Global Plastics Outlook

Annex A. Modelling framework

This Annex presents the methodologies applied to provide the projections contained in this report and in the OECD Plastics Outlook Database. These projections include, for the 2019 -2060 period, plastics use, plastic waste generation, plastic waste management and related environmental impacts. The environmental impacts are described by theme: (i) leakage to the environment, detailing the macroplastics and microplastics fractions, (ii) leakage to aquatic environments, (iii) particulate matter emissions from tyre and brake abrasion, (v) greenhouse gas emissions from the plastics lifecycle, and (vi) lifecycle impacts related to the production and disposal of plastics.

The Annex contains the following sections:

• Overview of the ENV-Linkages modelling framework.

• Overview of the data sources used for the plastics module calibration.

• Modelling plastics use in ENV-Linkages.

• Modelling plastic waste and end-of-life fates in ENV-Linkages.

• Modelling plastic leakage to the environment (Technical University of Denmark, DTU).

• Modelling plastic leakage to terrestrial and aquatic environments (University of Leeds).

• Modelling plastic leakage to aquatic environments (Laurent Lebreton).

• Modelling particulate matter emissions to air from tyre and brake wear (Norwegian Institute for Air Research, NILU).

• Modelling greenhouse gas emissions from plastics in ENV-Linkages.

• Modelling the effects of higher penetration rates of biobased plastics (CGE Box model).

• Modelling other lifecycle health and environment impacts from plastics (Ghent University).

Overview of the modelling framework

This section explains in more detail the methodologies employed to prepare the database, which is also part of the OECD Plastics Outlook Database (OECD.stat, 2022[1]), and used for the projections. Estimates for the 2019 base year have been generated by building on output from the OECD computable general equilibrium (CGE) model (ENV-Linkages) (Chateau, Dellink and Lanzi, 2014[2]), by filling existing data gaps and by generating projections on the environmental impacts. The projections are also based on the ENV-Linkages model.

The modelling of economic flows, plastics use, plastic waste and environmental impacts involves different steps, as illustrated in Figure A A.1. Plastics use is linked to sectoral and regional economic projections, which therefore drive the evolution of plastics use over time. Volumes of plastics are then used to calculate generated waste, based on product lifespans of different applications. The waste generated is further broken down by waste treatment, i.e. recycled (collected for recycling), incinerated, landfilled, mismanaged and littered waste (see Chapter 2 for definitions), taking into account differences across regions. Finally, projections for a subset of environmental impacts are calculated: leakage of microplastics and macroplastics to the environment, leakage to aquatic environments, particulate matter linked to tyre and brake wear, greenhouse gas (GHG) emissions, the effects of higher penetration rates of biobased plastics and other lifecycle health and environment impacts.

The analysis relies on a suite of modelling tools. More specifically, projections of the economic flows, plastics, plastic waste, and greenhouse gas emissions (Steps 1-4) rely the OECD in-house modelling tools, while other environmental impacts rely on external models (Step 5). The methodology is not fully linear: some of the information provided by external models in Step 5 have been used to calibrate the ENV-Linkages models in Steps 1-4.

Figure A A.1. Methodological steps

Source: Authors’ own elaboration.

The OECD’s in-house dynamic computable general equilibrium (CGE) model ENV-Linkages is used as the basis to estimate the economic activities that drive plastics use in 2019. ENV-Linkages is a multi-sectoral, multi-regional model that links economic activities to energy and environmental issues. A more comprehensive model description is given in Chateau, Dellink and Lanzi (2014[2]). A description of the Baseline scenario construction procedure is given in Chateau, Rebolledo and Dellink (2011[3]), while recent baseline results are illustrated in OECD (2019[4]).

The model is based on the Social Accounting Matrices (SAM) contained within the GTAP 10 database (Aguiar et al., 2019[5]). This database describes bilateral trade patterns, production, consumption and intermediate use of commodities and services, including capital, labour and tax revenues and use. The base year of the SAM and of the model is 2014. Therefore, to obtain estimates for 2019, the ENV‑Linkages model was run to 2019 (Box A A.1 for an overview of the functioning of the model). The short-term changes to the economy from 2014 to 2019 reflect short-term economic changes from international databases: the OECD Economics Department (OECD, 2020[6]) and the International Monetary Fund (2020[7]).

Box A A.1. The ENV-Linkages model

Production in ENV Linkages is assumed to operate under cost minimisation with perfect markets and constant returns-to-scale technology. The production technology is specified as nested Constant Elasticity of Substitution (CES) production functions in a branching hierarchy. This structure is replicated for each output, while the parameterisation of the CES functions may differ across sectors. The model adopts a putty/semi-putty technology specification, where substitution possibilities among factors are assumed to be higher with new vintage capital than with old vintage capital. In the short run this ensures inertia in the economic system, with limited possibilities to substitute away from more expensive inputs, but in the longer run this implies a relatively smooth adjustment of quantities to price changes. Capital accumulation is modelled as in the traditional Solow/Swan neo classical growth model, where economic growth is assumed to stem from the combination of labour, capital accumulation and technological progress.

Household consumption demand is the result of static maximisation behaviour, which is formally implemented as an “Extended Linear Expenditure System”. A representative consumer in each region – who takes prices as given – optimally allocates disposal income among the full set of consumption commodities and savings. Saving is considered as a standard good in the utility function and does not rely on forward looking behaviour by the consumer. The government in each region collects various kinds of taxes in order to finance government expenditures. Assuming fixed public savings (or deficits), the government budget is balanced through the adjustment of the income tax on consumer income. In each period, investment net-of-economic depreciation is equal to the sum of government savings, consumer savings and net capital flows from abroad.

International trade is based on a set of regional bilateral flows. The model adopts the Armington specification, assuming that domestic and imported products are not perfectly substitutable. Moreover, total imports are also imperfectly substitutable between regions of origin. Allocation of trade between partners then responds to relative prices at the equilibrium.

Market goods equilibria imply that, on the one side, the total production of any goods or services is equal to the demand addressed to domestic producers plus exports; and, on the other side, the total demand is allocated between the demands (both final and intermediary) by domestic producers and the import demand.

ENV-Linkages is fully homogeneous in prices and only relative prices matter. All prices are expressed relative to the numéraire of the price system that is arbitrarily chosen as the index of OECD manufacturing exports prices. Each region runs a current account balance, which is fixed in terms of the numéraire.

As ENV-Linkages is recursive-dynamic and does not incorporate forward-looking behaviour, price-induced changes in innovation patterns are not represented in the model. The model does, however, entail technological progress through an annual adjustment of the various productivity parameters, including e.g. autonomous energy efficiency and labour productivity improvements. Furthermore, as production with new capital has a relatively large degree of flexibility in choice of inputs, existing technologies can diffuse to other firms. Thus, within the CGE framework, firms choose the least-cost combination of inputs, given the existing state of technology. The capital vintage structure also ensures that such flexibilities are larger in the long run than in the short run.

Source: (Chateau, Dellink and Lanzi, 2014[2]).

For the development of this Outlook, ENV-Linkages has been enhanced to include data on plastics use, waste and waste treatment. In ENV-Linkages, plastics projections follow economic projections, and, more precisely, the evolution of the production and consumption of goods in different sectors and regions.

The sectoral aggregation of the model adopted in this report is given in Table A A.1, while the regional aggregation is presented in Table A A.2.

Table A A.1. Sectoral aggregation of ENV-Linkages

Agriculture, fisheries and forestry	Manufacturing
Paddy rice	Food products
Wheat and meslin	Textiles
Other grains	Wood products
Vegetables and fruits	Chemicals
Oil seeds	Basic pharmaceuticals
Sugar cane and sugar beet	Primary rubber and plastic products
Fibres plant	Secondary plastic products
Other crops	Pulp, paper and publishing products
Cattle and raw milk	Non-metallic minerals
Other animal products	Fabricated metal products
Fisheries	Electronics
Forestry	Electrical equipment
	Motor vehicles
Non-manufacturing Industries	Other transport equipment
Coal extraction	Other machinery and equipment
Crude oil extraction	Other manufacturing including recycling
Natural gas extraction	Iron and steel
Other mining	Non-ferrous metals
Petroleum and coal products	Services
Gas distribution	Land transport
Water collection and distribution	Air transport
Construction	Water transport
Electricity transmission and distribution	Insurance
Electricity generation (8 technologies)	Trade services
Electricity generation: Nuclear electricity; Hydro (and Geothermal); Solar; Wind; Coal-powered electricity; Gas-powered electricity; Oil-powered electricity; Other (combustible renewable, waste, etc.).	Business services n.e.s.
	Real estate activities
	Accommodation and food service activities
	Public administration and defence
	Education
	Human health and social work

Source: Authors’ own elaboration.

Table A A.2. Regional aggregation of ENV-Linkages

Macro regions		ENV-Linkages countries and regions	Most important comprising countries and territories
OECD	OECD America	USA	United States of America
		Canada	Canada
		Other OECD America	Chile, Colombia, Costa Rica, Mexico
	OECD Europe	OECD EU countries	Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden
		OECD Non-EU countries	Iceland, Israel,1 Norway, Switzerland, Turkey, United Kingdom
	OECD Pacific	OECD Oceania	Australia, New Zealand
		OECD Asia	Japan, Korea
Non-OECD	Other America	Latin America	Non-OECD Latin American and Caribbean countries
	Eurasia	Other EU	Bulgaria, Croatia, Cyprus,2 Malta, Romania
		Other Eurasia	Non-OECD European and Caspian countries, including Russian Federation
	Middle East and Africa	Middle East & North Africa	Algeria, Bahrain, Egypt, Iraq, Islamic Rep. of Iran, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, Syrian Arab Rep., Western Sahara, Yemen
		Other Africa	Sub-Saharan Africa
	Other Asia	China	People’s Republic of China, Hong Kong (China)
		India	India
		Other non-OECD Asia	Other non-OECD Asian and Pacific countries

Source: Authors’ own elaboration.

Overview of the data sources used for the plastics module calibration

Table A A.3. Data sources and methodologies

Category	Variable	Source
Production	Primary and secondary economic split	OECD ENV-Linkages model, based on GTAP10 (Aguiar et al., 2019[5]) split using Exiobase for cost structure (Stadler et al., 2018[8]), Grand View Research (2020[9]) data for total shares (in tons).
	Plastic sectors	OECD ENV-Linkages model projections, resulting from mapping of sectoral/polymer flows to economic baseline. Secondary plastics incorporates recycling loss rates from the literature (Cottom et al., 2022[10]; Chruszcz and Reeve, 2018[11]; Roosen et al., 2020[12]; VinylPlus, 2019[13]).
Use by region, application and polymer	Historical use	Global consumption from Geyer, Jambeck and Law (2017[14]) for 1950-2014. Regional split based on waste weight estimates from Kaza et al. (2018[15]) The split by polymers and applications per region is based on weight estimates from Ryberg et al. (2019[16]) in 2015, and is constant for 1950-2014.
	Use	For the calibration year (2015), primary plastics use by polymer and application from Ryberg et al. (2019[16]) has been associated to different sectors and regions in the OECD ENV-Linkages model. Secondary plastics use stems from waste generation (derived in the model), recycling rates (see below) and recycling loss rates from the literature (Cottom et al., 2022[10]; Chruszcz and Reeve, 2018[11]; Roosen et al., 2020[12]; VinylPlus, 2019[13]). For future years, OECD ENV-Linkages model projections result from the mapping of sectoral/polymer flows to economic baseline.
Waste by region, application and polymer	Historical waste	OECD ENV-Linkages model, based on historical consumption (for 1950-2015), and product lifespans from Geyer, Jambeck and Law (2017[14]).
	Waste	OECD ENV-Linkages model projections, based on product lifespans from Geyer, Jambeck and Law (2017[14]).
Waste management end-of-life fates	Recycling share	For 1980-2019: Country sources (Table A A.5), Geyer, Jambeck and Law (2017[14]), and Kaza et al. (2018[15]). Rates for non-MSW assumed to match MSW.
	Incineration share	For 1980 -2019: Geyer, Jambeck and Law (2017[14]) and Kaza et al. (2018[15]) Rates for non-MSW assumed to match MSW.
	Sanitary landfilling	Cross country regression (residual) based on What a Waste 2.0 (Kaza et al., 2018[15]) (*) Rates for non-MSW assumed to match MSW, when excluding littering.
	Littering share	(Jambeck et al., 2015[17]) for share in MSW and zero for non-MSW.
	Mismanaged share	Cross-country regression based on Kaza et al. (2018[15]) (*) Rates for non-MSW assumed to match MSW, when excluding littering.
Environmental impacts	Total leakage of macroplastics and microplastics to the environment by category	Based on plastic consumption, waste and waste management projections from OECD ENV-Linkages model, adapted from Ryberg et al. (2019[18]) methodology. The central estimate for macroplastic leakage from mismanaged waste (the largest source of leakage) is equal to the average between the estimate provided with the methodology of Ryberg et al. (2019[18]) and the estimate provided by Leeds University (Cottom et al., 2022[10]).
	Plastic leakage and accumulation in aquatic environments	Based on waste management projections from OECD ENV-Linkages model, and the leakage estimates described above, adapted from the Lebreton and Andrady (2019[19]) methodology.
	Plastic leakage to air from terrestrial transport	Based on transport projections from OECD ENV-Linkages model, adapted from Evangeliou et al., (2020[20]) methodology.
	GHG emissions for plastic lifecycle	Based on plastic consumption, waste and waste management projections from OECD ENV-Linkages model, based on Zheng and Suh (2019[21]).

Note: (*) The cross-country regressions based on the What a waste 2.0 database (Kaza et al., 2018[15]) include:

a. ${waste\_pc}_i\mathrm{ }=\mathrm{ }\alpha \mathrm{ }+\mathrm{ }\beta \mathrm{ }\mathrm{*}\mathrm{ }ln\left({gdp\_pc}_i\right)\mathrm{ }+\mathrm{ }{r}_i$

b. ${inc}_i/({inc}_i+\mathrm{ }{dis}_i)\mathrm{ }=\mathrm{ }\alpha \mathrm{ }+\mathrm{ }\beta \mathrm{ }\mathrm{*}\mathrm{ }ln\left({gdp\_pc}_i\right)\mathrm{ }+\mathrm{ }{r}_i$

c. ${mis}_i/{dis}_i\mathrm{ }=\mathrm{ }\alpha \mathrm{ }+\mathrm{ }\beta \mathrm{ }\mathrm{*}\mathrm{ }ln\left({gdp\_pc}_i\right)\mathrm{ }+\mathrm{ }{oecd}_i$

where waste_pc = MSW per capita, mis = mismanaged waste, inc = incinerated waste, dis = mismanaged + landfilled, gdp_pc = GDP per capita, oecd = dummy for OECD economies, r = regional dummies for 15 regions of ENV-Linkages, i = country.

Source: Authors’ own elaboration.

