Step 3: Build the First Draft of the Data Inventory

2.4 Step 3: Build the First Draft of the Data Inventory

2.4.1 Purpose

1By the end of Step 2, a country has a list of individually scored data sources, but not yet a single, usable picture of its plastics data. Step 3 brings that list together into a first version of the Plastics Data Inventory: a working document that records, in one place, what is known, how confident that knowledge is, and where the clear gaps lie. It will not be complete, nor is it intended to be. Its purpose is to provide Steps 4 to 6 a concrete basis on which to build.

2.4.2 Key Actions

1. 1Create the Plastics Data Inventory as a basic spreadsheet, with a separate sheet for each category in the plastics value chain, and populate it using the scored Checklist from Step 2.
2. 2Within each category sheet, structure the entries as a time series, covering as many years as data availability or the country’s specific requirements allow.
3. 3Clearly flag data gaps, low-confidence entries, and assumptions rather than leaving them blank. An explicit “not yet available” is more useful to the next reader than an empty cell.
4. 4Circulate the draft internally for a brief review, to check for entries that appear clearly incorrect or disproportionate, before it goes into the gap analysis in Step 4.

2.4.3 Outputs

1A first draft Plastics Data Inventory, with gaps and confidence levels visibly marked and entries tagged by end use. This is the input to Step 4.

2.4.4 Detail and supporting guidance

1There is often a temptation to delay sharing an inventory until it appears complete. In practice, this rarely happens without outside help. Most of the gaps remaining after Step 2 can only be closed with support from outside the lead agency, which is the purpose of Steps 4 to 6. Waiting for a complete picture before sharing the draft tends to delay progress rather than protect it.

2A rough but honest first version is more useful than a polished but incomplete one. If a category is missing, this should be stated explicitly, rather than left as a blank cell that a later reader might mistake for a zero. Where a figure rests on an assumption — for instance, that this year’s waste composition is the same as last year’s — that assumption should be recorded alongside the figure.

3This distinguishes an inventory that stakeholders can meaningfully engage with from one that risks misleading whoever relies on it next. This first version should be treated as a working document, not a finished product. It will be revisited and improved in Step 6, once institutional and stakeholder input (Step 5) has had a chance to fill in what this step could not.

Populating the inventory, category by category

1The most manageable way to populate the Inventory is one life cycle category at a time, moving through the same categories used for the Checklist in Step 2:

• 2Production
• 3Consumption
• 4Trade (imports and exports, of primary plastics, plastic products, and plastic waste)
• 5Waste generated
• 6Waste collected — formal and informal channels
• 7Waste left uncollected
• 8Waste treated (recycled or upcycled, used for energy recovery)
• 9Waste disposal (sent to landfills or dumpsites, incineration and open burning)
• 10Plastic leaking into the environment

11Working category by category, rather than trying to complete the whole document at once, makes it easier to see which parts of the inventory are already strong and which still rest on a single source or an assumption.

Box 2.3: A Quick Way to Tag Data Status

Add one word next to each entry, even before the fuller record is complete:

• 12No data found — nothing usable exists yet for this category.
• 13Estimated — calculated using a method such as mass balance or a regional default.
• 14Partial — some real data exists, but coverage is incomplete.
• 15Measured — based on direct, primary data collection.

This single column shows reviewers at a glance where the inventory is solid and where it still needs work.

Filling data gaps

1Some categories will have no directly measured data at all. Rather than leaving these blank, two approaches can be used, provided the method used is written down next to the figure.

2Approach 1: Estimate by difference (mass balance). Where a category cannot be measured directly but sits between two categories that can, it can often be estimated by difference. For instance:

Waste left uncollected = waste generated − waste collected (formal) − waste collected (informal)

3Where possible, this kind of derived figure should also be checked against a second, independent estimate, even a rough one, to confirm the result is plausible.

4Approach 2: Borrow a default value, and flag it clearly. When no domestic figure exists, a default from a regional or international study can be used instead, such as a standard composition breakdown or an average collection rate for a comparable country context. This is a legitimate way to avoid an empty inventory, but the entry should be clearly labelled as a placeholder, so it is replaced with a domestic figure as soon as one becomes available, rather than mistaken for local evidence.

5Where only two data points exist for a category (for example, two survey years several years apart), a simple growth-rate calculation between them can be used to estimate the years in between, again with the assumption stated alongside the figure.

Additional sources of data to consider

1A first inventory can often draw on data sources that are not immediately obvious:

• 2Survey questions already being asked. An existing national population or household survey, even where originally designed for another purpose, can serve as a proxy for how households dispose of waste, once the relevant questions are identified and extracted.
• 3Regional or international technical studies. These can supply a default figure for a category with no domestic data at all, pending a more locally specific source.
• 4Community groups, youth-led initiatives, and informal-sector operators. They can hold some of the most detailed and disaggregated data available, despite limited institutional recognition or support.

5The first inventory should actively look for useful data across this full range of sources, not only within formal government or industry bodies.

Coordination challenges to anticipate

1Two recurring challenges can arise at this step, even though they are not fully resolved until Step 5.

2Challenge 1: Centrally held data may only be released in aggregate. The authority best placed to hold much of the relevant data, most often the National Statistics Office, may only be able to release figures at a national or aggregated level, owing to its own data-sharing arrangements with the agencies that supply it. Disaggregated figures, where needed, should be requested directly from the individual agencies or organisations that generate them.

3Challenge 2: Willingness to share is uneven across the life cycle. Organisations on the collection, recycling, and disposal side of plastics tend to be more willing to share, while private-sector actors further upstream are often more cautious, citing commercial sensitivity. Where this is the case, an indirect proxy, such as trade data or a sector-level index, can serve as an interim stand-in in the first inventory while direct engagement is pursued through Step 5.