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Methodological problems in the generation of household waste statistics
Applied
Pergamon
Geography, Printed
PII: SOl43-6228(96)00031-S
Vol. 17, No. 3, pp. 231-244, 1997 0 1997 Elsevm Science Ltd in Great Britain. All riehts reserved 0143.6228/97 rSr7.00 + 0.00
Methodological problems in the generation of household waste statistics An analysis of the United Kingdom’s National Household Waste Analysis Programme Julian P. Parfitt Centre for Social and Economic Research on the Global Environment, East Anglia, Norwich NR4 7TJ, UK
University of
Robin Flowerdew North West Regional Research Laboratory, Department of Geography, Lancaster University, Lancaster LA1 4YB, UK The establishment of national targets designed to bring about more sustainable waste management in many developed countries has not generally been accompanied by the creation of reliable information systems for policy formulation and monitoring. In the first part of this paper the factors that complicate the collection of reliable household waste statistics are examined from both applied and theoretical perspectives. The second part presents a critique of the recent UK National Household Waste Analysis Programme (NHWAP). This was based on waste-collection-round samples selected by means of a geodemographic classification package (ACORN). NHWAP data are currently the only national data on household waste composition and the results have already been widely used by policy-makers at national and local levels. However, it is concluded that the NHWAP sample was too limited for there to be much confidence in the results. A national research programme based on household samples is required in order to understand the relationships between household waste arisings and socioeconomic, institutional, spatial and temporal variables. An outline methodology is suggested. 0 1997 Elsevier Science Ltd
Keywords: household waste, sampling, UK National Household Programme, waste management policy
Waste Analysis
United Kingdom policy towards waste has evolved rapidly in recent years, partly in response to home-grown public pressures to curb the environmental impacts of waste disposal practices. In addition, there have been important external influences from European Union environmental policy and the wider debate on sustainability fostered by the 1992 Rio Earth Summit. A common problem has emerged in countries that have embarked on policies promoting greater sustainability in waste management through waste recycling and waste 231
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The generation of household waste statistics: J. P Parjitt and R. Flowerdew
reduction. The pace of policy-making has not been matched by an equal effort to provide meaningful waste statistics. This paper examines the problem through the example of the UK National Household Waste Analysis Programme (NHWAP), which was set up in 1991 to generate household waste composition and weight data. Although some of the issues raised are specific to the UK context, the general conclusions are relevant to similar national programmes in other European and North American countries.
National household waste analysis: aspects of research design One fundamental difficulty in collecting household waste compositional data is that the analysis of waste composition is expensive and the physical process of hand-sorting waste into categories is often an unpleasant task. Another problem is that household wastes are highly heterogeneous and are susceptible to significant fluctuations in quantity and composition. A large sample size is therefore required to achieve a representative sample and sampling error is likely to be particularly large for individual categories of waste that are less often discarded, such as textiles (Klee, 1980: 45). In general, the need for greater waste categorization increases as policies higher up the waste hierarchy are pursued. The relationship between sustainable waste management and increasing data complexity of household waste statistics is summarized in Figure 1. In the UK, current interest in the quality of household waste statistics is largely derived from the need to monitor material recycling against a national target set by the government in 1990, that 25 per cent of household waste was to be recycled by the year 2000. In addition, the EC Packaging Directive requires specific data on the packaging element of the waste stream (Department of the Environment and Welsh Office, 1995). Household wastes typically have high coefficients of variation in terms of the total weight of different component categories generated by households. The composition varies through random week-to-week fluctuations in what individual households discard and through the influence of seasonal, regional and socioeconomic factors (Tchobanoglous et al., 1993: 56). Furthermore, differences exist between local authorities in waste service provision, such as facilities for the collection of recyclable materials and the method of containment provided for door-to-door waste collection. Sampling strategy can be made more cost effective by controlling for various combinations of the most important of these variables. However, the identification of such variables is a research problem in itself as few studies have systematically related socioeconomic, geographical and waste service provision variables to household waste generation. The most direct way in which this can be approached is by means of research focusing on the basic unit of waste production-the household. One extensive study that adopted this method was carried out in the West Midlands by Rufford (1984). Although now rather dated, Rufford’s research has yet to be surpassed by any other household-based research, either in the UK or elsewhere. The basic method involved the generation of variables through a house-to-house questionnaire survey. These were then related to the results of waste composition analyses conducted on samples taken from individual households. All the variables listed in Table I were found in analysis of variance to be significantly associated with at least one waste category. Those variables found to be the most strongly associated with quantity of household waste were then selected as the basis for categorizing households for an area-based waste arisings model. The main variables selected were: household size, tenure, and whether or not the head of household was economically active. Rufford’s research was a cross-sectional study based on a sample drawn from one region at one particular time of year. It did not quantify seasonal and regional differences nor investigate longer-term temporal variations. The mathematical form adopted by 233
The generation of household waste statistics: J. t! Pa@tt and R. Flowerdew
Table 1 Household characteristics with at least one waste category
found to be significantly
Smoke control zone Property type Tenure Occupation of head of house
Frequency of shopping trips for food Age of domestic adult Employment status of second adult Family life cycle Number of males Number of females Total household size Mode of newspaper purchase Car ownership
Type of domestic heating Property size (bedrooms) Pet ownership Freezer ownership Milk delivered (Source Rufford
associated
1984: 167)
Rufford to express the wider sample design required for national estimates, household type on a simple set of waste-related variables, was as follows: Wi,k
=
%~(~a)ijk%
W,,
where
(WO)$
Collection
= the quantity of waste generated in a given area in region i, season j and point in trend k = waste generation coefficient associated with households of type a in region i, season j and point in trend k (Rufford, 1984: 280).
rounds
/ J
>
,,,,,,
Bulky waste
\
2
main outlets. 234
Estimated total household Source:
Patfitt
Mixed rounds Other sources
--
Figure
classifying
Civic amenity
sites
wastes for England and Wales in 1993/4 (t X 106yr-‘)
et al. (1997)
by
The generation of household waste statistics: J. P. Parfitt and R. Flowerdew
A more up-to-date sample design would require modification to reflect recent changes in household waste management since Rufford’s research was undertaken. For instance, the introduction of 240-litre wheeled bins in some parts of the UK has resulted in an increase in refuse-collection-round waste (Civic Amenity Waste Disposal Project et al., 1993). In addition to the problems of sampling strategy and waste analysis, defining the systems boundary for household waste must also be addressed by the research design. Household waste collected by local authorities from ordinary refuse collection rounds is not equivalent to the total household waste generated. In a recent review of UK household waste statistics conducted for the Department of the Environment (Parfitt et al., 1977: 81), it was estimated that one-third of household waste in England and Wales was collected by means of outlets other than the local authority house-to-house collection round (Figure 2).
