The ecological and energy transition, in addition to protecting the environment, can have positive impacts on energy poverty. Renewable Energy Communities (RECs) are a tool that has been recognised and supported at European level since 2018, allowing citizens to produce and share renewable energy with economic, environmental and social benefits. In Italy, these initiatives are beginning to spread sporadically, and the drivers behind their adoption are not yet clear. To understand age-related propensities in the ecological transition and the fight against energy poverty, this paper analyses data on renewable energy communities (RECs) and the age of citizens in Italian municipalities. The analysis focuses on the effect of the average age of residents on the probability of creating a REC. The aim is to understand the level of participation of citizens belonging to older age groups and to identify the presence of constraints or potential benefits in terms of social inclusion and energy poverty. Our study aims to fill the research gap on the role that age can play in the context of the energy transition and offer ideas for more inclusive policies about the elderly.

Short abstract

This research investigates the interplay between energy poverty and active ageing among Italian seniors, a concern heightened by recent global crises. The study aims to determine if these phenomena are mutually reinforcing, potentially creating a vulnerability cycle for the elderly. It addresses three questions: whether active ageing mitigates energy poverty, if energy poverty hinders active ageing components like participation and lifelong learning, and if these vulnerabilities are persistent.

The study notes a literature gap concerning their interaction using longitudinal data, crucial for understanding causal effects. Data from waves 5 to 9 of the Survey of Health, Ageing and Retirement in Europe (SHARE) for Italy will be used. Energy poverty is measured using a Low Energy Price Resilience (LENRES) indicator, identifying those experiencing cold due to heating costs and financial fragility. Active ageing is assessed via an Active Ageing Index (AAI) based on WHO pillars, constructed using dimensionality reduction techniques like PCA.

Long abstract

The confluence of recent global crises, including the Great Recession, the COVID-19 pandemic, and the ongoing energy crisis, has amplified the vulnerabilities of elderly populations across Europe. This research proposes to investigate the relationship between energy poverty and active ageing among senior citizens in Italy, a topic of increasing socio-economic relevance. The study aims to explore whether these two phenomena are mutually reinforcing, potentially trapping the elderly in a cycle of vulnerability.

This paper will address three primary research questions:

1. Does active ageing alleviate energy poverty? Engagement in social activities and lifelong learning may equip seniors with skills and information to navigate the energy transition, for instance, by improving energy-saving habits or making better choices regarding energy suppliers (Lorenc et al. 2013). Conversely, diminished physical and cognitive resources in less actively ageing individuals might impede effective energy-related decision-making.

2. Does energy poverty hamper the participation and lifelong learning components of active ageing? Financial constraints imposed by energy poverty might compel the elderly to prioritize essential needs, thereby forgoing opportunities for learning and social engagement.

3. Are elderly vulnerabilities persistent, in terms of (in)active ageing and energy poverty? Affirmative answers to the preceding questions would suggest a mutually reinforcing dynamic, leading to a vulnerability trap for the elderly and making them less resilient (Hallegatte 2014).

The existing literature reveals a growing focus on energy poverty on the one hand (Bardazzi et al. 2021, 2024; Besagni and Borgarello 2019; Betto et al. 2020; Delugas and Brau 2021; Faiella et al. 2017; Faiella and Lavecchia 2021), and active ageing on the other (Barslund et al. 2019; Berenger et al. 2023; Olivera 2022; Paul et al. 2012; Rowe and Kahn 1997; Walker and Foster 2013; Walker 2014; Zaidi 2008, 2014, 2015; Zaidi et al. 2013, 2017). Yet, there is a lack of research exploring their interplay using longitudinal data, which is crucial for controlling for unobservable preferences and capturing the causal effects of time-varying conditions like policies and weather. Only few papers have focused on energy poverty of the elderly, e.g. Sardianou (2023) and Jiang et al. (2024). This study seeks to fill this gap.

To address these questions, we utilize data from the Survey of Health, Ageing and Retirement in Europe (SHARE) for Italy. While SHARE is primarily for active ageing research, it also provides consensual measures of energy poverty. We construct a Low Energy Price Resilience (LENRES) indicator, inspired by Burlinson et al. (2024), identifying individuals experiencing both the physical hardship of cold due to heating cost concerns and financial fragility (difficulty making ends meet or being in debt for utility bills). For active ageing, we develop an Active Ageing Index (AAI) based on the WHO pillars (Health, Lifelong Learning, Participation, Security), operationalized by Rojo-Perez et al. (2022). We build a longitudinal dataset covering SHARE waves 5 to 9, as comprehensive energy poverty-related questions are available from wave 5 onwards.

