The term “Triple Transition” has been used to describe the concomitant demographic, digital, and green transitions that the European Union (EU) aims to achieve through the integration of social inclusivity, technological advancement, and environmental sustainability [1]. According to the Organisation for Economic Co-operation and Development (OECD) [1], these transitions are reshaping societal structures, presenting unique challenges and opportunities in many sectors, including health care and, more specifically, long-term care (LTC) services.

In terms of demographic transition, the European LTC sector faces growing challenges such as rapid demographic aging and a shrinking working-age population. In Europe, median age increased from 39.0 to 44.5 years (2003‐2023), life expectancy increased by almost 4 years (2002‐2023), and the old-age dependency ratio grew by 9.3 percentage points (2001‐2020) [2,3]. These shifts might have serious implications for care provision, creating an unprecedented demand for care services together with a growing shortage of care staff [1,4].

The digital transition uses technology to enhance care delivery through electronic health records, telehealth, AI-powered diagnostics, and remote monitoring, among others [5,6]. Although some researchers predict digitalization will improve care efficiency [7], others caution that the benefits may be unequally distributed, causing a deeper digital exclusion and discrimination [1,8].

The green transition aims at fostering environmentally friendly practices as well as monitoring climate change [1]. As the health care sector contributes about 4.4% of global greenhouse gas emissions [9], care systems have faced increasing pressure to adopt more sustainable practices and policies [10,11].

While population aging is causing high pressure on the LTC sector and creating new challenges [12,13], digitalization and sustainability have been investigated as potential approaches to reduce resource constraints and improve the delivery of care [7,14-16].

In fact, while digital technologies have been primarily studied for enhancing care efficiency and quality [7,17-20], environmental practices seem to have been mainly investigated for their impact on resource efficiency, waste reduction, and management systems [21,22].

According to the OECD [1], these transitions have typically been studied in isolation, and potential synergies between digitalization and sustainability have been largely overlooked [23,24]. For example, digital solutions might simultaneously improve care efficiency as well as reduce environmental impact [25,26]. Similarly, LTC research has also maintained a siloed approach, with only a few studies exploring the integration and potential synergies between the digital and environmental transition [27-30]. Finally, Eurofound [3] calls for a more comprehensive understanding of these intersecting challenges, as this might inform solutions that jointly address rising care demand, technological change, and sustainability requirements in care systems.

To better understand the interconnections between the demographic, digital, and green transitions, this study uses the social-ecological-infrastructural systems (SEIS) framework developed by Ramaswami et al [31]. This framework belongs to a broader family of conceptual approaches that have been used to examine the relationships between the social, technological, and ecological dimensions of sustainability [32-34]. These approaches have been used to investigate how such dimensions are interconnected and to verify to what extent changes in one domain might affect the other ones [35,36]. Specifically, the SEIS framework merges 3 interconnected dimensions: social systems (people, communities, and institutions), infrastructural systems (transportation networks and engineered solutions), and ecological systems (natural environments and resources). Although the SEIS framework was originally conceived for urban sustainability [31], its application has been extended to other settings, including health care and sustainability [37]. More recent applications have also included digital technologies as a critical component of the infrastructural system [38,39]. Therefore, the SEIS framework is particularly suited to study the Triple Transition in LTC, as it directly aligns with the demographic (social), digital (infrastructural), and environmental (ecological) transitions occurring in the care sector.

The demographic transition primarily affects social systems through changes in population structure (ie, population aging), care needs, and the role of informal caregivers, for example, family or nonfamily members who provide unpaid care, outside of a formal framework, to someone with a chronic illness, disability, or other long-lasting care need [40]. The digital transition impacts the infrastructural system through the introduction of new tools, platforms, and devices for care delivery. Finally, the green transition seeks to develop more sustainable approaches for the use of resources and waste management.

While the Triple Transition has been viewed at these systems as separate pillars, the SEIS framework focuses on the integration within and among them. Specifically, it distinguishes 3 interfaces: the socioinfrastructural (how digital tools are integrated into caregiving contexts), the socioecological (how caregivers engage with eco-friendly practices), and the eco-infrastructural interface (how digital solutions contribute to environmental sustainability). The central intersection of the 3 systems represents the research gap addressed in this paper: the relationship between the use of digital tools and eco-friendly practices among informal caregivers (Figure 1).