The UK National Household Waste Analysis Programme No consensus has emerged in the international research literature as to the most appropriate methodology for conducting compositional analysis of household wastes. A variety of different methods have been developed based on sampling units at different levels of aggregation, ranging from individual households through to bulk samples of refuse collection rounds (Pa&t et al., 1997: 36). There have been recent EU proposals to standardize the approach across member states. This process could benefit from the issues raised by this UK case study. From 1991 to 1994 the UK Department of the Environment’s NHWAP generated data on household waste composition and weight. The results have already been widely disseminated as they are the only national statistics for household waste composition in the UK and the first to be collected since 1980 (Department of the Environment, 1992: 10). Furthermore, in the absence of local waste analysis, NHWAP statistics are the main source of waste compositional data used by local authorities in compiling their household waste recycling plans (Coopers and Lybrand, 1993: 7). The present authors were commissioned by the Department of the Environment to review the methodology adopted by the NHWAP and to comment on the results (Parfitt et al., 1994). The physical method of waste analysis adopted by the NHWAP involved collection of 5-tonne samples of household waste from selected collection rounds (waste from approximately 400-600 households). Each sample was sorted into different size fractions by means of a trommel (a large rotating drum with walls fitted with apertures of specific size) from which subsamples were then taken for hand-sorting into compositional categories (Warren Spring Laboratory and Aspinwall & Co. Ltd, 1993: 11-12). The NHWAP approached the question of sampling strategy by use of a commercially available system of classified residential area profiles, called ACORN (A Classification of Residential Neighbourhoods), marketed by CACI (Crichton, 1992). The 1981 census version of ACORN was based on a set of 40 census variables available for small geographical areas in Great Britain (enumeration districts). These census variables were used as the raw material for a multivariate classification of enumeration districts, with districts grouped together if they were relatively similar on the set of census variables taken as a whole. The classification process could produce any desired number of groupings of enumeration districts, but the 1981 census-based ACORN system was marketed as a classification with two levels, of 38 area types and of 11 larger neighbourhood types which are shown in Table 2. These 11 ACORN groups were used as the basis of the NHWAP sampling strategy. Each enumeration district was assigned to one of these groups, and the NHWAP sampled collection rounds that were concentrated as far as possible in enumeration districts in one of 23s
The generation of household waste stutistics: J. I? Parfitt and R. Flowerdew
B
4
a
236
The generation of household waste statistics: J. f? Patjitt and R. Flowerdew
Table 2 The 11 1981 ACORN Groups Group
% UK population
A B C D E F G H I .I K
3.3 16.8 17.8 4.2 12.7 9.0 6.1 3.4 4.7 17.7 4.2
Agricultural areas Modem family housing, higher incomes Older housing of intermediate status Older terraced housing Council estates-Category I Council estates-Category II Council estates-Category III Mixed inner metropolitan areas High-status non-family areas Affluent suburban housing Better-off retirement areas
the groups. The expectation was that other enumeration districts elsewhere in the UK would have similar amounts and composition of household waste to those sampled by the NHWAP for the same ACORN group. Collection rounds were selected that represented each ACORN group in different districts around the country; in addition, four collection rounds representative of the four most common ACORN groups were selected in Leeds. The districts to be sampled were also selected using an ACORN-related methodology. The same 40 variables were computed for 1981 at the district level, and input to a multivariate classification of districts. The districts sampled were intended to be representative of the families identified at the district scale. Main findings
of the NHWAP
Compositional analysis of the 31 household waste collection rounds sampled during 1992-4 are shown in Figure 3 in terms of kilograms per household per week. Each bar contains the 11 main categories into which the sample was sorted. A number of the categories require brief explanation: Putrescibles: organic wastes mostly of kitchen or garden origin. Fines: any materials with a diameter < 10mm. Miscellaneous combustibles: disposable nappies are the single largest component, otherwise this category contains other composite wastes that are predominantly combustible. Miscellaneous non-combustibles: contains rubble, lumps of soil, cinders from solid fuel burning (greater than 1Omm diameter), plaster and other inorganic wastes from DIY activities. Although considerable variation is apparent between samples, paper (which includes newspaper, magazines, card and cardboard) and putrescibles are the two categories accounting for the greatest weight in all samples but one. The Mendip ACORN A collection round, classified as a predominantly agricultural neighbourhood, contained a large quantity of wood ash from wood-burning stoves, which is represented in the large ‘fines’ category in Figure 3 (New and Davies, 1995: 19). It is difficult to generalize from the raw data without further grouping of the waste categories presented. The present authors have identified two distinct groups of wastes, each containing components that were highly positively correlated with one another (the 237
The generation
of household
waste statistics: J. P: Parfitt and R. Flowerdew
Table 3 Key to ACORN Group and District for NHWAP Samples 1992-94 Code
ACORN
Area
Month
Year
1A 2B 3B 4B SB 6B 18 8C 9c JOC 11C 12c 13c 14D 15E 16F 17F 18F 19F 20F 2lF 22G 23H 241 255 265 275 285 295 305 3lK
A B B B B B B C C c C C C D E F F F F F F G H
Mendip
February. April March August November March
1994 1992 1993 1992 1992 1993 1993 1992 1992 1993 1993 1992 1993 1994 1994 1992 1992 1993 1993 1992 1993 1994 1994 1994 1992 I992 1993 I993 1992 1993 1994
K
Leeds Leeds Warrington Warrington Warrington Warrington Charnwood Charnwood Charnwood Chamwood Leeds Leeds Blaenau Gwent Bromsgrove Gateshead Gateshead Gateshead Gateshead Leeds Leeds Glasgow Wolverhampton Merton St Albans St Albans St Aibans St Albans Leeds Leeds Colwyn
May August November February May June April April March August November February May April April May March February July October January April April April May
correlation matrices for this analysis are presented in the Appendix). The first is dominated by paper/card and contains plastics and glass (correlation coefficients between 0.53 and 0.83). This grouping contains the main post-consumer and packaging-related wastes and constitutes 51 per cent of the total weight of wastes sampled. In the second grouping, which comprises 3 1 per cent of the total waste sampled, putrescible waste is the main component by weight, with fines and the miscellaneous non-combustible categories highly correlated with it and with one another (correlation coefficients between 0.53 and 0.61). This grouping contains the main non-consumer household wastes (such as garden wastes, stones, soil and dusts), as well as organic kitchen wastes. Together the two basic groupings comprise 82 per cent of the total weight of waste analysed by the NHWAP. The mean weight of waste for the two waste groupings is compared for ACORN groups B, C, F and J (those best represented in the NHWAP sample) in Figure 4. The Leeds component of the ACORN B sample (two rounds) has not been combined with the Warrington sample due to the difference in method of waste containment between the two areas. Wastes sampled from areas on wheeled-bin collection, such as at Warrington, are known to be different in composition to collections from non-wheeled-bin areas, particularly with respect to non-consumer wastes.
