2. Materials and Methods: Unpacking Justice in UK Carbon Emissions
This section outlines the statistical sources and methods we used in this analysis. A graphical representation of the research approach is given in Figure 1
at the end of this section.
Unfortunately, there is no national UK database explicitly containing CO2
emissions for households (see for example [14
]). Earlier studies of household CO2
emissions have largely been based on the income and expenditure surveys produced by the ONS in combination with detailed data about carbon emissions of different classes of expenditure from bread to clothing. The basis of this study is the Understanding Society database (more correctly, the “UK Household Longitudinal Study”) which has run since 2009; we benefit from better data than available to earlier researchers. It contains annual data on the same households for many items (such as income, housing size and occupants, schooling, expenditure and car use) and triennial data on a range of less central areas (such as transport spend, environmental beliefs), covering both objective and subjective areas. In particular, money spent on fuels is asked every three or four years; in this case, 2012/13 was used as the latest available. The study uses the main survey containing around 47,150 individual records from 25,875 households weighted to the UK population using the 2001 and 2011 Censuses. All results are given on weighted observations. The level of emissions in each household and the total level in the sample population was established by applying standard factors for conversion of spend on electricity, gas, oil and coal to tons of CO2
throughout. Road fuel emissions were calculated by applying standard factors to the car mileages collected in the survey. The conversion factors were taken from an earlier paper [45
]. PCAs only consider fuel emissions, not embedded carbon in other purchases, and, therefore, only fuel spend values were converted to emissions.
The value of an individual’s total annual carbon allowance (as anticipated in a hypothetical PCA scheme) is produced from the sum of database emissions divided by the total number of qualifying individuals in the sample, using information on household composition (numbers of adults and ages of children) in the database. Each adult is allocated an allowance and each child a one-third allowance (as above, [14
]). Each household’s entitlement is then the value of this allowance multiplied by the qualifying adults and children in that household alone. This allows comparison of emissions to allowances on a household by household basis: households emitting above their allowances are classified as “over-emitters” who would have to purchase additional allowances, and the households emitting below their allowances are classified as “under-emitters” who would have surplus allowances to sell. In relation to our concerns with questions of just transitions, poverty and capacities to act, we are primarily interested in disadvantaged groups who, under this methodology, are identified as over-emitters, and would have to either change their carbon practices, or lifestyles, or purchase more carbon points.
In searching for links to over-emissions, the analysis studied associations between a large number of factors such as housing tenure, car ownership, benefit claiming, employment and environmental attitudes and self-reported environmental behaviours. Home ownership is associated with higher emissions [14
]; ownership is a choice, and those homeowners have future choices about fuel usage, insulation levels and/or moving home, though the concept of the elderly “aging in place” is discussed in Section 4.2
For those renting, choice is also prevalent at all income levels. It is generally considered that poverty measures are best calculated on incomes before housing costs rather than after housing costs [47
], the reason being that households exercise significant choice over the cost and nature of housing, particularly over the long term [48
]. On a more practical level, for those renting privately, is it reasonable to assume that they can rent properties appropriately sized (i.e., with the “correct” number of bedrooms) or might it be that (say) in certain areas there are no one-bedroom flats available? Consider that in the UK housing benefit restrictions apply to private renters with spare bedrooms, extra rooms cost money and tenants can relocate by a few miles relatively easily. These all suggest that under-occupancy by private renters is a choice. For those renting social housing, the position is more nuanced: whilst the authorities are likely to match the household size to the property offered initially, this will not always be the case and prospective tenants may have limited or no ability to refuse any property being offered to them. Over time occupancy may change with limited flexibility of the tenants to relocate. Vociferous opposition from housing charities [49
], political parties [50
] and sections of the media highlight examples where they consider the ability to relocate to smaller properties is a mirage: an investigation by one quality newspaper claimed that up to 96% of tenants were unable to do so [51
]. Consequently, it is considered in this paper that under-occupancy is a choice for owners and private renters but renters in social housing will be regarded as fully occupying.
Household net incomes (after taxes and including benefits) were equivalised using the modified OECD factors, being 1 for the first adult, 0.5 for second and subsequent adults and 0.3 for children under 14. Equivalisation approximates how well-off households of different incomes and sizes are, for example, a single person household earning GBP 15,000 a year is considered to be as well off as a couple with two children earning GBP 31,500 a year—15 divided by 1 equals 31.5 divided by 2.1. The sample was ranked by equivalised incomes and divided into two parts: those above and below the relative income poverty line calculated according to the “widely accepted” rule in the UK, being 60% of median net income before housing costs [47
]. This is straightforward to calculate but ignores household assets or measures of material deprivation. The majority of further analysis took place on those in designated poverty (the smaller sub-sample, about 14.0% of households, 2852 records). However, we also consider carbon injustice in relation to more qualitative capacities to act. Note that Understanding Society data relies on individual recall at one point in the year to produce data for the whole year, which is immediately approximate, though there is no apparent reason for it to skew the data in any direction.
