Embed Size (px)
Citation preview
Analyzing the Effectiveness of Recycling for
Wisconsin Responsible Units
2012
Dave Dolan and John Katers, Principal Investigators Paula Olig and Amanda Tuttle, Co-Investigators
Department of Natural and Applied Sciences
University of Wisconsin-Green Bay
UNIVERSITY OF WISCONSIN SYSTEM SOLID WASTE RESEARCH PROGRAM
Acknowledgement
We are deeply saddened by the recent passing of our friend and colleague Professor David Dolan. His primary focus was on the Great Lakes and his skills as a statistician contributed greatly to an understanding of the quality of their waters. He was the recipient of many awards during his tenure at UW-Green Bay, including the Founders Award for Excellence in Scholarship in 2012. He thoroughly enjoyed teaching and it showed in the help he freely gave to many students, both graduate and undergraduate, with completing their projects and theses. He was an enthusiastic member in the Natural and Applied Sciences department and worked tirelessly mentoring new faculty, serving on committees, and collaborating with his colleagues.
Abstract
The purpose of this research project is to utilize collection and recycling data from Responsible Units (RUs) in Wisconsin from 2007-2010 to analyze performance and determine opportunities to improve the economic efficiency of recycling in Wisconsin. Data for this project was provided by the Wisconsin Department of Natural Resources (WDNR) and includes information submitted by all RUs in the Annual Recycling Program Accomplishments and Actual Costs Report during this four year period. The most significant data categories in the annual RU reports include the following: collection methods, processing methods, materials and weights collected, actual recycling costs, and outreach and waste reduction efforts. Additional variables such as the geographic size of the RU, population density, etc., was also obtained using tools such as GIS and added to the RU databases provided by WDNR to allow for a more comprehensive analysis of recycling efficiency during this time period. A multiple regression analysis was then utilized to determine which variables are most significant for the efficient operation of recycling programs and to identify which RUs have the most efficient recycling programs.
2
Table of Contents
Acknowledgement………………………………………………………………………………………....2
Abstract…………………………………………………………………………………………………….2
Objective…………………………………………………………………………………………………...4
Background………………………………………………………………………………………………...4
Research Design……………………………………………………………………………………………6
Results – Descriptive Statistics……………………………………………………….................................8
Results – Costs per Capita………………………………………………………………………………...17
Results – Costs per Ton…………………………………………………………………………………...23
Level of Service Survey…………………………………………………………………………………..27
Survey Results…………………………………………………………………………………………….28
RU Case Studies…………………………………………………………………………………………..37
3
Objective
The objective of this project was to analyze the performance and effectiveness of Responsible
Unit (RU) recycling programs in Wisconsin. The data for this project was provided by the
Wisconsin Department of Natural Resources (WDNR) and consists of information submitted by
RUs in the 2007-10 Annual Recycling Program Accomplishments and Actual Costs Reports.
The most significant data categories in the annual reports include the following: collection
methods, processing methods, materials and weights collected, actual recycling costs, and
outreach and waste reduction efforts. Additional variables were supplemented to the database
such as the geographic size and population density of the RU using geographic information
system (GIS) software. This data allows for a more comprehensive analysis of recycling
efficiency during this time period. Cost per ton of recyclables collected and cost per capita for
recycling are the metrics used to analyze the performance and effectiveness of the RUs.
Background
In 1989, the State of Wisconsin passed one of the most progressive and comprehensive recycling
laws in the U.S. as a means of encouraging the reduction and recycling of specific materials. The
net effect of Wisconsin Act 335 was the implementation of mandatory recycling for the entire
state. Since 1990, the State of Wisconsin has made funds available to assist communities in
meeting these mandatory recycling requirements. However, these funds currently only cover on
average 30% of the total cost of recycling, with local municipalities covering the balance of the
costs.
In order to be eligible for these state recycling funds, a community must be designated as a
Responsible Unit. The original expectation, when Wisconsin Act 335 was enacted, was to have
72 RUs in Wisconsin, one for each county. However, this expectation failed to consider the
strong history of local government in Wisconsin, resulting in well over 1,200 RUs in the 1990s.
In 2010, despite a slight reduction, there were still more than 1,000 RUs. As a result of this large
number, the WDNR is required to process significantly more paperwork. It may also lead to
inefficiencies or duplication of recycling infrastructure in Wisconsin.
Due to the large number of RUs, as well as the real and perceived inefficiencies with recycling in
Wisconsin, the WDNR began offering Recycling Efficiency Incentive Grants. These grants were
available to RUs that demonstrated recycling program efficiency. To qualify, a RU’s recycling
4
program needed to be designated as an “effective recycling program” (as defined in s. NR
544.04, Wis. Admin. Code) by the WDNR. Applicants that undertook any of the following
efficiency measures were eligible to receive funding:
• Consolidation (per ch. 66 Wis. Stats.): a merger of at least two RUs into a single RU,
with an RU defined in s. 287.09 Wis. Stats.
• Entering into at least one cooperative agreement with at least one other RU where one or
more of the following efficiencies have been achieved:
o Collection and transportation of recyclables
o Sorting of recyclables at a material recovery facility
o Comprehensive program planning
o Education efforts about recycling
The greatest success, in terms of a cooperative agreement for recycling and solid waste
management, would likely be the agreement between Brown, Outagamie and Winnebago
Counties. The cooperative efforts of these three counties included shared landfills operating in
sequence and a recently constructed state-of-the art material recovery facility (MRF), which has
resulted in significant savings to date for all participants. It should be noted that the 2009-11
biennial budget in Wisconsin did not provide funding for the Recycling Efficiency Incentive
Grant for calendar year 2010 or 2011.
Research on the efficiency of recycling programs in Wisconsin dates back to 1993, when John
Katers completed a master’s thesis on this topic at the University of Wisconsin – Green Bay,
where he is currently an Associate Professor of Natural and Applied Sciences (Engineering) and
continues to work on waste management and recycling related issues. His thesis analyzed
recycling data from Wisconsin for the time period of 1991-93 using several of the same
performance metrics and statistical approaches utilized for this study, including regression
analysis. Several of the conclusions from his thesis are summarized below:
• A great deal of variation in the total costs per ton and per capita exist for residential
recycling in Wisconsin, which may allow for the identification of opportunities for
improvement in terms of performance and efficiency
• Economies of scale exist for recycling collection systems used in Wisconsin
• Given the large number of RUs, there appears to be a repetition of services
5
• A reduction in the number of RUs would result in a reduction in paper work for the
WDNR, a reduction in overall fixed program costs and more cost effective collection
programs in general
Dr. Wayne Carroll, Associate Professor at the University of Wisconsin–Eau Claire, later
published a research paper in the Eastern Economic Journal (1995) titled “The Organization and
Efficiency of Residential Recycling Services” which concluded that costs of residential recycling
are similar in some respects as the cost of garbage collection. However, it was also concluded
that collection costs are lowest in cities that enter into contracts with single haulers to provide
curbside collection, thereby providing economies of density.
Since these previous studies were completed there have been many major changes in recycling
and waste management practices in Wisconsin including: increased privatization of collection
and processing, the utilization of more efficient collection vehicles, the implementation of single
stream recycling, and significant increases in the landfill tipping fee. Additionally, there has also
been increased scrutiny of recycling in general due to decreasing state and local funding, volatile
recycling markets, and the elimination of most recycling related research and education grants.
Research Design
Data for this project was obtained by the WDNR through the Annual Recycling Program
Accomplishments and Actual Costs Report submitted by all RUs from 2007-10. All RUs
applying for a Responsible Unit Efficiency Grant are required to complete the report with the
following information: regulations, population, collection and processing methods, weights and
types of material collected, program costs, compliance violations, collection provider, outreach
and waste reduction efforts, and grant assistance.
The DNR reviewed the data for outliers and inconsistencies within consecutive reporting years
by calculating whether an RU’s total tonnage cost was 50% more than the previous year. If
submitted information was found to be inconsistent, DNR personnel contacted the corresponding
RU and made changes to the database if necessary. Additional quality assurance was conducted
which concluded in the correction and removal of certain records due to entering errors,
inconsistencies, and lack of information (these will be discussed further in the results section).
6
To acquire more detailed information beyond the DNR annual report, such as population density
and area, a spatial GIS database was compiled using ArcGIS 10.0 by ESRI®. Two feature
classes consisting of Wisconsin’s 2010 Municipal Boundaries and County Boundaries and 2010
American Indian Areas served as base layers. The Municipal Boundaries shapefile was obtained
from Wisconsin’s Department of Transportation (WisDOT). The American Indian Area
shapefile was obtained from the U.S. Census Bureau 2010 TIGER/LINE® Shapefiles: American
Indian Area database. Consolidated municipalities were dissolved into the one representative
RU. Population density was calculated through dividing the annual reports population for a given
year by the GIS configured area (square miles). Population density information along with data
from the RUs annual reports were related to the corresponding RUs geographic location.
