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This article delves into the critical evaluation of energy efficiency (EE) funding programs, emphasizing the need for a holistic assessment framework that goes beyond traditional operational energy savings. The study introduces an integrated approach that combines a Hybrid Input–Output Lifecycle Assessment (HIO-LCA) framework with the Portuguese Energy Consumption Efficiency Promotion Plan (PPEC) evaluation system. By incorporating upstream and systemic socio-economic and environmental effects, the proposed methodology aims to enrich the cost–benefit perspective and improve the prioritization of energy efficiency measures. The research demonstrates that incorporating additional co-benefits and lifecycle stages can significantly affect the evaluation of EE measures, leading to more robust and comprehensive results. The findings highlight the importance of capturing a wide range of positive and negative effects, including Gross Value Added (GVA), job creation, and greenhouse gas (GHG) emissions associated with the manufacturing, packaging, installation, and maintenance phases of EE measures. The study concludes that a more comprehensive evaluation approach can substantially inform and enhance decision-making processes related to EE investments, offering valuable insights for shaping broader national and EU-level energy policy agendas.
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Abstract
Investments in energy efficiency are widely recognized as a cornerstone of sustainable energy strategies and climate change mitigation efforts, offering not only environmental benefits but also delivering substantial socio-economic gains. However, conventional evaluation frameworks tend to focus predominantly on operational energy savings, often neglecting broader lifecycle effects associated with manufacturing, packaging, installation, and maintenance phases. These include wealth and job creation, as well as embodied energy and emissions. This study introduces a comprehensive approach that integrates the Hybrid Input–Output Lifecycle Assessment methodology into Portugal’s 2021 Energy Consumption Efficiency Promotion Plan framework. By incorporating Gross Value Added, Impact on the Public Budget and embodied energy and greenhouse gases emissions this approach extends conventional Primary Energy Savings, Net Present Value and Benefit–Cost Ratio calculations typically employed in cost-effectiveness analysis, allowing a comparative evaluation of the results. The findings reveal significant variations in evaluation outcomes, with Net Present Value rising by over 4,000%, Benefit–Cost Ratio increasing by more than 34%, and two measures changing position in the funding priority ranking. These results highlight the transformative potential of the proposed methodology for guiding energy policy, supporting a more equitable and effective allocation of public funds. By incorporating broader lifecycle impacts and societal benefits, the approach also enhances the robustness of energy efficiency evaluations, paving the way for improved decision-making in national and international energy programs.
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Introduction
Energy efficiency (EE) has long been recognized as a foundational element of sustainable energy systems and climate change mitigation strategies (Jaffe & Stavins, 1994). By reducing energy demand without compromising performance or comfort, EE supports energy security, lowers emissions, and drives economic competitiveness. Yet, a persistent gap remains between the potential and actual uptake of energy efficiency measures (EEMs), often due to financial, social, institutional, and technical barriers (Dolšak, 2023; IEA, 2014; Ryan & Campbell, 2012; Tzani et al., 2022). High upfront costs, limited access to affordable financing (Chlechowitz et al., 2022; Dolšak, 2023; Kola-Bezka, 2023), and investor uncertainty driven by performance gaps and long payback periods hinder financial viability (Mushafiq et al., 2023). Social challenges, including low public awareness, resistance to behavioral change (Chlechowitz et al., 2022; Dolšak, 2023; Kola-Bezka, 2023), and the “split incentive” dilemma, further constrain uptake, especially among low-income groups (Shnapp et al., 2020). Institutional fragmentation, regulatory instability, and limited administrative capacity reduce program effectiveness, while technical issues such as the shortage of skilled labor compromise implementation (Chlechowitz et al., 2022; Dolšak, 2023; Tzani et al., 2022). In addition, conventional evaluation frameworks often adopt a limited scope, prioritizing operational energy savings while overlooking lifecycle, rebound, and economy-wide effects, thus failing to fully capture the multidimensional value of EE (Russell et al., 2015).
To address this gap, the European Union (EU) has adopted the “Energy Efficiency First” (EE1st) principle as a core policy approach. Enshrined in directives such as the Energy Efficiency Directive (EED) and the Energy Performance of Buildings Directive (EPBD) (European Parliament and of the Council, 2023, 2024), EE1st requires that energy demand reduction be prioritized before supply-side investments (BPIE, 2024). These policies increasingly call for a holistic assessment framework that not only accounts for operational savings but also captures the multiple environmental, social, and economic benefits of EEMs across their lifecycle, ensuring their integration into key planning and investment decisions.
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Studies incorporating multiple benefits (MBs) into cost–benefit analyses have shown results up to 3.5 times higher than traditional models focusing solely on direct investment costs and operational energy and emissions savings, highlighting the critical importance of a comprehensive evaluation approach to accurately reflect the true value of EE investments (IEA, 2014; Ürge-Vorsatz et al., 2016; Zhang et al., 2016).
Despite the evidence on the value of MBs, national EE funding schemes, such as Portugal’s Energy Consumption Efficiency Promotion Plan (PPEC), continue to apply conventional evaluation methods. This limited scope may lead to the undervaluation of impactful measures and hinder alignment with broader EU policy goals.
