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Plastic waste generation

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Plastic Pollution
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Plastic waste generation

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What you should know about this indicator

  • Municipal plastic waste is the plastic fraction of municipal solid waste (for example, packaging and consumer products discarded with household waste).
  • Cottom et al. (2024) developed the SPOT model model, which first fills gaps in municipal waste data using statistical predictions. It then estimates how plastic flows through the waste system and quantifies the uncertainty in those estimates. The model produces results for around 50,700 municipalities, which are subsequently aggregated to country and regional totals.
  • This data covers plastic that comes from land-based municipal solid waste (everyday waste from households and similar sources). It does not include pollution from making plastic, textiles, sea-based sources (like fishing gear), electronic waste, or plastic that is exported as waste and then lost elsewhere.
  • Values are model-based estimates and come with uncertainty. They should be interpreted as approximate estimates rather than exact measurements.
Plastic waste generation
Estimated amount of plastic waste generated in a year from municipal sources such as households, shops, and offices.
Source
Cottom et al. (2024) – with minor processing by Our World in Data
Last updated
January 14, 2026
Next expected update
January 2027
Date range
2020–2020
Unit
tonnes

Related research and writing

Thumbnail

Plastic Pollution

Hannah Ritchie, Veronika Samborska, and Max Roser

Sources and processing

This data is based on the following sources

Cottom et al. – A local-to-global emissions inventory of macroplastic pollution

This dataset provides national, regional, and global estimates of plastic waste generation, collection, and emissions from the SPOT (Spatial Plastic Optimization Tool) material flow analysis model. It includes comprehensive metrics on waste generation, collection coverage, disposal methods, and resulting emissions of macroplastics to the environment.

The data covers waste generation (WG), properly collected waste (PWG), per capita metrics, plastic debris emissions, burn emissions, litter, uncollected waste, collection and disposal emissions, recycling, collection coverage, disposal types (controlled/uncontrolled), management practices, and population without collection services.

Retrieved on
January 14, 2026
Retrieved from
https://www.nature.com/articles/s41586-024-07758-6
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Cottom, J., Cook, E., Veeken, A. et al. A local-to-global emissions inventory of macroplastic pollution. Nature (2024). https://doi.org/10.1038/s41586-024-07758-6

This dataset provides national, regional, and global estimates of plastic waste generation, collection, and emissions from the SPOT (Spatial Plastic Optimization Tool) material flow analysis model. It includes comprehensive metrics on waste generation, collection coverage, disposal methods, and resulting emissions of macroplastics to the environment.

The data covers waste generation (WG), properly collected waste (PWG), per capita metrics, plastic debris emissions, burn emissions, litter, uncollected waste, collection and disposal emissions, recycling, collection coverage, disposal types (controlled/uncontrolled), management practices, and population without collection services.

Retrieved on
January 14, 2026
Retrieved from
https://www.nature.com/articles/s41586-024-07758-6
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Cottom, J., Cook, E., Veeken, A. et al. A local-to-global emissions inventory of macroplastic pollution. Nature (2024). https://doi.org/10.1038/s41586-024-07758-6

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

We recalculated the world total by summing all countries instead of using the modeled world estimate from Cottom et al. (2024).

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Plastic waste generation”, part of the following publication: Hannah Ritchie, Veronika Samborska, and Max Roser (2023) - “Plastic Pollution”. Data adapted from Cottom et al.. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/plastic-waste-generation.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Cottom et al. (2024) – with minor processing by Our World in Data

Full citation

Cottom et al. (2024) – with minor processing by Our World in Data. “Plastic waste generation” [dataset]. Cottom et al., “A local-to-global emissions inventory of macroplastic pollution” [original data]. Retrieved April 14, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/plastic-waste-generation.html (archived on March 4, 2026).

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/plastic-waste-generation.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/plastic-waste-generation.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/plastic-waste-generation.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/plastic-waste-generation.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/plastic-waste-generation.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/plastic-waste-generation.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/plastic-waste-generation.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/plastic-waste-generation.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear