A novel Index of Vulnerable Homes: findings from application in Spain

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ Esta es la versión aceptada del artículo publicado en: This is an accepted manuscript of a paper published in: Indoor and Built Environment (2018): 28/03/2018 DOI: https://doi.org/10.1177/1420326X18764783 El acceso a la versión publicada puede requerir suscripción. Access to the published version may require subscription. “This is an Accepted Manuscript of an article published by SAGE Publications in Indoor and Built Environment on 28/03/2018, available at: https://doi.org/10.1177/1420326X18764783.” 1 A novel Index of Vulnerable Homes: findings from application in Spain Raúl Castaño-Rosa Department of Building Construction II, University of Seville, Spain. Jaime Solís-Guzmán Department of Building Construction II, University of Seville, Spain. Madelyn Marrero Department of Building Construction II, University of Seville, Spain. Corresponding author Raúl Castaño-Rosa, Department of Building Construction II, University of Seville, Spain, Avenue Reina Mercedes, 4-a 41012, Seville. Email: raucasd[email protected]. Abstract This paper develops a novel Index of Vulnerable Homes (IVH) in terms of fuel poverty (FP) that is based on the analysis of different existing FP indicators and their relationship to the residential sector. The new IVH unifies the many indicators hitherto employed, and assesses the home vulnerability situation regardless of whether or not it is in FP by using three dimensions: monetary cost, energy and thermal comfort. The monetary dimension analyses vulnerability in relation to the available net income to face everyday life. The energy variable assesses the vulnerability related to the constructive characteristics of the dwelling. Finally, the introduction of the thermal-comfort variable enables the evaluation of the vulnerability related to the inner temperature of the dwelling and its perception by tenants. The combination of the different resulting values in each dimension and its relationship to the quality of life of tenants establishes a hierarchy of vulnerable levels. As a result, a multidimensional index is defined which relates technical aspects (characteristics of the dwelling) and social aspects (quality of life of households). The IVH provides a more complete assessment of the home situation than the existing FP indicators by identifying which variables require a greater assistance. Keywords Fuel poverty, Energy poverty, Vulnerable homes, Vulnerability Index, Health, Thermal Comfort. Introduction Fuel poverty (FP) is a global problem that has been accentuated in Europe due to the economic crisis, has given rise to government and political party interest, and has achieved high public impact. For the analysis of FP in the most vulnerable households, there are several official definitions and indicators developed by countries such as United Kingdom, Ireland, France and Slovakia1. However, no official standard has been established across the European Union that assesses FP in member states, and therefore in certain countries, this type of poverty still remains to be recognized as a social problem. Although it is not the main reason, the lack of a specific legislative programme to address the issue can lead to “FP rates varying drastically from country to country, or even among communities within a country. For example, based on Eurostat data for 2012, 46.5% of Bulgaria´s population are unable to keep their homes adequately warm during the winter time, while in Estonia this figure is officially only 4.2%, Denmark 2.6%, and Sweden 1.4%” (see EnAct project2 for a review). According to some sources, 50 million people in Europe live in FP, but other research indicates the amount to be under 20% of the total EU population (101 million people)2. 2 The FP concept not only indicates the inability to keep a home adequately warm during different periods of the year, or the existence of problems paying for the energy consumed or the high cost of energy prices, but it has also evolved into a multi-dimensional concept. Fuel poverty is currently defined as that situation which may deprive households not only of heating or cooling, but also of hot water, electricity and other essential household needs3,4. Fuel poverty was introduced by Isherwood and Hancock5 in 1979 after the energy price inflation due to the petroleum crisis (1973-1974). However, the concept was defined for the first time in 1991 by Dr Brenda Boardman, who stated that FP occurs when households are unable to “have adequate energy services for 10 per cent of income” (Boardman).6 The European Commission (EC), contrary to arguments from many of the EU institutions, “has stated that they do not support a European definition of FP, and that a common definition would be inappropriate due to the diverse energy contexts found across the Union” (Thomson).1 There is much discussion on how to define and identify FP, and several researchers, Braubach and Ferrand7, Rosenow et al.8, and Boardman9, are identifying energy efficiency in households as an effective policy approach in order to reduce FP in society. Currently, there are numerous FP definitions developed in countries such as the United Kingdom, France, Ireland and Slovakia1, as well as a diversity of indicators for their analysis. These indicators can be divided into two categories: those based on a household’s income and expenses (Table 1) and those based on self-reported household conditions (Table 2). Table 1. Indicators based on household income and expenditures. Indicator Description 10 percent measure4 A household is said to be in FP if it needs to spend more than 10% of its income on fuel to