238
The generation of household waste statistics: J. P. Parjitt and R. Flowerdew
An important compositional difference between ‘affluent suburban housing’ (ACORN J group) and the less affluent ACORN groups B, C and F is apparent. The ACORN J collection rounds contained a weekly average 9.6 kg per household of the predominantly post-consumer waste grouping compared with the combined mean of only 5.2kg per household from ACORN B, C and F collection rounds (t-value = 9.30, significant at 99.9 per cent level). This result suggests a strong link between affluence and the total weight of paper, glass and plastics in collection-round household waste. Comparisons between ACORN groups in terms of total weight of the ‘putrescibles, fines and miscellaneous non-combustible’ grouping are more difficult to interpret as some of the important constituent wastes, such as garden and DIY wastes, are particularly likely to be taken by householders to civic amenity sites. The research was not designed to measure the extent to which households were using this important alternative outlet for their waste. However, it is known from civic amenity site studies that more affluent households use civic amenity sites more often than others, but that the heaviest mean loads are associated with the intermediate ‘skilled manual’ social group (M. E. L. Research Ltd. 1989: 28). An appraisal of the NHWAP ACORN methodology An advantage of using the ACORN system as the basis of the NHWAP sampling is that the classification reflects variation between places in several potentially relevant respects. The input variables include many of those listed by Rufford (TabZe I), as well as others that are either thought to be related to household waste generation or can act as surrogates
_
0
Paper, glass
and plastics
Putrescibles,
fines and miscellaneous
non-combustibles
_
il=
2 2 Leeds ‘B’
4 Warrington
4
6 6 LeedslCharnwood ‘B’
6
6
‘C’ LeedslGateshead
6 Leeds/St
6 Albans
‘J’
‘F’
ACORN group and district
Figure 4
NHWAP
compositional analysis: comparison of main ACORN groups sampled by waste
grouping 239
The generation of household waste statistics: J. P. Pa&t
and R. Flowerdew
for such factors. The ACORN system does appear to reflect real differences between places on dimensions that are probably relevant to household waste, as suggested by the results that have been presented. However, there are a number of drawbacks in using the ACORN system in this way. Firstly, variables known or suspected to be important are omitted from the ACORN classification. These include the waste service provision variables mentioned earlier. If data are available for an ACORN D area, for example, which has wheeled-bin collection, little guidance is available for one that has plastic sack collection. Other variables, such as the use of solid fuel, size of garden, or nearness to civic amenity or recycling facilities, may have important effects on collection-round wastes but are excluded from ACORN. Secondly, irrelevant variables may be included. ACORN is designed as a generalpurpose marketing tool, and has been used very effectively for targeting likely sales prospects for direct mail deliveries. Such a system may not discriminate effectively in of individual variables in the what people throw away. Further, the weighting classification may not be appropriate for predicting household waste. One might expect, for example, the population aged O-4 to be a better predictor of the quantity of disposable nappies entering the waste stream than the balance between manufacturing and service workers or the means of transport to work. The argument is essentially that a classification for use in sampling for the estimation of household waste should be designed to emphasize those variables relevant to the matter at hand, rather than being designed as a generalpurpose tool. A problem with the use of classified residential area profiles is the difficulty of understanding exactly why observed differences exist between and within the ACORN groups sampled. For example, results of compositional analysis presented in Figures 3 and 4 suggest important differences between ACORN J neighbourhoods and less affluent ACORN groups. ACORN J collection rounds were sampled in Leeds (one collection round sampled once in April 1992 and again in April 1993) and St Albans (one collection round repeat sampled in July 1992, October 1992, January 1993 and April 1993). The Leeds sample rounds have a weekly average total weight of 17.3 kg per household, whereas the St Albans sample of the same ACORN group has an average of 13.6 kg. It is not clear to what extent this difference represents random fluctuations, the influence of seasonal effects (Leeds Js were sampled in April only), or some other systematic source of variation, such as differential use of other outlets for household wastes such as recycling or civic amenity sites. The number of samples taken is too small to draw any firm conclusions and the extent to which ‘other outlets’ for household wastes were being used by any of the sampled neighbourhoods was not an integral part of the research design. Even if ACORN groups do discriminate effectively between neighbourhoods that generate different types of waste, it is impossible to be sure why and exactly how the differences arise. One also cannot be sure whether some other form of classification would discriminate better, and hence be more useful in estimating waste generation for other areas. Classified residential area profiles are based on the predominant characteristics of an area. There may be important minority groups within an area that have major effects on household waste generation. For example, an area predominantly inhabited by middleclass pensioners may include one or two properties shared between groups of students, whose patterns of waste generation are very different. Although efforts have been made to sample from ‘pure’ ACORN areas, many real collection rounds will be made up of enumeration districts from different ACORN groups, or from enumeration districts that fall into a particular group but are less typical of it. There are also problems in updating the ACORN classification and hence in updating estimates of waste generation obtained in this way. It is dependent on census data, and the 240
The generation of household waste statistics: J. P. Parfitt and R. Flowerdew
census is only taken every ten years, Very few of the census variables can be updated between censuses, and the ACORN classification will therefore become dated by the end of the decade. Things are somewhat worse if ACORN is used to construct a sampling frame. When NHWAP was designed, the most recent census data were for 1981, and the classification was done on the basis of data for that year. Accordingly, the NHWAP sampling frame is based on 1981 data and the sampling was done in the early 1990s on the basis of the assignment of 1981 enumeration districts to groups that emerged from the classification of 1981 data. While visual evidence was used to ensure that sampled areas appeared to be typical of the group stereotype, when the model is used predictively, there is no way of ensuring that enumeration districts that were in group J in 1981 have not changed markedly in character since then. The 1991 census data have generated new classifications on this basis which are necessarily incompatible with 198 1, because of the availability of new census variables and because social change since 1981 means that different groups have emerged. It is far from clear how a sample based on 1981 ACORN groups can sensibly be updated to take account of new data and new groups. Some of these criticisms could be overcome by relating the collection-round data to 1991 census characteristics of the enumeration districts sampled (Flowerdew, 1995). This would allow relationships to be calibrated directly between household waste and relevant social and demographic variables, without them being filtered through the ‘black box’ of the ACORN classification. However, this strategy would still suffer from the defect that the number of collection rounds sampled by the NHWAP was very small (31); it is difficult to establish more than the most obvious relationships from such a small sample size.
