Geoforum 43 (2012) 44–57

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Geoforum

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Longitudinal environmental justice analysis: Co-evolution of environmentalquality and deprivation in England, 1960–2007

Gordon Mitchell a,b,⇑, Paul Norman a

a The School of Geography, The University of Leeds, Leeds LS2 9JT, UKb The Institute of Transport Studies, The University of Leeds, Leeds LS2 9JT, UK

a r t i c l e i n f o a b s t r a c t

Article history:Received 10 August 2009Received in revised form 7 July 2011Available online 10 September 2011

Keywords:Environmental justiceEnvironmental equitySocial deprivationLongitudinal analysisTime-seriesLandscape intrusion

0016-7185/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.geoforum.2011.08.005

⇑ Corresponding author at: The School of GeograpLeeds LS2 9JT, UK.

E-mail addresses: g.mitchell@leeds.ac.uk (G. Mitch(P. Norman).

We describe contemporaneous changes in environmental quality and social deprivation in English localauthority districts over four decades, using secondary source GIS modelled data on environmentallyintrusive development. The distribution of this development is described with respect to the Townsendmaterial deprivation score, corroborated against the Breadline Britain index. Spatial patterns of environ-mental intrusion and material deprivation change markedly over the period, although a clear environ-mental inequality remains throughout. However, it is not the most deprived who experience thegreatest decline in their environmental quality, as most of the increase in environmental intrusion occursin those districts whose population were amongst the most affluent in the early 1960s. We note that theenvironmental justice implications of these observations are dependent upon conceptions of justice held,and reflect on the challenge of testing, through empirical longitudinal analysis, the notion that environ-mental sustainability and social justice are incompatible.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction: Environmental justice, evidence and response

Environmental justice (EJ) has emerged as a key theme in thesustainable development paradigm, bridging goals of environmen-tal protection and social justice. EJ is conceptually broad, address-ing the fair distribution of environmental impacts, goods andservices within and between generations, and between peopleand the natural world. However, the principal concern of the EJmovement has been intra-generational equity issues, particularlythose related to exposure to environmental hazard.

US studies from the 1970s onwards concluded that environ-mental hazards, particularly waste disposal facilities, were dispro-portionately sited in predominantly coloured and low incomecommunities. Charges of ‘environmental injustice’ led to a Presi-dential Order (President, 1994) directing federal agencies to ensureminority communities are not disproportionately affected by ad-verse environmental burdens, and the establishment of an Envi-ronmental Protection Agency EJ office. In Europe, EJ has emergedas an important policy issue more recently. Unlike the USA, wherepolicy responded to grass roots civil rights activism, European EJpolicy is being shaped internationally, in response to the AarhusConvention (UN ECE, 1999), and ensuing EU directives on: PublicAccess to Environmental Information (EC, 2003a); Participation in

ll rights reserved.

hy, The University of Leeds,

ell), p.d.norman@leeds.ac.uk

Environmental Decision Making (EC, 2003b); and Access to Justicein Environmental Matters. Of these, the EJ directive remains at theproposal stage (EC, 2003c), as member states disagree over whocan bring a case to court on EJ grounds, reflecting fears that if allare eligible, courts will be swamped and development paralysed.The Aarhus Convention is, however, being applied in full to ECinstitutions (EC, 2006). The UK has also seen EJ policy speechesby the Prime Minister (and Scottish First Minister), EJ research pro-grammes supported by the Environment Agency and Scottish Exec-utive, whilst EJ is a policy goal of the National SustainableDevelopment Strategy (HMG, 2005).

Evidence for environmental inequality in Europe is limited (Pyeet al., 2007), with most studies conducted in the UK where the EJmovement is more developed, albeit not on the scale of the USA.Noting concerns with the quality of the USA evidence base (Bowen,2002; Bowen and Wells, 2002), empirical analyses of the social dis-tribution of environmental burdens in the UK have largely pursuednational coverage small-area studies that provide a more robustpicture of environmental inequalities than population samplestudies. These reveal clear inequalities in the social distributionof environmental risk and quality, including that:

� 2.5 million people live in wards where nitrogen dioxide (NO2)concentrations exceed the National Air Quality Strategy annualmean standard, of which more than half are in the mostdeprived 10% of wards in the country (Mitchell and Dorling,2003).

G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57 45

� The largest industrial facilities (IPC sites) are more clustered indeprived areas, with greater numbers of emission sources pre-senting a greater pollution hazard and producing more offen-sive emissions than in less deprived areas (Walker et al., 2003).� Of those people resident within 1 km of a waste incinerator or

waste disposal facility, 52% are in the most deprived 10% ofhouseholds, compared to 4% in the least deprived 10% of house-holds (Walker et al., 2003).� Children living in the most deprived areas are five times more

likely to be killed in a road accident as children in more affluentareas (HMG, 2005, p. 135).� For every affluent household (upper deprivation decile) in a

coastal flood risk area, there are eight households from the mostdeprived class (lower decile). Deprived households are morevulnerable to flooding, as they often lack insurance to aid recov-ery (Walker et al., 2003).� Several studies indicate that deprived communities are exposed

to much greater levels of cumulative impacts, to local incivili-ties (fly-tipping, neighbourhood noise), and enjoy poorer accessto environmental goods and services (Lucas et al., 2004).

A review for government thus concluded that: ‘‘environmentalinjustice is a real and substantive problem within the UK; prob-lems of environmental injustice afflict many of our most deprivedcommunities and socially excluded groups; both poor local envi-ronmental quality and differential access to environmental goodsand services have a detrimental effect on the quality of life experi-enced by members of those communities and groups; and that insome cases not only are deprived and excluded communities dis-proportionately exposed to an environmental risk, they are alsodisproportionately vulnerable to its effects’’ (Lucas et al., 2004).The review argued for more research into the causes of environ-mental injustice to support the development of appropriate policymeasures. The UK Sustainable Development Strategy echoes thisposition, outlining government’s intention to focus on improvingthe environment in areas already identified as most deprived, butin the long term, to conduct research on the causes of environmen-tal inequality to support design of measures to tackle it (HMG,2005, pp. 133–135).

Where environmental injustice exists (unequal is not necessar-ily unjust) what responses are possible? One approach is to reduceproximity of minority communities to environmental hazards. Thismay involve redirecting the hazard using the planning system, socommunities do not become ‘hazard havens’ where having onehazard makes it easier to gain consent for others. Difficulties withthis approach include: the issue of whether dispersing hazardsplaces more people in total at risk; the ‘not in my back yard’ syn-drome (whereby those able to articulate their protest about adevelopment gain an advantage over the less articulate else-where); and whether it is right to deny communities’ jobs andwider benefits that development may bring to an area. Conversely,proximity of hazard and minority community may be reduced bypolicies that reduce factors restricting where minority householdscan live, and which encourage social mixing. Thus housing policycould be used to guarantee new developments contain some socialhousing, rather than developing bulk social housing in areas whichtend to be less desirable. Another approach is to accept theinequality and provide community compensation as better hous-ing, health or education services (Blackman, 2006). This may beparticularly appropriate where a group bears an environmentalburden of a hazard without enjoying any of the benefits, such asjobs or services, the hazard offers others.

