A report for Friends of the Earth by Duncan McLaren, Olivier Cottray, Mary Taylor, Susan Pipes and Simon Bullock.
This report is based on the data and support provided by Business Geographics, Autodesk and Kingswood Ltd and ESRI.
Friends of the Earth would particularly like to thank Ed Parsons of Autodesk for all his help.
Friends of the Earth would also like to thank Laura Winton, Mike Childs, Zoe Gillard, Chas Linn, Leslie St James, Joe Short and Ian Willmore for all their help, comments and guidance.
Published by Friends of the Earth Trust, April 1999
All across England and Wales the poorest families (reporting average household incomes below £5,000) are twice as likely to have a polluting factory close by than those with average household incomes over £60,000. This is the sharp end of social exclusion - on top of unemployment and crime - these families and communities face the grime of industrial pollution. Here pollution is as far from a middle-class concern as it can get.
Over ninety per-cent of London's most polluting factories are located in communities of below average income. London is just the most extreme example. A similar pattern is found throughout England and Wales. Overall, almost two-thirds of the most polluting industrial facilities are to be found in areas of below average income.
Using the interactive map even at a national scale (England and Wales) the visual correlation between polluting factories and low income areas is clear (although in some areas the density of factories is such that the (red) colour of the area below is masked!).
In the North East areas with the most polluting factories have average household incomes almost £2,000 (or 14%) lower than where there are no such factories, and have over 20% more of the poorest households (with incomes below £5,000 per year) than where there are no such factories. Across England and Wales as a whole the income difference was over £1,500 (or about 9 per cent) and there are over 10% more of the poorest households in areas with the most polluting factories.
The effects are more severe in areas with multiple factories. At the extreme, Seal Sands on Teesside has 17 of the most polluting factories in one small area. The average income here is just £6,200 (just 45% of the regional average income, or 36% of the national average) and over half its households have annual incomes under £5,000.
This is a clear cut issue of 'environmental injustice' in which poorer people are subjected to greater risks and impacts of pollution, and have less control over their environment while the benefits of the industrial activity largely accrue elsewhere. Measures to reduce industrial pollution from these factories would be clearly socially progressive.
This report first sets out the methodology used, then outlines the analysis of the maps and data (using charts and tables), followed by a discussion of the possible relationships. Data interpretation issues are addressed in an Appendix.
This research has linked two data sets in a Geographic Information System and database analysis. The data sets are:
The latter data set effectively covers the 'entire population' - all the potentially most polluting factories in England and Wales - while the former has been weighted according to census data by the data provider, so can be treated as a statistically valid representation of the entire population. Further explanation of the underlying data is given in the Annex.
The maps show the location of IPC sites and the average household incomes of postcode sectors. The visual correlation between IPC sites and lower income areas is striking at all scales from the whole of England and Wales down to specific urban areas.
The data allows us to undertake two main types of analysis:
In both cases we can examine whether the income characteristics of households or areas are different for postcode sectors with IPC sites in them (the 'IPC sectors') than for all sectors, or for those with no IPC sites (the non-IPC sectors)(2).
In this section we present a number of alternative analyses of the data available, beginning with analysis based on sector incomes:
We then turn to two analyses by income bands:
Then we use income band analysis nationally and by region to estimate the size of the income effect:
Finally, we return to the effects of concentrations of IPC sites:
This section answers a series of key questions in turn. First is there a pattern to the data, suggesting a relationship between income and IPC sites, or is it just random? Second, what is the direction of any relationship - are there, as we might predict, more sites in lower income areas? Third, how strong is the relationship? This we look at in two ways, the relative chance of living near an IPC site according to income, and the average effect on local incomes of an IPC site. Finally, we ask whether greater numbers of IPC sites in a sector have a reinforcing effect.
Table 1 below compares the actual distribution of IPC sites according to average income by postcode sector, with the distribution we might expect if the IPC sites were equally distributed according to population. As they are designed to contain a consistent number of addresses, postcode sectors can be assumed to be of the same average population (rather than the same average area). The table also examines the distribution of IPC-authorised processes, of which there may be several at any given site.
Table one [back to text]
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Table 1 (and Graph 1) shows an apparent pattern with a consistent and significant deviation from the 'random' distribution clearly visible across the income scale. In the poorer areas (up to £15,000 average income), there are far more IPC sites than would be predicted on the basis of their total population, whilst in the richer areas (over £20,000) there are many fewer sites than the 'random' distribution would suggest. In the intermediate band (£15-20,000) in which average household income falls (£17,280), there are almost exactly as many sites as the 'random' distribution suggests.
