sample thesis - Bemidji State University

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School of Graduate Studies
Bemidji State University
1500 Birchmont Dr NE, #48
Bemidji, MN 56601-2699
218-755-2027
i
IMPACT OF RECYCLING AND WASTE MANAGEMENT POLICIES ON
RESIDENTIAL RECYCLING RATES IN MINNESOTA
by
Katelyn J. Larsen
____________________
A Thesis Submitted to the Faculty of the
CENTER FOR ENVIRONMENTAL, EARTH, & SPACE STUDIES, ECONOMICS &
SOCIOLOGY
In Partial Fulfillment of the Requirements
For the Degree of
MASTER OF SCIENCE IN ENVIRONMENTAL STUDIES
BEMIDJI STATE UNIVERSITY
Bemidji, Minnesota, USA
August 2013
ii
STATEMENT BY THE AUTHOR
This thesis has been submitted in partial fulfillment of requirements for an advanced
degree at Bemidji State University and is deposited in the University Library to be made
available to borrowers under the rules of the library.
Brief quotations from this thesis are allowable without special permission, provided
accurate acknowledgement of the source is indicated. Requests for permission to use
extended quotations or to reproduce the manuscript in whole or in part may be granted by
the Center for Environmental, Earth, & Space Studies, Economics & Sociology or the
Dean, School of Graduate Studies when the proposed purpose is in the interest of
scholarship. In all other instances, however, permission must be obtained from the author.
Signed: _________________________
__________________________________________________
APPROVAL BY THESIS ADVISOR
THIS THESIS HAS BEEN APPROVED ON THE DATE SHOWN BELOW:
__________________________________________
Dr. Patrick Welle,
Committee Chair
Professor of Environmental, Earth, & Space Studies,
Economics & Sociology
_______________________
Date
__________________________________________
Dean, School of Graduate Studies
_______________________
Date
iii
IMPACT OF RECYCLING AND WASTE MANAGEMENT POLICIES ON
RESIDENTIAL RECYCLING RATES IN MINNESOTA
Katelyn J. Larsen
Several counties in Minnesota are currently struggling to meet recycling goals.
Recycling is important because it preserves the life of landfills and prevents recyclable
materials from being incinerated. The purpose of this study was to determine what
variables positively and negatively impact residential recycling rates in Minnesota. This
study examined variables from all 87 counties in Minnesota during 1996-2011. It relied
heavily on the use of surveys and informal interviews with county environmental officials
to gain information. Nine out of 21 variables contributed significantly to the model
explaining annual residential recycling rates. These variables included population
density, number of drop-off sites, annual tons of municipal solid waste (MSW) sent to
landfills and processing facilities, percentage of MSW required to be sent to resource
recovery centers, annual amount spend on educating the public, percentage of county
population served by volume-based pricing, percentage of county population with
organized pick-up for MSW, percentage of county population with open-market pick-up
for recycling, and percentage of county population with open-market pick-up for MSW.
The results of this study suggest areas in which counties can improve in order to increase
residential recycling rates. However, the results of this study should be read with the
understanding that waste management and recycling in Minnesota is very complex, so the
model could not capture all variables that might impact residential recycling rates.
Approved by:
__________________________________________
Committee Chair
__________________________________________
Committee Member
__________________________________________
Committee Member
__________________________________________
Graduate Faculty Representative
_______________________
Date
iv
ACKNOWLEDGMENTS
First and foremost, I would like to thank my committee, Dr. Patrick Welle, Dr. Patrick
Donnay, and Erika Bailey-Johnson, for their constant feedback and support. Without
their suggestions, this study would not have been as strong.
Secondly, I would like to thank Arlene Vee, Peder Sandhei, and Cathy Jensen from the
Minnesota Pollution Control Agency for their input and provision of data and resources.
I cannot express my gratitude to them enough.
Third, I would like to thank all of the county environmental officials who responded to
my survey and provided detailed information on policies within their counties.
Lastly, I would like to thank my sister and brother-in-law for believing in me and helping
me get through the tough times in the past two years.
v
TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................vii
LIST OF FIGURES ........................................................................................................ix
Chapter
Page
1.
Introduction
Statement of the Problem ............................................................................. 1
Background and Need .................................................................................. 2
Purpose of the Study .................................................................................... 4
Significance of the Study .............................................................................. 4
Definitions ..................................................................................................... 5
Limitations.................................................................................................... 6
Ethical Considerations ................................................................................. 6
2.
Review of Literature
Recycling Behaviors by Individuals and within States ............................... 8
Individuals ........................................................................................ 8
States ............................................................................................... 12
Curbside Recycling .................................................................................... 13
Unit-Based Pricing ..................................................................................... 14
Curbside Recycling and Unit-Based Pricing ............................................. 19
Discussion ................................................................................................... 21
Conclusion .................................................................................................. 22
3.
Research Methodology
Counties ...................................................................................................... 23
Instrumentation .......................................................................................... 24
Variables ..................................................................................................... 27
Hypotheses .................................................................................................. 28
Statistical Analysis...................................................................................... 30
4.
Results
Descriptive Statistics .................................................................................. 31
SCORE report data ........................................................................ 31
Survey data ..................................................................................... 33
Inferential Statistics ................................................................................... 35
Paired-samples t test ....................................................................... 35
Multiple linear regression............................................................... 39
Model 1: SCORE report variables ........................................ 39
Model 2: SCORE report and survey variables ...................... 43
Alternative models ................................................................ 49
5.
Discussion
Paired-Samples T Test ............................................................................... 51
Multiple Linear Regression........................................................................ 52
vi
Model 1 ............................................................................................ 52
Model 2 ............................................................................................ 54
Limitations.................................................................................................. 57
Recommendations for Future Research .................................................... 58
Conclusions ................................................................................................. 58
References .................................................................................................................... 60
Appendix A. Minnesota County Data, 2007-2011 ...................................................... 63
Appendix B. Survey for Environmental Services Officials ........................................ 66
Appendix C. Frequencies and Means Reported for Survey for Environmental
Services Officials .............................................................................................. 71
vii
LIST OF TABLES
Table
1.
Page
Determinants of Recycling Programs ............................................................. 9
2.
Effects of Unit-Based Pricing ....................................................................... 17
3.
Determinants of Unit-Based Pricing ............................................................. 19
4.
Description of Variables............................................................................... 27
5.
SCORE Report Data Descriptive Statistics ................................................... 31
6.
Survey Data Descriptive Statistics ................................................................ 34
7.
Means of County Board and Individual Prioritization of Waste Management
and Recycling ............................................................................................... 36
8.
Paired-Samples T Test.................................................................................. 36
9.
Test for Normality between County Board and Individual Prioritization of
Waste Management and Recycling ............................................................... 37
10.
Wilcoxon Test for Difference between County Board and Individual
Prioritization of Waste Management and Recycling ..................................... 38
11.
Significance of Wilcoxon Test ....................................................................... 39
12.
Model 1 Summary ........................................................................................ 40
13.
Analysis-of-Variance: Model 1 ..................................................................... 40
14.
Coefficients of Model 1................................................................................. 41
15.
Independent Variables Strongly Correlated with Other Independent
Variables in Model 1 .................................................................................... 43
16.
Model 2 Summary ........................................................................................ 45
17.
Analysis-of-Variance: Model 2 ..................................................................... 45
18.
Coefficients of Model 2................................................................................. 46
19.
Model 2 Variables Statistically Different from 0 ........................................... 47
viii
20. Independent Variables Strongly Correlated with Other Independent
Variables in Model 2................................................................................................ 49
ix
LIST OF FIGURES
Figure
1.
Page
Map of Minnesota displaying county names ................................................. 23
1
Chapter 1: Introduction
During the 1970s, recycling became a popular policy meant to divert materials
from landfills and incinerators. However, recycling has other benefits as well.
According to a report published by the Natural Resources Defense Council, recycling
conserves natural resources…prevents pollution caused by manufacturing from
virgin resources…saves energy…reduces the need for landfilling and incineration
and helps avoid the pollution produced by these technologies…helps protect and
expand manufacturing jobs in America…[and] engenders a sense of community
involvement and responsibility (Hershkowitz, 1997, Introduction, para. 4).
Although there has been criticism from conservatives regarding the benefits of
recycling, it cannot be argued that recycling diverts materials from the waste stream that
would otherwise end up in landfills or incinerators. Conservatives, and other like-minded
citizens, may think that recycling is a waste of resources and argue that landfilling is
cheaper, but if the world was to stop recycling, landfills would indeed fill up at a much
quicker rate. Of course, conservatives could argue that there are other, more inexpensive
ways of solving the solid waste issue such as reducing consumption or
reusing/repurposing materials, but if a consumptive nation like the United States cannot
achieve a high recycling rate, then it would surely not be able to achieve high rates of
reduction or reuse. Furthermore, recycling provides a market and jobs whereas reduction
and reuse do not. Therefore, it is important to continue to focus on recycling.
Statement of the Problem
According to the Environmental Protection Agency, the United States currently
recycles about 34.7% of its total solid waste (U.S. Environmental Protection Agency
[U.S. EPA], n.d.). While this is very close to the nation’s recycling goal of 35% by 2005
2
(Hershkowtiz, 1997), it is less than half of the entire waste thrown away. More can be
done to improve recycling rates.
Background and Need
In the 1990s, two policies emerged as an attempt to improve recycling rates:
curbside recycling and unit-based pricing for solid waste. Several factors caused this
recycling trend. As a result of the environmental movement from 1970-1980, the
Resource Conservation and Recovery Act (RCRA) of 1976 was passed. Subtitle D of
that act implemented new guidelines for landfills, causing older ones to shut down out of
noncompliance (Jakus, Tiller, & Park, 1996; Kinnaman, 2006). From 1988 to 1997, the
number of landfills decreased from 8000 to less than 3000. According to Kinnaman and
Fullerton (2000), the reason for the decline was the change from multiple small, local
dumps to large, regional landfills. Local dumps could not keep up with the new
guidelines for landfills, and it was more cost-efficient to construct a large regional landfill
that met all of the RCRA codes. However, Kinnaman and Fullerton (2000) also noted
that while the number of landfills decreased, the capacity increased due to the
construction of larger landfills. They indicated that voters at the time may have been
confused, thinking that fewer landfills meant reduced capacity. If constituents believed
there was a landfill crisis, it only increased the need for a solution to America’s waste.
As the world’s population grew, so did the amount of household waste. This did
not weigh heavily on the United States until 1987 when the Mobro barge from New York
City traveled alongside the Atlantic coast attempting to find a suitable place to dump its
garbage (Kinnaman, 2006; “The Truth About Recycling,” 2007). Along the coast, the
barge was turned down by city upon city as a result of the Not in My Backyard (NIMBY)
3
phenomenon that became popular during the environmental movement (Kinnaman,
2006). Finally, in 1988, the EPA proposed a national recycling goal of 25% by 1992
(Khator, 1993). Later, the EPA increased that goal to 35% by 2005 (Hershkowitz, 1997).
This put pressure on states to adopt waste and recycling policies to meet the national
goal.
Minnesota attempted to meet the national goal by implementing recycling goals
for counties. According to Minnesota statute 115A.551 subdivision 2A, counties outside
of the metropolitan area are required to recycle 35% by weight of their total solid waste
while counties within the metropolitan area are required to recycle 50% by weight of
their total solid waste (Office of the Revisor of Statutes, n.d.b). The metropolitan area
refers to Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington (Office of
the Revisor of Statutes, n.d.d).
However, not all counties are meeting these goals. According to county responses
in the Report on 2011 SCORE Programs: A Summary of Recycling and Waste
Management in Minnesota, twenty-four counties reported recycling rates lower than the
required amounts (Minnesota Pollution Control Agency [MPCA], 2012). The penalties
for failing to meet the goal include “(1) notify[ing] county residents of the failure to
achieve the goal and why the goal was not achieved; and (2) provid[ing] county residents
with information on recycling programs offered by the county” (Office of the Revisor of
Statutes, n.d.b, subd. 5). These penalties do not represent an incentive for the county to
improve recycling rates. Rather, they serve as a passive approach to implementing
policy. A more sufficient approach would be taking away government funding for failing
to meet recycling goals or monetarily rewarding counties that did meet recycling goals.
4
Understanding the variables that are linked to improving recycling rates could help
counties that are currently not meeting recycling goals.
Purpose of the Study
The purpose of this study is to determine what variables negatively and positively