Modelling plastics use in ENV-Linkages

Volumes

The ENV-Linkages model has been extended to include plastics volumes, for both primary and secondary (recycled) plastics use. The plastics use data is presented in million metric tonnes (Mt) and plastics use is split by region, polymer and application.

Volumes of primary plastics for 2015 rely on data from Ryberg at al. (2019[18]), that updates and expands on the seminal work by Geyer, Jambeck and Law (2017[14]). Since the estimates provided by Ryberg et al. (2019[18]) were either by region and application or by application and polymers, an assumption of homogeneity of polymers by application was taken to estimate the primary plastics use by region, polymer and application.

Secondary plastics volumes for 2015 were estimated following a methodology deriving secondary plastics through waste collected for recycling and recycling losses. Loss rates including sorting losses and reprocessing losses were estimated using a methodology developed by the University of Leeds based on a review of the literature (see next section on Losses from sorting and reprocessing).

The estimates for 2019 are based on the 2015 year, using the link between plastics volumes in Mt and plastic inputs to sectors in USD, as described below. In addition, these are complemented with plastics use for the past between 1950 and 2014, for two reasons. The first reason is to be able to accurately compute waste flows in the future, since plastic lifespans can span up to decades. The second reason is to form the basis for the computation of environmental impacts, as for instance plastic leaked in the ocean accumulates over time.

The 1950-2014 historical plastics use is calculated following a step-wise approach. First, global plastics use is taken from the Geyer, Jambeck and Law (2017[22]) study. The regional split of plastics use is then based on weight-based estimates of waste, from a cross country regression of municipal solid waste on GDP per capita using What a Waste 2.0 (Kaza et al., 2018[15]), multiplied by the regional consumption shares in 2015. Finally, for each region, the split by polymer and application is assumed to be constant prior to 2014, based on the estimates from Ryberg et al. (2019[18]). This methodology is constrained by data availability (and thus necessarily imperfect) but provides estimates of plastics use by region, polymer and application.

Losses from sorting and reprocessing – University of Leeds

Plastic waste that has been collected for recycling almost always includes some non-plastic materials and articles. Moreover, collected plastic waste typically includes a multitude of plastics with varying chemical and physical composition. The degree to which these items, objects and fragments are useful to a plastics reprocessor depends on wide range of factors that influence the value of the material. In general, high income countries implement recyclate collection schemes (programmes) that are designed to yield high material mass through an accessible and simplified system that is easy for people to understand. Conversely, in low- and middle-income countries, plastic waste collection for recycling is carried out by informal workers (IRS) who selectively collect (cherry pick) items and objects that are most valuable, focusing on quality and concentration rather than high yield. Even with diligent, selective collection, plastic articles contain a multitude of intentionally and non-intentionally appended, entrapped, adhered and entrained materials and objects that must be removed from the dominant plastic before it can be most often comminuted and remelted under pressure in an extruder. A list of characteristics of waste plastics and their influence on the value of materials and hence their recyclability is reported by Cottom et al. (2022[10]) and shown in (Table A A.4).

Table A A.4. Characteristics of waste plastics and their influence on sorting reprocessability

Characteristic	Example	Effect on value
Plastic type	Usually denoted by the polymer that makes up the majority of the mass (e.g. polypropylene or polyvinyl chloride)	Must be technically recyclable which usually excludes non-melting plastics (thermosets) with the exception of polyurethane foam that can be recycled into an agglomerate using adhesive Must be abundant enough to support the business case for recovering from a material mixture. For instance it is extremely rare for post-consumer polystyrene or PVC packaging to be reprocessed in high income countries due to their low abundance in the municipal waste stream
Object shape	Bottles are largely cylindrical, trays for meat are often rectangular with an opening along one or two faces	Non-conforming –dominant objects may not be easily separable (sortable) by mechanical methods which may be calibrated to particular target shapes
Dimension	Packaging containers are often three dimensional whereas wrapping is comparatively two dimensional	Sorting equipment may select two dimensional plastic items alongside other two dimensional objects such as writing or news paper
Material thickness	Commonly occurring plastic objects can range in thickness from a few microns to tens of millimetres	Material that is too thin may not be easily sortable from materials that are thicker as the sorting machinery will
Object or fragment size	Plastic objects and fragments range in size from larger items such as panels in vehicles or water reservoirs to very small items such as toothpicks or drink closure gaskets	Larger objects are sometimes challenging to store, comminute or compress. They are also less common and storage, transport and sorting systems may not be calibrated to handling them Smaller objects are sometimes less likely to be targeted for reprocessing because a large quantity of small items is required to achieve a mass that is commercially viable to store, transport and reprocess. Smaller items may also be challenging to sort or may escape from sorting, storage and transport systems.
Flexibility	Films (foils) are more flexible than rigid containers for instance	Flexible foils are challenging to separate using machinery which often relies on classification using air jets which are calibrated for rigid items and which are less accurate for selecting flexible plastics of different sizes.
Co-occurrence as part of an assembly	The dominant, or ‘target’ plastic type may occur with other plastic or non-plastic items, objects or material as an assembly or composite material (sometimes referred to as multi-materials).	If components or items are not easily separable, then the whole object may be rejected: It is technically challenging to separate polyethylene from food and drink cartons to a high enough quality for commercially viable reprocessing Plastic packaging waste reprocessors are experienced at removing labels and closure assemblies from drinks bottles
Residues from previous use	Food or beverage residues are common in packaging plastics. In particular, flexible plastics have a high surface area and high static attraction which sometimes results in high surface contamination with residues	Though expected by reprocessors in food packaging, food residues require cots and effort (e.g. surfactant and hot water) to remove and damage the business case for reprocessors. Some critical contaminants can spread through a load causing rejection or very high decontamination costs.
Residues from handling, sorting and comingling	Single stream collection results in co-occurrence of materials with plastic. Material recovered from dumpsites or landfills is likely to have a high degree of surface contamination and may also include items and objects that have become adhered to or entrained with the plastic item.	These types of residue may be of unknown composition. The risk that these substance or materials might be passed on to the secondary pro duct may preclude their use as such lowering their potential value.

Source: Based on Cottom et al. (2022[10]).

Robust and generalisable loss rates during sorting and reprocessing for plastic waste that has been collected for recycling are not commonly reported. Hestin, Faninger and Milios (2015[23]) proffered 18% and 30% for sorting and reprocessing respectively, based on surveys of European reprocessors. However, the nature of the survey was not reported and it is possible that plastic and non-plastic material and objects may have been reported alongside plastic losses. The ENV-Linkages model is only concerned with plastic so data for non-plastic were excluded from this component of the model.

A theoretical model based on material value was developed by the University of Leeds for plastic waste collected for recycling in high-income countries and low- and middle-income countries. Acknowledging that collection and sorting systems vary enormously worldwide, these two generalised groups were chosen because high income countries largely operate, either single stream collection of dry recyclate or co-collection of mixed plastic waste alongside metal packaging. Conversely in low-income and middle-income countries, collection of plastic waste for recycling is largely carried out by the informal recycling sector whose participants selectively collect materials and have much lower loss rates.

To estimate recycling losses, for packaging waste collected for recycling in high income countries, a dataset that reports a weighted average for all collection scheme types across the United Kingdom was used (Chruszcz and Reeve, 2018[24]). For LDPE, an approximation was made based on data reported by Lau et al. (2020[25]) (P₂O model). The reason for this is that LDPE is predominantly used as a as a flexible foil in packaging. Although LDPE is commonly collected for recycling, if it is from a post-consumer household source, it is almost never reprocessed in high income countries due to the challenges associated with surface contamination and selectivity detailed in Table A A.5. On the other hand, post-consumer LDPE from commercial sources is commonly recycled in high-income countries as it is easily collectable and separately and can be extruded dry, often without undergoing substantial cleaning. The result is a low loss rate. The assumptions from Lau et al. (2020[25]) were used to determine the proportion of material that was from commercial/institutional sources compared to household sources.

A probability of plastic waste items being selected at the sorting stage based on material, value was applied to each of the packaging and plastic types as detailed in Table A A.6 and Table A A.7. These probabilities were estimated using cost data summarised by SystemiQ and the Pew Charitable Trust (2020[26]), recyclability imperatives detailed by Recoup (2019[27]) and data on material actually recycled reported by Antonopoulos, Faraca and Tonini (2021[28]) and Plastics Recyclers Europe (2020[29]). In general HDPE, PET and LDPE were considered to have a 100% chance of being selected for reprocessing at the MRF and PVC and PS were considered to have 0% chance of being selected for reprocessing at the MRF. Although the evidence for PVC is more clear-cut, Antonopoulos, Faraca and Tonini (2021[28]) reported some post-consumer PS selection taking place in Europe. However these quantities are reported by Plastics Recyclers Europe (2020[29]) to be small and unusual, there is a likelihood that they do not refer to post-consumer material. The probability was set to zero for packaging but an overall probability of 98.5% was set to allow for some small occurrences of non-packaging material.

The loss rates at the reprocessor were approximated using data on plastic content reported by Roosen et al. (2020[30]); non-plastic content reported was excluded and the relative masses normalised.

High-income countries were assumed to have formal collection and the plastic packaging reported there was subject to loss rates at both sorting and reprocessing. Low and middle-income countries were assumed to have informal collection and the loss rates were therefore assumed to occur only at the reprocessing stage as informal actors selectively collect.

The assumptions for non-packaging applications were based largely on estimates from the project expert team, as there are no published data to support them. Consumer and institutional products were assumed to be the same as packaging except for PVC for which evidence from VinylPlus (2019[31]) indicates some recycling takes place. For the textiles (fibres), an estimate of 20% from financial modelling by Thompson et al. (2012[32]) was used in the absence of any other robust data. Readers should note that this loss rate is approximated on the basis that post-consumer textiles that been recycled into shoddy fibres and/or flocking (stuffing) rather than items that have been ‘reused’ and are out of scope of this study.

Table A A.5. Assumptions used to determine loss rates for plastic packaging waste that has been collected for recycling

Plastic item1	Plastic type by dominant polymer1	Weighted composition1	High-income countries			Low- & middle-income countries
			Probability of being rejected before reprocessing2	Loss rate at reprocessor adjusted for wastage3	Net losses after sorting & reprocessing4	Probability of being rejected before reprocessing2	Loss rate at reprocessor adjusted for wastage3	Net losses after sorting & reprocessing4
Film LA recycling sacks	LDPE	2.9	100	0.00	2.90	25	1.00	0.75
FILM Other film	LDPE	11.2	100	0.00	11.20	25	1.00	2.88
FILM Carrier bags	LDPE	1.5	100	0.00	1.50	25	1.00	0.39
B PET NATURAL	PET	26.4	0	13.45	3.55	0	13.45	3.55
B PET JAZZ	PET	3.1	0	13.45	0.42	0	13.45	0.42
B HDPE Milk Bottles	HDPE	13.2	0	15.93	2.10	0	15.93	2.10
B HDPE All non-milk bottles	HDPE	7.7	0	15.93	1.23	0	15.93	1.23
B PVC ALL	PVC	0	100	0.00	0.00	100	0.00	0.00
B PP ALL	PP	0.4	50	21.31	0.24	0	21.31	0.09
Pack PET NATURAL	PET	10.3	0	14.63	1.51	0	14.63	1.51
Pack PET JAZZ	PET	0.5	0	14.63	0.07	0	14.63	0.07
Pack HDPE NATURAL	HDPE	0.1	100	0.00	0.10	0	14.63	0.01
Pack HDPE JAZZ	HDPE	0.6	100	0.00	0.60	0	14.63	0.09
Pack PVC ALL	PVC	0.1	100	0.00	0.10	100	0	0.10
Pack PP NATURAL	PP	4.4	100	0.00	4.40	0	2.08	0.09
Pack PP JAZZ	PP	5.3	100	0.00	5.30	0	2.08	0.11
Pack PS ALL	PS	1.5	100	0.00	1.50	100	0	1.50
Pack EPS ALL	EPS	0.4	100	0.00	0.40	100	0	0.40
Black PET	PET	1.9	100	0.00	1.90	100	0	1.90
Black PP	PP	0.6	100	0.00	0.60	100	0	0.60
Black Other	Mixture	1.1	100	0.00	1.10	100	0	1.10
Other	Mixture	0.2	100	0.00	0.20	100	0	0.20
Unidentified	Mixture	1.9	100	0.00	1.90	100	0	1.90
Plastic non-packaging	Mixture	4.4	100	0.00	4.40	100	0	4.40

1. (Chruszcz and Reeve, 2018[24]).

2. Assumptions based on polymer value SYSTEMIQ & The Pew Charitable Trust (2020[26]), recyclability reported by Recoup (2019[27]), and material reported to have been recycled by Antonopoulos, Faraca and Tonini (2021[28]) and Plastics Recyclers Europe (2020[29]).

3. (Roosen et al., 2020[30]). 4. Calculated.

Table A A.6. Average loss rates by plastic type and application for high income countries and low- middle income countries (MSW)

Plastic type by dominant polymer	Consumer & Institutional			Electrical/Electronic			Packaging1			Textile sector - clothing			Textile sector - others			Total (t y-1)
	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)
Fibres										0.17	20.04	20.04	0.10	20.04	10.0	0.27
HDPE										0.2	20.04	20.04	0.1	20.04	10.0	0.3
LDPE, LLDPE	1.9	18.7	15.9	0.2	95.0	40.0	11.8	18.72	15.92							13.9
Other	1.4	79.0	80.5	0.2	100.0	50.0	6.9	79.0	80.5							8.5
PET	0.0	98.0	98.0	0.0	100.0	50.0	0.0	98.0	98.0							0.1
PP							9.6	17.6	17.6							9.6
PS	2.8	98.5	8.3	0.6	95.0	40.0	6.8	98.5	8.3							10.2
PUR	0.4	100.0	100.0	0.1	100.0	100.0	0.5	100.0	100.0							0.9
PVC	0.2	40.03	10.03	0.1	100.03	100.03	0.0	100.03	100.03							0.2

Source: Cottom et al. (2022[10]).

Table A A.7. Average loss rates by plastic type and application for high income countries and low- middle income countries (Non-MSW)

Plastic type by dominant polymer	Building and Construction			Industrial/Machinery			Other			Transportation - Other			Total (t y-1)
	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)	Mass (t y-1)	Loss rate HIC (%)	Loss rate LMIC (%)
Fibres							0.1	100.0	100.0	0.0	100.0	100.0	0.1
HDPE	1.0	20.0	5.0	0.1	20.0	5.0	1.1	20.0	5.0	0.6	98.0	90.0	2.9
LDPE, LLDPE	0.1	2.0	2.0	0.1	2.0	2.0	0.7	2.0	2.0	0.1	98.0	90.0	1.0
Other	0.0	100.0	100.0				0.2	100.0	100.0	0.1	100.0	100.0	0.4
PP	0.2	20.0	5.0	0.1	20.0	5.0				1.2	100.0	100.0	1.5
PS	0.1	100.0	100.0				0.1	100.0	100.0				0.2
PUR	0.1	40.0	10.0	0.0	40.0	10.0	0.4	40.0	10.0	0.2	100.0	100.0	0.6
PVC	0.6	18.73	18.73				0.4	18.73	18.73	0.1	100.03	100.03	1.1

1. Calculated from Chruszcz and Reeve (2018[24]) and Roosen et al. (2020[30]).

2. Calculated from Lau et al. (2020[25]).

3. Approximated from data reported by VinylPlus (2019).

4. Thompson Willis and Morley (2012[32]).

Source: Estimated by project team, if not stated in the notes.

