2.1. Conceptual framework

Based on the idea that low-carbon transition policies should engage with moral reasoning (Granqvist & Grover 2016; United Nations 2015; WCED 1987), this paper links the capability approach to questions of equity in an Energy Efficiency Obligations (EOO) programme. Considerations about equity address the social dimension of sustainable developments, which is based on the premise that people should lead a good and fulfilling life and that avoidable differences should be removed (WCED 1987). Assessing the ethical implications of energy policies is relevant in the context of energy poverty as a risk to health and well-being and global efforts to reduce distributional inequalities (United Nations 2019). The analysis of (in)equalities may focus on the distribution of resources, opportunities or outcomes (Burchardt & Hick 2017). This study follows the approach of Day, Walker & Simcock (2016), who have placed a capabilities framework as central to a broader view of energy inequality and advanced the theoretical foundation of strategies that aim to recognise and ameliorate energy deprivation. Hence, this paper explores to what extent known factors of energy deprivation and constraints in the capability to retrofit are associated with the distribution of benefits of retrofit subsidies and how energy policies may result in more equitable outcomes.

The capability approach, as developed by the economist and philosopher Amartya Sen, offers a ‘framework for the evaluation and assessment of individual well-being and social arrangements, the design of policies, and proposals about social change in society’ (Robeyns 2005: 93). Capabilities are opportunities that allow people to do or be things, so-called functionings, and to achieve a good quality of life (Robeyns 2005; Sen 1990). As capabilities are positively linked to freedom and autonomy, constraints in enacting functionings equate to limited capabilities. Analyses of equality may focus on thresholds or distributional inequality and explore horizontal comparisons between groups or vertical comparisons within groups or populations (Burchardt & Hick 2017).

Energy poverty or inequality has no single definition, but can be understood as the lack of access to affordable, renewable and reliable essential energy services (Bouzarovski 2013; Thomson, Bouzarovski, & Snell 2017; United Nations 2019). Day et al. (2016) argued that energy inequality is more than a symptom of low income. Causes may include energy-inefficient dwellings and restricted choice in energy supply. They suggest that interventions should address the multidimensionality of conditions that shape deprivation. The present paper focuses on the capability or opportunity to retrofit the home. Retrofitting the home may be regarded as a functioning to reduce energy costs, increase comfort and improve health (Willand, Ridley, & Maller 2015). Constraints in retrofitting may be financial in nature, such as low discretionary income, or have structural causes, such as the lack in agency among renters.

Central to this paper is the aspect of fairness in the design of energy-transition policies. In particular, it addresses distributional (in)equity of subsidy benefits, which implies a moral value judgement of the extent of distributional (in)equality. Distributional (in)equality reflects the pattern of the distribution of resources, benefits and burdens across populations and space. Relevant approaches to distributive equality of resources are strict egalitarianism, which is the even distribution across all parties, utilitarian equality, which aims to maximise the desired outputs, and the principle of democratic equality, which advocates for raising valued outcomes for disadvantaged parties (Hillman 2004). In line with the quotation at the start of this paper, our argument is guided by the fairness criterion that the political management towards a lower carbon society should be progressive in the sense that it reduces the level of energy-related socioeconomic disadvantage in a society and leads to more even outcomes in well-being (Granqvist & Grover 2016).

2.2. Energy Efficiency Obligations (EOOs)

Retrofitting in owner-occupied-dominated, lightly regulated housing policy jurisdictions is largely a voluntary, private, self-driven and often costly activity (Willand & Horne 2013). In this context, EEOs are market-based policy instruments that encourage the uptake of energy-efficiency improvements through subsidies. Energy-reduction targets are placed on organisations such as energy utilities, which then discount retrofit activities to achieve these goals (Fawcett, Rosenow, & Bertoldi 2019). Notwithstanding the variety in designs, the costs of the EEOs are often passed from the energy utilities onto consumers through bill surcharges (Rosenow, Cowart, & Thomas 2019).

EEOs are used in over 50 jurisdictions worldwide (Rosenow et al. 2019), and they are expected to become more widespread (IEA 2017). They are popular as they allow flexibility in delivery and place minimal financial burdens on governments (IEA 2017; Rosenow & Bayer 2017). They have also successfully promoted private consumer investment in energy-efficiency measures (Rosenow et al. 2019). However, EOOs may also support free-riders, i.e. participants who are able and willing to pay and who would not have needed subsidies. Free-ridership may be interpreted as a market failure and an expression of an unequitable distribution of EEO benefits (Fawcett et al. 2019; Rosenow et al. 2019).

