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The accumulation of mismanaged plastic waste (MPW) in the environment is a global growing concern. Knowing with precision where litter is generated is important to target priority areas for the implementation of mitigation policies. In this study, using country-level data on waste management combined with high-resolution distributions and long-term projections of population and the gross domestic product (GDP), we present projections of global MPW generation at ~1 km resolution from now to 2060. We estimated between 60 and 99 million metric tonnes (Mt) of MPW were produced globally in 2015. In a business-as-usual scenario, this figure could triple to 155–265 Mt y−1 by 2060. The future MPW load will continue to be disproportionately high in African and Asian continents even in the future years. However, we show that this growth in plastic waste can be reduced if developing economies significantly invest in waste management infrastructures as their GDP grows in the future and if efforts are made internationally to reduce the fraction of plastic in municipal solid waste. Using our projections, we also demonstrate that the majority of MPW (91%) are transported via watersheds larger than 100 km2 suggesting that rivers are major pathways for plastic litter to the ocean.
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ARTICLE
Future scenarios of global plastic waste generation
and disposal
Laurent Lebreton1,2 & Anthony Andrady3
ABSTRACT The accumulation of mismanaged plastic waste (MPW) in the environment is
a global growing concern. Knowing with precision where litter is generated is important to
target priority areas for the implementation of mitigation policies. In this study, using country-
level data on waste management combined with high-resolution distributions and long-term
projections of population and the gross domestic product (GDP), we present projections of
global MPW generation at ~1 km resolution from now to 2060. We estimated between 60
and 99 million metric tonnes (Mt) of MPW were produced globally in 2015. In a business-as-
usual scenario, this gure could triple to 155265 Mt y1by 2060. The future MPW load will
continue to be disproportionately high in African and Asian continents even in the future
years. However, we show that this growth in plastic waste can be reduced if developing
economies signicantly invest in waste management infrastructures as their GDP grows in
the future and if efforts are made internationally to reduce the fraction of plastic in municipal
solid waste. Using our projections, we also demonstrate that the majority of MPW (91%) are
transported via watersheds larger than 100 km2suggesting that rivers are major pathways for
plastic litter to the ocean.
https://doi.org/10.1057/s41599-018-0212-7 OPEN
1The Ocean Cleanup Foundation, Rotterdam, The Netherlands. 2The Modelling House, Raglan, New Zealand. 3North Carolina State University, Raleigh, NC,
USA. Correspondence and requests for materials should be addressed to L.L. (email: laurent.lebreton@theoceancleanup.com)
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Introduction
Commercial production of plastics that started around
1950s has enjoyed exceptional growth, to reach the pre-
sent global annual production of 330 million metric
tonnes (Mt) for 2016 (Plastics Europe, 2017). Including the resin
used in spinning textile bres (Lenzing Group, 2016), this gure
is closer to 393 Mt, a value that interestingly matches the global
human biomass. At the present rate of growth, plastics produc-
tion is estimated to double within the next 20 years. This
impressive success of plastics is unparalleled by any competing
materials used in packaging or construction, the two major
applications areas of plastics. Plastics production is energy
intensive with resins having an embodied energy of 62108 MJ kg1
(inclusive of feedstock energy) much higher than for paper, wood,
glass or metals (except for Aluminium) (Hammond and Jones,
2008). About 4% of fossil-fuel extracted annually is presently used
as raw materials for plastics (British Plastics Federation, 2008)
and it is the natural gas liquid fraction or low-value gaseous
fraction from petroleum rening that is mostly used to make
plastics. The demand on fossil fuel, energy, as well as the asso-
ciated carbon emissions by the industry, will increase as the future
consumer demand for plastics increases. By year 2050 plastics
manufacturing and processing may account for as much as 20%
of petroleum consumed globally and 15% of the annual carbon
emissions budget (World Economic Forum, 2016). There is
considerable interest in switching to biomass feedstock to make
bioplastics that include the most-used synthetic plastic,
polyethylene.
It is the considerable societal benets of plastics (Andrady and
Neal, 2009) that account for its popularity as a material. Plastics
represent a low-cost, easily formable, high-modulus, hydro-
phobic, bio-inert material that nds use in a bewildering range of
consumer products. It is often the preferred, and with some
products an indispensable, choice in consumer packaging that
accounts for 42% of the global annual resin production (Geyer
et al., 2017). Transparent packaging lms that are both strong and
impermeable to gases and moisture, facilitate conformal packa-
ging (vacuum packs) or controlled-environment packaging (as in
red-meat packs). The exceptional thermal insulation of expanded
polystyrene foam has ensured its lead in hot-food service appli-
cations (Andrady and Neal, 2009). The handful of resins that
dominate packaging and food service applications are also the
most frequently found in municipal solid waste as well as in
marine debris (Andrady, 2011): these are polyethylene, poly-
propylene, polyethylene terephthalate, and polystyrene. Gen-
erally, the lighter, more durable and less expensive plastics
(especially polyvinyl chloride) have replaced metal and even
wood in building applications that accounts for about 20% of
global production (Plastics Europe, 2017). The same is true for
comfort bres in fabric and in carpeting where plastic has
replaced natural bres such as wool, cotton or silk, for the most
part. Plastic products are indispensable in medical applications
that require sterility and microbial inertness.
Projected increase in future plastic use will result in a con-
comitant increase in post-consumer plastic waste. For instance,
by 2025 the global urban population is estimated to generate >
6 Mt of solid waste daily (Hoornweg et al. 2013). Even using the
present fraction of ~10% plastics in the solid waste stream, this
amounts to over 200 Mt of waste plastics: this was the entire
global plastic resin production in 2002 (Plastics Europe, 2014).
