Plastics MFA

Design

The figure below presents a diagram of the plastics MFA. It covers the whole life-cycle of plastics from virgin production to end-of-life with different levels of recycling: recycling of polymers via mechanical recycling, recycling of monomers via chemical recycling, and utilization of captured emissions from incineration. The MFA is built as a succession of processes (or plastics stocks) connected by flows of polymers, either raw, embedded in goods, or in waste. They are listed in detail further below. What happens in each process to each flow is driven by exogenous parameters such as product lifetimes and waste collection rates that are provided as input data. Historic plastics consumption and trade flows are also provided as input data and serve for extrapolating future plastics stocks and trades. All exogenous parameter are also listed in detail further below.

PlasticsMFAdrawio Figure 1: Diagram of the plastics MFA

Processes

The following table lists the processes that are modelled in the plastics MFA.

Name
System environment
Feedstock(fossil)
Feedstock(biomass)
Feedstock(daccu)
Feedstock(ccu)
Virgin Production
Processing
Fabrication
Primary Market
Intermediate Market
Waste Market
Good Market
Mechanical Recycling
Chemical Recycling
Use Phase
EoL
Incineration
Landfilled
Collected
Uncollected
Uncontrolled
Emissions
Captured
Atmosphere

Dimensions

The following table presents the dimensions over which parameters and variables (stocks and flows incl. trades) are defined in the plastics MFA.

Name Letter
Time t
Historic Time h
Element e
Region r
Material m
Good g

Stocks

The following table presents the processes that are modelled as stocks in the plastics MFA with their respective dimensions and the lifetime model that is employed. We use an auxiliary stock for the stock extrapolation with only the dimensions that are regressed separately (r, g) to save dimensions and computation time. The auxiliary stock is computed using a dynamic stock model. The results are then transferred to the higher-dimensional in-use-stock in the MFA system by multiplying stock, inflow and outflow with the parameters "material shares in goods" and "carbon content materials". This stock is modelled as a simple flow driven stock.

Dimensions Name Process Stock Type Lifetime Model
t, r, g in_use_dsm StockDrivenDSM LogNormalLifetime
t, e, r, m, g in_use Use Phase SimpleFlowDrivenStock
t, e, r atmospheric Atmosphere SimpleFlowDrivenStock
t, e, r, m Landfilled Landfilled SimpleFlowDrivenStock
t, e, r, m Uncontrolled Uncontrolled SimpleFlowDrivenStock

Stock extrapolation

For plastics stock extrapolation, the general method as described in the Model overview was slightly adjusted: plastics stock per capita regressions over GDP per capita did not align well for different regions, as regions with lower GDP per capita currently have higher stocks per capita than more developed regions had at the same GDP per capita in the past. This was attributed to the fact that plastics is a rather novel material compared to steel and cement, and historic stocks of developed regions are therefore not representative for today's stocks in developing regions. To account for this effect, a weighted sum of logGDP per capita and time was used as a predictor in stock extrapolation according to the formula: 'log10(gdppc) * weight + time'. The weight (documented in the assumptions below) was determined from a regression of time vs. log10(gdppc) at constant stock per capita (that is not part of the repository). Regression parameters for the stock extrapolation are derived in two steps: As a first step, in-use stocks are regressed per product category with common parameter sets for all regions. An upper bound to the saturation levels is set at the current maximum historic stock per capita for each product category over all regions. From this regression, the fitted global saturation level for each product category is used for a second regression: In-use stocks are now regressed separately per product category and region using the fitted global saturation level from the previous regression as upper bound with the exception of those regions which already exceed that stock level, where the maximum historic stock per capita in the respective region was used as upper bound. Hence, in-use stocks are extrapolated by using separate parameter sets for each region and product category. The extrapolated in-use stocks for all product categories then add up to the total in-use stock of a region.

Flows

The following table presents all flows in the plastics MFA with their respective dimensions and the processes that they connect.

Dimensions Origin Process Destination Process
t, e, r, m System environment Feedstock(fossil)
t, e, r, m System environment Feedstock(biomass)
t, e, r, m System environment Feedstock(daccu)
t, e, r, m System environment Feedstock(ccu)
t, e, r Atmosphere Feedstock(biomass)
t, e, r Atmosphere Feedstock(daccu)
t, e, r, m Feedstock(fossil) Virgin Production
t, e, r, m Feedstock(biomass) Virgin Production
t, e, r, m Feedstock(daccu) Virgin Production
t, e, r, m Feedstock(ccu) Virgin Production
t, e, r, m Virgin Production Processing
t, e, r, m Virgin Production Primary Market
t, e, r, m Primary Market Processing
t, e, r, m Primary Market System environment
t, e, r, m System environment Primary Market
t, e, r, m Processing Fabrication
t, e, r, m Processing Intermediate Market
t, e, r, m Intermediate Market Fabrication
t, e, r, m Intermediate Market System environment
t, e, r, m System environment Intermediate Market
t, e, r, m, g Fabrication Good Market
t, e, r, m, g Good Market Use Phase
t, e, r, m, g Fabrication Use Phase
t, e, r, m Good Market System environment
t, e, r, m System environment Good Market
t, e, r, m, g Use Phase EoL
t, e, r, m EoL Collected
t, e, r, m EoL Uncollected
t, e, r, m Collected Mechanical Recycling
t, e, r, m Collected Chemical Recycling
t, e, r, m Collected Landfilled
t, e, r, m Collected Incineration
t, e, r, m Uncollected Uncontrolled
t, e, r, m Mechanical Recycling Processing
t, e, r, m Chemical Recycling Virgin Production
t, e, r, m Mechanical Recycling Uncontrolled
t, e, r, m Mechanical Recycling Incineration
t, e, r Incineration Emissions
t, e, r Emissions Captured
t, e, r Emissions Atmosphere
t, e, r Captured Feedstock(ccu)
t, r System environment Good Market
t, e, r, m Waste Market Collected
t, e, r, m Collected Waste Market
t, e, r, m Waste Market System environment
t, e, r, m System environment Waste Market