EEOs may provide distal social benefits, such as avoided energy infrastructure upgrade costs and better air quality (Rosenow et al. 2019). Participating householders may also experience better thermal comfort and increased asset values. Participants may also gain financially, when the costs savings due to the retrofits exceed the apportioned cost burdens of the scheme (Rosenow & Bayer 2017). Cost savings may be negated, however, when potential financial benefits are traded for improved comfort or undermined by changes in energy practices (Fawcett et al. 2019; Rosenow et al. 2019). Such rebound and pre-bound effects are frequently observed in low-income and fuel-poor households (Galvin 2015; Sunikka-Blank & Galvin 2012; Teli et al. 2016), and may challenge the estimation of deemed savings in emissions (Rosenow et al. 2019).

Green certificates are a similar market-based policy tool. Whereas white certificates aim to reduce energy demand, green certificates seek to accelerate the decarbonisation of the electricity supply by providing financial incentives to install solar PV systems and other renewable microgeneration systems. Owing to the reallocation of costs to consumers, there are concerns that market-based policy tools such as white and green certificate programmes may be regressive for non-participants (IEA 2017). Common mitigation measures are targets for low income, most notable the 100% objective in the UK (Rosenow et al. 2019). Other strategies that promise more equitable distributions of costs and benefits across households allocate costs only to households who can bear the burden, or provide low-income households with income support to balance the surcharges (Granqvist & Grover 2016).

2.3. Case study: The Victorian context

Victoria is Australia’s second most populous state (ABS 2019b), with three-quarters of its inhabitants living in its capital, Melbourne, and a relatively high population growth rate, which is driving housing demand. Responses include high-rise developments in the city centre, higher density housing in the inner suburbs and new developments in outer suburbs with predominantly detached houses. As inner-city suburbs are experiencing gentrification (Buxton, Goodman, & Moloney 2016), lower income families are pushed out onto the periphery (Lim 2018). The growth in coastal and regional areas is amplified by older Melbournians retiring to their second or more affordable homes (Luck, Race, & Black 2010; Saberi et al. 2017).

In Australia, policies aiming to improve the energy performance of homes have focused on new housing and the setting of minimum performance requirements (Moore, Horne, & Morrissey 2014). However, as retrofits of the existing housing stock have the potential to achieve substantial reductions in energy emissions and costs, they are promoted by national and state governments (COAG Energy Council 2018; Sustainability Victoria 2015; Victorian Government, Department of Economic Development, Jobs, Transport and Resources 2015). Market-based mechanisms are favoured (COAG Energy Council 2018). In contrast to European countries, which are obliged to address energy inequalities in their climate change-mitigation initiatives (European Commission 2019), in Australia there is little governmental commitment to alleviate energy hardship. Nonetheless, the need to make provisions for low-income householders has been acknowledged (COAG Energy Council 2018).

Low-income and rental households in Australia are at a higher risk of energy stress than wealthier population groups and owner-occupiers (ACOSS & Brotherhood of St Laurence 2019; Colmar Brunton 2018; Poruschi & Ambrey 2018). Renters are also more likely to lack energy-efficiency features that are common in owner-occupied dwellings (ABS 2010; Colmar Brunton 2018; Energy Consult 2009). While restrictions in tenant agreements prevent renters from making material changes to the property without the consent of their landlord (Acil Allen Consulting 2018), profit-driven landlords may lack the motivation to invest in energy-efficiency measures as demand for rental properties exceeds supply (AHURI 2017). Renters may also have little knowledge or understanding of the energy performance of their home or interest in this matter (Pitt & Sherry 2014; QCOSS 2017). In response, community services and advocacy organisations in Victoria have joined in a call for political action to protect renters through mandatory housing standards (ACOSS & Brotherhood of St Laurence 2018). These would translate into retrofit obligations for existing, rented homes.