However, such measures have received little serious implemen-tation attention, and currently, the principal approach to address-ing environmental injustice is to raise environmental quality in‘‘pollution-poverty hotspots’’ in the expectation that this will re-

duce inequalities, an approach favoured by the UK government.However, a further approach, raising environmental quality over-all, may have a more profound effect on the social distribution ofthe environmental burden. This is illustrated in two UK air quality(NO2) studies. The first (Walker et al., 2003) compared the socialdistribution of air quality for England in 2001 and 2010, and foundthat improvements in air quality achieved by 2010 greatly reducedthe number of deprived households in areas of poor air quality.Secondly, Mitchell (2005) found that, when comparing traffic man-agement strategies including several road pricing schemes,improvements in both air quality and road network performancecould be achieved without deterioration in air quality of deprivedneighbourhoods (a concern with road pricing, where re-routingto avoid a charge zone is common).

Unfortunately, evidence that improving environmental qualitycan bring about more socially equitable environmental distribu-tions is scarce and limited. The above air quality studies providean insight, but use static deprivation data, thus ignoring the impor-tant co-variation of environmental and social metrics. Longitudinalanalysis of observed data offers a way to explore the dynamic evo-lution of observed inequalities, and in the EJ field studies includethose adopting a narrative approach (e.g. Pulido (2000) and Booneand Modarres (1999) on hazardous industrial development in LosAngeles), an empirical approach (e.g. Oakes et al., 1996; Been andGupta, 1997; Stretesky and Hogan, 1998; Pastor et al., 2001; Sahaand Mohai, 2005), and a combination of the two (Baden and Cour-sey, 2002). These studies all relate to siting of hazardous wastefacilities in the USA, and arrive at different conclusions as towhether these ‘locally unwanted land uses’ were sited in low in-come or ethnic communities, or whether these communitiesevolved subsequently. With the exception of these studies, empir-ical EJ research has been limited to cross sectional analysis,neglecting the longitudinal dimension so important to understand-ing how environmental inequality evolves. Our analysis, aims toadd to this small body of literature, through examination ofchanges in the social distribution of a composite measure of envi-ronmental quality in England over the last 40 years.

2. Data

2.1. Overview

We use secondary data on environmental intrusion in England,reported in a study commissioned by the Campaign to Protect RuralEngland (CPRE) conducted by Land Use Consultants (LUC, 2007) (seeSection 2.2), and deprivation data (see Section 2.3), measured usingthe Townsend material deprivation index (Townsend, 1987; Town-send et al., 1988) with relationships corroborated against the geo-graphical distribution of poverty using the Breadline Britain index(Gordon, 1995; Dorling et al., 2007). The analysis uses sub-regionaldata on environmental intrusion and deprivation at three points intime from the early 1960s to 2007. Judged by the standard of recentUK small-area studies the analysis includes several data limitations,which we describe below. However, to our knowledge, this is thefirst empirical EJ study outside the USA to have attempted a nationallongitudinal analysis, and the first longitudinal analysis to addressan environmental measure other than waste disposal facilities.The results are hopefully useful in giving a first look at how environ-mental inequality has evolved in the UK, and may have relevance forother countries with similar development profiles.

2.2. Environmental intrusion

A common constraint on robust EJ analysis is the availability ofenvironmental data which reflects an environmental issue of

46 G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57

concern, presented in a consistent manner for the study area ofinterest, and at a useful spatial unit of analysis. Thus, as notedabove, there are relatively few national level small-area analyses.Such problems are compounded when attempting longitudinalanalysis. The few available longitudinal EJ studies are limited toUS waste facilities, drawing on the Federal register on hazardouswaste treatment, storage and disposal facilities (TSDFs) developedunder the 1976 Resource Conservation and Recovery Act. Whilstthe UK has a similar register (managed by the Environment Agencyunder the 1996 EC IPPC directive) it lacks temporal consistencywith earlier records, and to date is only able to support cross sec-tional analysis. The environmental issue most regularly addressedin UK EJ studies is air quality, reflecting public concerns particu-larly with respect to health, and the availability of small area na-tional scale data for several years since the mid 1990s. Thesedata are modelled (based on observed traffic and other emissions),and whilst it is theoretically possible to model air quality for pastdecades, in practice this would be a major task, probably con-strained by the lack of requisite data on the independent variables.

To gain an insight into long-run changes in the relationship be-tween environmental quality and deprivation in the UK, it is there-fore necessary to turn to less well studied environmentalmeasures. Here, we draw on secondary source data on environ-mental intrusion, which provides the best available long run of na-tional coverage environmental data appropriate to EJ analysis forthe UK. The data are for England, for the ‘early 1960s, ‘early1990s’ and 2007, with the early maps produced in 1995 for theCPRE and the then Countryside Agency, with a repeat exercise for2007 (LUC, 2007). All three maps were generated by the CPRE totrack the loss of ‘tranquil’ areas, defined as areas sufficiently farfrom noise or visual intrusion of development and traffic to be con-sidered unspoilt. The maps are available in the LUC (2007) reportand online as national and regional spatio-temporal animationsat the CPRE website (CPRE, online). This is a little studied environ-mental metric in the EJ field, but does help to address the narrowscope of past longitudinal EJ analyses. Noonan (2008) notes thatwhilst the EJ literature is vast, it is hard to generalise from its lim-ited scope, and argues for more research with a temporal dimen-sion and which addresses environmental amenities, such asaesthetics, parks and wilderness.

Factors addressed in the intrusion maps are presented in Ta-ble 1, with relevant buffer distances and data sources. LUC(2007) and Jackson et al. (2008) describe and critique the processof choosing the variables and thresholds. Table 2 provides a sum-mary of the intrusion data at the regional level, illustrating thatfor England as a whole, the intrusion area has doubled from theearly 1960s to 2007, with the most intruded area being the SE,and the least the SW. However, the SW has experienced the largestincrease in intrusion, with the rate of intrusion growth twice thenational average, and four times that of the northern regions. Theearly maps were not made from raw data (now unavailable), butwere digitised from 1:750,000 scale (A0 size) hard copy mapsand geo-referenced to 2007 digital boundaries. Whilst criteriaand thresholds are reported for the earlier maps, full details of datasources were not, hence the first two maps are for the ‘early’ 1960sand 1990s, rather than specific years (it is anyway likely that com-ponent data would be sourced from several years, as with the 2007map, based on 2001–2007 data). The data used in our EJ analyses isthus secondary source data, and we had no access to the compo-nent datasets they were built from. Whilst this does not precludean EJ analysis, it does present a series of data constraints.

Firstly, we must use the CPRE definition of intrusion. The 1995CPRE study was criticised on the grounds that it only considereddetractors from tranquillity, ignoring factors that contribute to it(hearing running water, or seeing a natural landscape or the starsat night), and failed to take public perceptions into account when

defining tranquillity criteria. These issues are addressed in morerecent mapping (Jackson et al., 2008), but not in the 2007 map,as methodological consistency across the 3 years was critical tothe comparative study (to avoid confusion with the 2008 tranquil-lity mapping studies, earlier CPRE maps are referred to as environ-mental intrusion maps). Whilst access to environmental goods andservices (including parks and recreational areas) is considered arelevant and important aspect of EJ analysis in the UK, most anal-yses, and indeed policy, focuses on environmental detractors (airquality, waste sites, flood risk, etc.). The absence of access to envi-ronmental goods and services from our EJ analysis is then typical ofmost of the EJ literature, although this is an area worthy of moreattention. However, the absence of environmental intrusion mayin itself be considered an environmental good, and part of our sub-sequent analysis addresses this correlate directly.

