1. Introduction
Since the sharp rise in energy prices beginning in 2020, the number of Dutch households experiencing energy poverty has increased by an estimated 90,000 [
1]. As a result, approximately 602,000 households, equivalent to 7.4% of the national population, were living in energy poverty. Energy poverty occurs when households cannot afford the necessary energy services to maintain a comfortable and healthy standard of living [
2]. This may stem from (1) the affordability of the energy bill (households may lack sufficient income to pay their energy bills), (2) the energy quality of the home (poorly insulated houses with low energy labels often suffer from unhealthy indoor climate with mould, damp and draughts), and (3) the ability to independently make the home more sustainable (e.g., dependency on landlord or own financial constraints).
The average variable costs (including VAT) of natural gas rose from EUR 0.30/m3 in 2020 to EUR 0.85/m3 in 2022, while electricity costs increased from EUR 0.07/kWh to EUR 0.28/kWh in the same period. This includes existing and new energy contracts. Since the rise in energy prices, the government has introduced several financial policy measures to support households manage their energy bills. These include tax reduction measures (e.g., lower VAT tax on energy), an energy surcharge to compensate households for high energy costs, and energy price caps, among other measures. In addition, the government has also increased budgets for supporting households in adopting energy saving measures. These budgets are drawn from national funds like the National Insulation Program (NIP) and the National Heat Fund, both aimed at helping households become more energy-efficient. Additional support is provided through so-called ‘SPUK funds’ (subsidies-specific grants), which allocate national resources to municipalities for the local policy implementation for health, sports, culture and social basis (that includes energy poverty as well). In addition, many municipal budgets are used to support measures such as energy coaches (providing energy advice and installing small energy saving measures in the dwelling) and white goods schemes (vouchers for the purchase of energy-efficient household appliances).
Long-term support for energy-poor households is likely to remain necessary for an extended period, as energy prices are unlikely to return to pre-crisis stable lows. While these measures primarily help to reduce the energy bills, they are also expected to improve other aspects of living conditions associated with energy poverty, such as poor living comfort conditions, health problems, and social isolation. Understanding how these measures improve households’ living situations beyond just reducing energy costs is crucial. Since previous research has mainly examined these interventions in isolation, a comparative analysis is also needed to understand how these interventions differ in impact and how they might reinforce one another.
This research therefore addresses the following question: What are the effects of the various governmental support measures on the energy poverty-related aspects in Dutch households, specifically regarding (1) perceived living comfort; (2) perceived physical health; (3) energy costs and consumption; (4) financial concerns and perceived mental health; (5) sense of social connection, (6) involvement with the neighbourhood; and (7) sustainable behaviour? This study examines eight different governmental support measures, categorized into three types: three energy coaching programmes, three renovation programmes, and two white goods schemes. In this study, we specifically aim to investigate the effects of these governmental support measures on these energy poverty-related aspects rather than measure energy poverty itself. Moreover, since the methodology of measuring the energy poverty-related aspects was consistent across the three types of support measures, a direct comparison of their effectiveness can be made.
2. Background and Literature Review
2.1. Case Descriptions of the Governmental Support Measures
The first type of governmental support measure comprises energy coaching programmes implemented by three organizations operating in the city of Amsterdam, Utrecht, and Arnhem. These organizations have several years of experience running energy coaching programmes across various regions of the Netherlands. All three programmes target low-income households, helping them to save on energy bills by installing simple energy-saving measures (e.g., draught excluders, radiator foil) in the home and/or providing behavioural advice (e.g., in the form of an advisory report). Energy coaches visit the households 1–6 times. They are often job seekers, status holders, volunteers, or interns, gaining work experience.
The second type of governmental support measure includes renovation programmes, carried out by social housing associations in the city of Haarlem, Geleen, and Arnhem. These social housing associations were all founded before 1930 and each has housing stock exceeding 10,000 social rental units. The renovation projects focus on significantly improving the energy performance of the older stock including both apartment buildings and terraced houses. Renovations include roof insulation, cavity wall insulation, replacement of single glazing and window frames, new mechanical ventilation, LED lighting, and replacement of boiler systems and pipework (not leading to the complete removal of the gas heating system). Some projects also include toilet and kitchen renovation.
The third type of governmental support measure involves white goods schemes, implemented in the municipalities Den Haag and Leiden. In both cities, an executive organization was assigned to hand out sustainability vouchers. These vouchers provided a certain budget for the purchase of more energy efficient/economical white goods. Both schemes target households with a low (minimum) income.
2.2. Reviewing the Effects of Support Measures on Affordability of Energy and Other Living Conditions
Energy poverty has various negative consequences. Direct effects include unaffordable or high energy bills, limited access to energy services, and poor-quality homes in terms of energy performance. These factors may lead to indirect effects such as lower living comfort [
3], manifesting as poor indoor air quality, dampness, mould, and thermal discomfort; as well as maladaptive coping strategies and insecurity regarding e.g., housing and food access [
4]. These conditions may result in a wide range of negative health outcomes. Physical health problems include respiratory complaints, asthma, osteoarthritis, cardiovascular disease [
5], allergies/dermatological issues, and even excess winter/summer deaths [
3,
6]. Energy poverty can also lead to mental health issues like (financial) stress, anxiety, sadness, and (maternal) depression [
7,
8]. According to research [
9], this leads to higher healthcare costs. Beyond health effects, energy poverty may also hinder social participation and engagement in neighbourhood activities [
10,
11,
12]. Finally, households experiencing energy poverty may also show less support for the energy transition [
13,
14].
Although a limited number of studies have mapped the effects of energy coaching programmes, several foreign studies and a few Dutch studies offer some initial insights. These studies suggest that energy-saving measures and energy advice can improve the living conditions of (energy-poor) households. Benefits include enhanced perception of warmth and comfort in the home [
15,
16], increased sense of control over the indoor climate, and changes in energy-related behaviour, such as heating and cooking behaviour, which helps households save costs [
17,
18]. A recent study in Amsterdam [
19] indicates that energy coaching effectively enhances home energy efficiency, reduces energy bills, and ultimately lowers the percentage of income spent on energy, particularly when combined with smart information. During the heating seasons, monthly electricity consumption fell by 62 kWh (33%), gas consumption reduced by 41 m
3 (42%), bills by ERU 104 (53%), and the percentage of income spent on energy from 10.1% to 5.3%. However, the impact of energy coaching seems to depend on various factors. For example, the effectiveness of the advice increases when the energy coach visits more often and has sufficient technical and social skills [
17]. Results also showed that energy needs and usage patterns differ between households [
15] and that the advice is most effective when it is adapted to the situation of the household [
17]. Households with higher initial energy consumption tend to save more energy than households that already use less energy [
20].
