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Using citizen science to evaluate extended producer responsibility policy to reduce marine plastic debris shows no reduction in pollution levels

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As governments around the world grapple with the challenge of responding to increased plastic pollution, extended producer responsibility (EPR) policy, which shifts the responsibility for waste management of a product to producers, is quickly becoming a cornerstone of legislative approaches to this issue. However, the effectiveness of this policy has never been assessed in terms of reducing plastic marine debris. Understanding if a policy like EPR is having an effect requires collecting and analyzing information before and after a policy is introduced. Using British Columbia, Canada as a case study, we evaluate the influence of an EPR policy for packaging to reduce shoreline pollution. We use available citizen science data to demonstrate that there has been no reduction in pollution levels after the introduction of the policy. The findings also highlight some limitations when using citizen science data for a purpose it was not intended for. To effectively evaluate prevention of plastic pollution in the marine environment, plastic policy interventions require monitoring programs tailored to their specific requirements.
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Full length article
Using citizen science to evaluate extended producer responsibility policy to
reduce marine plastic debris shows no reduction in pollution levels
Lucas Harris
*
, Max Liboiron , Louis Charron , Charles Mather
Memorial University of Newfoundland, Department of Geography, 230 Elizabeth Ave, St. Johns, NL A1C 5S7, Canada
ARTICLE INFO
Keywords:
Extended producer responsibility
Citizen science
Plastic marine debris
Policy evaluation
ABSTRACT
As governments around the world grapple with the challenge of responding to increased plastic pollution,
extended producer responsibility (EPR) policy, which shifts the responsibility for waste management of a product
to producers, is quickly becoming a cornerstone of legislative approaches to this issue. However, the effectiveness
of this policy has never been assessed in terms of reducing plastic marine debris. Understanding if a policy like
EPR is having an effect requires collecting and analyzing information before and after a policy is introduced.
Using British Columbia, Canada as a case study, we evaluate the inuence of an EPR policy for packaging to
reduce shoreline pollution. We use available citizen science data to demonstrate that there has been no reduction
in pollution levels after the introduction of the policy. The ndings also highlight some limitations when using
citizen science data for a purpose it was not intended for. To effectively evaluate prevention of plastic pollution
in the marine environment, plastic policy interventions require monitoring programs tailored to their specic
requirements.
1. Introduction
Plastic is a common and ubiquitous type of pollution in the marine
environment around the world. The San Francisco Bay area in California
alone is estimated to receive seven trillion plastic pieces annually [1].
Plastics are introduced into the marine environment from a variety of
land, air and sea-based sources [2]. As the global production of plastic
continues to increase every year [3], marine plastic pollution represents
a growing threat to socio-economic [4] and ecological [5] systems.
Packaging material in particular represents the single largest component
of plastics market demand around the world, accounting for 40% of total
plastic use in Europe [2] and 33% in Canada [6]. Packaging materials,
including food wrappers, plastic drink bottles, bottle caps, plastic gro-
cery bags and plastic lids, have also featured as the most frequently
found items during marine litter surveys [79].
Market-based strategies and policy interventions (e.g. bans, levies) to
minimize single-use plastics and packaging (e.g. plastic bags, microbe-
ads) have been rapidly increasing in the past 10 years [1012]. One
prominent policy approach that has been suggested to reduce marine
plastic pollution is Extended Producer Responsibility (EPR) [13]. As a
policy tool, EPR shifts the responsibility for waste management of a
product or its packaging from local governments to producers [14],
which provides incentives to producers to prevent waste from being
generated in the rst place (i.e. source reduction) [15], and reduces
material going to landll or leaking into the environment by funding,
creating or expanding infrastructure for post-consumer recycling [16].
Recently, several jurisdictions have adopted EPR as a pillar of their
legislative approach aimed at marine plastic pollution prevention, with
Canadian federal government [17], certain Canadian provinces [18] and
the European Union being recent examples [19].