Note also that several factors (Table 1) have been addressed inearlier single issue environmental equity studies, either directly(e.g. power stations and mineral extraction sites were addressedby Walker et al., 2003), or indirectly (e.g. roads, urban areas, powerstations collectively determine air quality). Thus, although theCPRE data were originally derived to assess loss of tranquillity fromdevelopment, it clearly has resonance with the environmental fo-cus of many recent UK national small-area EJ analyses. The addi-tion of factors not previously addressed (power lines, windfarms), and the longitudinal nature of the analysis, add new ele-ments to the growing UK EJ evidence base.

Secondly, the intrusion map is based on the observed spatialdistribution of factors (e.g. urban development, location of roadsand traffic), which we can be confident of, but assumptions as tohuman response to those factors, in terms of perception of intru-sion, have been made; these assumptions are reflected in thechoice of buffer distances adopted in the intrusion mapping. Jack-son et al. (2008) held public workshops to quantify perceptions ofintrusion, whereas the intrusion maps we use draw on the percep-tions of a smaller group of experts. However, we felt maintainingtemporal consistency of method more critical than embedding amore refined public perception of intrusion in the data. In develop-ing the 2007 map, LUC (2007) expended significant effort to ensureconsistency across years, although ambiguity over thresholds anddata sources used in the earlier maps means some inconsistenciesare likely (e.g. it was unclear in early mapping what was meant bya ‘‘heavily trafficked’’ railway, or how heavy industrial areas wererepresented). However, the authors assert that there is a high de-gree of temporal consistency in the intrusion mapping, certainlysufficient to support a comparative assessment.

Thirdly, we note that in developing the intrusion map in thisway, the environmental equity analysis is essentially a proximityanalysis with all the caveats of this reported in detail elsewhere(Mitchell and Walker, 2007). For example, proximity to a road isa somewhat crude environmental quality measure; emission ofgases (or noise), concentrations, exposure, and health impacts aresuccessively better measures, but much more demanding to derive.Whilst these more functionally resolved analyses are highly desir-able, proximity analyses necessarily remain the dominant formatin cross sectional EJ studies, and is the approach to which we aresimilarly limited due to the nature of the source data.

Fourthly, our analysis is based on a coarse, mixed geography.Each map was divided into temporally consistent sub-regions (de-fined using OS BoundaryLine 2006 data) by LUC (2007), and theland area subject to intrusion measured. The sub-regions ad-dressed in our analysis are thus those reported by Land Use Con-sultants (2007, Table 12), which comprise 117 mid-tier planningareas in England. This comprises a mix of Local Authority districts,Counties, Unitary Authorities, and Metropolitan Districts. Thesesub-regions have a median size of 268 km2, but this masks a con-siderable size range, with a quarter of sub-regions having an area

Table 1Criteria and data used in intrusion mapping. Source: Land Use Consultants (2007).

Feature Data set(s) used Definition Distance threshold

Roads Road network from OS Strategi (2006). Annual Average DailyTraffic (AADT) Flows from GB National Road Traffic Survey (DfT,2005)

Over 75,000 AADT 3 km

25000–75,000 AADT 2 km10,000–25,000 AADT 1 km5000–10,000 AADT 0.5 kmBelow 5000 AADT No disturbance

Railways National Rail Principal Routes from OS Strategi (2006) Mainline routes 0.5 km

Airports Airports and noise contours (Civil Aviation Authority) Civil Airports, distance from 57Leq noisecontour

1 km

Military airports with jet air squadrons (MOD) Military airports, ellipse along axis of runway 7 km � 1 km

Built up areas Urban Areas and Urban Area Key Statistics (ONS/Census 2001) Over 270,000 people 3 km4000–270,000 people 2 km2500–4000 people 0.5 km

Areas of mineralextraction

Britpits data set for all mineral extraction sites (BritishGeological Service, 2007)

0.5 km from pointdata

Electrical installations Power Stations (CESA, 2006) Power stations 3 kmLocation of 400 kV and 275 kV power lines and grid stations(National Grid, 2007)

400 kV and 275 kV power lines 0.5 km

Grid stations 0.5 kmWind power

developmentsLocation of operational wind power developments (AEA, 2007) Operational wind power developments 0.5 km

G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57 47

<100 km2, and a quarter >2000 km2. Whilst it would be preferableto express intrusion data nationally for small geographic units(such as electoral wards) as is desirable in EJ analysis (Mitchelland Walker, 2007), or more reasonably the 326 local authority dis-tricts (although these still vary in area by three orders of magni-tude), we were not able to do so, given a lack of access to thedigital mapped data.

Fifthly, we note that an issue of intrusion saturation arises, inpart a function of the geographical sub-regions used. That is,there are areas where the degree of intrusion reaches 100% ofthe sub-region. This is predominantly in urban areas, where thedensity of roads, built up area and other infrastructure mean thatno ‘tranquil’ areas remain. Where this intrusion saturation occurs,there is a danger of not adequately comparing areas, particularlyurban ones. Arguably, this is addressed through the consistentapplication of thresholds to the variables underpinning the intru-sion maps, as these thresholds are based on a consensus percep-tion (albeit of experts not the public) of what constitutes adetractor from tranquillity. Nevertheless, this will not representspatial coincidence of intrusion metrics (see Walker et al.(2003) and Wheeler (2004) for UK examples of such cumulativeassessment), and two areas may both be at intrusion saturation,but one may experience more dense development and traffic,with consequently lower environmental quality in terms of noiseand air quality than the other. This intensification effect is notpicked up in the intrusion metric in general, and its absence islikely to be most significant in the saturation sub-regions, whichmay thus misrepresent any longitudinal relationship betweenintrusion and deprivation. Given the constraints imposed on theEJ analysis by the secondary source data, this issue is difficultto overcome; however, we make an initial effort below, byrepeating some of the analysis with 33 intrusion saturation sub-regions excluded. Sub-regions close to intrusion saturationremain in the analysis, and may have an adverse effect, but acomparison of results with and without the 33 saturated sub-re-gions should allow the approximate impact of this factor to begauged. We also conduct analysis in which we consider the per-centage loss to 2007 of area that was not subject to environmen-tal intrusion in the early 1960s, which also helps to gauge theimportance of the saturation issue.

2.3. Deprivation

The principal social concern within the UK EJ movement is notethnicity, as in the USA, but deprivation. It is government’s mainconcern with respect to social exclusion, most EJ concerns at grassroots level are with respect to poverty, and there is little evidencethat ethnic groups bear a disproportionate burden of environmen-tal impact (but little research has been done). Note also that claimsof ‘environmental racism’ in the USA often proved suspect, as thesupporting studies gave insufficient attention to controlling forauto-correlation between race and poverty (Bowen, 2002).