Graph One[back to text]
Graph Two[back to text]
The pattern is similar, albeit even more striking, when we look at the distribution of processes (Table 1 and Graph 2). Quite clearly the IPC sites with more than one authorised process are more concentrated in poorer areas than IPC sites on average. We return to this question later.
By using the interactive mapping system it can be clearly seen that the majority of IPC sites in each region are concentrated in lower income areas, indicated by more intense red background shading. The pattern is most distinct in Yorkshire and Humberside, the North-West, the North-East and the West Midlands where both IPC sites and low income areas tend to be clustered and concentrated in traditional industrial urban areas such as Manchester and Birmingham. On the other hand, the relatively wealthy peri-urban regions of the same cities have relatively few IPC sites. The pattern is least clear - although still visible - in the more generally high-income South East and Eastern regions. We return to regional commentary later.
The above analysis is based on the average income of each post-code sector, which tells us nothing about the distribution of income within each sector. Below we aggregate data from income bands across all sectors, but first we look at another simple indicator of poverty: the proportion of households in the lowest income band (less than £5,000 pa).
On average, this is around 19% of households, but it varies widely - with 0.5% of sectors (42) having 50% or more of their households in Band 1. Those 42 sectors contain 23 IPC sites (1.74% of the total) - or over three times as many as would be expected were the sites distributed randomly. There are 978 sectors (12.3%) with 30% or more of households in Band 1(3) and these contain 261 (19.8%) of IPC sites - or over half as many again as would be expected.
In Table 2 below we look simply at the proportion of IPC sites found in areas of below regional average income - for all the regions of England and Wales. This is a crude but still useful indicator of the extent to which these sites are found primarily in poorer areas(4).
Table 2 shows a strongly consistent pattern, with just under two thirds of IPC sites being located in postcode sectors of below average income. Two regions exhibit starker relationships - almost 80% of the North-East's polluting factories are in communities of below average income and over 90% of the capital's polluting factories are in such areas! This may actually be due to the fact that London has no poor rural communities within its boundaries, and the possible masking effect on the overall correlation that such areas - poor, but with no IPC sites - would have is not present. The area with the least strong pattern is the East Midlands with only just over half of polluting factories in poorer areas. If we look at the maps we can see some areas of severe poverty which would appear to fall in ex-coalfield communities in this area, an effect which may also be having an impact in Wales. In both cases this may contribute to the less strong relationship found.
Table two [back to text]
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Once again, we can also analyse the distribution of IPC authorised processes (rather than simply sites). As we found above, this makes the pattern of discrimination more striking. Almost three-quarters of all processes (72.8%) are located in areas of below average income.
Tables 1 and 2 are based on average incomes for each sector. A more sophisticated comparison can be achieved by examining the distribution of household incomes in postcode sectors with or without IPC sites with the average distribution for all sectors. Such analysis eliminates any distortions that may be introduced by using the average income of the postcode sector as fully representative of its income characteristics and instead takes full account of the distribution of incomes within the relevant sectors.
Table 3 and Graph 3 show a small but significant pattern - lower income band households make up a higher proportion of the total in postcode sectors with IPC sites, whilst higher income band households make up a smaller proportion. The inverse holds true for non-IPC sectors. At the extremes, there are over 10% more Band I households in IPC sectors than in those with no IPC sites, and almost only half as many (48% less) households in band 10 in sectors with one ore more IPC sites, than there are in sectors with none.
In Graph 3 we have normalised the distribution of households by income band for sectors with no IPC sites. This highlights the percentage differences from that distribution that are found in the sectors with IPC sites (where there are more poor households and less rich households).
Table three [back to text]
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Graph three [back to text]
Table 4 below shows broadly the same phenomenon as Table 3, but here we calculate the proportion of households in each income band that share their postcode sector with a IPC site.
More than one in eight of the lowest income households (less than £5,000 pa) lives close to a IPC site (by this definition) whilst for the richest (£60,000 pa plus), only one in sixteen do so. Comparing the poorest fifth of the population with the richest fifth(5) across the whole of England and Wales the proportion found in postcode sectors with IPC sites declines from over one in eight (12.7%) to one in ten (10.0%) respectively - a difference that equates to an additional eighty-four thousand poor households exposed to pollution.
