Step 6: Improve, Verify and Consolidate the Inventory

2.7 Step 6: Improve, Verify and Consolidate the Inventory

2.7.1 Purpose

1Step 5 should have brought in new data, or at least firm commitments to provide data. Step 6 incorporates that data into the Inventory. If there are any gaps stakeholders could not fill, these are addressed through primary data collection. The improved Inventory is presented to stakeholders for confirmation before it becomes the final working version.

2.7.2 Key Actions

1. 1Incorporate the data received in Step 5 into the Inventory.
2. 2Where priority gaps remain unfilled by stakeholders, commission primary data collection (waste audits, surveys, sampling) using the cross-cutting methodologies and sampling guidance in Part IV.
3. 3Re-score updated entries using the Step 4 rubric, and record what changed between the first and improved versions.
4. 4Present the improved Inventory to the stakeholders engaged in Step 5, to verify it is correct before treating it as the working version.
5. 5Flag scheme-dependent gaps — most often EPR producer-level data — that cannot realistically be closed until the relevant scheme or legal instrument exists.

2.7.3 Outputs

1A verified, improved, higher-confidence Plastics Data Inventory, endorsed by the stakeholders who contributed to it. This is the basis for Step 7 and, in time, for a Plastics Account.

2.7.4 Detail and supporting guidance

1Expect to go round Steps 4 to 6 more than once. This is a normal part of building an inventory, not a sign that something has gone wrong. Each pass should leave the Inventory measurably better than the last, and how many passes it takes depends on the country’s own capacity and resourcing, not on a fixed schedule.

2Verification matters as much as the improvement itself. An inventory that has picked up new data but was never checked back with the stakeholders who supplied it runs a risk of embedding a misunderstanding — a double-counted figure, a misapplied definition, a number attributed to the wrong year. Closing the loop by presenting the improved version for confirmation is what allows the country to treat it as a reliable working document from Step 7 onward.

Incorporating new data and tracking progress

1Incorporating the data received in Step 5 is largely mechanical: each entry is updated with the new figure, its source, and its date, and re-scored against the Step 4 rubric so the confidence rating reflects the improved evidence rather than the original placeholder.

2What is easy to lose track of, across several rounds of Steps 4 to 6, is which entries have actually improved, and which have not. A simple way to keep this visible is to mark, alongside each entry, whether it has moved from a borrowed default to a domestic figure, from an estimate to directly measured data, or has not changed since the previous version. Reviewed together, this record makes it straightforward to see how much of the inventory now rests on primary data and helps focus the next round on the categories that have not yet moved.

Box 2.7: Tracking Progress Toward a Fully Measured Inventory

For each updated entry, note which of the following applies:

• 3Regional/default → domestic — a borrowed placeholder has been replaced with a country-specific figure.
• 4Estimated → measured — a derived or mass-balance figure has been replaced with primary data.
• 5No change — still awaiting data; carried forward to the next round.

Commissioning primary data collection to fill data gaps

1Where Step 5 has not been able to fill a priority gap, the next option is primary data collection to gather new data directly, rather than relying further on outreach. The Handbook’s cross-cutting guidance on surveys, material flow analysis and sampling sets out the general methods. The examples below illustrate how they can apply in practice for a first improvement round.

1. 2Waste characterisation audits at disposal sites. Physically sampling waste arriving at a landfill or dumpsite is one of the most direct ways to convert a tonnage figure into a plastic-type breakdown, and to check whether a composition assumption borrowed from elsewhere still holds locally. Waste audits of households and commercial entities can also help establish waste composition and disposal pathways.
2. 3Sector-specific surveys with major resource-dependent industries. Where a sector’s waste or production figures rest on a general regional default — for example for tourism, fisheries or agriculture — a short survey or consultation with the relevant industry association or ministry can produce a figure specific to the country, replacing the default.
3. 4Combining spatial or satellite mapping with targeted ground-truthing. Where a broad spatial dataset already exists, for example mapping the size and location of dumpsites, pairing it with a smaller set of on-the-ground audits can convert a size estimate into a plastic tonnage estimate without requiring a full survey of every site.
4. 5Pilot studies to test assumption-based rates. Leakage percentages, informal collection rates and similar assumptions can be tested, even at a small scale, through a targeted pilot, and refined with real evidence rather than carried forward indefinitely on the basis of a borrowed default.
5. 6Using an existing periodic index as a proxy. Where no dedicated study exists for a category, for example production, a country’s own periodic economic or production statistics can sometimes be adapted, with any assumptions used to translate the index into a plastics figure documented clearly alongside the number.

Closing the loop: verifying the improved inventory

1Once the Inventory has been updated, it should be presented to the stakeholders engaged in Step 5, and to anyone else who will rely on it, before it is treated as the working version. The aim is not simply to inform them of the update, but to actively invite feedback — what looks wrong, what is unclear, and what does not yet meet their needs.

2Consolidating this feedback into a single list of confirmed changes, and resolving each point before moving on, is what allows the Inventory to be used with confidence in Step 7 and beyond.

Scheme-dependent gaps

1Not every remaining gap can be closed through further outreach or primary data collection. Some categories, most often producer-level data tied to an Extended Producer Responsibility (EPR) scheme, will show little or no data simply because the reporting mechanism that would generate it does not yet exist. Without a legal obligation or scheme in place, there is no established channel through which a producer would routinely report this information.

2These gaps are worth flagging clearly as scheme-dependent, distinct from an ordinary data-quality gap that further stakeholder engagement could still close. Once flagged, the appropriate next step is not more data collection, but the institutional and legal work covered in Step 7 — establishing the scheme, mandate or reporting obligation that would allow the data to exist in the first place.