Foreign studies examining the effects of home renovation show that the residents’ living conditions generally improve following such renovations. Residents of homes where renovation measures have been implemented (such as insulation measures, improvements to windows and doors, or improvements to the heating supply) show fewer respiratory complaints, report better general (mental) health, and report sick less often at work or school [
21,
22,
23,
24,
25,
26,
27]. They also indicate that they are more satisfied with their homes (better thermal comfort and less indoor dampness and mould) and that they are better able to pay their energy bills [
25,
28,
29]. Furthermore, research shows that these improvements are more pronounced among low-income residents, children, and residents of homes with low energy quality [
23,
24,
27]. The magnitude of these benefits appears to correlate with the number renovation measures taken; effects seem to ‘add up’ [
28]. Although studies have identified positive results, there are also studies showing that low-income households, in particular, experience little improvement, possibly because they still have a lot of financial stress [
29]. In addition, studies also show that renovations can be experienced as very stressful, which can dampen their positive effects [
30,
31,
32]. Insulation also does not always yield benefits if residents do not ventilate their homes properly afterward [
28].
Although we did not find any research on energy savings and possible other improvements by replacing old white goods appliances, it is known that, for example, old refrigerators or freezers use significantly more energy than newer, more energy-efficient models. For example, a 15-year-old fridge–freezer combination consumes approximately 380 kWh per year, while a new fridge–freezer combination with energy label C consumes approximately 150 kWh per year. This can save EUR 50 to 160 per year [
33]. Moreover, European studies highlight that energy-poor households often rely on energy-inefficient and/or poorly functioning appliances [
34,
35]. These data in combination with results from European studies suggest that replacing old white goods leads to positive effects on the energy bill. Moreover, from conversations with energy coaches, we hear that a better washing machine or dryer can lead to a healthier indoor climate (less damp in the house).
3. Methods
3.1. Study Design
This study compared households participating in eight distinct support measures (intervention group;
N = 688) with those yet to participate (control group;
N = 536).
Table 1 provides an overview of participating households for each support measure. Participants who underwent multiple interventions (3.9%) were excluded from the dataset. The resulting between-subjects design enabled statistical comparisons to evaluate the effects of the support measures on various energy poverty-related aspects.
Control groups for all eight individual support measures were selected by the same implementing organisations as those delivering the interventions, to ensure comparability between control and intervention groups. For the three energy coaching programmes, the control group consisted of households that had applied but not yet received coaching. The control group in the three renovation programmes lived in similar dwellings, owned by the same social housing associations, located in the same neighbourhood, and scheduled for future renovations. For the two white goods schemes, the control group comprised households that had received a voucher but had not yet redeemed it.
The targeted households were all living in urban areas. Using respondents’ zip codes and house number (voluntarily provided), questionnaire data were linked to microdata by the Central Bureau of Statistics [
36] from 2020, with the respondents’ consent. This enabled a detailed demographic analysis, confirming that the control and intervention groups do not differ significantly from each other (See
Table 2).
CBS microdata also helped to determine the extent of energy poverty among the participants. Households were classified as energy poor if they had low incomes (below 130% of social minimum in the Netherlands) and either faced high energy bills (above the median of Dutch households) and/or lived in low-energy-efficient housing. Overall, 20% of the surveyed households experienced energy poverty in 2020 (two years before this study was conducted). In comparison, the national average was 6.4% [
1]. This suggests that the support measures seem to have effectively reached energy-poor households.
3.2. Questionnaire
A standardized questionnaire was used to measure the effects of the support measures on various energy poverty-related aspects. The questionnaire incorporated a range of indicators derived from other ongoing European studies. Direct energy poverty-related aspects (i.e., affordability of the energy bill and the energy quality of a home), indirect energy poverty-related aspects (i.e., several negative consequences of energy poverty found by previous research), and behavioural factors that could help reduce energy poverty (i.e., sustainable behaviour) were all administered in the questionnaire. Open-ended questions captured energy costs, gas usage, electricity usage, and thermostat temperature. All other aspects were rated on a 6-point Likert scale (1 = never, 2 = infrequently, 3 = sometimes, 4 = regularly, 5 = often, 6 = always). See
Table 3 for a full list of questionnaire items. By working together with a welfare organisation for the comprehensibility of the questions, we were able to re-phrase items to an easy language level (B1: simple Dutch). The questionnaire was available in five languages: Dutch, English, Turkish, Arabic, and Polish. It was conducted in the period from 12 December 2022 to 19 March 2023. Completing the questionnaire took about 5 min and participation was entirely voluntary and without reward.
3.3. Procedure
Recruitment strategies differed depending on the type of support measure. For the three energy coach programmes, participating organisations sent an invitation e-mail with a link to the online questionnaire. Households in the intervention group were invited by mail to participate in the questionnaire after the energy coach’s assistance. Households in the control group were invited by mail to participate in the questionnaire upon confirmation of their (first) appointment with the energy coach. The three renovation programmes used either an invitation e-mail with a link to the online questionnaire or an invitation letter with attached questionnaire by post. This letter/mailing was sent by the social housing corporation. To improve response rates, door-to-door recruitment was also conducted. For the two white goods schemes, e-mail invitations were sent by the implementing organisation to households that had received a voucher. The questionnaire included a specific question about whether the voucher had already been used to purchase new white goods. Due to the dependencies of the organisations involved in the support programmes we could not eliminate the possibility of response biases.