Deprivation is a state of disadvantage relative to the local com-munity, wider society or nation to which an individual, family orgroup belongs (Townsend, 1987). People can be deprived of ade-quate education, good quality housing, rewarding employment,sufficient income, good health and enjoyment opportunities (Dor-ling, 1996). To identify relatively deprived areas in the UK, variousindexes have been devised including: the Jarman UnderprivilegedArea index (1983); the Townsend index (1987); the Carstairs index(Carstairs and Morris, 1989); Breadline Britain (Gordon, 1995; Dor-ling et al., 2007); the Index of Local Conditions (DoE, 1983, 1994);the Index of Local Deprivation (Noble et al., 2000); and the Index ofMultiple Deprivation (IMD) (IMD, 2004, 2007; Noble et al., 2006).Indexes have also been developed in the US, Canada, New Zealandand elsewhere (Bell et al., 2007). UK indexes have mainly been atward scale and are predominantly based on a composite of cen-sus-derived variables indicating relative conditions between areas,although in the recent IMD (2007) alternative geographies and in-put variables are used.

Deprivation measures are highly influential on the allocation ofpublic resources (Blackman, 2006; Brennan et al., 1999; Chattertonand Bradley, 2000; Simpson, 1996) and are often used as explana-tory variables in models of various outcomes, including health, inboth the UK (Boyle et al., 2002; Dibben et al., 2006; Diez Roux,2005; Law and Morris, 1998; Norman et al., 2005, 2008) and othercountries (Karpati et al., 2006; Lorant et al., 2001; Pearce et al.,2006; Tello et al., 2005). Index choice is debated (Davey Smithet al., 2001; Mackenzie et al., 1998) but a high degree of correlationbetween schemes is found (Hoare, 2003; Morris and Carstairs,1991).

Table 2Regional summary of environmental intrusion. Source: Land Use Consultants (2007).

Region Area disturbed, early 1960s Area disturbed, early 1990s Area disturbed, 2007 Change (%) in disturbed area

km2 % of region km2 % of region km2 % of region 1960s–1990s 1990s–2007

East Midlands 4080.3 25.8 6460.6 40.9 7934.2 50.2 58.3 22.8East of England 4275.7 21.8 7549.6 38.6 9715.0 49.6 76.6 28.7North East 2127.1 24.5 2644.2 30.5 3010.0 34.7 24.3 13.8North West 4548.5 30.5 6185.7 41.5 7245.0 48.6 36.0 17.1South East and London 7947.4 37.8 12380.0 58.9 14541.2 69.2 55.8 17.5South West 3565.5 14.6 7351.7 30.1 10356.0 42.5 106.2 40.9West Midlands 3650.4 28.1 5578.9 42.9 6396.9 49.2 52.8 14.7Yorkshire & Humber 3739.2 24.0 5774.4 37.1 7141.4 45.9 54.4 23.7ENGLAND 33934.0 25.5 53925.2 40.6 66339.5 49.9 58.9 23.0

48 G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57

Currently, the IMD (2007) is the government’s preferred mea-sure of material deprivation in England, which it measures acrosssix domains using 33 variables. The complexity of the index andlack of pre-2000 data means that for this study, it was not feasibleto construct historical datasets with the required internal consis-tency. The IMD and its predecessors are not comparable over timeor between the UK’s constituent countries (Dorling et al., 2007;Whynes, 2008). Morelli and Seaman (2007) acknowledge these dif-ficulties mean that deprivation scores at different time points can-not usually be compared hence assessments of trends are difficult.Since its input variables (see below) are available and robust overtime, we focus on the Townsend index of material deprivation(Townsend et al., 1988) one of the most widely used indices (Na-tional Centre for Health Outcome Development, 2005), althoughsuperseded by the IMD in many applications.

Townsend scores are based on four variables representing: lackof material resources and insecurity, material living conditions,wealth and income. These are unemployed residents aged 16–59/64 as a proportion of all economically active residents aged 16–59/64; households without access to a car as a proportion of allhouseholds; households that are not in owner occupied accommo-dation as a proportion of all households; and overcrowded house-holds (>one person per room) as a proportion of all households.The unemployment and overcrowding variables are log trans-formed to produce less skewed distributions, and the variablesthen expressed as standardised z-scores and summed, with eachvariable having an equal weight, to give a final score. Higher scoresindicate greater levels of material deprivation.

Given debate on the merits of variables included in differentschemes (see Bradford et al., 1995; Senior, 2002; Eroglu, 2007),we should consider the availability and definition of the input vari-ables over time. Area statistics available from the 1971, 1981, 1991and 2001 censuses allow us to define and calculate very closelyequivalent indicator variables at the different time points so thatdifferences in levels of unemployment, overcrowding and non-ownership of home or car should provide a useful indication ofchange in aggregate population characteristics. Only periodicallyavailable and less justifiable over time are variables used as depri-vation indicators in other schemes such as: lacking householdamenities or central heating; born in the New Commonwealth;and socio-economic group (Norman, 2010).

Raw data are available from the 1971, 1981, 1991 and 2001 cen-suses so we can calculate Townsend scores for this period but notfor the earlier decade. The input variables have been transferredfrom the census geographies for which the data were originallydisseminated to be consistent with the district boundaries usedin the CPRE study. To convert data between geographies, varioustechniques have been developed including areal interpolation(Gregory, 2002) and dasymetric mapping (Mennis, 2003). Normanet al. (2003) demonstrate that socio-demographic data can beapportioned between different geographical systems to estimate

a data time-series for one set of boundaries. This apportionmentis computed using a proxy for population distribution, the countsof postcodes (weighted by the number of addresses at each) thatfall in the intersections of the ‘source’ and ‘target’ geographicalunits. Here the source units are the 1971, 1981 and 1991 censuswards and the target units are the 2001 census wards which canbe aggregated into the CPRE districts. Whilst conversions of databetween geographies cannot be perfect, this method is reliable en-ough that a similar approach has been adopted by the Office forNational Statistics (ONS) to provide population estimates fornon-standard areas (Bates, 2008). Aggregating from smaller to lar-ger geographies, as we do here, improves the accuracy of conver-sions (Simpson and Yu, 2003).

Conventionally, Townsend scores (and other area measures) aretime-point specific. Observations for areas for each input variableare expressed relative to that variable’s national rate (using zscores), e.g. a location’s unemployment rate is relative to UK unem-ployment rate. Here we needed to identify change between time-points. To achieve this, numerators and denominators of the fourinput variables for 1971–2001 were stacked in one file. Thus, whencalculating deprivation, for each CPRE district, rates in every yearfor each variable are expressed relative to the UK 1971–2001 aver-age of that variable. The resulting Townsend scores are then com-parable so that if an area changed from a (relatively deprived)score of +6 in 1991 to +5 in 2001 then, according to the compositeof the input variables, the area became less deprived over time(Norman, 2010). Of particular relevance here is that changing lev-els of deprivation over time can be related to changes in anotherphenomenon (Norman et al., 2008).

Fig. 1 shows how the national level of each indicator variablechanged between 1971 and 2001 (including for illustrative pur-poses 1981, not used in our analysis). Levels of non-home owner-ship and no-car access steadily decline along with a reduction inhousehold overcrowding which has become increasingly rare.Unemployment rose after 1971, being relatively high in 1981 and1991 when the economy was in recession. Unemployment wasmuch lower in 2001. The distributions of Townsend scores acrossthe CPRE districts are illustrated by boxplots (Fig. 2) with the meandeprivation gradually reducing over time along with reductions inthe range and standard deviation.