A number of variables were constructed: in particular, a yardstick for occupancy is required to determine the number of bedrooms required by the household (and hence over- or under-occupancy). The recognised yardstick which exists is that set out in the Welfare Reform Act 2012 which requires one bedroom for each person aged 16 or over not cohabiting, one bedroom for different sex children aged 11–16 and a maximum of two children under 10 or two same-sex children aged 11–16 per bedroom. In this study, the definition was relaxed somewhat to allow one bedroom for each child over 11 irrespective of gender. Given the apparent widespread under-occupancy of social housing under this definition (36.6% in the database), it demonstrates the aim of the legislation, being to persuade social housing under-occupiers to move to accommodation which the government regards as appropriately sized in order to make better use of the social housing stock, but it is widely suggested that this is not possible. For example, at November 2013, the Scottish Government reported that 52,000 households were required to downsize to one-bedroom properties; that 20,000 became available each year; and that 22,000 homeless people needed to be housed each year and so were in competition for the same properties [52
]. The definition imposes what is widely referred to as the “bedroom tax” on under-occupancy by social housing tenants who cannot or will not move. Tenants are penalised 14% of their housing benefit for one spare bedroom and 25% for two or more. The policy has been much maligned, in particular because those who lose out include a disproportionate number of registered disabled—an estimated 80% of affected households in Scotland contained a disabled adult [53
] or “almost ⅔” overall [54
]. There are also issues about cultural differences in the use of rooms not being taken account of [55
]. Importantly, and given the concern about the impact of the definition on social housing, none of the key results presented here are dependent on occupancy levels of social housing. The discussion in this paper is not about whether the Welfare Reform Act works in terms of persuading people to relocate or whether it causes future social housing tenancies to be appropriately sized at their outset, or whether it is blunt, cruel and inappropriate-but about whether it is a broadly sensible yardstick for analysing the population as a whole. In overall numbers of households, the social housing sector is relatively small (whole population, 16.5% [56
]: of those in poverty, 31.8% are in social housing (authors)), and there is a much larger issue of under-occupancy in the private renting and house-owning population; for example, in this database, 55% of those in poverty own their homes, and 64% under-occupy on the definition used (67% and 70% in the database as a whole). At a time of a fuel emissions crisis, as a yardstick to divide the population as a whole into over- and under-occupied dwellings to enable analysis of patterns in the data, we believe it is not unreasonable.
Initially, general analysis was undertaken to understand the composition of households: Section 3.1
and Section 3.4
, Figure 2
, Figure 3
, Figure 4
and Figure 5
, Table 1
and Table 2
. This was followed by principal component analysis and rotated factor analysis to try to establish key variables. For principal component analysis, 14 factors were required to explain 50% of the data variance—not a helpful result. A better result was obtained for rotated factor analysis, 4 factors explained 50% of the data in each of the individual cases (Appendix A
). Two understandings were gained: firstly, that only one of the components (“consumers”), linking wages, adult numbers, cars, employment, rooms, home ownership and consumer goods, was significantly related to emissions, and, secondly, as a corollary of this, environmental beliefs and habits were virtually unrelated to emissions.
To determine any underlying relationships, a very large number of least-squares multivariate regressions were then performed on the database, split into under- and over-emitting households. There is a huge choice of data fields to use including household specific data such as household income, dwelling tenure type, size, ages and gender of occupants, fuel type(s) and expenditure(s), numbers of cars and types of household appliance used. It also includes personal data and opinions of the occupants, such as types of benefits received and multiple questions on feelings about health, lifestyle and the environment. Stepwise elimination of factors was undertaken to reduce the number of factors to the main indicators shown in Table 3
and Table 4
by eliminating factors with β-values of less than 0.05 in both under- and over-emitter models and then re-running the model. With such low β-values, the factor eliminated changed the adjusted r2
figure minimally, typically at the 3rd or 4th decimal place. The final results are shown in Section 3.6
, before being placed in the context of “just transitions”, poverty and capacity to act.
Given that about one-third of households in poverty would emit more than the allowances they would be given under a PCA scheme, it would be conventional to conclude that twin goals of social and environmental justice cannot be achieved under such a scheme. Whilst this research grappled with the complex relationships between poverty, lifestyle attributes, choice and emissions levels, it produced key results by comparing average individual carbon usage across society to actual consumption on a household-by-household basis.
The first result is that over-emissions are predominantly made by those households who under-occupy their dwellings, using a definition broadly in line with the UK government welfare system and treating those in social housing as fully occupying. This is true of those in poverty and those above the poverty line, and implies a significant element of housing choice driving emissions levels.
Secondly, for households in poverty, the average emissions from an over-emitting purpose built flat, theoretically the most energy-efficient dwelling type, are greater than those for under-emitting detached houses, theoretically the least energy efficient housing type. In isolated examples, this might be explicable by construction methods, but in aggregate, this is a very clear statement that lifestyle is a key driver of emissions, even for those in poverty. This result is also true for the population as a whole.
Thirdly, regression analysis shows that emissions related to adults and children are the key drivers of emissions for households in poverty (as opposed to being driven purely by housing or material possessions), and that these vary by more than a factor of three between average under-emitters and average over-emitters. Again, a very clear statement that lifestyle is the main driver of emissions, even for households in poverty, and it is also true across the income spectrum.
Based on this research, advocates of PCAs would suggest that worries about impacts on households in poverty are overemphasised, since emission levels are driven more by lifestyle than any other single factor, and ultimately the only aim of PCAs is to change high-emitting lifestyles. Avoiding all household emission reduction policies because some high emitters have low incomes is not a credible strategy. Instead, perhaps more research should be undertaken into resolving the political and presentational problems of introducing PCAs, and how high emitters can be persuaded and educated to reduce consumption, than to a preoccupation with compensation for current behaviours.