White and green certificates are also common policy tools designed to promote retrofit in Australia. White certificate schemes operate at the state level, e.g. in Victoria, through the VEU programme and the Energy Efficiency Improvement Scheme in the Australian Capital Territory (ACT). Green certificates are generated through the Small-Scale Renewable Energy Scheme (SRES), which offers grants for solar PV systems to householders and businesses on a national scale (Clean Energy Regulator 2018). In contrast to the UK (Rosenow et al. 2013), in Australia, white and green certificates are not used to address energy inequality (Hillman 2004; Walker 2012). The VEU programme and SRES pursue the distributive principle of utilitarian equality. They aim to achieve the maximum in carbon emission reductions by focusing on opportunities inherent in the material characteristics of dwellings and treating all households as equal. The ACT’s scheme, however, takes a democratic equality approach by setting targets for activities in ‘priority households’, which are low-income private, public and community housing renters on energy retailers’ hardship programmes (ACT Environment Planning and Sustainable Development Directorate 2019).

2.4. The spatial dimension of retrofits

The concept of retrofit poverty offers a framework for exploring the socioeconomic, demographic and spatial dimensions that shape the opportunities to improve residential energy outcomes. While much of the work hitherto has focused on disparities in the functionings of affording energy or achieving adequate indoor temperatures in Europe (Burholt & Windle 2006; Healy 2004) and in Australia (Baker et al. 2019), interest in the spatial distribution of intervention benefits is emerging (Bouzarovski & Simcock 2017). European research suggests that regional polarisation may lead to disparities in retrofit projects and subsidies, with metropolitan regions benefiting more than peripheral areas (Bouzarovski & Tirado Herrero 2017; Lihtmaa, Hess, & Leetmaa 2018; Turcu 2016). In Australia, investigations into the spatial distribution of low-carbon technology uptake have focused on solar PV installations (Best, Burke, & Nishitateno 2019; Chandrashekeran & Li 2019). In this study, we undertook geographical research on retrofit subsidies to provide a better understanding of the mechanisms that shape the capabilities needed for retrofitting and to reveal inequalities of opportunities to reduce energy costs and emissions.

2.5. Victorian Energy Upgrade (VEU) programme

The VEU is a market-based retrofit subsidy programme launched by the Victorian government in 2009 in order to reduce greenhouse gas emissions (Victorian Parliament 2007). It also aims to promote the introduction of new products and investment in the retrofit service sector and to create jobs (DELWP 2019; Victorian Parliament 2007). Retrofit activities cover residential and commercial buildings. The primary outcome of the VEU is measured in Victorian Energy Efficiency Certificates (VEECs). These represent the activities’ potential savings in carbon dioxide equivalents (CO₂-e). One VEEC represents 1 tonne of avoided CO₂-e. Annual targets range between 2.7 million and 6.5 million certificates (ESC 2020). The VEECs of each VEU programme activity reflect the avoided emissions of the energy-related service that is addressed and, hence, vary among the various VEU programme activities. The Victorian government attributes the state’s flattening energy demand and the availability of affordable, efficient lighting products to the VEU (DELWP 2019). The VEU is also estimated to have created more than 2000 jobs (DELWP 2019). The programme will continue until 2029.

The residential activity sector of the VEU offers activities in 11 categories which include appliance upgrade options and provision of convenience and electricity consumption feedback tools for houses, apartments and hotels. Some VEEC activities, such as the upgrade of tungsten bulbs and halogen lights to LED lamps, low-flow shower roses and stand-by power controllers are offered to householders free of charge. All other activities require financial co-contributions by householders. Activities are implemented by service providers and require householder signatures as evidence. Table 1 presents a list of these categories, the relevant activities (ESC 2017, 2019) and the abbreviations used for the categories in the diagrams of the Results sections.

Although VEECs primarily reflect avoided carbon emissions, they may also be interpreted as a proxy for energy costs savings. VEECs may directly equate to potential savings in energy costs when the activity does not involve a fuel change, e.g. in lighting upgrades. VEECs from activities that involve a fuel change save more energy costs than VEECs without a switch in fuel type. For example, the switch from an electric water heating system to a gas or solar-powered device reduces energy costs substantially as gas or solar electricity unit costs are lower than electricity from the grid. However, such fine differentiation or the estimation of exact savings in energy expenditure was outside the scope of this study.

3.1. Data sources

The study used open-source data from the Register of Victorian Energy Efficiency Certificates (ESC 2019) and Census data from the Australian Bureau of Statistics (ABS) (2013b, 2018). It focused on the VEU residential activities from its launch in 2009 until the end of the financial year in June 2017.