Descriptive statistics for each RU were summarized and included general program features
(number of single stream versus dual stream facilities, etc.), as well as program metrics used to
assess performance and efficiency of each program, such as: tons collected per capita, cost per
ton of recyclables collected, and the cost per capita for recycling. Changes in these program
features and metrics were then compared over this time period.
Once the descriptive statistical analysis was completed, a multiple regression statistical analysis
was utilized to determine which variables are most significant for the efficient operation of
recycling programs and to identify which RUs have the most efficient recycling programs after
covariates are accounted for. Quantitative variables such as costs, weights, member count
(number of municipalities within a consolidated RU), population and population density were
employed as explanatory factors in the multiple regression models. Qualitative variables such as
curb-side vs. drop-off and government type were expressed as indicator or dummy variables to
assess their significance as well. Interactions between quantitative and qualitative variables were
tested to see if relationships change with collection category or municipality. The General Linear
Model (GLM) statistical framework (of which multiple regression is a part) is ideally suited for
this type of analysis.
Based on the results of the GLM, individual RUs either above or below expectations for each
year of the analysis (when considering their individual program features and overall program
performance) were identified. RUs on both ends of the spectrum, with particular emphasis on
those that are consistently in the top or bottom for each year, were contacted to gather additional
7
information on their program and clarify potential unknown circumstances (beyond those
reported in the Annual Recycling Program Accomplishments and Actual Costs Reports) that
could explain the results of the GLM.
Results – Descriptive Statistics
2007 Annual Report Data
Annual report data from 2007 represents the 17th year of Wisconsin’s recycling program. Data
from 1,058 RUs was submitted with a breakdown of 129 cities, 628 townships, 246 villages, 34
counties, 10 tribes and 11 “other” entities (“other” represents consolidated RUs containing more
than one type of government, i.e. village and city). These units provided services to 5,666,038
people within Wisconsin. The average RU served 5,355 people. The Town of Finley in Juneau
County represented the smallest population at 87 people and the City of Milwaukee represented
the largest population at 590,190 people. From a population density standpoint, the average RU
had 436 people per square mile (sq mi) compared to a median of 55 people per sq mi. This
signifies that more RUs have a smaller density, while a couple municipalities have larger
densities. In total, the 1,058 RUs represent 1,860 municipalities. The majority of the
municipalities have their own programs while 866 have consolidated to form 64 RUs.
Consolidated RUs range in size from 2 to 35 members with an average member count (number
of municipalities/tribes within a RU) of 14.
RUs collected a total recyclable weight of 405,393 tons. Standard recyclables recorded under this
report include newspaper, corrugated paper, magazines, aluminum containers, steel & bi-metal
containers, plastic containers, glass containers, and foam polystyrene packaging. The City of
Milwaukee had the maximum amount collected with 24,017 tons followed by Waukesha County
with 22,029 tons. The two RUs that collected the smallest tonnage are the Town of Decatur in
Green County and the Town of Bartelme in Shawano County with 1.0 and 1.31 tons,
respectively. On average, RUs collected 383 tons, although a median of 83 tons signifies the
distribution of tons is substantially skewed.
8
Table 1. 2007 Annual recycling program report data by population.
Table 2. 2010 Annual recycling program report data by population.
A better representation of weight is seen in a pounds per capita comparison. On average, each
person in Wisconsin recycled 144 lbs during 2007, compared to each person recycling 136 lbs
during 2010 (Table 1 and Table 2). The Town of Muscoda, in Grant County obtained the highest
ratio of 1,067 lbs/capita followed by the City of Mauston with 514.1 lbs/capita. At the lower end,
the Towns of Decatur and Bartleme reported the lowest ratios with 1.0 and 3.3 lbs/capita.
Recycling program costs are based on single-family and 2-4 unit residential portions of the
overall program. All other portions, such as the cost of collection, processing or marketing of
9
Average Population
Density # % # % people/sq mi Tons %
Less than 2,500 765 72.3 784,754 13.9 223 55,226 13.6 1382,500 - 4,999 122 11.5 422,583 7.5 612 36,200 8.9 1695,000 - 9,999 69 6.5 487,053 8.6 899 39,575 9.8 16410,000 - 24,999 60 5.7 919,354 16.2 1,501 73,465 18.1 16125,000 - 49,000 26 2.5 915,943 16.2 1,052 67,066 16.5 14850,000 - 99,999 10 0.9 653,113 11.5 2,436 40,817 10.1 123Greater than 100,000 6 0.6 1,483,238 26.2 2,010 93,045 23.0 137Total 1,058 100 5,666,038 100 405,393 100Average 436 144
Average Pounds
per Capita
Responsible Units Population WeightPopulation
Average Population
Density# % # % people/sq mi Tons %
Less than 2,500 761 71.9 784,993 13.7 226 52,351 13.3 1282,500 - 4,999 127 12.0 442,934 7.7 590 36,420 9.3 1635,000 - 9,999 67 6.3 479,484 8.4 915 37,885 9.6 15910,000 - 24,999 61 5.8 941,847 16.5 1,507 71,942 18.3 15525,000 - 49,000 26 2.5 927,254 16.2 1,060 65,737 16.7 14250,000 - 99,999 10 0.9 659,438 11.5 2,445 39,382 10.0 117Greater than 100,000 6 0.6 1,482,235 25.9 2,001 89,750 22.8 130Total 1,058 100 5,718,185 100 393,467 100Average 439 136
Responsible Units
Population WeightPopulation
Average Pounds per
Capita
recyclables and yard waste from residences with five or more units or commercial, retail,
industrial or governmental facilities are excluded from the annual report and are not eligible for
grant assistance. Actual total costs are calculated by adding up expenses for education,
collection, processing & marketing, and administration & enforcement. An overall total cost of
$110,759,378 was incurred with the most expensive program being the City of Milwaukee at
$11,091,940. Franklin Township was the least expensive program with total costs of $456.
Among the 1,006 RUs who submitted total cost data, the average spent was $110,099.
To calculate eligible costs used for grant determination, ineligible costs, other deductible
revenue, and revenue from the sale of recyclables was subtracted from the calculated total cost.
Ineligible costs include the costs associated with, but not limited to, the collection and handling
of lead acid batteries, waste oil, oil absorbents, major appliances, tires, and electronics. Other
deductible revenue can include revenue collected from other RUs for recycling services, profits
from the sale of equipment, or property purchased using grant funds. The final product produces
the net eligible cost (NEC) associated with the recycling program, which is used to calculate the
grant awards. NECs for all grant eligible RUs totaled $95,880,708 with an average of $95,309.
Although, the average is drastically higher than the median of $15,680, indicating smaller costs
for most RUs.
According to the Wisconsin Legislative Fiscal Bureau, 1,010 RUs received grants1. Grant
awards totaled $24,417,295. Individual grants ranged from $115 for the Town of Franklin to
$2,796,195 for the City of Milwaukee. Once again, the average grant amount of $24,223 is
skewed based on the award median of $4,157. The median demonstrates that the majority of
grants were distributed for lower amounts. If divided equally, the total grant amount per person
would have been $4.31 or 25% of the NEC.
Net exclude yard waste cost (NEYC) is NEC minus the yard waste costs. NEYC for 2007 ranged
between $-20,876 and $7,261,876 and totaled $66,901,714. As an equal comparison among RUs,
the cost per capita ($/cap) and cost per ton ($/ton) ratios was calculated by using NEYC as the
cost factor. The mean cost per ton among RUs was $218. Two RUs showed a negative cost per
ton of $-193.54 and $-182.50, due to reporting errors of not including yard waste in the total
cost. The next lowest cost per ton of $0 was recorded by 13 RUs whose NEC equaled its yard
1 According to the Wisconsin Legislative Fiscal Bureau, 1,010 RUs received a grant award totaling $24,423,080. Two RUs did not include report grant information in the DNR Annual Report (Bonderud, 2011).
10
waste costs. RUs to report an actual low cost per ton included the City of Mequon, $0.28/ton,
and Town of Antigo, $3.19/ton. Mole Lake Tribe reported the highest cost per ton at $3,746.40
followed by the Town of Decatur with $3,355, which collected one ton among its 1,945
residence for the year. Two other tribes made the top four highest list including Forest County
Potawatomi with $2,696 and Bad River Tribe with $3,284.
Cost per capita among RUs varies exceedingly between $-14.99 and $363.57. Equivalent to the
two lowest cost per ton, the two lowest cost per capita rates of $-14.99 and $-10.56 were due to
inaccurate reporting. The next 13 lowest RUs reported cost per tons of $0 with the yard waste
cost equaling the NEC. Two RUs with more than yard waste costs are the City of Mequon with
$0.02 and the Town of Antigo with $0.16. RUs experience an average cost per capita of $13.66.