The discouragingly slow growth in recycling rates and the likely
increase in single-use products, both exacerbate this situation.
Packaging products are almost always discarded with their
functional characteristics virtually intact, permitting both facile
re-use and recycling, however only about 9.4% of plastics (E.P.A.,
2016) is presently recycled in the US mainly due to collection
costs, lack of requisite infrastructure and poor demand by pro-
cessors for recycled plastic granulate.
Adding to risks of local ooding by clogging drains and
degradation of air quality from open dumps, a serious concern is
where mismanaged waste located near inland waterways or in
coastal regions serves as an input of plastics into rivers and the
oceans. Microplastics or small fragments (<5 mm in size), mostly
derived by surface weathering degradation of plastic debris
(Andrady, 2017), now ubiquitous in soil (Rillig, 2012), rivers and
lakes (Lebreton et al., 2017) as well as in the oceans (Barnes et al.,
2009). Virgin pellets and some manufactured products such as
microbeads, also nd their way into oceans (Mason et al., 2016).
The smaller the dimension of the microplastic, the wider will be
the range of marine organisms that are able to ingest or otherwise
interact with them. Microplastics absorb and concentrate
hydrophobic pollutants present in sea water at very low con-
centrations and these can be bioavailable to the ingesting species.
Over 660 species (Secretariat of the Convention on Biological
Diversity, 2012), ranging from seabirds, sh, bivalves to the
zooplanktons at the bottom of the marine food chain, are known
to be affected by plastic debris (Ivar do Sul and Costa, 2014; Van
Cauwenberghe and Janssen, 2014) and there is credible evidence
of the bioavailability of pollutants concentrated in the plastic
(Heskett et al., 2012; Chen et al., 2017) to the ingesting organisms.
Potential trophic transfer of the plastics and pollutants along the
food chain (Au et al., 2017) and their potential tainting of human
seafood (Santillo et al., 2017) are particularly serious concerns.
However, broad estimates of global plastic production or MPW
generation are inadequate to assess the regional impacts of plastic
waste on the ecosystem that would dictate the need for mitiga-
tion. Future increase in population density and therefore regional
or even country-level plastic waste generation are spatially het-
erogeneous. For instance, while the global population is predicted
to increase to over 9.5 billion in 2025 over 97% of this growth will
be in Asia and Africa (United Nations, 2015). Coastal commu-
nities in those regions will place a disproportionate plastic waste
burden on the environment and especially the ocean. Under-
standing this spatial variation in plastics inux into the ocean
requires the development of a high-resolution map of global
plastic usage that would indicate geographic bias in future plastic
waste trends.
Generally, plastics in the global ecosystem is distributed
between three fractions: plastics in use, post-consumer managed
plastic waste, and a mismanaged plastic waste (MPW) fraction,
the last of which includes urban litter (Geyer et al., 2017).
Packaging-related plastics have a particularly short in-use phase
and therefore dominate municipal plastic waste and subsequently
the mismanaged waste as well. In addition to urban litter, mis-
managed waste also includes inadequately contained waste such
as open dumps and are therefore transportable via runoff and
wind. Some mismanaged waste may be collected by street swee-
pers and concerned citizen groups and be re-introduced in one of
the two rst categories. Managed waste is accounted for and is
typically disposed of by incineration or landlling. Both per
capita use of plastics and the population density at a given
location determines the local plastic demand by consumers,
reected in the in-use fraction. The former generally scales with
the local gross domestic product (GDP) (Hoornweg et al., 2013)
with the more afuent countries using as much as over 100 kg/pp/
year (Waste Atlas, 2016). But in populous countries such as India
or China a relatively low per capita use of plastics coupled with a
high population density can still yield large tonnage of plastic
waste. A recent study (Jambeck et al., 2015) based on a World
Bank dataset (Hoornweg and Bhada-Tata, 2012) on country-
specic waste generation and management concluded that the
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fraction of this waste that is reaching the oceans represented 4.8
to 12.7 Mt of plastics in 2010 from populations living within
50 km from the coastline.
The present effort is aimed at examining the possibility of
improving the granularity of country-level plastic waste generation
data, using reasonable assumptions based on population density
and afuence. A comprehensive dataset of municipality-level
waste generation data for different countries is not presently
available. While we appreciate the limitations of evolving country-
level waste generation data into higher-resolution maps of ner
granularity, we believe the exercise is important as it yields likely
global hot spotsfor plastic waste at present and progressed into
the near future. An important trend is the increased migration
into urban areas that in general would tend to exacerbate the
developing hot-spots. We therefore employ high-resolution (30 ×
30 arc seconds) population density and GDP distributions to
model waste data at cells in a ner geographic grid. The use of
these two indicators allows to represent plastic waste generation
near large urban areas but also to possibly predict the likely
accumulation near major transportation axes such as roads and
railways which may not be represented by municipality-level data.
Methods
Consumer demand for plastics and per capita GDP relation.
We use data distributed by Waste Atlas (2016) for per capita
municipal solid waste generation (expressed in kg y1), mis-
managed fraction (expressed in %) and plastic fraction in
municipal solid waste (expressed in %). Hitherto, models of
plastic waste generation had relied on the data set compiled by
the World Bank (Hoornweg and Bhada-Tata, 2012). In this effort
we use an alternative source, the Waste Atlas, a resource compiled
using country-level data submitted by individual experts from
each country. More recent and detailed data are available from
this source compared to the World Bank dataset. A comparison
between the two datasets is provided in Supplementary Figure 1.