Conclusions
and future prospects
The ACORN collection-round approach has merits in the way in which it simplifies spatial complexity and highlights differences in collected household waste arisings between geographic areas. However, the method may not represent an efficient use of sampling resources and there is limited scope for explaining the differences observed between neighbourhood types. Furthermore, the NHWAP has not attempted to integrate collectionround data with other outlets for household waste (civic amenity sites, recycling, litter, and so on). The basic data on household waste composition for the UK are therefore incomplete and the data that do exist are too limited. Effective policy formulation towards future household waste reduction will partly depend on whether or not this information gap is filled and a more effective methodology developed for the UK NHWAP Despite these criticisms, the NHWAP has been a first attempt to address the issue of geo-demographic variation and waste sampling on a national scale. The lessons learnt have important implications for household waste analysis programmes elsewhere. Other European programmes have tended to neglect the question of how to take representative samples of either household types or area types, preferring instead to concentrate on the physical problem of handling, separating and subsampling the wastes once delivered for analysis (Par&t et al., 1997: 37). The explanation for this might simply be that household waste has been an area traditionally dominated by engineering disciplines and questions of statistical validity have tended to be confined to within-plant sampling operations. What is required now is for other applied disciplines to contribute to an understanding of the societal dimensions of waste; its relationship with demographic and economic factors in particular. An important initial component of this would be to develop further household-based research. Research at the household level of aggregation has the advantage of directly measuring the relationship between households and the socioeconomic, institutional, spatial and 241
The generation
of household waste statistics; J. P. Patjitt und R. Flowerdew
temporal variables influencing waste quantity and composition. Sorting through the waste to establish compositional trends could be accompanied by surveys designed to collect census and non-census variables. The latter could include such elements as recycling behaviour, use of civic amenity sites and questions relating to household waste minimization. While there is likely to be major variation between households of similar characteristics, the much greater sample size that would be possible using this approach could establish the range of behaviour for such households as well as the mean. An additional advantage would be that questionnaire survey work could be used to update between censuses. For instance, in monitoring the household waste generation from a specific area, it might be necessary to assess possible changes from 1995 to 1998. With the aggregate collection-round approach, waste analysis could be repeated but only 1991 census data would be available to provide the background socioeconomic variables. With the household-level methodology, changes could be easily monitored, regardless of the census. It would therefore be possible to detect whether changes at the local level were due to changes in population make-up or changes in behaviour by certain population groups. The household-based approach has so far only been conducted on any scale in the West Midlands (Rufford, 1984; M. E. L Research, 1994). Other districts need to be surveyed, selected according to regional and socioeconomic type and relevant waste collection policies (primarily type of bin provided, and provision of recycling points and civic amenity sites). The method should include sufficient repeat sampling to characterize seasonal differences properly. The data derived from a household-based programme could be used to construct a linear waste arisings model which would be used for predictive purposes. This should ideally be restricted to data accessible from publicly available sources at the district level or below. Those variables not so available (such as freezer ownership or newspaper delivery) should be replaced by census or other variables from which they can be predicted. This could again be done using household-level survey data. Validation could take place by collecting household waste data in certain areas without administering the questionnaire survey. Census data could then be used to estimate how much waste of certain types is likely to be collected in an enumeration district, and the estimate compared to the actual results. The model, assuming the validation process has been reasonably successful, could be used to predict waste quantities and composition for any area for which census data and policy variables are available. This could be done at the national scale, the district scale, or the local scale. Predictions from the suggested approach would be based directly on the number of households of a particular tenure, size, car ownership, and so on, rather than on the ACORN group to which an enumeration district had been assigned. The proposal to test out such an approach would also fulfil some basic guiding principles that are in accordance with the NHWAP objectives. The technique would be open to scrutiny and flexible enough to be translated to different geographic levels. Given that a national programme is unlikely to meet data needs at all geographical levels equally, it is better that a technique is based on readily accessible socioeconomic variables so that a consistent methodology may be encouraged at the more local level. Furthermore, it would be a method easily adapted for a regular monitoring programme into which new information could be added as and when resources permit.
Acknowledgements This research was funded by the Department of the Environment and the Department of Trade and Industry (via the Energy Technology Support Unit, Harwell). The opinions expressed in this paper are those of the authors and do not necessarily represent those of the funding bodies. 242
The generation of household waste statistics: J. I? Parjitt and R. Flowerdews
References Atkinson, W. and New, R. (1993) An Overview of the Impact of Source Separation Schemes on the Domestic Waste Stream in the UK and Their Relevance to the Government’s Recycling Target. WSL Report LR 943, Warren Spring Laboratory, Stevenage,. Barton, J. R. (1983) Sampling and analysis of household waste in the planning and operation of resource recovery plant. Paper presented to joint IWM/DoE Seminar at Aston University, 20 September 1983. Civic Amenity Waste Disposal Project, M. E. L. Research Ltd and Warren Spring Laboratory (1993) Monitoring and Evaluating Household Waste Recycling Programmes: Waste Definitions and Monitoring Parameters. Report for the Department of the Environment, No. CWM/070/93. Crichton, L. (1992) The development and application of GIS in waste collection and disposal. In Waste Location. ed. M. J. Clark et al.. Routledge, London. Coopers and Lybrand (1993) A Survey of English Local Authority Recycling Plans. Department of the Environment, London. Department of the Environment (1992) A Review of Options--A Memorandum Providing Guidance on the Options Available for Waste Treatment and Disposal. Waste Management Paper No. 1, HMSO, London. Department of the Environment (1996) Indicators of Sustainable Developmentfor the United Kingdom. HMSO, London. Department of the Environment and Welsh Office (1995) Making Waste Work-A Strategy for Sustainable Waste Management in England and Wales. HMSO, London. European Recovery and Recycling Association (1992) Nomenclature: Secondary Materials. ERRA, Brussels. Flowerdew, R. (1995)Re-analysis of the National Household Waste Analysis Programme Data. Research Report No. 30, North West Regional Research Laboratory, Lancaster University. Flowerdew, R. and Par&t, J. P. (1994) Choice of variables for modelling household waste. Final report to Department of the Environment, September 1994. Klee, A. .I. (1980) Quantitative Decision Making. Design and Management For Resource Recovery Series, Vol. 3, Design and Management for Resource Recovery, Ann Arbor Science, Ann Arbor, MI. M. E. L. Research Ltd (1989) Quantities and composition of civic amenity wastes arising in the West Midlands. A report to the West Midlands Joint Waste Disposal Sub-Committee 89/16, December 1989. M. E. L Research Ltd (1994) Trends in household waste. Final report to the Department of the Environment, August 1994. New, R. and Davies, D. (1995) NHWAP background and results. In Conference Prospectus: Household Waste Arisings and Composition 1st National Conference, Culham, Oxfordshire, 29 March 1995. Parfitt, J. P. and Flowerdew, R. (I 995) The National Household Waste Analysis Programme: assessment of methods and design criteria for the future. In Conference Prospectus: Household Waste Arisings and Composition 1st National Conference, Culham, Oxfordshire, 29 March 1995. Parfitt, J. P., Flowerdew, R. and Doktor, P. (1994) Socio-economic Variables in Household Waste Modelling: Two Case Studies. CSERGE Working Paper WM 94-02, University of East Anglia, Norwich. Parfitt, J. P., Flowerdew, R. and Pocock, R. (1997) A Review of the United Kingdom Household Waste Arisings and Compositional Data. Report prepared under contract to the Department of the Environment, Wastes Technical Division EPG 7/10/21 CLO201, Environment Agency, London. Rufford, N. M. (1984) The analysis and prediction of the quantity and composition of household refuse. Unpublished PhD thesis, University of Aston, Birmingham. Tchobanoglous, G., Theisen, H. and Vigil, S. A. (1993) Integrated Solid Waste Management; Engineering Principles and Management Issues. McGraw-Hill, New York. Warren Spring Laboratory and Aspinwall & Company (1993) The Technical Aspects of Controlled Waste Management: Development of the National Household Waste Analysis Programme. Summary Report No. CWM/059/93, Department of the Environment, London. (Revised manuscript