Different from the highest cost per ton RUs, the highest cost per capita RUs are represented by
Forest County Potawatomi Tribe with $363.57, City of Glendale with $274.49, the Mole Lake
Tribe with $185.71, and the Town of La Pointe located on Madeline Island in Ashland County
with $171.44. With the exception of Glendale, the population of the three other RUs is less than
515 people. Glendale had a population of 12,970 people suggesting they have a disproportionate
cost in relation to other RUs.
2008 Annual Report Data
For the 2008 annual report, the number of reporting RUs decreased to 1,055, representing 1,857
municipalities. The decrease was due to two municipalities not reporting 2007 data but did
submit reports in 2008, six municipalities lacked 2008 reports and the Town of Holway in Taylor
County only reported data in 2008. As a result, the number of unconsolidated RUs decreased to
991, townships decreased to 624, and the number of villages increased to 247. The population
represented by the recycling programs increased to 5,691,259 people with the average RU
representing 5,395 people. Average RU population density also increased by one person/sq mi to
437 pp/sq mi.
The decrease in total RUs, despite an increase in population, had a negative effect on the amount
of recyclables collected during the year. The amount of recyclables collected was reduced by 659
tons, for a new total of 404,734 tons. This change caused the average tonnage to slightly increase
to 384 tons, while the average lbs/cap was reduced to 139 tons. The most drastic change was
observed in the maximum lbs/cap. In 2007, the Town of Muscoda had a recorded tonnage of 397
11
tons bringing their lbs/cap to 1,067. In 2008, the observed maximum was in Town of Mound Ida
in Grant County with 619 lbs/cap.
Costs accrued in 2008 from 1,018 RUs totaled $112,038,405 with an average of $110,057. The
Town of Franklin accrued the lowest cost at $285, while the City of Milwaukee had the highest
cost at $11,888,706. NEC increased over the year to $96,669,775, even though there was a
decrease in the number of RUs. Alternatively, a lower average NEC of $94,960 shows the
additional RUs reporting in 2008 incurred lower NEC.
An increase in state funding to $30,785,142, despite an increase in eligible RUs (1,017), resulted
in a larger amount of distributed money. On average, each RU received $30,271. Further
calculations show the WDNR distributed $5.31 per person and covered 32% of NEC. Once
again, the City of Milwaukee received the largest grant at $3,535,679.
Unlike NEC, NEYC decreased to $66,317,572 with an average of $65,145. A median of $14,871
indicates more RUs had smaller NEYC values. Once again, cost per capita and cost per ton were
calculated using NEYC with 1,044 RUs supplying sufficient data for the calculation. The highest
$/ton, $2,394, was achieved by the Town of Sanborn in Ashland County. At the low end, the
Town of Antigo in Langlade County collected recyclables for $4.91/ton. There is a slight
difference between the average $/ton of $236.15 and the median of $178.18, showing fewer RUs
have higher than average cost ratios. Due to their small populations, Forest County Potawatomi,
462, and Mole Lake Tribe, 512, had the two highest $/cap of $298.71 and $202.39, respectively.
Other than the RUs who provided little to no cost data, the two lowest ratios were the Towns of
Antigo and Franklin with $0.27 and $0.24/capita. The average $/cap was $14.37 with a median
of $11.50.
Specific collection data were first gathered for 2008 with information on type, method,
frequency and provider. The most popular collection type was curbside used by 497 RUs. An
additional 248 RUs used curbside collection, but also provided drop-off services as well. The
remaining 310 RUs provided drop-off sites only for their residents. Single stream was the most
frequently used method with 332 RUs, followed by 311 RUs providing dual stream or sorted
recycling. The remaining 59 RUs of the 702 RUs to have responded to this question, used a
combination of the two methods. Out of the 702 RUs, 373 collect recyclables on a weekly basis.
12
Biweekly was second with 252 participating RUs, followed by monthly and “other” with 61 and
16 RUs, respectively.
RU programs utilized five combinations of curbside providers. RU contracted service was the
most common provider with 525 RUs. The second most common program with 108 RUs was
subscription service, where residents contract with a private hauler. The third type of service
with 87 RUs was municipal service, where the RU collects the recyclables. The last two
programs were a combination of these methods. Thirty-two RUs used contracts by the RU and
resident, while 14 RUs used all three methods.
Drop off collection only had two options for a collection provider. The most frequently used
choice was when an RU contracted out services with 189 RUs selecting this method. 178 RUs
collected the material themselves and lastly, 63 RUs used a combination of the two services.
Combination services could be due to separate municipalities, within a consolidated RU, having
multiple drop-off sites and therefore each municipality decides how they contract out services.
2009 Annual Report Data
In 2009, recycling program numbers returned to 1,058 RUs, due to six RUs reporting 2009 data
but not 2008 data. The Town of Holway in Taylor County and Town of Rochester in Racine
County reported 2008 data, but not 2009 data, and the Town of Reedsburg in Sauk County did
not report data for 2009. The increase in two townships and one village brought the municipality
total to 1,860. Of these municipalities, 866 were consolidated into 64 RUs with the maximum
RU consisting of 35 municipalities. The total population represented was 5,707,329 with an
average population served by an RU at 5,394 people. RUs became denser on average by 2 people
with the highest density of 8,298 people per square mile in the Village of Shorewood.
Wisconsin recycled 386,412 tons with an average tonnage of 365 for each RU. The Town of
Clyman in Dodge County collected the least at zero tons followed by the Town of Bartelme with
three tons. At the top of the list was the City of Milwaukee with 22,174 tons, followed by the
County of Waukesha at 21,096 tons. On a lbs/cap basis, the Towns of Clyman and Bartelme
collected the least recyclables with zero and three tons, respectively. The Forest County
Potawatomi Tribe had the highest ratio of 587 lbs/cap. The Town of La Pointe on Madeline
Island in Ashland County came in second with a ratio of 551 lbs/cap. An average of 136 lbs/cap
was achieved among the 1,058 RUs.
13
In total, the 1,020 RUs that reported cost data spent $117,854,900. While the average RU spent
$115,544 total on their programs for the year. After subtracting the ineligible costs, the NEC was
lowered to $107,997,735 (reported by 1,036 RUs). This resulted in an average cost of $105,880.
The WDNR used the same proportions of NEC from 1999 to distribute $27,826,367 or 26% of
the total NEC2. Grant recipients increased to 1,020 RUs as appropriated funds remained the same
at $31 million (Bonderud, 2011). On average, each RU received $27,281 with the highest
amount of $3,182,595 again received by Milwaukee
Grant funding recipients increased to 1,020 RUs together, while the grant amount decreased to
$27,826,367. This represented 26% of NEC and $4.88/cap. The average RU received $27,281
with the majority of RUs receiving less than $5,000, as shown by the median of $4,667.
Milwaukee obtained the largest grant with $3,182,595. The lowest grant recipient was the
Village of Bell Center in Crawford County, receiving $150. In total, 38 RUs did not receive
grants.
The total NEYC for the 1,020 reporting RUs equaled $76,069,964. The average RU had $74,578
in NEYC, while the median was $16,031. Again, the City of Milwaukee accrued the highest
costs with $8,435,003, followed by the City of Madison with $2,989,644. The average RU cost
was $258 per ton and the median $194 per ton. The highest ratio was achieved by the Mole Lake
Tribe at $4,677 per ton. The Forest County Potawatomi Tribe achieved the highest per capita
ratio of $302/cap, followed by the Mole Lake Tribe with $208.36/cap. These amounts exceed the
average cost per capita ratio of $15.51/lb.
Curbside was again the leading collection type with 488 RUs providing this method of
collection. A combination of both curbside and drop-off collection was the second most common
method with 264 RUs. The remaining 306 RUs provided only drop-off collection. Of the 708
RUs who provide curbside information, 370 used the single stream method and 275 used dual
stream or sorted. The remaining 63 that reported used a combination of the two methods for their
programs. Weekly collection was the most utilized frequency with 375 RUs followed by
biweekly with 261 RUs. Monthly came in third with 59 RUs and lastly, 13 RUs used a different
or “other” frequency.
2 Wisconsin Fiscal Bureau reported the DNR distributed $27,829,064 to 1,021 RUs (Bonderud, 2011).
14
Of those who provided curbside collection, 14 RUs utilized the services of all three providers to
collect recyclables, including municipal haulers, private haulers contracted by the RU and private
haulers contracted by the residents. Contracted services by both the RU and resident were
conducted by 32 RUs. For 79 RUs, the RU provided municipal collection of the material.
Subscription only occurred in 115 RUs, while almost half or 532 RUs contracted for service.
Drop off collection was mostly municipal service for 252 RUs. A combination of municipal and
RU contracted service occurred for 43 RUs. The other 149 RUs contracted services.
