Determinants of Per Capita Municipal Solid Waste Generation
in the Southeastern U.S.
running title: Per Capita Solid Waste Generation
Daniel Hockett
Douglas Lober*
Keith Pilgrim
Duke University
School of the Environment
P.O. Box 90328
Durham, NC 27706
9 19-613-8029
9 19-684-8741 (FAX)
A u p t 4, 1994
* address all correspondence to this author
Abstract
This study's goal is to identify and measure the variables which influence per capita
municipal solid waste generation in the southern U.S., using the 100 counties of North
Carolina as a data set and regression models for statistical analysis. Variables are selected
to capture the residential, institutional, and commercial components of the municipal solid
waste stream as well as the overall structure of waste management through inclusion of
waste disposal fees. An additional goal of this study is to examine the influence of the
components of retail sales, including sales of eateries, merchandise, food stores, and
apparel stores, as the retail industries have been suggested to contribute significantly to
waste generation.
The results indicate that retail sales and the waste disposal fee are the significant
determinants of waste generation while variables to account for manufacturing,
construction, personal income, and degree of urbanization did not prove significant. Of the
components of retail sales, per capita sales of eating establishments proved the greatest
influence on waste generation. Policy implications are that an increasing effort should be
made to focus on eating establishments and to establish appropriate tipping fees if a goal
of waste reduction is to be achieved.
1. Introduction
The U.S. currently generates and disposes of almost 200 million tons per year of
paper, plastics, yard waste, glass and other materials, which are collectively labeled
municipal solid waste (MSW) or more commonly, garbage (U.S. EPA, 1992). This waste
is primarily produced by residential, institutional, and commercial sources. Municipal solid
waste is regulated under subtitle D of the main law covering waste management, the
Resource Conservation and Recovery Act (RCRA). Municipal solid waste is distinguished
from other subtitle D wastes such as the 7.6 billion tons of industrial waste and the 5.2
billion tons of mining, oil and gas waste generated annually. It is also separate from the
200 million tons generated per year of hazardous waste which is classified as a subtitle C
waste under RCRA.
When municipal solid waste generation data is calculated on a per capita basis,
each individual in the U.S. is said to generate around .75 tons per year or 4 pounds per
person per day (U.S. EPA, 1992). However, there is considerable variation in waste
generation across regions in the U. S. Chattanooga, Tennessee, for example, generates 9.4
pounds per person per day while Yakima, Washington produces 1.9 pounds per person
per day (U.S. Congress, 1989). A number of variables, such as local climate, the economy,
demographic characteristics of the population, the amount of tourism in the region, and
population density are potential variables which may account for the variation in amount
and type of waste produced when waste is measured on a per capita basis.
This paper's goal is to identifl and measure variables which influence per capita
MSW generation, using the counties of North Carolina as a data set. The paper first
describes the nature of the economy, landscape, and waste generation in North Carolina.
It then briefly reviews the literature concerning the determinants of waste generation.
Next, a model to characterize the influence of a number of variables on waste generation is
created. The results indicating significant determinants of waste generation are presented
and policy implications are discussed.
2. The North Carolina Wasteshed
2.1 North Carolina landscape, economy and population
North Carolina, with a population of 6,628,000 people, is an extremely diverse
state in terms of landscape, economy, and population. The State has four main geographic
regions: Coastal, Coastal Plain, Piedmont, and Mountain. The Coastal region, with 10
percent of the population, includes the Cape Hattaras National Seashore. The Piedmont
region, with 55 percent of the population, has three major population centers, the RaleighDurham-Research Triangle area, the Greensboro-Winston-Salem Triad area, and
Charlotte, as well as the Interstate 40 corridor, one of the fastest growing business
locations in the United States. The Mountain region, with 14 percent offhe population,
includes both the Blue Ridge Parkway and Great Smoky Mountains National Park, the
most visited national park in the United States. In fact, North Carolina is a major tourist
destination with sixty-one million people visiting tourist sites in North Carolina in 1988
(North Carolina Department of Travel and Tourism, 1988 ).
Approximately 75 percent of the land area in North Carolina is classified as
farmland or forest land. Fifty seven percent of the state lives in urban areas where urban is
defined as any county classified as a federally-designated Metropolitan Statistical Area
(MSA). The Piedmont area is particularly urbanized with 8 1 percent of its population
2
!
