The majority of adults in the United States agree that the country's recycling system for plastic needs improvement, with more than ** percent of respondents agreeing with this statement in 2022. It is estimated that just **** percent of U.S. plastic waste was recycled in 2021. The share of respondents that that agree that the U.S needs to reduce its reliance on plastic has increased from ** percent in 2020 to ** percent in 2022.

An estimated 360 million metric tons of plastic waste was generated in 2020. In a business-as-usual scenario, in which no further policy measures are implemented, plastic waste generation is forecast to nearly double by 2040, surpassing 615 million metric tons.

In 2022, roughly ** percent of U.S. adults reported that they recycle most or all of their household's plastic waste - a slight increase from 2020. Meanwhile, ** percent of respondents said that they recycled very little or none of their household's plastic waste in 2022. Plastic recycling is a growing issue in the U.S., with ** percent of adults believing the U.S. plastic recycling system needs improvement.

The amount of plastic waste generated worldwide is projected to triple by 2060, to surpass *********** metric tons. This dramatic growth is set to be fueled by rising populations and economic growth. With so much plastic waste generated, proper waste management will be urgently needed to reduce environmental impact. However, projections show that landfilling will continue to be the main disposal method worldwide by 2060, with recycling accounting for less than ** percent. Much of the plastic waste generated over the coming **** decades will be caused by packaging.

Short description The annual rate of plastic debris emissions per person  Summary This indicator measures the release of plastic debris emissions, reported in kilograms per capita per year. It is modelled with the ‘Spatio-temporal quantification of plastic pollution origins and transport’ model (SPOT). The model begins at the point where waste is generated and discarded by the user and ends when these materials are either recycled, recovered, stored in disposal facilities or emitted into environment. The model focusses on macroplastic, referring to plastic particles larger than 5 mm, and includes both rigid and flexible formats.  Plastic emissions are defined as material that has moved from the managed or mismanaged systems (in which waste is subject to a form of control, however basic; contained state) to the unmanaged system (the environment; uncontained state) with no control. Plastic debris emissions are further classified as physical particles >5 mm that enter the environment. Sources for emissions were quantified with five land-based sources: (1) uncollected waste; (2) littering; (3) collection system; (4) uncontrolled disposal; and (5) rejects from sorting and reprocessing.  The study uses the UN-Habitat definition for MSW which includes discarded products and materials from households, commerce, and institutions, excluding waste from construction, demolition, industry, and sewage treatment. Additionally, textiles, electrical and electronic equipment waste, and waste material arising at sea were excluded.  The layer displays the mean value, and the modelled uncertainty, expressed as percentiles, can be seen in the pop up of each country.   Description Title: Plastic debris emissions per capita annually  Entity: University of Leeds, United Kingdom Source: University of Leeds: https://www.leeds.ac.uk/ Publication: Cottom, J.W., Cook, E. & Velis, C.A. A local-to-global emissions inventory of macroplastic pollution. Nature 633, 101–108 (2024). https://doi.org/10.1038/s41586-024-07758-6 Supplementary material: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-024-07758-6/MediaObjects/41586_2024_7758_MOESM1_ESM.pdf Data: https://datadryad.org/stash/dataset/doi:10.5061/dryad.8cz8w9gxb Time Period: 2020 Methodology: The SPOT model is plastic pollution emissions inventory that operates at the municipal level to reflect the level at which waste is managed and where waste data is typically collected. The model uses existing published data on solid waste management to train a machine learning model, which in turn is used to predict solid waste management variables for all municipalities globally. These predictions are used to perform material flow analysis for each municipality, including quantifying plastic emissions. Results are aggregated from the municipal level to the global scale, with the national level results shown here. Frequency Update: Unknown Last Update: 08-11-2024 Geo-Coverage: Global Licensing: Public

Short description Export - Plastic waste (thousand metric tonnes) Summary This indicator shows the development in total exported value of plastic at country level. The total plastic trade encompasses full life-cycle of plastic, covering primary forms, intermediate forms, intermediate manufactured goods, final manufactured goods, and plastic waste. Despite the relative small volume of trade in this category compared to other categories, plastic waste holds high significance due to the involvement of numerous stakeholders in waste processing. There is evidence suggesting that a considerable portion of waste exported abroad is not effectively recycled, leading to significant environmental and health risks. This is particularly evident in cases where waste is sent to countries lacking adequate infrastructure for environmentally sound waste management. Furthermore, the recycling process may involve various approaches such as re-cycling, up-cycling, downcycling, or waste-to-energy applications, contributing to a cyclic process where new products are generated from recycled waste. The trade data collection is from the UNSD Comtrade database, which has a dedicated methodology. Reporting is conducted globally by individual countries annually, covering the period from 2017 to 2022. The compartment is anthropogenic activities within the domain of waste. The data is presented in the unit thousand metric tonnes.Description Entity: United Nation Conference on Trade and Development (UNCTAD) United Nations Commodity Trade Statistics Database (UN Comtrade) Source URL: UNCTAD: https://unctad.org/ Data set: https://unctad.org/publication/global-trade-plastics-insights-first-life-cycle-trade-database Global trade in plastics: insights from the first life-cycle trade database: https://unctad.org/system/files/official-document/ser-rp-2020d12_en.pdf UN Comtrade: https://comtradeplus.un.org/ Time Period: 2017-2022 Methodology: UN Comtrade Methodology Frequency Update: Unknown Last Update: 25-07-2024 Geo-Coverage: Global Licensing: Public