keep the home adequately warm 2M Indicators10 - Double the median of household expenditure on energy - Double the mean of household expenditure on energy - Double the median share of household expenditure on energy - Double the mean share of household expenditure on energy Minimum Income Standard (MIS)11,12 The amount needed to achieve an acceptable standard of living. A household is said to be in FP due to its domestic energy expenses Low Income High Cost (LIHC)13 A household is in FP if the cost of adequate energy services pushes them below the threshold of monetary poverty, and if the energy costs are higher than those of a median household After Fuel Cost Poverty (AFCP)13–15 A household is said to be in FP if its net income (after subtracting energy and housing costs) falls below the threshold of poverty Source: Schuessler10 Due to the limitations of an expenditure approach, some authors propose the inclusion of subjective and objective data into the analysis, such as the presence of mould, the absence of central heating, and ability to keep dwellings warm. Furthermore, the European Commission (EC), in a staff working paper16, has suggested three criteria to evaluate FP: the inability to keep homes adequately warm; the delay in the payment of utility bills; and the occupation of defective dwellings (meaning they have leaky roofs, damaged walls or foundations, and conditions such as mould and rot). Arguably, this is the key advantage of a consensual FP indicator, particularly for comparative research on a European scale17, and caution is necessary given due to their subjective and culturally dependent nature18 (Table 2). 3 Table 2. Indicators based on self-reported household conditions. Indicator Description European Union Survey on Income and Living Conditions (EU-SILC)19 Ability to keep a home adequately warm This provides an insight into the perceived affordability of heating homes across the EU Household with arrears on utility bills This shows households reporting difficulties to pay their utility bills. It may indicate that a household is struggling to afford adequate services Defective dwellings (meaning they have leaky roofs, damaged walls or foundations, and conditions such as mould and rot) This shows households reporting the presence of a leaking roof, damp walls or rotten windows. The presence of damp indicates that the dwelling is not energy efficient. It may also be an indication of a continuously unheated or ineffectively heated home Survey on perceptions and statements from households (SPS)19,20 Ability to keep a home adequately cool in summertime This provides an insight into the perceived affordability of running air-conditioning in homes in regions with hot summers Source: Thomson and Snell21 Regarding the allegedly different meaning of Fuel and Energy Poverty terminologies, Energy Poverty (EP) “has been traditionally used to capture problems related to the inadequate access to energy supplies in developing countries, and involves a host of economic, infrastructural, social equity, education and health concerns” (Bouzarovski et al.).22 Moreover, a number of authors have been using EP in reference to the lack of access to modern energy services in developing countries, mainly in Central and South America, sub-Saharan Africa, and Asia23–26, leading to those areas with limited and inefficient infrastructures, such as socially excluded suburbs and rural areas to be identified. These areas face added disadvantages due to their isolation and/or geographical sprawl. The results obtained across Europe by Bouzarovski and Tirado Herrero27, and by Baker, White and Preston in 200828, help towards the understanding of this concept. However, according to a large number of key EU policy documents29, the terms FP and EP can be considered as the same phenomenon, and both are the manifestation of Energy Vulnerability (EV), which is defined as “the propensity of an individual to become incapable of securing a materially and socially needed level of energy service in the home” (Bouzarovski et al.).22 Following this premise, this paper adopts this latter position to unify the two terms. Hence, the term FP and EP mean the same throughout the paper. This paper contributes towards identifying those homes in a vulnerable situation by analysing the characteristics of the dwelling and the social situation of the tenants. The analysis of the concept of FP will allow the relationship between the technical and social aspects and an energy-efficiency improvement to be established, which will improve living conditions and welfare, motivated mainly by: - An increase in available income to face everyday life due to a reduction in energy expenses. - Maintenance of the home at healthy and comfortable temperatures. Background FP is a multi-faceted issue that varies spatially, temporally, culturally and longitudinally. Certain key factors identified in the literature base that affect the performance of homes, can be grouped into four main components: Geographic location, Energy prices, Income and Quality of dwelling (Figure 1). 