received II November
1996)
243
The generation of household waste statistics: J. P. Parfiirtand R. Flowerdew
Appendix Correlation matrices: NHWAP compositional Correlation
analysis
matrix 1
Paper Glass Plastic (Dense) Plastic (Film)
Paper
Glass
Plastic (Dense)
Plastic (Film)
1.00 0.59** 0.73*** 0.83*“*
0.59*** 1.00 0.53% 0.55**
0.73*** 0.53*
0.83*** 0.55** 0.78*** 1.00
1a0 0.78***
Note: n = 31. ***p = 0.000; **p = 0.001; *p = 0.002 (two tailed significance)
Correlation
Putrescibles Miscellaneous Fines
matrix 2
non-combustible
Putrwcibles
Misc. non-combustible
Fines
1.oo
0.61*** 1.00 0.53*
0.56** 0.53*
0.61*** 0.56**
Note: n = 31. **ep = 0.0~); **p = 0.001; *p = 0.002 (two tailed significance)
244
1.oo
Pergamon
Geography, Printed
PII: SOl43-6228(96)00031-S
Vol. 17, No. 3, pp. 231-244, 1997 0 1997 Elsevm Science Ltd in Great Britain. All riehts reserved 0143.6228/97 rSr7.00 + 0.00
Methodological problems in the generation of household waste statistics An analysis of the United Kingdom’s National Household Waste Analysis Programme Julian P. Parfitt Centre for Social and Economic Research on the Global Environment, East Anglia, Norwich NR4 7TJ, UK
University of
Robin Flowerdew North West Regional Research Laboratory, Department of Geography, Lancaster University, Lancaster LA1 4YB, UK The establishment of national targets designed to bring about more sustainable waste management in many developed countries has not generally been accompanied by the creation of reliable information systems for policy formulation and monitoring. In the first part of this paper the factors that complicate the collection of reliable household waste statistics are examined from both applied and theoretical perspectives. The second part presents a critique of the recent UK National Household Waste Analysis Programme (NHWAP). This was based on waste-collection-round samples selected by means of a geodemographic classification package (ACORN). NHWAP data are currently the only national data on household waste composition and the results have already been widely used by policy-makers at national and local levels. However, it is concluded that the NHWAP sample was too limited for there to be much confidence in the results. A national research programme based on household samples is required in order to understand the relationships between household waste arisings and socioeconomic, institutional, spatial and temporal variables. An outline methodology is suggested. 0 1997 Elsevier Science Ltd
Keywords: household waste, sampling, UK National Household Programme, waste management policy
Waste Analysis
United Kingdom policy towards waste has evolved rapidly in recent years, partly in response to home-grown public pressures to curb the environmental impacts of waste disposal practices. In addition, there have been important external influences from European Union environmental policy and the wider debate on sustainability fostered by the 1992 Rio Earth Summit. A common problem has emerged in countries that have embarked on policies promoting greater sustainability in waste management through waste recycling and waste 231
0
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INCREASING SUSTAINABILITY
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The generation of household waste statistics: J. P Parjitt and R. Flowerdew
reduction. The pace of policy-making has not been matched by an equal effort to provide meaningful waste statistics. This paper examines the problem through the example of the UK National Household Waste Analysis Programme (NHWAP), which was set up in 1991 to generate household waste composition and weight data. Although some of the issues raised are specific to the UK context, the general conclusions are relevant to similar national programmes in other European and North American countries.
National household waste analysis: aspects of research design One fundamental difficulty in collecting household waste compositional data is that the analysis of waste composition is expensive and the physical process of hand-sorting waste into categories is often an unpleasant task. Another problem is that household wastes are highly heterogeneous and are susceptible to significant fluctuations in quantity and composition. A large sample size is therefore required to achieve a representative sample and sampling error is likely to be particularly large for individual categories of waste that are less often discarded, such as textiles (Klee, 1980: 45). In general, the need for greater waste categorization increases as policies higher up the waste hierarchy are pursued. The relationship between sustainable waste management and increasing data complexity of household waste statistics is summarized in Figure 1. In the UK, current interest in the quality of household waste statistics is largely derived from the need to monitor material recycling against a national target set by the government in 1990, that 25 per cent of household waste was to be recycled by the year 2000. In addition, the EC Packaging Directive requires specific data on the packaging element of the waste stream (Department of the Environment and Welsh Office, 1995). Household wastes typically have high coefficients of variation in terms of the total weight of different component categories generated by households. The composition varies through random week-to-week fluctuations in what individual households discard and through the influence of seasonal, regional and socioeconomic factors (Tchobanoglous et al., 1993: 56). Furthermore, differences exist between local authorities in waste service provision, such as facilities for the collection of recyclable materials and the method of containment provided for door-to-door waste collection. Sampling strategy can be made more cost effective by controlling for various combinations of the most important of these variables. However, the identification of such variables is a research problem in itself as few studies have systematically related socioeconomic, geographical and waste service provision variables to household waste generation. The most direct way in which this can be approached is by means of research focusing on the basic unit of waste production-the household. One extensive study that adopted this method was carried out in the West Midlands by Rufford (1984). Although now rather dated, Rufford’s research has yet to be surpassed by any other household-based research, either in the UK or elsewhere. The basic method involved the generation of variables through a house-to-house questionnaire survey. These were then related to the results of waste composition analyses conducted on samples taken from individual households. All the variables listed in Table I were found in analysis of variance to be significantly associated with at least one waste category. Those variables found to be the most strongly associated with quantity of household waste were then selected as the basis for categorizing households for an area-based waste arisings model. The main variables selected were: household size, tenure, and whether or not the head of household was economically active. Rufford’s research was a cross-sectional study based on a sample drawn from one region at one particular time of year. It did not quantify seasonal and regional differences nor investigate longer-term temporal variations. The mathematical form adopted by 233
The generation of household waste statistics: J. t! Pa@tt and R. Flowerdew
Table 1 Household characteristics with at least one waste category
found to be significantly
Smoke control zone Property type Tenure Occupation of head of house
Frequency of shopping trips for food Age of domestic adult Employment status of second adult Family life cycle Number of males Number of females Total household size Mode of newspaper purchase Car ownership
Type of domestic heating Property size (bedrooms) Pet ownership Freezer ownership Milk delivered (Source Rufford
associated
1984: 167)
Rufford to express the wider sample design required for national estimates, household type on a simple set of waste-related variables, was as follows: Wi,k
=
%~(~a)ijk%
W,,
where
(WO)$
Collection
= the quantity of waste generated in a given area in region i, season j and point in trend k = waste generation coefficient associated with households of type a in region i, season j and point in trend k (Rufford, 1984: 280).
rounds
/ J
>
,,,,,,
Bulky waste
\
2
main outlets. 234
Estimated total household Source:
Patfitt
Mixed rounds Other sources
--
Figure
classifying
Civic amenity
sites
wastes for England and Wales in 1993/4 (t X 106yr-‘)
et al. (1997)
by
The generation of household waste statistics: J. P. Parfitt and R. Flowerdew
A more up-to-date sample design would require modification to reflect recent changes in household waste management since Rufford’s research was undertaken. For instance, the introduction of 240-litre wheeled bins in some parts of the UK has resulted in an increase in refuse-collection-round waste (Civic Amenity Waste Disposal Project et al., 1993). In addition to the problems of sampling strategy and waste analysis, defining the systems boundary for household waste must also be addressed by the research design. Household waste collected by local authorities from ordinary refuse collection rounds is not equivalent to the total household waste generated. In a recent review of UK household waste statistics conducted for the Department of the Environment (Parfitt et al., 1977: 81), it was estimated that one-third of household waste in England and Wales was collected by means of outlets other than the local authority house-to-house collection round (Figure 2).