Table four [back to text]
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
As well as quantifying the effect in terms of 'chances' we can quantify it in terms of the size of the income differential. In Table 5 we compare the average income of households in postcode sectors with IPC sites with the average for the whole population, and the average of those not in IPC sites. Throughout the analysis in this section we have rounded average annual incomes to the nearest £10.
Looking at the bottom line of Table 5 we find that in comparison to the overall average, average income is £1,380 pa (or 8.0%) lower in postcode sectors containing IPC sites. Compared with sectors with no IPC sites the contrast is a little starker - household incomes on average being £1,560 (or 8.9%) lower in sectors with IPC sites than in sectors without such polluting factories.
The income variations we have found are of a similar order to those between regions - regional average household incomes vary from £14,750 in Wales to £20,740 in London. Table 5 therefore presents the income analysis at a regional level too, comparing average incomes in sectors with and without IPC sites with regional average household incomes. The table shows quite a variation in this effect, with no immediately clear pattern: the North-West and Yorkshire and Humberside showing relatively little income depression in IPC sectors in general, whilst the North-East has the highest variation outside London.
Table five [back to text]
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
So far we have mainly concentrated on the comparison between postcode sectors with one or more IPC sites and those with none. Similarly in examining the effects by income bands, we have concentrated on the presence or absence of any IPC site within the relevant sectors. However, there are many sectors with two or more sites, and, as previously noted, any given site may have more than one process. Here we examine the relationship between average incomes and the proportion of households in the lowest income band and the number of sites.
Table 6 shows the effect of increasing numbers of IPC sites. As we saw above, the presence of one or more sites is associated with an 8% lower average household income. This effect grows with the number of sites in the sector - those sectors with two or more sites have 10% lower incomes, four or more sites is associated with 15% lower incomes, and seven or more with 20% lower incomes. There are only a handful of sectors with even more sites, but the trend continues: the two sectors with the most IPC sites have an average income almost 50% below the overall average, whilst the sector with the most sites (17 in all) - in Teesside - is one of the poorest in England and Wales with average household incomes 64% below the norm and over 50% of it's population in band 1(with household incomes below £5000pa)(6).
Table six [back to text]
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The final column of Table 6 shows that there is a similar pattern in the distribution of households in band1, with higher shares in sectors with more IPC sites.
Both measures are highly suggestive of a strong link between more pollution (or at least the potential of more pollution) and more severe poverty. Across England and Wales there are 11% more households in Band 1 (incomes below £5,000) in postcode sectors with IPC sites, than elsewhere. Graph 4, shows the same relationship visually.
Table 7 shows how this effect varies regionally, concentrating on the share of households in band 1. It presents figures for one or more, three or more, and eight or more sites to give a broad picture of the effects.
Rather like Table 5, Table 7 suggests that the pattern is not as consistent within regions as it appears to be across the country as a whole. However, with only one exception (the North West) there are greater percentages of band 1 households in sectors with IPC sites than in those without. In the extreme cases, again, London and the North East, the difference is over 20%. Even in the North West the expected effect emerges for sectors with multiple IPC sites.
Graph four [back
to text]
Before turning to a general discussion, it is worth examining the regional maps to seen if any reasons can be suggested for the different patterns and variations in the relationships found in different regions. Below we comment on each region in turn.
Table seven [back to text]
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
East Midlands: This region stretches across diverse areas from extremely poor ex-coalfields (with no IPC sites, although often severe dereliction) to relatively wealthy parts of the home counties. Although IPC sites do appear to be clustered in poorer urban areas in Derby, Nottingham and Leicester, there are enough sites scattered in less poor areas, that combined with some very poor areas without IPC sites, this region shows only a weak numerical relationship on all the measures we have calculated.
Eastern: This region includes both relatively rich home counties and poor rural areas in East Anglia. The map reveals clustering of IPC sites in poor areas along the north side of the Thames estuary and around Ipswich. Although there also appear to be many sites in the more wealthy south-west of the region, several of these are actually in small poor areas, and, because the south-west is more densely populated, the average income of the region is higher than a cursory glance at the map would suggest. As a result there is a clear relationship (although quite small) between poverty and pollution within this region, seen in all the numerical indicators.
London: This region has the highest average income and a very strong visual clustering of IPC sites into poorer areas in the East Thames Corridor and the Lea Valley. This visual relationship is replicated in the numerical indicators, all of which suggest that the inequalities associated with IPC sites are severe in the London region.