3.2. Key variables

The variables of primary interest were the intensity of VEECs generation as the dependent or outcome variable, and socioeconomic scores and prevalence of tenure as the independent or predictor variables. A differentiation into metropolitan and regional postcode areas served to test the relative (dis)advantages of location and economic geography. Calculation of indicators was performed in the spreadsheet software Microsoft Excel 2016. Statistical analyses were conducted in the software IBM SPSSv26, and geographical mappings of outcome variables in the data-visualisation software Tableau 2019.1.

Following previous research (Sullivan & Johnson 2012), VEECs generation intensity was defined as the number of residential VEECs generated for every 100 private dwellings (Equation 1). The numbers of private dwellings were derived from the ABS Census 2016 data and included occupied and unoccupied dwellings. This normalisation of VEECs generation allowed for the comparison of VEEC outputs across locations with varying dwelling numbers:

The main variables representing the areas’ socioeconomic levels were the SEIFA IER and SEIFA IRSAD scores. Socio-Economic Indexes for Areas (SEIFA) scores are calculated by the ABS based on the latest census data and area weighted variables (ABS 2013a). The Index of Economic Resources (IER) combines financial variables such as income and wealth. The Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) captures a more general level of ‘people’s access to material and social resources, and their ability to participate in society’ (ABS 2013a: 3). Changes in these indices since the 2011 Census were calculated to reflect levels of gentrification.

Tenure prevalence was expressed as the rate of rented dwellings, i.e. the percentage of occupied dwellings in each postcode area that were rented on the Census 2016 night. The allocation of postcode areas to metropolitan and regional locations followed the characterisation of areas in the Register of Victorian Energy Efficiency Certificates.

4.1. Descriptive statistics and independent sample t-tests

This analysis covered more than 3.5 million activities and more than 33.4 million VEECs or tonnes of avoided CO₂-e. The VEECs generation was dominated by lighting upgrades and stand-by power controllers. Together with shower roses, these free activities accounted for 84% of all white certificates (Figure 1). The total counts of VEECs varied considerably between metropolitan and regional parts of the state and among postcode areas. The number of VEECs generated in metropolitan Melbourne (69%) was more than double the number generated in regional Victoria (31%) (Figure 2). The mean number of VEECS per postcode area across the whole state was 48,504 certificates, with both the lowest (four VEECs) and highest counts (555,326 VEECs) found in metropolitan areas. On average, metropolitan postcode areas generated more than three times the number of certificates than regional areas (Table 2).

However, when the numbers of certificates in the two locations were normalised to 100 dwellings, the intensity of VEECs generation was lower in the metropolitan areas (1343 VEECs per 100 dwellings) than in regional Victoria (1540 VEECs per 100 dwellings) (Figure 3). In metropolitan areas, relatively more VEECS were generated from lighting upgrades than in regional areas (57% and 38%, respectively). By contrast, in regional Victoria, water heater upgrades accounted for a larger share of VEECs than in metropolitan Melbourne (21% and 3%, respectively). The mean VEECs generation intensity per postcode area across the whole state was 1506 certificates for every 100 dwellings, with the lowest intensity (4 VEECs per 100 dwellings) found in the metropolitan area and the highest intensity (3545 VEECs per 100 dwellings) in regional Victoria (Table 3).

The spatial mapping of the VEECs generation intensity (Figure 4) showed that most areas presented intensities just below the mid-value. Area clusters or neighbourhoods with similarly high VEECs generation intensities were revealed along the Murray River at the northern boundary of the state, west of Geelong and towards Lakes Entrance. Cold spots were isolated and included a military base with almost exclusively rented dwellings, and French Island in the Bass Strait with limited accessibility. Within the metropolitan area, the city centre with its new residential skyscrapers and the new suburbs in the south-east presented areas with low VEECs generation intensity. High intensities were found north-west of the city and along the main roads leading to the Mornington Peninsula (Figure 5).

At the postcode level, mean VEECs generation intensity in regional Victoria was 16% higher than in metropolitan Melbourne, a difference that was statistically significant (Table 4). The calculation and mapping of the share of the certificates that had been generated from the free activities in each postcode (Figure 6) showed that these activities accounted for more than half of all VEECs in most areas. A high share cluster was apparent in and around Melbourne. On average, the share of the certificates that had been generated from free activities was 25% higher in metropolitan areas (87%) than in regional areas (62%). This difference was highly statistically significant. At the postcode level, the mean VEECs generation intensity from the predominant free activity of lighting upgrades was 14% higher in metropolitan than in regional areas. By contrast, the mean VEECs generation intensity from water-heating upgrades, the predominant activity requiring a financial contribution by householders, was five times higher in regional areas (Table 4).