An alternative scheme through which to investigate intrusion–deprivation relationships temporally is the Breadline Britain index.This index measures relative poverty based on a lack of the per-ceived necessities of life, defined as those that more than 50% ofthe population believes ‘‘all adults should be able to afford andwhich they should not have to do without’’ (Gordon et al., 2000).Dorling et al. (2007) note that this approach can be replicated fora time-series when census data and poverty surveys occur atroughly the same time, which has been the case approximatelyevery 10 years since 1970. Information from detailed householdpoverty surveys is used to classify each household as ‘poor’ or

0

20

40

60

1971 1981 1991 2001

%

Unemployment Overcrowded householdsNo car access Non-home ownership

Fig. 1. Changing national levels of deprivation indicator variables, 1971–2001.

Fig. 2. Changing distribution of CPRE district Townsend deprivation, 1971–2001.

Fig. 3. Changing distribution of CPRE district Breadline Britain, 1970–2000.

G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57 49

‘not poor’. Information comparable to the census is included, suchas household composition, tenure, car ownership and social class.Survey data is analysed to assess the relationship between thesecensus-type variables and the poor/not poor classification ofhouseholds. These relationships are then applied to census areadata to estimate the number of poor households in each locationand has been applied to create a time-series relating to approxi-mately 1970, 1980, 1990 and 2000.

The main variable of interest is the percentage of householdswhich are ‘Breadline poor’. These households are under the povertyline, defined theoretically as the resource level that is so low thatpeople are excluded from participating in the norms of society(Dorling et al., 2007). To achieve geographical consistency overtime, the Breadline Britain data has only been released for rela-tively coarse geographies which lack the versatility of the ward le-vel data for which we have input data for the Townsend index.Whilst this is not problematical in terms of the study reported herewhich is for broad regions, should more local environmental intru-sion data become available, the lack of Breadline data at a consis-tent small area scale over time would be restrictive. Moreover, theuse of the Townsend index will mean that distributions of individ-ual deprivation indicator variables can potentially be comparedwith environmental intrusion to determine relationships with par-ticular dimensions of deprivation. For these reasons we use theTownsend index but, for corroboration, present sample findingsusing the Breadline Britain index.

Dorling et al. (2007) note that the geographical pattern of pov-erty revealed by the Breadline Britain scores demonstrates similargeographical distributions at every time period but that Britain be-came less polarised during the 1970s and then increased throughthe 1980s and 1990s. This is revealed for the geographical areas

used in the CPRE study in Fig. 3 which shows that the percentageof households below the poverty line decreased between 1970and 1980 but then increased up to 2000. The difference in patternwith the overall change in Townsend scores illustrated in Fig. 2 isbecause the perceived necessities of life are reappraised every10 years with the poverty level re-set, whereas the Townsendscores are relative to the same level throughout the time period.Despite their methodological differences, for the CPRE districtgeography there is a strong positive relationship between theTownsend scores and Breadline Britain index. The relationship isstrong in the early 1970s (correlation, 0.88) and then increasesby the early 1980s (correlation, 0.98) remaining at that level tothe early 2000s. The similarities in the distributions of the Town-send and Breadline Britain schemes suggests that when relatedto environmental intrusion very similar results will emerge.

Our analysis seeks to describe contemporaneous variation indeprivation and environmental intrusion in England. However,data constraints preclude the use of environmental and social datasets for corresponding years, and of necessity we relate data forbroadly equivalent periods. Thus the 2007 period relates 2001deprivation data to the 2001–2007 intrusion data; the 1990s peri-od relates 1991 deprivation data to intrusion data collected for ‘theearly 1990s’ (up to 1995); whilst the earliest analysis relates 1971deprivation data to intrusion data for the ‘early 1960s’ (exact yearsunreported). Whilst not ideal, the time span of the analysis exceeds40 years, hence these discrepancies are insufficient to precludeidentification of the general variation in environmental equityoccurring over the study period. We use descriptive statistics,including population weighted histograms (popular in recent na-tional small-area EJ studies) in our analysis. These are simple, butbecause we are dealing with a population rather than a sample,are able to provide a powerful description of the observed patterns.

3. Results

Fig. 4 illustrates the relationship between Townsend depriva-tion and environmental intrusion for all the English districts, foreach of the three time periods. Over time, there has been a clearshift in the distribution for both variables. Deprivation has easedsignificantly (high positive values indicate most deprived), withthe ‘cloud’ of points gradually and consistently moving from rightto left consistent with the Fig. 2 boxplots. Over the same period,intrusion increased, with the most noticeable change with respectto intrusion extremes. Between the 1960s and early 1990s thenumber of districts with low levels of intrusion fell substantially(17 districts with <20% area intrusion in the 1960s to one in

Fig. 4. Relationship of environmental intrusion to Townsend deprivation in England, 1960s–2007. Note: Higher Townsend scores indicate greater deprivation.

50 G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57

2007). Conversely, the increase in districts where no area is leftwithout some form of intrusion was relatively modest betweenthe early 1960s and 1990s (a rise from 10 to 15 districts over threedecades), but substantial from the 1990s to 2007 (a rise from 15 to33 districts with 100% intrusion over c. 15 years). These data illus-trate that many districts have reached a ‘saturation’ level of envi-ronmental intrusion where there are no areas left undisturbed byvisual or noise intrusion. This increase has been particularlymarked since the early 1990s.

Similarly, Fig. 5 illustrates the relationship between povertyindicated by the Breadline Britain index and environmental intru-sion area. The cloud of points moves up the y axis as intrusion in-creases through time such that the number districts with very lowlevels of intrusion in the early 1960s decreases by the 1990s andthen further by 2007. Concurrently, the number of districts at sat-uration point increases. The gradual shift towards lower levels ofdeprivation as seen for the Townsend scores (Fig. 4) is not evidentbecause of the way the Breadline Britain index is calculated. How-ever, the relationship between poverty and intrusion is consistentin the early 1960s, 1990s and in 2007 and does not appear likely to

reveal aspects differently to the Townsend scores, especially giventhe close correlation between the schemes. Since we wish in futureto investigate individual deprivation indicator variables and theirrelationship with intrusion, and to conduct a more geographicallydetailed study all further work reported here uses the Townsendscores.

Fig. 6 plots Townsend deprivation against intrusion. For eachtime period, districts are placed in rank deprivation order, andgrouped into deciles of approximately equal population count,and the decile mean deprivation score calculated (the first decilecomprises the most deprived districts). From these plots it isapparent that, firstly, the more deprived districts have the greatestlevels of intrusion, an observation consistent across all three timeperiods. By 2007, the most deprived decile experiences 100% intru-sion, compared to 65% for the least deprived decile (and just 55%for decile eight). Secondly, it is evident that the least deprived dis-tricts do not enjoy the least intrusion; in the final quintile the levelof intrusion increases after having fallen through the other deciles.Fig. 6 also shows that, almost without exception, the degree ofintrusion has increased from the early 1960s to the early 1990s,

Fig. 5. Relationship of environmental intrusion to Breadline Britain in England, 1960s–2007. Note: Higher Breadline Britain scores indicate increasing poverty.