' .
living in urban counties. Fifty seven percent of the waste is disposed of in the urban
counties of the state (N.C. Department of Environment, Health, and Natural Resources,
1992).
The largest employers for North Carolina's 3,713,000 workers are the government,
manufacturing companies, service industries and retail operations (N.C. Department of
Environment, Health, and Natural Resources). In all four regions of the state, retail
employers account for between 16 and 19 percent of the total employment. Manufacturing
industries employ 11 percent of the workers in the Coastal Region and 22 to 25 percent in
the rest of the state. The service industry accounts for between 15 and 2 1 percent of the
workers in all four regions. The government employs 33 percent of the employees in the
Coastal Region, 23 percent in the Coastal Plain, 12 percent in the Piedmont, and 16
percent in the Mountain Region.
2.2 North Carolina waste generation
In 1990, North Carolina's counties disposed of 6,823,000 tons of municipal solid
waste. Ninety-eight percent of this was disposed in landfills, primarily at the county level.
The counties also recycled 436,000 tons of material and composted 267,000 tons of yard
waste for a state-wide recycling and composting rate of 9.2 percent (N.C. Department of
Environment, Health and Natural Resources, 1993). Given the state's population of
6,628,000 people, the per capita waste generation was 1.1 tons per person per year or 6.2
pounds per person per day. Fifteen counties accounted for over 50 percent of the waste
disposed, with Mecklenburg County, the location of the State's largest city, Charlotte,
contributing the greatest amount of waste disposed, 8.8 percent of the total. All but six of
North Carolina's counties had landfills receiving municipal solid waste during the study
period.
These state-wide figures obscure considerable variability in waste generation
among counties (N. C. Department of Environment, Health, and Natural Resources,
1994). For example, Dare County, which contains the Cape Hatteras National Seashore
and therefore has a high tourist component, disposed of 2.2 tons per person per year or 12
pounds per person per day. Wilson County, which did not have a tipping fee when the
data were collected, produced 1.8 tons per person per year or nearly 10 pounds per capita
per day. Caswell and Camden counties produce .25 and .3 1 tons of MSW per person per
year.
Figure 1 cartographically represents the per capita waste generation in North
Carolina. No clear pattern exists to account for the differences in waste generation among
counties. This raises the main research question of this study: what can account for the
difference in waste generated at the county level, when controlling for population
differences?
[figure 1 3
3. Determinants of waste generation
The overriding variable influencing total waste generation is population. The
Piedmont region contains 55 percent of the population and disposes of 59 percent of the
State's waste. The coastal plain has 21 percent of the population and disposes of 19
percent of the waste. The Coastal region, with 10 percent of the population, disposes of
10 percent of the waste. The Mountain region disposes of 13 percent of the waste and
4
contains 14 percent of the population. Figure 2a shows this strong correlation for the
counties of North Carolina between the population in a county and the amount of waste
produced.
As would logically follow from figure 2a, when the amount of waste produced is
presented on a per-capita basis as in figure 2b, there is no relationship between population
and waste generation More populous counties simply do not produce more waste per
person than do less populated counties.
[figure 2a and figure 2b]
A variety of studies have attempted to account for differences in per capita waste
generation although this remains an area needing hrther research (Vesilind, et al, 1977).
We briefly mention some of the findings, as reviewed by Ali Khan and Burney (1989),
Henricks (1984), and ourselves, with an emphasis on the variables included in our study
to provide context and support for our hypotheses, which are presented in section 5.
AIncome and affluence have often been found to be positively correlated with
waste generation (Chang et al, 1993; Dayal et al, 1993; Jenkins, 1993; Rhyner, 1976;
Richardson and Havlicek, 1978; Wertz, 1976) although there are null findings (Ali Khan
and Burney, 1989, Cailas et al, 1994; Henricks, 1994) and negative ones (Grossman,
1974; Rathje and Murphy, 1992) as well. Cailas et a1 (1994) and Jenkins (1993)
determined that a negative correlation exists between population density and waste
generation while Ali Khan et a1 (1989) found no correlation. Studies by Cailas et a1 (1994)
and Henricks (1994) showed a positive correlation between degree of urbanization and
waste generation while Rhyner's (1976) examination found none. Dayal et a1 (1993) and
5
,
Jenkins (1993) discovered a correlation between climate and waste generation while Ali
Khan et a1 (1989) found no effect. Jenkins (1993) found that there was a positive
correlation between waste generation and age while Richardson and Havlicek (1978)
found that those who were middle-age rather than young or old produced more waste.