Short description Total imported plastic (thousand metric tonnes) Summary This indicator presents a time series of trade data containing the total imported value of plastic at country level. The total plastic trade encompasses full life-cycle of plastic, covering primary forms, intermediate forms, intermediate manufactured goods, final manufactured goods, and plastic waste. The trade data collection is from the UNSD Comtrade database, which has a dedicated methodology. Reporting is conducted globally by individual countries annually, covering the period from 2017 to 2022. The compartment is anthropogenic activities within the domain of production and consumption. The data is presented in the unit thousand metric tonnes. Description Entity: United Nation Conference on Trade and Development (UNCTAD) United Nations Commodity Trade Statistics Database (UN Comtrade) Source URL: UNCTAD: https://unctad.org/ Data set: https://unctad.org/publication/global-trade-plastics-insights-first-life-cycle-trade-database Global trade in plastics: insights from the first life-cycle trade database: https://unctad.org/system/files/official-document/ser-rp-2020d12_en.pdf UN Comtrade: https://comtradeplus.un.org/ Time Period: 2017-2022 Methodology: UN Comtrade Methodology Frequency Update: Unknown Last Update: 26-07-2024 Geo-Coverage: Global Licensing: Public

This dataset contains images of various plastic objects commonly found in everyday life. Each image is annotated with bounding boxes around the plastic items, allowing for object detection tasks in computer vision applications. With a diverse range of items such as milk packets, ketchup pouches, pens, plastic bottles, polythene bags, shampoo bottles and pouches, chips packets, cleaning spray bottles, handwash bottles, and more, this dataset offers rich training material for developing object detection models.

This dataset is collected by DataCluster Labs, India. To download the full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

The dataset is an extremely challenging set of over 4000+ original Plastic object images captured and crowdsourced from over 1000+ urban and rural areas, where each image is ** manually reviewed and verified** by computer vision professionals at Datacluster Labs.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Dataset Features

• Dataset size: 4000+
• Captured by: Over 1000+ crowdsource contributors
• Resolution: 99.9% images HD and above (1920x1080 and above)
• Location: Captured across 500+ cities
• Diversity: Various lighting conditions like day, night, varied distances, different material view points etc.
• Device used: Captured using mobile phones in 2020-2022
• Usage: Plastic object detection, Recycling and waste management, Garbage segregation, Trash segregation, etc.

Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Global Post consumer recycled plastics market is expected to grow above a CAGR of 6% and is anticipated to reach over USD 10.2 billion by 2026. Post-consumer recycled plastic is obtained from a product that was used by the consumer and has completed its life cycle and it will be disposed as a solid waste product.

This dataset is a part of the manuscript "Modelling macroplastic fluxes in the Imus catchment: Impacts of long-term accumulation and extreme events" by Clayer et al. The dataset was used with INCA-Macroplastics, a spatiotemporally explicit model for mismanaged plastic mobilization and transport from land to sea from the INtegrated CAtchment (INCA) family. INCA-Macroplastics encompasses all components of the catchment, is driven by available data (weather, population counts, solid waste production rates and composition) and enables calibration and validation against a range of observations (e.g., river monitoring, household surveys). The tool encompasses a litter source module driven by waste generation data as well as weather, land use and population data. It was applied to the Imus River, Cavite, Philippines, estimated to be among the top 50 most polluting rivers in the world. We modelled macroplastic transport, retention and export following two calibrations ("LandAcc" and "RiverAcc") and two emission scenarios ("default" and "Remediation2006", note that "Remediation2006" is referred to as "RA 9003 enforcement" in the manuscript) covering 1990-2020. Model outputs include water discharge, plastic stocks in various catchment and river compartments, as well as plastic exports to sea.

This repository includes 5 compressed folders (*.7z) with data files with outputs of a Monte Carlo sensitivity analyses performed for each calibration ("LandAcc" and "RiverAcc") and plastic emission scenario("default" and "Remediation2006") as described in the folder names. Variable names and units are given in the file names: - "dates.dat" are the datetime array over 1990-2020 - "Water_Discharge_m3_s.dat" is the water discharge at the river outlet in m3/s. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020. - "Total_Plastics_on_land_Free_and_Buried_Kg.dat" is the total (buried and free) weight of plastic litter on land in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020. - "Buried_Plastics_on_land_Kg.dat" is the weight of buried plastic litter on land in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020. - "Plastics_on_RiverBank_Kg.dat" is the weight of plastic litter on riverbanks in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020. - "Plastics_in_RiverVegetation_Kg.dat" is the weight of plastic litter stuck in river vegetation in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020. - "Plastics_exported_to_Sea_KgPerDay.dat" is the weight of plastic litter exported each day to the sea in kg/day. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020.