The UK National Household Waste Analysis Programme No consensus has emerged in the international research literature as to the most appropriate methodology for conducting compositional analysis of household wastes. A variety of different methods have been developed based on sampling units at different levels of aggregation, ranging from individual households through to bulk samples of refuse collection rounds (Pa&t et al., 1997: 36). There have been recent EU proposals to standardize the approach across member states. This process could benefit from the issues raised by this UK case study. From 1991 to 1994 the UK Department of the Environment’s NHWAP generated data on household waste composition and weight. The results have already been widely disseminated as they are the only national statistics for household waste composition in the UK and the first to be collected since 1980 (Department of the Environment, 1992: 10). Furthermore, in the absence of local waste analysis, NHWAP statistics are the main source of waste compositional data used by local authorities in compiling their household waste recycling plans (Coopers and Lybrand, 1993: 7). The present authors were commissioned by the Department of the Environment to review the methodology adopted by the NHWAP and to comment on the results (Parfitt et al., 1994). The physical method of waste analysis adopted by the NHWAP involved collection of 5-tonne samples of household waste from selected collection rounds (waste from approximately 400-600 households). Each sample was sorted into different size fractions by means of a trommel (a large rotating drum with walls fitted with apertures of specific size) from which subsamples were then taken for hand-sorting into compositional categories (Warren Spring Laboratory and Aspinwall & Co. Ltd, 1993: 11-12). The NHWAP approached the question of sampling strategy by use of a commercially available system of classified residential area profiles, called ACORN (A Classification of Residential Neighbourhoods), marketed by CACI (Crichton, 1992). The 1981 census version of ACORN was based on a set of 40 census variables available for small geographical areas in Great Britain (enumeration districts). These census variables were used as the raw material for a multivariate classification of enumeration districts, with districts grouped together if they were relatively similar on the set of census variables taken as a whole. The classification process could produce any desired number of groupings of enumeration districts, but the 1981 census-based ACORN system was marketed as a classification with two levels, of 38 area types and of 11 larger neighbourhood types which are shown in Table 2. These 11 ACORN groups were used as the basis of the NHWAP sampling strategy. Each enumeration district was assigned to one of these groups, and the NHWAP sampled collection rounds that were concentrated as far as possible in enumeration districts in one of 23s
The generation of household waste stutistics: J. I? Parfitt and R. Flowerdew
B
4
a
236
The generation of household waste statistics: J. f? Patjitt and R. Flowerdew
Table 2 The 11 1981 ACORN Groups Group
% UK population
A B C D E F G H I .I K
3.3 16.8 17.8 4.2 12.7 9.0 6.1 3.4 4.7 17.7 4.2
Agricultural areas Modem family housing, higher incomes Older housing of intermediate status Older terraced housing Council estates-Category I Council estates-Category II Council estates-Category III Mixed inner metropolitan areas High-status non-family areas Affluent suburban housing Better-off retirement areas
the groups. The expectation was that other enumeration districts elsewhere in the UK would have similar amounts and composition of household waste to those sampled by the NHWAP for the same ACORN group. Collection rounds were selected that represented each ACORN group in different districts around the country; in addition, four collection rounds representative of the four most common ACORN groups were selected in Leeds. The districts to be sampled were also selected using an ACORN-related methodology. The same 40 variables were computed for 1981 at the district level, and input to a multivariate classification of districts. The districts sampled were intended to be representative of the families identified at the district scale. Main findings
of the NHWAP
Compositional analysis of the 31 household waste collection rounds sampled during 1992-4 are shown in Figure 3 in terms of kilograms per household per week. Each bar contains the 11 main categories into which the sample was sorted. A number of the categories require brief explanation: Putrescibles: organic wastes mostly of kitchen or garden origin. Fines: any materials with a diameter < 10mm. Miscellaneous combustibles: disposable nappies are the single largest component, otherwise this category contains other composite wastes that are predominantly combustible. Miscellaneous non-combustibles: contains rubble, lumps of soil, cinders from solid fuel burning (greater than 1Omm diameter), plaster and other inorganic wastes from DIY activities. Although considerable variation is apparent between samples, paper (which includes newspaper, magazines, card and cardboard) and putrescibles are the two categories accounting for the greatest weight in all samples but one. The Mendip ACORN A collection round, classified as a predominantly agricultural neighbourhood, contained a large quantity of wood ash from wood-burning stoves, which is represented in the large ‘fines’ category in Figure 3 (New and Davies, 1995: 19). It is difficult to generalize from the raw data without further grouping of the waste categories presented. The present authors have identified two distinct groups of wastes, each containing components that were highly positively correlated with one another (the 237
The generation
of household
waste statistics: J. P: Parfitt and R. Flowerdew
Table 3 Key to ACORN Group and District for NHWAP Samples 1992-94 Code
ACORN
Area
Month
Year
1A 2B 3B 4B SB 6B 18 8C 9c JOC 11C 12c 13c 14D 15E 16F 17F 18F 19F 20F 2lF 22G 23H 241 255 265 275 285 295 305 3lK
A B B B B B B C C c C C C D E F F F F F F G H
Mendip
February. April March August November March
1994 1992 1993 1992 1992 1993 1993 1992 1992 1993 1993 1992 1993 1994 1994 1992 1992 1993 1993 1992 1993 1994 1994 1994 1992 I992 1993 I993 1992 1993 1994
K
Leeds Leeds Warrington Warrington Warrington Warrington Charnwood Charnwood Charnwood Chamwood Leeds Leeds Blaenau Gwent Bromsgrove Gateshead Gateshead Gateshead Gateshead Leeds Leeds Glasgow Wolverhampton Merton St Albans St Albans St Aibans St Albans Leeds Leeds Colwyn
May August November February May June April April March August November February May April April May March February July October January April April April May
correlation matrices for this analysis are presented in the Appendix). The first is dominated by paper/card and contains plastics and glass (correlation coefficients between 0.53 and 0.83). This grouping contains the main post-consumer and packaging-related wastes and constitutes 51 per cent of the total weight of wastes sampled. In the second grouping, which comprises 3 1 per cent of the total waste sampled, putrescible waste is the main component by weight, with fines and the miscellaneous non-combustible categories highly correlated with it and with one another (correlation coefficients between 0.53 and 0.61). This grouping contains the main non-consumer household wastes (such as garden wastes, stones, soil and dusts), as well as organic kitchen wastes. Together the two basic groupings comprise 82 per cent of the total weight of waste analysed by the NHWAP. The mean weight of waste for the two waste groupings is compared for ACORN groups B, C, F and J (those best represented in the NHWAP sample) in Figure 4. The Leeds component of the ACORN B sample (two rounds) has not been combined with the Warrington sample due to the difference in method of waste containment between the two areas. Wastes sampled from areas on wheeled-bin collection, such as at Warrington, are known to be different in composition to collections from non-wheeled-bin areas, particularly with respect to non-consumer wastes.