Jenluns( 1993) indicated that smaller household sizes produced more waste per capita
while Cailas et a1 (1994) and Rhyner (1976) found no effect. These studies analyze a
variety of localities and regions from cities in developing countries to several states. Only
the study by Rhyner (1976) applies to North Carolina and the surrounding region.
Correctly characterizing the causal mechanisms for these results is often difficult.
For example, wealthier people often use and discard more paper, particularly as they read
more newspapers. However, they produce less cans as they eat fewer canned foods. As
paper often constitutes a greater proportion of the average waste stream, the net effect
may be a positive correlation between income and waste generation. Nonetheless, the net
effect of income on waste generation can be ambiguous. One method to improve this
analysis is to characterize waste generation by material such as glass, paper, plastic, and
yard waste, as has been done in some studies (Henricks, 1994; Richardson and Havlicek,
1978; Ali Khan et al, 1989 )
Generally the models of per capita waste generation that exist have several
shortcomings. One, they often focus exclusively on demographic variables and therefore
measure primarily residential sources of waste, despite the importance of commercial and
institutional generators to the waste stream. For example, such contributors to the waste
stream as tourists would not be captured by variables to measure residential waste. Several
researchers have tried to incorporate indicators of commercial waste into waste generation
models. Henricks (1994) found that per capita retail sales was positively correlated with
6
total waste production in Florida. Gay et a1 (1993) develop a predictive model for waste
generation based on retail sales.
A second shortcoming of waste generation studies is that the models exclude key
structural variables, such as waste disposal fees, which may influence the amount of waste
disposed. The result of this exclusion is that the relative importance of all variables
influencing waste generation cannot be determined.
Another shortcoming of previous studies is that the data that is used to create
models is often collected by different methods or is inconsistent in definition, making it
non-comparable. For example, one set of data may include construction and demolition
debris while another excludes it in the measure of total waste generation.
These shortcomings provide an opportunity to develop studies which better reflect
the scope of the municipal waste stream and key variables which influence it while using
data which is comparable, an approach which is followed here.
4. Goals and Method
The main goal of this study is to create a model of the demographic, economic,
and structural determinants of per capita waste generation for the southern region of the
U. S. Specifically, the intent is to explain waste generation by identifling both the
significant determinants of waste production and their relative importance, with less
emphasis on constructing a predictive model. The independent variables chosen for
inclusion are those which are explicitly related to waste disposal patterns and include
economic, structural, and demographic variables. Economic variables include per capita
7
retail sales, per capita value added by manufacturing and per capita construction costs. A
structural variable examined is the cost per ton to dispose of the waste at the landfill,
which is called the tipping fee. Demographic variables include per capita income and urban
population percentage.
A second goal of the study is to closely examine the specific nature of the
contribution of retail sales to the generation of waste. To do so, the study examines how
per capita sales of apparel stores, food stores, merchandise, and eateries contribute to
overall production of waste.
The county was chosen as the unit of analysis for this study due to data availability
and the political appropriateness of this size unit for waste management. Furthermore, the
county level provides significant variation in both the dependent and independent variables
of interest to allow for the testing of their relationship. The amount of waste generated,
tipping fees, and populations for the 100 North Carolina counties were obtained from the
199 I-92 North Carolina Solid WasteManagement Annual Report (Department of
Environment, Health, and Natural Resources (NCDEHNR), 1993). Demographic
information was obtained from the Census Atlas of North Carolina. Economic data was
acquired from the Statistical Abstract of North Carolina Counties (1991) and from
Courtney et al's (1992) 1992 County and City Extra of North Carolina. Complete data
was available for 89 counties which is summarized in table 1 below.