Flows that enter or leave markets are the exports and imports of the trades listed in the following table.

Dimensions Name
t, r, m, e primary
t, r, m, e intermediate
t, r, m, e manufactured
t, r, m, e, g final
t, r, m, e, g waste

Parameters

The following table presents all exogenous parameters in the plastics MFA with their respective dimensions and the sources of the respective input data. The parameters chemical_recycling_rate, bio_production_rate, daccu_production_rate and emission_capture_rate are assumed to be historically 0 and their interpolation to future target rates can be adjusted via the scenario configuration.

Dimensions Name Description Sources
h, r collection_rate Collection rate of plastic waste (Jiang et al., 2020)1, (Yonggang et al., 2024)2, (Hunan Yinghong New Materials Co., Ltd., 2020)3, (OECD, 2022)4, (Plastics Europe, 2025)5, (US EPA, 2017)6
h, r mechanical_recycling_rate Mechanical recycling rate of collected waste (Jiang et al., 2020)1, (Yonggang et al., 2024)2, (Hunan Yinghong New Materials Co., Ltd., 2020)3, (OECD, 2022)4, (Plastics Europe, 2025)5, (US EPA, 2017)6
h, r chemical_recycling_rate Chemical recycling rate of collected waste
h, r incineration_rate Incineration rate of collected waste (Jiang et al., 2020)1, (Yonggang et al., 2024)2, (Hunan Yinghong New Materials Co., Ltd., 2020)3, (OECD, 2022)4, (Plastics Europe, 2025)5, (US EPA, 2017)6
h, r primary_his_imports Historic primary plastics imports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r primary_his_exports Historic primary plastics exports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r intermediate_his_imports Historic intermediate plastics imports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r intermediate_his_exports Historic intermediate plastics exports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r manufactured_his_imports Historic manufactured plastics imports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r manufactured_his_exports Historic manufactured plastics exports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r final_his_imports Historic final goods imports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r final_his_exports Historic final goods exports (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
t, r waste_imports Plastic waste imports (historic and future assumption) (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
t, r waste_exports Plastic waste exports (historic and future assumption) (Geyer et al., 2017)7, (OECD, 2022)4, (UNCTAD, n.d.)8
h, r bio_production_rate Share of bio-based plastics in virgin production
h, r daccu_production_rate Share of DACCU plastics in virgin production
t, r, m mechanical_recycling_yield Yield of mechanical recycling (Uekert et al., 2023)9
t, r, m reclmech_loss_uncontrolled_rate Rate of mechanical recycling losses to uncontrolled disposal (Brown et al., 2023)10
r, m, g material_shares_in_goods Share of materials in goods (OECD, 2022)4
h, r emission_capture_rate Carbon capture rate for emissions of incinerated plastics
e, m carbon_content_materials Carbon content of materials Data from stochiometric calculations and estimates based on expert judgement for broader categories
h, r, g consumption Historic consumption (Geyer et al., 2017)7, (OECD, 2022)4
g lifetime_mean Mean lifetime of goods (Geyer et al., 2017)7
g lifetime_std Standard deviation of lifetime (Geyer et al., 2017)7
t, r population Population (International Monetary Fund, 2021)11, (India Department of Economic Affairs, n.d.)12, (India Ministry of Health, 2019)13, (IIASA, 2024)14, (James et al., 2012)15, (Arujo et al., 2021)16, (World Bank, 2023a)17, (IIASA, 2024)14, (Crespo Cuaresma, 2017)18, (Dellink et al., 2017)19, (KC et al., 2024)20, (United Nations, Department on Economic and Social Affairs, Population Division, 2022)21, (World Bank, 2023b)22, (Gapminder, n.d.)23, (James et al., 2012)15, (Bolt et al., 2014)24
t, r gdppc GDP per capita (International Monetary Fund, 2021)11, (India Department of Economic Affairs, n.d.)12, (India Ministry of Health, 2019)13, (IIASA, 2024)14, (James et al., 2012)15, (Arujo et al., 2021)16, (World Bank, 2023a)17, (IIASA, 2024)14, (Crespo Cuaresma, 2017)18, (Dellink et al., 2017)19, (KC et al., 2024)20, (United Nations, Department on Economic and Social Affairs, Population Division, 2022)21, (World Bank, 2023b)22, (Gapminder, n.d.)23, (Bolt et al., 2014)24, (James et al., 2012)15, (Bolt et al., 2014)24

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