Unfortunately, this collection is non-exhaustive, and information
is provided for some countries only. Data is available for n=160
countries for per capita municipal solid waste generation, n=95
countries for unsound disposal fraction and n=105 countries for
plastic proportion in municipal solid waste.
When data was available, we compared these variables with per
capita GDP provided by the International Monetary Fund (IMF,
2016). We performed a Pearson product-moment correlation test
for the three waste variables against per capita GDP (Table 1).
Both per capita municipal solid waste generation and mismanaged
fraction showed a statistically signicant correlation with per
capita GDP. The correlation was expectedly positive for waste
generation reecting the higher consumption levels in rich
country and negative for the mismanaged fraction showing a
greater amount of waste unsoundly disposed for developing
countries (Fig. 1). However, per capita GDP and the proportion of
plastic in municipal solid waste did not demonstrate a statistically
signicant relationship. This suggests that the proportion of plastic
in solid waste is generally independent of the per capita GDP of a
country. The proportion of plastic in municipal solid waste for
countries provided by Waste-Atlas was homogeneously distrib-
uted when compared against per capita GDP with an average
value of 10.9% and standard deviation of 4.5% (n=105).
The statistically signicant correlations between per capita
municipal solid waste generation, mismanaged fraction and per
capita GDP allow us to predict these quantities when they are not
reported by Waste Atlas (n=160 countries reported). When the
proportion of plastic in municipal solid waste is not reported we
used the global reported average.
Model formulation. Current per capita plastic waste generation
is calculated by multiplying per capita municipal solid waste
generation by the fraction of plastic found within waste. When
waste generation data for a country was missing, we derived per
capita municipal solid waste generation (Ms
c
) from per capita
Table 1 Pearson product-moment correlation test between
per capita GDP and reported waste management data per
country
Per capita GDP (2016 USD) rp n
Per capita municipal solid
waste generation (kg y1)
0.67 <0.005 160
Unsound disposal fraction
(%)
0.62 <0.005 95
Plastic fraction in municipal
solid waste (%)
0.14 0.17 105
Information on per capita municipal solid waste generation rate, unsound disposal fraction and
plastic fraction in municipal solid waste. Pair with pvalues below 0.005 (in bold) are statistically
signicant. Per capita GDP demonstrates a statistically signicantcorrelation with per capita solid
municipal waste generation and unsound disposal fraction, respectively positive and negative.
Fig. 1 Per capita municipal solid waste (MSW) generation aand mismanaged fraction bfor different per capita GDP categories of country. Red line shows
the median, the blue box extends from the 25th to 75th percentile, the black whisker extends from minimum and maximum non-outliers and red crosses
shows outliers
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GDP (X
c
, in 2016 USD) by introducing an empirical function:
Msc¼aX
b
cð1Þ
Where a=11.434 (lower: 11.218, upper: 11.953) and b=0.3433
(lower: 0.3565, upper: 0.3298), best t parameters for n=160
countries (Supplementary Figure 2a). The production rates
reported by Waste Atlas completed with estimations from this
relation allowed us to draw an exhaustive list of per capita
municipal solid waste generation at national level, worldwide. For
any country, an estimate of total plastic waste produced (A) could
then be formulated:
A¼pMscYð2Þ
Where pis the average proportion of plastic in solid waste and Y
is the countrys total population. When pwas not reported by
Waste Atlas, we used the global average of 10.9 ± 0.85% (95% C.I.,
n=105). Value of pis assumed to be the same for all unmanaged
waste including litter. This method for estimating global plastic
waste generation has a drawback, however, in that it does not
account for spatial heterogeneity within a country. Furthermore,
when integrating national plastic waste generation at global scale,
the total was estimated to range between 212 and 229 Mt y1for
2015. A gure higher than the predicted amount derived from
production data, assuming conservatively that 50% of plastic
production goes to packaging and household items (i.e., ~50% of
380 Mt in 2015 is 190 Mt). A similar overestimation was found
using the World Bank dataset with which the same method
resulted in 212 Mt of global plastic waste for 2015. We explain
these results by a bias in estimating solid waste generation in rural
areas. Waste management data is mainly reported for urban
centres and we trust that levels of per capita use may not be
representative of rural areas.
An important model assumption here, is that per capita solid
waste generation scales with GDP inside a country. We used high
resolution (30 by 30 arc seconds, ~1 km) gridded population
density data (Landscan, 2014) and sub-national GDP data
(UNEP/DEWA/GRID-Geneva, 2012). The global GDP dataset
distinguishes between rural and urban population and allows the
mapping of per capita GDP at sub-national scale. We combined
these two datasets to estimate per capita solid waste generation
derived from computed per capita GDP. For a model grid cell i,
inside a given country, we computed the plastic waste generation:
Ai
ðÞ¼pMscþaxiðÞ
yiðÞ

b
Xb
c
! !
yi
ðÞ ð3Þ
Where x(i) is GDP and y(i) is population in cell i. The additional
correction term allows to scale generation between urban and
rural zones. If per capita GDP in cell iis lower than the national
average X
c
, the per capita waste generation, term is reduced from
the national value. We computed national and global generation
by integrating over land cells. Our global estimated municipal
plastic waste generation of 181 Mt for 2015 is closer to predictions
from production data and conrms our assumptions.