Several regression models were constructed to determine the significance and
importance of variables of interest. A number of statistical tests were done to assure that
the assumptions necessary to use ordinary least squares regression were met. These tests
are presented in section 6 .
8
5. Hypotheses and Model Creation
5.1 The dependent variable: per capita waste generation
The dependent variable used in this study is the amount of waste generated by
weight per capita per day. This figure included municipal solid waste primarily from
residential, commercial, and institutional sources, but including some construction and
industrial waste. Each county determined and reported to the State the amount of waste
which was disposed of at its landfill as well as the tons of material recycled and the
amount of yard waste disposed so that a total waste generation figure could be calculated
by summing these amounts.
5.2 Independent variables
Many variables can be considered as possible determinants of waste generation in
the state. North Carolina's waste managers indicate that the most important factors
influencing the generation and disposal of waste are the "geography, population, economic
base, income, land use, and available transportation routes'' (NC Department of
Environment, Health, and Natural Resources, 1992, p. ES-3). The State waste managers
also conclude that residential waste is a hnction of population and commercial and
industrial waste depend on tourism and the type of industry in the region. Variables to
capture all these potential sources of waste were included in this study along with others
suggested by the literature. In addition, the variables were selected so that they could be
easily reproducible in examinations of other regions. U.S.Bureau of the Census Standard
Industry Codes (SIC) provided the basis for the data on sales for different sectors of the
9
economy. The variables were measured on a per capita basis where appropriate to make
them consistent with the dependent variable of interest. Table 1 identifies the independent
variables of interest for this study as well as the units of measurement and the source of
the data.
[Table 13
The type of economic activity of the state is hypothesized to influence the
generation of waste. This economic activity was partially captured by including variables
to measure retail sales, construction activity, and manufacturing. It should be noted that
retail sales is also a measure of tourism. Tourists spend considerable amounts of money on
food, clothing, and merchandise which generate packaging, shipping, food, and other
waste. Value added to manufactured goods by companies in the county was the best
known available surrogate measurement for general industrial activity which can
contribute packaging or scrap to waste generation. Construction costs were included in
the analysis to account for possible construction or demolition debris deposited in the
landfill.
It is hypothesized that the degree of urbanization effects the waste generated.
Urban population was considered as an independent variable because this sector of the
population is believed to rely heavily on standard municipal solid waste pickup versus
alternative methods of disposal such as backyard disposal or burning.
The size of the tipping fee has the potential to influence waste disposal as greater
tipping fees might lead to less waste generated, alternative disposal methods or efforts to
find cheaper disposal sites, including out of the county. The average tipping fee in the
10
State was $16.06/ton with a range from $60 to $6 per ton for those counties who charged
for waste disposal. Twenty-six counties did not charge to dispose of waste.
The full model to be tested takes the following form, as presented in equation 1:
WASTE=bo + blTIPFEE + b2RETSAL + b3VALUAD + b4CONST + b5lNCOM +
b6URBPOP
(1)
6. Data analysis
Analyses were performed to ensure that the independent variables included in the
model, per capita retail sales, value added manufacturing, per capita construction costs,
and per capita income did not violate the regression assumptions of a linear relationship
between independent and dependent variables, independence of the independent variables,
and constant variance and normality of errors.
First, potential independent variables were analyzed for correlation. Variables that
were highly correlated with another explanatory variable were not considered independent.
A decision was then made to exclude one of the correlated variables based on which
variable alone was the best predictor of per capita waste generation.
Score tests were utilized to check for non-constant variance of the errors. A
general Score test was first performed by regressing the scaled squared residuals (Ui’s)
against the fitted values of the large model. The results of this test demonstrated that the
errors exhibited constant variance. Score tests were also pedormed to investigate the
influence of the independent variables on the variance of the residuals. These tests were
11
conducted by regressing the Ui'S against each independent variable. Results of these tests
demonstrated that the variance of the residuals were not a function of any of the
descriptors. Plots of the residuals versus each independent variable did not exhibit any
non-linear trends. A plot of the standard normal quartiles versus the residuals
demonstrated a strong linear trend indicative of normal distribution of the residual errors.
Case diagnostics from this model revealed that none of the counties were outliers nor did
any bear disproportionate influence on the model.