However, EPR is difcult to assess in terms of its ability to reduce
plastic pollution in aquatic environments. The effectiveness of EPR
programs is typically measured using the volume of material recovered
from the conventional waste stream (i.e. curbside pickup, depot
collection) on an annual basis as a metric [20]. Monitoring the volume of
material that leaks from the waste stream into the marine environment
is not a reporting commitment for most programs. Furthermore, from a
methodological perspective, one core approach to understanding if an
intervention is having the desired outcomes uses quantication before
and after the intervention is introduced to evaluate impact [21]. Un-
fortunately, despite new [11] and existing strategies to address marine
plastic pollution, in many cases there is insufcient long-term moni-
toring data available that can be used to evaluate the effectiveness of
interventions [22]. To our knowledge and after sustained review of
* Correspondence to: Memorial University of Newfoundland, Department of Geography, 1440 Myrtle Ave, Victoria, BC V8R 2Z6, Canada.
E-mail address: ljharris@mun.ca (L. Harris).
Contents lists available at ScienceDirect
Marine Policy
journal homepage: http://www.elsevier.com/locate/marpol
https://doi.org/10.1016/j.marpol.2020.104319
Received 28 March 2020; Received in revised form 14 October 2020; Accepted 9 November 2020
Marine Policy 123 (2021) 104319
2
English-language literature, there are no existing studies of the effec-
tiveness of EPR in reducing marine plastic pollution.
Citizen science has become integral in the development of data and
information on shoreline plastic marine pollution levels [23], lling a
gap in monitoring that often occurs due to constraints of time, resources
or geography [24]. Studies have found citizen science data were of
equivalent quality to those collected by accredited researchers [25] and
can be used to evaluate the effectiveness of a policy that has been
implemented [9,24]. In particular, the National Oceanic and Atmo-
spheric Administration (NOAA) states that standardized monitoring
frameworks used by citizen scientists can be used to evaluate the
effectiveness of policies to mitigate debris, such as recycling incentives
or EPR [26].
Here we provide an evaluation of the ability of EPR policy for
packaging material to reduce shoreline packaging pollution, using pre-
existing citizen science data generated during shoreline cleanup events
in British Columbia (BC), Canada as a case study. BC is the only juris-
diction in North America to have introduced 100% industry-funded EPR
legislation for packaging, beginning in 2014. Since the Recycle BC EPR
program launched, it has consistently recovered at least 75% of the
packaging and printed paper sold in the BC market on an annual basis
[2731], a major improvement from the estimated 52% recovery rate
pre-2014 [32] (see Table 1). BC also has eight organizations that
conduct citizen science shoreline monitoring work, three of which that
have used standardized frameworks to collect data on three packaging
pollution categories (plastic bags, food wrappers and six pack holders)
before and after the introduction of the EPR policy. However, recent
research of citizen science programs in BC demonstrates that most data
collection programs exist as a patchwork, with the majority of moni-
toring work focused at informing various policies, not evaluating them
[33]. This is the case in many jurisdictions around the world, in terms of
marine debris monitoring. As a result, this research provides a rare and
practical analysis of a policy that has been in place for several years,
using a data set similar to ones encountered in other jurisdictions. By
doing so, we outline the challenges and considerations of using this
citizen science data to evaluate policy interventions.
2. Materials and methods
EPR policy for packaging material in BC was regulated in 2011 [34]
and introduced on May 19, 2014 in a majority of municipalities across
the province [35]. Our analyses use pre-existing shoreline pollution
datasets that have been generated by, and obtained from, eight citizen
science shoreline cleanup organizations operating in BC. None of these
datasets were created to evaluate EPR. After reviewing the data sets,
only three of the organizations had data that recorded
packaging-specic categories before and after the introduction of EPR in
2014. The Great Canadian Shoreline Cleanup (GCSC) use a tally sheet
modeled from the International Coastal Cleanup, and two chapters of the
Surfrider Foundation use the eld guide developed by NOAA [36].