3.4. Data Analysis
To test whether there were significant differences between the control and intervention group of the energy coaching programmes or the renovation programmes or white goods schemes (i.e., main effect of each support measure), as well as whether these differences differed between the support measures (i.e., interaction effect between the three support measures and household’s participation), analysis of variance was used. This statistical model captured the association of relevant independent factors on the outcome variables tested. The independent factors in this case were household’s participation (control group, intervention group) in the support measure and type of support measure (energy coach, renovation, white goods scheme). The outcome variables were all energy poverty-related aspects measured through the questionnaire: living comfort, physical health, energy costs and consumption, financial concerns and mental health, social connection, involvement with the neighbourhood, and sustainable behaviour. To control for potential variation due to the timing of questionnaire completing, daily precipitation, mean temperature, sunshine duration, and maximum hourly mean wind speed were included as co-variables in the model. The data came from [
37] and details were entered for each participant based on date of participation and place of residence.
4. Results and Discussion
The findings indicate that energy coaches, renovations, and white goods schemes positively impact various aspects related to energy poverty. However, the specific effects and their magnitudes vary for each type of support measure. See
Table 4 for an overview of all three different support measures and their significant effects. More detailed findings are described in the subsections.
4.1. Living Comfort
Households that received a visit from an energy coach experienced significantly less cold and fewer draughts in their homes compared with those that had not yet received a visit. Households living in renovated homes experienced significantly less cold, draughts, and damp and/or mould compared with those living in non-renovated homes. Households that used a white goods scheme experienced significantly less damp and/or mould in their home compared with those that had not yet used a white goods scheme. No differences were found for households’ participation in any type of heating support measures in their homes. See
Table 5 for all detailed results.
Among the three support measures examined, renovations had the strongest positive effect on living comfort regarding cold, draught, and damp and/or mould, followed by the energy coaches and white goods schemes. The significant interaction effect shows that the effect of household’s participation (control group, intervention group) indeed differed between the three types of support measures (energy coach, renovation, white goods scheme) for cold (
F(2, 1213) = 13.72;
p < 0.001; η
p2 = 0.022), draught (
F(2, 1212) = 13.36;
p < 0.001; η
p2 = 0.022), and damp and/or mould (
F(2, 1210) = 4.57;
p = 0.010; η
p2 = 0.008). This effect is likely to have resulted from the difference in intensity of the support measure. Renovations included substantial improvements such as roof insulation, mechanical ventilation, HR++ glass, floor insulation, and new central heating boilers. Energy coaches installed window draught excluders, radiator foil, door brush seals, and window insulation foil. White goods schemes enabled the replacement of washing machines with suboptimal spin performance, thereby reducing the need to air-dry wet clothes indoors. These findings align with previous research demonstrating the positive effects of renovations and energy coaches on home comfort [
15,
21,
28]. However, these two support measures had not previously been compared, and the effects of for white goods schemes had not been studied before. A more detailed analysis comparing the programs within a single category of interventions shows that greater improvements in energy labels through renovation are linked to higher gains in living comfort. Differences in the impact of fixer/energy coach programs can be explained by their focus; those emphasizing practical implementation of energy-saving measures had stronger effects than those offering only advice. Finally, white goods schemes that included washing machines significantly reduced moisture issues, whereas schemes covering only other appliances had no measurable effect [
38].
4.2. Physical Health
Households that received a visit from an energy coach reported marginally significantly fewer respiratory problems than households that had not yet received a visit. Households living in renovated homes experienced significantly fewer respiratory problems, joint complaints, and less fatigue than households living in non-renovated homes. No differences in physical health were observed between households that participated in a white goods scheme and those that had not yet participated. Overall, respiratory problems were reduced the most, followed by joint complaints and then, fatigue. See
Table 6 for all detailed results.
The observed health effects are most likely to have been due to improvements in the living comfort in the home, as results show positively correlating between living comfort and physical health (all Pearson correlation coefficients (
r) are between 0.16 and 0.43 and all
p-values < 0.001). Hence, physical health is improved most by renovations, followed by energy coaches. A significant interaction effect confirms that the effect of household’s participation differs between the three types of support measures for joint complaints (
F(2, 1208) = 3.24;
p = 0.040; η
p2 = 0.005), and a marginally significant interaction was found for respiratory problems (
F(2, 1209) = 2.47;
p = 0.085; η
p2 = 0.004). While previous research has shown the health benefits of renovations [
4,
6,
39,
40,
41,
42], these effects had not previously been studied or reported for energy coaches and white goods schemes.
4.3. Energy Use and Costs
Households that received a visit from an energy coach reported significantly lower energy costs and electricity usage, and they also experienced fewer financial worries about paying the energy bill compared with households that had not yet received a visit. Similarly, households living in renovated homes reported significantly lower energy costs and electricity usage and experienced fewer financial worries about paying energy bills than households living in non-renovated homes. Households that participated in a white goods scheme reported significantly lower energy costs and experienced marginal significant fewer financial worries about paying the energy bill than households that had not yet participated. No significant differences were found in gas consumption across any type of support measure. See
Table 7 for all detailed results.
Roughly calculating the self-reported monthly energy saving results in the following annual savings: EUR 996 for renovations, EUR 276 for energy coaching, and EUR 156 for participation in white goods schemes. These savings could potentially increase by 20% in 2023 due to rising energy prices from 2022 to 2023 (based on CBS data [
43] on the average increase in energy bills). Hence, the extent to which participation in a support measure affects the monthly energy costs differs among the three types of support measures. The significant interaction effect confirms that the effect of household’s participation differs across the three types of support measures for the monthly energy costs (
F(2, 939) = 9.57;
p < 0.001; η
p2 = 0.020). A similar effect was observed for the monthly electricity consumption (
F(2, 364) = 11.33;
p < 0.001; η
p2 = 0.059). As a result of these savings, households across all three support measures reported fewer financial concerns about paying their energy bills. This is supported by significant positive correlation between energy costs and financial concerns (
r(945) = 0.325;
p < 0.001).
These findings are consistent with previous research indicating energy cost savings for renovations and energy coaches [
15,
18,
19,
28,
29]. For white goods schemes, thus far, only theoretical savings have been reported. For example, replacing a 15-year-old fridge-freezer with a new model rated energy label C can save EUR 50 to EUR 160 per year [
33]. Hence, the current research substantiates this with actual household data.