influence residential recycling rates in Minnesota. As previously stated, several counties
in Minnesota struggle to meet current recycling goals. However, other counties far
surpass recycling goals. Understanding why some counties are struggling while others
are not in terms of residential recycling should help improve overall recycling in
Minnesota.
In order to determine the variables responsible for achieving high residential
recycling rates, data was obtained on all 87 counties in Minnesota from several sources.
The bulk of the data came from the SCORE Reports from 1996-2011, produced by the
MPCA. Other data was retrieved from the Minnesota State Demographic Center and
U.S. Census American FactFinder. Additionally, a survey was developed to gain further
information about each county.
By studying the impact recycling and waste management policies have on
residential recycling in Minnesota, this study aimed to determine which variables are
beneficial and which ones do not have an impact.
Significance of the Study
This study will contribute significantly to the available literature on the impact
policies have on recycling rates. As the first comprehensive study conducted on the
subject on Minnesota, the ultimate goal of this thesis is to advance knowledge on
5
Minnesota residential recycling and provide insight to policy makers about effective and
ineffective policies.
Definitions
Bag/tag-based pricing: a special bag, or a tag applied to bags, is used for the purpose of
disposing of municipal solid waste. Waste haulers will not pick-up waste
disposed in other bags or ones that do not show a tag (Dijkgraaf & Gradus, 2004;
Fullerton & Kinnaman, 1996; Hong, 1999; Miranda & Aldy, 1998; Tsai & Sheu,
2009; Van Houtven & Morris, 1999).
Curbside recycling: “A program with at least one route-based collection system that picks
up at least four broad material categories of recyclables from households”
(MPCA, n.d., p. 12).
Drop-off site: a drop-off center where residents can drop off their recyclables
Frequency-based pricing: There are two ways of administering this method of waste
disposal. In the first method, residents prescribe to a certain number of cans and
are charged for that number whether the cans are full or not (Dijkgraaf & Gradus,
2004). In the second method, residents are charged based on the number of times
they set their can(s) out for collection (Van Houtven & Morris, 1999).
Resource recovery: “reclamation for sale, use, or reuse of materials, substances, energy,
or other products contained within or derived from waste” (Office of the Revisor
of Statutes, n.d.a, subd. 27). Typically, resource recovery refers to waste-toenergy in Minnesota.
Unit-based pricing: designed as an incentive to reduce the amount of waste residents
throw away, unit-based pricing (also known as variable-rate pricing and pay-as-
6
you-throw) charges residents by the unit, whether that is by bag/tag, weight,
volume, or frequency.
Volume-based pricing: different sizes of collection cans are used for the purpose of
disposing of municipal solid waste. Residents are charged based on the size of
can(s) they subscribe to (Dijkgraaf & Gradus, 2004; Miranda & Aldy, 1998).
Weight-based pricing: special cans are used for the purpose of disposing of municipal
solid waste and are weighed during waste pick-up. Residents are charged for the
weight of the waste they throw away (Dijkgraaf & Gradus, 2004; Linderhof,
Kooreman, Allers, & Wiersma, 2001).
Limitations
Although the study includes many variables, it was also limited in several ways.
First, the study was limited by time as it was designed to be completed in one year.
Second, the study was limited by the scope of the data. Although accounting for many
variables, it could not capture all of the variables that might impact residential recycling
rates, such as residents’ behaviors. Rather, the study aimed to examine the main
variables thought to impact residential recycling rates. It should provide value to
counties in evaluating current recycling and waste management programs and showing
them areas that can be improved.
Ethical Considerations
Because the design of the study included the use of a survey to gain information
from human subjects, the researcher followed guidelines outlined by the Bemidji State
University Human Subjects Committee. These included submitting a proposal to the
committee for review as well as completing the National Institutes of Health Training for
7
Human Subjects research. Since the survey was sent out to public service officials and
was designed to gain information about public services, there was minimal risk to
subjects. After review by the Human Subjects Committee, the study was approved.
8
Chapter 2: Review of Literature
Since the implementation of curbside recycling and unit-based pricing policies,
researchers have studied these two methods as well as recycling behaviors of individuals
and communities. This chapter will provide an analysis of the literature on people’s
recycling behaviors, curbside recycling, and unit-based pricing.
Recycling Behaviors by Individuals and within States
Individuals. The first study to explore human behaviors in relation to recycling
was in 1989 when De Young examined how recyclers and non-recyclers varied in
attitudes and behaviors. Assuming that non-recyclers had negative attitudes about
recycling, he used a telephone survey composed of 92 people along with actual
observations of 2,266 residences to conduct his research. De Young (1989) found that
recyclers actually did not vary from non-recyclers. Both had similar scores on prorecycling attitudinal, extrinsic motivation, trivialness, and frugality scales but scored
differently on the perceived difficulty scale. He determined that the most important
factor to increase recycling was familiarizing people with the proper ways of carrying it
out (De Young, 1989). Yet, at the time of De Young’s study, recycling was a relatively
new policy, so his results may be different if the same study was conducted today. Aside
from De Young, there have been several other researchers interested in how people feel
about recycling and how they react to it.
Oskamp et al. (1991) examined attitudes, behaviors, and knowledge as predictors
of recycling in Ontario, California. They conducted a telephone survey to examine the
correlation between participation in curbside recycling and knowledge, attitudinal
variables, and behavioral variables. However, by using a telephone survey, Oskamp et al.
9
(1991) only received a 27% response rate. This created a volunteer bias in the sampling
data, which may have affected the interpretation of the results. Nevertheless, Oskamp et
al. (1991) argued that convenient recycling increased recycling rates, so curbside
recycling programs would increase recycling. Table 1 shows their findings along with
the findings of several other studies.
Table 1
Determinants of Recycling Programs
Authors
Oskamp et al. (1991)
Study Area
Ontario, CA
Variable
Income
Type of dwelling
Derksen & Gartrell (1993)
Alberta, Canada
Know someone who
recycles
Concern for environment
Rural
Convenient recycling
program
Type of dwelling
Reschovsky & Stone (1994)
Jakus et al. (1996)
Tompkins County, NY
Williamson County, TN
Married
High education
Storage space
Knowledge of drop-off
center
Bag/tag program
Bag/tag program +
mandatory recycling +
curbside pickup
Well-informed
Know someone who
recycles
Storage space
Generation of recyclables
Age
Time
Note:
i
Low correlation (r=.07)
ii
Increased recycling for glass, cans, plastic, and cardboard; no effect on newspaper
Result
More likely to recycle
with higher incomes
More likely to recycle in
single-family house
Small impact on
recyclingi
Less likely to recycle
More likely to recycle in
single-family house
with ample storage
More likely to recycleii
No effect on recycling
23% increase in
newspaper; 37%
increase in glass
Less likely to recycle
with small storage
Less likely to recycle if
not enough recyclables
are generated
glass if older
Less likely to recycle if
it takes too much time
10
Oskamp et al. (1991) determined from their results that campaigns should focus on
promoting recycling rather than promoting an environmental conscience, specifically
targeting people who do not live in single-family homes. However, other researchers
found that promoting a general environmental conscience is more effective in increasing
recycling rates (Derksen & Gartrell, 1993; Khator, 1993). From results concluded by
Oskamp et al. (1991), the most successful curbside recycling programs are located in
residential areas with single-family homes and high house values.
Derksen and Gartrell (1993) examined the impacts that social context has on
recycling in Alberta, Canada. Edmonton provided curbside recycling while Calgary did
not provide any recycling opportunities to residents. Like Oskamp et al. (1991), Derksen
and Gartrell (1993) assumed that inconvenience and lack of knowledge inhibited
recycling. Therefore, they thought that the convenience of a curbside recycling program
would increase recycling rates. Derksen and Gartrell (1993) found that urban residents
and people living in single-family homes were more likely to recycle than rural residents
and people in non-single family homes (see Table 1). However, the strongest predictor of
recycling was having access to a convenient recycling program (Derksen & Gartrell,
1993). Therefore, curbside recycling programs were effective in increasing recycling
rates in urban areas and in areas with single-family homes.
In one of the more recent studies on recycling behavior in individuals, Feldman
and Perez (2012) examined people’s preferences to policy instruments. They argued that
deposit schemes had greater public support than fine schemes. Deposit schemes place an
additional fee on an item at the time of purchase, but the consumer can get that amount
back by returning the empty container for recycling. Fine schemes, on the other hand,
11
penalize residents for failing to recycle, otherwise known as criminal sanctions. Feldman
and Perez’s basis for their hypothesis came from several economic and behavioral
theories. First, they discussed the theory behind economic gains and losses in that people
prefer gains to losses. They recognized that both the fine and deposit schemes represent
losses, but the deposit scheme emphasized gaining at the time of returning the item for
recycling while the fine scheme only represented losing. Feldman and Perez also cited
McCaffery and Baron’s study as providing further support based on penalty aversion.
According to McCaffery and Baron, “penalty aversion refers to situations in which
people prefer bonuses over penalties, when the policies differ only in the form of
presentation” (as cited in Feldman & Perez, 2012, p. 409). Feldman and Perez argued
that the fine scheme was similar to a penalty while the deposit was similar to a bonus
(Feldman & Perez, 2012).
After conducting a web-based survey with 1800 participants, Feldman and Perez
(2012) found that people preferred deposit schemes over fine schemes. They concluded
that deposit schemes gave people more control and were more effective in providing
incentives for people to recycle. They also found that legal intervention created a
contradictory effect on recycling: it created a positive effect on non-recyclers who would
not normally recycle and a negative effect on people who would normally recycle
(Feldman & Perez, 2012). Therefore, using fines and mandatory recycling are not
effective policies in increasing recycling rates.
The studies discussed above were conducted in primarily urban areas where
curbside recycling is effective (see Table 1). In rural areas, it is not cost effective to
provide curbside recycling, so drop-off sites remain the viable option. Jakus, Tiller, and
12
Park (1996) studied recycling in rural Williamson County, Tennessee and determined
that, like Oskamp et al. (1991), people were more likely to recycle if they knew someone
who recycled (see Table 1). Furthermore, Jakus et al. (1996) found that people were
willing to pay $4.12 per household to recycle glass but only $1.66 per household to
recycle paper. While curbside recycling may not be effective in rural areas, Jakus et al.’s
(1996) study shows that rural residents have some of the same characteristics as urban
residents in recycling behavior.
According to De Young (1989), Oskamp et al. (1991), Derksen and Gartrell
(1993), and Jakus et al. (1996), curbside recycling is effective in urban areas with singlefamily homes and high incomes. As noted above, recycling is increased if people know
someone else who recycles. Additionally, legal intervention and fine schemes are not
effective policies for recycling (Feldman & Perez, 2012).
States. Rather than looking at recycling behaviors by individuals like the above
mentioned studies, Khator (1993) examined the strength of hypothesized predictors on
states’ recycling measures. She tested hypotheses in four different models: political,
economic, policy-perpetuation, and physical-factor. The political model examined the
variables party control, public participation, policy participation, innovation, and local
government discretion; the economic model examined the variables per capita income,
financial difficulty, and expenditure per capita; the policy-perpetuation model examined
the variables environmental commitment, per capita environmental expenses,
environmental policy innovativeness, and environmental bureaucratic strength; and the
physical-factor model examined the variables density and region. Khator found that the
physical-factor model was the strongest in predicting recycling. States with high
13
population density and located in particular regions (i.e. North Atlantic and Great Lakes)
within the United States showed more commitment to recycling. Khator (1993) noted
that although these physical demographic factors are out of policy makers’ control, they
can still be good predictors of where to locate effective curbside recycling programs
(Khator, 1993). This further shows that curbside programs are more effective in urban
areas.
Curbside Recycling
In 1982, before curbside recycling programs increased in the United States,
Jacobs and Bailey (1982) conducted an experiment on 615 homes similar in size and
value in different neighborhoods to determine the impact that promotions, incentives, and
convenience programs had on recycling rates in Tallahassee, FL. After assigning homes
to different groups (information only, penny-a-pound, lottery, weekly pick-up, and the
control), Jacobs and Bailey (1982) recorded the amount of recyclables set out by each
house on the first and third Saturdays of the test months. The lottery group, consisting of
homes entered into a drawing to win $5 each collection day for recycling, increased by
11.34% in participation during the study period. It also increased new participation by
23% during the study. However, it had the greatest deficit at -10.66 cents per home once
costs and revenues were determined. Yet, all groups showed deficit, even the control
group that did not receive any treatments (Jacobs & Bailey, 1982). Jacobs and Bailey’s
(1982) results showed that promotions, incentives, and convenience programs are
effective in increasing recycling rates, but they are not cost effective. Therefore, policy
makers should look to other means to increase recycling rates. One possibility is unitbased pricing.
14
Unit-Based Pricing
According to Fullerton and Kinnaman (1996), unit-based pricing has several
advantages and disadvantages. Unit-based pricing can reduce garbage, and it is fairer
than the traditional means of disposal because each household pays for its use of the
service. However, unit-based pricing programs have higher administrative costs and
produce an incentive to illegally dispose of waste. Because of these disadvantages, the
effectiveness of these programs is widely debated.
In Minnesota, volume-based pricing or weight-based pricing is required if the
local government unit charges waste generators for solid waste collection. Residents who
self-haul solid waste, as many do in rural counties in Minnesota, do not have to pay to
dispose of their trash at transfer stations and therefore do not face a monetary incentive to
reduce solid waste. Local government units can be exempt from the variable-rate pricing
requirement if they satisfy certain recycling requirements or have been exempt by the
commissioner (Office of the Revisor of Statutes, n.d.c). However, according to Peder
Sandhei, Principal Planner at the Minnesota Pollution Control Agency, many volumebased programs in Minnesota are ineffective because they do not have significant price
differentials between can sizes (personal communication, April 24, 2013) . According to
Skumatz (2008), effective price differentials between can sizes must be 60-80% in order
to provide incentives to reduce the amount of waste thrown away. Skumatz also
recommended that starting out with smaller container sizes like 20-32 gallons would be
more effective than starting out with can sizes around 60-90 gallons.
There have been five studies that examined the impacts of unit-based pricing on
waste disposal. Fullerton and Kinnaman (1996) conducted a study in Charlottesville,
15
VA, focusing on a bag/tag based program. Their results indicated that the volume of
bags decreased more than the weight thrown away after the implementation of unit-based
pricing (see Table 2). In other words, residents compressed their waste to fit into fewer