4.2. Linear regression analyses

Linear regression models were calculated to test the association of VEECs generation intensity and the socioeconomic and tenure variables. The mean 2016 IER and IRSAD were significantly higher, but presented a larger distribution in metropolitan than in the regional areas. The mean changes in SEIFA scores between 2011 and 2016 were positive in both locations, but on average higher in regional than in metropolitan areas. The proportion of rented dwellings was significantly higher in metropolitan than in regional areas; the inverse was true for the rate of unoccupied dwellings (Table 5).

Highly statistically significantly positive associations of VEECs generation intensity across all postcode areas were observed for the 2016 IER scores and their change since 2011. The rates of rented homes and unoccupied dwellings were negatively associated with VEECs generation intensity at the 0.0001 level. The relationships between the variables of IER and prevalence of rented dwellings and VEECs generation intensity were stronger in metropolitan areas than in regional areas. The very low p-values in many of the tests meant that the sample provided good evidence of an association between the predictor and the outcome variables (Table 6).

The disaggregation of certificate intensities into the predominant activities of lighting and water heater upgrades (Tables 7 and 8, respectively) concurred that economic resources were a stronger predictor of VEECs intensities in metropolitan than in regional areas. Relative socioeconomic advantage predicted higher VEECs generation intensities from free lighting activities in regional, but not in metropolitan, areas. The reverse was found for VEECs intensities from water-heating activities that required householder co-payments, which were associated with relative socioeconomic advantage in Melbourne, but not in regional Victoria. Increases in wealth and socioeconomic advantage predicted higher VEECs generation intensity from water heating upgrades across metropolitan, but not in regional, areas. The prevalence of rented dwellings negatively predicted the VEECs generation intensity from free lighting upgrades in metropolitan, but not in regional, areas. There was a negative association between the VEECs generation intensity from water heater upgrades and the rate of rented dwellings for both location groups.

4.3. Multiple regression analyses

A multiple regression was run to predict the VEECs generation intensity from the postcode areas’ prevalence of rented homes and share of unoccupied dwellings. The areas’ SEIFA IER scores were not included because of their negative correlation with the areas’ share of rented homes (r = –0.621). The rates of rented homes and of unoccupied dwellings highly statistically significantly predicted the VEECs generation intensity (F(2, 688) = 88.821, p = 4.98E–35) and explained 20.6% of the variance (Table 9). Within the metropolitan region, these two factors explained almost half the VEECs generation intensity (F(2, 274) = 130.506, p = 1.84E–40, r² = 0.49) (Table 10). This suggests that renters in metropolitan Melbourne were at a higher risk of missing out on retrofit subsidies than owner-occupiers.

4.4. Sensitivity analysis of VEECs generation intensity per household

A sensitivity analysis was conducted to explore the VEECs generation intensity by households rather than dwellings. In contrast to other work that equated dwellings with households (Best et al. 2019), this study worked on the premise that only occupied dwellings contained households, namely one household per occupied dwelling. More than 11% of dwellings in Victoria were unoccupied in the Census 2016 (ABS 2019a). The VEECs generation intensity was recalculated as the number of VEECs generated for every 100 occupied private dwellings (Equation 2):

When mapping, one outlier (POA 3944) was removed to provide meaningful gradation in colour. The spatial mapping of the VEECs generation intensity per household showed scattered areas of high intensity across Victoria. Small area clusters with similarly high VEECs generation intensities were apparent at the tip of the Mornington Peninsula (Figure 7), along the Murray River, in Lake Eildon, along the Surf Coast and Bass Strait, and north of Lakes Entrance (Figure 8). These hotspots accommodate weekend and summer retreats of Melbournian families. Many of these were empty on Census night, a Tuesday evening in the middle of winter, e.g. Lake Eildon (48.5% unoccupied dwellings).

4.5. Outlier case study

The extreme outlier of Portsea (Postcode 3944) at the tip of the Mornington Peninsula was investigated further to try to explain the unusually high VEECs generation intensity (Figure 9). The distribution of certificates across activities was not unusual. The postcode area’s SEIFA indices in the top two deciles indicated that the residents were wealthy and well-educated. The rate of rented dwellings was low, the rate of empty dwellings unusually high. Portsea is promoted as the holiday location of Melbourne’s ‘rich and famous’ (Visit Melbourne 2019).