For future projections we used country-scale population
projections distributed by the United Nations (2015) from 2015
to 2060. We sourced national GDP projections for 2015 to 2021
from the IMF (2016) and long-term growth rate projections for
2022 to 2060 from the Organisation for Economic Cooperation
and Development (OECD, 2014). Since the gridded distribution
of population and GDP used in this study are representative of
the current situation, we scaled the distribution in time using the
country scale projections. As such we neglected the possible
migration of population or variations in domestic product inside
countries for our future projections as we considered the relative
distribution to remain the same over time. For time tand in
model cell iof a given country, we computed the plastic waste
generation A(i,t) following:
Ai;tðÞ¼
pMsct0
ðÞþaxiðÞPc
iyiðÞ
yiðÞPc
ixiðÞXctðÞ

b
aXct0
ðÞ
b
!
yiðÞ
PiyiðÞYtðÞ
ð4Þ
Where Ms
c
(t
0
)and X
c
(t
0
)are the current national value for
respectively per capita solid waste generation and per capita GDP.
x(i) and y(i) are the current GDP and population in cell i.Xc(t)
and Y(t) are the respective national projections of per capita GDP
and population at time t. This model allowed us to produce high
resolution maps of future plastic waste generation from 2015 to
2060, every ve years. An additional map was created for 2010 to
compare results with previous estimates (Supplementary Figure 3).
Municipal waste management scenarios. We derived misman-
aged plastic waste generation from fraction of unsound disposal
per country reported by Hoornweg and Bhada-Tata (2012)as
proposed in a previous assessment (Jambeck et al. 2015). When
data was not reported (50% of countries) we proposed an
empirical formulation to estimate the fraction of mismanaged
waste Kfrom per capita GDP (X
c
in 2016 USD) based on the
correlation introduced earlier:
K¼eX
cþfð5Þ
With e=3.13 103and f=104 (lower: 70, upper: 138), best
t parameters for n=95 countries (Supplementary Figure 2b).
Many countries with a high GDP per capita had a reported
mismanaged fraction of 0%. To account for accidental and
deliberate littering in these countries, Jambeck et al. (2015) used a
minimum threshold of 2% of mismanaged waste. In this study,
we conservatively considered a minimum of 1% for our mid-
point estimates. However, as a signicant degree of uncertainty is
associated with this value, we considered using an upper and
lower threshold by varying orders of magnitude from 0.1 to 10%
minimum mismanaged waste. As some countries reported 100%
of mismanaged waste, we also applied a threshold on the upper
limit to account for opportunistic recycling and waste picking.
The upper threshold for mismanaged waste was respectively set to
90, 99 and 99.9% for lower, mid and upper estimates.
Reported and estimated mismanaged fractions Kwere
associated to plastic waste generation per country Ato produce
quantities of MPW B:
B¼KAð6Þ
For future projections, we assumed three scenarios. For
scenario A, we considered the case of business-as-usual where
the mismanaged fraction reported by Waste Atlas (2016) were
maintained into the future years (estimated from equation (5)at
2015 levels when not reported). In scenario B, we varied the
mismanaged fraction K(t) for each country over time from an
initial value K(t
0
)using the following:
KtðÞ¼Kt
o
ðÞþeXctðÞXct0
ðÞðÞð7Þ
Since eis negative, a growth in per capita GDP (Xc) from t
0
to t
results in the decrease of mismanaged fraction (i.e., as the
economy grows, waste management infrastructure improves). A
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minimum threshold ranging from 0.1 to 10% and maximum from
90 to 99.9% were also applied to this relation. Finally, in scenario
C, we used the same assumption as in scenario B and further
reduced the input of individual countries by capping the fraction
pof plastic in solid waste to 10% by 2020 and to 5% by 2040.
Plastic waste and watersheds. We introduced a spatial indicator
representing the size of the watershed of which a model cell
belongs to. Starting from the HydroSHEDS database (Lehner
et al., 2008) regrouping global watershed boundaries as polygonal
features, we computed the area covered by individual watersheds
and created a 30 arc second resolution global grid to match with
our plastic waste generation maps. Individual grid cells were
associated to the watershed they belonged to and assigned the
watersheds surface area as value. If a grid cell was not located in
any watershed, it was considered as oceanic cell. We categorised
grid cells from small watersheds (<10 km2) to continental rivers
(>1,000,000 km2) by orders of magnitude of surface area (7
categories in total). The main motivation behind this analysis was
to identify the fraction of global plastic waste generated in coastal
areas against the fraction generated inland that may reach the
marine environment via rivers. The surface area of watersheds
naturally decreases while approaching the coast from land. Some
smaller watershed may be found in land generally in arid regions
such as desert or mountains. As the waste generation in these
areas is very small since they are generally unpopulated, we
neglected the contribution of these regions from inland inputs.
Results
Global plastic waste generation. Combining country-level data
on self-reported per capita municipal solid waste (Waste Atlas,
2016) generation with high resolution (30 arc seconds) distribu-
tions of global population (Landscan, 2014) and GDP (UNEP/
DEWA/GRID-Geneva, 2012), we estimated plastic waste gen-
eration at local level. The estimate reects differences in con-
sumption within a country, differentiating between consumption
rates in urban and rural areas. Based on self-reported levels of
inadequate disposal, we estimated between 60 and 99 (mid-point:
80) million metric tonnes (Mt) of municipal plastic waste were
inadequately disposed globally into the environment during 2015
(Fig. 2). The quantity represents about 47% of the global annual
municipal plastic waste generation (mid-point estimate of 181 Mt
for n=188 countries).
Hosting 60% of the global population (United Nations, 2015),
the Asian continent was in 2015 the leading generating region of
plastic waste with 82 Mt, followed by Europe (31 Mt) and
Northern America (29 Mt). Latin America (including the
Caribbean) and Africa each produced 19 Mt of plastic waste
while Oceania generated about 0.9 Mt. However, the proportion
of produced waste that was inadequately disposed varied across
regions (Waste Atlas, 2016). We derived quantities of MPW from
reported waste management data per countries and per capita
GDP. As a signicant level of uncertainty is associated with the
data on municipal waste management, we introduced lower and
upper ranges along with our midpoint estimate and thereafter
reported the ranges in brackets. With an average of 63% of
inadequately disposed waste for 2015, Asia released 52 (4258)
Mt of plastic waste into the environment, representing 65% of the
global MPW generation. Africa, however, had the highest rate of
unsound waste disposal with an average of 88.5% resulting in a
total of 17 (1020) Mt of unsoundly disposed plastic waste despite
the low levels of resin production. Latin America and the
Caribbean were the third generating region with 7.9 (6.78.3) Mt,
followed by Europe with 3.3 (1.39.1) Mt, Northern America with
0.3 (0.033.0) Mt, and Oceania with 0.14 (0.050.32) Mt.
Interestingly, except with Asia, the regions with unsoundly
disposed plastics, do not correspond to production volumes of
plastics in these regions; the Middle East and Africa account for
only 7% of resin production while US and Europe each account
for ~20% of global plastics production. The unfair practice of
importing waste, especially e-waste, from developed nations, is to
a large part responsible for this problem in Africa for example
(Schmidt, 2006).
We ranked contributors by mid-point estimate of MPW
generation at regional and national scale (Table 2). The
generation of waste from the Asian continent was distributed
between Southern, Eastern and South-East Asia with respectively
18.4, 17.4 and 11.7 Mt y1of annual MPW generation. Inputs
from China with 17.2 Mt y1, and India with 14.4 Mt y1
dominated the waste generation gures for Asia. These two
countries provide over a third of the global MPW generation. The
Philippines were the third generating country with 4.52 Mt y1
but Manila, its capital, was predicted as the largest urban centre
for the generation of MPW with at least 0.81 Mt y1for the
Fig. 2 Global mismanaged plastic waste (MPW) generation in 2015. Plastic waste generation is computed globally on a 30 by 30 arc seconds resolution
reecting geographical heterogeneity based on population and GDP distributions. National data on waste management reported per countries (Waste
Atlas, 2016) is derived to estimate the mismanaged fraction at local scale. The 10 largest producing urban centres are labelled on the map with Manila,
Cairo and Kolkata as the leading agglomerations
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agglomeration, the same annual amount as produced by countries
like Sudan or Algeria. Five of the top 10 countries for
mismanaged plastic waste identied by this model include only
5 of the top ten countries identied by Jambeck et al. (2015) for
2010. Except for Tanzania, all ten were identied in the top 20
countries in this previous work. However, at least a part of this
discrepancy might be due to differences in methodology adopted
in previous and present studies. The ner granularity of the
present model allows us to predict local scale accumulation. Solid
waste is mostly an urban phenomenon (Hoornweg et al., 2013)
and our model conrms this by predicting substantial accumula-
tion around cities and near axes of transportation with 89% of
global MPW produced over 10% of the total modelled landmass,
and 64% over less than 1.5% (Fig. 3). High resolution renderings
of MPW generation on land are presented in Supplementary
Figure 4. We ranked global urban centres by MPW generation.
Manila, the largest contributor predicted by our model, was
followed by Cairo and Kolkata with 0.53 and 0.48 Mt y1. Sao
Paolo in Brazil was ranked fourth with 0.47 Mt y1. The rest of
the top 10 cities were in Asia with Bangkok (0.45 Mt y1),
New Delhi (0.41 Mt y1), Shanghai (0.5 Mt y1), Kuala
Lumpur (0.29 Mt y1), Beijing (0.28 Mt y1) and Guangzhou
(0.27 Mt y1).
Future scenarios. We predicted future plastic waste generation by
considering population (United Nations, 2015) and GDP (IMF,
2016; OECD, 2014) growth rates per country. By 2020, under a
business-as usual-scenario for plastic consumption, our predictive
model suggested the world will produce above 200 Mt of muni-
cipal plastic waste annually and around 230 Mt by 2025. This is in
good agreement with previous projections of solid waste gen-
eration (Hoornweg et al., 2013) with daily estimates above 6 Mt in
2025. Considering global average proportion of plastic in muni-
cipal solid waste (10.9%, lower: 8.3%, upper: 13.2%), a daily input
of 6 Mt of solid waste may represent around 239 (182290) Mt of
annual generation of municipal plastic waste. Based on long term
projections of population and GDP per countries, we estimated
the global municipal plastic waste generation could reach 300 Mt
annually by 2040 and 380 Mt by 2060. This increase is also in
good agreement with municipal solid waste generation projec-
tions which are not expected to peak within this century
(Hoornweg et al., 2013). To predict the total mismanaged fraction
(MPW) of future plastic production, we initially considered two
scenarios. In scenario A, we assumed the case of business-as-usual
where the level of waste management remains at the current
status in different countries worldwide. In scenario B, we assumed
waste management efforts will improve with increased invest-
ment in infrastructure as the economies in individual countries
grow in the future.
Following a growing demand of plastic by end-users, the MPW
generation in scenario A would nearly triple from the present
value of 80 (6099) Mt y1to 213 (155265) Mt y1by 2060.