For simplicity, OE6, O22, USA, and CAN were considered to have formal collection and all other regions were considered to have predominantly informal collection for recycling. The exception was People’s Republic of China (hereafter ‘China’) which has been undergoing a partial transition from informal to formal collection for recycling. Due to the lack of robust data on the informal recycling sector, this component of the model assumed a 70 : 30 ratio for informal: formal collection for recycling. Table A A.8 puts forward the outcome of the technical calculations. The loss rates of PS and other have been lowered to 72.3%, the second highest level of losses between polymers, to represent that these polymers are sometimes recycled, but only in small quantities. Furthermore, to reflect that a large share of recycling of PET is rather a downcycling transformation of PET into fibres, the modelling assumes 35% of recycled PET is transformed into fibres.

Table A A.8. Average loss rates by plastic type and OECD region for MSW and non-MSW combined

Region	HDPE	LDPE, LLDPE	Other	PP	PS	PUR	PVC	Fibres	PET	Mean
USA	23.7	71.4	99.8	96.1	100	58.1	47.8	35	17.6	51.8
Canada	23.6	71.5	99.8	96.2	100	58.1	48.3	34.9	17.6	51.9
Other OECD America	16.7	70.8	95.2	18.1	100	30.8	52.9	42.2	17.6	31.3
OECD EU countries	22.6	69.9	99.8	93.8	100	54	38.2	41.7	17.6	49.7
OECD Non-EU countries	22.2	70.9	99.8	95.1	100	54	42.2	41.1	17.6	50.2
OECD Oceania	19.1	76.4	98.7	23.2	100	46.9	53.7	19.1	17.6	33.3
OECD Asia	21.2	72	99.8	94.9	100	51.8	39.9	33.3	17.6	49.6
Latin America	16.6	71.6	95.2	16.7	100	30.1	59.8	41.4	17.6	31.1
Other EU	21.1	74	97.3	30.2	100	51	51.9	35.2	17.6	35.8
Other Eurasia	21.1	74.3	97.2	29.4	100	50.8	56.5	34.4	17.6	35.9
Middle East & North Africa	17.7	73.3	95.9	20.2	100	37.2	52.6	33.2	17.6	32.4
Other Africa	16.5	71.9	95.5	15.9	100	29.3	61.1	44.4	17.6	31
China	18.5	73.9	97	41.4	100	40.7	63.3	31.8	17.6	37.7
India	17.2	76	96.1	16.1	100	35.2	69.2	20.2	17.6	31.9
Other non-OECD Asia	19.2	75	96.6	23.1	100	45.7	67	27.3	17.6	33.7
Mean	20.3	72.3	98.1	59.8	100.0	47.2	48.9	34.1	17.6	42.1

Source: Estimated by project team.

Economic flows

The ENV-Linkages model has been modified to include primary and secondary plastics production. While in the original database that the model relies on - the GTAP 10 database (Aguiar et al., 2019[5]) - primary and secondary plastic production are aggregated in the same sector (Rubber and plastic products; rpp), this study enhanced the representation of plastic to allow the distinction of a technology producing primary plastic and an alternative technology producing secondary plastics.

Similar to coal power plants and gas power plants both providing the same good (electricity), these two technologies produce a similar plastic good, with an elasticity of substitution of two. The production of plastic goods was thus split with two data sources. First, the total shares in production for primary and secondary plastics was taken from the volumes in tonnes described above (Ryberg et al. (2019[18]) for primary and own estimates for secondary plastics). Table A A.9 describes the calculated share for the secondary plastic production technology. Furthermore, the Exiobase 3 database (Stadler et al., 2018[8]) was used to adapt the cost structures. The main difference stem from the material inputs: the primary technology uses fossil fuels, while the secondary technology uses inputs from the chemical sector.

Table A A.9. Share of the secondary production technology

	Region	Share of secondary technology in 2015 (in tonnes)
OECD America	USA	3.9%
	Canada	4.8%
	Other OECD America	8.3%
OECD Europe	OECD EU countries	9.5%
	OECD Non-EU countries	6.1%
OECD Asia	OECD Pacific	5.6%
	OECD Oceania	2.8%
Other America	Latin America	9.6%
Eurasia	Other EU	5.2%
	Other Eurasia	3.6%
Middle East and Africa	Middle East & North Africa	3.6%
	Other Africa	5.9%
Other Asia	China	10.1%
	India	7.0%
	Other non-OECD Asia	5.0%

Source: Own calculations, based on Ryberg et al. (2019[18]) for primary and GVR (2020[9]) for secondary plastics.

Link between volumes and economic flows

To model plastics use in ENV-Linkages, data on plastics volumes by application and polymer have been linked to the detailed sectoral production structure of the model and the GTAP database that underlies the model. This is done for 14 polymer categories (Table A A.10).

Table A A.10. The large range of polymers allows for a multitude of plastics applications

Polymer	Abbreviation	Examples of use
Polypropylene	PP	Food packaging, automotive parts
Low-density polyethylene	LDPE	Reusable bags, food packaging film
High-density polyethylene	HDPE	Toys, shampoo bottles, pipes
Polyvinylchloride	PVC	Window frames, floor covering, pipes, cable insulation
Polystyrene	PS	Food packaging, insulation, electronic equipment
Polyethylene terephthalate	PET	Beverage bottles
Polyurethane	PUR	Insulation, mattresses
ABS, elastomers, biobased plastics, PBT, PC, PMMA, PTFE, …	Other	Tyres, packaging, electronics, automotive, …
Fibres made of different polymers	Fibres	Textile applications but also in many other sectors

Note: ABS stands for Acrylonitrile butadiene styrene, PBT for Polybutylene terephthalate, PC for Polycarbonates, PMMA for Poly (methyl methacrylate) (also known as plexiglas) and PFTE for Polytetrafluoroethylene.

Two main sources of data (volumes and economic flows described above) were used and put in coherence: (i) plastics production and consumption by economic sector by GTAP10 adapted with a primary and secondary production technology in monetary values, and (ii) regional flows of a range of plastic polymers and application-specific flows of plastics in tonnes. Table A A.11 summarises the mapping of the economic sectors and plastics applications. The initial values for this mapping are calibrated using data from (Ryberg et al., 2019[18]), combining polymer distribution by application at the global level with distribution of total plastics use by region and application. The polymer distribution was taken from the global averages and applied for each region taking into account the specific economic structures of the various regions.

Based on the initial picture in 2014, primary plastics use is projected following the flows of “plastics products” into the various corresponding demand sectors, from initial values, following the methodology developed for the OECD’s Global Material Resources Outlook (OECD, 2019[4]). In particular, the model incorporates a series of plastics chains from initial production to final demand, either partially or in full depending on the particular structure of each regional economy. The basis for the chain includes flows from “oil” or ”biomass” to “chemicals”, that are then used for the production of “plastic products” which serve as intermediate goods or for sectors such as food product/appliances/motor vehicles/construction, before reaching final demand. The underlying assumption is that the coefficient (tonne/USD per polymer, per application, per region) that links monetary flows to physical flows (in tonnes), is kept constant. Plastics production then follows these demands, based on trade flows and plastics use.

There are three steps to project plastics use and the split of primary and secondary plastics to fulfil demand in baseline projections. First, total demand for plastics use is estimated following the evolution of the demand for the plastic commodity (produced by both the primary and secondary technologies). Second, as collected and sorted materials (further referred to as plastic scrap) are – after correcting for loss rates (see Annex section on Losses from sorting and reprocessing) - generally fully used to produce secondary plastics, the tonnes of secondary plastics follow the growth of the secondary sector in the ENV-Linkages projections. Third, the volumes of primary are calculated as a residual between the two. This is fully consistent as the demand for the plastic commodity relies on the growth of the primary and secondary technology, such that total demand for plastics is met.

Table A A.11. Mapping of plastics use by application to economic sectors

Input sectors	Applications	Output sectors	Polymers*
Plastic products	Building & Construction	Construction	ABS, ASA, SAN; Bioplastics; HDPE; LDPE, LLDPE; PP; PS; PUR; PVC; Other
	Consumer & Institutional products	Accommodation and food service activities; Air transport; Education; Health; Insurance; Lumber; Non-metallic minerals; Business services; Other manufacturing; Public services; Land transport; Pulp, paper and publishing; Real estate; Textile; Water transport	ABS, ASA, SAN; Bioplastics; HDPE; LDPE, LLDPE; PP; PS; PUR; PVC; Other
	Electrical/Electronic	Electrical equipment; electronics	ABS, ASA, SAN; Bioplastics; HDPE; LDPE, LLDPE; PP; PS; PUR; PVC; Other
	Industrial/Machinery	Fabricated metal products; iron and steel; nonferrous metal; Machinery and equipment	HDPE; LDPE, LLDPE; PP; PUR
	Packaging	Food products; Chemical products	Bioplastics; HDPE; LDPE, LLDPE; PET; PP; PS; PUR; PVC; Other
	Personal care products	Chemical products	HDPE; PET
	Transportation - other	Motor vehicles; Public services; Other transport equipment	ABS, ASA, SAN; Bioplastics; Fibres; HDPE; LDPE, LLDPE; PP; PUR; PVC; Other
	Other	Other sectors	Other
Chemicals	Marine coatings	Other manufacturing, other transport equipment	Marine coatings
	Road markings	Construction	Road markings
	Textile sector - clothing	Textiles	Bioplastics; fibres
	Textile sector - other	Textiles	Fibres
	Transportation - tyres	Plastic products	Elastomers (tyres)

* See Table A A.10 for abbreviations and examples of use for those polymers.

Source: OECD ENV-Linkages model.

Modelling plastic waste and end-of-life fates in ENV-Linkages

Plastic waste is calculated linking plastics use to the lifespan distribution of different products. Specifically, it is calculated as a function of plastics use (in volumes), following Geyer, Jambeck and Law (2017[22]), using a methodology based on lifespan distributions,1 under the assumption of global homogeneity.2

Plastic waste of different applications are grouped into three main categories: Municipal Solid Waste (MSW), Other and Markings & Microbeads. MSW includes packaging, consumer & institutional products, electrical/electronic and textiles. ‘Other’ incorporates waste that is not included in MSW, therefore mostly reflecting waste from industrial applications (including building and construction, industrial and machinery applications, transportation applications). Markings & Microbeads include marine coatings, road markings and personal care products.

End-of-Life Fates

Plastic waste is divided into different waste management streams (end-of-life fates) by applying end-of-life shares that vary across countries, polymers and waste categories. MSW and Other plastic waste categories can be (i) recycled, (ii) incinerated or (iii) discarded. The latter is further disaggregated into waste that is disposed of in sanitary landfills, mismanaged waste and, in the case of MSW, littering.3 Littering is presented as included with mismanaged waste. It is is set as a constant share of municipal solid waste following the assumption in Jambeck et al. (2015[17]). Markings & Microbeads form a very small stream (by mass) that is assumed not to be managed and to leak directly to the environment.

The sources of end-of-life fate shares for the base year, 2019, vary across regions. Recycling (defined here as material that has been collected for recycling) shares for plastics are exogenously fixed based on a range of sources, primarily country sources (Table A A.12). Notably, for the EU the recycling rate reported by Plastics Europe (2020[33]) was adjusted to ensure that polymer specific recycling rates are within the range of the EU plastics packaging recycling rates. For China, the official recycling rate in 2017 was used (Ministry of Commerce, 2019[34]). Recycling rates for other non-OECD regions were based on estimates of MSW recycling rates from What a Waste 2.0 (Kaza et al., 2018[15]) and consultations with experts. For the Middle East & North Africa, Other Africa, Other Eurasia and Latin America regions, projections were adjusted to account for informal recycling that is not reported but typically recovers high value streams such as HDPE and PET bottles.

The recycling shares are further split across polymers by multiplying the recycling shares for plastics by factors that reflect the recyclability and value of individual polymers based on expert consultations and ensuring that the estimated recycled volumes do not exceed the recycling capacities subject to data availability. Overall, PET and HDPE are assumed to have the highest recycling rates, followed by LDPE, PP and PVC (for construction). PUR, fibres, elastomers, bioplastics, marine coatings and road markings are not recycled, while only a very small proportion of PS, ABS, ASA, SAN and other polymers can be recycled.

Table A A.12. Data sources for plastic recycling rates in base year

Region	Recycling Rate Source and Assumptions
USA	United States Envrionmental Protection Agency (EPA) (2020[35]; 2020[36])
Canada	Environment and Climate Change Canada (2019[37]) *
Other OECD America	Based on SEMARNAT (2020[38]) and FCH (2021[39])
OECD EU countries	Polymer-specific recycling rates have been determined based on expert opinion and applied to the volumes of polymers collected for recycling by ENV-Linkages.**
Other EU
OECD Non-EU countries	Based on EU adjusted by the proportion of region’s MSW recycling rate to EU MSW recycling rate from What a Waste 2.0 (Kaza et al., 2018[15])
OECD Asia	Plastic Waste Management Institute (2019[40]) and expert judgement to account for recycling rates in Korea
OECD Oceania	Australian Government (2020[41]) ***
Latin America	Based on Other OECD America adjusted to account for a larger informal sector
Other Eurasia	What a Waste 2.0 (Kaza et al., 2018[15])
Middle East & North Africa	What a Waste 2.0 (Kaza et al., 2018[15])
Other Africa	What a Waste 2.0 (Kaza et al., 2018[15])
China	China Recycling Industry Development Report (2013-2018) by the Ministry of Commerce (2019[34])
India	Central Pollution Control Board (2019[42]) and UNIDO (2020[43])
Other non-OECD Asia	What a Waste 2.0 (Kaza et al., 2018[15])

* An updated report is available: (Statistics Canada, 2022[44]).

** For the EU, the calculated recycling rate for total plastics has been benchmarked with the numbers presented by Plastics Europe (2020[33]). In ENV-Linkages, the total amount of plastics collected for recycling is slightly higher (the numerator of the recycling rate),while the amount of plastics taken into account for the calculation is substantially higher (the denominator: total plastics includes fibres and other rarely recycled plastics). So the total recycling rate of plastics in ENV-Linkages is lower than Plastics Europe (2020[33]).

*** An updated report is available: Government of Australia (2021[45]).

To account for unreported informal recycling (which leads to understating plastic recycling rates) or overly optimistic reported recycling rates, all reported recycling rates were sense-tested, adapted and validated leveraging on consultations with experts and modelling carried out by Ed Cook, Josh Cottom and Costas Velis from the University of Leeds.