5.1. Low-carbon gentrification

The observed link between water heating upgrades and increases in affluence and advantage in the metropolitan areas endorsed the concept of ‘low-carbon gentrification’, i.e. the interaction of housing improvements and upward social change (Bouzarovski, Frankowski, & Tirado Herrero 2018). More research is needed to explore to what extent and in which ways this phenomenon may have led to the displacement of low-income tenants or other potentially disadvantaged groups.

The regressive outcome of subsidies to improve domestic energy demand among low-income households and renters seems to be compounded by a lack of access to decarbonise and reduce the costs of their energy supply. Recent work investigating the spatial coverage of the SRES found that, across Australia, renters were less likely to benefit from solar microgeneration subsidies (Best et al. 2019). This highlights that non-targeted low-carbon incentives are likely to (re)produce social inequalities. In contrast to the present study, which showed a linear association of VEECs generation intensities and economic wealth of areas, the solar PV subsidy uptake was particularly high in postcode areas with middle-income households (Best et al. 2019). Hence, it appears that varied mechanisms shape the uptake of the different types of low-carbon material home improvements.

The spatial concentrations of high VEECs generation intensities both within and outside the metropolitan boundary of Melbourne echo the neighbourhood effect that has been observed in solar PV installations in Victoria (Chandrashekeran & Li 2019) and solar PV subsidy penetration across Australia (Burke, Best, & Nishitateno 2019). The spatial concentration of hotspots may be due to the geographical coverage of retrofit provider services (Gooding & Gul 2017), to a snowballing effect of successful retrofit initiatives (Lihtmaa et al. 2018) or shared prevailing contextual conditions. One possible reason may be the sea and tree change phenomenon, the migration of affluent householders retiring to the Mornington Peninsula, Bass Strait, country Victoria and small towns along the Murray River (Luck et al. 2010). The hypothesis that affluent baby boomers may play a key role in the upscaling of the energy refurbishment of homes is supported by evidence that self-funded retirees have been more likely to install solar PV panels than lower income state pension recipients (Best et al. 2019). This also means that both white and green certificate schemes in Australia may inadvertently promote free-rider problems.

Another possible explanation may be the social normalisation of residential retrofit activities that extends beyond the main place of residence to the second home. Numerous of the holiday areas on the Mornington Peninsula with high VEECs generation intensities per household and low occupation rates have also presented high penetrations of solar PV panels (Chandrashekeran & Li 2019). This suggests that energy improvements of homes may have become a routinised feature in home maintenance, at least among the more affluent households. Beyond concerns about equity, the use of VEU grants for second homes raises questions about the effectiveness of the scheme. If intermittently occupied homes received subsidised activities, then the expected carbon emissions reductions may not have been achieved.

The value of spatial research on inequities in access to retrofit opportunities lies in its usefulness in monitoring and comparing outcomes over time and across areas. This study showed that retrofit poverty is a multidimensional issue with monetary, social and structural indicators which may require a multifactorial index. Maps may visualise variations in retrofit poverty or the outcomes of initiatives that aim to promote energy-efficiency improvements, assist in identifying areas that seem to be missing out and facilitate targeted interventions. However, the marketing of retrofit programmes in specific geographical areas may need sensitive communication as they may be interpreted as stigmatising if they imply high levels of energy hardship (Reid, McKee, & Crawford 2015).

5.2. Limitations

A key variable in understanding the geography of access to retrofit subsidies is the unit of analysis. This study aggregated data to the postcode level. This method limited conclusions referring directly to conditions that may have explained the divergence of outcomes. In addition, the association between the various independent variables and VEECs generation intensity does not signify a causal relationship. The spatial and sociodemographic differences in access to retrofit activities may, however, advance research into the phenomenon of retrofit poverty as more than a financial disparity.

Future research may consider sampling at dwelling unit level and approach energy-efficiency improvements as the dynamic sociotechnical system characterised by the interactions among people, place, properties and providers. Drawing on a public health framework (Duncan, Jones, & Moon 1998), it is recommended that future investigations aim for a deeper understanding of the relationship between contexts (including geographical and climatic region, time, regulations, localised programmes/innovations), compositions (such as diversity of people, dwellings and retrofit services) and conditions (which may be systemic, social, individual or pertain to the capabilities and practices around private and professional retrofits).