These data sets were then included in three different statistical analysis
routine: GCSC, Surfrider Vancouver and Vancouver Island Chapters
(combined) and urban versus remote sites. The number of material
categories (e.g. buoys, oats, cigarette butts, etc.) in these data sets used
to track pollution levels range from 26 to 56. For analytical purposes,
categories were removed from each dataset that were not aligned with
the denition of packagingin the BC Environmental Management Act,
narrowing the categories to three that have been consistently recorded
throughout each data set: plastic bags, six-pack holders and food
wrappers.
2.1. Great Canadian Shoreline Cleanup (GCSC)
The GCSC, which uses a tally method (count) similar to the Inter-
national Coastal Cleanup, frequently hosts cleanup events. The GCSC
data set includes 5752 records for shoreline cleanup events in BC, from
2008 to 2017. For analytical purposes, a subset of data was generated
that eliminated records that lacked material-specic results and
included sites with consistent records for three years before and after the
introduction of EPR in 2014, for a total of seven years of data
(20112017). The remaining data set includes 147 records, across 21
different shoreline cleanup sites.
The GCSC data collection method does not control for length of the
beach cleaned (kilometers) or the amount of people (people) partici-
pating in a cleanup event. However, these factors can each be expected
to have an inuence on the collection results. To address this, the
number of collected packaging items (combined total of plastic bags,
food wrappers and six-pack holders) was divided by the length of beach
surveyed and linear mixed-effect models were constructed with the
random effects: site and number of people. Models were constructed
using the function lmer of the lme4′′ package [37]. To meet the as-
sumptions of residuals homogeneity, normality and independence, the
response variable was ln-transformed +0.0001. To evaluate the inu-
ence of the EPR policy on packaging shoreline debris levels after it was
introduced, we performed model selection based on Akaikes informa-
tion criterion (AICc; to control for small sample size). Model selection
was performed on a set of candidate models (Table 2). Two models were
used: (1) a null model, including the random effects of site and number
of people and (2) a complete model, including the xed effect of year
and the random effects. Marginal and conditional goodness-of-t (R
2
)
were computed following the procedure of Nakagawa et al. [38]. Sum-
mary of the models allowed us to determine the importance of year
(proxy for EPR policy) on packaging debris levels.
We also performed a t-test on pre- and post-2014 packaging debris
levels, and an ANOVA. These analyses were limited and not adequate
when compared to the mixed-model approach, as they do not control for
beach size, site or the number of people, but showed that there was no
signicant difference in packaging debris levels from year to year.
2.2. Surfrider Foundation: Vancouver and Vancouver Island Chapters
Data from the Vancouver and Vancouver Island Chapters of the
Surfrider Foundation were used to analyze packaging material shoreline
deposition trends in the Vancouver and Victoria areas. The Surfrider
datasets are created in accordance with the NOAA Shoreline Survey
Field Guide (2012). The NOAA Field Guide uses a tally method (count)
and controls for length of shoreline by utilizing a 100 m transect. It also
controls for effort by typically having a limited number of people
perform the monitoring work and recording the number of people.
There is a combined total of 81 records from both Surfrider datasets
for shoreline cleanup events at locations in both Vancouver and Victoria
from 2011 to 2017. For analytical purposes, a subset of data was
generated that eliminated records that lacked data before and after the
introduction of EPR in 2014. The data set was also limited to sample sites
in municipalities that are participating in the EPR program. The
remaining dataset includes 37 records, across eight different shoreline
cleanup sites (seven in Victoria and one in Vancouver).
To test the effect of the introduction of EPR policy for packaging
(combined total of plastic bags, food wrappers and six-pack holders) on
Table 1
Annual recovery rates for all packaging and printed paper managed in the
Recycle BC EPR Program.
Recycle BC packaging and paper products annual recovery rates
Year 2014
a
2015 2016 2017 2018
Product sold (tonnes) 145,351 243,191 238,062 234,847 235,655
Product collected
(tonnes)
116,457 186,509 185,477 174,942 183,983
Recovery rate 80% 77% 78% 75% 78%
a
As per the regulation, the program launched on May 19, 2014, therefore it
did not operate for the entire year. As a result, this data reects 7.5 months of
operation.