The use of incineration as a waste treatment type is country-specific and related to historic elements and local population densities. The share of plastic waste that is incinerated is strongly correlated with the share of total solid waste that is incinerated. Therefore, the incineration shares are set so that the ratio of the incineration share to the non-recycled share is equal to the corresponding ratio for total MSW from the What a waste 2.0 database (Kaza et al., 2018[15]). Moreover, the same incineration shares apply for non-MSW plastic waste, namely the ‘Other’ waste category.

Regarding discarded waste, its share is equal to the residual, under the assumption that 2% of MSW is littered at all times to account for the unaccounted potential losses to the environment (Jambeck et al., 2015[17]). The discarded share is further split into sanitary landfilled and mismanaged waste. In this analysis, mismanaged waste includes open dumping and unaccounted waste treatments for all income levels apart from lower and lower middle income countries, for which also unspecified landfilling, waterway treatment and other categories are included based on country level data for MSW (Kaza et al., 2018[15]) and building on assumptions for the previous version of the database in (Jambeck et al., 2015[17]). In general, mismanaged plastic waste as a share of total plastic waste is expected to decrease with income level. Following this assumption and using MSW data from (Kaza et al., 2018[15]) , the share of mismanaged plastic waste was estimated by regressing the ratio of mismanaged waste to discarded waste on GDP per capita, accounting for regulatory differences between OECD and non-OECD countries using an OECD dummy. Specifically, the following regression was estimated for 156 countries for which complete data was available:

${MIS}_i/({MIS}_i+{LAN}_i)\mathrm{ }=\mathrm{ }\alpha \mathrm{ }+\mathrm{ }\beta \mathrm{ }\mathrm{*}\mathrm{ }ln\left({gdp\_pc}_i\right)\mathrm{ }+\mathrm{ }{OECD}_i\mathrm{ }$

where ${MIS}_i$ = mismanaged waste/MSW, ${LAN}_i$ = Landfilled waste/MSW, ${gdp\_pc}_i$ = GDP per capita and ${OECD}_i$ = dummy for OECD countries, $i$= country. Finally, the share for landfilled waste is equal to the residual.

Historical data for recycling, incineration and discarded shares of plastic waste are taken from Geyer, Jambeck and Law (2017[22]) for the period 1980-1990 for four regions – United States, EU, China and Rest of the World. Following, using granular data for MSW recycling and incineration rates from Kaza et al. (2018[15]), the historical shares for 1990 were mapped to the 15 regions within ENV-Linkages, and were linearly interpolated for the period 1990-2018 in line with the methodology previously applied in Geyer, Jambeck and Law (2017[22]). Historical data for mismanaged and landfilling following the same methodology as in the base year.

Modelling international trade in plastic waste

The model has been extended to include inter-regional trade in plastic waste per application and polymer type. Volumes of plastic waste exports and imports are calculated based on data from UN Comtrade (United Nations Statistics Division, 2020[46]) following two steps. First, total exports of plastic waste per country and polymer are estimated using the share of plastics exports (Comtrade) to plastic waste (output of ENV-Linkages). Second, exports are split into partner countries and polymers using the country and polymer weights in 2019 for projections, and historical data for the years before. Bilateral exports and imports weights per country (row weights) were calculated based on the bilateral data on exports and imports values for the period 2010-2019 (most recent and complete year) and for the four subcategories of plastic waste reported in the UN Comtrade database. The later were mapped to the polymer types included in ENV-Linkages (Table A A.13). To ensure that global trade balances, bilateral plastic waste imports per reporter-partner pair correspond to the bilateral export of the corresponding partner-reporter pair. Note that trade flows between countries that are grouped in a single region in the modelling framework are subsumed in the intra-regional accounting and thus excluded from inter-regional trade flows. Consequently, total trade flows in the model are around one-third lower than trade flows based on national data.

The end-of-life fates of plastic waste traded flows differ from the domestically treated waste to reflect the fact a high proportion of traded plastic waste tends to be recyclable. In particular, 50% of traded plastic waste is expected to be recycled, with the remaining being distributed across the other waste streams following the same proportions of end-of-life fates as domestically treated waste excluding littering.

Table A A.13. UN Comtrade plastic waste series mapping to polymers in ENV-Linkages

UN Comtrade code	Series Description	Polymers types in ENV-Linkages
3915	Waste, parings and scrap, of plastics
391510	of polymers of ethylene	HDPE,LDPE, LLDPE, PET, PP,PUR, Elastomers (tyres)
391530	of polymers of styrene	PS
391530	of polymers of vinyl chloride	PVC
391590	of other	Fibres, Marine coatings, Road marking coatings, ABS, ASA, SAN, Other

Source: United Nations Statistics Division (2020[46]) and OECD ENV-Linkages.

Modelling plastic leakage to the environment (Technical University of Denmark, DTU)

Estimations on the leakage of plastics are based on an interaction of the ENV-Linkages Model with other dedicated models. Each of the dedicated models builds on earlier work that has passed peer review with respect to estimations for current plastics leakage. The sources for leakage to the environment are varied. Consequently, the modelling techniques to make projections on these flows differ. This section explains the methodology and parameters employed by Teddy Serrano, Alexis Laurent, and Morten Ryberg from the section for Quantitative Sustainability Assessment at the Technical University of Denmark (DTU) to make projections on leakage of macro and micro plastics into the environment.

Leakage of macroplastics

For losses of macroplastics, four main categories have been considered: mismanaged municipal solid waste, mismanaged non-municipal solid waste, littering, and losses from marine activities. Plastic waste generation is calculated by the ENV-Linkages model as explained in the previous sections. The methodology employed for projections in the four categories are as follows:

• Mismanaged MSW was calculated from plastic waste generation and the estimated shares of MSW which is mismanaged, i.e. disposed of in landfills located in low-income countries or in dumpsites. Mismanaged MSW was retrieved from the ENV-Linkages model.

• Mismanaged non-MSW was also retrieved from the ENV-Linkages model. Due to a lack of data on the fate of mismanaged non-MSW, the share of mismanaged non-MSW lost to the environment is assumed to be equal to the share of mismanaged MSW lost to the environment (32%).

• Losses occurring via littering were calculated as the fraction of MSW in two steps. First, in line with Jambeck et al. (2015[17]) and studies carried out for the United Kingdom and Belgium (OVAM, 2018[47]; Resource Futures, 2019[48]), it was assumed that 2% of MSW is littered. Second, a substantial fraction of this littered waste happens in an urban environment and is cleaned up before it makes it to the environment. It is assumed that between 15% and 40% of littered waste is not captured by street sweeping, storm drain catchments and pump stations (Jambeck et al., 2015[17]). The estimated share of litter lost to the environment in each region was established according to the income level (as GNI/cap, US dollars), with lower shares for the high-income countries, as illustrated in (Table A A.14).

• Losses from marine activities (fishing gear and non-netting waste) were calculated based on production data on fishing gear in Europe (PRODCOM, 2016) (Eunomia, 2018[49]; Eurostat, n.d.[50]) upscaled to the rest of the world based on the projected growth of fishing activity to 2060 (from ENV-Linkages model), the assumption that 28% of plastic waste in the fishing and aquaculture sector comes from netting (Viool et al., 2018[51]), and the assumption that 15% of fishing gear material is lost every year during use (Viool et al., 2018[51]).

Table A A.14. Share of litter lost the environment considered based on income levels of the regions

Category	Low and lower-middle income	Upper-middle income	High income
Income level as GNI/cap [USD]	< 4 045	4 045-12 535	> 12 535
Share of litter lost to the environment	35%	25%	15%

Note: The World Bank country classifications by income level: 2020-2021 were used to allocate regions into low and lower-middle income, upper-middle income, or high income categories.

Source: World Bank (2020[52]).

Leakage of microplastics

For losses of microplastics, ten categories have been considered: microbeads, primary pellets, textile wash, tyre abrasion, road markings, brake dust, artificial turf, marine coatings, microplastics dust and wastewater sludge. This section presents the methodology employed to calculate projections of microplastics from the sources considered. For microplastics assumed to be collected by municipal wastewater networks, their fate is discussed at the end of this section.

The category “microbeads” includes losses of microplastics intentionally added to rinse-off personal care and cosmetic products, detergents, and maintenance products and discharged into municipal wastewaters during use. Projections for microbead consumption in personal care and cosmetics products (PCCPs) are derived from the output of the ENV-Linkages model. Based on data from ECHA (2020[53]), microbeads in detergents and maintenance products are twice the quantity of microbeads used in PCCPs. Because current policy trends show a progressive phase-out in the use of microbeads in rinse-off PCCPs (representing ca. 75% of total microbeads employed in PCCPs) (ECHA, 2020[53]), emissions of rinse-off microbeads were assumed to decrease from 2020 onwards. Based on the classification by Anagnosti et al (2021[54]), it was assumed that regions where bans have already been implemented would stop generating rinse-off microbeads losses by 2025; by 2030 for regions that proposed a ban; by 2035 for regions that reached an agreement on phase-out, and by 2040 for other regions. All microbeads are assumed to end up in the sewage system the year they are consumed.

The category “primary pellets” includes losses of primary plastic pellets occurring during production, transportation, and handling. Eunomia (2018[49]) estimated losses of plastic pellets occurring in 2015 in the EU, as originating from pellet production from raw materials, intermediary handling processes, processing and conversion, off-site waste management, and transportation and shipping. Assuming that leakage is proportional to the quantity of plastics produced, losses for the EU were scaled up to the entire world based on the European production share of plastics in 2015 (Plastics Europe, 2017[55]), and then allocated to geographical regions based on production shares. Losses from producers, recyclers, processors and offsite waste management were assumed to enter the sewage network as part of wastewater. Losses from Intermediary facilities and Shipping were assumed to be directly lost to the environment.

The category “textile wash” includes losses of synthetic microfibres lost during the washing of textile and apparel products. Projections are computed based on the total volume (tonnes) of plastics used in the category ‘Wearing apparel’ in a given year, and the assumption that during the lifespan of a textile product 0.4% of material is lost during washing. The share of material lost during the lifespan of a textile and apparel product was calculated based on an assessment of existing studies accounting for the share of synthetic material lost due to washings over several wash cycles (De Falco et al., 2019[56]; Pirc et al., 2016[57]). It was assumed that all microfibres released during washing enter the sewage system.

Three sources of microplastics emissions from road transport were taken into account:

• The category “tyre abrasion” includes losses of elastomers originating from the abrasion of tyre treads of cars, trucks, and motorcycles. Emission projections are derived from traffic data on the yearly activity in vehicle-km for passenger cars and in tonne-km for trucks from 2016 to 2060 in each region (retrieved from ENV-Linkages model). Wear rates (i.e. average mass of tyre tread lost per vehicle-km, by vehicle type) employed are those reported from Eunomia (2018[49]) and illustrated in (Table A A.15). For trucks, an average freight tonnage of 16t/vehicle was estimated, based on data from Eurostat (2018[58]). It was assumed that 45% of tyre treads is of elastomer content (Sommer et al., 2018[59]), and that the fate of the particles is as follows: 45% are retained in the asphalt pavement or remain close to the road, 45% is transported by road runoff and 10% is airborne, in line with available estimates of the fate of these particles following emission (OECD, 2021[60]). The share of particles lost into the environment is dependent on the rural/urban population share of each region from 2016 to 2060 (as also used in the ENV-Growth and therefore ENV‑Linkages model). In rural regions, road runoff and airborne emissions are considered as lost to the environment, whereas the particles trapped in the asphalt/road sides are not. In urban regions, airborne emissions are considered as lost to the environment, particles trapped in the asphalt/road sides are not, and particles as part of road runoff are assumed to go to a sewer system and treated as in wastewater the region where the loss occurs.

• The category “road markings” includes losses from markings applied to road surfaces. Plastics use projections for road markings are generated by the ENV-Linkages model, and the fate of road marking particles has been assumed to be similar to that of tyre abrasion particles due to a lack of data.

• The category “brake wear” includes losses of synthetic polymers originating from the wear of brake pads and other components. From the average composition of brake pads described by Hallal et al. (2013[61]), the polymer content of brake pads was assumed to be 23%. Similarly to the methodology used for tyre abrasion, loss estimations were based on annual traffic data from 2016 to 2060 and abrasion rates based on calculations by Eunomia (2018[49]) and illustrated in (Table A A.16).The fate of brake dust microplastics was assumed to be similar to that of tyre abrasion particles.

Table A A.15. Tyre wear rates used

Values in grams per vehicle-kilometre (g/vkm)

Vehicle type	Passenger cars	Trucks
Upper	0.05	0.6
Lower	0.25	1
Average	0.1	0.8

Source: ETRMA values, as reported in (Eunomia, 2018[49]).

Table A A.16. Brake pads wear rates used

Values in grams per vehicle-kilometre (g/vkm)

Vehicle type	Passenger cars	Trucks
Upper	0.011	0.047
Lower	0.02	0.084
Average	0.016	0.066

Source: (Eunomia, 2018[49]).

The category “artificial turf” includes losses of plastics from the infill of sport turfs. Estimates in the literature find losses of 300-730 kg / year per field in Denmark and 550 kh/year in Sweden (Løkkegaard, Malmgren-Hansen and Nilsson, 2018[62]; Swedish EPA, 2019[63]). According to ECHA (2020[53]), the number of artificial sport pitches will reach 39 000 by 2020 and average infill use is between 40 and 120 tonnes of material. Assuming that annual infill consumption is 1-4% of the total volume (ECHA, 2020[53]; Eunomia, 2018[49]), average yearly infill is 101 400 tonnes. Estimates for Europe were upscaled to other regions based on artificial turf market size figures (from (ResearchNester, 2021[64])) and GDP growth projections to 2060 (from the ENV-Linkages model). Based on the composition of rubber granulate used as infill, it was assumed that 96% of all infill is microplastics.4 In terms of losses and environmental fate, it was assumed that:

• 10% of rubber granulate particles are lost to the surrounding soil (and therefore considered as lost to the environment).

• 10% are discharged with water. Based on the rural share of the population in each region provided by the ENV-Linkages model from 2016 to 2060, it was assumed those 10% are considered as directly lost to the environment in rural areas. In urban areas, they are considered to enter the wastewater network. For those reaching a treatment system (primary, secondary, tertiary), all particles are assumed to be removed and therefore end up in sewage sludge, since turf crumbles’ significant size allows them to be usually well removed in treatment plants (Løkkegaard, Malmgren-Hansen and Nilsson, 2018[62]).

The category “marine coatings” includes losses of paint and coatings worn off from ships and marine structures. It is expected that 10% of plastics employed in the production of marine coatings is lost over the lifespan of the product, directly into the environment (Boucher and Friot, 2017[65]).