A growing body of literature and European-funded research, including the COMBI (2018), sEEnergies (2022), Triple-A (2022), EERAdata (2022) and MICAT (2023) projects, has highlighted the need for a more holistic understanding of EE’s MBs and reveals persistent gaps that need to be filled. Most studies often focus mainly on operational savings, overlooking key lifecycle phases (Clinch & Healy, 2001; Reuter et al., 2020; Scheer & Motherway, 2011; sEEnergies, 2022; Triple-A, 2022). Assessments that do consider additional lifecycle phases typically rely on Process Lifecycle Analysis (P-LCA) to assess the environmental impacts related to energy consumption (COMBI, 2018; MICAT, 2023). Additionally, EERAdata employs P-LCA to assess the energy and environmental impacts of specific EE measures, but it omits the social and economic effects of their use. Moreover, the use of Input–Output (IO) models which are capable of capturing broader cross-sectoral impacts and the complex interdependencies along supply chains remains limited (COMBI, 2018; MICAT, 2023; Reuter et al., 2020; Triple-A, 2022). This is particularly evident in EE assessments, where analyses are typically limited to the use phase and focus on a narrow set of benefits. As a result, simplified evaluation approaches are commonly adopted, overlooking the wider repercussions across multiple sectors of activity. Existing research is summarized in Tables 9 and 10 of the Appendix. Table 9 focuses on scientific articles, while Table 10 looks at European-funded projects.
This study proposes an integrated framework that combines a Hybrid Input–Output Lifecycle Assessment (HIO-LCA) framework with the Portuguese PPEC evaluation system. The core contribution of this work lies in extending of the conventional cost–benefit analysis applied in PPEC by incorporating the HIO-LCA model. This integration enables the assessment of upstream and systemic socio-economic and environmental effects generated at national level that are typically excluded from standard evaluations. Specifically, it allows for the quantification of a wider range of positive and negative effects, including Gross Value Added (GVA), financial contributions from job creation, as well as energy use and greenhouse gases (GHG) emissions associated with the manufacturing, packaging, installation and maintenance (MPIM) phases of EEMs. These impacts are incorporated into standard feasibility assessments based on Primary Energy Savings (PES) and Net Present Value (NPV), and the merit-based ranking system grounded in the Benefit–Cost Ratio (BCR) and policy relevance of PPEC. With the proposed approach we aim to enrich the cost–benefit perspective and assess whether the inclusion of additional co-benefits and lifecycle stages materially affects the prioritization of EEMs, thereby strengthening the evaluation mechanisms of EE funding programs. Using Portuguese PPEC as a case study, this work further investigates whether the HIO-LCA methodology proves to be a valid and robust alternative for improving the assessment frameworks employed by financing programs to evaluate EEMs submitted for support.
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Applying the proposed approach to a real-world policy instrument yields novel insights, demonstrating its potential to produce more robust and comprehensive results than conventional evaluation methods. This, in turn, can substantially inform and enhance decision-making processes related to EE investments. Beyond testing the robustness and equity of the approach, the study also provides findings that are relevant to shaping broader national and EU-level energy policy agendas.
The paper opens with a presentation of the research problem and the study's primary objective. Next section outlines the methodology, including a description of the PPEC program, the HIO-LCA model for assessing MPIM impacts, the selected EE measures, the benefits considered, and the study’s novel contributions. Then results are presented and discussed, while last section concludes with key findings, policy implications, and recommendations for future research.
Methods
The approach employed in this research (see Fig. 1) seeks to provide a transparent and replicable framework to enhance EE funding evaluation frameworks using MBs. It builds upon and refines the existing methodology of the PPEC by broadening the scope of analysis to include additional socio-economic and environmental benefits, as well as often neglected lifecycle stages of EEMs. This expanded approach aims to improve the accuracy and relevance of the evaluation by incorporating upstream impacts and new dimensions of value creation.
The methodology proposed in this study is structured into four sequential phases. In phase one, data are collected on technology-related-costs including installation, removal, disposal, administrative, and transaction costs, as well as energy consumption data for both current and replacement technologies. Avoided electricity and gas supply costs resulting from energy savings are also considered.
In the second phase, the analysis extends beyond operational impacts by incorporating upstream effects using an HIO-LCA model. This model targets the MPIM phases of EE technologies. By incorporating investment values and detailed information on the technological components and associated services (such as installation), the HIO-LCA framework estimates both direct and indirect impacts. These include GVA, job creation, and the embodied energy and GHG emissions. This expansion allows the framework to assess the broader economic activity triggered across various industrial sectors involved in EE technology deployment. Further details of the HIO-LCA model are presented in Section “Hybrid input–output lifecycle analysis”.
Phase 3 integrates the results from both operational and MPIM phases into a refined evaluation model aligned with the PPEC. This improved framework monetizes the avoided energy supply, net GHG emission reductions, GVA, and positive impacts on public budgets resulting from the fiscal effects of job creation (Impact on Public Budget, IPB), as detailed in Section “Benefits considered”. The evaluation process applies three main criteria: the PES test, the Societal Test incorporating the NPV, and the BCR. Measures must first satisfy both the PES and NPV criteria to advance to merit-based ranking through BCR. Additionally, the alignment of each measure with existing national policies on EE is considered to ensure coherence with broader strategic objectives. These metrics are further described in the next Section.
In phase 4, the results from the refined evaluation are used to produce a new ranking of EE measures. This ranking is directly compared with that produced by the conventional PPEC methodology. The differences between the two rankings highlight the added value of the enhanced approach, particularly its capacity to capture upstream and broader socio-economic impacts that are typically overlooked in standard evaluations.
The methodological details of the refined PPEC framework and the HIO-LCA model are provided in the following sections.