238
The generation of household waste statistics: J. P. Parjitt and R. Flowerdew
An important compositional difference between ‘affluent suburban housing’ (ACORN J group) and the less affluent ACORN groups B, C and F is apparent. The ACORN J collection rounds contained a weekly average 9.6 kg per household of the predominantly post-consumer waste grouping compared with the combined mean of only 5.2kg per household from ACORN B, C and F collection rounds (t-value = 9.30, significant at 99.9 per cent level). This result suggests a strong link between affluence and the total weight of paper, glass and plastics in collection-round household waste. Comparisons between ACORN groups in terms of total weight of the ‘putrescibles, fines and miscellaneous non-combustible’ grouping are more difficult to interpret as some of the important constituent wastes, such as garden and DIY wastes, are particularly likely to be taken by householders to civic amenity sites. The research was not designed to measure the extent to which households were using this important alternative outlet for their waste. However, it is known from civic amenity site studies that more affluent households use civic amenity sites more often than others, but that the heaviest mean loads are associated with the intermediate ‘skilled manual’ social group (M. E. L. Research Ltd. 1989: 28). An appraisal of the NHWAP ACORN methodology An advantage of using the ACORN system as the basis of the NHWAP sampling is that the classification reflects variation between places in several potentially relevant respects. The input variables include many of those listed by Rufford (TabZe I), as well as others that are either thought to be related to household waste generation or can act as surrogates
_
0
Paper, glass
and plastics
Putrescibles,
fines and miscellaneous
non-combustibles
_
il=
2 2 Leeds ‘B’
4 Warrington
4
6 6 LeedslCharnwood ‘B’
6
6
‘C’ LeedslGateshead
6 Leeds/St
6 Albans
‘J’
‘F’
ACORN group and district
Figure 4
NHWAP
compositional analysis: comparison of main ACORN groups sampled by waste
grouping 239
The generation of household waste statistics: J. P. Pa&t
and R. Flowerdew
for such factors. The ACORN system does appear to reflect real differences between places on dimensions that are probably relevant to household waste, as suggested by the results that have been presented. However, there are a number of drawbacks in using the ACORN system in this way. Firstly, variables known or suspected to be important are omitted from the ACORN classification. These include the waste service provision variables mentioned earlier. If data are available for an ACORN D area, for example, which has wheeled-bin collection, little guidance is available for one that has plastic sack collection. Other variables, such as the use of solid fuel, size of garden, or nearness to civic amenity or recycling facilities, may have important effects on collection-round wastes but are excluded from ACORN. Secondly, irrelevant variables may be included. ACORN is designed as a generalpurpose marketing tool, and has been used very effectively for targeting likely sales prospects for direct mail deliveries. Such a system may not discriminate effectively in of individual variables in the what people throw away. Further, the weighting classification may not be appropriate for predicting household waste. One might expect, for example, the population aged O-4 to be a better predictor of the quantity of disposable nappies entering the waste stream than the balance between manufacturing and service workers or the means of transport to work. The argument is essentially that a classification for use in sampling for the estimation of household waste should be designed to emphasize those variables relevant to the matter at hand, rather than being designed as a generalpurpose tool. A problem with the use of classified residential area profiles is the difficulty of understanding exactly why observed differences exist between and within the ACORN groups sampled. For example, results of compositional analysis presented in Figures 3 and 4 suggest important differences between ACORN J neighbourhoods and less affluent ACORN groups. ACORN J collection rounds were sampled in Leeds (one collection round sampled once in April 1992 and again in April 1993) and St Albans (one collection round repeat sampled in July 1992, October 1992, January 1993 and April 1993). The Leeds sample rounds have a weekly average total weight of 17.3 kg per household, whereas the St Albans sample of the same ACORN group has an average of 13.6 kg. It is not clear to what extent this difference represents random fluctuations, the influence of seasonal effects (Leeds Js were sampled in April only), or some other systematic source of variation, such as differential use of other outlets for household wastes such as recycling or civic amenity sites. The number of samples taken is too small to draw any firm conclusions and the extent to which ‘other outlets’ for household wastes were being used by any of the sampled neighbourhoods was not an integral part of the research design. Even if ACORN groups do discriminate effectively between neighbourhoods that generate different types of waste, it is impossible to be sure why and exactly how the differences arise. One also cannot be sure whether some other form of classification would discriminate better, and hence be more useful in estimating waste generation for other areas. Classified residential area profiles are based on the predominant characteristics of an area. There may be important minority groups within an area that have major effects on household waste generation. For example, an area predominantly inhabited by middleclass pensioners may include one or two properties shared between groups of students, whose patterns of waste generation are very different. Although efforts have been made to sample from ‘pure’ ACORN areas, many real collection rounds will be made up of enumeration districts from different ACORN groups, or from enumeration districts that fall into a particular group but are less typical of it. There are also problems in updating the ACORN classification and hence in updating estimates of waste generation obtained in this way. It is dependent on census data, and the 240
The generation of household waste statistics: J. P. Parfitt and R. Flowerdew
census is only taken every ten years, Very few of the census variables can be updated between censuses, and the ACORN classification will therefore become dated by the end of the decade. Things are somewhat worse if ACORN is used to construct a sampling frame. When NHWAP was designed, the most recent census data were for 1981, and the classification was done on the basis of data for that year. Accordingly, the NHWAP sampling frame is based on 1981 data and the sampling was done in the early 1990s on the basis of the assignment of 1981 enumeration districts to groups that emerged from the classification of 1981 data. While visual evidence was used to ensure that sampled areas appeared to be typical of the group stereotype, when the model is used predictively, there is no way of ensuring that enumeration districts that were in group J in 1981 have not changed markedly in character since then. The 1991 census data have generated new classifications on this basis which are necessarily incompatible with 198 1, because of the availability of new census variables and because social change since 1981 means that different groups have emerged. It is far from clear how a sample based on 1981 ACORN groups can sensibly be updated to take account of new data and new groups. Some of these criticisms could be overcome by relating the collection-round data to 1991 census characteristics of the enumeration districts sampled (Flowerdew, 1995). This would allow relationships to be calibrated directly between household waste and relevant social and demographic variables, without them being filtered through the ‘black box’ of the ACORN classification. However, this strategy would still suffer from the defect that the number of collection rounds sampled by the NHWAP was very small (31); it is difficult to establish more than the most obvious relationships from such a small sample size.