L. Harris et al.
Marine Policy 123 (2021) 104319
3
shoreline packaging debris levels, we performed an analysis of variance
(ANOVA) on a generalized linear model with Poisson distribution, to
account for count data. We used site, yearand their interaction as
covariates. Models were constructed using the function glm. All
models met the assumptions of residuals homogeneity, normality and
independence. We also performed a paired t-test to determine packaging
pollution trends over time, by pairing site before and after the intro-
duction of EPR in 2014. For sites that had more than one cleanup event
before or after 2014, the average value of packaging debris over these
groups of years was used.
2.3. Urban versus remote sites
The Vancouver Island Chapter of the Surfrider Foundation is the only
organization that has collected data from sample sites in both urban and
remote locations consistently over time. Their data was used to analyze
packaging material shoreline deposition trends between urban and
remote sample sites. There is a total of 56 records between 2011 and
2017 across 13 different sample sites on the south west coast of Van-
couver Island. 29 records are from sample sites in remote locations and
27 records are from sample sites in urban areas.
To test the difference in packaging pollution levels (combined total
of plastic bags, food wrappers and six-pack holders) between shoreline
“type (urban or remote), we performed an analysis of variance
(ANOVA) on a generalized linear model with Poisson distribution, to
account for count data. We used year, typeand their interaction as
covariates. Models were constructed using the function glmin R. All
models met the assumptions of residuals homogeneity, normality and
independence.
All analyses were done using R version 3.6.2 [39]. The data from this
study is subject to third party restrictions. The data that support the
ndings from this study are available from the various citizen science
organizations that participated but restrictions apply to the availability
of these data, which were used under license for the current study, and
so are not publicly available. Data are however available from the au-
thors upon reasonable request and with permission of the particular
citizen science organization.
3. Results
3.1. Great Canadian Shoreline Cleanup
Mixed-effect modeling demonstrated that the introduction of EPR in
2014 did not signicantly inuence packaging pollution levels on
shorelines. The AICc selection procedure (Table 2) determined that the
null model (only including the random effects) was the most parsimo-
nious. Year did not improve the model, meaning that year does not have
a signicant inuence on packaging debris levels (marginal R
2
=0.009)
(Fig. 1). It can be noted, however, that a higher proportion of the vari-
ance in shoreline packaging debris levels was explained by the random
effects of site and people (conditional R
2
=0.193).
3.2. Surfrider Foundation - Vancouver and Vancouver Island Chapters
Data from the Vancouver and Vancouver Island Chapters of the
Surfrider Foundation was also used to analyze packaging material
shoreline deposition trends in the Vancouver and Victoria areas, in
relation to the introduction of EPR policy. The results of the ANOVA
demonstrate that there is a strong effect of the interaction of site and
year (p-value <0.00001), with different trends occurring over time at
every site. Some sites show an upward trend in pollution levels, while
others show a constant or downward trend (see Fig. 3). Nevertheless,
there is also a year effect, showing an overall trend in which packaging
pollution is increasing over time (p-value 0.001486) (see Figs. 2 and 3).
Since site has a strong effect, we also performed a paired t-test (by
site) on packaging pollution levels before (pre) and after (post) the
introduction of EPR in 2014. Sites before 2014 had a mean value of 11.1
pieces of packaging, while sites after had a mean value of 19.9 pieces.
Each site has its own trend (increase, decrease or constant in packaging
pollution) and the difference pre- post- EPR is not statistically signicant
(p-value 0.2; see Fig. 3).
This data set has a strong site-specic trend. In particular, the results
of the ANOVA demonstrate that packaging pollution levels at each site
are very different from the next (p-value 0.00001). The t-test, which
accounts for site, does not show any signicant difference in pollution
levels before or after the introduction of EPR in 2014 (p-value 0.2). This
result is driven by a few sites that have seen their pollution level increase
drastically (e.g. Willows Beach in Victoria). Using the NOAA method
allowed the two Surfrider chapters to develop a good quality records
within their data sets. However, the sample size is somewhat limited.