Midpoint plastic demand by end-users in Asia was projected to
steadily increase from 99 Mt y1in 2020 to 151 Mt y1in 2040
and 193 Mt in 2060. If no efforts are made in waste management,
generation of MPW in Asia could double from 52 (4258) Mt y1
in 2020 to 129 (104150) Mt y1in 2060. India would become
the largest MPW generating country by 2035 and would reach
46.3 (38.652.0) Mt y1by 2060, followed by China with 33.3
(28.136.8) Mt y1and the Philippines with 11.6 (10.112.4) Mt
y1. According to scenario A, cities like Manila, Cairo, Kolkata or
New Delhi would reach the 1 Mt y1mark for annual MPW
generation before 2060. However, if we assume a gradual
improvement of waste management infrastructures with scenario
B, we estimated that global MPW generation could peak before
2020 and decrease to 50 (2294) Mt y1by 2060. In this case, the
decrease of global MPW generation would mainly be driven by
the rapid economic development in Asia. We found that the
continent could reduce MPW generation below 30 Mt y1by
2040 and below 10 Mt y1by 2060. This scenario would mean
reducing mismanaged waste below 10% of total generated waste
by around 2030 for current major contributors such as China,
Thailand, Indonesia or Turkey, and around 2060 for countries
like India, the Philippines, or Vietnam. Our model shows how the
main contribution to global MPW production would then shift
from Asia to Africa. Our projections suggested that African
demand by consumers for plastic will increase exponentially in
the future decades from 23 Mt y1of municipal plastic waste in
Table 2 Top regional and national generators of MPW in Mt y1for 2015
Top 10 regions Mt y1Top 10 countries Mt y1
Southern Asia 18.4 (15.120.2) China 17.2 (14.718.2)
Eastern Asia 17.4 (14.718.9) India 14.4 (12.415.1)
South-East Asia 11.7 (9.4812.8) Philippines 4.52 (3.914.72)
Eastern Africa 5.89 (2.937.10) Brazil 3.68 (3.193.84)
South America 5.81 (5.056.05) Turkey 2.15 (1.862.24)
Western Africa 4.47 (2.695.22) Nigeria 1.90 (1.432.05)
Western Asia 3.31 (2.484.45) Tanzania 1.77 (0.802.24)
Northern Africa 3.40 (2.673.83) Thailand 1.77 (1.521.86)
Eastern Europe 2.66 (0.955.34) Indonesia 1.63 (1.371.73)
Middle Africa 2.39 (1.342.86) Egypt 1.60 (1.281.76)
The regions and countries are ranked by midpoint values, the condence interval is provided inside the parenthesis. See Supplementary Table 1 for a denition of regions per countries.
Fig. 3 Distribution of annual MPW generation over total land surface area.
Modelled cells were classied by rate of MPW generation (01, 110, ,
>10k kg y1). Total land surface area (blue, expressed in % of total
modelled surface) and total MPW generation (grey, expressed in % of total
generation) are represented for each cell category
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Fig. 4 Future projections of global mismanaged plastic waste (MPW) generation and distribution per continent under three scenarios. Scenario A
corresponds to a business-as-usual case where the level of waste management corresponds to data for 2015 and consumer demand for plastic increases
with economy. Scenario B considers that waste management infrastructures improve as per capita GDP grows. Scenario C reects a reduction in plastic
demand per capita with fraction of plastic in municipal solid waste capped at 10% by 2020 and 5% by 2040, waste management gradually improves as in
scenario B. (Top, graph) The global midpoint estimates for MPW generation are represented with thick lines while the shaded areas represent our
condence interval. (Bottom, maps) Continental distribution of MPW generation in 2020, 2040 and 2060 under the three investigated scenarios
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2010 to 72 Mt y1in 2060. The demand for plastic by end-users
in Africa was projected higher than in Northern America or
Europe by 2035. Under this scenario, by 2060, 8 out of the 10
generating country will be an African country with Nigeria
(3.086.46 Mt y1), Congo (2.226.33 Mt y1), Tanzania
(1.166.42 Mt y1), Ethiopia (1.014.10 Mt y1), Niger
(0.882.69 Mt y1), Sudan (0.382.00 Mt y1), Mozambique
(0.821.64 Mt y1) and Mali (0.471.78 Mt y1).
Finally, we investigated an alternative option where demand of
plastic per capita would substantially decrease in the future. We
introduced a third scenario C, where waste management efforts
improve as the economy of a country grows like in scenario B, but
also where plastic use by households is reduced to 10% of
municipal solid waste by 2020 and to 5% by 2040, reecting
willingness from country to curb waste generation and ban
single-use plastics. This objective is reachable as some developed
economies already demonstrates such low rate. Notably Den-
mark, rst country to introduce a tax on plastic bags in 1993 for
example, now reporting 1% of municipal waste composed of
plastic (Waste Atlas, 2016). We found no signicant relation
between fraction of plastic in solid waste and per capita GDP.
Some strong economies for example reported large fractions such
as The Netherlands, largest reported gure for a developed
economy with 19% of plastic in municipal solid waste. Under a
scenario where countries would align to reduce fraction of plastic
in municipal waste to below 5%, the global plastic waste
generation would drop to around 140 Mt per year by 2040 but
increase again to 2015 levels with nearly 180 Mt per year in 2060
as global population grows. Combined with a gradual investment
in waste management infrastructure however, the global MPW
generation could be reduced to a third of current value with
annual average generation below 25 Mt y1before 2060 including
74% generated in Africa and 21% in Asia. Long term projections
for global MPW generation for scenario A, B and C are presented
in Fig. 4as well as distributions per continent. A full breakdown
of results per sub-region with condence interval for 2020, 2040
and 2060 is given for scenario A, B and C in Supplementary
Tables 2, 3 and 4.