The category “microplastics dust” is used to refer to unintentional losses of microplastics occurring during the use phase of a number of products. Specifically, in the model five sources were taken into account: microplastics in household textile dust, the wear off of paint from interior surfaces, the wear off of paint from exterior surfaces, losses from construction and demolition activities, and shoe sole abrasion. These categories do not embody an exhaustive list of all microplastics losses not reported in other sections, but only those for which sufficient literature has been found to include them in this model.

For each source, with the exception of household textile dust, projections are based on reported losses at the scale of a country or the European Union, which have been scaled down to calculate per capita emissions or per USD of GDP at constant PPP created, and finally scaled up to calculate the emissions for the entire world for each year between 2016 and 2060, using data provided by the ENV‑Linkages model. For interior and exterior paints, as well as exterior construction and demolition sources of dust, GDP was used as a scaling proxy under the assumption that the use of these materials is correlated to wealth.

For shoe sole abrasion, population data was considered a more relevant proxy. Because a person can only wear one pair of shoes at a time, the wear of shoes is assumed to be dependent on the activity of the person and not on wealth. In lack of better data, trends in the use of shoes are assumed to follow population trends. The model does not take into account future changes in the number of people using shoes nor future developments in shoe sole material composition.

The losses estimations of household textile dust are based on a recent study, according to which airborne-emitted synthetic fibres from textile and apparel products could represent a third of those lost to water during washings (De Falco et al., 2020[66]). Therefore, the emissions of textile fibres previously calculated during textile wash were used to calculate the losses of household textile dust.

A summary of the sources used to calculate those losses can be found in Table A A.17. It was assumed that 15% of household textile dust (Kawecki and Nowack, 2020[67]) and 100% of microplastics from interior paints ends up in wastewater. For other sources, particles emitted in urban areas were also assumed to enter wastewater systems, whereas they were considered lost to the environment for rural areas.

Table A A.17. Losses sources of microplastics dust and losses values for the year 2060

Microplastics dust sources	Reference (Country or region)	Scaling method
Household textile dust	ENV-Linkages model’s textile projections	-
Interior paints	(Eunomia, 2018[49])(EU)	GDP (USD, PPP)
Exterior paints	(Eunomia, 2018[49]) (EU)
Exterior construction and demolition	(Kawecki and Nowack, 2020[67]) (Switzerland)
Shoe sole abrasion	(Lassen et al., 2016[68]) (Denmark)	Population

The category “wastewater sludge” includes losses of microplastics occurring via the application of wastewater sludge to land, as detailed in the next section.

The wastewater pathway and losses via sludge application to land

A large share of the emitted microplastics end up in wastewater or stormwater runoff (OECD, 2021[60]). Hence, an overview of relevant end-of-pipe treatment systems is needed in order to estimate the quantities of microplastics that reach the environment. The model considers a number of possible fates for microplastics, in line with Ryberg et al. (2019[18]) and as illustrated in Figure A A.2. Ultimately, microplastics can either be retained by wastewater treatment or be lost to the environment.

Figure A A.2. Fate of microplastics in wastewaters

Source: Methodology adapted from (Ryberg et al., 2019[18]).

The share of microplastics emissions ending up in different pathways varies according to state of wastewater infrastructure coverage in different countries. Allocation shares for each fate were estimated on a regional level. For each region, most allocation shares leading to treatments (represented by yellow boxes in Figure A A.2) were calculated using allocation shares averages of the countries composing the region, weighted by the population of each country. An assessment of data for 187 countries showed high variability in data availability and quality across countries. For most OECD countries, as well as Brazil, Colombia and South Africa, the latest available data from (OECD.stat, 2017[69]) was used and considered representative for wastewater treatment in 2016. For China and India, allocation shares were based on Kalbar, Muñoz and Birkved (2017[70]).

For other countries, it has been decided to rely on data from the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP, 2020[71]). This data is used for monitoring development in SDG 6.3.1 “Proportion of safely treated domestic wastewater flows (%)”. In the dataset, the following classification is used:

• Safely managed: use of improved facilities, which are not shared with other households and where excreta are safely disposed in situ or transported and treated off-site.

• Basic: use of improved facilities, which are not shared with other households.

• Limited: use of improved facilities shared between two or more households.

• Unimproved: use of pit latrines without a slab or platform, hanging latrines or bucket latrines.

• Open defecation: disposal of human faeces in fields, forests, bushes, open bodies of water, beaches and other open spaces or with solid waste (JMP 2020).

The “safely managed” share of the wastewater was assumed to at least undergo primary treatment. The remaining share of the wastewater is modelled as being directly released to the environment. Although this is a conservative assumption, it was not possible to retrieve more detailed data on the treatment levels for certain regions.

Based on information from the literature, a microplastics removal rate was assigned to different levels of wastewater treatment (primary, secondary, and tertiary, as illustrated in Table A A.18) and employed to calculate the fate of microplastics passing through wastewater treatment, following the approach by Ryberg et al. (2019[18]). The removal rate of unspecified and independent wastewater treatment was assumed equal to the removal rate for primary treatment. Regional data on loss of wastewater due to overflow (represented by blue boxes in Figure A A.2) is generally lacking and the loss share was therefore modelled using the same loss shares for all regions. It is estimated that 0.6% and 2.4% of the wastewater is lost due to overflow of the sewer system and of the waste water treatment plant (WWTP), respectively (Magnusson et al., 2016[72]; Ryberg et al., 2019[18]).

Table A A.18. Microplastics removal rate for different levels of wastewater treatment

Treatment technology	Microbeads	Fibres	Other microplastics
Primary treatment	86%	87%	69%
Secondary treatment	92%	92%	96%
Tertiary treatment	99%	96%	99%

Source: Calculations from (Michielssen et al., 2016[73]).

Because the share of wastewater treated is likely to evolve between 2019 and 2060, multiple linear regressions (MLRs) were carried out to estimate the development in the share of wastewater going to a treatment plant and the treatment technology in place (i.e. primary, secondary or tertiary). The microplastics removal rates within a country were also derived based on MLRs. The MLR models use values for 2019, GDP per capita [USD PPP] and the region on which the country is located as input parameters. The MLRs were weighted with the population of each country. Due to a lack of data, the development in wastewater losses due to overflow of the sewers or WWTPs, the share of wastewater undergoing independent treatment, or the share of sewage sludge applied to land were modelled as being constant between 2016 and 2060.

Wastewater sludge is the waste by-product of wastewater treatment containing the water pollutants removed from the influent. Sludge reuse for agricultural applications is encouraged in several countries, mainly due to the high nutrient content and its beneficial effects on crops, as well as to reduce the need for landfilling or incineration. However, recent evidence suggests that this practice leads to the transfer of a share of the microplastics retained during wastewater treatment to agricultural land (Nizzetto, Futter and Langaas, 2016[74]).

Losses into the environment via agricultural land were calculated based on the share of sludge generated in a given year that is applied on agricultural land. Due to data scarcity on the fate of microplastics during sludge treatment, it was assumed that there is no further removal of microplastics before sludge is applied to land (Ryberg et al. 2019). For Canada, China and the United States, the share of sludge applied to agricultural land follows the fractions reported by Rolsky et al. (2020[75]) (i.e. 43%, 45% and 55% for Canada, China and the United States, respectively). Due to a lack of data, the share of wastewater sludge applied on agricultural fields in all other countries was assumed to be equal to the European average (i.e. 46%) (Eurostat, 2020[76]).

Modelling plastic leakage to terrestrial and aquatic environments (University of Leeds)

This section explains the methodology and parameters employed by the experts from the University of Leeds to make projections on the fate of plastics after it becomes waste.

Waste management and leakage to environment

The end-of life fate, including plastic waste emissions to the environment from the waste management system were quantified for the Baseline 2019 scenario using the Spatiotemporal Quantification of Plastic Pollution Origins and Transportation (SPOT) model (Cottom et al., 2022[10]). The SPOT model predominantly estimates material flow at Level 2 and 3 administrative boundary resolution, and therefore it had to be adapted to provide outputs at national (Level 0) which were aggregated to OECD regional level. Material flow analysis (Brunner and Rechberger, 2016[77]) was the general methodological approach underpinning the distribution of plastic waste generation estimates provided by the ENV-Linkages model and used to describe its flow through the waste system as illustrated in the conceptual diagram (Figure A A.3). This hybrid model is described hereafter as the ‘ENV-Linkages-SPOT plugin’.

Figure A A.3. ENV-Linkages-SPOT plugin model structure

Source: Cottom et al. (2022[10]).

Model structure and basic assumptions

Data were processed using the SPOT model in three stages: 1) Municipal waste generation, composition and management data from 2007 to 2021 from four sources, Waste Wise Cities Tool (WaCT) (UN Habitat, n.d.[78]); Wasteaware Cities Benchmark Indicators (WABI) (Wilson et al., 2012[79]); United Nations Statistical Division (UNSD) (UNSD, 2021[80]); and What a Waste 2.0 (WAW2) (Kaza et al., 2018[15]), were cleaned and normalised according to a common denominator, resulting in approximately 500 data records; 2) Random forest machine learning used predictive variables to model data for the remaining 85 088 global municipalities that had no data; 3) Probabilistic material flow analysis used the interpolated data to allocate the flow of waste from the point of generation through managed, mismanaged and unmanaged process nodes.

The ENV linkages-SPOT plugin uses the aggregated country level (Level 0) mass of rigid and flexible plastic waste estimated by the SPOT model, to determine transfer coefficients used to allocate material between process nodes. However, the SPOT does not present all data in the format required for the ENV-Linkages-SPOT plugin to function, so adjustments are made.

Incineration data were not specifically reported in this version of the SPOT model due to the lack of spatial granularity in the source data, which resulted in their aggregation with other types of recovery. Therefore, data obtained from Kaza et al. (2018[15]) were used in the ENV-Linkages-SPOT plugin alongside further research which was used to verify or amend some data points as detailed in Table A A.19.

Table A A.19. Validation of incineration data

Country	Mass of MSW incinerated (t y-1)	Proportion of MSW incinerated (%)	Verification / addition	Source
Liechtenstein	8 268	25.4	add	Liechtenstein Institute for Strategic Development (2020[81])
Azerbaijan	400 000	9.6	add	Islamic Development Bank (2020[82])
Vietnam	1 602 764	5.4	add	Tun et al. (2020[83])
Thailand	1 389 627	5.0	Verified	Tun et al. (2020[83])
Ethiopia	350 000	2.5	add	Cleere et al. (2020[84]) and Muuben et al. (2019[85])
Lao PDR	32 637	2.0	add	Tun et al. (2020[83])
India	1 916 250	0.7	add	Central Pollution Control Board (2021[86])
Myanmar	21 900	0.2	add	JFE Engineering Corporation (2017[87])

Source: Kaza et al. (2018[15]).

The proportion of waste collected for recycling by the informal recycling sector was estimated using a model adapted from one first presented by Lau et al. (2020[25]) (P₂O). Additional data reported by (Cottom et al., 2022[10]) for average productivity per waste picker, number of waste pickers per head of urban population, proportion of waste collected that is plastic (Table A A.20) and an assumption that workers operate for 235 days on average accounting for sickness, vacation and other downtime.

Table A A.20. Data used to model the activities of the informal recycling sector

Income group (World Bank)	Proportion of urban population that is an informal waste worker (%)	Productivity per waste picker (kg d-1)	Proportion of waste collected by informal recycling sector that is plastic (%)
High income	0.01	37.0	5
Upper middle-income	0.26	37.0	28
Lower middle income	0.19	37.0	35
Low income	0.14	37.0	35

Source: Cottom et al. (2022[10]).

Dumping mismanaged waste in water

Data to support the deliberate dumping of waste into water by waste generators are scarce. This section presents a rapid review of census data that indicate the mass deposited directly into water by householders in the absence of a waste collection services (Table A A.21). Acknowledging the uncertainty in the data, high variability and the fact that the data do not necessarily represent the global population, a conservative approach was adopted and approximated by using the mean of the country level median proportion treated in this way (4.8% of uncollected waste).

Table A A.21. Deliberate dumping into water

Country	Proportion of population engaged in behaviour (median % of uncollected waste)	Source
Malawi	1.0	(National Statistical Office, 2020[88])
Guatemala	1.8	(Guatemala, Instituto Nacional de Estadística, 2018[89])
Indonesia	7.6	(Sub Direktorat Statistik Lingkungan Hidup, 2014[90])
Fiji	0.5	(Fiji Bureau of Statistics, 2018[91])
Brazil	0.4	(Instituto Brasileiro de Geografia e Estatística, 2010[92])
Bolivia	15.6	(Instituto Nacional de Estadistica, 2012[93])
Samoa	0.4	(Samoa Bureau of statistics, 2019[94])
Ethiopia	10.9	(Population Census Commission, 2007[95])

Plastic debris emissions to the environment

Waste transfer from the terrestrial to aquatic environment was estimated using transfer ratios suggested by Lau et al. (2020[25]) and detailed in Table A A.22.The GWPv4 (2015) (United Nations, 2019[96]) UNAdj population density map (CIESIN, 2018[97]) was used to estimate the proportion of rural and urban inhabitants using definition from was estimated using Dijkstra and Poelman (2014[98]) that a grid cell has >300 population and >5 000 inhabitants in contiguous cells. The urban and rural attribution was mapped onto the HydroSHEDS 30 arc river and coastline dataset. Population data for countries above 60°N latitude were approximated using ratios for nearest similar countries which were below 60°N.

Waste transfer from the terrestrial to aquatic environment was estimated using transfer ratios suggested by Lau et al. (2020[25]) and detailed in Table A A.22. The GWPv4 (2015) (United Nations, 2019[96]) UNAdj population density map (CIESIN, 2018[97]) was used to estimate the proportion of rural and urban inhabitants using definition from was estimated using Dijkstra and Poelman (2014[98]); that a grid cell has >300 population and >5 000 inhabitants in contiguous cells. The urban and rural attribution was mapped onto the HydroSHEDS 30 arc river and coastline dataset. Population data for countries above 60°N latitude were approximated using ratios for nearest similar countries which were below 60°N.

Table A A.22. Plastic waste transfer rate from terrestrial to aquatic environment

Flexibility	Distance population to aquatic environment	Proportion plastic waste in terrestrial environment that transfers to the aquatic environment (% wt.)
Rigid	<1 km	10
	>1 km	3
Flexible	<1 km	35
	>1 km	8

Source: Lau et al. (2020[25]).

Projected mismanaged waste data to 2060

Transfer coefficients from the 2019 baseline in ENV-Linkages-SPOT plugin were used to distribute mismanaged waste emissions to the environment for future years, driven by the mass of mismanaged waste projected by the ENV-Linkages model. Table A A.23and Table A A.24 show these multipliers by region for mismanaged waste and Table A A.25 and Table A A.26 show the multipliers used to distribute waste that has not accumulated in dumpsites through each of the societal and waste management nodes through which it may transfer.