Having more records over time would allow for the ability to better
Table 2
AIC model selection results for GCSC data. Results demonstrate how year accounts for less than 1% of the variance in the data, while site and people account for 19%.
Model K AICc Delta AICc LL Marginal R
2
Conditional R
2
Null Packaging/km ~ (1|Site) +(1|People) 4 623.768 0.000 307.743 0.000 0.193
Year Packaging/km ~ Year +(1|Site) +(1|People) 5 624.212 0.444 306.893 0.009 0.206
Fig. 1. GCSC Mean Packaging Pollution Levels. Plot of GCSC mean packaging
pollution levels for three years before and after the introduction of packaging
EPR in BC in 2014.
Fig. 2. Surfrider Vancouver and Vancouver Island Mean Packaging Pollution
Levels. Plot of mean packaging levels for each year in the data set, demon-
strating the increase in packaging pollution after 2014.
L. Harris et al.
Marine Policy 123 (2021) 104319
4
dene the trend.
3.3. Urban versus remote sites
Analysis of the Surfrider Vancouver Island data set by itself is aimed
at determining the difference on packaging pollution levels between
urban and remote locations. The results of the ANOVA demonstrate that
there is a strong effect of the interaction of year and type, with shoreline
types not reacting the same way over year (see Table 3 and Fig. 4). But
even across year, the mean packaging number has been consistently
lower in remote sites, when compared to urban.
Overall, the packaging pollution deposition pattern over time be-
tween remote and urban shoreline sites has varied. Deposition patterns
appear similar in earlier years. But it is evident that remote areas have a
constant rate of pollution, while urban sites have seen levels increase
over time. This increase in urban areas in driven by some extreme values
in 2015 and 2016.
4. Discussion
EPR is a relatively new policy approach for marine plastic pollution
prevention. While many have suggested that EPR policies can prevent or
reduce plastic pollution in the marine environment [13,15,16], there is
no pre-existing research available to test this assumption. Here we show
that there is no signicant reduction in packaging debris levels after the
introduction of EPR in 2014 British Columbia, Canada based on existing
citizen science data, even though the EPR program has been able to
demonstrate an increase in material recovery. This data is unable to
evaluate whether this is due to an overall increase in plastic waste
despite some signicant amount being diverted to material recovery,
whether there is an increase in plastic waste from areas outside of BC
coming onto BC shorelines, or whether there are different trends in
plastics outside of the three packaging types investigated. Other trends
detected include that the mean packaging number has been consistently
lower in remote sites, when compared to urban areas and that there is
high variability within collection sites.
While we believe these results are valid, they are also based on
acutely limited data. Citizen science has been identied as a legitimate
source of information that can support research [23], education [40]
and the introduction [41] and evaluation [24] of a particular pollution
prevention policy or legislation. However it should be clear that we used
pre-existing citizen science datasets generated by organizations that
collected the data for other reasons, such as supporting policy devel-
opment and informing education and awareness programs. As detailed
in the methods, to make the data correspond with the denition of
“packagingin the BC Environmental Management Act so it could be used
to evaluate EPR, there was an acute reduction of the amount of data that
could be used. Thus, this study is using limited data that mainly de-
scribes only three types of packaging rather than the breadth of mate-
rials covered under the packaging EPR program and the BC
Environmental Management Act. This is not a limitation of citizen science
data per se but rather the limitation of using citizen science data for
purposes other than those for which it was collected.