In summary, the assumptions for ordinary least squares regression with no
necessary transformations were met for the model of waste generation against per capita
income, per capita retail sales, per capita value added by manufacturing, per capita
construction costs, and tipping fee.
7. Results and discussion
7.1 Significant variables
The model with parameter estimates and standard errors in parentheses is as
follows:
WASTE= 3.725 -.034*TIPFEE + .323* RETSAL + .059*VALUAD+ .227*CONST(.859) (.013)
(.069)
(.043)
(.441)
.OOO* INCOM + .007*URBPOP
(.OOO)
(.009)
Two variables, per capita retail sales (p = .OOOi) and tipping fees (p = .0104),
proved to be significant determinants of waste generation. As retail sales increased, so did
12
the amount of per capita waste generation. Higher tipping fees were associated with lower
levels of total waste levels.
According to our model, a $1000 increase in per capita retail sales will result in a
.323 pound per day increase in per capita waste generation. This finding of the importance
of retail sales is consistent with the results of Gay et a1 (1993) who found retail sales to be
an excellent predictor of waste generation.
Since per capita retail sales was the single best predictor of per capita waste
disposal, we decided to hrther investigate the nature of the effect of retail sales on waste
disposal. Data for retail sales receipts was divided into four major sub-classes: eating
establishments, food stores, general merchandise stores, and apparel stores (Courtney, et
al, 1992). A separate model, equation 3, was then constructed with these four sub-classes
used as independent variables.
WASTE= bo + blCLOTHES + b2EATS + b3MERCH + b4FOOD
(3)
Equation 4 presents the parameter estimates for this model, with standard errors in
parentheses. These results illustrate that per capita sales of eating establishments produced
the only significantly non-zero slope, demonstrating the significant contribution of
restaurants to the waste stream.
WASTE=3.47 - .000"CLOTHES + ,002"EATS + .001*MERCH + 001"FOOD
(.581) (.002)
(.002)
(.001)
(4)
(.001)
13
r2
=
.371
Tipping fee is the only significant variable with a negative beta, indicating that an
increase in tip fee is associated with a decrease in waste both deposited at the landfill and
recycled. A dollar increase in tipping fee is estimated to result in a .034 pound per day
decrease in per capita waste generation. This finding of the significance of tipping fees is
relevant to waste management for several reasons. One, it strongly indicates the value of
having a tipping fee to control waste generation as those counties without tipping fees had
higher rates of waste disposal. Two, it indicates the relatively small importance of
demographic variables relative to structural ones in determining waste generation. What
remains unexplained by our finding is what happened to the waste when tipping fees were
higher. Was there less waste generated or was it disposed of illegally? We do know that
11 counties received waste from other counties (N.C. Department of Environment,
Health, and Natural Resources, 1993). However, the county waste generation data has
been adjusted for this. Research on unit based pricing, which involves charging individuals
by the amount of waste generated, has indicated that this can lead to 10 to 30 percent
declines in the amount of waste generated (Miranda, forthcoming). It has not yet been
determined exactly how the waste is reduced. Some mechanisms include changes in
consumer purchasing behavior and illegal disposal.
Determining the relative importance of these two variables, per capita retail sales
and tipping fees, is difficult given the different scales used to measure each. One such
method is to examine the sum of the squares for each variable. Since per capita retail sales
has a higher sum of the squares, 33.44, than does the tipping fee, 10.77, it can be
considered more important using the sum of the squares criterion. Another approach to
determining relative importance is to examine the standardized estimates of the two
14
variables. These show that as one moves from the lowest to highest value within the
observed range of the retail sales variable, the effect on waste generation is greater relative
to the effect of the tipping fee variable within its observed range.
7.2 Non-significant variables
Interestingly, the slope estimates for income, urbanization, manufacturing, and
construction were not significantly different from zero, indicating that these three variables
do not explain variability in per capita waste generation. The statistically insignificant beta
for construction costs might suggest that construction debris is either not deposited in
municipal solid waste landfills or is not significant (Apotheker, 1990). However, in
general, construction and demolition debris in North Carolina is deposited in municipal
solid waste landfills. Land clearing and inert debris, on the other hand, is put in special
demolition landfills and is not measured in the waste generation data. This would lead to
the expectation that a variable measuring this type of waste would not be significant as a
determinant of total waste generation as measured in North Carolina. Our null finding
concerning income and degree of urbanization is consistent with the rather ambiguous
findings in the literature.