G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57 51

and again to 2007, for all deprivation deciles. That is, no particulargroup escapes the added intrusion occurring over the last fourdecades.

The social distribution of these increases in intrusion are noteven, however. Fig. 7a plots change in deprivation score from1971 to 1991 against change in intrusion from the early 1960s tothe early 1990s; Fig. 7b presents a similar plot for the early1990s–2007 interval. For both intervals, all districts experiencedan improvement in deprivation, and the intrusion data has anapproximately bell shaped distribution, with respect to the changein deprivation score. That is, districts which experienced the least,and the greatest improvement in deprivation status, tend also tohave experienced the least additional intrusion, whilst intrusionhas been greatest in those districts that experienced more interme-diate levels of deprivation reduction.

These data are summarised in Fig. 8. Districts have been placedin bands according to change in deprivation (Townsend unit inter-vals) from 1971 to 2001. For each band, the mean change in intru-sion over the associated period from the early 1960s to 2007 is

then given, with the mean deprivation score in 1971. From thesedata, three broad groups of districts can be identified. The first iswhere additional intrusion over the four decades has been low(c. 10% growth in area), and where deprivation status has improvedsubstantially (>7 Townsend units). The second group also experi-enced little added intrusion over 40 years (c. 9% growth), but itsdistricts have experienced the least improvement in deprivation(<4 Townsend units). These districts include those that had inter-mediate levels of deprivation in 1971. The third group comprisesthe remaining districts, which were the least deprived in 1971,have experienced intermediate levels of improvement in depriva-tion status, but account for the bulk (>80%) of the growth in intru-sion since the early 1960s.

Figs. 9 and 10 present similar analyses confirming this generalpattern. Fig. 9 is a comparable analysis, but excludes the 33 sub-re-gions where intrusion reaches 100%, and hence represents an at-tempt to better address the intrusion saturation effect. Fig. 10looks at the intrusion data in another way; here we analyse intru-sion as a share of the remaining tranquil area in sub-regions, rec-

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10Population weighted deprivation decile

(% d

istr

ict,

mea

n by

dec

ile) Early 1960s Early 1990s 2007

(Deprivation declines L to R)

Intr

usio

n ar

ea

Fig. 6. Environmental intrusion by deprivation decile, England 1960s–2007. Note:Districts are placed in order of decreasing Townsend material deprivation score andthen grouped into deciles of roughly equal population (c. 4.9 million per decile).Missing bars at 2nd and third decile arises as Greater London Authority population(7.5 million) exceeds decile size (and so appears in next decile).

(a) Early 1960s to early 1990s

-50

0

50

100

150

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250

-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0Change in deprivation 1971-1991 (Townsend index units)

199

0s (%

of 1

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intr

usio

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ea)

(b) Early 1990s to 2007

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-4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0Change in deprivation 1991-2001 (Townsend index units) C

hang

e in

intr

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n ar

ea e

arly

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0s-2

007

(% o

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ly 1

990s

intr

usio

n ar

ea)

Cha

nge

in in

trus

ion

area

ear

ly 1

960s

- ea

rly

Fig. 7. Change in environmental intrusion and deprivation for districts in England:(a) Early 1960s to early 1990s; (b) Early 1990s to 2007.

0

20

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60

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120

2.5 3.5 4.5 5.5 6.5 7.5 8.5Change in Townsend deprivation score 1971-2001

Cha

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ear

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007

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ore

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971Change in intrusion

area early 1960s-2007Townsend deprivationscore in 1971

Fig. 8. Relationship of changes in environmental intrusion and deprivation inEnglish districts from the early 1960s to 2007.

0

5

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140

2.5 3.5 4.5 5.5 6.5 7.5 8.5

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(% fr

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Change in Townsend deprivation score 1971-2001

Change in intrusion area early 1960s-2007Townsend deprivation score in 1971

Fig. 9. Relationship of changes in environmental intrusion and deprivation inEnglish districts from the early 1960s to 2007: Districts with 100% intrusionexcluded.

52 G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57

ognising that even if one particular sub-region experiences a rela-tively small aggregate loss of tranquil area, the impact of this losscould be greater if it has less of this resource to lose.

4. Discussion

4.1. Evolution of environmental inequality

For some conceptions of justice (e.g. a just distribution is onewhere people get what they deserve) understanding how aninequality arises is important in judging whether that inequalityis unfair. For other conceptions (e.g. rights based), the causal pro-cess is less relevant to judging fairness, but it may still be impor-tant to understand this process, so as to design effectivemitigation. There are many processes by which environmentalinequalities may develop (see e.g. Liu, 1997; Been, 1994). Of these,deliberate discrimination in decision-making (e.g. regarding devel-opment location or planning consent) is perhaps the only one con-sidered overtly unjust (and rarely demonstrated), and a variety ofother processes have been theorised which may produce inequalitypatterns (these often lack robust testing due to a lack of time-seriesdata). Four main theories can be identified, relating to locationchoice, risk perception, neighbourhood transition, and planningpractice (see Liu (2001) for an extended discussion).

0

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2.5 3.5 4.5 5.5 6.5 7.5 8.5

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in 1

971

Loss

in tr

anqu

il ar

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rese

nt in

ear

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-20

07 (m

ean

of d

istr

icts

in d

epriv

atio

n ch

ange

ban

d)

Change in Townsend deprivation score 1971-2001

Mean % loss 60-2007 of 1960 tranquil area Townsend deprivation score in 1971

Fig. 10. Relationship of changes in environmental intrusion and deprivation inEnglish districts from the early 1960s to 2007: Intrusion measured as loss of 1960snon-intruded area.

G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57 53

Location theory argues that households locate to meet a pack-age of needs, maximising utility (Tiebout hypothesis). As valuationof, and ability to pay for, environmental quality is non-uniformamongst households (the affluent are deemed to give these attri-butes a higher value), an environmental inequality results. A sim-ilar utility maximising process may also operate with respect tofirms who seek to minimise costs, and so may locate in low incomeareas with cheaper land and labour. Risk theory argues that peo-ple’s risk perception varies according to individual and social orcultural group characteristics, thus affecting household location.Those persons who value the environment less than average mayalso perceive environmental risk as less than average, and will lo-cate closer to the risk than average. Neighbourhood transition the-ory argues that certain social groups become closer to areas oflower environmental quality through interactions between house-holds. For example, invasion–succession sees minorities (e.g. ofimmigrant stock, or of colour, who often have below average in-come) arrive in a neighbourhood of low environmental quality,do well, and make it more attractive for other minorities who lo-cate there, creating a concentration of such households in a lowenvironmental quality area. Alternatively, location of a hazardousfacility in an area may cause more affluent householders to moveaway, with their properties then occupied by lower income groupsattracted by the higher quality housing available, thus producing aminorities concentration close to the hazard. Finally, the land useplanning system tends to protect high quality environments bydirecting threats to environmental quality towards areas that arealready degraded. Thus, it is theorised that risks and hazards canbe concentrated by the housing market: people with the meansto live in higher quality protected environments do so, leaving low-er income groups to locate in unprotected areas where hazards aremore common. Additionally, housing developers seek to maximisereturns by developing properties for high income households inareas of high environmental quality, with social housing morelikely to be directed to lower environmental quality areas.

