Between Habit and Investment: Managing Residential Energy Saving Strategies in Polish Households

Abstract

Escalating energy prices have positioned households as pivotal agents in advancing demand-side energy efficiency. This study examines three complementary energy-saving strategies among Polish households: (1) habitual, low-cost actions such as switching off unnecessary lighting; (2) capital-intensive investments, including LED lighting and energy-efficient appliances; and (3) time-based and prosumptive strategies linked to dynamic tariffs and photovoltaic systems. The empirical analysis is based on a nationwide survey conducted using the Computer-Assisted Web Interviewing method, involving 401 respondents. The study’s contribution lies in integrating these strategies within a single analytical model and providing the first empirical evidence on their socio-demographic determinants in Central and Eastern Europe, with Poland as a representative case. The results show that older individuals more often adopt everyday habitual practices, whereas higher income and education levels are associated with investment-oriented behaviours. Urban households tend to favour technological solutions, while rural households more frequently adopt time-of-use tariffs and PV systems. Two complementary pathways are identified: a behavioural–habitual path and an investment–technological path. The findings offer guidance for public policy by showing that energy savings increase when financial incentives are combined with clear communication and low-effort decision tools that help households optimise energy use regardless of demographic profile.

1. Introduction

The growing volatility in energy prices, the increase in climate pressures, and the digitalisation of metering systems (e.g., smart meters, mobile applications) have made households increasingly important actors on the demand side of the energy market [1]. As emphasised by Teixeira, an effective energy transition requires not only technological change but also transparent and trustworthy financial mechanisms such as green bonds and decentralised, blockchain-based investment systems [2]. Literature distinguishes two complementary approaches to energy savings: habitual (behavioural) actions and investment-orientated measures. Habit is understood as everyday low-cost behaviours and simple improvements, and investment is understood as permanent technological changes that require capital outlays [3]. In this paper, we propose to extend the savings measures to a third dimension: the management of consumption timing and self-production of energy (including time-based tariffs, photovoltaic installations and storage systems), These three components can be viewed as a continuum from “use differently”, through “buy better”, to “produce and shift consumption in time” [4].

To date, studies indicate that all three levels of intervention bring measurable results. Behavioural and informational interventions, ranging from simple stimuli in a common space to advanced feedback systems, can significantly increase the frequency of desired behaviours, such as turning off lighting [5,6]. Technical solutions in the field of lighting, including occupancy sensors, daylight-dependent control, and individual control systems, systematically contribute to reducing energy consumption [7]. Modernization of light sources is also a stable basis for efficiency, e.g., LED technology, which combines high efficiency, long durability, and decreasing costs, making lighting replacement one of the most cost-effective investments in the home [8,9].

On the purchasing decision side, energy labels are an important instrument of the consumer market. Research shows that a clear label design, especially showing the cost of energy throughout the life cycle, makes it easier to choose more efficient models [10,11]. These decisions also depend on environmental values, beliefs, and motivations [12,13]. In everyday operation, standby power is an important but often overlooked source of losses. The published results of measurement and implementation studies clearly confirm that limiting idle power leads to measurable energy savings, without reducing user comfort [14,15]. Micro-habits in the kitchen, such as pouring only the necessary amount of water into the kettle and avoiding the temperature maintenance function, also have a measurable impact on the energy balance [16,17].

The third layer is the strategy for managing the time of consumption and the consumers who produce it at the same time (prosumers). Reviews of dynamic tariff programmes (TOU/CPP) show that households respond to a price signals, and additional feedback reinforces this response [18,19]. In homes equipped with photovoltaics, it becomes crucial to increase self-consumption: shifting device operation to production hours and, where economically justified, using energy storage to balance evening peaks [20]. In the context of flexibility management, modern Demand Response (DR) models, including bi-level, price-signal-based frameworks, offer analytical tools for prosumer decision-making [21]. Behavioural models, on the other hand, allow us to account for real consumer behaviour in the mechanisms of demand shifting [22].