Renovations were expected to reduce gas consumption due to the thoroughly installed energy saving measures (e.g., roof insulation, HR++ glass, floor insulation, and renewed central heating boilers). However, in two of the three renovation programmes households could not report their individual gas consumption due to a collective gas connection. Combined with a generally low response to this question the resulting sample size was small (N = 14), which probably explains why no effects on gas consumption were found. Still, a decreasing trend of gas consumption was visible from 119 m3 (control group) to 108 m3 (intervention group), which might result in a significant difference with a larger sample. In addition, the monthly gas consumption per flat from one of the renovation programmes with a collective gas connection was on average 68 m3 per household in non-renovated flats, compared with 53 m3 per household in renovated flats. Although this difference cannot be tested statistically, it suggests a reduction in gas consumption after renovation, which could potentially lead to annual savings of EUR 320 in variable costs (based on a natural gas price of EUR 1.78 per m3 in 2022).
4.4. Mental Health
White goods schemes are the only support measure that showed a positive effect on mental health. Households that participated in a white goods scheme reported marginally significant lower levels of gloom and significantly lower levels of stress and anger. No effect of households’ participation on gloom, stress, and anger was found for energy coaches and renovations. See
Table 8 for all detailed results. A significant interaction effect further indicates that the effect of households’ participation differed across the three types of support measures for anger (
F(2, 1167) = 3.44;
p = 0.032; η
p2 = 0.006) and a marginally significant interactions was found for stress (
F(2, 1167) = 2.87;
p = 0.057; η
p2 = 0.005).
A possible explanation for this finding is that a white goods scheme is a very low-threshold form of support. Households received a white goods voucher by post or email, which they could redeem at a participating retailer, who would deliver the new white goods to their home and would also be responsible for the collection of the old white goods. Furthermore, households also reported that they perceived the new white goods as a gift, which alleviated concerns about the potential breakdown of their previous appliances.
Whereas previous research has shown that both physical and mental health can improve following renovations [
22,
23,
24,
26,
27], the current research found improvements only on physical health and not on mental health after renovations (as well as energy coaches). There are three possible explanations for this discrepancy. First, households that had not yet participated in a renovation or energy coach programme reported rather low levels of gloom, stress, and anger, leaving little room for improvement. This is supported by the finding showing that the control group for white goods schemes reported significantly higher levels of gloom, stress, and anger than the control group for renovations and energy coaches (all
p-values < 0.001). Second, households that participated in a renovation program may have experienced stress due to the duration of the process or the disruption to their daily lives, see [
12,
30,
32], potentially diminishing observable differences between the intervention and control groups. Third, the current study may not have found an effect on mental health due to limitation in the measurement method. The questions were asked once, whereas emotions can vary greatly. Hence, in future studies, mental health would perhaps be better measured by experience sampling, a structured diary technique that captures subjective experiences in daily life [
44].
4.5. Social Connection
No significant differences were found between households that participated in any of the three types of support measures and those that did not, across all aspects of social connection (loneliness, inviting people home, contact with neighbours). See
Table 9 for all detailed results.
4.6. Involvement with the Neighbourhood
Energy coaches showed a unique positive main effect on attending neighbourhood activities and doing volunteer work in the neighbourhood. Households who received a visit from an energy coach attended significantly more neighbourhood activities and were involved in significantly more volunteer work in the neighbourhood than those who had not yet received a visit. No such effects were found for households participating in renovations or white goods schemes. Additionally, no differences were found for helping neighbours across any type of support measure. See
Table 10 for all detailed results.
The positive effect of energy coaches on neighbourhood involvement might be explained by the fact that energy coaches often tailor their support to the specific needs of each household [
17] and may be volunteers resident in the neighbourhood. This finding that a visit from an energy coach enhances neighbourhood involvement is of great significance, as previous research has shown that energy poor households are more likely to experience social isolation [
10]. Moreover, the effects of renovations, energy coaches, and white goods schemes on social isolation have not previously been studied.
4.7. Sustainable Behaviour
Renovations have varying effects on sustainable behaviour. For instance, households living in renovated homes more often turned off lights in empty rooms compared with those living in non-renovated homes. However, the same households tended to take longer showers than households whose homes were not renovated. No significant effect of a household’s participation on turning off lights in empty rooms or showering less than 5 min was found for households participating in energy coaching programs or white goods schemes. Additionally, thermostat settings during the day and the frequency with which households put on a sweater or used a blanket when feeling cold did not differ across any type of support measure. See
Table 11 for all detailed results.
Based on previous research showing that energy coaches promote more sustainable behaviour [
15,
17,
18], a similar effect in this study was anticipated. However, this research shows limited effects of the support measures on sustainable behaviour. A possible explanation is the context of the energy crisis that was prominent in the winter, making households highly conscious about saving energy to lower their energy bills. For example, households that had not yet participated in a support measure set the thermostat to 17.5 degrees on average and often used a sweater or blanket when it was cold instead of turning up the heating. These households also often turned off lights in empty rooms and regularly shower less than 5 min.
The finding that households in renovated homes turned off lights in empty rooms more often than those in non-renovated homes can possibly be explained by the spillover effect. This effect entails that sustainable actions also foster other sustainable actions [
45]. Hence, the renovation which increases the house’s energy label spills over to the sustainable behaviour of turning off lights. However, there was variation in the effect of household participation across the three types of support measures (
F(2, 1158) = 2.40;
p = 0.091; η
p2 = 0.004), and the absence of spillover effect for energy coaches and white goods schemes remains a topic for investigation. The finding that households in renovated homes showered longer might indicate a rebound effect [
46]. As energy bills decrease after renovation, households can afford longer showers, thus exhibiting more unsustainable behaviour. This may be supported by the results showing that showering for less than 5 min significantly relates to the monthly energy costs (
r(143) = 0.22;
p = 0.010).
5. Conclusions, Policy Implications, and Further Research
Since the start of the energy crisis in 2020, the Dutch government has introduced various programmes to support households, particularly those experiencing energy poverty, in adopting energy saving measures. These support programmes primarily focus on improving the affordability of the energy bills but may also address broader adverse effects associated with energy poverty, such as poor living conditions, health problems, and social isolation. This study examines eight distinct governmental support measures categorized into three types of programmes: three energy coaching programmes, three renovation programmes, and two white goods schemes. The main research question guiding this study was ‘What are the effects of different governmental support measures on various energy poverty-related aspects in Dutch households?’. These energy poverty aspects include (1) perceived living comfort, (2) perceived physical health, (3) energy costs and consumption, (4) perceived mental health, (5) sense of social connection, (6) involvement with the neighbourhood, and (7) sustainable behaviour. This study evaluates the impact of the eight support measures across these dimensions, providing insights into their effectiveness and differences in outcomes between programme types.