Table A A.23. Transfer coefficients used to distribute mismanaged plastic waste from MSW to four main components of the ENV-Linkages-SPOT plugin

Percentage of. mismanaged waste per year in million tonnes (Mt)

Region	Dumpsite (uncontrolled)	Open burning	Terrestrial environment	Aquatic environment
Canada	60%	26%	10%	4%
China	39%	45%	10%	6%
Other EU	26%	54%	13%	7%
India	57%	30%	8%	5%
Latin America	51%	36%	8%	5%
Middle East and North Africa	60%	31%	7%	3%
OECD EU countries	50%	31%	14%	5%
Other Africa	38%	31%	21%	10%
Other OECD America	42%	43%	9%	6%
OECD Oceania	60%	27%	10%	3%
Other non-OECD Asia	49%	33%	12%	6%
OECD Non-EU countries	41%	43%	11%	5%
OECD Pacific	26%	36%	29%	9%
Other Eurasia	57%	33%	7%	4%
USA	56%	28%	11%	5%
World	49%	35%	11%	6%

Source: ENV-Linkages-SPOT plugin.

Table A A.24. Transfer coefficients used to distribute mismanaged plastic waste from Non-MSW to four main components of the ENV-Linkages-SPOT plugin

Percentage of mismanaged waste per year in million tonnes (Mt)

Region	Dumpsite (uncontrolled)	Open burning	Terrestrial environment	Aquatic environment
Canada	60%	26%	12%	3%
China	39%	45%	12%	4%
Other EU	26%	54%	15%	5%
India	57%	30%	9%	3%
Latin America	52%	37%	9%	3%
Middle East and North Africa	60%	31%	7%	2%
OECD EU countries	50%	31%	15%	4%
Other Africa	39%	31%	25%	6%
Other OECD America	42%	44%	10%	4%
OECD Oceania	60%	27%	10%	3%
Other non-OECD Asia	49%	33%	14%	4%
OECD non-EU countries	41%	43%	12%	4%
OECD Pacific	26%	36%	32%	6%
Other Eurasia	57%	33%	8%	2%
USA	56%	28%	13%	3%
World	50%	35%	12%	3%

Source: ENV-Linkages-SPOT plugin.

Table A A.25. Transfer coefficients used to attribute mismanaged plastic waste from MSW excluding waste in dumpsites to components of the ENV-Linkages-SPOT plugin

Percentage of mismanaged waste excluding dumpsites per year in million tonnes (Mt)

Region	Uncollected litter	Uncollected dumped on land	Uncollected dumped into aquatic environment	Collection system debris	Disposal system debris	Uncollected open burned	Uncontrolled disposal open burned	Losses from sorting & reprocessing open burned	Losses from sorting & reprocessing mismanaged on land
Canada	18%	25%	18%	5%	2%	27%	35%	1%	3%
China	3%	17%	3%	2%	1%	55%	15%	3%	4%
Other EU	3%	18%	3%	1%	0%	63%	8%	1%	3%
India	2%	18%	2%	1%	1%	36%	31%	3%	5%
Latin America	2%	16%	2%	1%	1%	47%	25%	2%	3%
Middle East and North Africa	2%	16%	2%	2%	2%	38%	35%	2%	3%
OECD EU countries	15%	27%	15%	7%	1%	37%	24%	2%	3%
Other Africa	1%	34%	1%	1%	1%	31%	15%	4%	8%
Other OECD America	2%	15%	2%	2%	1%	55%	17%	2%	4%
OECD Oceania	14%	24%	14%	5%	2%	31%	36%	1%	2%
Other non-OECD Asia	2%	23%	2%	1%	1%	39%	23%	3%	5%
OECD non-EU countries	4%	18%	4%	2%	1%	54%	17%	2%	3%
OECD Pacific	25%	39%	25%	11%	0%	39%	8%	2%	4%
Other Eurasia	2%	16%	2%	1%	1%	42%	32%	2%	3%
USA	15%	25%	15%	7%	1%	31%	30%	2%	3%
World	3%	21%	3%	2%	1%	43%	22%	3%	4%

Source: ENV-Linkages-SPOT plugin.

Table A A.26. Transfer coefficients used to attribute mismanaged plastic waste from Non-MSW excluding waste in dumpsites to components of the ENV-Linkages-SPOT plugin

Percentage of mismanaged waste excluding dumpsites per year in million tonnes (Mt)

Region	Uncollected litter	Uncollected dumped on land	Uncollected dumped into aquatic environment	Collection system debris	Disposal system debris	Uncollected open burned	Uncontrolled disposal open burned	Losses from sorting & reprocessing open burned	Losses from sorting & reprocessing mismanaged on land
Canada	18%	25%	18%	5%	2%	27%	35%	1%	3%
China	3%	17%	3%	2%	1%	55%	15%	3%	4%
Other EU	3%	18%	3%	1%	0%	63%	8%	1%	3%
India	2%	18%	2%	1%	1%	36%	31%	3%	5%
Latin America	2%	16%	2%	1%	1%	47%	25%	2%	3%
Middle East and North Africa	2%	16%	2%	2%	2%	38%	35%	2%	3%
OECD EU countries	15%	27%	15%	7%	1%	37%	24%	2%	3%
Other Africa	1%	34%	1%	1%	1%	31%	15%	4%	8%
Other OECD America	2%	15%	2%	2%	1%	55%	17%	2%	4%
OECD Oceania	14%	24%	14%	5%	2%	31%	36%	1%	2%
Other non-OECD Asia	2%	23%	2%	1%	1%	39%	23%	3%	5%
OECD non-EU countries	4%	18%	4%	2%	1%	54%	17%	2%	3%
OECD Pacific	25%	39%	25%	11%	0%	39%	8%	2%	4%
Other Eurasia	2%	16%	2%	1%	1%	42%	32%	2%	3%
USA	15%	25%	15%	7%	1%	31%	30%	2%	3%
World	3%	21%	3%	2%	1%	43%	22%	3%	4%

Source: ENV-Linkages-SPOT plugin.

Modelling plastic leakage to aquatic environments (Laurent Lebreton)

This section explains the methodology and parameters employed by Laurent Lebreton to make projections on the fate of waste plastics after they enter the environment. More specifically, the model calculates the amount of leaked plastics ending up in aquatic environments and assesses their mobility as well as degradation in rivers and oceans.

With a wide variety of polymer types, object shapes and sizes, and the dynamic nature of aquatic environments, quantifying sources and the fate of plastics in rivers, lakes, and the ocean is not trivial. Some studies have recently attempted to quantify the amount of mismanaged plastic waste generated by countries worldwide, which likely reach an aquatic environment (Borrelle et al., 2020[99]) and subsequently the ocean (Meijer et al., 2021[100]). These studies utilise spatial models describing the generation of mismanaged plastic waste in relation to topography and other environmental parameters. This section raised country-scale emission results to the modelled global regions represented in the ENV-Linkages model. The transport of emitted plastics was estimated considering geographical variations. Then the fate of plastics for the different regions was modelled as a function of polymer types predicted by projections of waste generation from various sectors of the economy. Finally, the mass of plastics accumulated in different aquatic environments for each region is reported.

To calculate inputs of plastics by region into aquatic environments, results from a previous study by Borrelle et al. (2020[99]) which estimated leakage of mismanaged plastic waste into rivers, lakes, and the ocean at a global scale were used. The model supporting the results of this study includes global high-resolution distribution of plastic waste generation derived from population density, gross domestic product (GDP) per capita, and country scale municipal waste statistics (Lebreton and Andrady, 2019[19]). The model then computes the probability for mismanaged plastic waste to reach an aquatic environment (rivers, lakes, and oceans) as a function of distance and terrain slope direction. By integrating over land, the study reports the national probability of emissions of plastics into aquatic environments, which is independent of the total amount of waste generated but may differ around the world as a function of population location and topography of countries (adapted from Borrelle et al. (2020[99])). In this study, the probability of emissions by region was computed by weighing country scale emission probability by population size and formulating a regional average including confidence intervals (Figure A A.4). The likelihood of plastic waste emissions varies by region. OECD Oceania (Australia and New Zealand) and OECD Pacific (Japan and Korea) have the highest chance of leakage into aquatic environments, reflecting inputs from island nations with predominantly coastal populations.

Figure A A.4. Probability of mismanaged and littered plastic waste emissions into aquatic environments

Note: The probability was calculated as the average of the country-scale fraction of emissions weighted by the population size of countries.

Source: Calculations based on Borrelle et al. (2020[99]).

Statlink

In freshwater, floating plastics may be transported downstream and sinking plastics (plastics with a larger density than freshwater, e.g. PET, PVC or PS) will inevitably reach bottom sediments. Floating plastics may also be retained in freshwater environments in vegetation bordering the river, sediments in the river banks, artificial barriers (e.g. dams), or lakes. Some floating plastics may also be colonised by organisms and sink due to loss of buoyancy. A recent study estimating direct global inputs of plastics into the ocean via waterways reported that only 1% to 2% of mismanaged plastics generated annually have a chance to reach the sea globally within a year (Meijer et al., 2021[100]). The study utilised the same probability framework derived from location and quantities of mismanaged waste generation to the nearest river network. Still, it computed additional transport probabilities to river mouth from distance to the river mouth, river discharge, and river network order.

Table A A.27. Fraction of mismanaged plastic waste entering aquatic environment and fraction reaching the ocean environment

Macro region	Region	Fraction of mismanaged and littered waste entering aquatic environments	Fraction of aquatic plastics reaching the ocean
OECD America	USA	32%	3%
	Canada	36%	3%
	Other OECD America	21%	5%
OECD Europe	OECD EU countries	34%	3%
	OECD non-EU countries	34%	4%
OECD Asia	OECD Pacific	43%	11%
	OECD Oceania	44%	2%
Other America	Latin America	28%	5%
Eurasia	Other EU	27%	1%
	Other Eurasia	32%	1%
Middle East and Africa	Middle East & North Africa	27%	4%
	Other Africa	23%	4%
Other Asia	China	28%	2%
	India	26%	4%
	Other non-OECD Asia	34%	14%

Source: Fraction of mismanaged and littered plastic waste entering aquatic environments (adapted from Borrelle et al. (2020[99])) and fraction of waste in aquatic environment entering the ocean environment (adapted from Meijer et al. (2021[100])) by region.

In the ocean, plastics with a larger density than seawater will sink at the bottom, accumulating in deep-sea canyons and trenches by the action of gravity. Floating plastics, however, will be transported by the action of waves, wind and currents. The most significant fraction of these plastics, however will rapidly reencounter land and beach on a coastline. A study presenting a model of dispersion of plastics in the ocean from global coastal sources reported that within a year, around 97% of released model particles had resided near a coastline for more than two consecutive days (Lebreton and Andrady, 2019[19]), suggesting a significant fraction had likely beached in that time. Rich coastal ecosystems will also facilitate the retention of floating plastics near the coastline as, similarly to freshwater environments, organisms in the marine environment will colonise floating plastics. Objects with smaller volume to surface ratios, such as plastic films or small microplastics, will likely sink near the coastline. Fragments and objects with a sufficiently large volume to maintain their buoyancy can escape the coastal environments. Over time debris tend to accumulate offshore in subtropical latitudes. Five accumulation zones have been widely reported in the literature from field observations and numerical models. The largest one is located in the North Pacific Ocean between Hawaii and California (Lebreton et al., 2018[101]).

Environmental conditions will also dictate the fate of plastics during their journey in freshwater and marine environments. Particularly under the action of sunlight, plastics degrade by photo-oxidation. As such, it is expected that plastics near the surface in rivers, lakes, or in the ocean are more likely to degrade into smaller particles, commonly referred to as microplastics with varying definitions (usually, particles below 1-5 mm and larger than one micron). Due to the large complexity of mechanisms and under varying conditions, data on the degradation of plastics in natural environments is scarce. Still, results are starting to appear with long-term experiments on the degradation of plastics in controlled environments. Fragmentation rates expressed in the percentage of weight loss per year did not exceed 5% in a laboratory seawater microcosm for various conventional thermoplastics (Gerritse et al., 2020[102]). This is in good agreement with modelled whole-ocean plastic degradation rates expected by numerical models (i.e. 3% of total ocean plastic mass degraded per year from macro- to microplastics, (Lebreton and Andrady, 2019[19]).

For the purpose of this work, the whole-ocean plastic mass budget model presented in (Lebreton and Andrady, 2019[19]) was expanded to a simplified representation of the global aquatic environment. The model now differentiates between annual inputs in freshwater and the ocean, allowing floating plastic waste to circulate from one compartment to the other over time. The model was also enhanced by differentiating inputs by polymer types using the OECD ENV-Linkages model estimates and waste projections presented in this report. The likely fate of emitted plastics was determined depending on their density. Additionally, the degradation rates varied between polymers based on laboratory results (Gerritse et al., 2020[102]). The general model framework is presented in Figure A A.5. To differentiate between freshwater and marine environment inputs, the model uses the results from Meijer et al. (2021[100]), which provides country-scale probabilities of emissions to the ocean. These results were upscaled to the modelled region by following the same weighted method as for inputs into aquatic environments (see the previous section). Thus was estimated the fraction of waste emitted in freshwater and the fraction emitted directly into the ocean for every region and per year. Starting the model in 1951, plastics were emitted into the modelled aquatic environment from every region. Polymers with a density higher than water were assumed to sink on the riverbed, lakebed, or seabed. Floating polymers circulating at the surface could directly reach the coastal ocean surface within the first year or remained in the freshwater system, likely stranded on river and lakeshores. The model also remobilised accumulated waste in river and lakeshores, adding onto inputs from the following year. Floating polymers in the coastal ocean surface followed the same dynamics as in the model presented in Lebreton and Andrady (2019[19]), with recirculation between the shoreline and the sea surface and transfer from coastal to offshore waters. Floating plastics accumulated in river and lake shore or on the ocean surface and shoreline were considered in contact with sunlight, and a fraction of their mass was degraded yearly to a sink term representing the mass of microplastics accumulated in freshwater and marine environments. The cycle was repeated every year until 2019.

Figure A A.5. Mass balance budget model for plastic in global aquatic environments

Note: Mass inputs by modelled region, characterised by polymer types, are accumulated from 1951 to 2060 into the plastic fate model. Plastics with a density higher than water sink and accumulate in riverbed, lakebed and seabed. Floating plastics (density lower than water) are transported between different aquatic compartments and are allowed to degrade into microplastics over time from contact with sunlight. The region-specific parameter ‘i’ is the ratio between plastics remaining in freshwater and the plastics entering the marine environment (Table A A.27). The parameters ‘s’ and ‘r’ represent the fraction of stranding and release from the global shoreline. The parameter ‘t’ is the fraction of floating plastic circulating from the coastal to the offshore ocean. (s = 97%, r = 3%, t = 33%, (Lebreton, Egger and Slat, 2019[103])). Finally, ‘d’ is the mass fraction degrading into microplastics annually and varies with polymer types (Table A A.28).

Source: OECD ENV-Linkages model, adapted from Lebreton and Andrady (2019[19]) methodology.