As policy interventions are introduced, there is a temptation to use
citizen science data to evaluate them. But there are acute limitations to
using data this way. Instead, we recommend that policy interventions
create data frameworks for monitoring and evaluation that directly
measure their full range of targets and anticipated outcomes, including
plastic marine debris reduction. In particular, a successful data collec-
tion approach for evaluating the ability of EPR for packaging material to
reduce shoreline plastic pollution levels should address several key
factors, which include identifying the source of debris, performing
frequent monitoring, establishing material categories consistent with
the policy and using count as the primary metric for tracking, due to the
fact that recent light-weighting of packaging items has the potential to
skew collection results based on weight.
Ethics
The data in this study was obtained under the Interdisciplinary
Committee on Ethics in Human Research (ICEHR) at Memorial Univer-
sity of Newfoundland approval Aligning Citizen Science and Extended
Producer Responsibility Policy for Marine Plastic Reduction
(20190638-AR) for the period 08/24/18 08/31/19.
Funding
This work was supported by the Canadian Social Science and Hu-
manities Research Council [grant number 430-2015-00413].
Fig. 3. Surfrider Vancouver and Vancouver Island Paired Data. Plot of paired
data for mean values of packaging for sites before (pre) and after (post) the
introduction of EPR in 2014. Each line represents a specic site.
Table 3
ANOVA for Surfrider Vancouver Island data. Results of the ANOVA for Surfrider
Vancouver Island data demonstrate that there is a strong interaction of year*-
type effect, with shoreline types not reacting the same way over year.
Df Deviance Resid. Df Resid. Dev Pr (>Chi)
Null 55 935.08
Year 1 62.9 54 872.21 2.212 ×10
15
Type 1 186.7 53 685.51 <2.2 ×10
16
Year:Type 1 52.1 52 633.41 5.292 ×10
13
Fig. 4. Surfrider Vancouver Island Urban and Remote Mean Pollution Levels.
Plot of mean packaging levels (combined total of food wrappers, plastic bags
and six-pack holders) in the Surfrider Vancouver Island data set each year,
showing results for both urban and remote shoreline site types. Error bars
represent the standard error of the mean.
L. Harris et al.
Marine Policy 123 (2021) 104319
5
CRediT authorship contribution statement
Lucas Harris: Conceptualization, Methodology, Software, Valida-
tion, Formal analysis, Investigation, Data curation, Writing original
draft, Writing review & editing, Visualization, Project administration.
Max Liboiron: Conceptualization, Methodology, Formal analysis, Re-
sources, Writing review & editing, Supervision, Funding acquisition.
Louis Charron: Methodology, Software, Validation, Formal analysis,
Writing review & editing, Visualization. Charles Mather: Conceptu-
alization, Writing review & editing, Supervision.
Declaration of interest
Lucas Harris is a previous executive committee member of the
Vancouver Island Chapter of the Surfrider Foundation, one of the or-
ganizations that provided shoreline pollution data for this research.
Lucas is also currently a Senior Policy Analyst with the extended pro-
ducer responsibility section of the BC Ministry of Environment and
Climate Change Strategy. He was previously the le lead for the pack-
aging EPR program but is currently assigned to a different le.
Acknowledgements
We thank the volunteers and staff from the citizen science organi-
zations, EPR program and government agency who contributed infor-
mation used in the analysis herein. We gratefully acknowledge shoreline
litter data supplied by the Great Canadian Shoreline Cleanup, a con-
servation partnership by Ocean Wise and WWF-Canada. We also thank
Dr. Brett Favaro at the Fisheries and Marine Institute of Memorial Uni-
versity of Newfoundland for guidance on statistical analysis related to
this project. This research was conducted in the Civic Laboratory of
Environmental Action Research (CLEAR) at Memorial University of
Newfoundland and we acknowledge the generous feedback and support
from lab members during the span of this project. In particular, we thank
Kaitlyn Hawkins and Pamela Murphy for providing administrative
support, with regard to funding for this project. We would also like to
thank Dr. Kate Parizeau and Dr. Josh Lepawsky for reviewing Lucas
Harriss MSc thesis, which this article is based on. We also thank Neil
Nunn, Kelsey Singbeil, Owen Harris for providing care and support
throughout the research process.
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L. Harris et al.
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