This research demonstrates that energy coaches, renovations, and white goods schemes each have multiple positive effects on energy poverty-related aspects. However, the specific effects and magnitude of the effects vary by the type of support measure. Renovations emerge as the most effective, significantly enhancing living comfort by reducing cold, drafts, moisture, and mould. Energy coaches also contribute to reducing cold and drafts. White goods schemes primarily impact issues of moisture and mould, particularly through the replacement of poorly functioning washing machines. Renovations also positively impact households’ physical health. Respiratory problems are reduced the most, followed by joint complaints and then, fatigue. Energy coaching results in fewer respiratory problems. White goods schemes do not have an impact on the perceived physical health. Financially, renovations provide the largest savings on energy costs, followed by energy coaches and white goods schemes. These savings reduce financial stress and enable households to allocate resources to other activities. White goods schemes are the only support measure that has a positive impact on mental health. This may be attribute to the ease of using the voucher, the perception of receiving a valuable gift, and the reduced concerns about appliances potentially breaking down. None of the governmental support programmes seem to have impact on sense of social connection. However, energy coaches do uniquely foster involvement with the community by attending neighbourhood activities and participating in volunteer work in the neighbourhood. Renovations are the only governmental support programme that seem to have various, but contradictory, impacts on sustainable behaviour. While they lead to more sustainable actions, such as turning of lights in empty rooms, they also enable less sustainable behaviours such as taking longer showers.
The policy implications of this research include the following. Overall, interventions of policymakers should be tailored to address specific aspects of energy poverty that require improvement, such as energy costs, living comfort, social isolation, and physical and mental health. The complementary effects of energy coaches and white goods schemes suggest potential benefits from integrated approaches. Policy makers could pilot programs that combine the energy coaching service (such as identifying inefficient appliances) with financial assistance for energy-efficient replacements. More specifically, policy makers should prioritize renovations for maximum impact, since this yields the greatest benefits in reducing energy poverty related aspect. Policy makers should also better address the role of social connections in wellbeing when designing energy poverty measures. Finally, energy-poor households should be targeted more effectively, to ensure support reaches those most in need.
There are two important directions for future research. First, this study assessed the impact of different governmental support measures on energy poverty-related aspects for all households; future research could explore differences in outcome between energy poor and non-energy poor households. Second, longitudinal research using difference-in-differences design is needed to ensure equivalence between groups before intervention and to determine whether the support measures decrease the actual prevalence of energy poverty over time.
Author Contributions
Conceptualization, A.J.v.d.W. and C.v.O.; Methodology, A.J.v.d.W. and C.v.O.; Formal analysis, A.J.v.d.W.; Investigation, A.J.v.d.W. and C.v.O.; Writing—original draft, A.J.v.d.W., C.v.O., K.S. and M.R.; Writing—review & editing, A.J.v.d.W. and M.R.; Project administration, C.v.O. All authors have read and agreed to the published version of the manuscript.
Funding
This paper is part of the report of the National Research Program on Energy Poverty conducted by the Netherlands Organization for Applied Scientific Research (TNO). This program was funded by the Ministries of the Interior and Kingdom Relations, Social Affairs and Employment, and Economic Affairs and Climate, and various Dutch provinces (North Holland, South Holland, Flevoland, and North Brabant).
Institutional Review Board Statement
Ethical review and approval by the internal TNO Institutional Review Board was obtained. According to the Medical Scientific Research legislation, social scientific research such as the current study is excluded from further ethical review, because of limited risks.
Informed Consent Statement
Informed consent for participation was obtained from all subjects involved in the study.
Data Availability Statement
The original contributions presented in this study are included in the article.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
Number of households that participated in the research.
Table 1.
Number of households that participated in the research.
| Control Group: Not Participated in the Support Measure | Intervention Group: Participated in the Support Measure | Total |
---|
Energy coach 1 | 35 | 83 | 118 |
Energy coach 2 | 56 | 78 | 134 |
Energy coach 3 | 95 | 120 | 215 |
Renovation 1 | 39 | 42 | 81 |
Renovation 2 | 47 | 40 | 87 |
Renovation 3 | 33 | 15 | 48 |
White goods 1 | 206 | 261 | 467 |
White goods 2 | 25 | 49 | 74 |
Total | 536 | 688 | 1224 |
Table 2.
Demographic aspects of control and intervention group.
Table 2.
Demographic aspects of control and intervention group.
Demographic Aspects | Specification | Control Group | Intervention Group |
---|
Type of home | Semi-detached house | 1.1% | 0.6% |
| Corner house | 4.7% | 4.2% |
| Terraced house | 18.7% | 14.7% |
| Apartment | 59.5% | 68.4% |
Year of construction | | Average 1962 | Average 1958 |
Surface area | | Average 81 m2 | Average 75 m2 |
Ownership | Owner-occupied home | 7.8% | 7.7% |
| Renting social housing | 69.4% | 71.9% |
| Renting free sector housing | 6.9% | 8.0% |
Home of low energy quality | | 29.7% | 30.3% |
Household type | Single | 42.2% | 48.2% |
| Couple with children | 12.7% | 10.7% |
| Couple without children | 13.5% | 12.8% |
| Single-parent family | 15.3% | 15.9% |
| Otherwise | 0.6% | 0.4% |
Number of inhabitants in household | | Average 2.0 | Average 1.8 |
Low-income household | | 45.1% | 50.2% |
Income type | Work | 29.1% | 27.8% |
| Pension | 25.0% | 21.3% |
| Social assistance | 18.7% | 21.8% |
| Sickness Benefits Act | 6.0% | 10.9% |
Table 3.
Survey questions.
Table 3.
Survey questions.