4 The characteristics of the area where the home is located exert direct or indirect influence on the performance of the dwelling, such as the availability of infrastructure and equipment, and social and climatology characteristics of the area (old town, suburbs, social exclusion, etc.). This factor is related with those areas with higher levels of poverty, far from the city centre, with inefficient infrastructure and equipment and a population with high levels of economic deprivation. It will therefore be possible to identify those areas with high numbers of households in a vulnerable situation. From this context, the econometric analysis by Legendre and Ricci in France30, and that by Miniaci, Scarpa and Valbonesi in Italy31, identify groups with a high vulnerability for entering FP, but fail to identify FP itself. These analyses examine the pattern within the demographic, socio-economic and physical conditions, which might give rise to households dropping below the FP threshold. To this end, the potential area-based platforms have been shown to improve the targeting of anti-fuel-poverty policies by using a wide range of demographic and socio-economic variables, such as income, housing tenure, household type and age of dwelling.32 Area-based, Geographic Information Systems (GIS)-based approaches, which lead to all relevant information being brought together and analysed, are emerging as a realistic strategy for the identification of areas which are at high risk of FP33. Energy price, which is used in the Low Income High Cost (LIHC) indicator34,35 and other FP indicators (Table 1), allows the fuel price used by households to be analysed. This factor depends mainly on the type of fuel and the power company, and also on the area where the home is located36 (Figure 1). Household income is applied to evaluate the monetary vulnerability of tenants and has been considered by all FP indicators since their conception. This depends on factors such as: housing expenses, employment situation of tenants, type of family (presence of the elderly, young children, or teenagers), and size of family37. Household income, or economic resources to deal with the payment of the minimum energy consumption, is currently considered the most influential factor in FP38. The FP situation can therefore be a consequence of the current economic crisis across the EU. In general, households in FP have high energy consumption expenses, and hence their net available income for food decreases39. This situation can cause malnourishment in children in particular and is generally called the ‘‘heat-or-eat’’ effect (Liddell).40 In addition, these households have shown a higher risk of suffering from mental illnesses, such as depression, with a probability greater than 25%, whereas there is only a 5% risk in the rest of the population41. The dwelling quality is the most complex factor in the evaluation of the home, and is analysed by the energy consumption of the dwelling. The energy efficiency of the dwelling can be considered as a starting point in order to assess its quality according to its relationship with FP. Dwelling energy efficiency depends on factors such as energy rating, building envelope, systems, ventilation level, maintenance and year of construction (Figure 1). Homes with insufficient heating most of the time can affect children’s school progress and emotional wellbeing: it is nearly twice as likely for children living in homes with insufficient heating to suffer from respiratory diseases. It has also been noted that children living in FP homes run a higher risk of being admitted to hospital or of needing primary care facilities, and suffer increased severity and risk from asthma (see Liddell and Morris42 for the literature), and hence children and the elderly can be considered as vulnerable customers who have a higher possibility of suffering illnesses.43 The dwelling quality can also be evaluated by means of comfort analyses (thermal, light and acoustic), in order to consider as many variables as possible. This paper is focused on the thermal comfort and its relationship with FP, the remaining factors will be analysed at a future time. There are many studies on this topic that show that improvements in thermal comfort reduce the energy consumption of a dwelling44–46, and improve the health of the household47,48. It is known that those households with inadequate temperatures during the winter suffer from minor health problems, such as colds and flu, and from worsening arthritis and rheumatism (see Gilbertson et al.49 for evidence). The impact of extreme temperatures on the elderly is well known, in that “it increases the probability of suffering from chronic medical conditions, such as cardiovascular and cerebrovascular diseases, 5 diabetes, respiratory and renal diseases, Parkinson’s disease, Alzheimer’s disease, and epilepsy” (Ormandy and Ezratty),50 and it can cause a higher mortality rate during wintertime. This is known as excess winter mortality,51 and is estimated as the increased number of deaths occurring during the winter season (December to March inclusive) as compared to the average during the rest of the year; there is an empirical relationship between seasonal mortality and housing energy efficiency. Healy52 reported that excess winter mortality in Europe is 5% to 30%. However, there remains an open debate on why certain countries experience dramatically higher rates of seasonal mortality than others, leading a number of authors to advocate alternative measures to quantify health impacts associated with current and future wintertime weather53,54. In this regard, Portugal suffers the highest rates (28%) of seasonal mortality followed closely by Spain, Ireland and Greece (21%, 21%, and 18%, respectively) (Healy).52 The Eurowinter Group (1997)51 concluded that mortality increased to a greater extent in regions with warm winters and in populations with cooler homes. Although their study has not proved causality, the strong, positive correlation between temperature and thermal standards in construction indicates that improving the thermal efficiency of housing in southern and western Europe could play a strong part in reducing the large seasonal variations in mortality found in these countries55. Thermal comfort has been assessed by other researchers, in terms of FP indicators, by using