Energy consumption efficiency promotion plan—PPEC
Launched in 2006 by the Energy Services Regulatory Authority (ERSE), the PPEC was originally designed to promote efficiency in the electricity consumption (ERSE, 2006). In its most recent version, the scope was expanded to comprise natural gas efficiency measures, aligning with the vision of an integrated energy system. The program supports projects aimed at fostering the adoption of efficient technologies and rational energy use, reinforcing Portugal’s commitment to the objectives of the NECP 2020–2030 (ERSE, 2021b).
PPEC operates through a competitive selection process open to various entities, including businesses, municipalities, and organizations, which submit EE proposals. These proposals fall into two categories: tangible measures, involving direct interventions such as the installation of EE technologies or the replacement and disposal of inefficient equipment, primarily targeting the residential, commercial and services, and industrial and agricultural sectors; and intangible measures, which focus on promoting behavioral change through initiatives such as training programs, awareness campaigns, and energy audits aimed at encouraging sustainable practices.
Tangible measures are assessed based on technical feasibility, cost-effectiveness, and their estimated potential energy savings. Approved solutions receive up to 75% funding, with the remaining 25% covered by the project promoter, partner, or participating consumers. In contrast, intangible measures are evaluated against a set of qualitative criteria, including the clarity and quality of the proposal, its potential to address market barriers and generate multiplier effects, equity considerations, the level of innovation, and the promoter’s capacity to implement the initiative. These measures may receive up to 95% funding, with the remaining 5% provided by the promoter, partner, or participating consumer (ERSE, 2021b).
Although intangible measures contribute to long-term energy-saving efforts, this work focus exclusively on tangible measures within the residential sector, as they provide quantifiable indicators for assessing the effectiveness of a new holistic framework applied to EE funding programs. Other sectors have been excluded from this study due to the current lack of sufficient data for an in-depth evaluation.
To determine the eligibility for funding under PPEC, tangible measures undergo two key evaluations:
1.
PES Test – Measures the reduction in primary energy consumption achieved by replacing standard equipment with more efficient alternatives. It is computed as:
where C(toe) represents the primary energy consumption (in tons of oil equivalent) for a given technology in year t; i denotes the standard technology equipment, while j corresponds to the efficient technology equipment, and n represents the technology lifespan.
2.
Social Test – Determines the societal value of a measure by calculating the NPV of its long-term benefits, incorporating both benefits and costs. It is given by Eq. (2).
where Bst represents the benefits from a societal perspective associated with the efficiency measure in year t. These benefits comprise the avoided costs of electricity or gas supply, as along with the avoided costs of environmental externalities resulting from reducing electricity and natural gas consumption. Such externalities include GHG emissions, their related impacts on public health, and resource-use impacts linked to energy transportation, distribution and consumption and investments in infrastructure. Cst corresponds to the costs incurred in year t due to the implementation of the efficiency measure. These costs encompass installation, removal and disposal of the replaced equipment, net of its residual value, along with administrative and transaction costs supported by the promoter, the participating consumer and the partner in the measure. Finally, d is the discount rate, which is set at 5%, as specified by the PPEC.
Only measures with a positive PES and NPV values are admissible for selection. Costs and benefits are evaluated incrementally against a standard technology, and recoverable Value Added Tax (VAT) is excluded from calculations.
Once measures meet eligibility criteria, they are ranked using a merit-based system, with the final score determined by two components:
1.
50% assigned by ERSE (economic assessment);
2.
50% assigned by the Directorate-General for Energy and Geology (DGEG) (policy alignment).
The score of the first criterion is determined by summing the points awarded across all weighting criteria illustrated in Table 1, and these are attributed based on “A. Benefit–cost analysis” and the “B. Share of equipment investment in total measure cost”.
Table 1
ERSE criteria for tangible measures within PPEC
ERSE criteria
Weighting
A. Benefit–cost analysis
75 points
A1. Proportional benefit–cost ratio
50 points
A2. Ordered benefit–cost ratio
25 points
B. Share of equipment investment in total measure cost
25 points
The Benefit–cost Analysis score is determined using two metrics: the Proportional Benefit–Cost Ratio (BCRp) and the Ordered Benefit–Cost Ratio (BCRo), as defined by Eqs. (4) and (5), respectively. Prior to applying these scoring metrics, the BCR for each measure must be calculated using Eq. (3):
where \({C}_{{PPEC}_{t}}\) represents the costs subsidized by the PPEC related to the EE measure in year t and d represents the discount rate.
Once the BCR for each measure has been established, the scoring proceeds as follows:
1.
Proportional Benefit–Cost Ratio (BCRp):
Each measure is compared against the maximum BCR (BCRmax) among all evaluated measures. Scores are assigned proportionally to each measure’s relative performance, up to a maximum of 50 points, using Eq. (4).
$${BCR}_{p}=50\frac{{BCR}_{j}}{{BCR}_{max}},$$
(4)
The highest-scoring measure receives the full 50 points, while all others are allocated a proportionate score.
2.
Ordered Benefit–Cost Ratio (BCRo):
Measures are ranked in descending order based on their BCR values. The top-ranked measure receives 25 points, with subsequent measures awarded decreasing scores according to their rank, as determined by Eq. (5):
$${BCR}_{o}=25-\left(k-1\right)\frac{25}{q},$$
(5)
where q is the number of measures and k is the measure’s position in the list.
Lastly, the “Share of Equipment Investment in Total Measure Cost” aims to reward measures that prioritize direct investment in more efficient equipment, including installation costs, over indirect or administrative costs associated with the measure. Each EE measure, whether in electricity or gas consumption, is assessed based on the allocation of its budget between direct investment (DI) in equipment and indirect or administrative costs. The evaluation of this indicator is determined using the Direct Investment in Equipment Index (DI), calculated as shown in Eq. (6).
where \({DC}_{{PPEC}_{j}}\) represents the cost of equipment acquisition subsidized by the PPEC, and \({C}_{{PPEC}_{j}}\) corresponds to total cost of the measure subsidized by the PPEC.