1. Living comfort |
1.1 Do you experience any discomforting cold indoor climate? |
1.2 Do you suffer from draughts in your home? |
1.3 Do you suffer from damp and/or mould in your home? |
1.4 Did you suffer from heat in your home last summer? |
2. Physical health |
2.1 How often do you have problems with your respiratory tract? (e.g., coughing, cold, shortness of breath, shortness of breath) |
2.2 How often do you suffer from joint pain? (e.g., painful joints, rheumatic complaints) |
2.3 How often do you suffer from fatigue? (e.g., low energy, not physically fit) |
3. Energy costs and consumption |
3.1 How many Euros do you pay monthly for your energy bill? |
3.2 How much gas in m3 do you use monthly? |
3.3 How much electricity in kWh do you use monthly? |
3.4 Are you worried about paying your energy bill? |
4. Mental health |
4.1 How often do you feel gloomy? (e.g., not feeling like doing anything, seeing no way out) |
4.2 How often do you experience stress? (e.g., being anxious, worrying, worrying) |
4.3 How often are you angry? (e.g., irritation, frustration, anger, aggression) |
5. Connectedness |
5.1 Do you feel lonely? |
5.2 Do you invite people to your home? |
5.3 Do you have contact with other residents in the neighbourhood? |
6. Involvement |
6.1 Do you attend activities organized in your neighbourhood? |
6.2 Do you help your neighbours in your neighbourhood? |
6.3 Are you committed to neighbourhood activities and/or do you do volunteer work in the neighbourhood? |
7. Sustainable behaviour |
7.1 What temperature do you set the thermostat to during the day? |
7.2 Do you put on a warm sweater or grab a blanket when you are cold at home? |
7.3 Do you turn off the lights in rooms where no one is there? |
7.4 Do you shower for less than 5 min? |
Table 4.
The results on energy poverty-related aspects, by type of support measure and participation.
Table 4.
The results on energy poverty-related aspects, by type of support measure and participation.
| Energy Coaches | Renovations | White Goods Schemes |
---|
| Control Group | Intervention Group | Control Group | Intervention Group | Control Group | Intervention Group |
---|
Cold | 4.2 | 3.9 | 4.5 | 3.0 | | |
Draughts | 3.9 | 3.4 | 4.4 | 2.9 | | |
Damp and/or mould | | | 2.8 | 2.0 | 3.3 | 2.9 |
Respiratory problems | 3.3 | 3.0 | 3.4 | 2.7 | | |
Joint complaints | | | 3.6 | 2.9 | | |
Fatigue | | | 3.8 | 3.3 | | |
Energy costs (self-reported) | €186 | €163 | €162 | €79 | €183 | €170 |
Electricity consumption (self-reported) | 167 kWh | 133 kWh | 166 kWh | 90 kWh | | |
Financial worries | 3.6 | 3.4 | 3.7 | 2.9 | 4.5 | 4.1 |
Gloom | | | | | 3.5 | 3.2 |
Stress | | | | | 3.9 | 3.6 |
Anger | | | | | 3.3 | 3.0 |
Neighbourhood activities | 2.0 | 2.2 | | | | |
Volunteer work | 1.9 | 2.2 | | | | |
Turning off lights | | | 5.4 | 5.7 | | |
Shower shortly | | | 3.9 | 3.4 | | |
Table 5.
Results of support measures on living comfort.
Table 5.
Results of support measures on living comfort.
Living Comfort | Support Measure | Control Group | Intervention Group | F-Value | p-Value | ηp2-Value |
---|
Cold | Energy coaches | M = 4.21 (SD = 1.27) | M = 3.90 (SD = 1.33) | F(1, 457) = 6.70 | p = 0.010 | ηp2 = 0.014 |
| Renovations | M = 4.47 (SD = 1.39) | M = 3.04 (SD = 1.64) | F(1, 205) = 33.00 | p < 0.001 | ηp2 = 0.139 |
| White goods schemes | M = 4.41 (SD = 1.35) | M = 4.12 (SD = 1.40) | F(1, 533) = 0.06 | p = 0.813 | ηp2 < 0.001 |
Draughts | Energy coaches | M = 3.91 (SD = 1.56) | M = 3.44 (SD = 1.48) | F(1, 457) = 8.63 | p = 0.003 | ηp2 = 0.019 |
| Renovations | M = 4.44 (SD = 1.57) | M = 2.93 (SD = 1.71) | F(1, 204) = 43.38 | p < 0.001 | ηp2 = 0.175 |
| White goods schemes | M = 3.96 (SD = 1.58) | M = 3.71 (SD = 1.56) | F(1, 533) = 0.14 | p = 0.712 | ηp2 < 0.001 |
Damp and/or mould | Energy coaches | M = 2.56 (SD = 1.61) | M = 2.60 (SD = 1.52) | F(1, 456) < 0.01 | p = 0.973 | ηp2 < 0.001 |
| Renovations | M = 2.80 (SD = 1.74) | M = 1.97 (SD = 1.40) | F(1, 203) = 12.91 | p < 0.001 | ηp2 = 0.060 |
| White goods schemes | M = 3.30 (SD = 1.78) | M = 2.94 (SD = 1.73) | F(1, 533) = 3.88 | p = 0.049 | ηp2 = 0.007 |
Heat | Energy coaches | M = 3.73 (SD = 1.33) | M = 3.74 (SD = 1.54) | F(1, 285) = 0.78 | p = 0.378 | ηp2 = 0.003 |
| Renovations | M = 4.00 (SD = 1.56) | M = 3.73 (SD = 1.66) | F(1, 205) < 0.01 | p = 0.944 | ηp2 < 0.001 |
| White goods schemes | M = 3.80 (SD = 1.61) | M = 3.72 (SD = 1.51) | F(1, 533) = 0.43 | p = 0.511 | ηp2 = 0.001 |
Table 6.
Results of support measures on physical health.
Table 6.
Results of support measures on physical health.