subjective indicators based on self-reported household conditions (Table 2) and it has been found that better thermal comfort in the home is related to better health and well-being56. Furthermore, several projects also aim to show the relationship between quality housing and FP. Warm from better health57 (Regional, Economic and Social Centre, UK) evaluates the quality of the dwelling before and after the installation of improvements in energy efficiency, and also tenants’ opinions of improvements in their homes; a second project is Affordable Warmth and Health Impact Evaluation Toolkit (Department of Energy and Climate Change (DECC) and Centre for Sustainable Energy (CSE)58,59, UK), which reports case studies where the relationship between health and energy efficiency of the dwelling is made clear. The new index, the Index of Vulnerable Homes (IVH), as proposed in this paper, unifies a high number of influential factors in home performance, leading to the definition of those vulnerable households whose situation is determined by physical, social, economic, environmental and comfort factors that increase the risk of suffering health problems and social well-being problems. In addition, this paper considers it essential to additionally incorporate tenant characteristics, which may be considered the most influential factors, into the assessment of the vulnerable situation of the home (Figure 1). 6 Figure 1. Key factors in a situation of fuel poverty. Methodology In order to combine the aforementioned FP indicators together with the energy efficiency of buildings, a new index of vulnerable homes (IVH) has been defined, which allows a particular home to be analysed. This IVH depends on the previously defined concept of FP, and is obtained by analysing three dimensions (monetary poverty, energy and thermal comfort indicators) and the health-related quality of life of the household. The three dimensions of the IVH are based on FP indicators currently used (Tables 1 and 2), although the IVH unifies and strives to improve its implementation. Finally, to assess how well the IVH has been developed, a case study in Seville, Spain was analysed, and a sensitivity analysis was performed under various household conditions. Monetary Poverty Indicator (MPI) The proposed MPI is based on the first of the three components of the AROPE (At Risk of Poverty and/or Exclusion) indicator. The AROPE indicator was set up by the Europe 2020 strategy60, and it identifies people at risk of poverty or social exclusion. According to the first component of the AROPE indicator, 60 which represents the condition of living with a low income (60% of the median equivalised disposable income after social transfers), the poverty threshold was set in euros, and hence a home is considered at risk of poverty and social exclusion if its annual income divided by the number of household members falls below this threshold. The poverty threshold will depend on the household's size and composition, and it is calculated using equation (1): T = To × Uc (1) 7 where T0 is the poverty threshold for a person and Uc is the equivalised household size. It is expressed by using a standard (equivalence) scale, the modified scale provided by the Organisation for Economic Corporation and Development (OECD) 61, according to equation (2): Uc = 1 + (a-1) x 0.5 + b x 0.3 (2) where a is the number of adults, and b is the number of children. In order to improve the indicator, this paper considers two poverty thresholds: first, a monetary poverty threshold (MPT) which is set at 60% of the median equivalised disposable income in each region according to Eurostat statistics; and second, a severe monetary poverty threshold (SMPT), which defines a more precarious level of poverty, representing the lowest disposable income of a household. This threshold is set in accordance with the social benefit granted by the government to families in social exclusion. Regarding equation (1), T0 is 60% of the median equivalised disposable income for a person and the social benefit granted for a person in social exclusion in the studied area for the calculation of the MPT and SMPT, respectively. The monetary poverty indicator (MPI) is calculated using equation (3): MPI = (I + B) - (HE + OE + WMSW) T (3) where: I: household income. Annual household incomes are given after subtracting housing expenditures. Unlike the MIS, LIHC and AFC indicators analysed (Table 1), MPI excludes energy expenses in order to avoid “false negatives” that may arise due to an inadequate home performance or a poor monetary situation that results in a household not being able to afford the minimum energy consumption. B: additional monetary benefits (social benefits, rental incomes, etc.). HE: housing expenditure (rent or mortgage). Housing expenditure depends on the type of family and its social situation. Annual household incomes are therefore different in each home (it is calculated by the numerator in equation (3)). OE: other additional housing expenditures. It shows the relationship between a home and additional expenses from degenerative diseases, mobility, disabled relatives, etc. These types of expenses exert a major impact on the monetary situation of a home, and in most times, it can cause a severe vulnerable situation. For this reason, the inclusion of this “special expenses” in the MPI is justified. WMSW: expenditure of water and municipal solid waste management. These expenses have yet to be taken into account by the different FP indicators. They are catalogued as an essential need by the World Health Organization (WHO) and motivate its inclusion. T: poverty threshold, is calculated by equation (1). Therefore, the MPI is defined by a derived equation (4) taking into account of T and net income. MPI = NI T (4) where: NI: net Income (numerator in equation (3)). 