Beyond the economic assessment, projects undergo a political evaluation conducted by the Directorate-General for Energy and Geology (DGEG) (Office of the Assistant Secretary of State for Energy, 2021), which assigns qualitative scores, based on national relevance, policy alignment, and program coordination of each measure based (see Table 2), making up the remaining 50% of the final score for each measure according to the PPEC.
Table 2
DGEG qualitative criteria for tangible measures within PPEC
DGEG criteria
Weighting
National coverage
10 points
Alignment with national policy and legislation
40 points
Support for the development and implementation of measures to promote EE
30 points
Diversification of promoters
10 points
Coordination with other instruments to encourage EE
10 points
Since the EE measures considered remain the same, the political evaluation of the EE measures remains constant. The next section explores the integration of the HIO-LCA methodology to enhance impact evaluation within the PPEC framework.
Hybrid input–output lifecycle analysis
HIO-LCA has emerged as a robust framework for evaluating the wide spectrum impacts of EE investments. By integrating conventional process-based lifecycle assessment (P-LCA) with IO modeling, HIO-LCA bridges the limitations of P-LCA and economic IO lifecycle analysis (EIO-LCA) (Henriques & Sousa, 2023). This hybrid approach offers the dual advantage of system completeness and technological specificity, addressing key challenges such as truncation and time-consumption issues in P-LCA, as well as the sectoral aggregation and homogeneity assumptions inherent to EIO-LCA (Crawford, 2009; Säynäjoki et al., 2017; Suh, 2004). This methodological synergy is especially valuable in the context of evaluating funding programs aimed at promoting the widespread adoption of EEMs through competitive selection processes.
At its core, IO analysis provides a vectorized representation of intersectoral economic flows, tracking goods and services over defined timeframes (Leontief, 1986). When extended, such as to incorporate environmental dimensions, IO models offer two primary pathways: one expands the technical coefficients matrix to endogenize environmental interactions, enhancing dynamic modeling interactions but increasing complexity and limiting the flexibility of the analysis (Leontief, 1970, 1973; Miller & Blair, 2009), while the other externally extends the technical coefficients matrix to maintain a separation of economic and environmental accounts for greater methodological flexibility (Hendrickson et al., 2006; Miller & Blair, 2009). HIO-LCA aligns with the latter, retaining modularity to assess a wide range of environmental and economic indicators without recalibrating the entire model. This flexibility allows HIO-LCA capturing both embodied and operational impacts across multiple lifecycle phases, providing a more holistic representation of diverse EE technologies and making it well-suited for policy evaluation and investment planning.
Extended IO-LCA approaches have been used to explore EE economic and environmental impacts in buildings (Cellura et al., 2013; Choi et al., 2014) and appliances (Singh et al., 2018a, 2018b, 2019), yet they often fall short of capturing the technological diversity and full lifecycle of EE measures. More recent studies, (Henriques et al., 2015; Oliveira et al., 2014), however, have begun to explore additional benefits, such as employment generation and alignment with national climate and energy policies. Moreover, Henriques et al. (2020) used embodied energy along with economic indicators to evaluate investment strategies for industrial lighting systems. Yet, comprehensive lifecycle estimations of MBs remain limited.
Building on this foundation, this paper applies the HIO-LCA framework developed in Tenente et al., (2024, 2025) to estimate a broader set socio-economic and environmental indicators at the national scale. Incorporating these indicators into the evaluation of large-scale EE programs, such as the PPEC, allows the framework to extend beyond conventional assessments and capture more comprehensive, long-term impacts. This approach enables a holistic understanding of EE investments, ensuring that funding decisions account for economy-wide effects and supporting the design of more effective funding mechanisms and sustainable energy policies.
In this section the application of the HIO-LCA model employed to assess the MBs of EE is outlined. As an illustrative example of its application, Fig. 2 and chapter 2 of Supplementary Material illustrates the methodological steps using a representative case of the replacement of a standard gas conventional boiler with a Gas condensing boiler. This visual representation facilitates a clearer understanding of the transition from conventional technologies to more efficient alternatives and demonstrates the broader analytical approach applied across all technologies analyzed in this study.
Fig. 2
Schematic representation of the application of the HIO-LCA
The implementation of the HIO-LCA methodology begins with the identification of baseline and EE technologies. This forms the basis for evaluating the socio-economic and environmental effects associated with each technology by capturing both direct impacts on EE-related industries and indirect effects generated through their upstream supply chains. The analysis adapts and extends the framework initially proposed by Breitschopf et al. (2012) while leveraging the advancements introduced by Tenente et al. (2025), to assess 57,660 packages of EEMs across Portuguese residential buildings. An important premise of this approach is that the industries represented in the model serve as suitable proxies for national industries in the EE sector in terms of input structures and impacts and benefits per unit of output.
The HIO-LCA implementation (illustrated in Fig. 2) involves several key steps:
1.
System Boundary Definition: This step involves establishing the system boundaries. The process begins with the identification of standard technologies currently used in residential buildings, along with their corresponding EE alternatives. This identification is informed by data from the PPEC program (ERSE, 2022b).
2.
Cost estimation in EEMs: Costs associated with investment, installation, and maintenance of selected measures are compiled from PPEC (ERSE, 2022b), professional pricing tools (CYPE Ingenieros S.A, 2021) and supplier surveys. These costs are adjusted for inflation and excluding taxes and markups to reflect net economic input values (FFMS, 2020).