2010 Annual Report Data
RU numbers remained constant in 2010, even though three RUs only reported in 2010 and four
RUs lacked reports that year. This was in part due to two RUs consolidating with other RUs and
one changing municipal structure from a town to a village. The type of government represented
by each of the 1,058 RUs remained the same with 129 cities, 626 townships, 248 villages, 34
counties, 10 tribes and 11 others. Of the 1,863 municipalities, 868 were consolidated into 64
RUs. The largest consolidated RU contained 35 municipalities and the lowest comprised of two
municipalities. Population served increased by 10,856 people to 5,718,185 people throughout
Wisconsin. Population density also stayed the same with an average of 439 people per sq mi.
However, more RUs were found to be less dense due to a median of 56 people per sq mi. Density
ranged from one person per sq mi in the Town of Foster, in Clark County to 8,292 people per sq
mi in the Village of Shorewood.
Recyclable weight increased from 2009 to 2010, although it still did not reach 2007 and 2008
levels. The total tonnage was 393,467 with an average of 372. The Town of Clyman, with a
population of 878, collected the least weight with 0.01 tons (20 lbs.) of recyclables. Once again,
the City of Milwaukee collected the most recyclables with 22,317 tons. On a per capita basis, the
Town of Clyman also had the smallest ratio with less than one lb/cap. The Forest County
Potawatomi Tribe had the highest ratio with 578 lbs/cap. On average RUs collected 136 lbs/cap
with a median of 124 lbs/cap.
Total cost for the 1,024 RUs reached $121,209,188 for the year. The highest total cost was
recorded again by Milwaukee with $10,741,195, followed by the City of Madison with
$8,880,109. On average the RUs accrued costs of $118,373 with a median of $19,223. The
average RU had a NEC of $101,699 with a median of $18,304. Eleven RUs reported having a
15
NEC of $0, followed by the Town of Aniwa, in Shawano County with $128. Again, Milwaukee
had the maximum NEC with $9,751,565, followed by Madison with $7,040,876. Total NEC was
$104,139,581.
Grant recipients reached a maximum for the four years with 1,024 RUs receiving $29,292,030.
This amount covered 28% of NEC and equaled $5.12/cap. The median grant obtained by the
RUs was $4,950. A few RUs received larger grant amounts, resulting in an average of $28,606.
The Village of Bell Center received the lowest grant amount at $158, while Milwaukee received
the largest grant at $3,348,450.
NEYC totaled $71,676,512 for 2010 with an average cost of $69,997. The Town of Aniwa
accrued the least NEYC of $0 due to having no yard waste costs. Milwaukee was the highest at
$7,185,095, followed by Waukesha County with $3,049,260. The two highest $/ton of $4,808
and $3,591 were achieved by the Mole Lake Tribe and Bad River Tribe, respectively. The lowest
$/ton was the City of Hayward with $4.30. RUs achieved the highest average out of the four
years at $269/ton. Average $/cap was also the highest during the four years in 2010 with $16/cap
with a median of $13/cap. At $0.21/cap, Aniwa Township had the lowest cost ratio with the
Forest County Potawatomi Tribe achieving the highest at $265/cap.
Similar to the two previous years, curbside recycling was the most popular collection type with
491 users and 257 using both curbside and drop-off recycling. RUs using only drop-off
collection totaled 310. Of the 748 RUs who reported having curbside collection, 704 reported
what type of collection method they use. Single stream only was used by 374 RUs, followed by
264 using dual stream or sorted collection only. Sixty-six RUs used a combination of the two
methods. Curbside collection was the most frequent on a weekly basis with 358 RUs, followed
by biweekly with 277 RUs. The two other frequencies, monthly and “other,” were used by 53
and 16 RUs, respectively.
Collection providers were similar to previous years with RUs contracting out services to private
haulers the most with 532 RUs. Residents who contracted their services to a private collector
were second at 115 RUs. Municipal collection was third with 79 RUs. Having a combination of
the RU and resident contract services to a private collector was fourth with 32 RUs. A total of 14
RUs utilized a combination of the RU and resident contracting services to a private collector
with municipal collection. Municipal collection was the most frequently used provider for RUs
16
with drop-off collections at 267 RUs. The other drop-off collection provider was RUs contracted
collection services to a private collector, which happened in 129 RUs. The remaining 44
programs used a combination of the two providers.
Statistical Analysis
After analyzing several different regression models, it was determined necessary to take the
logarithm of some of the variables. Doing so reduces the skewness of the data which in turn
reduces the effects of observations with high residuals such as Mount Ida. If not log-transformed,
results and interpretations could be drastically different.
Annual report data for the four year period originally possessed 4,229 observations. 103
observations lacked cost data to calculate cost per capita and cost per ton, this led to their
removal from the data set. Next, observations with cost per capita and cost per ton of $0 were
eliminated due to a lack of cost data or because records showed the total cost of the program was
equivalent to yard waste costs. Meaning the RUs entire program costs equaled the yard waste
costs, leaving 4,047 observations. Since the model tests program components such as collection
type and method, and since no collection data was recorded for 2007, the year’s data was
eliminated. The last observation to be eliminated was the Town of Iron River in Bayfield County
due to misreporting their 2010 revenue from sale of $27,200. This number was questionably
higher than the total cost of $10,382 that was supposed to include the revenue from sale. This
resulted in a net exclude yard waste of $-16,818 and in turn created a negative cost per capita and
cost per ton. The remaining 3,053 observations were found to be complete and included
sufficient information.
Recycling Costs per Capita
A basic regression model was first formulated using a breakout of collection methods and
government type. Since collection method and government type are categorical variables, it was
necessary to create dummy variables for their use in the regression model. To achieve this, a 0 or
1 was used to signify the presence of the variable. Drop-off collection was used as the baseline
and its effect can be found in the intercept. Table 3 outlines the collection method classifications.
The same process was completed for government type with Town as the baseline variable and
can be seen in Table 4.
17
Table 3. Collection method classification for regression analysis
Collection Method COLL1 COLL2 Drop-off 0 0 Curbside 1 0 Drop-off & Curbside 0 1
Table 4. Government type classification for regression analysis
Government Type MUNI1 MUNI2 MUNI3 MUNI4 MUNI5 Town 0 0 0 0 0 Village 1 0 0 0 0 City 0 1 0 0 0 County 0 0 1 0 0 Tribe 0 0 0 1 0 Other 0 0 0 0 1
The first regression equation is as follows:
Log(Net Excluding Yard Waste Cost/Capita) (CTCAPL) = 1.93 + 0.39
Log(Pounds/Capita) (LBSL) – 0.094 Log(Population) (POPL) – 0.081
Log(Population Density) (POPDENL) - 0.033 Member Count (MEMCT) + 0.148
COLL1 + 0.053 COLL2 – 1.45 (Grant Amount/Net Eligible Costs) (PER) + 0.268
Village + 0.383 City + 1.505 County + 1.75 Tribe + 0.377 Other.
Parameter estimates can be seen in Table 5.
18
Table 5. First Regression Analysis for 2008-10 Net Exclude Yard Waste Cost Per Capita
Notes:
DF = Degrees of freedom
The t-values given in Table 3 indicate the significance of the variables in the equation. A t-value
can be positive or negative and still be significant. A higher t-value indicates the variable is of
great importance to the study, while a lower t-value indicates the variable is of less importance.
For this study, significance is determined using a two-tail 95% confidence interval. A probability
(Pr > [t]) (p-value) of less than 0.05 indicates the significance of the variable to the model.
The p-value shows that 11 of the 12 variables to be significant which can be seen with the low
probabilities. COLL2 was the only nonsignificant variable with a p-value of 0.13. The analysis
of variance shows 39.9% of the Log(Net Exclude Yard Waste Cost/Capita) can be accounted for
by the independent variables which is seen in the R2 value. After adjusting for the number of
independent variables, the Adjusted R2 becomes 39.7%. Although this model is good, there can
be an increased significance with interaction variables.
19
Variable Abbreviations DFParameter Estimate
Standard Error
t Value Pr > [t]
Intercept Inter 1 1.92968 0.12809 15.06 <.0001Log(Pounds/Capita) LBSL 1 0.39093 0.0224 17.45 <.0001Log(Population) POPUL 1 -0.094 0.0156 -6.03 <.0001Log(Population Density) POPDENL 1 -0.08146 0.01645 -4.95 <.0001Member Count MEMCT 1 -0.03289 0.00765 -4.3 <.0001Curbside COLL1 1 0.1482 0.02971 4.99 <.0001Curbside & Drop-Off COLL2 1 0.05278 0.03484 1.51 0.13Grant Amount/ Net Eligible Costs
PER 1 -1.4498 0.04739 -30.59 <.0001
Village MUNI1 1 0.26823 0.05368 5 <.0001City MUNI2 1 0.38261 0.05454 7.02 <.0001County MUNI3 1 1.50538 0.18021 8.35 <.0001Tribe MUNI4 1 1.75126 0.10918 16.04 <.0001Other MUNI5 1 0.37702 0.10885 3.46 0.0005N = 3053 R2 = 0.3991 Adj R2 = 0.3968
Source DF Mean Square F Value Pr > FModel 12 57.440 168.29 <.0001Error 3040 0.341Corrected Total 3052
Analysis of VarianceSum of Squares
689.291037.611726.89
Interactions between collection type and type of government created nine interactions. These
interactions are shown in Table 6. Interactions between county and curbside do not exist;
therefore, no interaction was created. The addition of interactions decreased the F value to
100.36, which still shows the regression is a good fit. The R2 increased to 41.0% with an
adjusted R2 of 40.6%.