Conclusions
and future prospects
The ACORN collection-round approach has merits in the way in which it simplifies spatial complexity and highlights differences in collected household waste arisings between geographic areas. However, the method may not represent an efficient use of sampling resources and there is limited scope for explaining the differences observed between neighbourhood types. Furthermore, the NHWAP has not attempted to integrate collectionround data with other outlets for household waste (civic amenity sites, recycling, litter, and so on). The basic data on household waste composition for the UK are therefore incomplete and the data that do exist are too limited. Effective policy formulation towards future household waste reduction will partly depend on whether or not this information gap is filled and a more effective methodology developed for the UK NHWAP Despite these criticisms, the NHWAP has been a first attempt to address the issue of geo-demographic variation and waste sampling on a national scale. The lessons learnt have important implications for household waste analysis programmes elsewhere. Other European programmes have tended to neglect the question of how to take representative samples of either household types or area types, preferring instead to concentrate on the physical problem of handling, separating and subsampling the wastes once delivered for analysis (Par&t et al., 1997: 37). The explanation for this might simply be that household waste has been an area traditionally dominated by engineering disciplines and questions of statistical validity have tended to be confined to within-plant sampling operations. What is required now is for other applied disciplines to contribute to an understanding of the societal dimensions of waste; its relationship with demographic and economic factors in particular. An important initial component of this would be to develop further household-based research. Research at the household level of aggregation has the advantage of directly measuring the relationship between households and the socioeconomic, institutional, spatial and 241
The generation
of household waste statistics; J. P. Patjitt und R. Flowerdew
temporal variables influencing waste quantity and composition. Sorting through the waste to establish compositional trends could be accompanied by surveys designed to collect census and non-census variables. The latter could include such elements as recycling behaviour, use of civic amenity sites and questions relating to household waste minimization. While there is likely to be major variation between households of similar characteristics, the much greater sample size that would be possible using this approach could establish the range of behaviour for such households as well as the mean. An additional advantage would be that questionnaire survey work could be used to update between censuses. For instance, in monitoring the household waste generation from a specific area, it might be necessary to assess possible changes from 1995 to 1998. With the aggregate collection-round approach, waste analysis could be repeated but only 1991 census data would be available to provide the background socioeconomic variables. With the household-level methodology, changes could be easily monitored, regardless of the census. It would therefore be possible to detect whether changes at the local level were due to changes in population make-up or changes in behaviour by certain population groups. The household-based approach has so far only been conducted on any scale in the West Midlands (Rufford, 1984; M. E. L Research, 1994). Other districts need to be surveyed, selected according to regional and socioeconomic type and relevant waste collection policies (primarily type of bin provided, and provision of recycling points and civic amenity sites). The method should include sufficient repeat sampling to characterize seasonal differences properly. The data derived from a household-based programme could be used to construct a linear waste arisings model which would be used for predictive purposes. This should ideally be restricted to data accessible from publicly available sources at the district level or below. Those variables not so available (such as freezer ownership or newspaper delivery) should be replaced by census or other variables from which they can be predicted. This could again be done using household-level survey data. Validation could take place by collecting household waste data in certain areas without administering the questionnaire survey. Census data could then be used to estimate how much waste of certain types is likely to be collected in an enumeration district, and the estimate compared to the actual results. The model, assuming the validation process has been reasonably successful, could be used to predict waste quantities and composition for any area for which census data and policy variables are available. This could be done at the national scale, the district scale, or the local scale. Predictions from the suggested approach would be based directly on the number of households of a particular tenure, size, car ownership, and so on, rather than on the ACORN group to which an enumeration district had been assigned. The proposal to test out such an approach would also fulfil some basic guiding principles that are in accordance with the NHWAP objectives. The technique would be open to scrutiny and flexible enough to be translated to different geographic levels. Given that a national programme is unlikely to meet data needs at all geographical levels equally, it is better that a technique is based on readily accessible socioeconomic variables so that a consistent methodology may be encouraged at the more local level. Furthermore, it would be a method easily adapted for a regular monitoring programme into which new information could be added as and when resources permit.
Acknowledgements This research was funded by the Department of the Environment and the Department of Trade and Industry (via the Energy Technology Support Unit, Harwell). The opinions expressed in this paper are those of the authors and do not necessarily represent those of the funding bodies. 242
The generation of household waste statistics: J. I? Parjitt and R. Flowerdews
References Atkinson, W. and New, R. (1993) An Overview of the Impact of Source Separation Schemes on the Domestic Waste Stream in the UK and Their Relevance to the Government’s Recycling Target. WSL Report LR 943, Warren Spring Laboratory, Stevenage,. Barton, J. R. (1983) Sampling and analysis of household waste in the planning and operation of resource recovery plant. Paper presented to joint IWM/DoE Seminar at Aston University, 20 September 1983. Civic Amenity Waste Disposal Project, M. E. L. Research Ltd and Warren Spring Laboratory (1993) Monitoring and Evaluating Household Waste Recycling Programmes: Waste Definitions and Monitoring Parameters. Report for the Department of the Environment, No. CWM/070/93. Crichton, L. (1992) The development and application of GIS in waste collection and disposal. In Waste Location. ed. M. J. Clark et al.. Routledge, London. Coopers and Lybrand (1993) A Survey of English Local Authority Recycling Plans. Department of the Environment, London. Department of the Environment (1992) A Review of Options--A Memorandum Providing Guidance on the Options Available for Waste Treatment and Disposal. Waste Management Paper No. 1, HMSO, London. Department of the Environment (1996) Indicators of Sustainable Developmentfor the United Kingdom. HMSO, London. Department of the Environment and Welsh Office (1995) Making Waste Work-A Strategy for Sustainable Waste Management in England and Wales. HMSO, London. European Recovery and Recycling Association (1992) Nomenclature: Secondary Materials. ERRA, Brussels. Flowerdew, R. (1995)Re-analysis of the National Household Waste Analysis Programme Data. Research Report No. 30, North West Regional Research Laboratory, Lancaster University. Flowerdew, R. and Par&t, J. P. (1994) Choice of variables for modelling household waste. Final report to Department of the Environment, September 1994. Klee, A. .I. (1980) Quantitative Decision Making. Design and Management For Resource Recovery Series, Vol. 3, Design and Management for Resource Recovery, Ann Arbor Science, Ann Arbor, MI. M. E. L. Research Ltd (1989) Quantities and composition of civic amenity wastes arising in the West Midlands. A report to the West Midlands Joint Waste Disposal Sub-Committee 89/16, December 1989. M. E. L Research Ltd (1994) Trends in household waste. Final report to the Department of the Environment, August 1994. New, R. and Davies, D. (1995) NHWAP background and results. In Conference Prospectus: Household Waste Arisings and Composition 1st National Conference, Culham, Oxfordshire, 29 March 1995. Parfitt, J. P. and Flowerdew, R. (I 995) The National Household Waste Analysis Programme: assessment of methods and design criteria for the future. In Conference Prospectus: Household Waste Arisings and Composition 1st National Conference, Culham, Oxfordshire, 29 March 1995. Parfitt, J. P., Flowerdew, R. and Doktor, P. (1994) Socio-economic Variables in Household Waste Modelling: Two Case Studies. CSERGE Working Paper WM 94-02, University of East Anglia, Norwich. Parfitt, J. P., Flowerdew, R. and Pocock, R. (1997) A Review of the United Kingdom Household Waste Arisings and Compositional Data. Report prepared under contract to the Department of the Environment, Wastes Technical Division EPG 7/10/21 CLO201, Environment Agency, London. Rufford, N. M. (1984) The analysis and prediction of the quantity and composition of household refuse. Unpublished PhD thesis, University of Aston, Birmingham. Tchobanoglous, G., Theisen, H. and Vigil, S. A. (1993) Integrated Solid Waste Management; Engineering Principles and Management Issues. McGraw-Hill, New York. Warren Spring Laboratory and Aspinwall & Company (1993) The Technical Aspects of Controlled Waste Management: Development of the National Household Waste Analysis Programme. Summary Report No. CWM/059/93, Department of the Environment, London. (Revised manuscript
received II November
1996)
243
The generation of household waste statistics: J. P. Parfiirtand R. Flowerdew
Appendix Correlation matrices: NHWAP compositional Correlation
analysis
matrix 1
Paper Glass Plastic (Dense) Plastic (Film)
Paper
Glass
Plastic (Dense)
Plastic (Film)
1.00 0.59** 0.73*** 0.83*“*
0.59*** 1.00 0.53% 0.55**
0.73*** 0.53*
0.83*** 0.55** 0.78*** 1.00
1a0 0.78***
Note: n = 31. ***p = 0.000; **p = 0.001; *p = 0.002 (two tailed significance)
Correlation
Putrescibles Miscellaneous Fines
matrix 2
non-combustible
Putrwcibles
Misc. non-combustible
Fines
1.oo
0.61*** 1.00 0.53*
0.56** 0.53*
0.61*** 0.56**
Note: n = 31. **ep = 0.0~); **p = 0.001; *p = 0.002 (two tailed significance)
244
1.oo