In addition, there are two excel files with data from the household survey and from monitoring of macroplastic litter in the Imus River.

Switzerland Exports of waste, parings, scrap of plastics to Ukraine was US$358 during 2020, according to the United Nations COMTRADE database on international trade. Switzerland Exports of waste, parings, scrap of plastics to Ukraine - data, historical chart and statistics - was last updated on July of 2025.

These data are made of two files. One file provides the observed data we collected and cleaned from the World Bank database. The second file provides the simulation results from the STIRPAT model we designed based on the observed data abovementioned. Our results can be summarised as follows:

Since 2015, the detrimental effects of plastic pollution have attracted media, public, and governmental attention. Considering economic growth is inevitable and a key driver of plastic contamination, it is worthwhile to analyze the environmental Kuznets curve (EKC) relationship between economic development and plastic pollution. To this end, we contribute by being the first to (i) use the Stochastic Impacts by Regression on Population, Affluence, and technology model (STIRPAT model) to investigate this EKC relationship; (ii) provide a comprehensive analysis of how demographic factors affect plastic pollution; and (iii) use panel model techniques to examine the drivers of plastic pollution. Our empirical results support an inverted U-shaped relationship between plastic pollution and income. They show that at current trends, global plastic pollution (that is, annual discard of inadequately managed plastic waste) is expected to grow from 52 million tons per year in 2020 to 257 million tons per year in 2050.

Slovakia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data was reported at 205.466 Tonne th in 2022. This records an increase from the previous number of 152.722 Tonne th for 2020. Slovakia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data is updated yearly, averaging 187.726 Tonne th from Dec 2010 (Median) to 2022, with 7 observations. The data reached an all-time high of 219.460 Tonne th in 2014 and a record low of 152.722 Tonne th in 2020. Slovakia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovakia – Table SK.OECD.ESG: Environmental: Waste Generation: by Sector: OECD Member: Annual.

Slovenia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data was reported at 421.357 Tonne th in 2022. This records an increase from the previous number of 341.698 Tonne th for 2020. Slovenia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data is updated yearly, averaging 390.047 Tonne th from Dec 2010 (Median) to 2022, with 7 observations. The data reached an all-time high of 502.848 Tonne th in 2018 and a record low of 296.584 Tonne th in 2012. Slovenia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovenia – Table SI.OECD.ESG: Environmental: Waste Generation: by Sector: OECD Member: Annual.

The United Kingdom exported nearly *** thousand metric tons of polyethylene (PE) waste in 2023, making it the most exported plastic waste type. Meanwhile, polystyrene waste exports amounted to some ** thousand tons that year, nearly tripling from 2020 levels.

The global Municipal Solid Waste Management market in 2019 was approximately USD 80 Billion. The market is expected to grow at a CAGR of 2% and is anticipated to reach around USD 96 Billion by 2026.

Norway Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data was reported at 444.326 Tonne th in 2022. This records an increase from the previous number of 402.218 Tonne th for 2020. Norway Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data is updated yearly, averaging 396.360 Tonne th from Dec 2010 (Median) to 2022, with 7 observations. The data reached an all-time high of 444.326 Tonne th in 2022 and a record low of 296.394 Tonne th in 2012. Norway Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.ESG: Environmental: Waste Generation: by Sector: OECD Member: Annual.

Germany Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data was reported at 32,422.550 Tonne th in 2022. This records an increase from the previous number of 31,519.080 Tonne th for 2020. Germany Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data is updated yearly, averaging 31,046.790 Tonne th from Dec 2010 (Median) to 2022, with 7 observations. The data reached an all-time high of 32,932.370 Tonne th in 2014 and a record low of 20,957.510 Tonne th in 2010. Germany Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.ESG: Environmental: Waste Generation: by Sector: OECD Member: Annual.

Latvia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data was reported at 28.751 Tonne th in 2022. This records a decrease from the previous number of 35.096 Tonne th for 2020. Latvia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data is updated yearly, averaging 35.096 Tonne th from Dec 2010 (Median) to 2022, with 7 observations. The data reached an all-time high of 101.947 Tonne th in 2018 and a record low of 7.993 Tonne th in 2014. Latvia Waste Generation: Primary: Manufacturing: Chemical, Pharmaceutical, Rubber and Plastic Products data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Latvia – Table LV.OECD.ESG: Environmental: Waste Generation: by Sector: OECD Member: Annual.