This study investigates how the use of digital tools is interconnected with the adoption of eco-friendly practices among informal caregivers. In doing so, it aims to better understand how the 3 dimensions of the SEIS framework interact with each other within the specific context of LTC. This integrated approach is consistent with more contemporary developments in sustainability theories that emphasize the need for systems thinking rather than siloed approaches [40].

Studying the intersection between informal caregivers’ use of digital tools and environmental practices provides a unique opportunity to understand how the Triple Transition manifests in care settings. Informal caregivers are a particularly important population group, increasingly affected by demographic shifts, digitalization, and growing environmental concerns. Situated at this intersection, they are especially relevant for understanding how these transitions interact in practice and how policy might better integrate these interconnected domains rather than addressing them in isolation. Drawing on the SEIS framework, the following 3 subsections examine the relationship between the use of digital tools and environmental behaviors; the adoption of digital tools in caregiving contexts; and eco-friendly practices in LTC.

The relationship between the adoption of digital technologies and environmental behaviors has gained increasing attention as both digitalization and environmental awareness have become more widespread [41,42]. This relationship is theoretically grounded in the concept of behavioral spillover effects [43], in which engagement in one domain (use of digital tools) may influence behaviors in another domain (environmental practices) through shifts in values, knowledge, and capabilities. In fact, digital tools may serve as enablers of environmental behaviors in a number of ways, including more efficient access to information, facilitating the creation of support communities, and making eco-friendly consumption easier to perform. According to Hilty and Aebischer [44], digital technology’s environmental effects can be categorized into 3 distinct orders.

First-order effects concern the direct environmental impacts of the life cycle of digital devices from production through use to disposal [44]. In the health care context, these impacts include the environmental footprint of digital and telehealth devices [27]. Second-order effects involve how digital technologies enable process optimization, dematerialization, and the substitution of physical resources with digital alternatives [44]. In health care and caregiving, digital services might reduce transportation emissions through telemedicine [45] and lower paper and other resource consumption through the adoption of electronic health records [22,46,47].

Third-order effects, which are considered to have the most profound impact, are the focus of this paper. They involve how digital technologies enable broader behavioral changes [44]. According to Santarius et al [48], digital technologies provide easier access to information about sustainable options, enable remote activities that reduce transportation needs, and foster the formation of online communities, in which peers share and discuss sustainable behaviors. Digital tools can also increase environmental awareness through educational content and real-time feedback [49,50].

Digital platforms make eco-friendly behaviors more convenient by reducing transaction costs of purchasing eco-friendly products [51-53]. Finally, online communities reinforce environmental norms and values through shared experiences and peer influence [54-56]. This explains how digital technologies might relate to environmental behaviors, even when digital tools themselves are not explicitly used for environmental purposes. While the understanding of digital tools’ environmental impact has grown, significant gaps remain in understanding how these effects manifest in specific populations such as caregivers. Research has primarily focused on first- and second-order impacts of digital technologies on environmental sustainability in health care [22,46]. The third-order behavioral effects, specifically how the use of digital tools relates to caregivers’ environmental behaviors, remain largely unexplored.

Digital tools have become increasingly integrated into caregiving practices, though adoption remains uneven. In the United States, approximately 53% of informal caregivers report using at least one digital tool for caregiving, although this varies significantly by tool type and demographic factors. For example, about 80% report feeling comfortable using basic technologies, compared with only 50% with advanced tools like smart assistants [57,58]. In other countries, where comparable data are scarce, digital solutions seem less widespread but increasingly being used to support informal caregiving [3,59,60]. In fact, they have been used for several purposes, including providing information and educational material [61], platforms for organizing caregiving activities [62], remote monitoring technologies [20], communities for social support [59,63], and administrative tools [64]. Despite variations in the use of digital tools across different caregivers’ activities, specialized caregiving apps have a relatively low adoption rate (25%) [65]. Furthermore, a significant digital divide still exists based on the caregivers’ demographic characteristics. For example, Shaffer et al [66] found that age and education are strong predictors of technology adoption, with 40.4% of caregivers aged 65 years and older identifying themselves as noninternet users versus just 13.7% of younger caregivers. The likelihood of being offline decreases by 40% among tertiary-educated caregivers, while rural caregivers show higher rates of digital exclusion [67]. The most critical barriers to adoption include financial constraints, digital literacy limitations, and privacy issues [64,67].