Table A A.28. Parameters for fate of plastic in aquatic environments by polymer type

Polymer type	Micro	Floats	Degradation rate in % of mass per year
HDPE	No	Yes	0.6
LDPE, LLDPE	No	Yes	0.8
PP	No	Yes	0.0
PET	No	No	4.9
PS	No	No	0.1
PUR	No	No	3
PVC	No	No	-
ABS, ASA, SAN	No	No	-
Bioplastics	No	No	-
Elastomers (tyres)	No	No	-
Fibres	No	No	-
Road marking coatings	Yes	No	-
Marine coatings	Yes	No	-
Other	No	No	-

Source: Degradation rates are sourced from laboratory experiments (Gerritse et al., 2020[102]).

This model produced time series from 1951 to 2060 of inputs and accumulation of plastic waste from global regions into rivers, lakes, and the ocean. The main concerning result is a severe worsening of pollution in all aquatic environments in the Baseline scenario. The model allows us to produce first-order of magnitude estimates of mass distribution in different compartments of the global aquatic environment.

This simplified model has some limitations, and care should be given in the interpretation of the results. The fate of plastics will vary significantly depending on the situation. These projections should be seen as a whole, describing the regional quantity of plastic waste expressed by orders of magnitude of mass. Some assumptions were made in the design of the model, which does not always reflect reality. For instance, polymers such as PET, PVC or PUR were considered as sinking plastics, but by design, objects made with these polymers can float for a variable period of time (e.g. empty PET bottles with cap on, PVC buoys, or extended PUR foam). On the contrary, some floating plastics such as HDPE or LDPE may also sink rapidly (e.g. biobased plastic bags) in rivers while still considered movable in the model.

By investigating inputs, transport and fates of plastics from the beginning of mass production to 2060, the generation of secondary microplastics can be estimated in the environment, allowing comparisons between the contribution of old legacy plastics versus new inputs. By looking back, the contribution of early polluters can be assessed and observed throughout a century how the problem has and will continue shifting geographically. These results help target priority regions for mitigation of pollution, focusing on the Asian and African continents.

Modelling particulate matter emissions from tyre and brake wear (NILU)

This section explains the methodology and parameters employed by Nicolaos Evangeliou from the Norwegian Institute for Air Research (NILU) to make projections on the emission of airborne road-traffic-related microplastics and their contribution to particulate pollution.

Calculation of emissions of tyre and brake wear

Tyre and brake wear particles (TWPs and BWPs) are calculated using the GAINS (Greenhouse gas – Air pollution Interactions and Synergies) model (Amann et al., 2011[104]). GAINS is an integrated assessment model where emissions of air pollutants and greenhouse gases are estimated for nearly two hundred regions globally considering key economic activities, environmental regulation policies and region-specific emission factors. For emissions of particulate matter (PM), GAINS provides PM distinguishing PM₁, PM_2.5, PM₁₀, total PM, as well as carbonaceous particles (BC, OC) that derive from combustion processes, as described in Klimont et al. (2017[105]).

Emissions of non-exhaust PM in GAINS include TWPs, BWPs, as well as road abrasion. The calculation of these emissions is based on region-specific data and estimates of distance driven (km/vehicle-type/year) and vehicle-type specific emission rates (mg/km). The types of vehicles considered include motorcycles, cars, light-duty vehicles, buses, and heavy-duty vehicles. The estimates of distance driven for 2015 are derived using data on fuel use in road transport from the International Energy Agency’s Word Energy Outlook (IEA, 2011[106]), supported by national data on vehicle numbers and assumptions of per-vehicle mileage travelled. Considering vehicle-type specific emission rates and use, allows for better reflection of significant regional differences in fleet structure, e.g. large number of motorcycles in South and South-East Asia and lower car ownership numbers in parts of the developing world. GAINS emissions are estimated globally at the grid level (0.5°×0.5°) using road network data, assumptions about road-type vehicle density, and population data.

The vehicle-type specific TWP and BWP emission factors used in GAINS draw on a review of several measurement papers (Klimont et al., 2002[107]) that were recently updated (Klimont et al., 2017[105]) using primarily van der Gon et al. (2013[108]), EEA (2013[109]) and Harrison et al. (2012[110]). There are large uncertainties in emission factors including the PM size distribution. GAINS provides total suspended particulates (TSP), and then assumes that PM₁₀ from TWPs represent about 10% of TSP, and PM_2.5 about 1% of total TWPs, whereas PM₁₀ from BWPs is about 80% of TSP and PM_2.5 is 40–50% of total BWPs (Klimont et al., 2002[107]).

Atmospheric transport modelling

Emissions of PM₁₀ calculated with the GAINS model are used as input in the FLEXPART (FLEXible PARTicle) atmospheric transport model version 10.4 (Pisso et al., 2019[111]). Atmospheric dispersion of particulate matter, including both transport and deposition of particles, were simulated for the reference year 2014. The FLEXPART model was run in forward mode from 2014. Atmospheric processes affecting particle transport in clouds (e.g. boundary layer turbulent mixing and convection processes) are parameterised in the model (Forster, Stohl and Seibert, 2007[112]). The model was driven by 3-hourly 1°×1° operational analyses from the European Centre for Medium Range Weather Forecast (ECMWF), the spatial output resolution of concentration and deposition fields was set to 0.5°×0.5° in a global domain with a daily temporal resolution. In FLEXPART the dispersion of road microplastics is modelled assuming a spherical shape of particles (Pisso et al., 2019[111]).

The simulations also accounted for below-cloud scavenging and dry deposition, assuming a particle density for TWPs of 1234 kg/m³, which is in the middle of the densities of 945 kg/m³ for natural rubber and 1522 kg/m³ for synthetic rubber (Walker, 2019[113]; Federal Highway Administration Research and Technology, 2019[114]). This density is within the reported range for microplastics (940-2 400 kg/m³) (Unice et al., 2019[115]). For BWPs a higher density was assumed (2 000 kg/m³) considering that BWP may also contain metals (Grigoratos and Martini, 2014[116]). Plastics are generally hydrophobic and should therefore be rather inefficient cloud condensation nuclei (CCN) (Di Mundo, Petrella and Notarnicola, 2008[117]; Ganguly and Ariya, 2019[118]). However, coatings may make the particles more hydrophilic with time in the atmosphere (Bond et al., 2013[119]). The efficiency of aerosols to serve as ice nuclei (IN) is also not well known. Based on Evangeliou et al. (2020[120]), it is more realistic to use intermediate scavenging coefficients for CCN/IN in the model.

Extrapolation of the model results until 2060

The aforementioned emissions of TWPs and BWPs were extrapolated using the road passenger data from the IEA’s World Energy Outlook (IEA, 2018[121]) for 15 geographical regions with global coverage, following the regional aggregation of the ENV-Linkages model.

Year 2014 was taken as base year and the ratio to year 2014 was calculated for each year between 2015 and 2020 and for each of the 15 regions (from now on referred as “regional scaling factor”). This regional scaling factor could be negative, if the road passenger data decreased as compared to 2015, or positive, if an increase is shown.

Having obtained estimates of TWPs and BWPs, the FLEXIPART model was used to calculate their global annual transport and deposition (atmospheric dispersion) for each year between 2015 and 2060. Running FLEXIPART for so many years is computationally expensive. Furthermore, since meteorological fields necessary for FLEXPART are only available until present times (2021), it would be necessary to run a climate model until 2060 to generate the meteorological fields for FLEXPART, thus further increasing the computational time needed. To avoid these issues, it was assumed that the meteorology will remain approximately constant over the years, and that changes in the global dispersion of TWPs and BWPs will only be due to emission changes in the various regions.

With these assumptions, FLEXPART was run with the 2014 emissions for the 15 ENV-Linkages regions; thus creating 15 different model simulations, each representing the dispersion from the respective region. Then, the regional scaling factor was used to scale modelled dispersion that results from each individual regional emission for each year between 2015 and 2060. Finally, the 15 regional annually-scaled modelled dispersions were used to calculate global TWP and BWP estimates.

Modelling greenhouse gas emissions from plastics in ENV-Linkages

This section explains the methodology and parameters employed to make projections on the contribution of the lifecycle of plastics to GHG emissions, on a global level.

An emission-factor-based approach

In the GTAP database (Aguiar et al., 2019[5]), plastics production occurs in two sectors: Chemicals and Rubber and plastics products. The plastic products sectors has been split into primary and secondary plastics production. The plastics producing sectors use inputs from the electricity generation sector, fossil fuel extraction sectors and other sectors of the economy. However, since these plastics producing sectors also produce other goods, not all emissions can be attributed to plastics. Therefore, to approximate the global lifecycle emissions from plastics, an emission factor-based approach is retained, in line with the most recent estimates from the literature:

${Emi}_{g,t}^{plastics}= {\sum }_p\left({\lambda }_{g,p,t}^{prod}+{\lambda }_{g,p,t}^{conv}\right)C_{p,t}+{\sum }_f{\lambda }_{g,f,t}^{eol}{W}_{f,t}$

where ${Emi}_{g,t}^{plastics}$ are emissions of greenhouse gas $g$ (comprising CO₂, CH₄ and N₂O, measured in CO₂-equivalents5) from the plastics lifecycle at time $t$, ${\lambda }_{g,p,t}^{prod}$ and ${\lambda }_{g,p,t}^{conv}$ are respectively the emission factors per tonne of plastic product for production and conversion of plastic for polymer $p$ that are applied to the level of plastics consumption $C_{p,t}$ estimated by the model. Finally, ${\lambda }_{g,f,t}^{eol}$ is the emission factor for a specific end-of-life fate $f$ (incineration, sanitary landfilling and recycling only are considered, due to data availability), applied to the amount of plastic waste generated $W_{j,t}$.

The literature provides estimates of emission factors for year 2015 (Zheng and Suh, 2019[21])6 that are used to calibrate emissions for 2015 (Figure A A.6). These emission factors comprise emissions from the whole value-chain of plastics production, and are not constant over time due to structural changes in the production process the change in GHG intensity of plastics production and conversion over time is endogenously determined in the model. A GHG-efficiency index is computed based on the global average scope 2 emissions (direct emissions plus emissions from electricity demand) of the most relevant plastics-related sectors (Chemicals, primary Rubber and plastics products, Oil extraction, Gas extraction and Petroleum and coal products). Regarding the GHG intensity of recycling, an index is built based on scope 2 emissions of the secondary plastics sector, while for incineration and landfilling, emissions factors are constant.

Figure A A.6. Greenhouse gas emission factors for plastics lifecycle in ENV-Linkages

Note: Emissions from recycling and incineration are direct emissions from their respective industrial processes. Avoided emissions (emissions from electricity generation that are replaced by waste incineration and emissions from primary plastics production that are replaced by secondary plastics) are not included in the emission factors depicted here, because they are included directly in the ENV-Linkages model (changes in the input structure of the electricity generation sector and changes in the balance between primary and secondary plastics, both being driven by the change in the relative prices of products endogenously determined by the model).

Source: Adapted from Zheng and Suh (2019[21]).

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Decomposition of changes in plastics emissions

To analyse the changes in emissions $E$ between two situations, labelled 1 and 0 (e.g. between 2060 and 2019 in the baseline or between the policy scenario and the baseline), the following decomposition of emissions was used (e.g. for end-of-life):

$E_1-E_0=\left({\sum }_f{W}_{f,1}-{\sum }_f{W}_{f,0}\right) {\sum }_f{\lambda }_{f,0}\frac{W_{f,0}}{{\sum }_{ff}{W}_{ff,0}}+\left({\sum }_f{W}_{f,1}\right){\sum }_f{\lambda }_{f,0}\left(\frac{W_{f,1}}{{\sum }_{ff}{W}_{ff,1}}-\frac{W_{f,0}}{{\sum }_{ff}{W}_{ff,0}}\right)+{\sum }_f\left({\lambda }_{f,1}-{\lambda }_{f,0}\right)W_{f,1}$where the first term can be interpreted as a “scale” effect (change in total plastic waste generated at initial emission factor and composition), the second can be interpreted as a “composition” effect (change in the relative shares of the different waste management options) and the third term as a “GHG intensity” effect (change in emission factors at final composition and scale). The same decomposition is done for production and conversion emissions, where the “scale” effect corresponds to the changes in the amount of plastic produced at initial emission factor and polymer mix, the “composition” effect corresponds to the change in the share of the different polymers in total production, and the “GHG intensity” effect corresponds to the changes in emission factors at final composition and scale.

Modelling the effects of higher penetration rates of biobased plastics (Neus Escobar and Wolfgang Britz)

The assessment was carried out with the integrated GTAP-based framework “GGEBox” (Britz and van der Mensbrugghe, 2018[122]). First, the original GTAP 10 database (Aguiar et al., 2019[5]) was aggregated into 18 larger regions, while keeping the full sectoral resolution. Second, fossil-based and biobased plastics were split from the “rubber and plastic products” (rpp) aggregate in five major producing regions – Brazil, China, the EU, the United States and Thailand –, based on relative output shares. These regions currently represent around 60% of the global biobased plastic market. Although Thailand’s bioplastics market is relatively smaller, the country is expected to become a production hub of biodegradable and biobased plastics, in view of recent investment in the last years (Fielding and Aung, 2018[123]; OECD, 2013[124]). Besides wheat and sugarcane, already explicitly represented in the GTAP database, corn and cassava were disaggregated respectively from “other grains” and “fruits and vegetables”, to include relevant bioplastic feedstock. Additional adjustments were made to the GTAP database to increase the cost share of agricultural raw materials in the bioplastic industry relative to the original “rpp” sector, which uses petroleum as input instead of crops. China and the United States produce biobased plastics mainly from corn (>85%) but also wheat; OECD EU utilises both corn and wheat to almost the same extent (about 50% each); Brazil employs entirely sugarcane while Thailand also uses cassava at around 40%.

Substitution between fossil-based and biobased plastics in intermediate input demand was modelled through a Constant Elasticity of Substitution (CES) function. An initial value of 5 was assumed for the substitution elasticity (subelas) to capture the relatively large market shares of drop-in products, which are expected to be maintained in the future (IEA, 2020[125]). These refer to those plastics that have identical technical characteristics to their fossil counterparts and allow for direct market substitution, such as polyethylene and polyethylene (PE) and polyethylene terephthalate (PET).

CGEBox incorporates multiple GTAP extensions to be able to estimate GHG emissions from Indirect Land Use Changes (ILUC) as well as from endowment use and agricultural production. The land transformation module simulates land conversion across major uses (cropland, pasture and managed forest) at the Agro-Ecological Zone (AEZ) level, based on differences in returns to land according to a Constant Elasticity of Transformation (CET) function. Each land use is associated with AEZ-specific carbon pools in soil, above- and below-ground biomass and litter, including foregone carbon sequestration over a 30-year period (Gibbs, Yui and Plevin, 2014[126]; Pelvin et al., 2014[127]). Moreover, CGEBox introduces the possibility of agricultural land expansion into natural land, with a land supply elasticity (landelas) of 0.05 for all regions considered. Carbon stocks in natural land uses were estimated by assuming that natural forests have twice as much carbon as managed forest (Kindermann et al., 2008[128]); grassland, savannah, and shrubland have the same carbon content as pastureland; and the remaining “other” land has 10% of the carbon in pastureland – see (Escobar and Britz, 2021[129]) for further details.