bags, increasing the density of each bag. Nevertheless, the unit-based pricing program
was moderately effective in reducing waste and increasing recycling (Fullerton &
Kinnaman, 1996).
Miranda and Aldy (1998) studied the impacts of unit-based pricing in nine cities
in California, Illinois, and Michigan. By looking at city data, they found that high unit
prices and small container sizes improved waste reduction and recycling. However, not
all nine communities experienced the same trend (see Table 2).
Linderhof, Kooreman, Allers, and Wiersma (2001) studied the impact of weightbased pricing in the Oostzaan in The Netherlands and found that waste decreased by
56%. Some of the decreased waste resulted from residents taking their trash to other
areas without weight-based pricing, but the majority of the waste decreased as a result of
the residents buying less packaged products, using fabric shopping bags, using cloth
diapers, composting, and recycling (Linderhof et al., 2001). Linderhof et al. (2001) also
investigated whether the waste decreased as a result of illegal dumping but found it to be
negligible.
Dijkgraaf and Gradus (2004) also examined unit-based pricing in The
Netherlands, but they studied all forms of unit-based pricing. They found that weightbased and bag-based (13 or 16 gallon bag) pricing systems reduced waste more than
frequency-based (subscription) and volume-based (37 or 63 gallon can) systems (see
Table 2). According to Dijkgraaf and Gradus (2004), these results were not caused by
16
illegal dumping or waste tourism. Their results also showed that the bag-based program
had smaller administrative costs.
Table 2
Effects of Unit-Based Pricing
Researchers
Study area
Methods
Sample size
Program
type
Waste Reduction
Increase
recycling?
Fullerton &
Kinnaman (1996)
Charlottesville, VA
Household survey (before/after)
75 householdsi
Bag/tag
14% waste reduction by
weight; 37% reduction by
volumeii
16% increase in
weight
Miranda & Aldy
(1998)
Downers Grove, IL
City data
NA
Bag/tag
20% waste reduction
41-64% increase
Miranda & Aldy
(1998)
Hoffman Estates,
IL
City data
NA
Bag/tag
37.6% waste reduction
41-64% increase
Miranda & Aldy
(1998)
Woodstock, IL
City data
NA
Bag/tag
20% waste reduction
41-64% increase
Miranda & Aldy
(1998)
Grand Rapids, MI
City data
NA
Bag/tag
22% waste reduction
NAiii
Miranda & Aldy
(1998)
Lansing, MI
City data
NA
Bag/tag
50% waste reduction
Yes
Dijkgraaf &
Gradus (2004)
The Netherlands
Municipality survey
538
municipalities
Bag/tag
14-36% waste reduction iv
Yes
17
Miranda & Aldy
(1998)
Glendale, CA
City data
NA
Volume
20% waste reduction
60% increase
Miranda & Aldy
(1998)
Pasadena, CA
City data
NA
Volume
No
70% increase
Miranda & Aldy
(1998)
San Jose, CA
City data
NA
Volume
20% waste reduction
Yes
Miranda & Aldy
(1998)
Santa Monica, CA
City data
NA
Volume
>6% waste reduction
30% increase
Dijkgraaf &
Gradus (2004)
The Netherlands
Municipality survey
538
municipalities
Volume
6% waste reduction
No
Dijkgraaf &
Gradus (2004)
The Netherlands
Municipality survey
538
municipalities
Frequency
21% waste reduction
10% increase
Linderhof et al.
(2001)
Oostzaan in The
Netherlands
Demographic survey, weighing of
compostable waste (GFT) and solid
waste (RST)
3459
households
Weight
56% waste reduction
NA
Dijkgraaf &
Gradus (2004)
The Netherlands
Municipality survey
538
municipalities
Weight
50% waste reduction
21% increase in
glass, paper,
textiles
Note:
Not representative of the population; primarily single-family homes, higher incomes, homeowners, married couples, full-time workers
ii
28% of this due to illegal dumping
iii
At the time of the study, Grand Rapids did not have recycling data because its recycling program was very new
iv
Municipalities that used a bag/tag program for both non-recyclable and compostable waste saw a 36% waste reduction. Municipalities that did not charge for
compostable waste collection saw a 14% waste reduction.
i
18
19
Finally, Callan and Thomas (1999) examined what influences adoption of unit
pricing for waste disposal by communities in Massachusetts. Table 3 shows their results.
Table 3
Determinants of Unit-Based Pricing
Authors
Callan & Thomas (1999)
Study Area
Massachusetts
Variable
Result
Income
Less likely in higherincome areas
Housing value
More likely in wealthier
areas
Education
More likely in
communities with high
education levels
Age
Less likely in areas with
relatively young or old
residents
Rural classification
20.3% less likely in rural
areas
Callan and Thomas (1999) stated that their study could be used to predict whether a
community would adopt unit-based pricing. Their results are consistent with those found
by Oskamp et al. (1991) and Reschovsky and Stone (1994).
Curbside Recycling and Unit-Based Pricing
Although unit-based pricing has not always been effective in reducing waste on
its own, researchers have hypothesized that it is more effective when combined with a
curbside recycling program. Five studies examined the effectiveness of unit-based
pricing on reducing waste and enhancing recycling in several areas around the world.
As noted above, Callan and Thomas (1997) studied the phenomenon in
Massachusetts. They learned that unit pricing was effective in increasing recycling, but it
was even more effective if accompanied with a curbside pickup program. However,
because of differences in local government policies and structure, Callan and Thomas
20
(1997) argued that economic analyses on recycling cannot be used to determine a onesize-fits-all policy.
Hong (1999) found that bag/tag based unit pricing increased recycling by 26.8%
in South Korea, and it also reduced waste by 17.8%. She noted that her results could
have been different if South Korea used a weight based program rather than a bag/tag
based one. Like Fullerton and Kinnaman (1996), Hong (1999) also found that the bag/tag
based program provided an incentive for people to compact their garbage into fewer bags.
Yet, Van Houtven and Morris (1999) found that a bag/tag-based program in Marietta, GA
was effective in reducing waste by 51% but not in increasing recycling.
Jenkins, Martinez, Palmer, and Podolsky (2003) also found that unit-based pricing
did not impact recycling. Additionally, Tsai and Sheu (2009) found a bag/tag-based
pricing program in Taipei City, Taiwan to be effective in reducing waste by 22% but not
in enhancing recycling. Tsai and Sheu (2009) also found that 60% of the waste reduction
resulted from illegal dumping. The contradictory results of Hong (1999), Van Houtven
and Morris (1999), Jenkins et al. (2003), and Tsai and Sheu (2009) appear to support
Callan and Thomas’ (1997) argument that economic analyses on recycling cannot be used
to create a one-size-fits-all policy.
Of course, one of the elements of an effective program is cost effectiveness.
According to Kinnaman (2006), neither unit-based pricing nor curbside recycling is cost
effective. He analyzed several studies that performed economic analysis on unit-based
pricing and curbside recycling and examined the administrative costs of those programs.
He found that there are very low net benefits after costs are considered. Kinnaman
(2006) suggested that instead of implementing programs with high administrative costs, it
21
makes more sense to charge residents the marginal cost of solid waste disposal, which he
figured out to be $5.38-8.76 per ton. He argued this reduces administrative costs because
it relies on infrastructure already in place for waste disposal. Additionally, he stated this
prevents illegal dumping, which has been seen in cities that have adopted unit-based
pricing (Kinnaman, 2006). While Kinnaman’s method accounts for the external costs of
landfills (odor, sight, emission of greenhouse gases, etc.), it relies on filling up landfills
as the method of disposal. In a finite planet, this method is not sustainable even if it does
consider the environment. The key, then, is finding a solution that is sustainable while
keeping administrative costs low.
Discussion
The literature presented several things to consider when designing a research
project examining the effectiveness of recycling and waste management. First, curbside
recycling programs were most effective in urban areas that had medium to high-income
residents living in single-family homes (Derksen & Gartrell, 1993; DeYoung, 1989,
Jakus et al., 1996; Oskamp et al., 1991). Second, mandatory recycling helped improve
recycling rates when it was accompanied by a curbside recycling program and unit-based
program (Reschovsky & Stone, 1994). Otherwise, mandatory recycling was not effective
(Feldman & Perez, 2012). Third, curbside recycling programs and unit-based pricing
were associated with high administrative costs. Fourth, unit-based pricing programs were
effective in reducing waste or enhancing recycling when accompanied by a convenient
recycling program, but they were not effective on their own. Fifth, weight-based
programs were more effective in reducing waste and enhancing recycling, but they were
22
more expensive to run than bag/tag-based programs (Dijkgraaf & Gradus, 2004; Hong,
1999).
Conclusion
Throughout the world, researchers have been interested in examining the
effectiveness of current waste disposal and recycling policies. While not all programs
have shown effectiveness, they have shown effective and ineffective methods.
Furthermore, studies have shown that people’s demographics and behaviors play a role in
predicting whether they recycle or not. Communities with a strong environmental
conscience have shown a higher likelihood of recycling and lesser likelihood to illegally
dump garbage. Based on this review, communities with an environmental conscience,
high population densities, curbside recycling, and unit-based pricing programs should
have high recycling rates.
23
Chapter 3: Research Methodology
This project served as an observational study of Minnesota’s waste management
and recycling policies in all 87 counties. Several types of methods were used in this
study including analyzing data from surveys and interviews. This section includes a
description of the counties included in the research, discusses the instrumentation used,
describes the variables studied, explains hypotheses included in the research, and
provides a brief overview of the statistical tests performed.
Counties
The study area includes all of Minnesota’s 87 counties, which differ in terms of
population size, location, population density, household median income, and median
value of homes. Figure 1 shows a map of the study area.
Figure 1. Map of Minnesota displaying county names. From Minnesota County MapMinnesota Map, by Digital Map Store, n.d. Retrieved from http://www.digital-topomaps.com/county-map/minnesota.shtml
24
The largest counties in terms of population size in Minnesota, according to the
2011 U.S. census, are Hennepin, Ramsey, Dakota, and Anoka, which are all located in
the metropolitan region of Minnesota. Understandably, these are also the counties with
the highest population density. However, they do not have the highest household
incomes or household values. The counties with the highest household incomes include
Scott and Carver with a median over $80,000. Scott and Carver are also the two counties
with the highest median value of homes. For more detailed information, see Appendix A.
While the study area includes all 87 counties, some counties receive different
treatment in terms of management. For example, within St. Louis and Carlton counties
lies the Western Lake Superior Sanitary District (WLSSD). The entire district includes
the cities of Duluth, Cloquet, Carlton, Scanlon, Wrenshall, Hermantown, Proctor and
Thomson, and the rural townships of Silver Brook, Thomson, Twin Lakes, Canosia,
Duluth, Grand Lake, Lakewood, Midway, Rice Lake, and Solway (Western Lake
Superior Sanitary District, n.d.). However, for SCORE reporting purposes, Carlton
County reports on all of the cities included within its county while St. Louis County
reports on everything within its county that is outside of the WLSDD boundary. For this
reason, the WLSSD is viewed as a management district in this research and is treated as a
county. Additionally, Pope County and Douglas County are managed together in the
SCORE report and will thus be treated as one county for the purpose of this research
project.
Instrumentation
Every year since 1991, all Minnesota counties have been required to report to the
Minnesota Pollution Control Agency detailed information regarding their solid waste and
25
recycling policies and processes. Then, the information counties submit as part of a
survey is summarized in the SCORE Report, which is then submitted to the Minnesota
Legislature in odd-numbered years (Minnesota Pollution Control Agency, 2013). The
present study used county data reported in the SCORE Reports from 1996-2011.
Although data found on the MPCA website goes back to 1991, information on the
variables included in this research could only be obtained from the MPCA records since
1996. The SCORE Reports provided valuable information on several variables included
in the research: annual tons of residential recycling, cost of recycling programs,
education costs, annual tons of MSW sent to resource recovery centers, annual tons of
MSW sent to compost facilities, number of cities and townships per county that offer
curbside recycling at least once a month, and county population served by residential
curbside recycling. Because information was obtained on all 87 counties over a period of
16 years, this created a sample size of 1,392.
Although the SCORE report proved to be a valuable resource for this study, it did
not provide information on all variables included in the research. To obtain estimated
population data by county dating back to 1996, information from the Minnesota State
Demographic Center was used. Information on population density by county was taken
from the 2000 and 2010 censuses, obtained through the U.S. Census American
FactFinder. Population density by county for the remaining years was calculated by
dividing the population data from the Minnesota State Demographic Center by the land
area in square miles from the 2010 census.
Other variables considered in the present study included the type of fee structure
for waste disposal used in each county. There are two different types of fee structures
26
implemented for municipal solid waste: unit-based pricing (pay-as-you-throw) or annual
fixed cost. Because this information was not available through the SCORE Report, the
Survey for Environmental Services Officials was designed. It was used to obtain
information on several variables related to the different fee structures: type of unit-based
pricing (volume-based, frequency-based, bag/tag-based, weight-based, or none), number
of municipalities per county engaged in unit-based pricing, and county population that is
served by unit-based pricing. By using the survey, it could be better determined whether
waste management and recycling policies affected residential recycling rates within the
past 16 years. Because the survey asked respondents to indicate how long the various
policies were in place, the answers could be used for multiple years, creating sample sizes
of 816 to 896 (some counties left some questions blank, which is why there are various
sample sizes on the Survey for Environmental Services Officials).
Beyond unit-based pricing, the Survey for Environmental Services Officials also
asked several questions about other variables assumed to impact residential recycling in
Minnesota. The other variables gathered from the survey included resource recovery
requirements, presence of organized pick-up for recycling and solid waste, presence of
open market pick-up for recycling and solid waste, presence of a separate tax on solid
waste like an environmental charge, and how waste management and recycling is
prioritized within the county. For detailed information on these variables, see Appendix
B.
The Survey for Environmental Services Officials was sent to environmental
contacts from each county. Contact information was found on the Minnesota Pollution
Control Agency website (see Appendix B for a copy of the survey). Using that contact
27
information, environmental services officials were first e-mailed about the survey on May
20 and 21 of 2013 with a deadline of June 8, 2013. Reminders were sent out to those
who had not responded two weeks after the first e-mails were sent. Noting that the
response rate was quite low after the initial deadline, the deadline for the survey was
extended to June 17, and reminders were sent out on June 13. Phone calls were also
made during the week of June 10 to follow-up with respondents for clarification on
responses. The extension of the deadline allowed for a response rate of 64%, meaning 56
out of 87 counties replied (see Appendix A for a list of the counties that responded).
Variables
Several variables were studied in this research. Table 4 lists and describes all of
the variables included in the research.
Table 4
Description of Variables
Name
Annual residential recycling rate
County
Year
Number of years
Type
Dependent
Independent
Independent
Independent
Scale of Measurement
Ratio
Nominal
Ratio
Ratio
N
1392
1392
1392
1392
Source
SCORE Report
SCORE Report
SCORE Report
SCORE Report
Population density
Independent
Ratio
1392
Percent of county with access to
curbside recycling programs
Number of recycling drop-off sites
Percent of MSW sent to resource
recovery centers
Percent of MSW sent to compost
facilities