North East: The map of this region suggests very strong clustering of IPC sites in poor urban centres on Teesside and Tyneside. Indeed so strong is this effect, that despite the low average income of this region, all the numerical indicators suggest severe inequalities associated with IPC sites. There are a number of sectors in the region with very high numbers of sites within them (Seal Sands with 17 sites being the most in the country). This will help clarify the apparent relationship.
North West: The North West is an intriguing region. Visually, the map does not look that dissimilar from the North East, with IPC sites clustered in Manchester and on Merseyside but the numerical indicators suggest a much less strong relationship. In fact the region is unique in having less households in band 1 (on average) where IPC sites are present than where they are not (although this inverse relationship does not hold where there are three or more sites in a sector). A closer look at the map suggests that the densely populated areas of severe poverty in Liverpool, where there are no IPC sites could contribute to this effect at a regional level. This would suggest that a finer scale of analysis might well reveal patterns that are partly concealed at the regional level.
South East: The South East is similar to the Eastern region in pattern and indicators, although it has a higher average income and smaller areas of peripheral poverty. Like the Eastern region it has clusters of IPC sites on the Thames Estuary, and also around Portsmouth and Southampton. Combined with the overall relative wealth of the region this tallies well with the small but clear relationship indicated in the tables.
South West: The South West also spans a geographical range of prosperity, from the quite wealthy Western end of the M4 corridor, to the severe poverty of sparsely populated peripheral Cornwall. IPC sites in the region are clustered around Avonmouth and in Bristol, and to a lesser extent, around Plymouth and Bournemouth. As with the Eastern region the map perhaps suggests a lower average income than is the case (as a result of relative population densities), and once again there is a clear although small relationship between poverty and (potential) pollution.
Wales: Wales is another interesting region, with clusters of IPC sites, as expected, around Deeside, Swansea, Port Talbot, Cardiff and Milford Haven. However two factors would appear to be responsible for reducing the numerical relationship revealed in the tables. First, there is severe poverty in the Valleys areas, associated with ex-coal mining settlements, whilst enough of the IPC sites around Cardiff fall into relatively high-income areas to possibly affect the overall picture.
West Midlands: The West Midlands shows a clear relationship, both on the map and in the tables, despite having a relatively high share of IPC sites in areas of above average income. The clustering in the densely populated Birmingham-Wolverhampton conurbation and also in Coventry and Stoke on Trent is quite pronounced, but there are also enough scattered sites elsewhere that the relationship is not as strong as that in the North East.
Yorkshire and Humberside: In many respects, Yorkshire and Humberside is not dissimilar to the North West. Despite an apparent strong clustering of IPC sites in the main industrial and port cities: Leeds, Sheffield, Hull and Grimsby, the numerical indicators suggest at best a weak relationship. This could be partly explained by the relatively widespread scattering of IPC sites over the former metropolitan counties of South and West Yorkshire, many of which are in areas at or near the regional average income, and also partly by some areas of more severe poverty in Hull and in ex-coalfield areas in South Yorkshire, where there are no IPC sites. Unlike the North West, in this region there is not such a clear trend of more sites associated with lower incomes or more households in band 1. Indeed in this region the sector with the most sites (8) is noticeably above the regional average income.
In this commentary we have not been able to do more than begin to tease out different relationships. A more sophisticated approach would examine not only smaller areas, but use a finer scale of income data, and categorise the IPC sites according to their nature and/or scale, so as to capture effects which might arise from the differences between authorised processes.
The above analysis shows that poorer people in England and Wales are more likely to live in close proximity to a potentially polluting factory than richer people. It also shows that the relationship is stronger with increasing numbers of factories in an area.
There are different possible reasons for this. Industrialists might have located their factories in already poor areas, or the proximity of the factory might have degraded the area to the extent that richer people have left (as they have the choice), leaving the poorer people to 'grin and bear it'. What is certain is that, whatever the direction of the link, the injustice is real. Whatever the health impact may be from these factories - and it will vary greatly - this preliminary analysis shows that there is a disproportionate impact on poorer people. This suggests that action to tackle pollution from factories will reduce inequalities.
There are many uncertainties however. For example, it is not possible to assess the relative effects of income, race, housing quality, housing and planning policy, urban redevelopment and differential migration in generating the patterns we find in the maps and tables, although all these factors are interlinked and seem likely to have contributed in some way - albeit not consistently.