8 T: poverty threshold, which will depend on the country or region. A household is said to be in a monetary poverty or severe monetary poverty situation if its net incomes fall below the set threshold (MPI <1.00). Energy Indicator (EnI) The majority of FP indicators analyse the energy consumption of dwellings by using the energy expenses in their domestic energy services, and data is collected through utility bills or self-reported energy expenditures. Fuel Poverty can also be caused by living in energy-inefficient homes17, and therefore the proposed EnI aims to evaluate the energy efficiency of dwellings and benefits after an energy-efficiency improvement. The householdlevel fuel poverty indicator developed by Ryan Walker et al62, which is based on the UK’s Standard Assessment Procedure (SAP), leads to energy simulation software being considered as an option for the evaluation of the expected energy consumption of the dwelling if systems are correctly and fully used. Furthermore, the characteristics, priorities and customs of these households have no effect on the required energy consumption value, and consequently it can be considered more significant than the actual energy consumption (see Thomson et al.,63 for a review). With this premise, the EnI evaluates whether required energy consumption of the dwelling is standard for the median energy consumption required for the type of building in the located area (using the median value would allow those unusually high values of energy consumption to be considered). Hence, the energy threshold comes from the idea used in other FP indicators (Tables 1 and 2), and this is set as the median energy consumption required for the type of building in the area of study. Therefore, the energy indicator (EnI) is calculated using equation (5): EnI = EC MEC (5) where: EC: energy consumption required (obtained from the software simulation). MEC: median energy consumption required for the type of building in the area of study. Therefore, the housing energy consumption is adequate if it is below the energy threshold or “admissible” (EnI <1.00), otherwise it is considered “inadmissible” (EnI >1.00). Energy consumption based on the characteristics of the dwelling (from the energy simulation) prevents the inclusion of bias towards those homes with an inadequate systems usage or those in a poor monetary situation, who cannot afford minimum energy consumption. Hence, hidden energy poverty (HEP)64, which identifies those households whose energy expenditures are abnormally low, can be analysed. Comfort Indicator (CI) Thermal comfort is included in the proposed indicator in accordance with its relationship with FP indicators and its influence in the vulnerable situation of a home. The remaining factors (light and acoustics) are to be considered in future studies. The evaluation of thermal comfort in a home is complex mainly due to the influence of the following factors: 1. Indoor area65,66: position of rooms, ventilation control and natural light, control of physical conditions. 2. Environmental67,68: air temperature, radiant temperature, relative humidity and air velocity. 3. Personal69–71: health, age, gender, metabolism, activity level, clothing, thermal sensation. Most FP indicators evaluate the thermal comfort based on subjective data from Survey on Living Conditions (SLC) and EU-SILC (Table 2). However, this research aims to evaluate thermal comfort by using the adaptive model ASHRAE 55-201072 and by supplementing it with the normative UNE EN 15251:200773. Both models evaluate the 15 - EnI: this will require the adaptation of the MEC, which is the median energy consumption required for the type of building in the area of study. - CI: this will require the adaptation of the thermal comfort range, which can be calculated by using the correspondent equation in the UNE EN 15251:200773. - The cost-effectiveness value of a human life, which is set by the National Health Service within the studied country, will be required for the calculation of the HRQLC. In order to assess how well the IVH has been developed, this section presents the results for the case study in which the IVH has been applied to the Spanish context. This context is chosen mainly due to the availability of the data sources required. Case study The IVH was applied to a dwelling in a residential block in Seville (Spain), specifically in the working-class district of El Tardón, located on the outskirts of the city. This choice represents the typical type of building in the different districts of the city, and all necessary data for the application of the indicator is available93. The analysed flat was built in 1978 before the introduction of first energy-efficiency regulations (CT-79)94 and therefore it has poor energy conditions. The dwelling comprises a living-dining room, a kitchen, a bathroom and three bedrooms. The living room and one of the bedrooms are oriented towards the southeast (diverted 4° towards the east), and the other two bedrooms and kitchen towards the northwest (diverted 4° towards the west) (Figure 2). The structural characteristics, energy and economic data of the home are defined in Tables 6-8. The basic data for the calculation of the IVH is summarized in Table 9. Figure 2. Geographic localization and plan of the analysed dwelling93. Table 6. Structural characteristics of the dwelling. Dimension data System data Structural data 16 Rooms Floor Area (m2) Type Description Component Transmittance (W/m2K) Bedroom 1 8.17 Heating Propane gas boiler, 100% climatised area Façade 1.35 Bedroom 2 9.06 Floor 1.78 Bedroom 3 