Annual tons of MSW sent to
landfills and processing centers
Total annual MSW generated
(includes recycled material)
Annual amount spent on recycling
programs
Annual amount spent on educating
the public
Presence of volume-based pricing
Independent
Ratio
1392
Minnesota Demographic
Center
SCORE Report
Independent
Independent
Ratio
Ratio
1392
1392
SCORE Report
SCORE Report
Independent
Ratio
1392
SCORE Report
Independent
Ratio
1392
SCORE Report
Independent
Ratio
1392
SCORE Report
Independent
Ratio
1392
SCORE Report
Independent
Ratio
1392
SCORE Report
Independent
Nominal
864
Percentage of county population
served by volume-based pricing
Independent
Ratio
864
Survey for Environmental
Services Officials
Services Officials
28
Presence of frequency-based pricing
Independent
Nominal
864
served by frequency-based pricing
Independent
Ratio
864
Presence of bag/tag-based pricing
Independent
Nominal
880
served by bag/tag-based pricing
Presence of weight-based pricing
Independent
Ratio
880
Independent
Nominal
864
served by weight-based pricing
Percentage of MSW required to
send to resource recovery centers
Presence of organized pick-up for
recycling
served by organized pick-up for
recycling
Presence of organized pick-up for
MSW
served by organized pick-up for
MSW
Presence of open pick-up for
recycling
served by open pick-up for
recycling
Presence of open pick-up for MSW
Independent
Ratio
864
Independent
Ratio
896
Independent
Nominal
896
Independent
Ratio
896
Independent
Nominal
880
Independent
Ratio
880
Independent
Nominal
880
Independent
Ratio
880
Independent
Nominal
896
served by open pick-up for MSW
Presence of tax on MSW
Independent
Ratio
896
Independent
Nominal
880
served by tax on MSW
How county board prioritizes waste
management/recycling
How environmental services official
prioritizes waste
Independent
Ratio
880
Independent
Interval
816
Independent
Interval
832
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Services Officials
Hypotheses
This research project served to study several areas related to waste management
and recycling policies. These hypotheses included:
1. H1: There is a difference in the average values reported for how county boards
versus environmental service officials prioritize waste management and recycling.
2. H2: High population density will positively impact residential recycling rates.
29
3. H3: High county percentage of people with access to curbside recycling programs
will positively impact residential recycling rates.
4. H4: High numbers of recycling drop-off sites will positively impact residential
recycling rates.
5. H5: High county percentage of MSW sent to resource recovery facilities will
negatively impact residential recycling rates.
6. H6: High county percentage of MSW sent to compost facilities will positively
impact residential recycling rates.
7. H7: High tonnage of MSW sent to landfills and processing centers will negatively
8. H8:High percentage of MSW required to be sent to resource recovery centers will
negatively impact residential recycling rates.
9. H9: High recycling expenditures will positively impact residential recycling rates.
10. H10: High education expenditures will positively impact residential recycling
rates.
11. H11: High county percentage of population with unit-based pricing options
(volume-based, frequency-based, bag/tag-based, and weight-based) will positively
12. H12: High county percentage of population with organized pick-up for solid waste
and recycling will influence residential recycling rates, either negatively or
positively.
30
13. H13: High county percentage of population with open market pick-up for solid
waste and recycling will influence residential recycling rates, either negatively or
positively.
14. H14: High county percentage of population with separate tax on MSW will
positively impact residential recycling rates.
15. H15: High prioritization of waste management and recycling by county boards and
environmental service officials will positively impact residential recycling rates.
Statistical Analysis
Several statistical tests were run, which will be explained in greater detail in
chapter four. Briefly, these tests included paired samples t-test, Wilcoxon matched-pairs
signed-rank test, and multiple linear regression.
31
Chapter 4: Results
As discussed in the introduction of this thesis, the main goal of the study was to
determine what variables negatively and positively impact residential recycling rates in
Minnesota. Therefore, the majority of the analysis comes from using multiple linear
regression. However, the paired-samples t test and Wilcoxon matched-pairs signed-rank
test were used to examine hypothesis 1. Before the results of the analysis of the pairedsamples t test and multiple linear regression are examined, a description of the data is
provided.
Descriptive Statistics
SCORE report data. Table 4 in chapter 3 provided an overall description of
variables. In order to understand the characteristics of the data, it is necessary to examine
descriptive statistics using measures of central tendency and measures of variability.
Table 5 provides this information for the SCORE report data.
Table 5
SCORE Report Data Descriptive Statistics
SCORE
report
variable
Residential
annual
recycling rate
Percent of
MSW sent to
compost
facilities
Percent of
MSW sent to
resource
recovery
Population
Population
density
Percent of
people in
county that
N
Mean
Median
Standard
deviation
Range
Skewness
1392
12.18%
10.90%
6.41%
54.40%
1.30
1392
1.28%
0.00%
6.06%
61%
5.82
1392
12.93%
0.00%
19.84%
80%
1.33
1392
1392
59,589.54
127.53
22,794.00
27.72
139,642.26
425.19
1,165,621
3,398.60
5.81
6.14
1392
54.46%
61.05%
29.58%
114%
-.47
32
have access to
curbside
recycling
Number of
recycling
drop-off sites
in county
Annual tons
of solid waste
sent to
landfills and
processing
centers
Total annual
solid waste
generated
(includes
recycled
material)
Annual
amount spent
on recycling
program
Annual
amount spent
on educating
the public
1392
14.69
10.00
28.90
272
7.72
1392
36,187.24
10,812.00
107,064.28
1,004,722
6.76
1392
65,259.97
21,450.00
182,291.81
1,624,519
6.44
1392
$172,632.37
$104,067.50
$219,106.52
$2,386,623
3.51
1392
$25,010.72
$5,590.00
$69,681.80
$1,598,331
10.38
The most common measure of central tendency is the mean, which is reported for
each SCORE variable in Table 5. However, the mean is not very useful when the data is
skewed. As is visible in the skewness column, all of the SCORE variables are skewed in
some way. The majority are negatively skewed, but there is one positively skewed
variable: percent of people in the county that have access to curbside recycling. In a
normal distribution, the skewness in SPSS would read 0. Residential annual recycling
rate, percent of MSW sent to resource recovery, and the percent of people in the county
that have access to curbside recycling are only slightly skewed. The other variables that
report skewness values greater than three are highly negatively skewed because the
majority of the values are in the lower end of the range. In those cases, the median is
more reliable as a measure of central tendency, which is also reported for each SCORE
variable in Table 5.
33
When viewing descriptive statistics, measures of variability are also important.
These show how spread out the data is. The range shows the difference between the
maximum and minimum values in the dataset while the standard deviation shows the
average amount of deviation between the individual values and the mean value. The
standard deviations reported in Table 5 show that the values of each variable are highly
spread out. Lower standard deviations would have indicated that the data did not have
much spread.
Another important piece of information that needs to be explained further is the
range of the percent of people in the county that have access to curbside recycling. Table
5 reports a value of 114%. Because the variable is reported as a percentage, this indicates
the maximum value is 114%. The reason for this high percentage could be related to two
factors. The variable was calculated by dividing the estimated population in the county
that has access to curbside recycling by the estimated population of the county and
multiplying that value by 100. However, as was mentioned in chapter three, the
estimated population values came from the Minnesota Demographic Center while the
estimated population in the county with access to curbside recycling came from the
SCORE Reports. Because the range of this variable is greater than 100%, it means that
either the counties overestimated the population that had access to curbside recycling, the
U.S. Census underestimated the population of the county, or it was a combination of the
two.
Survey data. Because the multiple linear regression equation uses data obtained
from the Survey for Environmental Services Officials, it is necessary to also show
descriptive statistics for that part of the dataset. Table 6 shows the results.
34
Table 6
Survey Data Descriptive Statistics
Survey variable
n
Mean
Median
Range
Skewness
81.00%
Standard
deviation
38.33%
Percentage of county
population served by
volume-based pricing
frequency-based
pricing
bag/tag-based pricing
weight-based pricing
Percentage of MSW
required to send to
resource recovery
centers
organized pick-up for
recycling
MSW
open pick-up for
recycling
open pick-up for
MSW
tax on MSW
How County Board
prioritizes waste
How environmental
services official
prioritizes waste
864
67.75%
100.00%
-.85
864
8.58%
.00%
25.26%
100.00%
2.88
880
13.75%
.00%
26.83%
100.00%
1.98
864
1.87%
.00%
13.49%
100.00%
7.15
896
15.74%
.00%
34.50%
100.00%
1.84
896
39.89%
28.00%
39.70%
100.00%
.35
880
21.46%
8.00%
28.46%
100.00%
1.27
880
34.06%
2.40%
40.50%
100.00%
.57
896
55.33%
66.00%
39.47%
100.00%
-.27
880
47.67%
00%
49.29%
100.00%
.09
816
6.84
8.00
2.06
9
-.77
832
8.67
9.00
1.67
8
-1.82
Compared to the SCORE data in Table 5, the data from the Survey for
Environmental Services Officials is much less dispersed. This is partly because the
majority of the variables in this part of the dataset were standardized into percentages. In
this case, the mean is a better measure of central tendency for all of the variables except
35
percentage of county population served by weight-based pricing. This variable shows a
skewness of 7.15, which means that it is negatively skewed. In the case of this variable,
the median serves as a better measure of central tendency.
Table 6 only shows the descriptive statistics for the ratio and interval level data
obtained from the Survey for Environmental Services Officials. To examine the
frequencies reported for the nominal level data, see Appendix C.
Inferential Statistics
While descriptive statistics are important for showing the characteristics of the
sample or population, inferential statistics help make conclusions about the population
that the sample represents. The paired-samples t test is an example of inferential
statistics, which was used to study hypothesis 1: there is a difference between the average
values reported for how county boards and environmental service officials prioritize
waste management and recycling.
Paired-samples t test. On the Survey for Environmental Services Officials, the
last two questions asked the survey taker to rate how the county board prioritized waste
management and recycling and how the person taking the survey prioritized waste
management and recycling. The literature review discussed the impact that attitudes and
beliefs can have on recycling. These questions were meant to analyze the effect of
attitudes and beliefs on recycling from a county environmental official perspective rather
than from the perspective of residents. Because these questions only asked about the
ratings for the present year, this data was put into a separate spreadsheet in SPSS in order
to avoid a great deal of missing data for the years 1996-2010. Additionally, this data
allowed for the use of the paired-samples t test because both ratings were provided by the
36
same survey taker, and the ratings also applied to the same county. Using the pairedsamples t test, the null hypothesis states that there is no difference between the average
values reported for how county boards and environmental service officials prioritize
waste management and recycling. Table 7 shows the means of both variables.
Table 7
Means of County Board and Individual Prioritization of Waste Management and
Recycling
Mean
N
Std. Deviation
Std. Error Mean
County board prioritization of
waste management and
6.82
50
2.097
.296
8.62
50
1.701
.241
recycling for 2011
Pair 1
Individual prioritization of
recycling for 2011
Table 7 shows that the survey taker tends to prioritize waste management and
recycling more than the county board. In order to test whether these results are
significant, it is necessary to examine further output. Table 8 shows the results of the
paired-samples t test.
Table 8
Paired-Samples T Test
Mean
Pair
1
County board
prioritization of
waste management
and recycling for
2011 - Individual
prioritization of
waste management
and recycling for
2011
-1.800
Std.
Deviation
2.040
Paired Differences
Std. Error
95% Confidence
Mean
Interval of the
Difference
Lower
Upper
.289
-2.380
-1.220
t
-6.238
df
Sig. (2tailed)
49
.000
37
The paired-samples t test shows that the mean difference between the two
variables is -1.800. Since the alternative hypothesis (H1) was for a two-tailed test, the
significance displayed is correct. This means that the null hypothesis that there was no
difference between the two variables can be rejected at the 1% level of significance. In
other words, there is 99% confidence that H1 is correct, but there is also a 1% chance of
rejecting a true null hypothesis.
In testing the paired-samples t test, it is assumed that the data comes from a
normal population. Based on the Central Limit Theorem, it can be assumed that the
distribution of sample means is approximately normal because the sample size is
sufficiently large (each variable in this case was over 50). However, the Shapiro-Wilk
and Kolmogorov-Smirnov tests can test for normality. Table 9 shows the results.
Table 9
Test for Normality between County Board and Individual Prioritization of Waste
Management and Recycling
Kolmogorov-Smirnova
Statistic
Difference in priorities between
county board and individual
.212
df
Shapiro-Wilk
Sig.
50
.000
Statistic
.828
df
Sig.
50
.000
a. Lilliefors Significance Correction
In testing for normality, the null hypothesis states that the distribution is normal.
Because of the low significance in both the Kolmogorov-Smirnov and Shapiro-Wilk
tests, there is reason to doubt the normality assumption. In other words, the null
hypothesis would be rejected in this case at the 1% level of significance. However, the
results of this test do not necessarily mean the paired-samples t test results were
inaccurate. Using a nonparametric test that does not require the normality assumption
38
may yield the same results as the paired-samples t test. In order to test this, the same
hypothesis can be run using the Wilcoxon matched-pairs signed-rank test. It does require
that the differences in the sample be symmetric, but that is a more flexible requirement
than the normality requirement. Table 10 shows the results of the Wilcoxon test.
Table 10
Wilcoxon Test for Difference between County Board and Individual Prioritization of
Waste Management and Recycling
N
Mean Rank
Sum of Ranks
Individual prioritization of
Negative Ranks
0a
.00
.00
Positive Ranks
32b
16.50
528.00
recycling for 2011 - County
Ties
18c
board prioritization of waste
management and recycling for Total
50
2011
a. Individual prioritization of waste management and recycling for 2011 < County board prioritization of
waste management and recycling for 2011
b. Individual prioritization of waste management and recycling for 2011 > County board prioritization of
c. Individual prioritization of waste management and recycling for 2011 = County board prioritization of
According to Table 10, the mean rank for cases where individual prioritization is
less than county board prioritization is 0 while the mean rank for cases where individual
prioritization is greater than county board prioritization is 16.50. This would indicate that
individual prioritization tends to be higher than county board prioritization, meaning the
null hypothesis that there is no difference between the two variables would be rejected.
Table 11 confirms this, showing that the null hypothesis can be rejected at the 1% level
of significance.
39
Table 11
Significance of Wilcoxon Test
Z
Asymp. Sig. (2-tailed)
a. Wilcoxon Signed Ranks Test
b. Based on negative ranks.
Individual prioritization of waste management
and recycling for 2011 - County board
prioritization of waste management and
recycling for 2011
-4.967b
.000
The Wilcoxon Test showed that the results of the paired-samples t test were
accurate even though there was reason to doubt the assumption of normality. Therefore,
H1 can be accepted with 99% confidence.
Multiple linear regression. In order to study H2 through H15, multiple linear
regression was used. The first model tested only the variables retrieved from the SCORE
Report while the second model combined the variables obtained from the Survey for
Environmental Services Officials to show how the model changed when more
information was supplied.
Model 1: SCORE report variables. The model specification for Model 1 was:
Y= β0 + β1(X1) + β2(X2) + β3(X3) + β4(X4) + β5(X5) + β6 (X6) + β7 (X7) + β8 (X8) + β9 (X9)
+ε
where