The adoption of eco-friendly practices in LTC, particularly among informal caregivers, represents an emerging research area. Recent research has revealed that informal caregivers of older adults engage significantly more in environmentally sustainable behaviors compared to noncaregivers [68]. Their performance is particularly strong in sustainable consumption choices (eco-friendly products, fair-trade items, and second-hand goods), avoiding single-use plastics, prioritizing local food products, and using low-carbon transportation. While caregivers outperform noncaregivers across all measured environmental behaviors, the gap is somewhat smaller for household practices such as recycling and resource conservation [68].

This pattern suggests that caregivers’ environmental engagement extends beyond basic resource-saving behaviors to reflect deeper environmental values. The experience of caring for vulnerable individuals may foster a broader sense of responsibility and interconnectedness that extends to environmental concerns [69] and fosters a heightened ethic of care that embraces environmental stewardship [70,71]. Furthermore, because caregivers are typically in charge of households, they make several environmental decisions about consumption, waste disposal, and resource use [72]. This might foster an increased awareness of how environmental factors impact an individual’s health, leading to the adoption of a long-term consciousness of the importance of sustainability for future generations [73].

Nonetheless, the distinctive constraints that caregivers face, including time limitations [74,75], financial pressures often associated with longer hours of care [76], and mental and physical exhaustion [76,77], could reduce their engagement in environmental practices. Understanding how digital tools might help overcome these barriers is particularly relevant to supporting sustainable behaviors among this substantial population group, which already shows a predisposition toward eco-friendly practices [75,78,79].

This study offers practical insights into potential interventions that could simultaneously promote digital inclusion and support environmentally sustainable behaviors among informal caregivers, a group that often faces considerable time limitations, financial pressures, and emotional demands. By empirically examining the relationship between the use of digital tools and eco-friendly practices in caregiving contexts, the study addresses a critical gap in the existing literature, which has largely considered digital and environmental transitions in isolation. In doing so, it contributes to a more integrated understanding of how the digital and environmental transitions intersect in everyday care and may inform policies that treat the 2 domains together rather than in isolation. Accordingly, this study addresses 2 key research questions:

Beyond estimating whether the use of digital tools is associated with environmentally sustainable behaviors, the analysis is intended to identify the behaviors for which digital interventions may be most relevant and those for which they are likely to be of limited use.

The focus on informal caregivers of older adults with LTC needs builds on previous research [68], which suggests that this population shows distinct patterns of environmental engagement compared to noncaregivers. These caregivers face unique challenges at the intersection of aging demographics, digital adoption barriers, and environmental concerns. As the population continues to age in Europe, understanding how they navigate both the digital and environmental transitions is increasingly relevant for the development of sustainable LTC policy.

To examine how the use of digital tools relates to environmental practices among informal caregivers of older adults, this study uses the theory of planned behavior (TPB) [80] as its primary analytical framework. TPB offers a well-established psychological model of the behavioral determinants, making it particularly suitable for exploring how digital tools might be associated with caregivers’ environmental behaviors. According to this theory, behavioral intentions are determined by 3 key factors: attitudes (evaluations of the behavior), subjective norms (perceived social pressure), and perceived behavioral control (perceived ease or difficulty of performing a behavior).