The analysis focuses on the effects of an increased market penetration of biobased plastics in Brazil, China, OECD EU, United States and Thailand, compared to the baseline in 2060.7 This requires that biobased plastics have a cost-advantage over fossil-based ones, which is here simulated with two alternative scenarios, namely (A) introducing fiscal policies to regulate the plastics market and (B) promoting technical progress in the bioplastic industry through R&D. The two scenarios are described as follows:

• Mandate scenario: represents a government intervention simulating a mandate to increase consumption of biobased plastics at the cost of conventional ones. This is done by subsidising bioplastics consumption by firms and final consumers to replace 5% of the total plastics market (in monetary values) in each of the five regions considered by 2060. The targeted level of market penetration is consistent with projections for the EU 28 region (Schipfer et al., 2017[130]). As a result, the level of the subsidy varies across bioplastic producing regions, depending on the respective sizes of both their bio- and fossil-based plastic sectors. The greatest drop in ad-valorem taxes on demand for biobased plastics is estimated for China (-47.0%) and the smallest for Brazil (- 14.0%), with an average decrease of 41.0% globally. At the same time, consumption taxes on oil, gas, coal, petroleum and fossil-based plastics increase in each region, referring to both domestic and import demand by all agents (consumers, firms, investors and governments). The same change in ad-valorem tariffs is applied to these five products in all regions to keep total indirect tax income constant in real terms. This can be interpreted as changes in value added tax rates to ensure that public services (health, education, etc.) are maintained.

• Efficiency scenario: introduces technical progress in the bioplastic industry beyond the baseline, as a result of R&D investment and subsequent upscaling of technologies that allow for enhanced biomass use efficiencies. These in turn refer to pathways based on non-food feedstock (e.g. algae, perennial crops or waste) or cascading uses and closed-loop approaches (e.g. in integrated biorefineries). Hence, technical progress is simulated as a more efficient use of crop-based inputs for plastic production, as combined with higher factor productivity. It is assumed that demand for agricultural raw materials per unit of bioplastic produced decreases by 60% in 2060, implying a rate of 1.3% per annum. Additionally, labour and capital requirements are reduced by 30% (0.65% per annum). Similar efficiency improvements were considered in other studies for the long-term development of bioenergy and biochemical sectors both in industrialised and emerging countries (Lee, 2016[131]; van Meijl et al., 2018[132]), and at the world level (Escobar and Britz, 2021[129]). At the same time, taxes on fossil-based plastics are introduced in all regions – not only in bioplastic producing ones – to keep real GDP constant.

Both the Mandate and Efficiency scenarios yield approximately the same levels of biobased plastics production in 2060 on a global scale (ca. 60 Mt), with these accounting for around 3% of the total plastics market. Whereas the market penetration of bioplastics is exactly the same across producing regions in Mandate (5%), the Efficiency scenario delivers different levels of bioplastics consumption across the five regions. The greatest market shares are obtained for Thailand (6.3%) and Brazil (17.6%) due to improvements in the conversion efficiency of sugarcane, which becomes the most cost-effective feedstock. The market share of bioplastics in China, OECD EU and the United States is around 4% in 2060 in Efficiency. Outcomes from the two scenarios were then assessed against the baseline, in order to understand the economy-wide impacts of each intervention.

Scenarios with alternative parameters were considered as part of an uncertainty analysis. First, the parameter reflecting the substitutability between fossil-based and biobased plastics (subelas) for different applications (e.g. packaging, electronics, buildings or automotive) was varied to understand how easily industries can replace conventional plastics with biobased plastics. Second, the ease of converting natural land into agricultural and managed forestland areas (landelas) was varied to understand how the implementation of different conservation policies and other governance strategies can promote or prevent natural land cover loss when biomass demand increases. These two parameters were varied around the central values considered (subelas=5 and landelas=0.05) to analyse the uncertainty of results (see Table A A.29).

Table A A.29. Uncertainty parameters for the analysis of biobased plastics penetration rates

Mandate scenario		Efficiency scenario
Subelas	Landelas	Subelas	Landelas
5	0	5	0
5	0.025	5	0.025
5	0.05	5	0.05
5	0.10	5	0.10
5	0.25	5	0.25
1	0.05	1	0.05
2.5	0.05	2.5	0.05
5	0.05	5	0.05
7.5	0.05	7.5	0.05
10	0.05	10	0.05

Note: subelas = elasticity of substitution between conventional and biobased plastics; landlelas: land supply elasticity. The central values of 5 and 0.05 were respectively taken for subelas and landelas in the assessment of both Mandate and Efficiency scenarios. The reference scenario using the central values is in bold in the table.

Source: CGE-Box model.

Projecting other health and environment impacts from the life cycle of plastics (Ghent University)

This section explains the methodology, parameters and impact categories employed by the experts of the Sustainable Systems Engineering Group of Ghent University to make projections for the health and environment impacts from plastics using a life cycle assessment (LCA) approach.

Goal and scope

The goal of the LCA is to analyse the environmental impacts of plastics on a global scale. The assessment includes the production from cradle to gate of polymers serving as feedstock for industry and the end-of-life (EoL) treatment of such plastics considering recycling, landfilling, incineration, dumping and open burning.

Seven polymer types are included in the analysis: Polyvinyl chloride (PVC), Polyurethane (PUR), Polystyrene (PS), Polypropylene (PP), Polyethylene terephthalate (PET), Low-density Polyethylene (LDPE), High-density Polyethylene (LDPE).

The functional unit is the production and end-of-life of polymers on a global scale in 2019. The system boundary includes the primary and secondary production from cradle to gate and selected waste management methods. Figure A A.7 shows a simplified scheme of the system boundary.

Figure A A.7. Considered stages of the LCA analysis for the seven polymer types

The geographical scope is global. OECD data for the global use of plastics were used as a base for the global production of plastics. Also for global waste management, OECD data were used. The assessment did not apply regional differentiation of production for the environmental impacts of plastics. Generally, global energy mixes of electricity and heat are used to calculate the environmental impacts. Some datasets related to incineration and landfilling do not have global averages; in those cases, estimates rely on Europe as a geographical reference.

The temporal scope relates to two periods: 2019 and 2060. The difference between the two periods only affects the projected volumes of plastics use and waste. The inventory information for plastics production per end-of life fate was not modified and no changes in future technology or future global energy mix were incorporated.

Some key limitations of this study: there is no consideration of the use stage of plastics, the manufacturing of plastic products and the impacts related to the production of other polymers not mentioned in this report.

Life Cycle Inventory

The compilation of inventory was made with SimaPro 9.1.1.1. Life cycle data of plastics was sourced from Ecoinvent v3.6, with the cut-off by classification model. In the cut-off by classification model, "recyclable materials are cut off from the producing product system" (Wernet et al., 2016[133]). This means for plastics that the feedstock for recycling (waste plastic) comes burden-free to the secondary producer. The secondary producer bears only the burden of the recycling and secondary material production processes; hence, no burdens from the primary production are attributed to secondary materials (Wernet et al., 2016[133]).

The foreground system boundary followed the structure of the database modelling approach. As presented in Figure A A.7, the secondary production at the beginning of life (Secondary) includes the activities after the collection of plastics until the production of the secondary polymer (e.g., regranulates). To keep consistency at the EoL, "recycling" only bears the impact of waste collection before the recycling process itself.

Data assumptions:

• Primary production: The technology pathway to produce a certain polymer can differ. Some of these pathways are reflected in the database and sometimes need to be manipulated before use in the assessment. For example, the production of PVC is detailed by three polymerisation technologies: suspension, emulsion and bulk polymerisation. Evidently, different technology pathways will show different environmental impacts, so an arithmetic average of the impacts from different technology pathways to produce polymers has been used for the projections.

• Secondary production (mechanical recycling): Data for secondary production of plastics is quite limited compared to primary production. Only data of secondary HDPE and PET via mechanical recycling is available in Ecoinvent. Hence, an arithmetic average of the environmental impact of these polymers was used for the calculation of PVC, PS, and PP. For LDPE, the same impact was assumed as for HDPE. PUR is not included as there is no reported secondary production of such material in the OECD model. The analysis starts with the collected plastic waste (burden-free) and includes the steps of separation, shredding, washing, floating, drying, cutting, and regranulation according to the Ecoinvent datasets information.

• Secondary production (chemical recycling): Data for thermochemical recycling was derived from (Civancik-Uslu et al., 2021[134]), considering sorted waste streams of PP and PE for naphtha production. Due to confidentiality, only aggregated environmental impact data can be presented. The downstream processes after naphtha production (i.e. cracking, polymerisation, and granulation) were based on calculations from (Civancik-Uslu et al., 2021[134]) using Ecoinvent data for primary production of PP and HDPE. On the one hand, the uncertainty for the estimation of these downstream processes is high. On the other, data for the production of naphtha via thermochemical recycling is based on high-quality measures derived from primary data collection in Belgium. Finally, data on the thermochemical recycling process was adapted using global energy production (electricity and heating) to represent global impacts.

• End-of-life (EoL): The EoL stage was evaluated in five groups: recycling, incineration, landfilling, dumping and open burning. For recycling, as commented in section 1.2, only impacts related to collection are attributed to this process (downstream processes are attributed to secondary material production). For the other waste management methods, polymer-specific datasets were used. For incineration, the data concerned municipal incineration with fly ash extraction and sanitary landfill.

The background data, i.e. the upstream data of energy, materials, infrastructure and auxiliaries for the provision of the above-mentioned processes, were not modified from the Ecoinvent database. In this respect, the energy mixes are related to the regions represented in the datasets

Life Cycle Impact Assessment (LCIA)

The environmental impact calculation used SimaPro 9.1.1.1. As LCIA methodology the ‘ReCiPe 2016 Midpoint (H) V1.04 / World (2010) H’ was used, in which 11 impact categories were selected for computation of environmental impact results: Ozone formation - Human health, Ozone formation – Terrestrial ecosystems, Terrestrial acidification, Freshwater eutrophication, Marine eutrophication, Terrestrial ecotoxicity, Freshwater ecotoxicity, Marine ecotoxicity, Human carcinogenic toxicity, Human non-carcinogenic toxicity and Land use.

Description of the LCA impact categories

Land use refers the land surface used to produce a resource or execute an activity, for example the area occupied by a mine, landfill or agricultural activity. This land is then temporarily unavailable for other uses, or for nature and ecosystems. The impacts are measured as land use (in m2).

Ozone formation or photochemical oxidation is the formation of reactive chemical compounds such as ozone by the action of sunlight on certain primary air pollutants, sometimes visible as smog. These reactive compounds may harm damage health, ecosystems, and crops. The impacts are measured as emissions of substances (VOC, CO) to air (in kg ethylene equivalents). These emissions are translated into a category indicator ‘tropospheric ozone formation’ using the Photochemical Ozone Creation Potential (POCP) of different gases (Jenkin and Hayman, 1999[135]; Derwent et al., 1998[136]; Derwent, Jenkin and Saunders, 1996[137]).

Euthrophication covers all potential impacts of excessively high environmental levels of macronutrients, the most important of which are nitrogen (N) and phosphorus (P). Nutrient enrichment may cause undesirable shift in species composition and elevated biomass production in ecosystems and affects sources suitable for drinking water. These emissions are translated into a category indicator ‘deposition/N/P equivalents in biomass’ using a stoichiometric procedure, which identifies the equivalence between N and P for both terrestrial and aquatic systems (Heijungs, 1992[138]). Marine eutrophication is measured in kg of N-eq, and freshwater eutrophication is measured in kg of P-eq.

Ecotoxicity refers to the impacts of toxic substances on species in freshwater aquatic or terrestrial ecosystems. The impacts are measured as emissions of toxic substances to air, water and soil (in kg 1.4-dichlorobenzene equivalents). These emissions are translated into a category indicator ‘predicted environmental concentration/predicted no-effect concentration’ using Freshwater Aquatic Ecotoxicity Potentials (FAETP) (Huijbregts, 2000[139]; Huijbregts, 1999[140]) and the USES 2.0 model developed by RIVM, describing fate, exposure and effects of toxic substances into Terrestrial Ecotoxicity Potentials (TETP) (Huijbregts, 2000[139]; Huijbregts, 1999[140]).

Human toxicity covers the impacts on human health of toxic substances in the environment, either by inhalation or via the food chain. Such impacts cover widely varying symptoms reaching from irritation to mortality. The impacts are measured as emissions of toxic substances to air, water and soil (in kg 1,4-dichlorobenzene equivalents). These emissions are translated into a category indicator ‘acceptable daily intake/predicted daily intake’ using Human Toxicity Potentials (HTP) (Huijbregts, 2000[139]; Huijbregts, 1999[140]).

Acidification is the corrosive impact that pollutants such as sulphur dioxide (SO₂) and Nitrous Oxides (NOx) have on soil, groundwater, surface waters, biological organisms, ecosystems and materials (buildings). The impacts are measured as emissions of acidifying gases to the air (in kg SO₂ equivalents). These emissions are translated into an indicator ‘deposition/acidification critical load’, describing the fate and deposition of acidifying substances as Acidifying Potentials (AP average Europe) of different gases (Huijbregts, 1999[140]).

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Notes

← 1.  As it is not possible to use lifespan distributions from historical years, in the first years an exogenous component of waste generated by earlier produced commodities is added.

← 2.  Due to lack of country/application specific lifespan data.

← 3.  Littering is included as a separate category to reflect the unaccounted potential losses to the environment. It is set as a constant share of municipal solid waste only following the assumption in (Jambeck et al., 2015[17]).

← 4.  In particular, ECHA (2020[53]) reports that the share of end-of-life tyre-derived granules would represent 78% on the infill, whereas EPDM and TPE would account for 18%, and cork 4%, by 2028. As artificial turf is only made up of the rubber part of tyres (EuRIC MTR 2020), 96% of all infill is assumed to be microplastics.

← 5.  The nominal emissions of CH₄ and N₂O are converted to CO₂-equivalents using the 100-year GWP from 2nd assessment report (IPCC, 1995[141]).

← 6.  The authors of this paper are gratefully acknowledged for providing for providing greenhouse-gas specific emission factors that are not available directly in their paper.

← 7.  The baseline was generated over the period 2014-2060 with the G-RDEM model in CGEBox (Britz and van der Mensbrugghe, 2018[122]; Britz and Roson, 2018[142]), based on projections of population and GDP from the OECD’s ENV-Linkages model under the impacts of COVID-19. The baseline also includes projections of both biobased plastics and total plastics consumption in physical units.