Although the effectiveness of individual interventions is well documented, knowledge of their actual implementation by households remains limited. This applies in particular to habits or spontaneous everyday behaviours, often not requiring investment, but difficult to consolidate [23]. Research shows that these practices can vary depending on socio-demographic characteristics [1,24], but there is a lack of consensus on which factors are decisive. Some studies indicate that older people are more economical, while others indicate that younger people are more environmentally conscious [25]. However, the effects of education and income can depend on the cultural context, the availability of technology, and the level of trust in institutions [13,26]. In this context, research on the profiling of financial consumer behaviour demonstrates that decisions may emerge from the interplay between sociodemographic characteristics and entrenched behavioural patterns, suggesting that energy-saving strategies may similarly reflect distinct decision pathways within households [27]. Literature on green finance similarly demonstrates that investment decisions, including those related to energy technologies, are strongly shaped by trust in financial instruments and the transparency of available information [2]. Thus, the results of previous studies remain partially divergent, which justifies the need for new empirical analyses.

The purpose of this study is to empirically verify whether the frequency of habits limiting energy consumption increases with age and a higher level of education. In addition, it was assumed that the size of the place of residence and the level of income are positively correlated with such habits. Therefore, a priori hypotheses were formulated:

H1 (age).

Older people are more likely to adopt saving habits than younger people.

H2 (size of the town).

Residents of larger towns are more likely to adopt saving habits than residents of smaller towns.

H3, H5 (education, education-time shifting/self-generation).

People with higher education are more likely to practice saving habits than people with lower education.

H4 (income).

Households with higher incomes are more likely to practice saving habits than households with lower incomes.

The study is both theoretical and applied in nature: (1) household profiles in which habitual interventions are most productive have been identified; (2) the conditions of complementarity between the behavioural and investment approaches were examined; (3) three energy-saving strategies (habit, investment, time/production management) are proposed. In the following sections, the theoretical framework, literature review, methodology, results, and discussion of policy and practical implications are presented.

2. Theoretical Framework

Empirical studies consistently show that age differentiates everyday energy-saving practices (curtailment), while its influence on investment decisions and flexibility strategies is markedly weaker. Older adults more frequently switch off unnecessary lighting or avoid standby mode, even after controlling for sociodemographic characteristics [28,29]. Yet panel and experimental research reveals substantial variation: in many settings the age effect is weak or statistically insignificant [30] especially where younger users—more fluent in digital technologies—adopt cost-saving solutions more readily [31]. This divergence indicates that the role of age is conditional on education, energy literacy, access to technology and cost perceptions. These inconsistencies arise from counteracting mechanisms. Older individuals rely on well-established saving routines and exhibit sensitivity to waste, but they also spend more time at home and demand higher thermal comfort, increasing total consumption [31,32].

Related research in personal finance demonstrates that behavioural segmentation uncovers distinct strategic profiles [27], a conceptually analogous structure to the differentiation of energy-saving strategies examined here. Simulation studies likewise show that moderate behavioural or technical adjustments, optimised settings, insulation, shading or ventilation, can reduce consumption without compromising comfort [33,34]. In the literature on the subject, three domains of energy interventions are often distinguished:

(1)

Behavioural and informational interventions reinforce curtailment: simple reminders increase light-switching with persistent effects [5]. while digital feedback systems documented in Energy Efficiency and Behaviour Change through ICT monitor and shape user behaviour [35,36,37].

(2)

Technical control and light management systems such as presence sensors, daylight harvesting mechanisms, and task-ambient systems allow for significant reductions in lighting consumption while maintaining visual comfort [22,38,39,40]. In the context of Demand Response, building control integrates with network management [41,42]. LED adoption produces unambiguous energy and cost benefits [43,44], though uptake depends on income, education and innovation readiness. Younger cohorts transition faster [8,9,31]. Communication that simplifies lifecycle costs, as evidenced in energy-label reforms, further reduces intergenerational differences [10,11].

(3)

Prosumer oriented interventions such as PV and storage solutions are driven primarily by economic incentives and openness to innovation [45,46]. Profitability increases when consumption aligns with production or when storage mitigates temporal mismatches, particularly under time-differentiated tariffs [20,47,48]. In this context age exerts only an indirect influence through income, property ownership, risk appetite and technological openness [45,49,50,51]. Transparent information and decision support tools further reduce generational disparities [18,52].