3.
Domestic output calculation: EE technologies are broken down into their constituent components and associated activities across MPIM phases. EE-related expenditures are then allocated to these components and activities based on detailed technical and survey data (see Tables 1 to 5 in Supplementary Material). In order to account only for the impacts and benefits generated nationally, the import shares, derived from OECD IO data (OECD, 2017b), are subtracted to isolate the domestic portion of expenditures. These are then attributed to the relevant industry sectors within the IO model, resulting in the sectoral alignment and the accuracy of derived impact coefficients.
4.
Impact estimation: The model quantifies the direct and total impacts resulting from increased final demand for EEMs. Eq. (7) estimates the immediate changes in sectoral outputs of industries engaged in the MPIM phases, in response to shifts in final demand (Miller & Blair, 2009). This means the quantification of the direct impacts on industries directly engaged in producing, installing, and maintaining the EE technologies. For example, the replacement of a boiler directly increases output in the machinery and services sector.
$${\varvec{r}}=R{\varvec{x}},$$
(7)
where \({\varvec{r}}\) is the vector of impact levels; R is the matrix of direct impact coefficients per unit of industry output, and \({\varvec{x}}\) is the output vector (Hendrickson et al., 1998, 2006).
To account for total impacts, the model extends the analysis using a rectangular IO framework, through the Eq. (8), conceptually aligned with the Leontief inverse matrix approach (Hendrickson et al., 1998, 2006). Total impacts capture both direct and indirect. Indirect impacts reflect the cascading effects across upstream industries that supply intermediate goods and services to primary EE technology industries (Miller & Blair, 2009). For instance, increased steel demand from boiler production leads to higher activity in metallurgic and energy sectors, which are incorporated into the model results.
where \(\left[S{\left(I-QS\right)}^{-1}\right]\) represents the total production required per unit of final demand; S is the industry-by-product market share matrix obtained from Supply table by normalizing each product column by that products’ total demand, capturing market share coefficients, indicating the proportion of each product supplied by different industries; I is the identity matrix; Q is the product-by-industry technical coefficients matrix derived from the Use table by dividing each industry column by that industry’s total output, detailing direct product input required per unit of industries’ output; and \({\varvec{y}}\) symbolizes the final demand for EE products.
By integrating R and \({\varvec{y}}\) into the total production required per unit of final demand, the model ensures that all chain reactions throughout the economy are captured and attributed back to industry-specific contributions. This means that the impulse created by the expenditures in EE technologies propagates through the system to generate sectoral production effects that are finally converted into environmental and socio-economic, impacts via technology- and sector-specific coefficients.
The rectangular IO model uses Supply and Use Tables (SUTs) in rectangular form to keep products and industries distinct and to preserve secondary production. The Use matrix represents the commodities used in industries, and the Supply/Make matrix representing what commodities are produced by industries. Due to the number of products can exceed the number of industries, the system is called rectangular (Horowitz & Planting, 2006). This structure allows for more accurate modeling of embodied energy and emissions, as well as GVA and job creation, as it better reflects real economic complexity and sectoral heterogeneity.
This analysis aligns the 2017 SUTs at basic prices (OECD, 2017a) with satellite accounts from Portugal’s National Statistics Institute (INE, 2017). Data from 2018–2020 were excluded due to data unavailability and COVID-related distortions. By integrating SUTs with satellite accounts, this approach streamlines the P-LCA process for assessing the environmental and socio-economic impacts.
Selected EE measures
As part of the 2021 PPEC edition, all proposed tangible measures underwent a rigorous evaluation and received formal approval (ERSE, 2021b). The assessment relied on a comprehensive dataset that included parameters such as technology lifespan, final energy savings, the number of installations, PPEC-subsidized costs, promoters and consumers’ contributions, and the associated social costs. The data were sourced from ERSE (2022a, 2022b) and are detailed in Table 11 of the Appendix. The EEMs analyzed comprise a range of equipment replacements and system upgrades aimed at reducing energy consumption and improving efficiency in the residential sector. Specifically, IBD_TR1 and IBD_TR2 involve the replacement of conventional air conditioning and domestic hot water systems with high-efficiency heat pumps, with the option of integrating photovoltaic systems for self-consumption. PORTGAS_TR1 focuses on replacing natural gas-fueled water heaters with safer and more efficient models, while GOLDENERGY_TR1 entails the installation of intelligent thermostats to optimize indoor climate control. LISGDL_TR1 addresses the replacement of outdated atmospheric boilers with sealed or condensing boilers to improve energy performance and reduce emissions. Table 3 summarizes the approved initiatives.
Table 3
Approved PPEC measures for the residential sector
Nomenclature
Promoter
Measure
Sector
IBD_TR1
Iberdrola
Efficient thermal energy
Residential
IBD_TR2
Iberdrola
DHW heat pump
PORTGAS_TR1
Portgas
More efficient water heaters
GOLDENERGY_TR1
Goldenergy
Smart thermostats
LISGDL_TR1
Lisboagas GDL
Replacement and disposal of energy equipment with more efficient equipment
Benefits considered
As previously noted, a comprehensive evaluation of EE initiatives must account for MBs to support more informed decision-making. This section outlines the benefits addressed in this work and how they were used to recalibrate the metrics of PPEC.
The following benefits were considered:
PES: Defined as the difference in primary energy consumption between business as usual technologies and best available EE technologies. For the PES test, embodied energy during MPIM phases of best available EE technologies are included alongside with operation consumption.