Sources to ocean. The rapid accumulation of MPW on land can
result in the contamination of waterways and eventually the
marine environment. Through runoff, winds, and gravity, plastic
debris slowly makes its way downhill and enters the sea from
coastal environments (Jambeck et al., 2015) and through rivers
(Lebreton et al., 2017). A rst global estimate of plastic inputs
from land to the sea for 2010 (Jambeck et al., 2015) proposed to
consider municipal plastic waste generation for population living
within 50 km from the coastline and assumed 25% (1540%) of
plastic waste from this population enters the ocean. Under this
condition, we predicted a total of 20.5 Mt of MPW for coastal
population in 2010. Following the study, this quantity may be
converted to an annual global input into sea of 5.1 (3.18.2) Mt.
The fraction of plastic waste entering the ocean may vary between
locations, however. The magnitude and timing of plastic waste
displacement on land is poorly known (Horton et al., 2017) and
may be a function of topography, land use, climate, vegetation
Fig. 5 Distribution of global mismanaged plastic waste (MPW) generation in 2015 by watershed size categories. The surface area of a watershed is a good
indicator to differentiate coastal watersheds from large rivers. An example (top) is shown for the Mississippi river mouth with watersheds of different size
categories. The global generation of mismanaged waste was binned into surface area categories (bottom) from coastal watersheds (<10 km2)to
continental rivers (>1,000,000 km2)
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and, particle shape and size (e.g., microplastics may be more
easily transported than larger, more complexly shaped debris).
Considering populations living within a xed distance from the
coastline may not always be representative of land-based sources
to the ocean as plastic waste generated inland can be transported
by rivers. Predicted accumulation of plastic waste on land can be
distributed into watersheds (Lebreton et al., 2017). Here, we
categorised watersheds by surface area considering that small
watersheds are located directly at the coastline while larger
watersheds may expand in land and form streams and rivers (Fig.
5). We grouped watersheds by orders of magnitude in surface
area from coastal watersheds (<10 km2) to continental rivers
(>1,000,000 km2). From our global distribution of mismanaged
plastic waste for 2015, we estimated that 5% of waste was
discarded directly near the coastline (watershed surface area <
10 km2) and 4% at proximity to the coastline (10 to 100 km2).
This result shows that the majority (91%) of MPW was generated
inside larger watersheds (>100 km2) and suggests that rivers may
be a major vector of transport of plastic waste from land into the
ocean. Over a quarter of the global waste was discarded into the
watersheds of only 14 continental rivers (>1,000,000 km2, namely
the Mississippi, the Nelson, the St Lawrence, the Amazon, the
Paraná, the Congo, the Niger, the Nile, the Zambezi, the Volga,
the Lena, the Amur, the Yangtze and the Ganges rivers).
Discussion
Here we present for the rst time, spatial distributions of mis-
managed municipal plastic waste generation at an order of 1 km
resolution, worldwide from now to 2060. As synthetized in this
study, these results can be interpreted at global, regional, national
and local scale. This information at this level of resolution for
GDP and population density, even with the limitation of low-
resolution waste generation data, is important as it can help
policy makers target priority areas for mitigation and develop-
ment of waste management infrastructures. Furthermore, pro-
jected into the future, this dataset can assist countries, regions and
municipalities in xing objectives to reduce generation of plastic
waste and limit releases into the environment. China and India
currently contribute to above a third of the global MPW gen-
eration, with GDP growth rate projected above 4% until mid-
2020s and early 2040 s respectively (OECD, 2014). Under a
business-as-usual scenario, it is fair to expect that consumer
demand for plastic will dramatically increase in these countries.
According to the World Bank (Hoornweg and Bhada-Tata, 2012),
70 and 85% of municipal waste is currently mismanaged in China
and India, respectively. It is crucial for cities and municipalities in
these countries to invest in waste management and prepare for
the surge in consumer plastic demand predicted for the future
decades and the associated waste management needs. A scenario
where China and India reduce the fraction of mismanaged waste
to 50% by respectively 2020 and 2030, and to the current western
standard by 2035 and 2050, along with other countries in similar
economic transition, shows that we could reach peak generation
of MPW at global scale in the next decade. Our results highlight
the urgency of the situation and the necessity of an international
law-abiding agreement between countries on the management of
plastic waste as we could be generating twice the current amount
of MPW per year, globally, by mid-century if the situation
remains unchanged. These should focus on anti-dumping of
waste in developing countries, developing better recycling archi-
tectures and standardising technologies to prevent escape of
microplastics from land-based sources into the ocean.
However, our results also suggest that gradual increase in waste
management infrastructure may not be enough for some parts of
the world. Our projections show some countries, particularly in
Africa, that may not reach this transition before 2060. The con-
sumer demand for plastic in Africa was projected to grow by 375%
from the current demand by 2060, the highest growth for a
continent, the global average being 210%. This exponential
increase in demand for plastics by the end-user is fuelled by GDP
growth but also by a substantial increase of population. Africas
demographic is expected to reach 2.9 billion by 2060 (United
Nations, 2015) representing a population growth of 245% from
2015, much higher than any other continents (global average:
138%). As a result, despite GDP growth, the per capita GDP
remains sufciently low for our model to consider a large portion
of produced waste to be likely mismanaged. These assumptions
may have to be further questioned however it is an indication that
resolving the global issue of plastic contamination in the envir-
onment may require cooperation between cities and countries as
well as the promotion of further international aid for waste
management in the future. But more importantly, it shows that
GDP-fuelled demand for plastics by end-users is not sustainable at
global scale and support the necessity to introduce quotas in
plastic use. In a scenario where the plastic fraction in municipal
solid waste is capped to 10% by 2020 and to 5% by 2040, the
release of plastic into the environment could be efciently reduced
to a third of the current level, assuming signicant improvement
are also made in terms of waste management to compensate for
population growth. Prohibition of single-use plastics is currently
being proposed in policies of cities, countries and regions around
the world. To efciently mitigate the release of plastic waste into
the environment in the future, our results emphasise two aspects
with (1) the improvement of waste management infrastructure as
well as our capacity of recycling waste and (2) the introduction of
a limit on fraction of plastic in solid waste per capita reecting
reduction in demand for single-use plastics.
