National renovation plans should prioritize buildings constructed before 1980 and municipalities with high energy poverty indices. Subsidies and technical support should focus on these groups to maximize energy savings and address socio-economic disparities.
• Regional Customization of Policies: Policies should reflect the unique characteristics of each region. For example, regions with high energy poverty and older building stock may require intensive renovation programs, while regions with modern buildings may benefit from incremental upgrades and maintenance initiatives.
• Dynamic Financial Incentives Allocation Models: Introduce adaptable financial schemes and programs that account for regional differences in energy efficiency renovation potential and socio-economic conditions. Dynamic weighting mechanisms could be used to adjust funding as data and priorities evolve.
• Integration of Advanced Technologies: Invest in data collection and analysis technologies, such as smart meters and machine learning algorithms, to enhance the precision and scalability of prioritization methods. This will enable real-time adjustments to policy objectives and improve decision-making processes.

By implementing these recommendations, policymakers can ensure that energy renovation efforts are both efficient and equitable, contributing to national sustainability targets while addressing the needs of vulnerable communities.