Avoided costs of electricity or gas supply: Estimated by monetizing the net energy savings of EE technologies, computed as the difference between PPEC-calculated savings and the energy consumed during their MPIM phases. Monetization parameters were retrieved from (ERSE, 2021a).
Environmental benefits (societal perspective): These benefits are valued using the unitary values of environmental benefits associated with avoided electricity and natural gas consumption provided by ERSE (2021a). For electricity, avoided GHG emissions are not included in the valuation since they are already internalized in electricity pricing; therefore, only other environmental externalities are considered. For natural gas, both avoided GHG emissions and additional environmental externalities are included. The same ERSE cost factors are also applied to estimate the costs of GHG emissions and environmental externalities generated by the energy consumed during the MPIM phases of EE measures. Accordingly, environmental benefits are calculated as the net avoided supply of electricity and natural gas, obtained by subtracting MPIM energy consumption from the total avoided energy use.
To ensure comparability between the standard PPEC methodology and the refined approach integrating HIO-LCA indicators, the environmental analysis is restricted to energy consumption and related externalities. Although the HIO-LCA framework can also estimate other pollutants (e.g., particulate matter), these are excluded from this work to align with ERSE’s unitary cost structure.
GVA: Represents the economic value generated during MPIM phases of EE technologies. This indicator is estimated via the HIO-LCA model.
IPB: Quantifies the fiscal return generated through employment creation across different industry sectors during the MPIM phases. This includes income taxes and social security contributions arising from the jobs created because of the adoption of EEMs. The total IPB is calculated using Eq. (9).
where \({FTE}_{j}\) represents the number of full-time equivalent jobs created in sector j; \({\overline{In} }_{j}\) is the average annual income per employee in sector j; \({Ir}_{j}\) denotes the effective income tax and social contribution rate in sector j; j represents each sector involved in the MPIM of the EE technology under evaluation and n is the total number of sectors considered.
The standard PPEC evaluation did not account for other positive externalities, such as socio-economic impacts, particularly those related to employment and GVA. Recognizing the importance of these factors, we incorporated them into this study to more comprehensively reflect a wide range of social benefits that a given measure may generate.
The lifecycle phases addressed in this work encompass MPIM (using HIO-LCA) and operation (using the PPEC methodology). Due to data limitations, the end-of-life impacts are excluded, however, prior studies estimate their contribution at under 5% of total lifecycle impacts (Ortiz et al., 2010).
Novelties
To address the limitations identified in the preceding sections, the proposed methodology seeks to refine existing EE evaluation frameworks through the introduction of several key advancements:
1.
Integration of the HIO-LCA model with the PPEC methodology: Although the HIO-LCA framework has been developed and applied in other contexts, its integration into the cost–benefit evaluation framework of the PPEC represents a novel contribution. This integration enhances the traditional analysis by incorporating national-scale socio-economic and environmental co-benefits arising from different lifecycle phases of EE measures. In doing so, the paper demonstrates the validity of HIO-LCA as a complementary tool for strengthening cost–benefit assessment methodologies, refining funding criteria, and providing policymakers with a more comprehensive and robust basis for decision-making in EE policy.
2.
Enhanced IO Modeling: This work employs national SUTs to trace economic interactions within supply chains of measures proposed for funding under the PPEC program. By capturing sectoral interdependencies at the national level, the approach generates detailed insights into how EE investments affect socio-economic and environmental outcomes. These insights strengthen the evidence base for policymakers, enabling the optimization of national EE strategies to maximize both economic returns and broader societal gains.
3.
Scalability and International Applicability: Portugal is used as the case study given the scale and relevance of its PPEC program, which provides a robust setting to test and demonstrate the proposed framework. Proving its effectiveness in improving cost–benefit evaluation models establish the methodology as a valid alternative that can be readily extended to EE funding schemes in other national contexts. By leveraging internationally standardized IO and SUT tables, available in at least 57 countries, the framework can be adapted and replicated to evaluate different national EE programs and enables meaningful cross-country comparisons. This adaptability ensures broader applicability, strengthening the consistency and comprehensiveness of impact assessments across diverse policy contexts. Additionally, by incorporating detailed technical and survey data for component decomposition and cost allocation within the IO model the approach can be readily adapted to assess a wide range of technologies, including those in the industrial and agricultural sectors, using the same procedural framework.
Results and discussion
This section presents the outcomes from the implemented methodology, followed by a comparative assessment and recalibration of key indicators.
Table 4 summarizes the PPEC impacts associated with each EEM analyzed, including operational energy savings, societal benefits, and costs. These values serve as a foundation for recalculating PES, NPV, and BCR by incorporating the additional effects derived from the HIO-LCA framework, as detailed in Table 5.