Micro-level analyses reinforce these dynamics. Standby devices average 67 W, accounting for 5–26% of household consumption; simple organisational interventions sharply reduce these losses [14,15]. Electric kettle behaviour displays similar inefficiencies, with avoidable waste arising from overfilling and temperature-maintenance functions; minimal behavioural adjustments yield consistent savings [53]. EU policy reviews converge on the conclusion that behavioural patterns rather than technology represent the largest latent efficiency potential [16,17,54] and older users tend to exhibit greater persistence in low-cost routines.

By contrast, age rarely differentiates responses to dynamic tariffs or load-shifting incentives [55,56]. TOU tariffs reduce peak demand by 3–6%, while CPP schemes yield 9–13% to 20–47% reductions [18,57]. The strongest effects arise when cognitive burdens are reduced through programmers, delayed-start functions, smart sockets and transparent pricing, enabling behavioural change irrespective of age [18,58]. In these settings, differences in technical infrastructure overshadow demographic variation.

Despite extensive evidence on the effectiveness of specific interventions, real world uptake remains poorly understood, especially for low-cost curtailment behaviours that require no financial investment yet are difficult to sustain. Findings differ across sociodemographic groups [23], with no consensus on whether older adults’ frugality or younger cohorts’ environmental concern predominates [59,60,61]. The effects of education and income vary by cultural context, technological availability and institutional trust [62,63]. The broader energy transition is also shaped by political and regulatory uncertainty, constraining renewable-energy investment even at the municipal scale [64].

A clear research gap persists. Few studies integrate curtailment, investment decisions and temporal management of consumption within a single analytical framework. Evidence from Central and Eastern Europe, including Poland, is limited, despite distinctive infrastructural and social characteristics [29,65]. Polish research focuses largely on self-reported energy efficiency, with minimal attention to demographic or infrastructural differentiators [66,67], although decisions clearly stem from the interplay of economic resources, social norms and technological availability [31,47,68,69]. Building on this evidence, we hypothesise that age remains a significant determinant of everyday curtailment, while its influence on investment and flexibility strategies is indirect and mediated by income, education, prosumer status, access to technology and infrastructural conditions.

3. Materials and Methods

The study used a diagnostic survey method based on the Computer-Assisted Web Interviewing (CAWI) technique and a proprietary questionnaire designed for individual energy consumers in Poland. The research tool contained 51 items: 50 closed-ended single-choice questions (some of which were assessed on a Likert-type rating scales), one multiple-choice question, and a sociodemographic metric. The selection and order of the questions were designed in such a way as to ensure consistency of the data obtained and transparency of the questionnaire form.

Participation in the anonymous study was entirely voluntary. All respondents were informed of the anonymity of participation and gave informed consent to participate in the study. Ethical review and approval were waived for this study because it involved anonymous survey data and did not include sensitive personal information. The CAWI technique, which involves self-administered online questionnaires, enabled rapid outreach to a wide group of respondents and significantly reduced the costs of conducting the survey. The use of Likert-type rating scales with a varying number of categories, adjusted to the nature of individual questions, made it possible to measure both the direction (e.g., positive or negative) and the intensity of the declared attitudes, which enabled the comparability of results and the use of advanced statistical analyses.

The research sample included 401 respondents. The majority of respondents (61.35%) lived in cities with over 200,000 inhabitants. Other groups included residents of rural areas (13.97%), cities with up to 50 thousand inhabitants (10.97%), cities with up to 100 thousand inhabitants (6.98%), and cities with up to 200 thousand inhabitants (6.73%). The vast majority of participants were people with higher education (71.57%), followed by people with secondary education (25.19%), and the smallest group comprised respondents with primary education (3.24%). The largest age group consisted of respondents aged 31–40 years (35.91%) and 41–50 years (26.93%). A smaller share was recorded in people aged 21–30 years (15.96%) and over 60 years of age (9.98%). The least represented age groups were respondents aged 51–60 (8.73%) and under 20 (2.49%).

In the study, each concept was assigned questions that refer to the actual behaviour of the study participants, i.e.,

Habits (HCI—Habitual, Cost-free Improvements)—everyday, low-cost behaviours, i.e.,

HCI1: I turn off unnecessary lighting,

HCI2: I reduce the number of bulbs in use,

HCI3: I limit the use of electric stoves, fans, air conditioning, etc.,

HCI4: I use side or task lighting more often than the main light source,

HCI5: I boil the right amount of water in the kettle.