Y= annual residential recycling rate

X1=number of years

X2=population density

X3=percent of county with access to curbside recycling programs

X4=number of recycling drop-off sites

X5=percent of MSW sent to resource recovery centers
40

X6=percent of MSW sent to compost facilities

X7=annual tons of MSW sent to landfills and processing centers

X8=annual amount spent on recycling programs

X9=annual amount spent on educating the public

ε= error term
Using SPSS, Model 1 was run to test its strength. Table 12 shows the strength of the
model.
Table 12
Model 1 Summary
Model
R
1
R Square
.156
Adjusted R Square
.024
Std. Error of the Estimate
.018
6.3497
According to the model, only 2.4% of the observed variability in annual residential
recycling rates is explained by the independent variables. This is a very weak model and
implies that the independent variables are not linearly related to the dependent variable.
However, the analysis-of-variance table says otherwise.
Table 13
Analysis-of-Variance: Model 1
Model
Sum of Squares
Regression
1
df
Mean Square
1383.923
9
153.769
Residual
55721.186
1382
40.319
Total
57105.109
1391
F
Sig.
3.814
.000
The analysis-of-variance tests three null hypotheses: that there is no linear
relationship in the population between annual residential recycling rates and the
independent variables, all of the population partial regression coefficients are 0, and the
41
population value for multiple R2 is 0. According to Table 13, the null hypotheses can be
rejected at the 1% level of significance. This means that there is some linear relationship
between the dependent and independent variables. It also means that at least one of the
population regression coefficients is not 0. The coefficients are provided by Table 14.
Table 14
Coefficients of Model 1
Model
1
Unstandardized
Coefficients
B
Std. Error
11.393
.579
.012
.038
(Constant)
Number of years
Percent of MSW sent
.057
to compost facilities
Percent of MSW sent
-.009
to resource recovery
Population density by
-.003
county
Percent of people in
county that have access
.019
to curbside recycling
Number of recycling
.025
drop-off sites in county
Annual tons of solid
2.068E-006
waste sent to landfills
Annual amount spent
-1.836Eon recycling program
006
Annual amount spent
1.271E-007
on educating the public
Standardized
Coefficients
Beta
t
Sig.
Collinearity
Statistics
Tolerance
VIF
.009
19.683
.313
.000†
.755
.942
1.061
.029
.054
2.004
.045**
.965
1.036
.009
-.028
-1.017
.309
.913
1.096
.002
-.212
-1.755
.079
.048
20.662
.007
.086
2.748 .006***
.727
1.376
.015
.113
1.630
.103*
.146
6.864
.000
.035
.443
.658
.116
8.626
.000
-.063
-1.911
.056
.654
1.529
.000
.001
.033
.974
.393
2.544
Note. Using 1-tailed test: * reject null at the 10% level of significance; ** reject null at the 5% level of
significance; ***reject null at the 1% level of significance. Using 2-tailed test: †reject null at the 1% level
of significance
According to the results of the coefficients, the estimated regression equation
would be written as:
̂
(
)
(
)
(
)
(
)
(
)
(
)
(
)
(
)
The coefficients table tests the null hypotheses that the population partial regression
coefficient for a variable is 0 and that the constant is 0. Using a two-tailed test, the null
hypothesis for the constant can be rejected at the 1% level of significance. Based on the
results, the variables that were not 0 were percent of MSW sent to compost facilities,
(
)
42
percentage of people in county with access to curbside recycling, and number of
recycling drop-off sites. These were the only variables whose null hypotheses could be
rejected based on hypotheses made in the methods chapter. Based on these results, H3,
H4, and H6 predicted correctly in stating residential recycling rates would be positively
impacted. Although these variables predicted correctly, they differ in confidence levels.
While percentage of people in the county with access to curbside recycling has the most
confidence in being accurate at 99%, the number of recycling drop-off sites has the least
confidence in being accurate at 90%. Table 14 explained the significance levels in which
to reject the null hypothesis for each variable. According to this model, all other
variables either predicted incorrectly or did not contribute significantly.
The variables that predicted incorrectly included population density and annual
amount spent on recycling program. In the methodology chapter, it was shown that these
variables would positively impact residential recycling rates. However, this model shows
that they predicted negatively. Because the alternate hypotheses were directional in
focus, the one-tailed test applied to these variables. Due to the negative sign displayed on
the coefficients in the model, these results are not significant. Therefore, the null
hypotheses stating that the coefficients for these variables are not different from zero
cannot be rejected.
The collinearity statistics of Model 1 show that multicollinearity could be a
potential problem. Tolerances that are less than 0.10 and variation inflation factors
(VIFs) higher than 10 signify a potential problem with multicollinearity. According to
Table 14, there is one variable that could be causing multicollinearity: “population
density by county.” Typically, the way to correct for multicollinearity is to omit the
43
culprit variable. However, doing so in this case would be erroneous because theory
suggests that population density does have an impact on recycling rates. Because only
one variable showed a low tolerance and high VIF, multicollinearity is not seen as a
serious problem in this model. Table 15 shows the independent variables in Model 1 that
are highly correlated with one another (Pearson correlation with absolute value greater
than .6). It shows that population density is strongly correlated with three other variables,
causing multicollinearity.
Table 15
Independent Variables Strongly Correlated with Other Independent Variables in Model 1
Variable
county
Annual amount spent on
educating the public
Strongly Correlated
with
Number of recycling
Pearson Correlation
Sig. (1-tailed)
.778
.000
.765
.000
.812
.000
.630
.000
Model 2: SCORE report and survey variables. In the second model, variables
obtained from the Survey for Environmental Services Officials were added to Model 1.
The model specification for Model 2 was:
Y= β0 + β1(X1) + β2(X2) + β3(X3) + β4(X4) + β5(X5) + β6 (X6) + β7 (X7) + β8 (X8) + β9
(X9)+ β10(X10) + β11(X11) + β12(X12) + β13(X13) + β14 (X14) + β15 (X15) + β16 (X16) + β17
(X17) + β18 (X18) + β19 (X19) + β20 (X20) + β21 (X21) + ε
where