Table 4
PPEC impacts
Measures
PES
Costs
Benefits
PPEC
Social
Environmental benefits from a societal perspective
Avoided costs of electricity or gas supply
Toe/year
(€)
(€)
(€)
(€)
IBD_TR1
43.72
112,344
334,798
19,287
488,107
IBD_TR2
13.55
41,098
157,601
5,803
152,227
PORTGAS_TR1
159.28
685,125
940,000
129,026
825,699
GOLDENERGY_TR1
41.18
104,545
150,560
33,360
330,132
LISGDL_TR1
254.26
799,433
1,279,609
205,965
1,318,074
Table 5
MPIM impacts
Measures
MPIM impacts
GVA
Employment
Impact on public budget
Embodied GHG emissions
Embodied energy
€
Nº of jobs
€
Tons of CO2eq
€
Toe
€
IBD_TR1
34,315.47
0.96
5,570.26
23.67
299.77
7.06
4,597.31
IBD_TR2
16,740.14
0.50
3,542.66
8.56
127.40
2.93
1,868.94
PORTGAS_TR1
362,279.42
10.25
51,686.85
310.19
3,577.04
82.96
87,754.63
GOLDENERGY_TR1
35,180.37
1.16
4,411.62
14.62
224.87
5.03
3,015.95
LISGDL_TR1
282,300.88
7.75
49,651.49
206.83
2,566.60
57.90
65,258.57
To account for environmental effects, a detailed analysis of the materials and services involved in the MPIM phases of each measure was carried out, reflecting the separate reporting of unit costs for electricity and gas. The energy balance tables from DGEG (2023) allowed to compute the proportional contributions of electricity and gas to the total energy input we estimated (see Table 6). It is important to note that the cumulative percentages did not sum 100%, as minor energy vectors were excluded due their negligible contribution to the overall energy use.
Table 6
Shares of energy vectors in each EE measure
Measures
Energy vectors
Gas (%)
Electricity (%)
IBD_TR1
32.28
64.76
IBD_TR2
33.79
62.77
PORTGAS_TR1
32.70
66.10
GOLDENERGY_TR1
36.23
57.80
LISGDL_TR1
32.90
61.00
GVA was already expressed in euros, while employment impacts were translated into IPB, reflecting the fiscal return generated through job creation. These indicators provide a monetary representation of broader socio-economic impacts.
New PPEC rankings
Using the refined methodology outlined previously, the PES, NPV, and BCR values were recalculated based on the updated inputs from Tables 4 and 5, with the results summarized in Table 7.
Table 7
Old and new scores for BCR, PES and social test
Measures
Old BCR
New BCR
Old PES (toe)
New PES (toe)
Old NPV (€)
New NPV (€)
IBD_TR1
4.52
4.83
874.4
867
172,595
207,584
IBD_TR2
3.85
4.29
271.0
268
428
18,715
PORTGAS_TR1
1.39
1.86
1,911.4
1,828
14,724
337,360
GOLDENERGY_TR1
3.48
3.82
494.2
489
212,932
249,283
LISGDL_TR1
1.91
2.24
3,051.1
2,993
244,430.3
508,557
Finally, the updated ERSE scores were determined (see Table 8). These revised scores were then combined with the unchanged DGEG values to obtain the final PPEC scores, which served as the basis for the updated ranking of EEMs.
Table 8
New PPEC rankings
Measures
ERSE – Old score
ERSE – New score
DGEG score
PPEC – Old score
PPEC – New score
Old ranking
New ranking
IBD_TR1
98.53
98.53
84.00
91.27
91.27
1º
1º
IBD_TR2
83.59
85.07
84.00
83.80
84.54
2º
2º
PORTGAS_TR1
45.43
49.14
79.00
62.21
64.07
3º
4º
GOLDENERGY_TR1
59.32
69.27
64.00
61.66
66.64
4º
3º
LISGDL_TR1
52.98
54.83
64.00
58.49
59.42
5º
5º
The findings of this study highlight the significant impact of incorporating MBs on EE assessment and decision-making. As reflected in Table 7, key performance metrics responded notably to the inclusion of additional effects.
PES saw modest reductions, with decreases of 1% observed in IBD_TR1, IBD_TR2, and GOLDENERGY_TR1, and a maximum decrease of 4% for PORTGAS_TR1. In contrast, NPV experienced dramatic increases, reaching up to 4,271% for the second EEM proposed by Iberdrola. BCR also improved, with a notable rise of 34% for efficient water heaters (PORTGAS_TR1). These results indicate that although the inclusion of embodied energy and GHG emissions slightly diminished the societal benefits, the positive contributions of GVA and IPB more than offset these impacts, ultimately enhancing the overall cost-effectiveness of all proposed EEMs. Without including MPIM phases’ impacts, the significant positive contributions of GVA and IPB would remain hidden, leading to an incomplete representation of societal benefits.
As depicted in Table 8, these changes had a direct effect on project rankings. While IBD_TR1 maintained its top position with an unchanged ERSE score (98.53), GOLDENERGY_TR1 moved ahead of PORTGAS_TR1, highlighting the influence of lifecycle performance in the evaluation. This shift illustrates the critical role that broader benefit integration plays in shaping funding priorities and investment decisions in EE programs.
Although all measures considered in the 2021 PPEC edition (used here as a case study) were ultimately approved, this has not always been the case in previous editions. Therefore, the methodological approach adopted in this study could prove instrumental in future decision-making contexts, potentially increasing the potential for borderline or underappreciated measures being approved.
Conclusions
Enhancing EE remains a priority for EU Member States, given its cost-effectiveness and crucial role in reducing environmental impacts. Aligning with this goal, the EU mandates national strategies to meet EE targets and reduce GHG emissions necessitating robust evaluation frameworks to guide and assess these strategies. Despite ongoing efforts, there is still considerable potential to better capture the full value of EE, particularly by integrating broader and societal benefits.
This paper introduces a refined methodology that extends conventional frameworks by incorporating MBs generated across different lifecycle phases, rather than focusing solely on the operational phase. Specifically, the approach integrates the HIO-LCA model into the existing PPEC framework, enhancing its analytical capacity to assess socio-economic and environmental impacts and benefits. It also leverages national SUTs for improved IO modeling and ensures scalability trough compatibility with international databases, supporting broader applicability and cross-country benchmarking.