The application of TPB is especially relevant for informal caregivers who face distinctive pressures and motivations compared with those of the general population. Caregivers often operate under significant time pressures, financial constraints, and emotional burdens that may influence their environmental practices. Through the TPB lens, we can examine how digital tools might affect each component in the caregiving context:

Building on recent extensions of the TPB [80,81], sociodemographic factors that may be associated with these relationships are also considered. Age, education, employment status, urbanization, and country of residence may be systematically related to both the adoption of digital tools and environmental behaviors. Previous research indicates that younger age [66], higher education, and urban residence [67] are associated with more positive attitudes toward digital technologies, potentially contributing to variation in the relationship between digital tools and environmental behaviors across different caregiver subgroups. This approach aligns with the SEIS conceptual framework by providing a psychological dimension for understanding how social (caregiving context), infrastructural (digital tools), and ecological (environmental behaviors) systems interact at the individual level. The analyzed eco-friendly behaviors include consumption choices (eco-friendly products, fair-trade items, second-hand goods, or plant-based options), resource conservation, waste management practices, and transportation decisions [82]. By examining how the use of digital tools relates to these different behavior categories, we can identify which TPB components may best account for the observed associations in the informal caregiving context.

The findings outlined in this study are based on the Survey of Gender Gaps in Unpaid Care, Individual, and Social Activities project (European Institute for Gender Equality; EIGE/2021/OPER/10)—commissioned by the EIGE—conducted between November 2021 and December 2022 by Eurocarers (Belgium), in collaboration with the European Centre for Social Welfare Policy and Research (Austria), the National Institute of Health and Science on Ageing (Istituto di Ricovero e Cura a Carattere Scientifico [IRCCS] Istituto Nazionale Ricovero e Cura Anziani [INRCA] Italy), and IPSOS GmbH (Germany). In total, 60,405 individuals aged 16-74 years participated in the survey, which was carried out across all 27 EU Member States using quantitative methods. The fieldwork took place between August and October 2022.

The primary data-collection instrument was a questionnaire designed for a 15-minute survey and structured to comply with the core principles of the General Data Protection Regulation (EU) 2018/1725 (GDPR). Data were collected by IPSOS GmbH using computer-assisted web interviews (CAWIs) across all participating countries except Malta and Luxembourg, where interviews were conducted via computer-assisted telephone interviews (CATIs). For the CAWI mode, the target population was limited to individuals who had registered with an online access panel, who were residents of one of the Member States, and who had access to online services on a digital device at the time of the survey. Pane­ls are pools of registered respondents who are willing to participate in market and opinion research. To register, respondents must reside in the country of the panel they are signing up for. Their language skills must be sufficient to answer the registration questions and surveys in the local language. Robust online access panels do not exist in all EU Member States, particularly in smaller countries, where the overall size of such panels is limited. In Luxembourg and Malta, the available panels could not guarantee a sufficient number of respondents to reach the national sample sizes established on the sampling design. Therefore, in these 2 countries, respondents were interviewed via CATIs [68,83].

The sample size varied by country according to population size, ranging from 500 respondents for smaller countries (eg, Cyprus) to 4000 for more populous countries (eg, France and Germany). Hard quotas were applied for age, gender, and region (Nomenclature of Territorial Units for Statistics–Level 3 [NUTS III] classification), while monitoring quotas covered factors such as household composition, number of children, degree of urbanization, and education level (according to International Standard Classification of Education [ISCED] level of low, medium, and high) [68]. Monitoring education in particular helped account for differing levels of digital literacy among respondents, mitigating potential bias in the CAWI sample.

The survey targeted the general population of individuals aged 16-74 years. In certain countries (Austria, Belgium, Spain, Lithuania, Luxembourg, Latvia, Malta, and Poland), a limited number of respondents aged 75 years and older were included in the survey to help meet quotas for the 65-74-year age group. In Romania, constraints related to low internet accessibility and limited digital literacy led to a narrower age range, restricting participation to individuals aged 16-64 years. The target quotas were based on official Eurostat statistics and reflected the population stratification within each Member State. The sampling approach for the CATIs differed from that for the CAWIs: no quotas were applied in Luxembourg or Malta, because the sampling was random, thereby reducing the potential for selection bias. Any discrepancies between the net sample and representative figures were subsequently adjusted through weighting. A random iterative proportional fitting procedure was used to weight multiple variables so that, after a predefined maximum number of iterations, the weighted distribution matched the target values of all selected variables [84].