This analysis mirrors much more detailed work on “environmental justice” in the United States, where poorer communities - and in particular ethnic communities - bear the greatest burden of toxic pollution. Research undertaken in the USA, by academics, activists and the US Environmental Protection Agency, has shown an apparent causal link in the citing of very polluting factories in poorer areas, especially those dominated by ethnic minorities (Bullard 1996; US EPA, 1992) - so called “environmental racism”. The benefits factory owners have obtained from locating their factories in poor, black communities include not only (in general) less opposition to development but also weaker enforcement of environmental standards and regulation (Bullard, 1999; Lavelle and Coyle, 1992).
Although we might expect to find concentrations of ethnic minorities near polluting factories in some parts of this country, in others race might be almost irrelevant, and, for example, the housing policies of the local authority may be most significant in shaping the pattern of residential location for poorer people (Walker, 1999). The greater degree of residential segregation on ethnic grounds in the USA means that the correlations between more risk and pollution and lower income are generally stronger than those we have identified in this report. However, we can expect that the driving economic forces are little different in the UK.
Any link between the location of factories and poverty is only one of a complex web of issues. The extent to which proximity to polluting factories equates to actual exposure to pollution also depends on a range of factors, whilst directly linking exposure to health or other impacts is also complex and difficult.
Even if the pollutant is emitted to air, the combination of chimney height, wind direction and other atmospheric conditions will determine the shape and direction of the pollutant plume. Nonetheless, proximity remains a significant factor increasing the likelihood of continued or repeated exposure - at least to air pollutants (and to a lesser extent ground- and surface water pollutants). Proximity is also a key factor in the likelihood of exposure in the event of a catastrophic accidental release - which is a real possibility for many of the facilities on the IPC list. It should be noted that the IPC list is not identical to that administered by the Health and Safety Executive (HSE) for the purposes of controlling Major Accident Hazards but the overlap is substantial(7). Thus it is helpful to outline the major emissions to air from IPC sites as a whole (see table 8). Many of these are hazardous and health-threatening.
Table Eight [back to text]
|
||||||||||||||||||||||||||||||||||||||||||
Even where exposure data is available, linking this to ill-health (morbidity) and premature deaths (mortality) is very difficult, and time consuming. In our view, even though direct causal links have yet to be established, if there is evidence of income related health inequalities, and the potential that some of them are pollution-related, then precautionary action to cut emissions is justified. It is better to act on a precautionary basis - factories should not get the benefit of the doubt.
This has been clearly demonstrated in the case of air pollution from traffic. Over much of the last 20 years a lack of proof of causal links was used by industry and government ministers to deny the possibility of health effects from air pollution (Bullock, 1998). In effect, the burden of proof was placed on people not pollution, a clear failure to apply the precautionary principle. Now the Government admits that these pollutants cause up to 24,000 premature deaths a year. Many lives could have been saved if precautionary action to reduce such pollution had been taken. In fact, the Government has repeatedly endorsed the precautionary principle - but failed to apply it in this case, as in many others.
Moreover, the Government has admitted that pollution is a contributory factor to poor health in deprived regions. Tessa Jowell, Minister for Public Health recently said: “More people suffer from poor health in the most deprived areas due to a range of factors including ... pollution” (Department of Health, 1998b). Below we outline some of the evidence to support this view.
The poorest communities suffer worst health. Sir Donald Acheson's recent report on inequalities in health noted that the highest mortality rates for both men and women are found among those who live in more deprived areas. The standard mortality rate for men in social class 1 is 280 per 100,000; for men in social class V this rate is 806. (Acheson, 1998). People living in households with incomes of £350 or more per week (approx £17,500 pa) have significantly lower rates of self-reported illness than those living in households with an income of £200 per week or less (our two lowest income bands - below approximately £10,000 pa).
A number of health conditions - heart disease, mental illnesses and respiratory diseases such as lung cancer - have been linked, by health researchers, to exposure to air pollution (Halpern, 1995; Freeman, 1998; TEESG, 1995). Poorer people as a whole experience higher mortality and morbidity rates from these conditions (Drever and Whitehead, 1997).
The preliminary analysis in this study suggests that poorer people also live nearer to factories which pollute. Our analysis also shows that in areas with multiple factory sites, the link between factories and low income is even stronger.