Table 6. Second regression analysis for 2008-10 Net Exclude Yard Waste Cost Per Capita
Although the overall model is significant (p-value <0.0001), eight of the 22 variables are not
significant compared to the baseline, including COLL2. Two of the interactions, M1C1 and
M4C1, were the only interactions found to be significant. A third interaction, M2C1, came close
with a p-value of 0.0565.
20
Abbreviations DFParameter Estimate
Standard Error
t Value Pr > [t]
Inter 1 1.99583 0.12801 15.59 <.0001LBSL 1 0.37912 0.0225 16.85 <.0001
POPUL 1 -0.10268 0.01559 -6.59 <.0001POPDENL 1 -0.07575 0.0164 -4.62 <.0001
MEMCT 1 -0.03278 0.00787 -4.17 <.0001COLL1 1 0.23907 0.03251 7.35 <.0001COLL2 1 -0.03512 0.04033 -0.87 0.3839
PER 1 -1.417 0.047901 -29.59 <.0001MUNI1 1 0.38044 0.09558 3.98 <.0001MUNI2 1 0.70143 0.24072 2.91 0.0036MUNI3 1 1.88778 0.4199 4.5 <.0001MUNI4 1 2.41647 0.33628 7.19 <.0001MUNI5 1 0.49539 0.13109 3.78 0.0002M1C1 1 -0.24424 0.09185 -2.66 0.0079M1C2 1 0.15575 0.1049 1.48 0.1377M2C1 1 -0.46318 0.24278 -1.91 0.0565M2C2 1 -0.13566 0.24499 -0.55 0.5798M3C2 1 -0.27006 0.35341 -0.76 0.4448M4C1 1 -1.14663 0.38889 -2.95 0.0032M4C2 1 -0.47853 0.36477 -1.31 0.1897M5C1 1 -0.32947 0.35981 -0.92 0.3599M5C2 1 -0.17588 0.23644 -0.74 0.457
N = 3053 R2 = 0.4102 Adj R2 = 0.4061
Source DF F Value Pr > FModel 21 100.36 <.0001Error 3031Corrected Total 3052
Mean Square33.7280.336
City*CurbsideCity*BothCounty*BothTribe*CurbsideTribe*BothOther*CurbsideOther*Both
Village*Both
Sum of Squares708.29
1018.601726.89
CityCountyTribeOtherVillage*Curbside
Member CountCurbsideCurbside & Drop-OffGrant Amount/ Net Eligible CostsVillage
Variable
Intercept Log(Pounds/Capita)Log(Population)Log(Population Density)
Analysis of Variance
Interactions between collection type & population and collection type & population density
created four new variables and represents the final regression model for 2008-2010 Cost per
Capita (Table 7). The final equation is as follows:
Log(Net Exclude Yard Waste Cost/Capita) = 3.13 + 0.36 Log(lbs) – 0.22
Log(Population) – 0.163 Log(Population Density) - 0.037 Member Count – 0.99
Curbside Collection – 1.49 Curbside & Drop-off Collection – 1.393 Grant
Amount/Net Eligible Costs + 0.565 Village + 0.967 City + 2.52 County + 2.60
Tribe + 0.55 Other – 0.58 M1C1 – 0.159 M1C2 – 0.91 M2C1 - 0.6 M2C2 – 0.89
M3C2 – 1.4 M4C1 – 0.64 M4C2 – 0.298 M5C1 - 0.25 M5C2 + 0.114 POPC1 +
0.15 POPC2 + 0.144 PDC1 + 0.13 PDC2.
The addition of the final interactions decreased the F value to 89.2, which still shows a good fit
for the model. However, the model increased slightly in strength with an R2 value of 42.4%.
After adjustment, 41.9% of the Log(Net Exclude Yard Waste Cost/Capita) can be accounted for
by the independent variables. The completed model is significant (p-value <0.0001) with 21 of
the 26 variables being individually significant. The four nonsignificant variables include M1C2,
M4C2, M5C1, and M5C2.
21
Table 7. Final regression analysis for 2008-10 Net Exclude Yard Waste Cost Per Capita
Variable Abbreviations DFParameter Estimate
Standard Error
t Value Pr > [t]
Intercept Inter 1 3.13008 0.23683 13.22 <.0001Log(Pounds/Capita) LBSL 1 0.36253 0.02237 16.2 <.0001Log(Population) POPUL 1 -0.22273 0.04095 -5.44 <.0001Log(Population Density) POPDENL 1 -0.16321 0.03389 -4.82 <.0001Member Count MEMCT 1 -0.03685 0.0079 -4.66 <.0001Curbside COLL1 1 -0.98907 0.24225 -4.08 <.0001Curbside & Drop-Off COLL2 1 -1.49385 0.26639 -5.61 <.0001Grant Amount/ Net Eligible Costs
PER 1 -1.393 0.047547 -29.3 <.0001
Village MUNI1 1 0.56548 0.12517 4.52 <.0001City MUNI2 1 0.96655 0.246 3.93 <.0001County MUNI3 1 2.51666 0.43535 5.78 <.0001Tribe MUNI4 1 2.59988 0.33337 7.8 <.0001Other MUNI5 1 0.55397 0.13681 4.05 <.0001Village*Curbside M1C1 1 -0.58005 0.14713 -3.94 <.0001Village*Both M1C2 1 -0.159 0.15953 -1 0.319City*Curbside M2C1 1 -0.90505 0.25821 -3.51 0.0005City*Both M2C2 1 -0.6002 0.26328 -2.28 0.0227County*Both M3C2 1 -0.89078 0.38421 -2.32 0.0205Tribe*Curbside M4C1 1 -1.40326 0.38595 -3.64 0.0003Tribe*Both M4C2 1 -0.63809 0.36144 -1.77 0.0776Other*Curbside M5C1 1 -0.29745 0.36169 -0.82 0.4109Other*Both M5C2 1 -0.24982 0.2424 -1.03 0.3028Population*Curbside POPC1 1 0.11444 0.04618 2.48 0.0133Population*Both POPC2 1 0.15096 0.04881 3.09 0.002Population Density*Curbside PDC1 1 0.14409 0.04172 3.45 0.0006Population Density*Both PDC2 1 0.13048 0.04422 2.95 0.0032N = 3053 R2 = 0.4242 Adj R2 = 0.4194
Source DFMean
Square F Value Pr > FModel 25 29.30 89.2 <.0001Error 3027 0.33Corrected Total 3052
732.53994.361726.89
Analysis of VarianceSum of Squares
22
Recycling Cost per Ton
Net exclude yard waste cost per ton was first modeled using the same variables. The first
regression model for cost per ton is shown in Table 8 and is represented by the following
equation:
Log(Net Exclude Yard Waste Cost/Ton) = 9.53 - 0.608 Log(Tons) - 0.094
Log(Population) – 0.081 Log(Population Density) - 0.033 Member Count + 0.148
Curbside Collection + 0.054 Curbside & Drop-off Collection – 1.145 Grant
Amount/Net Eligible Costs + 0.266 Village + 0.38 City + 1.5 County + 1.75 Tribe
+ 0.377 Other.
Table 8. First regression analysis for 2008-10 Net Exclude Yard Waste Cost per Ton
23
The regression model is a good fit with an F value of 173.39 and is shown to be significant (p-
value <0.0001). The dependent variables account for 40.6% of the Log (Net Exclude Yard Waste
Cost/Ton). After adjustment for the number of independent variables, this amount decreases
slightly to 40.4%. Out of the 12 variables, 11 are shown to be significant. The only
nonsignificant variable was COLL2 with a p-value of 0.1232.
To further this analysis, interactions were added to the model. The second regression model
(Table 9) included nine interactions between collection method and government type. Once
again, no counties reported curbside collection; therefore, no interaction was created.