Simple Improvements and Investments (LCU2 + CII—Low-Cost Upgrades & Capital-Intensive Investments)—permanent technological changes with varying capital intensity, i.e.,

LCUI1: I use energy-saving bulbs (LED),

CII1: I buy energy-efficient equipment (high-class household appliances/electronics).

LCUI2: I turn off devices, e.g., electronics, completely so that they are not in standby mode.

Time-shifting and self-production strategies (TSI + SPI), use of time tariffs, and RES micro-installations, i.e.,

TSI1: I have two tariffs: night and day,

SPI1: I use alternative energy sources, e.g., I have photovoltaic panels.

The following research hypotheses were empirically tested in subsequent sections of the paper:

H1. 

As consumers age, the frequency of energy-saving habits increases.

H2. 

With the increase in the size of the place of residence of consumers, the investment and technological orientation increases, and the frequency of habits decreases.

H3. 

Higher education of consumers promotes investment activities and reduces the frequency of habits.

H4. 

Higher consumer income is conducive to investment activities and reduces the frequency of habits.

H5. 

Education level differentiates consumers’ propensity to adopt time-shifting and self-production strategies.

Based on the adopted research hypotheses, a conceptual research model was developed (see Figure 1), which presents the assumed relationships between sociodemographic variables and energy-saving strategies.

The analysis began with hypothesis H1, which posits a relationship between respondents’ age and energy-saving practices; the Spearman rank correlation (R) was used. It is a nonparametric measure of monotonic dependence, suitable for variables of at least ordinal (e.g., positions on the Likert scale), does not assume a normal distribution of variables, and is relatively resistant to outlier observations. The use of the ρ coefficient allows for a reliable assessment of the relationship even if the relationship is not linear but remains monotonic [69].

4. Results

The verification of the research hypotheses began with hypothesis (H1), assuming the existence of a relationship between the age of the respondents and the use of energy-saving practices. Spearman’s rank correlation coefficients (R) were calculated between respondents’ age and indicators of declared energy-saving strategies (see

Table 1. Spearman’s rank correlations: age and methods of saving energy.

Factors	R	t(n − 2)	p
HCI1: I turn off unnecessary lighting	0.036	0.728	0.467
LCUI1: I use an energy-saving light bulb	0.048	0.962	0.337
CII1: I buy energy-efficient equipment	0.052	1.048	0.295
HCI2: reduce the number of bulbs in use	0.212	4.341	0.000
HCI3: I limit the use of electric stoves, fans, air conditioning, etc.	0.145	2.930	0.004
LCUI2: I turn off devices, e.g., electronics, completely so that they are not in standby mode	0.058	1.151	0.250
HCI4: I use the side lights/lighting more often than the main one	0.142	2.857	0.004
HCI5: I boil the right amount of water in an electric kettle (not too much, not too little)	0.178	3.604	0.000
TSI1: I have two tariffs: night and day	−0.025	−0.498	0.619
SPI1: I use alternative energy sources, e.g., I have photovoltaic panels	−0.030	−0.590	0.555

Note. Spearman’s R is reported together with t(n − 2) and two tailed p-values; bold indicates statistical significance (p < 0.05).

). The choice of a non-parametric measure was justified by the ordinal nature of the variable parts and the possibility of deviations from the normality of the distributions. Table 1 reports the ρ coefficients and their corresponding significance levels (p). Positive ρ values indicate that the propensity to use a given energy-saving practice increases with age, while negative values suggest a decrease in its use in older age groups. These associations are also illustrated in Figure 2. The analyses were performed for the entire sample using a significance level of α = 0.05. To aid interpretation, relationship strength was classified as follows: |R| ≈ 0.10—weak relationship, |R| ≈ 0.30—moderate, |R| ≥ 0.50—strong.

Figure 2 presents only those Spearman’s rank correlations between age and specific energy-saving behaviours (HCI2, HCI3, HCI4, HCI5) that proved statistically significant (p < 0.05). The arrows link the variable “Age” with HCI2, HCI3, HCI4 and HCI5, and each arrow is labelled with the value of Spearman’s rank correlation coefficient (R).