The study compares the conventional indicators used by PPEC, namely PES, NPV and BCR, which are traditionally based solely on monetized energy savings and avoided emissions, with revised results generated using the expanded framework. This updated approach incorporates GVA and IPB, while also accounting for embodied energy and emissions from MPIM phases.
The application of this enhanced methodology demonstrated that incorporating additional benefits can substantially affect the prioritization of EEMs. Two measures shifted in ranking, and all showed notable improvements in NPV and BCR, specifically NPV rose by over 4,000% and BCR by more than 34%, highlighting the value of a comprehensive evaluation. Although PES slightly declined across all measures due to the inclusion of embodied energy and GHG emissions, the overall cost-effectiveness improved, as the positive impacts of GVA and IPB more than compensated for these reductions. Ultimately, this work successfully addressed a central research question: Does considering additional benefits enhance the evaluation of EE measures? The findings confirm the relevance and effectiveness of the proposed methodological approach, which adds significant value by enhancing the robustness and reliability of the assessment process through a comprehensive, scalable, and data-driven framework that captures lifecycle, economic, and societal impacts of EE investments, supporting more informed, transparent, and impactful policy and funding decisions. Moreover, due to its adaptability, the approach can be extended to assess other technologies across different sectors, such as industry or agriculture, thereby broadening its applicability and contribution to sustainable development planning.
While the proposed methodology offers significant advantages, several limitations remain. First, the analysis relies on aggregated sectoral data in the IO model, which may obscure heterogeneity within industries. Improved disaggregation or the use of higher-resolution SUTs would increase accuracy but often requires restricted data. Second, the assumption that Portuguese industries are representative of the EE technology supply chain can introduce uncertainty when generalizing results. Future work should therefore explore strategies to enhance sectoral resolution without compromising data availability and reliability. In the context of employment impacts, for instance, further disaggregation could help identify the specific types of jobs generated and assess their alignment with the skills and workforce demands of the ongoing energy transition.
Another important limitation involves the exclusion of end-of-life effects due to limited manufacturer-specific information; although these typically account for less than 5% of lifecycle impacts, more targeted empirical data would strengthen robustness.
Future research could also broaden the application of this methodology by incorporating additional benefits, such as reductions in energy poverty or improvements in public health, and by extending its use to other EE programs. A relevant case is the Portuguese Environmental Fund's residential programs, which currently focus solely on operational energy savings. Employing this more comprehensive framework could support a more robust assessment of whether the funded measures yield the most favorable cost–benefit outcomes.
Acknowledgements
This research was funded by the Portuguese Foundation for Science and Technology (FCT) through the doctoral grant SFRH/BD/151353/2021 (https://doi.org/10.54499/SFRH/BD/151353/2021), supported by the European Social Fund under the PORTUGAL2020 framework and the Demography, Qualifications, and Inclusion Program (Pessoas 2030), via the Regional Operational Program of the Center (Centro 2020), and within the scope of the MIT Portugal Program. Additional funding was provided through the FCT Pluriannual Funding UID/308: Institute for Systems and Computer Engineering at Coimbra—INESC Coimbra and UID/5037: Centre for Business and Economics Research—CeBER, as well as by the European Union under the Horizon Europe framework program (Grant Agreement ID: 101075582). This work is also co-funded by the European Regional Development Fund (FEDER) and national funds through FCT, under the project COMPETE2030-FEDER-00888800, operation no. 15224.
Declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This work is neither a repetition of any work nor copied key data from other’s work.
Informed consent
All co-authors of this manuscript were notified about each step of the publication process, and all was done with their input and approval.
Clinical trial number
Not applicable.
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No evaluation of impacts at the different phases of the lifecycle of the EE technologies beyond their operation
IO analysis is applied only to the evaluation of Gross Domestic Product (GDP) and employment impacts connected to the energy sector and its supply chain
Table 10
Review of European-Funded projects assessing the MBs of EE
No evaluation of economic and social impacts of the EE technologies at different phases of their lifecycle beyond their operation
Assessment of the energy and environmental impacts of the technologies under study beyond their operation, using P-LCA
Time-consuming and truncation issues arising from the application of the P-LCA methodology in lifecycle evaluation
Table 11
Technical characteristics and costs of selected measures
Nomenclature
Lifespan
years
Energy consumption
(kWh/year)
Number of Equipments
PPEC cost
€
Promoter costs
€
Consumer costs
€
Social costs
€
Description
IBD_TR1
20
427,062
168
112,344.32
2,000.00
220,453.92
334,798.24
Replacement of air conditioning and hot water production equipment with heat pumps. In addition, the installation of a photovoltaic system for self-consumption
IBD_TR2
20
128,391
56
41,097.85
2,000.00
114,503.39
157,601.24
Replacement of low-efficiency domestic hot water equipment with DHW heat pumps. In addition, the optional installation of a photovoltaic system for self-consumption
PORTGAS_TR1
12
1,852,500
2,500
685,125.00
16,250.00
238,625.00
940,000.00
Replacement of domestic hot water equipment whose source of energy is natural gas, in particular water heaters, with safer, cleaner and more efficient equipment
GOLDENERGY_TR1
12
822,780
1,000
104,545.00
31,015.00
15,000.00
150,560.00
Installation of intelligent thermostats to improve the management and control of room air conditioning
LISGDL_TR1
12
1,950,463
1,049
799,432.74
49,949.99
430,226.37
1,279,609.10
Replacement of atmospheric boilers with more efficient equipment, such as sealed or ventilated condensing boilers
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