7.3 Model uncertainty and error
There are a number of sources of both uncertainty and error which can enter into
our model. These relate to the accuracy and precision of the data which are used for
parameter estimation as well as the adequacy of the measures which are used to represent
this waste generation.
15
One source of possible error is that there is likely to be considerable inaccuracy in
the measuring and reporting of waste by county. Scales to weigh garbage may not be
operational, waste may enter landfills without being weighed, personnel may not be
trained adequately in waste measurement, waste may be illegally disposed of or
transported out of state, and several landfills opened during the course of the year where
the data for this study was collected. The recycling rate is probably underreported as well
as some commercial establishments, such as supermarkets, may have their own nonreported recycling programs for materials such as corrugated cardboard.
A second source of potential error is that the per capita waste generation measures
by county may not be comparable as they might include different types of waste. Some
may include industrial or construction and demolition waste while others do not,
depending on both the training of those reporting and the range of disposal options
available to a county.
A third source of possible error is that the included variables may not be adequate
measures of the underlying economic or structural process which they seek to measure.
The finding, for example, that per capita construction costs was not significant does not
necessarily mean that construction and demolition waste is not a contributor to overall
waste generation but that this measure may not adequately capture it.
A fourth source of potential error is that the effect of time may not be adequately
considered (Chang et al, 1993). One such example of this is that some of the measures of
commercial activity are from different time periods than those concerning the amounts of
waste generated.
16
The purpose of this analysis was to identifl significant factors that explained
variability in per capita waste generation and to examine their relative importance rather
than predict per capita waste generation. A better predictive model of this complicated
process would require identification of other variables that are directly related to waste
production. Examples of such excluded variables are those to capture the government and
service sectors of the economy which can be significant contributors to waste generation.
These possible sources of uncertainty and error should be addressed in hture
research. Field studies, for example, can better characterize errors associated with waste
generation data or excluded waste streams. The model needs to be re-examined in hture
years as data for waste generation for each new year becomes available. New measures to
reflect different waste generation activities need to be developed. Reducing possible
uncertainty and error will contribute to making our findings more robust.
7.4 The model and waste management
The model constructed attempts to describe and explain the production of waste in
North Carolina. Clearly the size of the overall population will be the best predictor for the
total amount of waste generated in a region. This model's value for waste managers is to
identifl key determinants of waste generation on a per capita basis to improve waste
management strategy.
One of our findings, the importance of structural characteristics of waste
management relative to economic and demographic variables, is important given the waste
management changes currently occurring in North Carolina as well as nationally. A
structural component in the solid waste disposal scheme which could significantly effect
17
the generation and disposal of waste as characterized by this study is the evolution of
waste management activities from the county level to a regional level, corresponding with
the creation of large regional landfills. In fact, North Carolina has recently given approval
for eight regional landfills. Though only one landfill in the state is currently attracting
significant amounts of out-of-state waste, this could change with the construction of the
proposed landfills, given the low tipping fees in the region. The result is that this structural
component could have far more impact on waste disposal and generation than any of the
variables included in the study. Waste managers must account for these potential impacts
in permitting these disposal changes.
A second finding is the possible importance of restaurants as contributors to the
solid waste stream. This suggests that waste reduction efforts should target restaurants to
significantly impact the waste stream. However, it is also possible that the high correlation
between waste generation and restaurant sales may reflect a different underlying economic
process. The significance of restaurant sales may be indicative simply of a high
concentration of all types of waste generators and activities, such as the presence of
institutions and commercial establishments. One way to try to distinguish between these
alternative explanations for waste generation is to do a waste stream analysis (Crissman,
personal communication). In fact, waste stream characterizations do exist for several of
the counties in North Carolina. These can be examined to see if the actual studies of the
type of waste generated seem consistent with what is suggested by our model, i.e. whether
high restaurant sales are contributing to high overall waste generation.