The results of the analyses revealed several positive, statistically significant relationships with low to moderate strength. With age, the following were more frequently declared:

• Reducing the number of bulbs in use (R = 0.212; t = 4.341; p < 0.001);
• Boiling only the amount of water needed (R = 0.178; t = 3.604; p < 0.001);
• Limiting the use of stoves and air conditioning (R = 0.145; t = 2.930; p = 0.004);
• More frequent use of side lighting (R = 0.142; t = 2.857; p = 0.004).

However, no significant links were found in relation to practices such as the use of energy-saving light bulbs, the purchase of energy-saving equipment, having two tariffs, or the use of renewable energy sources (e.g., photovoltaics).

The tendency to engage in small everyday energy-saving actions increases with age. On the other hand, practices requiring higher investment (e.g., the purchase of new equipment or technological installations) do not show a clear relationship with age.

H2 assumes that the size of a respondent’s place of residence affects the nature of their energy-saving activities (see

Table 2. Spearman’s rank correlations: place of residence and methods of saving energy.

Factors	R	t(n− 2)	p
HCI1: I turn off unnecessary lighting	0.205	4.177	0.0000
LCUI1: I use an energy-saving light bulb	0.295	6.167	0.0000
CII1: I buy energy-efficient equipment	0.314	6.598	0.0000
HCI2: reduce the number of light bulbs	−0.181	−3.678	0.0003
HCI3: I limit the use of electric stoves, fans, air conditioning, etc.	−0.236	−4.860	0.0000
LCUI2: I turn off devices, e.g., electronics, completely so that they are not in standby mode	−0.297	−6.204	0.0000
HCI4: I use the side lights/lighting more often than the main one	−0.243	−5.005	0.0000
HCI5: I boil the right amount of water in an electric kettle (not too much. not too little)	−0.206	−4.212	0.0000
TSI1: I have two tariffs: night and day	−0.115	−2.319	0.0209
SPI1: I use alternative energy sources, e.g., I have photovoltaic panels	−0.163	−3.300	0.0011

Note. Spearman’s R is reported together with t(n − 2) and two tailed p-values; bold indicates statistical significance (p < 0.05).

). With the increase in the size of the consumer’s place of residence (from villages to large cities), investment and technological practices, such as purchasing efficient appliances or adopting LED lighting become more prevalent. At the same time, the frequency of everyday micro-saving habits, e.g., completely turning devices off from standby mode, limiting the use of stoves and air conditioning, or measuring water in the kettle, is decreasing.

H2 refers to differences in energy-saving strategies. These relationships are also illustrated in Figure 3. In large cities, the technological and purchasing approach dominates, while in smaller towns, simple habitual practices play a greater role.

The diagram presents all statistically significant Spearman’s rank correlations (p < 0.05) between the variable “Place of residence” and energy-saving behaviour indicators denoted by the abbreviations HCI1, LCUI1, CII1, HCI2, HCI3, LCUI2, HCI4, HCI5, TSI1 and SPI1. The arrows link the variable “Place of residence” with each indicator, and each arrow is labelled with the value of Spearman’s rank correlation coefficient (R).

As the size of the consumer’s place of residence increased (from rural areas to major urban centres), respondents exhibited a higher likelihood of disengaging non-essential lighting (R = 0.205; t = 4.177; p < 0.001), adopting energy-saving light bulb (R = 0.295; t = 6.167; p < 0.001) and investing in energy-efficient appliances (R = 0.314; t = 6.598; p < 0.001).

At the same time, behavioural saving practices such as completely disconnecting appliances from standby mode (R = −0.297; t = −6.204; p < 0.001), limiting the use of stoves and air conditioning (R = −0.236; t = −4.860; p < 0.001), using side lighting (R = −0.243; t = −5.005; p < 0.001) or measuring kettle water (R = −0, 206; t = −4.212 p < 0.001) were observed.

Importantly, the use of two tariffs (R = −0.115; p = 0.021) and photovoltaics (R = −0.163; p = 0.001) was more often declared by residents of smaller towns, which can be associated with infrastructural conditions, e.g., the predominance of single-family houses and greater possibilities of installing photovoltaic panels (RES).

Overall, the results indicate that the “technology and purchasing” approach dominates in large cities, while habitual and tariff-related practices play a greater role in smaller towns.

H3 assumes that the level of education differentiates the types of energy-saving actions undertaken (see

Table 3. Spearman’s rank correlations: education and methods of saving energy.