Our methodology unfolds in two stages. First, we construct an AAI at the individual level using dimensionality reduction techniques on SHARE variables. This poses methodological challenges, as SHARE includes continuous, dichotomous, and categorical variables. To ensure robustness, we apply both standard and polychoric PCA (Kolenikov et al., 2005), preceded by suitability checks (KMO, anti-image matrix, Cronbach’s alpha) as performed in Steinmayr et al. (2020). PCA is performed by wave, with additional checks on the stability of loadings from wave 5 across WHO pillars.

Second, we estimate panel regressions to assess the bidirectional relationship between energy poverty and active ageing, including panel autoregressive models. Controls include age, gender, education, marital status, household composition, and building type. Fixed and random effects models are compared using Hausman tests. We are also considering Variable selection is further supported by Bayesian Model Averaging (BMA) to check the robustness of the results.

This study is expected to provide crucial insights into the dynamics of vulnerability among the elderly in Italy. By employing a longitudinal approach, it aims to offer more robust evidence on the potential causal links between energy poverty and active ageing. The findings will be valuable for policymakers seeking to design targeted interventions to break the cycle of vulnerability and promote well-being in an ageing society.

References

Bardazzi, R., Bortolotti, L., Pazienza, M. G. (2021). To eat and not to heat? Energy poverty and income inequality in Italian regions. Energy Research & Social Science, 73, 101946.

Bardazzi, R., Bortolotti, L., Pazienza, M. G. (2024). Are they twins or only friends? The redundancy and complementarity of energy poverty indicators in Italy. Italian Economic Journal, 10(2), 585-623.

Barslund, M., Von Werder, M., Zaidi, A. (2019). Inequality in active ageing: evidence from a new individual-level index for European countries. Ageing & Society, 39(3), 541-567.

Berenger, V., Silber, J. Are Older People Aging Well in Europe? A Multidimensional Deprivation Index Among Older People. A Multidimensional Deprivation Index Among Older People.

Besagni, G., Borgarello, M. (2019). The socio-demographic and geographical dimensions of fuel poverty in Italy. Energy Research & Social Science, 49, 192-203.

Betto, F., Garengo, P., Lorenzoni, A. (2020). A new measure of Italian hidden energy poverty. Energy Policy, 138, 111237.

Boardman, B. (2013). Fixing fuel poverty: challenges and solutions. Routledge.

Bollino, C. A., Botti, F. (2017). Energy poverty in Europe: A multidimensional approach. PSL Quarterly Review, 70(283), 473-507.

Bosch-Farré, C., Garre-Olmo, J., Bonmatı-Tomas, A., Malagon-Aguilera, M. C., Gelabert-Vilella, S., Fuentes-Pumarola, C., Juviny`a-Canal, D. (2018). Prevalence and related factors of Active and Healthy Ageing in Europe according to two models: Results from the Survey of Health, Ageing and Retirement in Europe (SHARE). PLoS One, 13(10), e0206353.

Burlinson, A., Davillas, A., Giulietti, M., Price, C. W. (2024). Household energy price resilience in the face of gas and electricity market crises. Energy Economics, 132, 107414.

Delugas, E., Brau, R. (2021). Evaluating the impact of energy poverty in a multidimensional setting. The Energy Journal, 42(1), 39-66.

Faiella, I., Lavecchia, L., Borgarello, M. (2017). Una Nuova Misura Della Povertà Energetica Delle Famiglie (A New Measure of Households’ Energy Poverty). Bank of Italy Occasional Paper, (404).

Faiella, I., Lavecchia, L. (2021). Energy poverty. How can you fight it, if you can’t measure it?. Energy and Buildings, 233, 110692.

Fry, J. M., Farrell, L., Temple, J. B. (2022). Energy poverty and retirement income sources in Australia. Energy economics, 106, 105793.

Gan, H., Lin, C., Zhou, Y., Zhuo, Z. (2025). Adult children’s education and elderly parents’ energy poverty: Evidence from China. Energy Policy, 202, 114604.

Hallegatte, S. (2014). Economic resilience: definition and measurement. World Bank Policy Research Working Paper, (6852).

Jiang, L., Shi, X., Feng, T., Yan, M. (2024). Age-driven energy poverty in urban household: Evidence from Guangzhou in China. Energy for Sustainable Development, 78, 101369.

Lorenc, A., Pedro, L., Badesha, B., Dize, C., Fernow, I., Dias, L. (2013). Tackling fuel poverty through facilitating energy tariff switching: a participatory action research study in vulnerable groups. Public Health, 127(10), 894-901.

Olivera, J. (2022). Ageing unequally in Europe. Socio-Economic Review, 20(1), 401-422.

Paul, C., Ribeiro, O., Teixeira, L. (2012). Active ageing: An empirical approach to the WHO model. Current Gerontology and Geriatrics Research.

Rojo-Perez, F., Rodriguez-Rodriguez, V., Molina-Martinez, M. A., Fernandez-Mayoralas, G., Sanchez-Gonzalez, D., Rojo-Abuin, J. M., ... Forjaz, M. J. (2022). Active ageing profiles among older adults in Spain: A Multivariate analysis based on SHARE study. PloS one, 17(8), e0272549.