However it is not valid to conclude from this evidence that proximity to polluting sites is necessarily a significant contributory factor in health inequalities. As discussed above, establishing causality for even a tight cluster of health effects around a factory or incinerator is a major task given our current understanding of pollutant pathways and health effects. When considering much larger populations it is easier to suggest that there may - on average - be a contributory effect, but there are also many more confounding factors - including lifestyle (smoking and drinking); housing quality (cold and damp); poorer access to medical services; and higher unemployment rates - all of which can be correlated with a range of health impacts.
Little more can be said with confidence, because there has been only limited research into the direct and indirect impacts of pollution on the health of poorer people. One study of 27 poor estates in Teesside and Sunderland held all other factors constant and found a significant causal link between elevated death rates from lung cancer in women under 65 and industrial emissions (TEESG, 1995). However, there has not been much research in this area so far.
It is not possible to say that factory emissions cause worse health inequalities, but it is reasonable from the above evidence to advocate a precautionary approach - to assume unless it can be proven otherwise that actions to reduce emissions would reduce exposure which in turn reduces the likelihood and severity of health effects - which would reduce health inequalities.
It is also very likely that the indirect effects of pollution may also add heavily to the burden of psycho-social ill-effects. These effects are increasingly identified as a prime cause of the inequalities in health found between rich and poor (Wilkinson, 1996). Feelings of inadequacy and the injustice generated by poor housing and high rates of unemployment can only be exacerbated by living in a polluted environment. It may not be possible to say what the overall health effect is, but pollution adds to the burden of ill-health for excluded communities. The psychological effects of a poor environment do have real health outcomes.
In a more detailed examination of these interlinked issues, one might also want to ask whether residents gained employment opportunities from the proximity of the factory. The net effects on health and well-being could be different if exposure to pollution also brought with it the self-esteem of employment. We do not have data to answer this question, although circumstantial evidence suggests that increasingly those who gain employment from such industrial sites do not live in the near vicinity (Walker, 1999).
There is clear inequality in risk of exposure to a range of health-threatening pollutants - the factories that emit them are more likely to be found in poor areas and communities. Average incomes decline in areas with more factories, whilst the share of households with incomes below £5000 increases quite rapidly.
Cleaning up pollution from IPC factories is therefore a progressive social policy. Justice for poor people in England and Wales must include cleaning up their environment and reducing their exposure to health-threatening pollution. The UK is a signatory to the World Health Organisation's 'European Charter on Public Health' which sets out agreed principles for public policy including : “the health of every individual, especially those in vulnerable and high-risk groups, must be protected. Special attention should be paid to disadvantaged groups” (WHO, 1990).
Friends of the Earth believes that the Government should be aiming to reduce emissions of hazardous substances to air water and land by 80% by 2005 with the ultimate goal of reducing it to zero. As probably the largest contributors to such emissions, effective and rapid reductions of pollution from IPC sites will be essential to achieving such a goal. Measures to deliver such a reduction would be justified not only to protect the environment, but also to improve the quality of life and health of many of Britain's disadvantaged communities.
The Government has already recognised that pollution is a factor in poor health in poor communities. In 1998 Public Health Minister Tessa Jowell said “there is compelling evidence that people who live in disadvantaged circumstances suffer from more illness, greater distress and have shorter lives than those who are more affluent. That is why we have set new goals for making our nation healthier which recognise the impact of ... a polluted environment” (Department of Health, 1998a). However, the Government has yet to take action to attack this pollution at source. Such precautionary action is clearly justified.
Alongside aggressive measures to reduce industrial pollution from IPC sites, further research needs to be conducted to confirm these findings and identify more specifically the areas and groups most affected. Further research is also needed to examine the range of other environmental factors - such as traffic pollution, water quality and housing quality - which one can hypothesise will be similarly unfairly imposed on our poorer families and communities.
Acheson, D. 1998. Independent Inquiry into Inequalities in Health. London, The Stationery Office.
Bullard, R.D.. 1993. Race and environmental justice in the United States. Yale Journal of International Law 18(1) pp.319-335.
Bullard, R.D.. 1996. Unequal Protection: environmental justice and communities of color. San Francisco, Sierra Club Books.
Bullard, R.D.. 1999. Dismantling Environmental Racism in the USA. Local Environment 4(1) pp.5-20.
Bullock, S. 1998. UK progress towards World Health organisation targets on health and the environment. London, Friends of the Earth.
Drever, F. and Whitehead, M. (eds) 1997. Health Inequalities - decennial supplement. (DS15, Office For National Statistics). London, The Stationery Office.