Factors	R	t(n − 2)	p
HCI1: I turn off unnecessary lighting	0.173	3.516	0.000
LCUI1: I use an energy-saving light bulb	0.417	9.176	0.000
CII1: I buy energy-efficient equipment	0.231	4.743	0.000
HCI2: reduce the number of light bulbs	−0.136	−2.734	0.007
HCI3: I limit the use of electric stoves, fans, air conditioning, etc.	−0.163	−3.303	0.001
LCUI2: I turn off devices, e.g., electronics, completely so that they are not in standby mode	−0.242	−4.989	0.000
HCI4: I use the side lights/lighting more often than the main one	−0.211	−4.311	0.000
HCI5: I boil the right amount of water in an electric kettle (not too much, not too little)	−0.182	−3.702	0.000
TSI1: I have two tariffs: night and day	−0.073	−1.458	0.146
SPI1: I use alternative energy sources, e.g., I have photovoltaic panels	−0.115	−2.319	0.021

Note. Spearman’s R is reported together with t(n − 2) and two tailed p-values; bold indicates statistical significance (p < 0.05).

). People with higher education are more likely to choose solutions that require financial investments (e.g., purchase of energy-efficient appliances, use of modern technologies), while being less likely to engage in simple, everyday energy-saving behaviours, such as completely turning off devices from standby mode or measuring water in the kettle. These patterns are also visualised in Figure 4.

A higher level of education is indeed associated with more frequent use of energy-saving light bulbs (R = 0.417; t = 9.176; p < 0.001), as well as, albeit to a lesser extent, the purchase of energy-saving equipment (R = 0.231; p < 0.001) and turning off unnecessary lighting (R = 0.173; p < 0.001).

At the same time, negative correlations are observed between the level of education and the micro-saving behaviours, such as reducing the number of light bulbs lit (R = −0.136; p = 0.007), limiting the use of stoves and air conditioning (R = −0.163; p = 0.001), completely turning appliances off from standby mode (R = −0.242; p < 0.001), using side lighting (R = −0.211; p < 0.001), or measuring water in the kettle (R = −0. 182; p < 0.001).

The diagram presents Spearman’s rank correlations between the variable “Education” and energy-saving behaviour indicators denoted by the abbreviations HCI1, LCUI1, CII1, HCI2, HCI3, LCUI2, HCI4, HCI5, and SPI1. The arrows link “Education” with each indicator, and each arrow is labelled with the value of Spearman’s rank correlation coefficient (R); statistically significant correlations are marked with asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001).

No significant relationship was found for using two tariffs (R = −0.073; p = 0.146). Respondents with higher education were less likely to report on owning photovoltaic systems (R = −0.115; p = 0.021), which probably reflects differences in housing conditions, rather than the effect of knowledge alone. This should be regarded as an indicative interpretation rather than a causal inference.

H4 assumes that income level differentiates the types of energy-saving measures adopted (see

Table 4. Spearman’s rank correlations: income and energy-saving practices.

Factors	R	t(n − 2)	p
HCI1: I turn off unnecessary lighting	0.146	2.943	0.003
LCUI1: I use an energy-saving light bulb	0.397	8.647	0.000
CII1: I buy energy-efficient equipment	0.311	6.525	0.000
HCI2: reduce the number of light bulbs	−0.169	−3.421	0.001
HCI3: I limit the use of electric stoves, fans, air conditioning, etc.	−0.241	−4.954	0.000
LCUI2: I turn off devices, e.g., electronics, completely so that they are not in standby mode	−0.345	−7.342	0.000
HCI4: I use the side lights/lighting more often than the main one	−0.209	−4.265	0.000
HCI5: I boil the right amount of water in an electric kettle (not too much, not too little)	−0.236	−4.860	0.000
TSI1: I have two tariffs: night and day	−0.066	−1.324	0.186
SPI1: I use alternative energy sources. e.g., I have photovoltaic panels	−0.010	−0.193	0.847

Note. Spearman’s R is reported together with t(n − 2) and two tailed p-values; bold indicates statistical significance (p < 0.05).

). Households with higher income are more likely to invest in technologies and solutions that require financial outlays, such as the purchase of energy-saving light bulbs or energy-saving equipment. At the same time, they are less likely to engage in small, everyday habits that reduce energy consumption, e.g., completely turning devices off from standby mode, using side lighting, measuring water in the kettle, or reducing the number of light bulbs that are lit. These relationships are also illustrated in Figure 5.