Table 9. Second regression analysis for 2008-10 Net Exclude Yard Waste Cost per Ton
Variable Abbreviations DFParameter Estimate
Standard Error
t Value Pr > [t]
Intercept Inter 1 9.59155 0.12806 74.9 <.0001Log(Pounds/Capita) LBSL 1 -0.62011 0.02251 -27.55 <.0001Log(Population) POPUL 1 -0.10277 0.01559 -6.59 <.0001Log(Population Density) POPDENL 1 -0.07521 0.0164 -4.58 <.0001Member Count MEMCT 1 -0.03279 0.00787 -4.16 <.0001Curbside COLL1 1 0.23866 0.03252 7.34 <.0001Curbside & Drop-Off COLL2 1 -0.03333 0.04035 -0.83 0.4088Grant Amount/ Net Eligible Costs
PER 1 -1.41573 0.04792 -29.54 <.0001
Village MUNI1 1 0.3788 0.09562 3.96 <.0001City MUNI2 1 0.6994 0.24082 2.9 0.0037County MUNI3 1 1.88919 0.42008 4.5 <.0001Tribe MUNI4 1 2.4161 0.33643 7.18 <.0001Other MUNI5 1 0.49578 0.13114 3.78 0.0002Village*Curbside M1C1 1 -0.24429 0.09189 -2.66 0.0079Village*Both M1C2 1 0.15339 0.10494 1.46 0.1439City*Curbside M2C1 1 -0.46285 0.24288 -1.91 0.0568City*Both M2C2 1 -0.13757 0.24509 -0.56 0.5746County*Both M3C2 1 -0.27374 0.35356 -0.77 0.4389Tribe*Curbside M4C1 1 -1.14652 0.38906 -2.95 0.0032Tribe*Both M4C2 1 -0.4802 0.36493 -1.32 0.1883Other*Curbside M5C1 1 -0.32879 0.35996 -0.91 0.3611Other*Both M5C2 1 -0.17806 0.23655 -0.75 0.4517N = 3053 R2 = 0.4171 Adj R2 = 0.4131
Source DFMean
Square F Value Pr > FModel 21 34.73984 103.28 <.0001Error 3031 0.33635Corrected Total 3052
729.536631019.484041749.02067
Analysis of Variance
Sum of Squares
24
Interactions decreased the F value to 103.3, due to increasing degrees of freedom with the same
significance (p-value <0.0001). The R2 increased to 41.7% with an adjusted value of 41.3%. The
same variables remained significant from the first model with COLL2 increasing its p-value to
0.4088. Only two interactions, M1C1 and M4C1, were found to be significant, although M2C1
came close with a p-value of 0.0568.
An addition of four collection method interactions among population and population density
formed the final analysis (Table 10). The equation is as follows:
Log(Net Exclude Yard Waste Cost/Ton) = 10.73 - 0.64 Log(Tons) - 0.223
Log(Population) – 0.16 Log(Population Density) - 0.037 Member Count - 0.99
Curbside Collection – 1.499 Curbside & Drop-off Collection – 1.39 Grant
Amount/Net Eligible Costs + 0.565 Village + 0.966 City + 2.52 County + 2.6
Tribe + 0.554 Other – 0.58 M1C1 – 0.167 M1C2 – 0.904 M2C1 - 0.608 M2C2 –
0.895 M3C2 – 1.4 M4C1 – 0.64 M4C2 – 0.297 M5C1 - 0.25 M5C2 + 0.115
POPC1 + 0.151 POPC2 + 0.144 PDC1 + 0.132 PDC2.
The interaction addition decreased the F value to 91.74 and continues to show the variables are a
good fit. A R2 value of 43.1% signifies an increased proportion of the variability of Log(Net
exclude yard waste cost/ton) explained by the model. Of the original 13 variables, all of them
including COLL2 were found to be significant. Four interactions were not significant, including
M1C2, M4C2, M5C1, and M5C2.
25
Table 10. Final regression analysis for 2008-10 Net Exclude Yard Waste Cost Per Ton
Variable Abbreviations DFParameter Estimate
Standard Error
t Value Pr > [t]
Intercept Inter 1 10.7273 0.237 45.28 <.0001Log(Pounds/Captia) LBSL 1 -0.6367 0.022 -28.45 <.0001Log(Population) POPUL 1 -0.2228 0.041 -5.44 <.0001Log(Population Density) POPDENL 1 -0.1632 0.034 -4.81 <.0001Member Count MEMCT 1 -0.0369 0.008 -4.67 <.0001Curbside COLL1 1 -0.9891 0.242 -4.08 <.0001Curbside & Drop-Off COLL2 1 -1.4989 0.266 -5.62 <.0001Grant Amount/ Net Eligible Costs
PER 1 -1.3910 0.048 -29.25 <.0001
Village MUNI1 1 0.5652 0.125 4.51 <.0001City MUNI2 1 0.9658 0.246 3.92 <.0001County MUNI3 1 2.5207 0.436 5.79 <.0001Tribe MUNI4 1 2.6002 0.333 7.8 <.0001Other MUNI5 1 0.5542 0.137 4.05 <.0001Village*Curbside M1C1 1 -0.5797 0.147 -3.94 <.0001Village*Both M1C2 1 -0.1668 0.160 -1.04 0.2961City*Curbside M2C1 1 -0.9040 0.258 -3.5 0.0005City*Both M2C2 1 -0.6081 0.263 -2.31 0.021County*Both M3C2 1 -0.8954 0.384 -2.33 0.0199Tribe*Curbside M4C1 1 -1.4036 0.386 -3.64 0.0003Tribe*Both M4C2 1 -0.6401 0.362 -1.77 0.0768Other*Curbside M5C1 1 -0.2972 0.362 -0.82 0.4116Other*Both M5C2 1 -0.2511 0.242 -1.04 0.3005Population*Curbside POPC1 1 0.1145 0.046 2.48 0.0133Population*Both POPC2 1 0.1510 0.049 3.09 0.002Population Density*Curbside PDC1 1 0.1440 0.042 3.45 0.0006Population Density*Both PDC2 1 0.1324 0.044 2.99 0.0028N = 3053 R2 = 0.4311 Adj R2 = 0.4264
Source DFMean
SquareF
Value Pr > FModel 25 30.157 91.74 <.0001Error 3027 0.329Corrected Total 3052
753.930995.0911749.021
Analysis of VarianceSum of Squares
26
Level of Service Survey
On March 21, 2012, a survey was issued to all of the responsible unit (RU) programs with e-mail
addresses provided by the WDNR. This survey contained an individualized link to a Qualtrics
based survey that allowed the survey responses to be tracked by the email address of the
distribution invitation. A total of 360 RU’s (34.6%) responded to the survey.
Numeric values were assigned to each available response within the survey as illustrated in Table
11. A value of 0 was given to the answer option with the lowest level of convenience, with
subsequent values of 1, 2, and 3 given as convenience levels increased. A value of 4 was given
when the answer option “other” was selected. Table 3 lists the answer options for each survey
question and the numeric scores they received.
Table 11. - Survey Answer Option Scores
The sum of the numeric values for each RU’s responses were found, giving the program a total
numeric value. These numeric values were used to sort the survey respondents into 3 groups;
Basic Level of Service, Moderate Level of Service, and High Level of Service. For example, a
program that reports curbside collection, every week, single stream, on the same day as
residential waste, with a basic scope of materials would receive scores of 1, 3, 2, 1, and 0,
respectively. The numeric total for this program would be 7 placing them in the “High Level of
Service” grouping. A program that reported: drop off location, not collected, dual stream, on the
same day as residential waste with a basic scope of material would receive scores of 0, 0, 1, 1,
and 0, respectively. This program would achieve a numeric total of 2 placing them in the “Basic
Level of Service” group.
Survey QuestionAnswer option score = 0
Answer option score = 1
Answer option score = 2
Answer option score = 3
Answer option score = 4
Where are recyclable materials collected? Drop-off location Curb-side Porch-Side OtherHow often is recyclable material collected?
Not collected, drop-off only Monthly
Every other week Weekly Other
Are recyclable materials comingled?
No (everything is separated) Dual stream Single Stream Other
Are recyclable materials collected on the same day as No YesWhat is the scope of materials collected?
Basic (paper, glass, aluminum, some
Moderate (additional plastics, foams, films)
Expanded (organics) Other
27
If survey respondents did not answer all questions or selected “other” as an answer option, then
their RU was removed from the pool of survey answers (36 in total). Since “other” received a
numeric score of 4, its use would have artificially inflated the total scores of those programs that
utilized the “other” option.
Survey Results
Collection Method
Collection method refers to the combination of factors comprising the collection system for an
RU program. The factors examined by the level of service survey include: comingling policy,
collection frequency, collection location, scope of material collected, and if collection occurs for
recyclable materials on the same day as municipal solid waste. Each factor has several options
that provide residents with varying levels of convenience; this is discussed in more detail below.