Rowe J.W., Kahn R.L.. Successful Aging. Gerontologist. 1997; 37(4):433–40. PMID: 9279031

Sardianou, E. (2023). Understanding Energy Poverty among the Elderly: Insights from a Household Survey in Greece. Energies, 17(1), 94.

Scobie, J., Adfour, L., Beales, S., McGeachie, P., Gillam, S., Mihnovits, A., Mikkonen-Jeanneret, E., Nisos, C., Rushton, F., Zaidi, A. (2015). Global Age-Watch Index 2015: Insight report. HelpAge International.

Steinmayr, D., Weichselbaumer, D., Winter-Ebmer, R. (2020). Gender differences in active ageing: Findings from a new individual-level index for European countries. Social Indicators Research, 151(2), 691-721.

Tian, P., Feng, K., Sun, L., Hubacek, K., Malerba, D., Zhong, H., ... Li, J. (2024). Higher total energy costs strain the elderly, especially low-income, across 31 developed countries. Proceedings of the National Academy of Sciences, 121(12), e2306771121.

Walker, A., Foster, L. (2013). Active ageing: Rhetoric, theory and practice. In The making of ageing policy (pp. 27-52). Edward Elgar Publishing.

Walker, A. (2014). The concept of active ageing. In Active ageing in Asia (pp. 14-29). Routledge.

Zaidi, A. (2008). Features and challenges of population ageing: The European perspective. Policy brief, 1, 1-16.

Zaidi, A. (2014). Enabling environments for active and healthy ageing in EU countries. Gerontechnology, 12(4), 201-208.

Zaidi, A. (2015). Creating and using the evidence base: the case of the Active Ageing Index. Contemporary Social Science, 10(2), 148-159.

Zaidi, A., Gasior, K., Hofmarcher, M. M., Lelkes, O., Marin, B., Rodrigues, R., ... Zolyomi, E. (2013). Active ageing index 2012. Concept, Methodology, and Final Results, Research Memorandum, Methodology Report, European Centre Vienna. Available at.

Zaidi, A., Gasior, K., Zolyomi, E., Schmidt, A., Rodrigues, R., Marin, B. (2017). Measuring active and healthy ageing in Europe. Journal of European Social Policy, 27(2), 138-157.

The research project developed an evidence-based evaluation checklist designed for local policymakers to support the planning and monitoring of age-inclusive and sustainable public policies. Anchored in the framework of the 2030 Agenda for Sustainable Development, the project promotes a multidimensional and proactive vision of ageing, recognising older adults as active agents of social and territorial transformation. Using a three-round Delphi method (n=22, n=17, n=8), the research progressively identified, validated, and refined the checklist items, which are organised into five key domains: inequalities, well-being, resources, innovation, and participation. In the third round, the checklist was structured as a practical evaluation tool, enabling the assessment of local implementation levels for relevant policies and services. Each item was rated based on adequacy, coherence, and completeness, and matched with clear implementation indicators (fully implemented / partially / planned / not implemented / not applicable). Results indicate strong internal consistency and thematic relevance, offering a replicable and adaptable tool to support integrated, measurable, and socially responsive ageing policies. The checklist bridges different domains - health, social care, environment, technology—facilitating intersectoral governance and promoting intergenerational participation. The contribution proposes a novel approach to the local monitoring of sustainable ageing, grounded in expert consensus and oriented toward operational impact.

In an aging society, it is essential to develop tools that can accurately capture housing and health vulnerabilities among older people.

Inadequate housing conditions can exacerbate chronic illnesses and reduce mobility, negatively impacting overall well-being. Problematic health conditions can undermine the ageing in place, when coupled with housing issues and weak social and institutional support. Against this background, this study presents and critically discusses the research findings related to the empirical analyses conducted within the project/cascade funding Spoke 1 Age-It: VAI – Vulnerabilità abitativa e di salute degli Anziani in Italia.

It considers data from the 2019 European Health Interview Survey (EHIS) for Italy, focusing on individuals aged 65 and over, to construct two indices at macro and regional levels for housing (VAA) and health (VSA) vulnerabilities.

Both indices are developed using the Alkire and Foster (2011) dual cut-off methodology. This method identifies vulnerable individuals through a two-step process. The resulting Adjusted Headcount Ratio reflects both the incidence and intensity of vulnerability.

Research findings reveal that high housing vulnerability does not always correlate with high health vulnerability. To address the limitations of simple aggregative methods that produce compensatory effects, the Voronoi algorithm is applied (Mariani et al., 2024).

This spatial partitioning method allows simultaneous analysis of both dimensions by assigning each region to a distinct Voronoi cell. The resulting regional rankings differ from those based on single indices, offering a more nuanced tool for targeting policy interventions aimed at supporting vulnerable older population.