Higher incomes are clearly linked to the investment orientation in energy saving. They correlate more strongly with the use of energy-saving light bulbs (R = 0.397; t = 8.647; p < 0.001) and the purchase of energy-saving equipment (R = 0.311; t = 6.525; p < 0.001) and correlate less with turning off unnecessary lighting (R = 0.146; p = 0.003).

At the same time higher incomes are negatively associated with the micro-saving practices such as: completely turning off appliances from standby mode (R = −0.345; t = −7.342; p < 0.001), limiting the use of stoves and air conditioning (R = −0.241; p < 0.001), using side lighting (R = −0.209; p < 0.001), measuring water in a kettle (R = −0.236; p < 0.001), or a reduction in the number of bulbs lit (R = −0.169; p = 0.001).

However, no significant correlations were found between the level of income and the use of two tariffs (R = −0.066; p = 0.186) or the use of photovoltaics (R = −0.010; p = 0.847).

The general pattern indicates that higher-income households are more likely to invest in efficient technologies, while less likely to declare the use of restrictive, everyday energy-saving practices.

The analysis of the data reveals two distinct patterns of behaviour: investment orientation, which includes purchases and the use of modern technologies, and habitual orientation, which is associated with daily energy reduction practices. The use of tariffs and renewable energy sources is determined to a greater extent by location and housing conditions than by income level. The observed relationships are characterised by weak to moderate strength and should be interpreted as co-occurrences rather than evidence of causal relationships.

The diagram shows Spearman’s rank correlations between the variable “Income” and energy-saving practices (HCI1, LCUI1, CII1, HCI2, HCI3, LCUI2, HCI4, HCI5). Arrows link “Income” with each practice and are labelled with the corresponding Spearman’s rank correlation coefficient (R); statistically significant correlations are marked with asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001).

5. Discussion and Conclusions

This study makes an important contribution to the literature on household energy behaviour by integrating three energy-saving strategies—habitual, investment-orientated, and those based on time-of-use management and self-consumption—within a single analytical model. In doing so, it addresses a significant research gap, as previous analyses primarily focused on individual strategies, making it difficult to assess their complementarity, synergies, and interdependencies. An additional value of this work is its focus on the Central and Eastern European context, which remains under-represented in the existing literature.

The results indicate the existence of two main complementary pathways for improving energy efficiency in households. The first is oriented towards investments and technology and is primarily determined by higher income, higher levels of education, and an urban environment. The second pathway is based on habitual behaviours, more common among older individuals and reinforced by simple behavioural stimuli and default device settings. The most effective strategies combine these two approaches: on the one hand, supporting and incentivising investments where they are economically justified, and on the other hand, designing home environments that facilitate the automatic and repeatable implementation of simple, everyday energy-saving practices. This dual-path model confirms the findings of previous studies that highlighted the coexistence of technological and behavioural actions to improve energy efficiency [50].

The findings of this study also confirm the importance of financial resources, energy literacy, and access to infrastructure in shaping investment decisions, which is consistent with the literature [59,63]. As indicated in the literature [29,59,63], higher income determines investment decisions but does not automatically translate into more regular use of simple energy-saving actions. The results also confirm the observed tendency of Polish households to transition to more efficient technologies and fuels [70].

The analysis further revealed that the influence of socio-demographic factors on energy behaviours is not uniform. According to previous research [28,29,32], age fosters the adoption of daily actions to reduce energy consumption, which can be explained by increased routine stability and stronger embedding of social norms among older age groups. Moreover, according to the findings of other authors [31,47,50], its influence on investment decisions remains limited and indirect, depending on factors such as financial situation, housing status or readiness to adopt innovations.

The level of education also proved to be an important determinant of investment decisions. The findings indicate that a higher level of education is associated with a greater ability to interpret technical and economic information, increasing the propensity to invest. However, consistent with previous analyses [59], it does not systematically translate into greater regularity in simple energy-saving practices, which are primarily shaped by household routines and norms.