We aimed to describe how the social distribution of an environ-mental quality measure changed over time, but not to test which ofthese causality theories drives the observed distribution. However,in describing how the environmental equity pattern evolvednationally, over four decades, it is possible to more confidentlyspeculate as to which of these processes may be more significant.Firstly, it is clear that in the early 1960s there was already a wellestablished social distribution of environmental quality, with highlevels of intrusion (75–85% by area) in the most deprived districts,and progressively lower levels of intrusion for less deprived dis-tricts. The exception is with the least deprived districts (last decileof Fig. 6) where levels of intrusion are elevated, although still wellbelow those observed for the most deprived districts. This observa-tion is consistent with those reported elsewhere for air quality inthe UK (Mitchell and Dorling, 2003; Walker et al., 2003). In thesestudies the below average air quality observed for the least de-prived deciles was interpreted as the least deprived householdschoosing to locate in areas of lower environmental quality thanthey might otherwise be expected to, so as to take advantage ofbenefits of that location (e.g. access to certain jobs and services isoften better in large urban areas where air quality tends to beworst). This overall pattern is evident in the intrusion data fromthe 1960s to date, when the level of intrusion increased acrossall deprivation classes.

If we consider the contemporaneous changes in deprivation andintrusion (Figs. 7–10), a coarse longitudinal pattern emerges, withno straightforward relationship (e.g. linear correlation) betweenthe universally observed improvement in deprivation scores andincreases in intrusion. Rather, the relationship is non-linear, withrelatively little growth in intrusion for those districts experiencingeither little or much improvement in deprivation, and major

growth in intrusion occurring in those districts that were amongstthe least deprived in 1971, and which experience intermediate lev-els of improvement in deprivation status.

Of the three groups of districts defined by deprivation changethat identified in Section 3, two experienced little added intrusionover the last four decades (c. 10% of total area growth). One ofthese groups experienced substantial improvements in deprivationscore (>7 Townsend units), but from a very low base in 1971. Thisgroup includes the districts (Table 3) of Knowsley in the NorthWest (the most deprived district in England in 1971) and NorthEast districts such as Sunderland and Durham County, amongstthe most deprived of all districts in 1971. From our knowledge ofthese areas, we speculate that the small growth in intrusion isdue to a declining industrial economy and a relative lack of jobsand demand for development.

The second group with little intrusion growth has a rather dif-ferent deprivation profile. Districts in this group were, on average,of intermediate deprivation in 1971, and experienced the smallestimprovement in deprivation. These districts (Table 3) are predom-inantly urban districts in the North, Midlands and Greater LondonAuthority area. Whilst low demand for development may be a fac-tor behind low intrusion growth for some of these districts, it is nota satisfactory explanation for all (e.g. SE districts where the econ-omy is more buoyant and demand for development land high).Rather, it would appear that the potential to increase intrusion be-came constrained after the early 1990s for these urban districts,and predominantly those that are the most deprived, when maxi-mum possible levels of environmental intrusion were reached (i.e.entire districts became affected by visual or noise intrusion).

The final group of districts has very high levels of intrusiongrowth, and deprivation levels, lowest of all districts in 1971, haveshown intermediate improvement. This group comprises largelyrural counties and districts with medium sized cities, where urban-and sub-urbanisation is likely responsible for the major growth inintrusion, with housing, roads and infrastructure developed inareas accessible to major urban centres, yet attractive to develop-ers and householders. In seeking out the benefits of suburban liv-ing, particularly larger cheaper houses in attractive areas withaccess to major centres, householders are of course contributingto the decline in the environmental quality that they value. It is

Table 3English districts in selected deprivation change (1971–2001) classes.

Deprivation score improvement 1971–2001 (Townsend units)

64 P5 6 6 P7

City of Leicester Wirral District South TynesideDistrict

Luton NorthamptonshireCounty

NorthumberlandCounty

Greater London Authority City of Plymouth Sunderland DistrictBlackburn with Darwen Sefton District North Tyneside

DistrictOldham District Suffolk County Durham CountyBlackpool Dorset County Knowsley DistrictBirmingham District Warwickshire CountyManchester District YorkDudley District HaltonCoventry District Gloucestershire CountyMilton Keynes North LincolnshireSlough SwindonBradford District Norfolk CountyReading East Riding of YorkshireSandwell District Salford DistrictCity of Peterborough Nottinghamshire CountySolihull District Cheshire CountySouthend-on-Sea Cambridgeshire CountyCity of Nottingham Staffordshire CountyNorth East Lincolnshire County of HerefordshireCity of Wolverhampton

DistrictCumbria County

Bolton District Telford and WrekinWalsall District Liverpool DistrictCity of Stoke on Trent Oxfordshire CountyBury District North Yorkshire County

Lincolnshire CountyHampshire CountyBracknell ForestThurrockShropshire CountyWigan DistrictHartlepoolNewcastle Upon TyneDistrictDerbyshire CountyWest BerkshireStockton-on-TeesRotherham DistrictWiltshire County

-70

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-10

0

-50 0 50 100 150 200 250% Change in population 1971-2001

% C

hang

e in

no

car a

cces

s

Milton Keynes grew from 66k in 1971 to 207k in 2001 19

71-2

001

Fig. 11. Relationship between changes in population and non-car access: 1971–2001.

54 G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57

particularly telling that the districts which experienced the largestpopulation increases have also seen the largest fall in non-car ac-cess (Fig. 11), which we would expect to have an association withenvironmental intrusion.

We would like to place our findings in the context of previouslongitudinal EJ studies. However, these studies (Section 1)although addressing relatively long time periods (usually 20 years,and 40 in the case of Baden and Coursey (2002)), only addresswaste siting in the USA, and are heterogeneous in terms of studyarea and unit, waste facility type, social metric (e.g. ethnicity, in-come), and method. The conclusions drawn from these studiesvary widely; some conclude waste facilities have been dispropor-tionately sited in ethnic and low income communities, and othersthat these communities evolved close to the waste sites; somestudies have concluded that housing market dynamics drive theobserved current social distributions of waste facilities, others thatthey do not. It is therefore difficult to draw parallels between thesestudies and our own, although we note with interest, that Been andGupta (1997), from analysis of a random sample of 608 regulatedwaste facilities across the USA, found it was not the poor that borethe brunt of waste facility siting over the previous 25 years, butmiddle income groups. This has resonance with our findings thatit is those areas that experience intermediate levels of socio-eco-

nomic advancement (as reduction in deprivation score) that expe-rience the greatest increases in environmental intrusion. Given thelimitations of our analysis we can only speculate on which causalprocesses considered above underlie our observations; however,for England as a whole, market forces regulated by the land useplanning system, would seem to offer the most plausible explana-tion for the social distribution of intrusion observed in the 1960sand maintained to the present day. The possibility that other pro-cesses also act to influence this distribution cannot be discounted,but their influence is likely to be significant at more local levels.