The total EIGE project sample numbered 60,405 participants. The exclusion criteria for this study were as follows:

The resulting subsample selected for this paper, after the above exclusion criteria were applied, comprised 5761 individuals, who were included in the following analysis.

For descriptive statistics, the distribution normality of continuous variables was assessed using the Shapiro-Wilk test; means and SDs were reported. Statistical significance of between-group comparisons was assessed using one-way analysis of variance. For categorical variables, absolute frequencies and percentages were reported, and statistical significance was assessed using the Pearson chi-square test. The relationship between the frequency of use of digital tools in the caregiving role and the overall SESB score and each individual environmentally sustainable behavior was evaluated using Spearman rank correlation coefficients, with Bonferroni-adjusted P values.

For the multivariate analyses, a linear mixed effects model using maximum likelihood estimation was applied to the SESB score, while 10 mixed effects ordered probit models were estimated for the individual environmentally sustainable behaviors. Because of the hierarchical nature of the data, the statistical approach needed to account for the nesting of individuals within countries [85]. To address this, multilevel mixed effects models using maximum likelihood were employed, enabling the total variability to be partitioned into 2 levels: the primary level (individual-specific variability) and the secondary level (country-level variability) [84].

The validity of the models was evaluated using the Wald chi-square test. Additionally, to identify potential multicollinearity issues among covariates, the standard errors of each coefficient were assessed to mitigate variance inflation.

A 2-tailed P value of <.05 was considered statistically significant. All data analyses were conducted using STATA/MP (version 19.0; StataCorp LLC).

The survey microdata are publicly available on the GESIS public repository [86].

The study adhered to the ethical principles outlined in the Declaration of Helsinki, and the survey protocol (including ethical aspects) received approval from IPSOS’ Global Data Privacy and Research Compliance Team [87]. Some modifications to the questionnaire were required before approval, including the inclusion of explicit consent for sensitive data, the addition of refusal options, and the use of bracketed/grouped responses when referring to personal details about another adult individual (ie, the partner).

The survey was conducted in full compliance with national and EU data protection regulations, including the GDPR (Regulation [EU] 2016/679), Regulation [EU] 2018/1725, and Regulation [EC] No. 223/2009 regarding the processing of data for statistical aims. An integrated EU-27 dataset was generated from anonymized national datasets, ensuring that respondents could not be identified. A separate informed-consent clause was embedded in the survey’s introduction, informing participants of their rights regarding data access, rectification, withdrawal, and restriction, as well as their right to object to data processing and to file a complaint with the relevant data protection supervisory authority.

Respondents did not receive any compensation for participating in the survey. Informed consent was obtained from all participants, as the survey included questions related to political engagement and health. Respondents were provided with a dedicated section explaining the survey’s purpose, content, and data storage policies. Only after giving their consent were they allowed to participate in the survey.

This study provides large-scale empirical evidence that the use of digital tools among informal caregivers is positively associated with environmentally sustainable behaviors, expanding current understanding of how digital and ecological transitions intersect in LTC.

The main finding of a weak positive association [88] between the frequency of digital tools use and environmental behaviors (ρ=0.28; P<.001) in caregiving contexts provides empirical evidence that demographic, digital, and environmental transitions are interconnected rather than occurring in parallel. Extending the work by Tur-Sinai et al [68] on caregivers’ already heightened environmental engagement, our findings are consistent with the possibility that the use of digital tools is associated with caregivers’ preexisting environmental responsibility, an interpretation aligned with the ethics of care framework [70,71].

The comparatively larger associations with consumption-related behaviors suggest that improved access to information may be one possible pathway linking the use of digital tools to these behaviors, by helping caregivers identify, compare, and locate sustainable options. Digital platforms offer ways to identify eco-friendly products, compare fair-trade alternatives, locate second-hand goods, arrange delivery, or participate in online exchanges of reusable care-related items; these are the behaviors for which the associations were the largest. By contrast, the weaker associations with behaviors that depend on infrastructure (recycling, mindful resource consumption, and low-carbon transport) suggest that digital solutions alone may be insufficient: a caregiver may recognize the importance of recycling, but, without accessible municipal services, clear local instructions, or convenient collection points, digital engagement has limited practical leverage. The findings thus point to a differentiated intervention model, in which digital tools facilitate informed and convenient choices but must be complemented by physical infrastructure and social support where environmental action depends on local systems.