The place of residence plays a significant role in shaping energy strategies, especially in the context of the development of the consumer. The findings are consistent with previous analyses [45,71]. which highlight the importance of spatial context in the adoption of self-consumption solutions. Strategies related to managing time-of-use and installing photovoltaic systems are more common in smaller towns and rural areas, where infrastructure conditions (e.g., the prevalence of single-family housing) are more conducive to their implementation. This pattern aligns with observations by Mills and Schleich, who show that household transitions to energy-efficient technologies result not only from economic incentives but also from behavioural routines and contextual infrastructure conditions [52,72].

Another important conclusion from the study is the confirmation of the role of technologies that support consumer decision-making. In line with previous findings [18,56], access to technologies such as programmable devices, smart sockets, electric vehicle charging schedules, or delayed start functions significantly increases demand flexibility. Our results suggest that the use of such solutions does not depend heavily on socio-demographic characteristics but rather on the availability of infrastructure and technical tools.

The study also showed that decisions about the adoption of renewable energy are shaped by economic factors, technical level of knowledge, and perceived risk, which is consistent with previous analyses [71]. Our findings align with broader patterns observed in studies of financial decision-making, where age, income and educational attainment function as primary determinants of preferred behavioural strategies [27]. The willingness to pay higher prices for renewable energy increases with income and the quality of available information [73]. This aligns with Teixeira, who argues that investment willingness increases when institutional frameworks offer transparency, credibility and reduced financial risk. At the microeconomic level, the greatest cost savings are achieved through strategies that combine demand reduction with technological investments, confirming the principle of ‘reduce consumption first, then invest’ [74].

The findings have significant implications for public policy. The effectiveness of energy interventions increases substantially when behavioural and technological strategies are implemented in parallel. As Gao demonstrates at the urban level, household energy strategies are likewise shaped by structural conditions and risk perceptions, underscoring the need for policies that integrate financial instruments with clear and transparent communication [62]. Financial incentives alone prove insufficient if not accompanied by appropriate communication and decision-support tools that facilitate the adoption of simple, low-cost practices. In the case of households charged according to time-of-use tariffs, both price signals and technologies that enable the shift in energy-intensive activities to lower-cost hours play a key role. In line with Teixeira, enhancing the clarity and transparency of financial instruments supporting household energy investments is essential for strengthening the effectiveness of energy policy [2].

The results also emphasise the importance of the concept of energy literacy, understood as the combination of technical understanding and the ability to assess the economic consequences of decisions. Educational and communication policies should focus not only on promoting technologies but also on developing users’ cognitive skills, allowing them to independently evaluate the efficiency and cost-effectiveness of solutions. The way information is presented is also crucial. Misinterpretation of the new A–G energy labels after the scale reform limits the shift in demand towards more efficient technologies, indicating the need to simplify and clarify informational messages.

In summary, the findings of this study, consistent with previous literature, confirm that achieving a balance between technological and behavioural approaches is a necessary condition for a sustainable energy transition. The best results are achieved when public policies and individual actions integrate both approaches: they support and financially incentivise investments where appropriate, while simultaneously creating an environment in which simple habits and schedules become part of everyday energy use.

Despite its broad scope and significant findings, this study has certain limitations. The CAWI method relies on self-reported data, which may lead to self-assessment biases and discrepancies between declarations and actual behaviours. The sample was dominated by respondents with higher education and residents of large cities, which limits the generalisability of the results. It should also be noted that the CAWI method inherently overrepresents certain demographic groups. The cross-sectional nature of the study allows only for the identification of co-occurrences of the analysed variables and does not enable the establishment of causal relationships. Causal inference cannot be drawn from this type of research design and would require longitudinal or experimental data. Furthermore, the model does not account for psychological and cultural factors such as values, social norms, risk perception, or environmental awareness, which may influence energy decisions. In future stages of the project, we plan to extend the scope of analysis to include additional housing, family, and psychological variables, which will require a dedicated research instrument and a broader dataset than the one used in the present study.

Future research directions include analysing behavioural changes over time and integrating survey data with actual measurements from smart meters, which will allow verification of the reliability of self-reported behaviours. The planned inclusion of psychological and contextual variables, as well as international comparisons covering Central and Eastern European countries, will enable a more comprehensive understanding of household energy management mechanisms and provide deeper insights into how economic, technological, and environmental factors jointly shape consumer decisions in the context of the energy transition.