Y=annual residential recycling rate

X1=number of years

X2=population density

X3=percent of county with access to curbside recycling programs
44

X4=number of recycling drop-off sites

X5=percent of MSW sent to resource recovery centers

X6=percent of MSW sent to compost facilities

X7=annual tons of MSW sent to landfills and processing centers

X8=annual amount spent on recycling programs

X9=annual amount spent on educating the public

X10=percentage of county population served by volume-based pricing

X11= percentage of county population served by frequency-based pricing

X12= percentage of county population served by bag/tag-based pricing

X13= percentage of county population served by weight-based pricing

X14= percentage of MSW required to be sent to resource recovery centers

X15= percentage of county population served by organized pick-up for recycling

X16= percentage of county population served by organized pick-up for MSW

X17= percentage of county population served by open-market pick-up for
recycling

X18= percentage of county population served by open-market pick-up for MSW

X19= percentage of county population served by separate tax on MSW

X20=county board prioritization of waste management/recycling

X21=environmental service official prioritization of waste management/recycling

ε= error term
Using SPSS, Model 2 was run to test its strength. Table 16 shows the results.
45
Table 16
Model 2 Summary
Model
R
1
R Square
.466
Adjusted R Square
.217
Std. Error of the Estimate
.193
5.2950
Although still weak, this model shows that 21.7% of the observed variability in annual
residential recycling rates is explained by the independent variables. Like Model 1,
Model 2 also shows that there is some linear relationship between annual residential
recycling rates and the independent variables according to the analysis-of-variance output
in Table 17.
Table 17
Analysis-of-Variance: Model 2
Model
Sum of Squares
Regression
1
df
Mean Square
5297.093
21
252.243
Residual
19121.605
682
28.038
Total
24418.698
703
F
Sig.
8.997
.000
According to Table 17, the null hypotheses indicating no linear relationship in the
population between the dependent and independent variables, all population partial
regression coefficients are 0, and the population value for multiple R2 is 0 can all be
rejected at the 1% level of significance. This means that at least one of the population
regression coefficients is not 0. The coefficients are provided by Table 18.
46
Table 18
Coefficients of Model 2
Model
1
Unstandardized
Coefficients
B
Std. Error
21.009
1.401
-.033
.046
(Constant)
Number of years
Percent of MSW sent
-.031
to compost facilities
Percent of MSW sent
.067
to resource recovery
.006
county
Percent of people in
county that have access
-.023
to curbside recycling
Number of recycling
.055
-2.386Ewaste sent to landfills
005
Annual amount spent
3.757E-007
on recycling program
Annual amount spent
1.511E-005
on educating the public
population with
.022
volume-based pricing
population with
-.054
frequency-based
pricing
population with bag.008
tag-based pricing
population with
.016
weight-based pricing
Percentage of solid
waste county is
required to send to
-.037
resource recovery
center
population with
.005
recycling
population with
-.022
solid waste
population with open.035
market pick-up for
recycling
Standardized
Coefficients
Beta
t
Sig.
Collinearity
Statistics
Tolerance
VIF
-.026
15.000
-.715
.000†
.475
.896
1.116
.101
-.011
-.312
.755
.964
1.037
.019
.219
3.487
.001
.290
3.445
.003
.316
1.754
.080**
.035
28.260
.013
-.102
-1.816
.070
.365
2.741
.031
.100
1.794
.073**
.371
2.697
.000
-.546
-3.144 .002***
.038
26.243
.000
.017
.369
.712
.511
1.958
.000
.132
1.967
.050**
.254
3.934
.008
.147
2.777 .006***
.411
2.430
.015
-.187
-3.578
.000
.422
2.371
.010
.038
.778
.437
.481
2.078
.021
.039
.735
.463
.397
2.517
.009
-.236
-4.135 .000***
.353
2.829
.009
.032
.543
.587
.326
3.066
.009
-.106
-2.328
.020†
.559
1.789
.010
.237
3.508
.000†
.251
3.978
47
population with openmarket pick-up for
solid waste
population with
environmental charge
on solid waste
County board
prioritization of waste
management and
recycling for all years
Individual
prioritization of waste
management and
recycling for all years
-.073
.009
-.507
-7.751
.000†
.268
3.727
-.006
.005
-.053
-1.213
.226
.608
1.644
.189
.149
.069
1.269
.205
.394
2.541
-.910
.166
-.250
-5.489
.000
.553
1.807
Note. Using 1-tailed test: ** reject null at the 5% level of significance; *** reject null at the 1% level of
significance. Using 2-tailed test: †reject null at the 1% level of significance
According to the results of the coefficients, the estimated regression equation
would be written as:
̂
(
)
(
(
)
)
(
(
)
)
(
(
(
)
(
)
(
)
(
)
)
(
)
(
(
)
)
)
(
(
(
)
)
)
(
(
(
)
)
(
Similar to Model 1, the null hypothesis that the constant is 0 can be rejected at the 1%
level of significance using a two-tailed test. Table 19 shows the variables that were
statistically different from 0.
Table 19
Model 2 Variables Statistically Different from 0
Variable
X2
X4
X7
X9
X10
X14
Name
Population density
Number of drop-off sites
Annual tons of MSW sent to
landfills and processing facilities
Annual amount spent on educating
the public
served by volume-based pricing
Percentage of MSW required to be
sent to resource recovery centers
Corresponding Hypothesis
H2
H4
H7
H10
H11
H8
)
)
48
X16
with organized pick-up for solid
waste
with open-market pick-up for
recycling
with open-market pick-up for solid
waste
X17
X18
H12
H13
H13
The variables listed in Table 19 were the only ones whose null hypotheses could
be rejected based on hypotheses made in the methods chapter. Based on these results, H 2,
H4, H10, and H11 predicted correctly in stating that residential recycling rates would be
positively impacted. In other words, as X2, X4, X9, and X10 increase, so do residential
recycling rates. H7 and H8 also predicted correctly in assuming that residential recycling
rates would be negatively impacted, which means that as X7 and X14 increase, residential
recycling rates decrease. Furthermore, H12 and H13 predicted correctly in stating that
residential recycling rates would be impacted in some way. Since X 16 and X18 have
negative coefficients, as these variables increase, residential recycling rates decrease. X 17
has a positive coefficient, meaning that as it increases, so do residential recycling rates.
According to Model 2, all other variables either predicted incorrectly or did not
contribute significantly.
The variables that predicted incorrectly included percent of MSW sent to resource
recovery, percentage of people with access to curbside recycling, percentage of county
population with frequency-based pricing, and environmental service official prioritization
of waste management and recycling. According to the hypotheses shown in the
methodology chapter, it was predicted that percent of MSW sent to resource recovery
centers would have a negative impact on residential recycling rates while the other three
variables would have a positive impact on residential recycling. However, the model
49
shows that the opposite is true. Because the hypotheses for these variables were
directional, the one-tailed test applied. Yet, because the wrong sign was displayed on the
coefficients, the null hypotheses for these variables had to be accepted at the 10% level of
significance, meaning these coefficients are not different from zero.
In Model 2, the collinearity statistics showed that multicollinearity could also be
present based on two variables: “population density by county” and “annual tons of solid
waste sent to landfills.” However, omitting these variables would also be erroneous
based on theory. Furthermore, this does not represent a serious problem with
multicollinearity since only two variables raised questions. Table 20 shows the variables
that are strongly correlated with each other (Pearson correlation with absolute value
greater than .6). Both of the variables causing multicollinearity appear on the table.
Table 20
Independent Variables Strongly Correlated with Other Independent Variables in Model 2
Variable
county
Percent of MSW sent to
resource recovery
Number of recycling
population with openmarket pick-up for
recycling
Strongly Correlated
with
Percentage of solid
waste county is required
to send to resource
recovery center
population with weightbased pricing
population with openmarket pick-up for solid
waste
Pearson Correlation
Sig. (1-tailed)
.967
.000
.788
.000
.688
.000
.608
.000
.744
.000
.712
.000
Alternative models. Three multiple linear regression models were run in order to
determine the best model to use for Model 2. The first alternative model tested every
50
variable found in Model 2 with the exception of county board and individual
prioritization of waste management/recycling. This resulted in a significant F test but
lower R square value of .129, meaning only 12.9% of the variability in residential
recycling rates was explained by the independent variables.
The second alternative model added in the variables of county board and
individual prioritization of waste management/recycling but only included those values
for 2011 (since the Survey for Environmental Services Officials was done in 2013, the
values were first input into the most recent year to align with variables from the SCORE
report in order to avoid making a large assumption that the attitudes of environmental
services officials remained the same from 1996-2011). However, this resulted in the
variable “number of years” becoming a constant, forcing SPSS to delete it from the
analysis. This test resulted in an insignificant F test (.564) even though it had a stronger
R square value of .447. Due to the results of the F test, this model would not have
provided an in-depth analysis for looking at how variables influenced residential
recycling rates.
The third model tested all of the variables indicated in Model 2, which was the
model that was chosen due to its significance and strength. It should be noted that this
model included the variables of county board and individual prioritization of waste
management/recycling by extending those values reported in 2011 in the second
alternative model to 1996, assuming that attitudes of environmental services officials did
not change from 1996-2011. Therefore, the results of Model 2 are accurate as long as
there were no attitudinal shifts during that time span. Because this assumption is most
likely not the case, the results of Model 2 should be read with that in mind.
51
Chapter 5: Discussion
Reducing the amount of solid waste produced by humans is important in order to
achieve sustainability on Earth. Recycling is one method of diverting solid waste from
landfills and incinerators, thereby preserving space for future generations. As discussed
earlier, several counties in Minnesota are having a difficult time meeting the required
recycling goals of 35% outside of the metropolitan area and 50% within the metropolitan
area. This study aimed to find which variables negatively and positively impacted
residential recycling rates in order to suggest areas that counties could improve in. The
literature review pointed out that areas with curbside recycling and unit-based pricing had
higher recycling rates. Additionally, curbside programs were more successful in highly
populated areas with medium to high-income residents in single-family homes. If
Minnesota followed world-wide trends, then the percentage of people with access to
curbside recycling, percentage of people with unit-based pricing, and population density
would have positively impacted residential recycling rates.
Paired-Samples T Test
In the methodology chapter, it was hypothesized that there was a difference in the
average values reported for how county boards versus environmental service officials
prioritized waste management and recycling. The paired-samples t test and Wilcoxon
matched-pairs signed-rank test confirmed that the hypothesis was accurate with 99%
confidence. In both tests, it was shown that environmental service officials had higher
ratings on average than county boards. This finding is noteworthy for several reasons.
First, environmental service officials may have provided higher ratings for themselves
because they wanted to appear superior to the county board on a psychological level.
52
Second, this result could mean that while environmental service officials are highly
dedicated to their positions, county boards have too many other responsibilities to
consider recycling and waste management a top priority. Finally, it could be a mixture of
these two reasons or other reasons. While both reasons are plausible, the second reason
carries heavier implications with it. If county boards are weighed down with too many
responsibilities, thereby pushing recycling and waste management to a low priority, this
could be a reason for some counties being unable to meet the recycling requirements.
This could also be a reflection of the attitude and ideology of the county administrator.
For example, conventional wisdom and political theory have shown that Republican
leaders tend to prioritize business over the environment while Democrat leaders prioritize
the environment over business. Even though county environment positions are nonpartisan, the ideologies of county officials could still affect their actions. While these
variables were not explored in this thesis, these could very well be impacting residential
recycling rates as well as county board prioritization of waste management and recycling.
Multiple Linear Regression
Model 1. Model 1 tested linearity between annual residential recycling rates and
variables obtained from the SCORE Report. Although it was very weak, it did show that
some variables were linearly related to annual residential recycling rates. The variables
that contributed the most to the model were percentage of MSW sent to compost
facilities, percentage of people with access to curbside recycling, and number of
recycling drop-off sites. Each of the three variables correctly predicted that annual
residential recycling rates would be positively impacted.
53
Based on this model, Minnesota slightly followed global trends. A prediction was
made based on the literature review theorizing that a high percentage of people with
access to curbside recycling would positively impact annual residential recycling rates.
The reasoning behind this was that when people are presented with a convenient method
of recycling, they tend to recycle more. However, the literature review also pointed out
that curbside recycling programs were more successful in areas with high population, so
it was predicted that population density would have a positive impact on annual
residential recycling rates. Yet, the model showed that population density had a negative
impact on the dependent variable, meaning that as population density increased, annual
residential recycling rates decreased. This suggests that, when statistically controlling for
the presence of curbside recycling programs in Minnesota, high population density leads
to lower recycling rates. It could mean that where curbside recycling programs are
readily available in highly populated areas, people are choosing not to recycle.
Nevertheless, the evidence from this model, unlike Model 2, implies that population
density does not improve residential recycling rates.
The model also showed that the percentage of MSW sent to compost facilities
positively impacted residential recycling rates. The original theory behind this
hypothesis was that if counties were inclined to send MSW to compost facilities, they had
a higher environmental conscience and would be more likely to push recycling.
However, composting can also be thought of as a step higher than recycling, so if a
county is composting, then they are also most likely recycling. This finding from Model
1 shows that this train of thought makes sense.
54
Model 2. Model 2 also tested linearity between annual residential recycling rates
and variables obtained from the SCORE Report, but it also added in variables obtained
from the Survey for Environmental Services Officials. It was significantly stronger than
Model 1 but still weak overall. In this version of the model, the variables that were
significant included population density, number of recycling drop-off sites, annual tons of
MSW sent to landfills and processing centers, annual amount spent on educating the
public, percentage of county population with volume-based pricing, percentage of MSW
counties are required to send to resource recovery, percentage of county population with
organized pick-up for MSW, and percentage of county population with open-market
pick-up for MSW and recycling. The only variable that was significant in both models
was the number of recycling drop-off sites.