The analysis of our results may not only be constrained to
political boundaries as in this study, we show how considering
physical boundaries may help in understanding the generation of
plastic waste in watersheds and indirectly sources of plastic to the
ocean (Lebreton et al., 2017; Schmidt et al., 2017). Our data is
made available to assist hydrologist and geologist in quantifying
sources of plastic litter in rivers and soils (Gonzalez et al., 2016;
van Emmerik et al., 2018). This is an important aspect as we also
show that most of the produced MPW each year is located inside
larger watersheds characterised by a river and several conuents.
Understanding the geography of waste generation can help tar-
geting rivers for mitigation and eventually reduce the source of
plastic waste to the ocean. The transport of plastic litter on land is
poorly understood and additional site-specic work may be
required to quantify the portion of litter that is stored in soils and
the portion that enters waterways, and subsequently the ocean.
Other physical boundaries of interest are the average ultraviolet
radiation levels received by unit of surface area or the average
ground air temperature as these environmental parameters may
dictate degradation processes for plastic waste on land and
therefore microplastics generation rate. Well outside the scope of
this study, we noticed however that the current plastic demand by
end-users is dominantly distributed in regions with a temperate
climate, but our projections shows that this demand will shift
towards lower latitude, in much warmer climate, within the course
of this century with predicted major consumers in Southern Asia,
South-Eastern Asia or in Africa. The foregoing discussion was
based on MPW in general. However, recent research specically
points to ecological impacts of meso-plastics, micro-plastics and
nano-plastics in the ocean environment (Avio et al., 2017). These
smaller fragments are generated by weathering degradation of
plastic debris in the outdoor environment, especially beaches,
followed by mechanical action of wind and waves fragmenting the
degraded plastics (Andrady, 2017). A combination of solar UV
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radiation and high sample temperatures are well known to control
the rates of degradation and therefore the fragmentation of MPW
into micro-particles (Abdelhadia et al., 2015). Fragmentation of
common plastics into micro- and nano-sized particles has been
demonstrated in laboratory (Wohlleben and Neubauer, 2016;
Kalogerakis et al., 2017). It is difcult to accurately quantify the
rates of potential micro-plastics generation at a given location as
the fraction of MPW exposed to solar radiation cannot be easily
assessed. But those locations of high solar irradiance and high
ambient temperatures might be qualitatively expected to promote
faster weathering and fragmentation of exposed plastics waste.
Considering the already developed hotspots, those in African
continent will experience both high downward solar UV ux as
well as high ambient temperatures followed by those in India and
East Asia. The signicance of these hotspots is therefore magnied
because of the increased propensity to generate micro-plastics at a
faster rate. Accordingly, they demand more stringent efforts at
plastic waste management to avoid the load of micro- and nano-
plastics they may generate via weathering.
It is important to note that some uncertainties are associated to
our estimates. To reect these, we introduced lower and upper
ranges along our mid-point projections. The calculations of these
condence intervals include the uncertainties related to per capita
municipal solid waste generation, proportion of plastic in waste
and proportion of waste that is mismanaged. This data was
reported at national level. However, values may vary inside
countries. As local information is unavailable, we proposed a
model to consider sub-national variations of plastic consumption.
This approach naturally yields uncertainties which are also
included in the calculation of our condence interval. The con-
dence interval for our projections naturally increase as we
progress in the future. For example, our projections neglect sub-
national change of population distribution since population
density was assumed to grow at the same rate as the national
average. This assumption may introduce a bias in our projections.
Rural exodus may further reinforce urban inuences on waste
production in the future. Also, our projections do not consider
plastic waste exports or imports for reprocessing: in 2012, for
instance, 15 Mt was traded, mainly between Europe and China.
The import of waste for recycling has recently been shut down
from China however and many countries now face difculties to
handle the accumulated plastic from curb-side collection. Simi-
larly, our model cannot account for future exceptional economic,
societal or nancial events. Advances in technology, consumer
product design, and behaviour patterns may change the per capita
GDP-consumption relationship as well as conversion rates of
waste to MPW. Generally, we encourage the systematic reporting
of waste management data at sub-national scale by countries to
reduce some of the uncertainties introduced above. Better
reporting of waste management practices could lead to the
development of a national plastic waste emission, similarly to the
creation of carbon emission index by climate scientists (Le Quéré
et al., 2018). Such index would foster international cooperation
and assist countries and municipalities in setting objectives to
reduce plastic waste releases into natural environments.
Data availability
Gridded distributions of total and mismanaged municipal plastic
waste generation are available on Figshare in.tif format as well as
a country scale results summary spreadsheet (Lebreton and
Andrady, 2018).
Received: 5 July 2018 Accepted: 13 December 2018
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Acknowledgements
This study was made possible thanks to the open-access distribution of several global
dataset including the Waste Atlas, the LandScan 2014 High Resolution Global Population
Data, the GDP distribution by UNEP/DEWA/GRID-Geneva and the HydroSHEDS by
USGS/WWF. We would like to thank the donors of The Ocean Cleanup Foundation who
helped funding this study, as well as Dr Tim van Emmerik for reviewing this manuscript.
The authors would also like to thank the participants of GESAMP Working Group 40 on
Sources, Fate and Effects of Microplastics in the Marine Environment, for the valuable
exchanges in the early stage of this study.
Additional information
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