Physical Health | Support Measure | Control Group | Intervention Group | F-Value | p-Value | ηp2-Value |
---|
Respiratory problems | Energy coaches | M = 3.28 (SD = 1.51) | M = 3.01 (SD = 1.35) | F(1, 454) = 3.21 | p = 0.074 | ηp2 = 0.007 |
| Renovations | M = 3.42 (SD = 1.46) | M = 2.68 (SD = 1.48) | F(1, 204) = 14.62 | p < 0.001 | ηp2 = 0.067 |
| White goods schemes | M = 3.71 (SD = 1.44) | M = 3.54 (SD = 1.40) | F(1, 533) = 0.41 | p = 0.524 | ηp2 = 0.001 |
Joint complaints | Energy coaches | M = 3.26 (SD = 1.66) | M = 3.33 (SD = 1.69) | F(1, 454) = 0.01 | p = 0.918 | ηp2 < 0.001 |
| Renovations | M = 3.55 (SD = 1.73) | M = 2.90 (SD = 1.76) | F(1, 203) = 6.32 | p = 0.013 | ηp2 = 0.030 |
| White goods schemes | M = 3.84 (SD = 1.65) | M = 3.86 (SD = 1.60) | F(1, 533) = 0.18 | p = 0.673 | ηp2 < 0.001 |
Fatigue | Energy coaches | M = 3.69 (SD = 1.43) | M = 3.74 (SD = 1.48) | F(1, 454) = 0.20 | p = 0.655 | ηp2 < 0.001 |
| Renovations | M = 3.81 (SD = 1.40) | M = 3.26 (SD = 1.52) | F(1, 205) = 5.46 | p = 0.016 | ηp2 = 0.028 |
| White goods schemes | M = 4.19 (SD = 1.39) | M = 4.10 (SD = 1.38) | F(1, 533) = 0.31 | p = 0.575 | ηp2 = 0.001 |
Table 7.
Results of support measures on energy use and costs.
Table 7.
Results of support measures on energy use and costs.
Energy Use and Costs | Support Measure | Control Group | Intervention Group | F-Value | p-Value | ηp2-Value |
---|
Energy costs (in Euro) | Energy coaches | M = 186.31 (SD = 85.94) | M = 163.40 (SD = 74.16) | F(1, 314) = 7.43 | p = 0.007 | ηp2 = 0.023 |
| Renovations | M = 161.80 (SD = 89.60) | M = 79.36 (SD = 50.34) | F(1, 140) = 46.86 | p < 0.001 | ηp2 = 0.251 |
| White goods schemes | M = 183.19 (SD = 84.39) | M = 169.88 (SD = 87.31) | F(1, 467) = 4.24 | p = 0.040 | ηp2 = 0.009 |
Gas use (in m3) | Energy coaches | M = 62.86 (SD = 39.68) | M = 63.71 (SD = 46.54) | F(1, 139) = 0.17 | p = 0.683 | ηp2 = 0.001 |
| Renovations | M = 119.15 (SD = 41.31) | M = 107.57 (SD = 28.07) | F(1, 14) = 1.74 | p = 0.208 | ηp2 = 0.111 |
| White goods schemes | M = 59.45 (SD = 56.48) | M = 63.43 (SD = 52.54) | F(1, 148) = 0.28 | p = 0.597 | ηp2 = 0.002 |
Electricity use (in kWh) | Energy coaches | M = 166.71 (SD = 75.91) | M = 133.17 (SD = 63.78) | F(1, 149) = 4.02 | p = 0.047 | ηp2 = 0.026 |
| Renovations | M = 166.47 (SD = 87.08) | M = 89.81 (SD = 46.68) | F(1, 60) = 29.24 | p < 0.001 | ηp2 = 0.328 |
| White goods schemes | M = 152.07 (SD = 77.09) | M = 171.37 (SD = 78.05) | F(1, 137) = 1.02 | p = 0.315 | ηp2 = 0.007 |
Financial concerns | Energy coaches | M = 3.62 (SD = 1.51) | M = 3.35 (SD = 1.46) | F(1, 435) = 11.81 | p < 0.001 | ηp2 = 0.026 |
| Renovations | M = 3.71 (SD = 1.53) | M = 2.94 (SD = 1.72) | F(1, 198) = 5.17 | p = 0.024 | ηp2 = 0.025 |
| White goods schemes | M = 4.49 (SD = 1.41) | M = 4.10 (SD = 1.57) | F(1, 533) = 3.47 | p = 0.063 | ηp2 = 0.006 |
Table 8.
Results of support measures on mental health.
Table 8.
Results of support measures on mental health.
Mental Health | Support Measure | Control Group | Intervention Group | F-Value | p-Value | ηp2-Value |
---|
Gloom | Energy coaches | M = 2.58 (SD = 1.31) | M = 2.70 (SD = 1.30) | F(1, 426) = 0.31 | p = 0.579 | ηp2 = 0.001 |
| Renovations | M = 2.62 (SD = 1.26) | M = 2.60 (SD = 1.28) | F(1, 189) = 0.04 | p = 0.838 | ηp2 < 0.001 |
| White goods schemes | M = 3.50 (SD = 1.35) | M = 3.23 (SD = 1.42) | F(1, 533) = 3.73 | p = 0.054 | ηp2 = 0.007 |
Stress | Energy coaches | M = 3.08 (SD = 1.29) | M = 3.19 (SD = 1.31) | F(1, 426) = 0.07 | p = 0.793 | ηp2 < 0.001 |
| Renovations | M = 3.04 (SD = 1.37) | M = 3.16 (SD = 1.42) | F(1, 190) = 0.37 | p = 0.545 | ηp2 = 0.002 |
| White goods schemes | M = 3.92 (SD = 1.41) | M = 3.60 (SD = 1.43) | F(1, 533) = 5.18 | p = 0.023 | ηp2 = 0.010 |
Anger | Energy coaches | M = 2.54 (SD = 1.08) | M = 2.64 (SD = 1.07) | F(1, 426) < 0.01 | p = 0.992 | ηp2 < 0.001 |
| Renovations | M = 2.50 (SD = 1.25) | M = 2.71 (SD = 1.14) | F(1, 190) = 0.60 | p = 0.438 | ηp2 = 0.003 |
| White goods schemes | M = 3.31 (SD = 1.34) | M = 3.01 (SD = 1.32) | F(1, 533) = 8.15 | p = 0.004 | ηp2 = 0.015 |
Table 9.
Results of support measures on connectedness.
Table 9.
Results of support measures on connectedness.