4.2. Compatibility of environmental sustainability and social justice

A recent review concluded that the major challenge for EJ re-search (along with better understanding links between socialinequality, environmental quality and health, and how theyevolve; see also Pearce and Kingham, 2008) is to test the long heldassumption that ‘‘achieving social justice would move us down theroad to environmental sustainability’’ (Mohai et al., 2009, p. 423;see also Walker and Bulkeley’s (2006) editorial in an EJ Geoforumspecial issue). Such a test is needed, as it is rash to assume thatimproving environmental quality is always socially progressive,with environmental benefits accruing disproportionately to themost disadvantaged. Indeed, it has been argued that: ‘‘. . .social jus-tice and environmental sustainability are not always compatibleobjectives. Assertions to the contrary are more often, I think, basedon wishful thinking rather than on clear sighted analysis of hardempirical evidence’’ (Dobson, 2003, p. 83).

Dobson argues that whilst movements for social justice andenvironmental sustainability may share some common environ-mental ground, they have different objectives, and that measuresto achieve one objective may constrain ability to meet the other.For example, environmental sustainability seeks to reduce envi-ronmental impact (say tonnage of waste to be disposed of), whilstsocial justice seeks to ensure that the costs and benefits of societaldevelopment, include environmental impacts of development (sayof waste incinerators, landfills) are more fairly distributed by socialclass or race. Dobson argues for empirical research on the relation-ship between environmental sustainability and social justice,including testing of such hypothesis as: ‘‘the environmental justicemovement is not a movement for environmental sustainability’’(Dobson, 2003, p. 85).

We have an ambition to address Dobson’s challenge by usinglongitudinal analysis to describe the co-variation of environmentaland social justice metrics (it would also be necessary to demon-strate model specification, i.e. that both measures are respondingto common development processes). Table 4 illustrates four possi-ble co-variation scenarios, of which only the first could support aconclusion of compatibility. As our analysis shows no decline in

Table 4Environmental sustainability – social justice scenarios.

Scenario Environmental sustainability Social justice Interpretation

1 Improves Improves Compatible2 Improves Declines Not compatible3 Declines Improves Not compatible4 Declines Declines Not compatible

G. Mitchell, P. Norman / Geoforum 43 (2012) 44–57 55

intrusion accompanied by a more equal social distribution of thatintrusion (scenario 1), it cannot be used to indicate compatibility.However, the analytical limitations have proved such that in prac-tice, neither can a conclusion of incompatibility be drawn. Theselimitations take a number of forms, and must be overcome if Dob-son’s central question is to be addressed.

Firstly, suitable environmental metrics are needed. The CPRE’sintrusion measure we use is arguably too blunt a measure of envi-ronmental sustainability. We see intrusion growing fastest in theleast deprived districts, but our measure addresses areal extentonly, and not the intensity or severity of intrusion. For the most de-prived districts intrusion has reached saturation levels in areaterms, but intrusion could continue to increase in ways that weare unable to quantify (e.g. growth in traffic noise on existingroads, urban infill within buffer zones of intrusion features, cumu-lative intrusion). Thus our intrusion measure masks other environ-mental attributes potentially important in measuringenvironmental sustainability. We also report that one reviewerfound it ironic that the data used in our analysis comes from theCPRE who, ‘focus on preserving the tranquil rural idyll and resist-ing development . . .in largely white middle class rural areas. . . thussupporting patterns of infrastructure and development being con-centrated in existing, often deprived, urban industrial spaces’. Thiscertainly invites debate (over the appropriateness of the intrusionindex measures (Table 1), the level of influence the CPRE has hadon patterns of infrastructure development, how exclusive theyare as an organisation, etc.), but clearly, this criticism illustrateswell the need to agree suitable environmental metrics.

Secondly, and fundamentally, a clear conception of what ismeant by sustainable is needed. For those adopting a strong sus-tainability perspective, intrusion may be considered inherentlyunsustainable as it represents loss of environmental capital (e.g.greenspace). However, for those holding a weaker sustainabilityperspective, intrusion growth may be judged sustainable, so longas the value of the environmental capital lost to that developmentis substituted with human capital (e.g. infrastructure) of equalvalue.

Thirdly, the conception of justice must also be defined. Forexample, our analysis shows that areas of greater deprivation tendto experience higher levels of intrusion in the 1960s (Fig. 6), butthe growth in intrusion to 2007 has largely been in the least de-prived districts (Fig. 8). The interpretation of whether these distri-butions are fair can only be made if the conception of justice isdefined. For those subscribing to justice as a merit based concep-tion, the observed distributions are likely just, as households locateaccording to their economic means (assuming the location pro-cesses discussed in Section 4.1 operate). For those who subscribeto a needs based conception of justice, the early 1960s intrusiondistribution might be considered unjust, but one that is becomingmore just as the least deprived bear a greater share of the addedintrusion (the distribution becomes more egalitarian).

Fourthly, problems with availability of longitudinal data, partic-ularly spatial environmental data, must be overcome. In principle,key data sets might be amenable to construction through model-ling. For example, given historical data on road networks, trafficflows, industrial activity, etc., it should be possible to produce airquality maps for past years using the same techniques (Stedman

et al., 1997) used to model air quality nationally today. If analysisthen focuses on atmospheric concentrations in breach of air qualitystandards, it would become possible to consider a clear environ-mental sustainability metric (one important to public health) in re-spect of a clear conception of justice (a universal right to air qualitycompliant with nationally agreed health based standards). Such ananalysis would be particularly interesting as the results are notintuitive; past development has increased the extent of pollutingactivities, but technology has substantially reduced emissions perunit activity for many pollutants. Finally, a proper investigationof Dobson’s assertion would require such analyses to be conductedacross a range of environmental issues so as to develop a suffi-ciently comprehensive evidence base.

5. Conclusion

We have presented a longitudinal EJ analysis using data on so-cial deprivation, and environmental intrusion from a range ofdevelopments in England spanning about 40 years. Data limita-tions mean that spatially and temporally this remains a coarseanalysis, but to our knowledge, this is the first national longitudi-nal EJ study outside the USA. A clear social distribution of environ-mental quality is maintained throughout the study period, with themore deprived districts subject to lower environmental quality.However, the bulk of the growth in environmental intrusion oc-curred in districts which were the least deprived at the beginningof the period studied.

Whilst evidence for environmental inequality is clear, it is hardto argue that environmental injustice occurs, in part as the marketand land-use planning processes thought to underlie the observedpatterns are not inherently discriminatory. If injustice does occur,it is arguably against those who suffered the greatest increase inintrusion whilst experiencing only modest improvements in depri-vation status. However, these were the least deprived in the early1960s, and likely had a greater opportunity to take remedial action(e.g. move somewhere better) than the economically more mar-ginal groups.

Our study also illustrates the challenge inherent in testing thehypothesis that environmental sustainability and social justiceare compatible. As Dobson, 2003, pp. 87–90) says, this requires ex-plicit definition of environmental sustainability (what is to be sus-tained, for how long, and for whose benefit?), and how justdistributions are identified (what is ‘fair’, who decides?). However,for the time being, it appears that investigation of the compatibilityissue must await the availability of appropriate spatially resolvedlongitudinal environmental data needed to tackle this fundamentalquestion.

Acknowledgements

Census data provided by the Office for National Statistics (viaESRC/JISC supported MIMAS CASWEB service) are Crown copyrightand reproduced with permission of the Controller of HMSO.

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