Furthermore, these behaviors are already widely established: 55% of respondents report engaging daily in recycling and mindful resource consumption, compared with 12%‐33% for other environmental behaviors. This suggests that recycling and resource conservation are long-established habits, likely formed before digital tools became widespread in caregiving. In line with previous research [82], such routine environmental behaviors tend to be less responsive to digital engagement, as they are embedded in habit rather than informed choice. By contrast, consumption-related behaviors—such as buying secondhand or ecolabeled products—are more variable and may be more closely associated with informational and motivational inputs enabled through digital tools [48,51,53]. Consequently, these behaviors show larger associations with digital engagement.

These results support the distinction drawn in the literature between habitual and intentional pro-environmental actions [89], while extending it to the specific context of informal caregiving. In doing so, they offer both theoretical insights and practical guidance for integrated action across the domains of care, digital inclusion, and sustainability. Overall, the observed associations between the use of digital tools and eco-friendly behaviors may operate through attitudes, subjective norms, and perceived behavioral control, although these mechanisms were not directly tested. Digital access to information about sustainable products [48,49] may inform caregivers’ evaluative judgments, particularly for consumption choices requiring informed decision-making. Online shopping may support perceived behavioral control by making sustainable choices more accessible [53], especially valuable for caregivers who cannot leave care recipients needing round-the-clock assistance. Online caregiver communities, where sustainable practices are shared and encouraged [56,62], may also relate to subjective norms about what is considered normal or expected. Regarding demographic patterns, consistent with established research on digital divides [66], the adoption of digital tools shows systematic variation across demographic groups, with higher rates among younger, more educated, and employed caregivers. Although the education gap is not substantial and the urban-rural divide is moderate, these disparities suggest that digital-environmental co-occurrence may be unevenly distributed. Older, less educated, and nonemployed caregivers—often those providing the most intensive care—appear less likely to use digital tools frequently. This may leave existing inequities unaddressed unless digital inclusion strategies specifically target these groups [66,67].

From a SEIS framework perspective [31], these findings demonstrate how the infrastructural system (digital tools) might shape behaviors within the ecological system through social system mediators (caregiver decision-making processes).

First, policy frameworks addressing demographic, digital, and environmental transitions in LTC should recognize their interconnections rather than treating them as separate domains. Integrated approaches that simultaneously address caregiving needs, digital inclusion, and environmental sustainability could bring collateral benefits across multiple transition domains.

Second, to avoid exacerbating existing inequalities in the care sector [1,8], digital inclusion initiatives should specifically target more vulnerable caregiver groups or those facing greater barriers—particularly older, less educated, and nonemployed caregivers—to ensure that the benefits of digital-environmental synergies are equitably distributed across the caregiver population.

Third, environmental policies and LTC service providers could consider caregivers’ existing predisposition toward environmental responsibility [68] when designing digital tools with dual functions, so that the same platforms supporting caregiving tasks could also provide access to sustainable consumption choices, particularly for those providing intensive care who may have limited mobility or greater time constraints. For example, existing caregiver support apps or portals could incorporate features such as mapping locally available second-hand or fair-trade suppliers and low-emission transport options for accompanying care recipients to appointments. Since the findings show a larger association with consumption-related behaviors, integrating sustainable-procurement guidance directly into the tools caregivers already use may be a relevant entry point.

Finally, educational programs for caregivers should integrate digital literacy with environmental awareness, helping caregivers understand how digital tools can facilitate both caregiving responsibilities and environmentally sustainable practices.

These recommendations must also take into account that family caregivers already perform demanding tasks that generate substantial waste. Expecting them to adopt greener practices without structural support risks adding further burden. Since caregivers already lean toward sustainability, the priority is not to demand more of them but to make sustainable action easier through the provision of efficient recyclable products and accessible disposal systems. Digital tools are most valuable here when they reduce, rather than add to, the effort of acting sustainably.