In all variables that proved to be significant to the model, the rate at which they
changed the dependent variable was very minimal. This means they did not have a large
impact on the dependent variable.
In the methodology chapter, it was thought that sending large amounts of MSW to
landfills and processing centers, including being required to send large amounts to
resource recovery, would have a negative effect on residential recycling rates. Model 2
showed that this was a correct assumption. This finding could mean that there are
recyclable materials currently being thrown away into landfills or sent to incinerators.
This would certainly cause lower recycling rates. However, some counties, such as
Beltrami County, have contractual obligations with resource recovery centers in that they
are required to send a certain percentage of their waste to those centers. This provides a
disincentive to recycle due to stricter penalties for not meeting the contractual obligations
55
versus not meeting county recycling goals. Nevertheless, requiring recyclable materials
to be recovered prior to landfilling or incineration would improve recycling rates. Some
counties are currently doing this by using machinery to recover metals. However, certain
recyclable materials like plastic are not as easily removed by machines. Hence, it is
necessary to look at other ways of improving recycling rates.
As mentioned earlier, the variable “number of recycling drop-off sites” was
significant in both models. Based on this finding, one of the ways counties can improve
residential recycling rates is by increasing the number of drop-off sites. Essentially,
increasing the number would make it more convenient for people to recycle, especially
rural people. However, this may be too costly for some counties.
According to the model, increasing the amount spent on educating the public
could also improve residential recycling rates. While the variable “annual amount spent
on educating the public” was significant at the 5% level of significance, the magnitude of
its slope coefficient (.00) indicated that it did not affect the dependent variable as much as
other variables such as the percentage of people with volume-based pricing (.02).
Model 2 showed that as the percentage of people with volume-based pricing
increased, so did residential recycling rates. Therefore, counties could improve
residential recycling rates by increasing the number of people required to throw MSW
away through volume-based pricing. However, as was mentioned in the literature
review, counties could also improve the effectiveness of volume-based pricing by
increasing the price differential between the different sizes of cans. Improving the
effectiveness of volume-based pricing would likely have a positive impact on residential
recycling rates.
56
There were four variables that required the use of the two-tailed test because it
was unclear how they would impact residential recycling rates. Three of those variables
were significant in the model. These included percentage of people with organized pickup for MSW, percentage of people with open-market pick-up for recycling, and
percentage of people with open-market pick-up for MSW. As was shown in Table 18,
percentage of people with organized pick-up for MSW and percentage of people with
open-market pick-up for MSW had a negative impact on residential recycling rates while
percentage of people with open-market pick-up for recycling had a positive impact.
Increasing access to either method of pick-up for MSW would have the opposite effect on
residential recycling rates. This could be a psychological issue in that when people are
given a convenient method of disposal for waste, they will be less inclined to recycle.
Nevertheless, it does not seem to matter which method of disposal people have access to;
both types reduce residential recycling rates.
In the case of organized pick-up vs. open-market pick-up for recycling, the type
of method does matter. While percentage of people with organized pick-up for recycling
was not significant in Model 2, percentage of people with open-market pick-up for
recycling was. Furthermore, increasing the amount of people with open-market pick-up
for recycling increases residential recycling rates. This finding indicates that it is not
having access to a convenient recycling program that is important; rather, having the
ability to choose a hauler is important. Of course, this does not mean that open-market
pick-up for recycling is superior to organized pick-up for recycling. It simply means that
giving people the option to choose a hauler seems to have a positive influence on the
57
amount they recycle. This is related to Feldman and Perez’ (2012) finding that giving
people more control increased recycling rates.
Limitations
Although the author worked diligently to design a well-planned research project,
there were several limitations to the study. First, the timeline of sending out surveys and
receiving feedback was too short. This meant a lower response rate (64%) than what
could have been achieved with more time. Additionally, it was unknown at the time of
designing the survey that some of the contact information on county environmental
officials from the MPCA website was outdated. This resulted in some county officials
not receiving the survey until later in the study.
The survey itself presented some limitations as not all county officials were clear
on how to answer questions or knowledgeable about the policies within their county.
This was mostly due to several policies being managed at the city level rather than the
county level.
Some of the data was limited as well. For example, the dependent variable
residential recycling rates was a function of the residential recycled material divided by
the overall waste collected, which included waste produced from the commercial and
industrial sectors. Regression results may have been different if information on
residential solid waste could have been obtained. Unfortunately, that was not a variable
included in the SCORE report.
Finally, as was mentioned in the introduction of the thesis, not all variables could
be accounted for in the regression analysis. There are several variables, like
environmental attitudes of Minnesotans, that would likely have an impact on residential
58
recycling rates. However, it was beyond the scope of this study to examine that type of
information.
Recommendations for Future Research
Based on the results from this study, there are several areas recommended for
future research. First, if this study was to be conducted again, it should incorporate data
on environmental attitudes of Minnesotans. Based on the literature review, this was a key
component that was missing in this research. The survey attempted to resolve the issue
by asking county environmental officials to rank their priority on waste
management/recycling. However, the officials do not represent the attitudes and believes
held by county constituents. Therefore, finding a way to include this information would
be crucial.
Additionally, a continuation of this study would be to examine the issue from a
household perspective, similar to the studies in the literature review. Knowing what the
results are from the household perspective could help counties further improve residential
recycling rates.
Conclusions
Three major conclusions can be drawn from this study. First, there was a
significant difference between how county environmental officials prioritize waste
management and recycling and how they felt county boards prioritize waste management
and recycling. As noted in the discussion earlier, this could be a reason for counties not
meeting current recycling goals. Second, the models tested using multiple linear
regression were not superb predictors for annual residential recycling rates. Although
they were significant, the addition of more variables could yield higher R square values.
59
Third, while the models were not very strong, they did provide some useful information,
such as identifying variables that counties can look at to improve residential recycling
rates. As the discussion noted, these variables included population density, number of
recycling drop-off sites, annual tons of MSW sent to landfills and processing centers,
annual amount spent on educating the public, percentage of county population with
volume-based pricing, percentage of MSW counties are required to send to resource
recovery, percentage of county population with organized pick-up for MSW, and
percentage of county population with open-market pick-up for MSW and recycling.
As mentioned at the beginning of this study, these results do not fully explain the
dynamic between the independent and dependent variables. There are several other
independent variables working together to explain annual residential recycling rates that
could not all be captured by this thesis. Therefore, the results of this thesis should be
used to suggest areas to improve in for struggling counties and not as a one-size-fits-all
type of policy.
60
References
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recycling effort. Eastern Economic Journal, 23(4), 411-423.
Callan, S. J., & Thomas, J. M. (1999). Adopting a unit price system for municipal solid
waste: Policy and socio-economic determinants. Environmental & Resource
Economics, 14, 503-518.
Derksen, L., & Gartrell, J. (1993). The social context of recycling. American Sociological
Review, 58(3), 343-442.
DeYoung, R. (1989). Exploring the difference between recyclers and non-recyclers: The
role of information. Journal of Environmental Systems, 18, 341-351.
Dijkgraaf, E., & Gradus, R. (2004). Cost savings in unit-based pricing of household
waste: The case of the Netherlands. Resource and Energy Economics, 26, 353371. doi: 10.1016/j.reseneeco.2004.01.001
Feldman, Y., & Perez, O. (2012). Motivating environmental action in a pluralistic
regulatory environment: An experimental study of framing, crowding out, and
institutional effects in the context of recycling policies. Law & Society Review,
46(2), 405-442.
Fullerton, D., & Kinnaman, T. C. (1996). Household responses to pricing garbage by the
bag. The American Economic Review, 86(4), 971-984.
Hershkowitz, A. (1997). Too good to throw away: Recycling’s proven record. Retrieved
from http://www.nrdc.org/cities/recycling/recyc/recyinx.asp
Hong, S. (1999). The effects of unit pricing system upon household solid waste
management: The Korean experience. Journal of Environmental Management,
57, 1-10.
Jacobs, H. E., & Bailey, J. S. (1982). Evaluating participation in a residential recycling
program. Journal of Environmental Systems, 12(2), 141-152.
Jakus, P. M., Tiller, K. H., & Park, W. M. (1996). Generation of recyclables by rural
households. Journal of Agricultural and Resource Economics, 21(1), 96-108.
Jenkins, R. R., Martinez, S. A., Palmer, K., & Podolsky, M. J. (2003). The determinants
of household recycling: A material-specific analysis of recycling program features
and unit pricing. Journal of Environmental Economics and Management, 45(2),
294-318.
Khator, R. (1993). Recycling: A policy dilemma for American states? Policy Studies
Journal, 21(2), 210-226.
Kinnaman, T. C. (2006). Policy watch: Examining the justification for residential
recycling. The Journal of Economic Perspectives, 20(4), 219-232.
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Kinnaman, T. C., & Fullerton, D. (2000). The economics of residential solid waste
management. In T. Tietenberg & H. Folmer (Eds.), The international yearbook of
environmental and resource economics 2000/2001 (pp. 100-147). Cheltenham:
Edward Elgar.
Linderhof, C., Kooreman, P., Allers, M., & Wiersma, D. (2001). Weight-based pricing in
the collection of household waste: The Oostzaan case. Resource Energy &
Economics, 23(4), 359-371.
Minnesota Pollution Control Agency. (n.d.). 2012 SCORE reporting form. Unpublished
document.
Minnesota Pollution Control Agency. (2012). Report on 2011 SCORE programs: A
summary of recycling and waste management in Minnesota. Retrieved from
http://www.pca.state.mn.us/index.php/view-document.html?gid=18888
Minnesota Pollution Control Agency. (2013). The SCORE report. Retrieved from
http://www.pca.state.mn.us/index.php/data/score/recycling-in-minnesota-thescore-report.html
Miranda, M. L., & Aldy, J. E. (1998). Unit pricing of residential municipal solid waste:
Lessons from nine case study communities. Journal of Environmental
Management, 52, 79-93.
Office of the Revisor of Statutes. (n.d.a.). 115A.03 definitions. Retrieved from
https://www.revisor.mn.gov/statutes/?id=115A.03
Office of the Revisor of Statutes. (n.d.b). 115A.551 recycling. Retrieved from
https://www.revisor.mn.gov/statutes/?id=115A.551
Office of the Revisor of Statutes. (n.d.c.). 115A.9301 solid waste collection; volume-or
weight-based pricing. Retrieved from https://www.revisor.mn.gov/
statutes/?id=115A.9301
Office of the Revisor of Statutes. (n.d.d). 473.121 definitions. Retrieved from
https://www.revisor.mn.gov/statutes/?year=2012&id=473.121#stat.473.121.2
Oskamp, S., Harrington, M. J., Edwards, T. C., Sherwood, D. I., Okuda, S. M., &
Swanson, D. C. (1991). Factors influencing household recycling behavior.
Environment and Behavior, 23(4), 494-519.
Reschovsky, J. D., & Stone, S. E. (1994). Market incentives to encourage household
waste recycling: Paying for what you throw away. Journal of Policy Analysis and
Management, 13(1), 120-139.
Skumatz, L. A. (2008). Model pay as you throw (PAYT)/ variable rates (VR) legislation:
Elements, options, and considerations for state or local level legislation in solid
waste. Retrieved from http://www.serainc.com/Publications_v1.html
62
The truth about recycling. (2007, June 9). The Economist. Retrieved from
http://www.economist.com/node/9249262
Tsai, T., & Sheu, S. (2009). Will unit-pricing enhance recycling? International Journal of
Sustainable Development & World Ecology, 16(2), 102-108. doi:
10.1080/13504500902796765
U.S. Environmental Protection Agency. (n.d.). Municipal solid waste. Retrieved from
http://www.epa.gov/epawaste/nonhaz/municipal/
Van Houtven, G. L, & Morris, G. E. (1999). Household behavior under alternative payas-you-throw systems for solid waste disposal. Land Economics, 75(4), 515-537.
Western Lake Superior Sanitary District. (n.d.). WLSSD legislative boundary (service
area). Retrieved from http://www.wlssd.com/about_servicearea.php
63
Appendix A: Minnesota County Data, 2007-2011
County
Population
Population Density
Median Household Income
Median Value of Home
Aitkin*
16,303
8.9
$41,301
$175,400
Anoka *
329,487
782.1
$69,139
$215,900
Becker *
32,422
24.7
$47,959
$169,300
Beltrami*
44,233
17.7
$43,989
$148,900
Benton *
38,357
94.2
$51,159
$165,800
Big Stone*
5,297
10.6
$44,438
$83,300
Blue Earth *
63,370
85.6
$48,911
$161,700
Brown *
25,926
42.4
$48,149
$122,400
Carlton *
35,141
41.1
$53,553
$163,300
Carver*
90,064
256.9
$82,710
$277,600
Cass
28,584
14.1
$43,042
$185,400
Chippewa*
12,383
21.4
$44,712
$94,900
Chisago*
53,334
129.9
$67,075
$224,700
Clay*
58,032
56.4
$52,108
$149,900
Clearwater*
8,654
8.7
$39,143
$117,300
Cook
5,213
3.6
$49,496
$240,600
Cottonwood
11,695
18.3
$43,111
$83,100
Crow Wing*
62,267
62.6
$45,853
$188,600
Dakota*
396,547
709
$73,723
$238,300
Dodge*
19,961
45.7
$66,216
$162,100
Douglas*
36,051
56.5
$48,436
$188,100
Faribault
14,690
20.4
$43,214
$87,300
Fillmore
20,946
24.2
$47,940
$137,000
Freeborn*
31,301
44.2
$43,447
$109,500
Goodhue*
46,051
61
$56,099
$188,000
Grant
6,055
11
$43,777
$102,600
Hennepin*
1,146,195
2081.7
$62,966
$244,100
Houston*
19,108
34.5
$53,017
$151,000
Hubbard
20,318
22.1
$45,733
$173,400
Isanti
37,910
86.8
$58,721
$196,400
Itasca*
44,955
16.9
$47,106
$151,900
Jackson*
10,326
14.6
$47,455
$97,000
Kanabec
16,344
31.1
$46,863
$162,000
Kandiyohi*
41,992
53
$49,915
$162,000
Kittson*
4,575
4.1
$50,049
$65,600
Koochiching*
13,365
4.3
$41,135
$102,500
Lac qui Parle*
7,298
9.5
$48,269
$80,300
Lake*
10,877
5.2
$47,450
$142,300
Lake of the Woods
4,085
3.1
$43,523
$130,100
La Sueur*
27,754
61.7
$58,074
$190,900
64
Lincoln*
5,898
11
$46,270
$77,400
Lyon*
25,716
36.2
$47,254
$137,600
McLeod*
36,719
74.6
$57,323
$166,200
Mahnomen
5,396
9.7
$39,385
$89,600
Marshall
9,519
5.3
$49,636
$87,500
Martin
20,821
29.3
$44,791
$98,800
Meeker
23,348
38.3
$51,929
$165,600
Mille Lacs*
26,095
45.6
$46,100
$163,100
Morrison
33,178
29.5
$46,054
$157,600
Mower*
38,985
55.1
$45,596
$108,300
Murray
8,729
12.4
$47,833
$88,100
Nicollet
32,471
73
$59,877
$170,000
Nobles
21,188
29.9
$45,552
$100,100
Norman
6,849
7.9
$43,333
$81,400
Olmsted*
142,934
220.8
$66,202
$173,000
Otter Tail*
57,442
29.1
$45,494
$161,400
Pennington
13,915
22.6
$45,007
$94,100
Pine
29,567
21.1
$44,463
$158,200
Pipestone*
9,572
20.6
$42,217
$87,100
Polk*
31,392
16
$49,257
$118,800
Pope*
11,013
16.4
$49,599
$150,500
Ramsey
506,608
3341.6
$52,713
$217,400
Red Lake
4,101
9.5
$47,719
$78,500
Redwood*