Comingling Policy
Comingling policy is also of interest when considering program efficiency. Studies have shown
that consumer participation increases with greater comingling of materials such as single stream
(Folz, 1999a; Gallardo, Bovea, Colomer, Prades, and Carlos, 2010). Comingling policy across
the state is primarily single stream, followed by dual stream. Both single and dual stream policies
place the burden of separation on the materials recovery facility (MRF), rather than on the
resident. Programs classified in the Basic Level of Service group had the greatest diversity in
comingling policy as seen in Figure 1. In contrast, the High Level of Service group was almost
exclusively single stream.
28
Figure 1. Number of RUs by Comingling Policy
Collection Frequency
As illustrated in Figure 2, one of the most revealing factors for establishing likely group
identification was collection frequency. The majority of programs classified in the Basic Level of
Service group did not have collection services. In fact, this is the only group that contained drop-
off only programs. Moderate Level of Service group and High Level of Service group contain
the majority of programs that offer every other week, or bi-monthly collection. The High Level
of Service group contains primarily weekly collection programs.
0
10
20
30
40
50
60
70
80
90
100
Basic Scope ofMaterial
Moderate Scopeof Material
Expanded Scopeof Material
Basic Level of Service
Moderate Level of Service
High Level of Service
29
Figure 2. Number of RUs by Collection Frequency
Across all groups, the most frequently reported response to the question “are recyclables
collected on the same day as residential waste,” was yes. This is an important aspect when
evaluating the overall convenience of the collection process because it makes it easier for
residents to remember the correct day to put out their recyclable materials. It is believed that the
higher occurrence of “no” in Basic Level of Service group is due to the inclusion of the word
“collected” in the question. Since the majority of programs in Basic Level of Service group are
drop-off only, including the word collected in the question may have prompted a negative
response. The results of this question are illustrated in Figure 3.
0
10
20
30
40
50
60
70
80
90
Not Collected Monthly Every otherweek
Weekly
Basic Level of Service
Moderate Level of Service
High Level of Service
30
Figure 3. Are Recyclables Collected the Same Day as Residential Waste?
Collection Location
While somewhat redundant in the case of the Basic Level of Service group, collection frequency
and collection location confirm the predominance of drop-off only locations being heavily
utilized within this category. The primary alternative to drop-off location collection, is curbside
collection, which is the primary collection location for both the Moderate Level of Service group
and the High Level of Service group. Interestingly, one program within High Level of Service
group continues to offer porch-side collection, which is considered to be the most convenient
collection location for residents. Residents must simply locate their recycling receptacles outside
on their property and are not tasked with remembering to take the bin or cart to the edge of their
property on the proper day.
0
20
40
60
80
100
120
140
Not collected Same Day Collected Same Day
Basic Level of Service
Moderate Level of Service
High Level of Service
31
Figure 4. Number of RUs by Collection Location
Scope of Materials
There is variation between programs regarding the types, or scope of material, collected. While
the majority of RU programs collect only the basic materials as required by state law (including
paper, paper board, glass, aluminum, and PET and HDPE), many programs are expanding their
collection to include additional plastics, films, foams and organic materials. An area for further
study may be the reasoning behind expanded collection: has past market development spurred
this inclusion, could future market incentives increase the number of programs offering this
service, is proximity to markets a factor? The scope of materials collected by group is exhibited
in Figure 5.
0
20
40
60
80
100
120
Not collected(drop-off only)
CurbsideCollection
Porch-sideCollection
Basic Level of Service
Moderate Level of Service
High Level of Service
32
Figure 5. Number of RUS by Scope of Materials
Table 12 illustrates state averages for per capita collection (in pounds), total costs,
awarded grant amount, cost per capita and cost per ton. Pounds per capita collection has
remained largely static while total costs and net eligible amounts have been increasing.
Table 12. State Average Costs and Collection Amounts by Year (2007-2010)
Figure 6 shows a slight dip in state wide averages for pounds per capita collection.
However, it should be noted that the values are quite small and overall, the collection amounts
have remained steady over the 4-year period.
0
20
40
60
80
100
120
Basic Level of Service Moderate Level ofService
High Level of Service
Basic Scope
Moderate Scope
Expanded Scope
YearPopulation
Amount
Per Capita Col lection
(lbs )Tota l Costs
Tota l Cost/ Capi ta
Tota l Cost/ton
Net Elgible Costs
Net Elgible Cost/
Capi ta
Net Elgible
Cost/tonAward Amount
Award Amount/
Capita
2007 5396 138.82 $112,572 $20.86 $301 $97,782 $13.98 $221 $23,079 $4.282008 5392 138.82 $111,010 $20.59 $297 $94,999 $14.84 $238 $28,553 $5.302009 5391 138.80 $112,497 $20.87 $301 $97,888 $15.00 $242 $26,553 $4.932010 5387 138.81 $114,768 $21.30 $307 $99,278 $15.19 $247 $26,555 $4.93
State Averages
33
Figure 6. State Average Per Capita Collection (in pounds)
In contrast to the relatively steady collection amounts, total costs have maintained a steady
upward trend over the same time period with a statewide average increase of $2,196 in the same
4 year time period (Figure 7).
Figure 7. State Average Total Costs
As depicted in Figure 8 the amounts awarded to RUs in grant money have also increased in this
four year time period. The statewide average amount has increased by $3,476. While this is not
quite as steep as the total costs increase, it is a sizable amount of money.
34
Figure 8. State Average Awarded Grant Amounts
As seen in Figure 9, state average costs per capita, based on net eligible reported expenses, have
also been climbing with only a slight dip that occurred in 2009. This equates to an additional
$1.21 per person, state wide.
Figure 9. State Average Cost per Capita
Perhaps the most telling statistic is the steady increase in costs per ton of material collected, as
seen in Figure 10. Increasing costs coupled with decreasing tonnage collected have resulted in a
high cost per ton for collection. Each ton of material collected in 2011 cost $20.00 more than a
ton collected in 2007.
Figure 10. State Average Cost per Ton
35
36
RU Case Studies
Basic Level of Service
Town of Cedarburg, Ozaukee County
The town administrator, who is not an elected official but does have other duties,
oversees the Town of Cedarburg’s RU. He spends less than 10 hours per week managing the RU
and has less than 5 years of experience in the recycling or solid waste industry. It is believed that
the recycling program is successful and that local residents support recycling. The administrator
spends less than 20% of RU management on reporting and feels that the reports are easy to
understand and that he has been adequately trained to fill them out.
A publically managed drop-off center is the method for collection in the Town of
Cedarburg. Education is employed to increase resident participation in the recycling program.
The educational program consists of emails, a website, and a newsletter. Increased participation
is cited as needed to make the program more successful; community acceptance is the strongest
point within the program. The weakest point of the program is that it is very labor intensive and
the biggest hindrance is funding. Within the reporting process, inadvertent measures of
performance are seen as beneficial. The Town Administrator finds reporting data to be a good
method for tracking local performance. No issues with regard to the reporting of yard waste have
been noted.
Per capita collection totals have been steadily declining from a high of 233 in 2001, when
net eligible cost per ton was at a low of $30.00, to a low of $152.75 in 2010 with a net eligible
cost per ton of $71.95. Table 11 details the information for the Town of Cedarburg.
Population Total 5759 Population Density (per sq mile) 231 Public or Private Hauler Drop-off Bin/Cart n/a Truck Type n/a Pounds per person per year 199.96 lbs Cost Average per person $4.78 Cost Per Ton Average $50.70
37
Table 13. Town of Cedarburg Average Costs and Collection Amounts by Year (2007-2010)
Moderate Level of Service
City of Reedsburg, Sauk County
Population Total 9028 Population Density (per sq mile) 42
Public or Private Hauler Private Bin/Cart Bin
YearPopulation
Amount
Per Capita Col lection
(lbs )Tota l Costs
Tota l Cost/
Capi ta
Tota l Cost/ton
Net Elgible Costs
Net Elgible Cost/
Capi ta
Net Elgible
Cost/ton
Award Amount
Award Amount/
Capita
2007 5759 233.00 $78,625 $13.65 $117 $29,135 $3.50 $30 $6,572 $1.142008 5789 220.01 $96,154 $16.61 $151 $25,550 $2.82 $26 $8,309 $1.442009 5798 194.08 $83,048 $14.32 $148 $57,322 $7.30 $75 $3,505 $0.602010 5794 152.75 $82,457 $14.23 $186 $47,838 $5.50 $72 $7,869 $1.36
2007 1792 118.39 $26,561 $14.82 $250 $20,730 $10.47 $234 $6,730 $3.752008 1813 119.23 $27,580 $15.22 $255 $21,750 $11.97 $243 $8,504 $4.692009 1823 111.49 $28,448 $15.61 $280 $23,820 $12.63 $238 $7,745 $4.252010 1811 109.51 $29,072 $16.06 $293 $21,387 $12.42 $246 $8,115 $4.48
2007 5396 138.82 $112,572 $20.86 $301 $97,782 $13.98 $221 $23,079 $4.282008 5392 138.82 $111,010 $20.59 $297 $94,999 $14.84 $238 $28,553 $5.302009 5391 138.80 $112,497 $20.87 $301 $97,888 $15.00 $242 $26,553 $4.932010 5387 138.81 $114,768 $21.30 $307 $99,278 $15.19 $247 $26,555 $4.93
RU: Cedarburg
Basic Level of Service Group Averages
State Averages
38
The city engineer/public works director oversees the City of Reedsburg RU. RU
management requires less than 10 hours per week and while the city engineer does not have
experience specifically in the recycling and solid waste industry, he does have 19 years of
experience in city government. Collection is done by a private hauler utilizing a trailer with bins
for on-site separation. Residents place recyclables in a bin, single stream; these materials are then
sorted during the collection process. Private contractors are selected with a regional RFP (request
for proposal) every three years; most recently, a five year extension was signed. It is believed
that the recycling program is successful and that residents support recycling. Education is used to
increase resident participation. The educational program consists of handouts, providing
residents with the hauler’s phone number and information on the town’s website.