Department of the Environment, 1996. English House Condition Survey 1991, Energy Report. London, HMSO.
Department of Health, 1998a. Press Release: £1.7 million on research into inequalities in health. 6 May.
Department of Health, 1998b. Press Release: Health Impact Assessments will help measure action in tackling inequalities says Tessa Jowell. 16 November.
Freeman, H. 1998. Healthy Environments. In Encyclopedia of Mental Health, Volume 2. London, Academic Press.
Halpern, D. 1995. More than Bricks and Mortar? Mental Health and the Built Environment. London, Taylor and Francis.
Lavelle, M & Coyle, M. 1992. Unequal protection. The National Law Journal, 21 Sept. pp1-2.
TEESG (Teeside Environmental Epidemiology Study Group). 1995. Health, illness and the environment in Teesside and Sunderland. Department of Epidemiology and Public Health, University of Newcastle.
US Environmental Protection Agency, 1992. Environmental Equity: reducing risk for all communities. Washington DC, US EPA.
Walker G. 1999. Risk, Justice, Perception and Choice. Paper to Geographies of the Future, RGS-IBG AGM, Leicester, 4-7 January.
Wilkinson, R.G. 1996. Unhealthy Societies: the afflictions of inequality. London, Routledge.
World Health Organisation, 1990. Environment and Health: The European Charter and Commentary. Copenhagen, WHO Europe
For this study we have utilised the best available and affordable data - both on income and polluting industrial facilities. In both cases there are some limitations to the data and the nature of the data-sets merits some explanation.
In this study we have used survey data to provide a value for the average household income for the postcode sector. The data-set is based on over 10 million consumer questionnaires from surveys undertaken by the research consultancy ICD. Respondents were asked to place their approximate annual family income within a consistent set of income bands(8). The lowest household income band (less than £5,000 pa) is likely to be dominated by the elderly, those on benefit and students. The base data has been re-weighted by Business Geographics in accordance with local population demographics derived from the 1991 census to provide data on the distribution of family income within each of 8936 postcode sectors in England and Wales. Where response levels were low, Business Geographics have also used analysis of the data from surrounding areas to partly impute the data for a given sector. This method has been applied to 18% of the sectors. A small number of sectors (70 or 0.8%) had too little data (less than 50 survey respondents) for even this method. These sectors and the five IPC sites they contain have been excluded from our analysis.
We compared the income band data for England and Wales with survey data on household income from the English House Condition Survey (EHCS). Whilst the bands applied are not identical, this cross-check did indicate that the income distribution of the weighted data was reasonably similar to that obtained in the EHCS. Business Geographics also derived an estimate of average family income using median values and the income band distribution(9).
Because the raw data is already banded, it is rarely possible to subdivide it in any other ways. Approximate comparisons can be made between the richest and poorest quintiles, but only because the numbers of households in the measured bands approximate reasonably well to these divisions (19% of all households falling in band one, and 23% in band 6 and above). On the other hand the official dividing line used to demarcate 'poverty' - half average income - falls in the middle of band 2, so we were not able to use this measure in this study. The official definition also is based on household incomes adjusted for household size, whereas the data available to us is not so adjusted. This factor also means that our 'quintile' comparisons are not necessarily comparable with such comparisons based on other data-sets.
The 'polluting sites' data-set used for this analysis is the list of industrial sites (registered under 'Integrated Pollution Control' (IPC) regulations) that report annual emissions for the official 'Chemical Release Inventory' (CRI). Across the UK around 2000 sites are registered under IPC. In this phase of research we have only examined England and Wales for which the most up-to-date information is for 1996, and covers 1320 sites. Eleven sites had no (or irresolvable) postcode data attached, so were excluded from the analysis along with those covered by confidentiality conditions(10).
Table Nine [back to text]
|
Integrated Pollution Control was established under the Environmental Protection Act 1990 and in England and Wales, is administered by the Environment Agency. IPC processes are those regarded as the more polluting or complex industrial processes (see tables 8 and 9). These are controlled with a view to preventing or minimising pollution of all three media: air, land and water.
One industrial site might be operating several different types of processes and each process will receive a separate permit (legally termed an “authorisation”). Each permit has various conditions for operation, usually including requirements to monitor for specified substances in waste streams at specified frequencies. This data is usually reported as a concentration (eg mgs per litre). The monitoring data is used in the first instance to judge performance of the plant against the imposed emission limits. The applications for permits, the permits themselves, and monitoring reports are available in public registers in England, Wales and Scotland.