16,039
18.3
$45,177
$85,100
Renville*
15,834
16
$48,442
$97,700
Rice *
63,594
129.4
$59,533
$204,200
Rock
9,625
20.1
$44,510
$104,100
Roseau
15,769
9.3
$48,612
$101,300
St. Louis*
199,849
32
$45,399
$141,800
Scott*
128,447
364.5
$83,415
$263,100
Sherburne*
87,881
204.4
$71,819
$218,600
Sibley
15,219
25.9
$52,482
$148,100
Stearns*
149,337
112.2
$53,035
$172,600
Steele*
36,529
85.1
$57,290
$158,800
Stevens
9,703
17.3
$47,712
$119,100
Swift
9,852
13.2
$43,846
$94,600
Todd*
24,848
26.3
$44,202
$135,400
Traverse*
3,603
6.2
$44,773
$69,200
Wabasha
21,701
41.4
$52,346
$160,300
Wadena
13,820
25.8
$35,307
$117,600
Waseca
19,154
45.2
$52,357
$142,700
Washington*
235,802
619.7
$79,571
$257,200
Watonwan*
11,190
25.8
$49,307
$93,400
65
Wilkin
6,613
8.8
$51,957
$103,300
Winona*
51,394
82.2
$44,848
$160,200
Wright*
123,019
188.5
$69,674
$213,900
Yellow Medicine
10,446
13.8
$50,740
$98,500
Note: All data was retrieved from the U.S. Census Bureau using five year estimates from 2007-2011 with
the exception of population density, which was obtained from the 2010 census. * denotes counties that
responded to the Survey for Environmental Services Officials. WLSSD also responded but is not
considered a county on the U.S. Census.
66
Appendix B: Survey for Environmental Services Officials
You are invited to participate in a short 10-15 minute survey as part of data collection for
a master's thesis at Bemidji State University. The purpose of the thesis is to study the
impact of recycling and waste management policies on residential recycling in Minnesota
from 1996-2011. Much of the data for the thesis is being obtained from the SCORE
reports published by the Minnesota Pollution Control Agency. This survey is designed to
gain further details about items presented in the SCORE report. Your participation in this
survey is completely voluntary.
Please indicate the county that you are reporting for.
The 2012 SCORE reporting form discusses variable-rate pricing, also known as unitbased pricing, as an incentive to encourage residents to reduce the amount of waste they
throw away. The four main categories include volume-based, frequency-based, bag/tagbased, and weight-based pricing:
 Volume-based involves the use of different sizes of collection containers.
Residents are charged the price of the size of container(s) they subscribe to.
 Frequency-based can be administered in two different ways. In the first method,
residents prescribe to a certain number of cans and are charged for that number
whether the cans are full or not. In the second method, residents are charged
based on the number of times they set their can(s) out for collection.
 Bag/tag-based involves the use of special bags or tags that are applied that
residents throw their waste into. Residents are charged per bag or per tag.
 Weight-based involves the use of special cans that are weighed during waste
pickup. Residents are charged for the weight of waste they throw away.
Volume-based pricing
1A. Based on the definitions above, does your county implement volume-based pricing?
(If no, skip to 2A).
Yes
No
1B. Is the policy implemented county-wide?
Yes
No
1C. If not, how many municipalities engage in volume-based pricing?
1D. Please estimate the county population that is served by volume-based pricing.
1E. Please indicate the years that the policy has been implemented.
67
Frequency-based pricing
2A. Based on the definitions above, does your county implement frequency-based
pricing? (If no, skip to 3A).
Yes
No
Yes
No
2C. If not, how many municipalities engage in frequency-based pricing?
2D. Please estimate the county population that is served by frequency-based pricing.
Bag/tag-based pricing
3A. Based on the definitions above, does your county implement bag/tag-based pricing?
Yes
No
Yes
No
3C. If not, how many municipalities engage in bag/tag-based pricing?
3D. Please estimate the county population that is served by bag/tag-based pricing.
Weight-based pricing
4A. Based on the definitions above, does your county implement weight-based pricing?
Yes
No
Yes
No
68
4C. If not, how many municipalities engage in weight-based pricing?
4D. Please estimate the county population that is served by weight-based pricing.
Resource Recovery
5A. Is the county required to send a percentage of its solid waste to a resource recovery
center (i.e. incinerator)?
Yes
No
5B. Please estimate the percentage that the county is required to send.
5C. Please indicate the years that the county has been required to do so.
Organized Pick-up
6A. Does the county engage in organized pick-up for recycling? Organized pick-up is
defined as all neighborhoods or municipalities within the county having one collector or
hauler; the collector or hauler picks up recycled material from a neighborhood on the
same day. (If no, skip to 7A).
Yes
No
6B. Is organized pick-up implemented county-wide?
Yes
No
6C. If not, how many municipalities engage in organized pick-up?
6D. Please estimate the county population that is served by organized pick-up.
6E. Please indicate the years that organized pick-up has been implemented.
69
7A. Does the county engage in organized pick-up for solid waste? Organized pick-up is
defined as all neighborhoods or municipalities within the county having one collector or
hauler; the collector or hauler picks up solid waste material from a neighborhood on the
same day. (If no, skip to 8A).
Yes
No
7B. Is organized pick-up implemented county-wide?
Yes
No
7C. If not, how many municipalities engage in organized pick-up?
7D. Please estimate the county population that is served by organized pick-up.
7E. Please indicate the years that organized pick-up has been implemented.
Open-market Pick-up
8A. Does the county engage in open-market pick-up for recycling? Open-market pick-up
is defined as residents finding their own collector or hauler. Under this program,
residents living next door to each other could have different collection days. (If no, skip
to 9A).
Yes
No
8B. Is open-market pick-up implemented county-wide?
Yes
No
8C. If not, how many municipalities engage in open-market pick-up?
8D. Please estimate the county population that is served by open-market pick-up.
8E. Please indicate the years that open-market pick-up has been implemented.
9A. Does the county engage in open-market pick-up for solid waste? Open-market pickup is defined as residents finding their own collector or hauler. Under this program,
to 10).
Yes
No
70
9B. Is open-market pick-up implemented county-wide?
Yes
No
9C. If not, how many municipalities engage in open-market pick-up?
9D. Please estimate the county population that is served by open-market pick-up.
9E. Please indicate the years that open-market pick-up has been implemented.
Tax on MSW
10A. Does the county impose a separate tax on solid waste? An example of this tax
would be a county environmental charge. (If no, skip to 11).
Yes
No
Yes
No
10C. If not, how many municipalities impose the tax?
10D. Please estimate the county population that is imposed by the tax
11. Please rate how the County Board prioritizes waste management and recycling on a
scale of 1-10 with 1 being low and 10 being high:
1
2
3
4
5
6
7
8
9
10
12. Please rate how you prioritize waste management and recycling on a scale of 1-10
with 1 being low and 10 being high:
1
2
3
4
5
6
7
8
9
10
Thank you for taking the time to participate in this survey! If you would like more
information about the study or wish to receive a copy of the completed thesis, please
contact Katelyn Larsen at (218)-556-2737 or at [email protected]
71
Appendix C: Frequencies and Means Reported for Survey for Environmental
Services Officials
You are invited to participate in a short 10-15 minute survey as part of data collection for
a master's thesis at Bemidji State University. The purpose of the thesis is to study the
impact of recycling and waste management policies on residential recycling in Minnesota
from 1996-2011. Much of the data for the thesis is being obtained from the SCORE
reports published by the Minnesota Pollution Control Agency. This survey is designed to
gain further details about items presented in the SCORE report. Your participation in this
survey is completely voluntary.
Please indicate the county that you are reporting for.
56 out of 87 counties responded; response rate=64%
The 2012 SCORE reporting form discusses variable-rate pricing, also known as unitbased pricing, as an incentive to encourage residents to reduce the amount of waste they
throw away. The four main categories include volume-based, frequency-based, bag/tagbased, and weight-based pricing:
 Volume-based involves the use of different sizes of collection containers.
Residents are charged the price of the size of container(s) they subscribe to.
 Frequency-based can be administered in two different ways. In the first method,
residents prescribe to a certain number of cans and are charged for that number
whether the cans are full or not. In the second method, residents are charged
based on the number of times they set their can(s) out for collection.
 Bag/tag-based involves the use of special bags or tags that are applied that
residents throw their waste into. Residents are charged per bag or per tag.
 Weight-based involves the use of special cans that are weighed during waste
pickup. Residents are charged for the weight of waste they throw away.
*Note: Your county may have more than one form of unit-based pricing. Please
indicate all types that your county operates. For example, if your county uses a
combination of volume-based and frequency-based, please mark yes for both
categories.
72
Volume-based pricing
1A. Based on the definitions above, does your county implement volume-based pricing?
Yes
698
No
166
1B. Please estimate the percentage of the county population that is served by volumebased pricing.
Mean: 67.75%; Standard deviation: 38.33%
1C. Only considering the years 1996-2011, please indicate the years that the policy has
been in place
No (%)
29.6
27.8
24.1
24.1
20.4
18.5
18.5
16.7
16.7
16.7
16.7
16.7
16.7
16.7
14.8
13.0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
70.4
72.2
75.9
75.9
79.6
81.5
81.5
83.3
83.3
83.3
83.3
83.3
83.3
83.3
85.2
87.0
100
90
80
70
60
Frequency of
volume-based 50
pricing (%)
40
30
Yes (%)
No (%)
20
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
10
0
73
Frequency-based pricing
2A. Based on the definitions above, does your county implement frequency-based
pricing? (If no, skip to 3A).
Yes
110
No
754
2B. Please estimate the percentage of the county population that is served by frequencybased pricing.
been in place
No (%)
90.7
90.7
88.9
88.9
87.0
87.0
87.0
87.0
87.0
87.0
87.0
87.0
85.2
85.2
85.2
85.2
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
9.3
9.3
11.1
11.1
13.0
13.0
13.0
13.0
13.0
13.0
13.0
13.0
14.8
14.8
14.8
14.8
100
90
80
70
60
Frequency of
frequency-based 50
pricing (%)
40
Yes (%)
30
No (%)
20
10
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
74
Bag/tag-based pricing
3A. Based on the definitions above, does your county implement bag/tag-based pricing?
Yes
303
No
577
3B. Please estimate the percentage of the county population that is served by bag/tagbased pricing.
been in place
No (%)
67.3
67.3
65.5
65.5
65.5
65.5
65.5
65.5
65.5
65.5
65.5
65.5
65.5
65.5
65.5
63.6
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
32.7
32.7
34.5
34.5
34.5
34.5
34.5
34.5
34.5
34.5
34.5
34.5
34.5
34.5
34.5
36.4
80
70
60
50
Frequency of
bag/tag-based 40
pricing (%)
30
Yes (%)
No (%)
20
10
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
75
Weight-based pricing
4A. Based on the definitions above, does your county implement weight-based pricing?
Yes
32
No
832
4B. Please estimate the percentage of the county population that is served by weightbased pricing.
been in place
No (%)
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
96.3
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
120
100
80
Frequency of
weight-based 60
pricing (%)
40
Yes (%)
No (%)
20
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
76
Resource Recovery
5A. Is the county required to send a percentage of its solid waste to a resource recovery
center (i.e. incinerator, waste-to-energy center)?
Yes
167
No
729
5B. Please estimate the percentage that the county is required to send.
been in place
No (%)
83.9
83.9
83.9
83.9
82.1
82.1
82.1
80.4
80.4
80.4
80.4
80.4
80.4
80.4
78.6
78.6
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
16.1
16.1
16.1
16.1
17.9
17.9
17.9
19.6
19.6
19.6
19.6
19.6
19.6
19.6
21.4
21.4
90
80
70
60
Frequency of 50
resource recovery
requirement (%) 40
30
Yes (%)
No (%)
20
10
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
77
Organized Pick-up
6A. Does the county engage in organized pick-up for recycling? Organized pick-up is
defined as municipalities or the county contracting for collection, meaning each
neighborhood, municipality, or the entire county has one collector or hauler; the collector
or hauler picks up recycled material from a neighborhood/municipality on the same day.
Yes
588
No
308
6B. Please estimate the percentage of the county population that is served by organized
pick-up.
been in place
No (%)
35.7
35.7
35.7
35.7
33.9
33.9
33.9
33.9
33.9
33.9
33.9
33.9
33.9
33.9
33.9
33.9
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
64.3
64.3
64.3
64.3
66.1
66.1
66.1
66.1
66.1
66.1
66.1
66.1
66.1
66.1
66.1
66.1
70
60
50
Frequency of 40
organized pick-up
30
for recycling (%)
20
10
0
Yes (%)
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
No (%)
78
7A. Does the county engage in organized pick-up for solid waste? Organized pick-up is
defined as municipalities or the county contracting for collection, meaning each
neighborhood, municipality, or the entire county has one collector or hauler; the collector
or hauler picks up solid waste from a neighborhood/municipality on the same day. (If no,
skip to 8A).
Yes
528
No
352
7B. Please estimate the percentage of the county population that is served by organized
pick-up.
been in place
No (%)
43.6
43.6
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
38.2
38.2
38.2
38.2
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
56.4
56.4
60.0
60.0
60.0
60.0
60.0
60.0
60.0
60.0
60.0
60.0
61.8
61.8
61.8
61.8
70
60
50
Frequency of 40
organized pick-up
30
for MSW (%)
20
10
0
Yes (%)
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
No (%)
79
Open-market Pick-up
8A. Does the county engage in open-market pick-up for recycling? Open-market pick-up
is defined as residents finding their own collector or hauler. Under this program,
to 9A).
Yes
458
No
422
8B. Please estimate the percentage of the county population that is served by open-market
pick-up.
been in place
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
No (%)
50.9
50.9
49.1
49.1
47.3
47.3
47.3
47.3
47.3
47.3
47.3
47.3
47.3
47.3
47.3
47.3
Yes (%)
49.1
49.1
50.9
50.9
52.7
52.7
52.7
52.7
52.7
52.7
52.7
52.7
52.7
52.7
52.7
52.7
54
53
52
51
50
Frequency of
open-market pick- 49
up for recycling (%) 48
47
46
45
44
Yes (%)
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
No (%)
Year
80
9A. Does the county engage in open-market pick-up for solid waste? Open-market pickup is defined as residents finding their own collector or hauler. Under this program,
to 10).
Yes
714
No
182
9B. Please estimate the percentage of the county population that is served by open-market
pick-up.
been in place
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
No (%)
23.2
23.2
21.4
21.4
19.6
19.6
19.6
19.6
19.6
19.6
19.6
19.6
19.6
19.6
19.6
19.6
Yes (%)
76.8
76.8
78.6
78.6
80.4
80.4
80.4
80.4
80.4
80.4
80.4
80.4
80.4
80.4
80.4
80.4
90
80
70
60
Frequency of
50
open-market pick40
up for MSW (%)
30
20
10
0
Yes (%)
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
No (%)
Year
81
Tax on MSW
10A. Does the county impose a separate tax on solid waste? An example of this tax
would be a county environmental charge. (If no, skip to 11).
Yes
420
No
460
10B. Please estimate the percentage of the county population that is imposed by the tax
been in place
No (%)
58.2
56.4
56.4
56.4
54.5
54.5
54.5
52.7
49.1
49.1
49.1
49.1
49.1
49.1
49.1
49.1
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Yes (%)
41.8
43.6
43.6
43.6
45.5
45.5
45.5
47.3
50.9
50.9
50.9
50.9
50.9
50.9
50.9
50.9
70
60
50
Frequency of Tax 40
on MSW (%) 30
Yes (%)
20
No (%)
10
Year
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
82
11. Please rate how the County Board prioritizes waste management and recycling on a
scale of 1-10 with 1 being low and 10 being high:
1
1
2
1
3
2
4
1
5
11
6
3
7
6
8
15
9
9
10
2
12. Please rate how you prioritize waste management and recycling on a scale of 1-10
with 1 being low and 10 being high:
1
0
2
1
3
0
4
1
5
1
6
1
7
5
8
10
9
11
10
22
25
20
15
Frequency
County Board
10
Env. Official
5
0
1
2
3
4
5
6
7
8
9
Prioritization of waste management/recycling rating
10

sample thesis - Bemidji State University

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