Introduction

3.1 Introduction

1This part sets out methods for generating and collecting plastics data across the life cycle. The stages may be addressed independently and in any order, allowing each country to begin where the need is greatest, where data are most readily available, or where existing capacity allows. In most cases, a country will build its inventory incrementally, adding stages over successive reporting cycles rather than attempting full coverage at once.

2Each section presents several methods for collecting data on the relevant stage, together with the advantages and limitations of each. No single method is universally appropriate: the most suitable choice depends on a country’s national context, the data already available, and the resources at its disposal. Users should weigh the options on that basis and select the methods best suited to their circumstances, drawing on the guidance on prioritisation in Part II. For a comprehensive national inventory, a combination of methods is generally most effective.

3The methods are presented in the following order, by life cycle stage:

• 4Plastic Production
• 5Plastic Trade (including Plastic Waste)
• 6Plastic Consumption
• 7Plastic Waste Generated
• 8Plastic Waste Collected
• 9Plastic Treatment
• 10Plastics Disposal
• 11Plastics in the Environment
• 12Social data

3.1.1 Core principles for data collection

1When collecting new plastics data, users are advised to observe the following principles:

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  Consistency: apply uniform methodologies over time, so that figures can be meaningfully compared and trends analysed.

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  Verification: where possible, draw on multiple data sources to confirm accuracy and cross-validate findings.

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  Transparency: document all assumptions, limitations, and methodological steps.

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  International alignment: align with international classifications and definitions wherever feasible.

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  Proportionality: balance the ambition of data collection with the resources and capacity available.

Minimum data requirements

1When collecting a new data point, at a minimum, users should record data on the:

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  Weight of plastics - the mass, in a stated unit (for example, 1,250 tonnes of plastic PET bottles exported).

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  Year of reporting - the calendar or reporting year the figure refers to (for example, 2024).

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  Data source - where the figure came from (for example, the national customs database, or municipal council’s weighbridge data records).

5However, for each data point, users are encouraged to also record data on:

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  Polymer type(s) - the resin or resins involved (for example, PET, HDPE, or PVC).

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  Industry where plastic is consumed and/or disposed of - the sector of use or end-of-life (for example, food and beverage packaging, construction, or agriculture).

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  Product lifespan, including data on formal reuse systems where applicable - how long the product remains in use (for example, single-use packaging discarded within a year, compared with a PVC pipe lasting several decades; or a refillable-bottle scheme in which containers are reused before disposal).

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  Any assumptions, conversion factors and methodological processes used to estimate the data point - the calculation steps behind the figure (for example, converting a count of bottles to a weight using an average bottle mass of 20 grams).

Dealing with inconsistent data

1Data gathered in different locations and at different times are frequently inconsistent, which impedes the comparison of figures and the tracking of progress over time. The following measures help to keep reporting as consistent as possible:

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  adopt standardised reporting templates and agreed conversion factors, so that figures are recorded on a consistent basis;

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  incorporate validation checks into data entry to identify errors at an early stage;

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  where data are missing, apply proxy measures or modelled estimates, clearly labelled as such;

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  where sources overlap, rank them by reliability and give precedence to the more robust source;

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  corroborate key figures against two or more independent sources;

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  refer questionable data for expert review;

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  communicate any uncertainty transparently, so that users can judge how much confidence to place in each figure.