Overall, these results highlight the importance of considering all 3 SEIS dimensions when designing interventions to support the triple transition in LTC. Importantly, digital tools cannot substitute for physical infrastructure. Their associations are the largest for behaviors individuals can act on directly (such as purchasing choices) and weakest where action depends on external provision, such as recycling and resource conservation. The observed associations therefore suggest that the environmental relevance of digital investments may depend on whether enabling infrastructure already exists. Policies should combine digital inclusion measures with complementary local infrastructure, such as low-carbon transport or accessible recycling facilities, so that the options digital platforms recommend are available to caregivers. Digital innovation and physical infrastructure should be considered coordinated investments rather than alternatives.

Although our study provides important insights into the intersection of the use of digital tools and environmental behaviors among informal caregivers, 2 primary limitations should be acknowledged. First, the cross-sectional nature of our data does not allow us to establish causality or temporal ordering in the relationship between the use of digital tools and environmental behaviors. The mechanisms discussed in this paper should therefore be understood as theory-guided and empirically plausible pathways rather than as directly observed causal relationships. Nevertheless, the differentiated pattern across behavior categories offers useful indirect evidence: larger associations for information- and consumption-related behaviors and weaker associations for infrastructure-dependent behaviors are consistent with the proposed TPB and SEIS interpretation. Second, our measurement of digital tools captured frequency rather than the specific type, content, or function of the tools. As a result, the study cannot directly test whether caregivers used digital tools for environmental information, online purchasing, care coordination, peer support, or service navigation, which limits our understanding of which digital applications are most closely associated with eco-friendly behaviors. In light of these limitations, further mixed method studies are encouraged—combining longitudinal data with semistructured interviews—to clarify the temporal ordering and direction of the relationship between digital tools and environmental behaviors, to identify which types of tools caregivers use in relation to eco-friendly practices, and to examine the determinants and motivations underlying environmental attitudes. A further limitation derives from the CAWI recruitment procedure. Because the CAWI sample was drawn from online access panels, participants in these countries had baseline internet access and were registered with an online panel. Consequently, the “never users of digital tools for caregiving” group should not be interpreted as equivalent to noninternet users in the general population. The present dataset therefore cannot provide evidence about caregivers who were offline or otherwise excluded from online access panels, and claims concerning digital exclusion should be interpreted accordingly. Although the sample was weighted by country according to sex, age, region, and educational level, weighting cannot remove this underlying coverage limitation. Despite these limitations, this study provides empirical evidence of the associations between the use of digital tools and environmental behaviors among informal caregivers, contributing to our understanding of how the triple transition manifests in everyday caregiving contexts.

This study provides empirical evidence of an association between the use of digital tools and environmental practices among informal caregivers across the EU, suggesting that demographic, digital, and environmental transitions may co-occur rather than operate in isolation. However, because the design is cross-sectional, these findings should be read as associations rather than as evidence that the use of digital tools changes environmental behavior, and they may not generalize to caregivers without baseline internet access. Overall, the findings support integrated approaches that combine digital inclusion, caregiver support, and environmental infrastructure, while recognizing that the strength of the observed associations varies across behavior types and that recycling and resource conservation were not significantly correlated with the use of digital tools. However, these potential benefits might not be equally accessible to all caregivers, increasing the risk that digital tools may inadvertently widen existing inequalities in the care sector. Realizing the full potential of digital tools to support both caregiving and environmental goals will require inclusive access and appropriate infrastructure support. In practical terms, digitalization in LTC should be valued not only for improving care efficiency but also for its potential to support caregivers’ environmental practices by reducing the burden of acting sustainably. This potential is behavior-specific: digital tools are most likely to help with information-, purchasing-, and coordination-related behaviors and least likely to help with practices constrained by physical infrastructure or established routines.

As European countries continue to face the triple transition, the evidence presented in this paper provides a foundation for developing more integrated approaches that combine sustainability-oriented digital platforms, caregiver training in both digital and environmental literacy, and the local infrastructure needed to translate digital information into feasible action.