A third finding was that commercial sources other than the retail sector did not
prove to be significant contributors to waste generation. In fact, though our though our
manufacturing value added variable didn't capture it, there is some evidence that special
18
manufacturing waste streams may account for a considerable portion of the waste
disposed of in landfills (Manuel, 1994). Wilson County, for example, has significant
amounts of tobacco clippings from storage and processing warehouses. Other county
landfills might receive ash from power plants and municipal waste water treatment sludge.
Some of the waste disposed of also may also be due to one time commercial events such
as plant closings or major construction projects. These commercial sources and amounts
of waste are unlikely to be accounted for by our predictor variables. Field research would
help to better examine the waste stream to reveal if the manufacturing sector might require
special targeting. However, any waste management plan aimed at this sector of the
economy must recognize the rapid change occurring in the generation patterns of
companies as they adapt to new environmental concerns and regulations.
8. Conclusions
There is considerable variation in the amount of waste generated on a per capita
basis in the U.S. The model constructed is valuable to address this issue by providing
some empirical evidence as to the relative importance of a variety of variables in
influencing waste generation while using a data set that is relatively consistent in data
collection and reporting methods. Specifically, this study finds that per capita retail sales
and tipping fee are the significant determinants of per capita waste generation while other
variables, particularly demographic ones, prove to not be significant as correlates of waste
production. The finding of the importance of tipping fee is particularly valuable for it
stresses the relative importance of waste management structural characteristics in
influencing waste generation and disposal rather then only socioeconomic ones. The study
also suggests that the retail sector, including eating establishments, should be a prime
target for waste reduction efforts. These findings can be extended beyond the population
19
of the counties of North Carolina. Many of the states in the Southeastern U.S. and in the
U.S . Environmental Protection Agency's Region IV (including South Carolina, Georgia,
Tennessee and Kentucky) share similar economic and population characteristics to North
Carolina and it is likely that the results are relevant to these states as well.
Waste generation, and specifically over generation, rather than problems related to
waste disposal, is increasingly recognized as the underlying cause of the waste problem.
Our study attempts to shed hrther light on this problem to better inform waste policy. Not
only do we identifl some key variables effecting waste generation but we point out the
sensitivity of any waste management plan not only to demographics and economy but to
structural variables influencing the overall management of waste. The challenge for waste
managers will be to link these findings to waste reduction and management efforts.
20
Acknowledgments
We would like to thank Paul Crissman, Andrea Borresen, and Susan Wright of the
Division of Solid Waste Management of the North Carolina Department of Environment,
Health, and Natural Resources.
List of tables and figures
Figure 1: Per capita waste generation by county in North Carolina
Figure 2a: Waste generation versus population by county in North Carolina
Figure 2b: Per capita waste generation versus per capita population by county in North
Carolina
Table 1: Descriptive statistics for determinants of per capita waste generation
Table 1. Hypothesized determinants of per capita waste generation in North
Carolina
Description
per capita MSW
disposal
tipping fee
per capita retail
sales
Units
Year
Variable
name
Ibs/person/day 1991- WASTE
1992
$/ton
1991- TIPFEE
1992
$1000/person 1990 RETSAL
value added by
manufacturing
11
1987
VALUADD
per capita
construction costs
per capita sales of
eateries
per capita sales of
merchandise
per capita sales of
food stores
per capita sales of
apparel stores
per capita income
<<
1990
CONST
urban population
percentage
Source
NCDEH
NR report
NC
Statistical
Abstract
County
and City
Extra
Mean
value mum
1.4
5.3
12.2
16.6
0
60
6.8
1.8
19.8
5.2
0.09
20.5
0.6
0
2.69
419
0
2197
396
0
1068
1136
3 19
3289
207
0
553
(1
$/person
1987
EATS
<<
<<
1987
MERCH
16
(1
1987
FOOD
11
16
1987
CLOTHES
11
(1
%
1990
INCOM
1990
URBPOP
Census
Atlas of
NC
13345 909 1
26.7
l<
0
20040
87.8
References
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Journal of Environmental Economics and Management, 2: 263-272.
Figure 1.
Per capita waste generation by county in North Carolina in 1991-92
Per Capita Municipal Solid Waste (tondperson)
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0
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0
O
Total Municipal Solid Waste (x10,OOO tons)
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