7.10 Air-conditioning Electric multi-split device, 100% climatised area Indoor wall 2.35 Living-dining room 21.74 Common wall 1.94 Kitchen 6.56 Hot water Propane gas boiler, nominal power of 9.40 kW, efficiency of 85% and 52.56 m3/day Windows 2.69 Bathroom 3.84 Hallway 6.86 Energy data (Table 7) was obtained from the official Spanish software, CE3_Viviendas95, for the energy simulation of buildings. The software also determines the energy efficiency of existing buildings in Spain96. This simulated data was used due to the lack of actual utility bills, and also in order to prevent an over- or underconsumption figure from being applied due to an inadequate usage of systems or a poor economic situation, which would result in less than the minimum energy consumption. Table 7. Energy data of the dwelling95. Heating Air-conditioning Hot water Light Electrical appliances Cooking appliance Total Energy consumption (kWh/m2·year) 51.67 15.29 19.73 3.55 8.37 2.34 100.96 Energy rating E Economic data (Table 8) shows the typical economic situation of a household in Spain97, which consists of two adults and one child. Income and monetary benefits were calculated from microdata of the Spanish Household Budget Survey (HBS) from the Spanish National Statistics Institute (SNSI)98. The rent expense was obtained from the average rent expense within the studied area99. The water and municipal solid waste cost was the minimum cost according to the official supplier100. Table 8. Economic data of the household. Income (€/month) Benefits (€/month) Housing expenditures (€/month) Other expenditures (€/month) Water and solid urban waste expenditures (€/month) 1200 0 200 0 50 Table 9 sums up all the data necessary to calculate the IVH from the previous tables. The floor area was calculated by adding the areas of the rooms of the dwelling (Table 6). The net income (numerator in equation (3)) was obtained after the subtraction of housing and water and municipal solid waste expenses (Table 8). Energy consumption was taken from the energy simulation (Table 7). 17 Table 9. Data for the IVH calculation. Household data Dwelling data U. area (m2) Net income (€/year) Nº people Energy consumption (kWh/year) Adults Children 63.59 11,400 2 1 6420 Regarding the thermal comfort factor, this was evaluated using the number of hours (%) the dwelling was inside or outside the comfort range during the analysed period, for which the comfort data obtained by Sanchez- Guevara et al.101 was used. This data was obtained from the dynamic thermal evaluation of the dwelling in the Energy Plus 7.0 modelling software. The climatological data of Seville used in the software was obtained from the IWEC data of EnergyPlus. The daily comfort ranges were calculated by using the corresponding equation73 depending on the assessed period: spring, summer and winter. The autumn was excluded from the analysis since its thermal values are similar to those in springtime. Figure 3 shows a graphic example of how the number of hours of comfort was calculated according to the thermal comfort ranges and the indoor temperature of the dwelling. The red and blue lines represent upper and lower limits of comfort for the interior of the dwelling, respectively, which were calculated by using the corresponding equation73, and the green line shows the indoor temperature of the dwelling during the analysed period in the studied area (from the dynamic thermal evaluation in the Energy Plus 7.0 modelling software). Note that these values were calculated in 2015 according to the characteristics of the specific dwelling. Therefore, depending on whether the characteristics of the dwelling were modified (after the improvements had taken place), these values might be different. The analysis of Figure 3 (the southeast orientation in springtime) shows that on only two days of the analysed period, 19 and 21 May of 2015, the indoor temperature of the dwelling was outside the limit of comfort, leading to 3.57 hours (0.51% of hours) outside the comfort range in the dwelling during that period. Figure 3. Upper and lower limit of thermal comfort ranges of the dwelling in the southeast orientation. Springtime93. Table 10 summarizes results obtained from the application of calculation of percentage of thermal comfort hours for each analysed period: springtime, summertime, and wintertime (autumn was considered to have similar thermal comfort hours (%) as compared to springtime in Seville). 18 Table 10. Percentage of Thermal comfort hours101. Springtime Summertime Wintertime Northwest façade Southeast façade Northwest façade Southeast façade Northwest façade Southeast façade Outside comfort range Hours 0 3.57 6.71 104.9 464.86 464.73 Percentage 0.00% 0.51% 0.93% 14.49% 96.85% 96.82% Within comfort range Hours 696 692.43 713.29 615.71 15.14 15.29 Percentage 100.00% 99.49% 99.07% 85.51% 3.15% 3.18% Monetary Poverty Indicator (MPI). In order to analyse the monetary factor, monetary poverty and severe monetary poverty thresholds were set according to the studied area (Seville) and the household size (Uc) (Table 11). The inter-professional minimum income102 (very similar to 60% of the median equivalised disposable income in Seville103 in 2015) was used as the monetary poverty threshold, and the granted social benefits by the Spanish government, named Plan PREPARA104 (€400 per month), was used as the severe monetary poverty threshold. The equivalisation factor defined in equation (2) was used to apply the household size (Uc) in Table 11, and the various thresholds were then calculated. Table 11. Poverty thresholds in Seville (Spain). Nº people Monetary poverty (€) Severe monetary poverty (€) 1 person 8114 4800 2 people 12,171 7200 2 adults and 1 child 