Connectedness | Support Measure | Control Group | Intervention Group | F-Value | p-Value | ηp2-Value |
---|
Loneliness | Energy coaches | M = 2.29 (SD = 1.17) | M = 2.53 (SD = 1.26) | F(1, 425) = 0.29 | p = 0.591 | ηp2 = 0.001 |
| Renovations | M = 2.14 (SD = 1.30) | M = 2.40 (SD = 1.52) | F(1, 189) = 1.12 | p = 0.291 | ηp2 = 0.006 |
| White goods schemes | M = 3.11 (SD = 1.50) | M = 2.84 (SD = 1.49) | F(1, 533) = 0.13 | p = 0.718 | ηp2 < 0.001 |
Inviting people home | Energy coaches | M = 3.16 (SD = 0.97) | M = 3.11 (SD = 1.06) | F(1, 426) = 0.01 | p = 0.927 | ηp2 < 0.001 |
| Renovations | M = 3.21 (SD = 1.19) | M = 2.94 (SD = 1.16) | F(1, 190) = 0.41 | p = 0.521 | ηp2 = 0.002 |
| White goods schemes | M = 2.71 (SD = 1.08) | M = 2.81 (SD = 1.23) | F(1, 533) = 0.01 | p = 0.905 | ηp2 < 0.001 |
Contact with neighbours | Energy coaches | M = 3.27 (SD = 1.23) | M = 3.22 (SD = 1.19) | F(1, 426) = 0.08 | p = 0.772 | ηp2 < 0.001 |
| Renovations | M = 3.03 (SD = 1.23) | M = 2.87 (SD = 1.19) | F(1, 190) = 1.94 | p = 0.165 | ηp2 = 0.010 |
| White goods schemes | M = 2.84 (SD = 1.28) | M = 2.94 (SD = 1.29) | F(1, 533) = 0.82 | p = 0.366 | ηp2 = 0.002 |
Table 10.
Results of support measures on neighbourhood involvement.
Table 10.
Results of support measures on neighbourhood involvement.
Involvement | Support Measure | Control Group | Intervention Group | F-Value | p-Value | ηp2-Value |
---|
Neighbourhood activities | Energy coaches | M = 2.01 (SD = 1.09) | M = 2.24 (SD = 1.23) | F(1, 422) = 4.61 | p = 0.032 | ηp2 = 0.011 |
| Renovations | M = 1.85 (SD = 0.99) | M = 1.74 (SD = 0.97) | F(1, 187) = 0.15 | p = 0.699 | ηp2 = 0.001 |
| White goods schemes | M = 2.00 (SD = 1.15) | M = 2.07 (SD = 1.14) | F(1, 533) = 0.01 | p = 0.911 | ηp2 < 0.001 |
Helping neighbours | Energy coaches | M = 2.88 (SD = 1.25) | M = 2.87 (SD = 1.17) | F(1, 421) = 1.27 | p = 0.260 | ηp2 = 0.003 |
| Renovations | M = 2.75 (SD = 1.26) | M = 2.92 (SD = 1.26) | F(1, 190) = 0.28 | p = 0.600 | ηp2 = 0.001 |
| White goods schemes | M = 2.62 (SD = 1.29) | M = 2.69 (SD = 1.27) | F(1, 533) = 1.51 | p = 0.219 | ηp2 = 0.003 |
Volunteer work | Energy coaches | M = 1.82 (SD = 1.23) | M = 2.18 (SD = 1.43) | F(1, 421) = 7.61 | p = 0.006 | ηp2 = 0.018 |
| Renovations | M = 1.57 (SD = 1.15) | M = 1.58 (SD = 1.20) | F(1, 189) < 0.01 | p = 0.979 | ηp2 < 0.001 |
| White goods schemes | M = 1.92 (SD = 1.34) | M = 1.96 (SD = 1.38) | F(1, 533) = 1.16 | p = 0.282 | ηp2 = 0.002 |
Table 11.
Results of support measures on sustainable behaviour.
Table 11.
Results of support measures on sustainable behaviour.
Involvement | Support Measure | Control Group | Intervention Group | F-Value | p-Value | ηp2-Value |
---|
Thermostat temperature | Energy coaches | M = 18.05 (SD = 1.86) | M = 17.84 (SD = 2.11) | F(1, 352) = 0.52 | p = 0.470 | ηp2 = 0.001 |
| Renovations | M = 17.35 (SD = 4.00) | M = 17.38 (SD = 2.48) | F(1, 36) = 0.03 | p = 0.871 | ηp2 = 0.001 |
| White goods schemes | M = 17.52 (SD = 2.46) | M = 17.59 (SD = 2.32) | F(1, 387) = 0.07 | p = 0.786 | ηp2 < 0.001 |
Using sweater or blanket | Energy coaches | M = 4.81 (SD = 1.22) | M = 4.97 (SD = 1.21) | F(1, 247) = 1.29 | p = 0.257 | ηp2 = 0.005 |
| Renovations | M = 4.86 (SD = 1.25) | M = 4.60 (SD = 1.42) | F(1, 190) = 1.73 | p = 0.190 | ηp2 = 0.009 |
| White goods schemes | M = 5.07 (SD = 1.10) | M = 5.13 (SD = 1.06) | F(1, 531) = 0.30 | p = 0.583 | ηp2 = 0.001 |
Turn off lights | Energy coaches | M = 5.19 (SD = 0.86) | M = 5.41 (SD = 0.85) | F(1, 415) = 0.03 | p = 0.860 | ηp2 < 0.001 |
| Renovations | M = 5.34 (SD = 1.40) | M = 5.67 (SD = 0.83) | F(1, 192) = 4.57 | p = 0.034 | ηp2 = 0.023 |
| White goods schemes | M = 5.65 (SD = 0.78) | M = 5.67 (SD = 0.85) | F(1, 533) = 2.60 | p = 0.107 | ηp2 = 0.005 |
Shower less than 5 min | Energy coaches | M = 3.97 (SD = 1.46) | M = 4.04 (SD = 1.56) | F(1, 415) = 0.26 | p = 0.609 | ηp2 = 0.001 |
| Renovations | M = 3.90 (SD = 1.68) | M = 3.38 (SD = 1.73) | F(1, 191) = 2.97 | p = 0.087 | ηp2 = 0.015 |
| White goods schemes | M = 4.10 (SD = 1.53) | M = 3.93 (SD = 1.63) | F(1, 533) = 0.68 | p = 0.410 | ηp2 = 0.001 |
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