In 1991, it was proposed to produce an annual inventory of emissions from the IPC processes covering the total quantities released. These reports are filed in the public registers and the Environment Agency also combines the data into a centrally held database, which forms the Chemical Release Inventory (CRI). At the time of writing the most recent verified data set held by the Environment Agency is for 1996 - covering approximately 2000 processes, over 400 different substances in total and nearly 10,000 records.
Unfortunately, the value of the CRI as a measure of the pollution emitted is somewhat undermined by fundamental flaws of the data set. The main problem is that reporting requirements have not been applied consistently. The CRI is based on permit data rather than being constructed from a standard list of pollutants, so inconsistencies and indeed lack of reporting requirements for many substances in the permits feed into the CRI data set. This has happened because the government at the time did not want to impose any extra monitoring costs on industry, so the Environment Agency could only require reports on chemicals already mentioned in the existing permits. Different inspectors from the Environment Agency had written permits with different monitoring requirements - even for similar processes. For example, one incinerator quantified releases of 41 substances in 1994, another quantified releases of only four substances. Out of a total of 15 municipal waste incinerators, ten reported dioxin releases, but five did not, even though all incineration processes will produce some dioxins and all of these incinerators had permits. Despite an overall list of over 400 chemicals, the average number of chemicals reported per process is only about five.
Another disadvantage of linking the CRI to the permits is that the system is being phased in over several years, so not all industries have reported annual releases from the beginning. Processes in existence when the IPC system began have been brought under IPC in phases, although new processes of any sector have had to seek immediate IPC authorisation before operation. So data for a full set of IPC processes is not yet available even though the first entries in the database date back to 1992.
There are also potential weaknesses relating to the postcode element of the IPC data. We discovered a border effect, in which 2 sites which are in England appear to have Scottish postcodes. These have been excluded from the analysis. There are two further relevant weaknesses in the data set. Our 1320 sites are those with a distinct name and postcode. Thus a very large IPC site, which could conceivably be allocated two six digit postcodes, would appear twice in our data set. We have not been able to correct for this problem, which we believe to be uncommon. Insofar as it does occur, it has the net effect of increasing the de facto weighting applied such very large sites. Secondly there may be postcode errors in the Environment Agency data which have the effect of locating the factory in a sector with a different income. Without detailed ground survey work we could not correct for this, which should have a random effect anyway.
These various weaknesses notwithstanding, for the purposes of this project, the CRI does offer an adequate data set allowing us to accurately locate those factories which are considered by the Environment Agency to be potentially the most polluting. Whilst we can say little with confidence about specific factories - not least because the data is several years old - it is also clear that these factories, as a set, are responsible for substantial emissions of hazardous and health-threatening pollution (see Table 8).
1. The exceptions are predominantly commercial areas, or locations where major business addresses have been allocated multiple postcodes, thus lowering the number of households in the sector.
2. When examining average incomes, we find that there are no sectors with an average household income in band 1 (below £5,000), nor any with an average in band 9 or above (over £50,000) leaving us with just seven bands. On the other hand, when using the income distribution data all ten bands are relevant to the analysis.
3. 30% in Band 1 is half as many again as average, and suggests relatively severe and concentrated poverty.
4. It is crude because it does not set any particular threshold for 'poverty', merely including areas with income below the regional average. Nor does it take account of any variation around the average within each area.
5. See technical annex on income data.
6. It's average household income is £6200, whilst the poorest sector in England and Wales is in Liverpool (L14 8) where household incomes average £5,700.
7. Preliminary, more sophisticated analysis of income levels around such sites - using the areas within risk contours defined by the HSE, undertaken at the University of Stafford suggests a similar bias towards exposure of poorer people to such risks (Walker, 1999).
8. Bands of £5,000 under £30,000 and bands of £10,000 above that level.
9. While the data-set does merge responses of both pre- and post-tax incomes, it is reasonable to assume that this will not create a significant consistent bias in the data set (even though it may apparently inflate incomes in relatively richer areas where the overall difference between pre- and post-tax income is greater).
10. IPC/CRI data is not available for Northern Ireland, and Friends of the Earth Scotland have an independent remit for Scotland.
April 1999
Duncan McLaren, Olivier Cottray,
Mary Taylor, Susan Pipes and Simon Bullock
Last modified: June 2007