14,605 8640 2 adults and 2 children 17,040 10,080 2 adults and 3 children 19,474 11,520 According to data in Table 11, the MPT was set at €14,605 and the SMPT corresponds to €8640. The home analysed has a net income of €11,400 per year (obtained from Table 9), and from the application of equation (4), the MPI was set at "monetary poverty" (MPI <1.00). Energy Indicator (EnI). The energy consumption threshold (as explained in the Energy Indicator section, as part of the methodology) was set according to the data on the median energy consumption required on the climatology of the studied zone (Seville, south of Spain, Mediterranean climatology) and the dwelling typology (the case study was a dwelling in a multifamily residential block). According to the latest data published by the IDAE105, the median energy consumption required for the type of building for the case study (energy consumption threshold) is 8360.46 kWh/year. The energy consumption required for the analysed dwelling is 6420 kWh/year (Table 9), from software CE3_Viviendas95, and hence, by applying equation (5), the EnI is "admissible" (EnI <1.00). Comfort Indicator (CI). The comfort threshold, according to the explanation of the comfort indicator outlined above, was taken as 80% of hours in which the evaluated room spaces must be within the thermal comfort ranges. An example of the thermal comfort range is as shown in Figure 3 for thermal comfort ranges in a dwelling 19 with a southeast façade during the springtime. As a result, the CI is "admissible" (CI >80%) during spring and summertime. On the other hand, the CI result is “inadmissible" (CI <80%) during wintertime since the percentage of hours that the indoor temperature stays within the thermal comfort range is lower than 80% (thermal comfort threshold) (Table 10). Table 12 summarizes results from each indicator (MPI: monetary poverty, EnI: admissible, CI: admissible for spring-summer and inadmissible for winter), and hence the level of vulnerability of the home can be obtained from Table 5. The final value of the IVH was then deduced by using equation (6). Table 12. Results after the IVH application. Season Monetary poverty indicator (MPI) Energy indicator (EnI) Comfort indicator (CI) IVH Spring-Summer Monetary poverty Admissible Admissible 4 Winter Monetary poverty Admissible Inadmissible 5 Sensitivity analysis To show the potential of the IVH, this was evaluated in several scenarios based on the case study. The following aspects were analysed: type of household, household income, energy-efficiency intervention, and increase in energy consumption. Scenario 1. Type of household. The first analysis considered a change in the number of occupants. As a result, the new household was composed of two adults and three children. The characteristics of the dwelling remained unchanged, and therefore the energy consumption and thermal comfort factors remained the same. However, the monetary factor was shown to have undergone major changes. The IVH results show an increase in its vulnerable situation (6 in the summertime, and 9 in the wintertime) regarding the case study (Table 13). The results show how vulnerability could be increased in larger households. Scenario 2. Household income. The second analysis considered a reduction in the net income due to medication expenses of €100 per month of a family member (OE), and an increase of €135 per month in the water price leading to a higher water and solid urban waste expenditure (WMSW) given in equation (3). The new situation caused a reduction of household net income for at least one year (€11,400 was the net income in the initial scenario and this was reduced to €8580). All other variables remained constant. The IVH shows an increase in the vulnerable situation (6 in the summertime, and 9 in wintertime) in relation to the case study (Table 13). Scenario 3. Energy-efficiency intervention. The third analysis evaluated the energy-efficiency improvements by changing the environmental heating and water-heating installations: changing the existing heating system to a natural gas boiler and the hot water to a solar thermal system, in addition to installing new windows. This equipment renovation costs €7850106, and the new windows €3500106, leading to a reduction in the energy consumption (from 6420 kWh/year originally to 5169 kWh/year post-improvement), and consequently a reduction in the energy consumption expenditure, and an improvement of the thermal comfort (adequate all year round) which was due to the greater efficiency of the windows (with a low transmittance and minor air leaks in the dwelling) and the hot-water system (100% renewable). This scenario enabled the social benefit to be evaluated for government investment in new systems for these vulnerable homes in order to make these dwellings energy efficient. 20 The IVH shows a reduction in the vulnerable situation during wintertime (from 5 initial case to 4 new scenario) in relation to the case study (Table 13). Scenario 4. Increase in energy consumption. This last analysis evaluated a significant change in the energy consumption due to a certain damage in the insulation of the dwelling, leading to a higher energy consumption (6420 kWh/year initial case to 8546 kWh/year new scenario). Since adequate thermal comfort was maintained in the home, and, in turn, the energy consumption expenses rose correspondingly. The increased energy consumption generated an “inadmissible” Enl (the threshold was established at 8360.46 kWh/year according to IDAE105). For this reason, the IVH shows a worsening in its vulnerable situation (7 in the summertime, and 8 wintertime) in relation to the case study (Table 13). Table 13. Scenarios of the sensitivity