An increase in education and advertising is cited as an option to make the program more
successful including highlighting the twice a year e-cycle and establishment of compact
fluorescent lamp (CFL) collection. The strongest point of the program currently is a “good
hauler”; the city does not receive complaints regarding collection. The weakest point within the
program is holiday confusion with regard to collection, but more education and advertising
would alleviate this problem. The biggest hindrance to the program is funding.
With regard to reporting, the RU believes that the reports are simple and while there was
no training, they were simple to “figure out” online. The most noteworthy change in reporting is
the switch to online submittal, which is much better and operates smoothly. Yard waste has not
become an issue, possibly because it is separate in the budget. The RU does operate a yard waste
Truck Type Manual –
trailer w/ bins for separation
Pounds per person per year 135.15 lbs Cost Average per person $8.42 Cost Per Ton Average $101.14
39
site with processing taking place once a year. The rural setting may also play a part in this as
there “are more places to get rid of yard waste”.
*The numbers provided in Table 12 for net eligible cost per ton and net eligible cost per capita
are shown exactly as reported to the WDNR in the annual reporting process.
Table 14. City of Reedsburg Average Costs and Collection Amounts by Year (2007-2010)
40
High Level of Service
Town of Blooming Grove, Dane County
The Town of Blooming Grove is located in Dane County and borders the city of
Madison, WI. The town clerk is responsible for filling out and filing RU reports . The town
clerk’s predecessor trained him and he relies heavily on previous years’ reports to identify
potential problems, or discrepancies within the reporting process. He received no training from
the WDNR and notes that other RU programs in the area have contacted him with questions as to
how they should complete the required reports. A positive potential improvement to this process
would be a simplification of the reports.
The belief of the town clerk is that the program is successful and well supported by
residents. As a personal opinion, he offered that he believes that the residents see the programs of
the surrounding areas and would prefer that the Town of Blooming Grove’s recycling program
more closely emulate their neighbors. He cites adjoining communities rolling carts and the desire
of Blooming Grove’s residents to recycle a larger volume of material.
The Town of Blooming Grove offers weekly, single stream, curbside collection on the
same day as municipal solid waste collection. These program features receive a numeric value of
7, placing it in High Level of Service group. A private contractor does the collection, utilizing a
manual truck to empty 27-gallon bins provided to residents. Contractors are hired after a request
for bid (RFB) process is executed approximately every 5 years.
The strongest point within the program is believed to be the community residents, “they
expect recycling and are proactive”. The largest hindrance to the program is frustration by
Population Total 1734 Population Density (per sq mile) 215 Public or Private Hauler Private Bin/Cart Bin Truck Type Manual Pounds per person per year 223.32 lbs Cost Average per person 53.83 Cost Per Ton Average 484.18
41
residents who wish to recycle more than the bins allow. The education program consists of a
newsletter and information on the town’s website, “If they want to find the information, it is
there.”
Yard waste has not presented itself to be an issue in the reporting process in the Town of
Blooming Grove. Public employees provide this service and the information regarding costs is
more readily available for this reason. Yard waste is collected weekly curbside for six weeks in
the spring and six weeks in the fall. The clerk notes that some programs may have problems in
this area if chipping is included in the cost of collection. They handle them as independent
processes. As seen in the data provided to the WDNR, yard waste was included in the annual
reports for 2007 and 2008, prior to the current town clerk’s tenure with the program. The total
costs for yard waste were $8607.00 and $9052.00 for 2007 and 2008, respectively. The program
costs as associated with the RU, reported annually to the WDNR are in Table 16.
42
Table 15. Town of Blooming Grove Average Costs and Collection Amounts by Year (2007-
2010)
YearPopulation
Amount
Per Capita Collection
(lbs)Total Costs
Total Cost/ Capita
Total Cost/ton
Net Elgible Costs
Net Elgible Cost/ Capita
Net Elgible
Cost/ton
Award Amount
Award Amount/
Capita
2007 1734 215.10 $86,207 $49.72 $462 $86,207 $44.75 $416 $13,254 $7.642008 1741 242.48 $90,517 $51.99 $429 $90,517 $46.79 $386 $16,759 $9.632009 1753 221.73 $118,796 $67.77 $611 $118,796 $67.77 $611 $15,086 $8.612010 1752 213.98 $98,123 $56.01 $523 $98,123 $56.01 $523 $15,872 $9.06
2007 7190 177.83 $186,115 $25.89 $291 $107,317 $14.93 $179 $31,137 $4.332008 7233 172.34 $191,773 $26.51 $308 $112,251 $15.52 $201 $39,390 $5.452009 7256 172.57 $211,682 $29.17 $338 $123,318 $16.99 $210 $35,397 $4.882010 7265 172.54 $210,722 $29.01 $336 $136,886 $18.84 $232 $37,298 $5.13
2007 5396 138.82 $112,572 $20.86 $301 $97,782 $13.98 $221 $23,079 $4.282008 5392 138.82 $111,010 $20.59 $297 $94,999 $14.84 $238 $28,553 $5.302009 5391 138.80 $112,497 $20.87 $301 $97,888 $15.00 $242 $26,553 $4.932010 5387 138.81 $114,768 $21.30 $307 $99,278 $15.19 $247 $26,555 $4.93
RU: Town of Blooming Grove
High Level of Service Group Averages
State Averages
43
Recommendations for WDNR
Based on contact with individual RU programs identified as outliers within this project we
recommend the following:
1. More intensive training in the reporting process for RU programs
a. The majority of the programs identified as outliers had clerical errors within the
reporting process that caused them to be labeled as such.
i. Regional training
ii. Webinars
b. Many of the RUs that were contacted felt that they were inadequately trained to
complete the reporting process.
2. Encourage communication
a. Between neighboring programs, many programs could benefit from partnerships
with surrounding areas.
i. Partnering for private contractor bidding
ii. Sharing equipment
iii. Combining aspects of programs without full consolidation
1. *consolidation is still optimal choice
iv. Consolidation
1. Fostering communication, building camaraderie, encouraging
consolidation
2. Bidding for private haulers by geographic region
b. With the DNR
c. Group similar programs or demographic regions for collaboration
3. Emphasize importance of accurate reporting
4. Simplify reporting process
5. Collect additional data that can be useful in future analysis of programs
6. Analysis of countywide, consolidated RU programs should be conducted.
a. These large programs are often managed by highly trained individuals, which may
lead to fewer clerical errors in the reporting process, offering a more accurate
view of programs’ efficiency levels.
44
b. The consolidated natures of these programs offer larger territories and potentially
contain more densely populated areas, which may lead to a more competitive
bidding process with private contractors.
c. The set-up of these programs appears to be widely varied, offering many different
approaches of management for study.
Recommendations for RUs
Expanded bidding areas
*Reporting requirements written into contracts with private haulers
Partnering with neighboring areas
Consolidation
Automated trucks
Carts – owned by RU or resident, not as part of bid from contractor due to surprise costs
or being “held hostage” at end of contract period.
45
References
This will be edited
46
Appendix 1
Interview Questionaire1
Interview questions
1. In your opinion, what would make your program more successful? __________________
2. What is the strongest point within your program? _______________________________
3. What is the weakest point within your program?_________________________________
4. What is the biggest hindrance to your program? _________________________________
Reporting:
5. What could be improved within the reporting process? __________________________
Collection:
6. If collected by a private contractor, what is the selection process? ___________________
7. If applicable: how does the pay as you throw program work? (sell stickers, bags etc.) ___
8. Have there been any alternate problems caused since implementation of the pay as you
throw program? _________________________________________________________
9. What does your educational program consist of? (if applicable) ___________________
1 Interviews were conducted with RUs that were available for contact and wished to participate
in the interview process.
47