analysis for the IVH. Season Monetary poverty indicator (MPI) Energy indicator (EnI) Comfort indicator (CI) IVH Case study Spring-Summer Monetary poverty Admissible Admissible 4 Winter Monetary poverty Admissible Inadmissible 5 1 Spring-Summer Severe monetary poverty Admissible Admissible 6 Winter Severe monetary poverty Admissible Inadmissible 9 2 Spring-Summer Severe monetary poverty Admissible Admissible 6 Winter Severe monetary poverty Admissible Inadmissible 9 3 Spring-Summer Monetary poverty Admissible Admissible 4 Winter Monetary poverty Admissible Admissible 4 4 Spring-Summer Monetary poverty Inadmissible Admissible 7 Winter Monetary poverty Inadmissible Inadmissible 8 Discussion Based on the results of the index, the new IVH can be affirmed to be able to identify the situation of a dwelling and clearly shows the nature of the problem. Consequently, IVH can contribute towards the analysis of the best solution for the improvement of the household situation of the case study: - Despite the adequate quality of the dwelling, the IVH shows a higher vulnerability during wintertime due to the negative results in the CI, mainly due to the poor quality of the existing windows. - The poverty situation in the household is the main cause of the situation of vulnerability. - The energy-efficiency intervention in scenario 3 could contribute towards a better quality of life due to a reduction to level 4 of vulnerability (IVH = 4) all year round. However, the household would need additional improvements in order to leave the vulnerable situation, either by means of an income increase or a reduction in housing expenses. - Inclusion of thermal comfort in the IVH enables the vulnerability of a home to be analysed in different seasons, in addition to allowing an analysis of the energy consumption of the dwelling, in contrast with the existing FP indicators. - The social benefit of an energy-efficiency intervention for vulnerable homes can be evaluated in order to make their dwellings energy efficient, resulting in a tool that would enable governments to improve the quality of life for the household if applied together with good energy policies. 21 Conclusions This paper offers a novel Index of Vulnerable Homes (IVH), which leads to the development of the first multi-dimensional index that assesses the home vulnerability situation, regardless of whether or not it is in Fuel Poverty (FP). The influence factors include: household monetary situation, quality of the dwelling, inefficiency in thermal comfort, inadequate usage of systems, poor choice of system equipment, fuel type, and fuel price used in energy consumption. The assessment of thermal comfort in the housing allows objective data to be obtained, and provides an analysis after an energy-efficiency improvement, and aids in choosing the optimal retrofit measure. Furthermore, the IVH would allow identification of a situation of vulnerability and of FP in seasons, which have never been taken into account before. FP has been traditionally related with countries where winters are very cold and long, but it is known that countries where summers are very hot have high energy consumption during the summer, which may result in an FP situation. Furthermore, through the application of the Health- Related Quality-of-Life measurement, all factors considered in the IVH can be related: monetary cost, energy, thermal comfort, health of the household, type and size of the household, and geographical location. Returning to the aim of the paper, the new IVH provides a more complete assessment of the situation of a home than the existing FP indicators, by identifying which factors have the greatest impact on the situation of vulnerability. In addition, the IVH relates the technical aspects (the characteristics of the dwelling) and social aspects (the quality of life of the tenants). It also unifies evidence, developed in the FP concept, from mixed methods and interdisciplinary studies: medicine, psychology, environmental sciences, energy, construction, and social studies. More importantly, the IVH may be used in different disciplines and in other countries due to the approaches taken into account in the definition of the three factors: monetary cost, energy and thermal comfort. To this end, new lines of research will be accomplished in order to be able to use the IVH for the analysis of different energy and Health-Related Quality-of-Life costs, income or culture. In contrast to the FP indicators, the new IVH contributes towards the assessment of vulnerability in terms of its consequence or intensity, and leads to the possibility of evaluating the optimal retrofit measurement in order to improve the quality of life of households, by promoting energy efficiency. The sensitivity analysis demonstrates how the IVH assesses real-life changes that could occur in a home, whether they are minor, as in Scenario 3 (modification in the type of housing systems and windows), or whether they are of greater impact, such as in Scenario 1 (with an increase in the household size), Scenario 2 (whereby the household suffers a reduction in its net income by €235 per month) and Scenario 4 (significant increase in the energy consumption of the dwelling). While there are numerous FP and vulnerable consumer studies that strive to yield a solution to the addressed problem, this paper brings a new perspective in the assessment and identification of vulnerable homes, and provides a starting point for new lines of research aimed at achieving optimal and efficient performances for households in great need. Authors’ contribution All authors contributed equally in the preparation of this manuscript. 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