Step 4: Analyse Gaps and Set Priorities
2.5 Step 4: Analyse Gaps and Set Priorities
Section titled “2.5 Step 4: Analyse Gaps and Set Priorities”2.5.1 Purpose
Section titled “2.5.1 Purpose”1With a first-draft Inventory in hand, the next task is to determine which gaps matter most, and in what order to address them. Step 4 turns the Inventory’s visible gaps into a short, well-supported list, which Step 5 uses to approach the right stakeholders with a specific request.
2.5.2 Key Actions
Section titled “2.5.2 Key Actions”- 1Assign scores for each data category.
- 2Assign scores for each plastics data type.
- 3Weight the resulting priority score by urgency as well as by data quality.
- 4Validate the gap analysis with stakeholders and data custodians.
2.5.3 Outputs
Section titled “2.5.3 Outputs”1A prioritised list of data gaps, weighted by both quality and urgency. This list is the input to Step 5.
2.5.4 Detail and supporting guidance
Section titled “2.5.4 Detail and supporting guidance”Scoring each data category
Section titled “Scoring each data category”1To decide what to improve first, assign a score for each data category using the descriptors in Table 2.14. The smaller the score, the stronger the case for prioritising that category’s improvement. Where a category has no data at all, score it 0, to flag it for urgent attention.
2Each sub-score uses the same 0–2 scale: 0 marks the answer that most strongly supports prioritising the category now, 2 marks the answer that most strongly supports deferring it, and 1 marks the answer in between. This keeps data availability, data reliability, ease to fill the gap, and impact of filling the gap equally weighted in the total, so no single sub-score can dominate the ranking on its own.
3The data availability and reliability scores should already exist from Step 2. At this stage, simply transpose those ratings into the numeric scores below, then add two further judgements:
- 4Expected ease to fill the gap: draw on stakeholders’ own expertise to understand why a gap exists (a missing collection mechanism, insufficient resources, a regulatory limitation) and how hard it would realistically be to close.
- 5Impact of filling the gap: draw on stakeholders’ expertise to gauge how much demand there is for the data — for example, how central it is to policy objectives or reporting requirements.
6Table 2.14. Scoring each data category. Lower totals are stronger candidates for prioritisation.
| Data availability | Score | Data reliability | Score | Ease to fill gap | Score | Impact of filling gap | Score |
|---|---|---|---|---|---|---|---|
| Yes | 2 | High | 2 | Challenging | 2 | Low | 2 |
| Partially | 1 | Medium | 1 | Moderate | 1 | Medium | 1 |
| No | 0 | Low | 0 | Easy | 0 | High | 0 |
7Sum the scores for each data category. The categories with the lowest totals are the strongest candidates for prioritised improvement.
Worked example: a category with all four sub-scores
Category: producer-reported packaging weights. Data availability = Partially → 1. Data reliability = Medium → 1. Ease to fill gap = Moderate → 1. Impact of filling gap = High → 0.
Total = 1 + 1 + 1 + 0 = 3, out of a possible 0–8 range (0 = strongest possible candidate for prioritisation). A total of 3 sits toward the lower end of that range, so this category is a reasonably strong candidate — mainly on the strength of its high policy impact, even though the data that exists is only partially reliable.
8Where data availability is “No”, do not separately score data reliability — there is not yet any data to assess for reliability. Base that category’s priority on the average of its three applicable sub-scores (availability, ease, and impact) rather than a four-term sum, so it remains comparable to categories where all four sub-scores apply; for all other categories, use the average of all four sub-scores.
Worked example: a category with no data at all
Category: informal-sector collection volumes — the plastic waste collected by waste pickers and informal recyclers, which sits outside municipal collection statistics. Data availability = No → 0. Data reliability is not scored — there is no data yet to assess. Ease to fill gap = Challenging → 2. Impact of filling gap = Medium → 1.
Only three sub-scores apply here, so average them rather than summing four terms: (0 + 2 + 1) ÷ 3 = 1.0. Compare that to the example above, expressed the same way: 3 ÷ 4 = 0.75.
On this shared 0–2 average, the packaging-weights category (0.75) is actually the slightly stronger priority candidate, even though “no data at all” sounds more urgent than “partially available, moderately reliable”. That is because this category’s gap is hard to close and only moderately impactful. This is exactly why an average, not a raw sum, is used to compare categories that have a different number of applicable sub-scores.
Scoring by plastics data type
Section titled “Scoring by plastics data type”1This is a separate prioritisation exercise from Table 2.14 and produces its own ranking; the two tables’ totals are not added together.
2A related decision is which type of plastic — for example primary forms, semi-finished products, finished products — to focus data collection on. If products are the focus, a further decision is needed on which kind of products: whether to prioritise 100% plastic items or composite products (pure plastic items are typically easier to measure), high-volume or low-volume categories, and environmentally problematic plastics.
3Score these choices the same way: the ease of identifying data for each plastics stream against the impact of having that data, using Table 2.15. The lower the score, the stronger the case for prioritising that stream.
4Table 2.15. Scoring by plastics data type.
| Ease to fill gap | Score | Impact of filling gap | Score |
|---|---|---|---|
| Challenging | 2 | Low | 2 |
| Moderate | 1 | Medium | 1 |
| Easy | 0 | High | 0 |
Worked example: choosing between two plastics streams
Stream A — single-use PET beverage bottles: a largely single-material stream already tracked through many deposit-return and recycling schemes. Ease to fill gap = Easy → 0. Impact of filling gap = Medium → 1. Total = 1, out of a possible 0–4 range.
Stream B — multi-material flexible packaging (laminated pouches, chip bags): mixed materials make it hard to identify and measure separately. Ease to fill gap = Challenging → 2. Impact of filling gap = High → 0. Total = 2.
Even though Stream B carries the greater policy and environmental significance, its total (2) is higher than Stream A’s (1), so Stream A is the stronger near-term candidate under this scoring. This is the same quick-wins logic used in Table 2.14: an easier-to-measure stream is prioritised ahead of a harder-to-measure one of greater underlying importance. Where a stream’s importance is judged to outweigh its difficulty, that judgement is applied through the urgency weighting below, not by adjusting the score itself.
Weighting priority by urgency, not only by quality
Section titled “Weighting priority by urgency, not only by quality”Box 2.4: A lower quality score is not always the higher priority
A data category with a moderate quality score can still be more urgent than one with a worse score, if it sits behind a binding, deadline-linked reporting obligation. As a rule of thumb:
- 1Basel Convention Reporting gaps are annual and binding — treat these as a floor priority regardless of how they score on the rubric above.
- 2Extended Producer Responsibility (EPR) scheme gaps are usually driven by a scheme’s own reporting calendar once it exists, and by the practical need for baseline data before a scheme is designed.
- 3National policy and reporting commitments (the INC process, SDG tracking, national targets) are typically the most flexible in timing and can absorb whatever prioritisation capacity is left over.
Validating the gap analysis
Section titled “Validating the gap analysis”1Once the initial gap analysis and prioritisation are complete, check the results before acting on them:
- 2Expert review — have subject matter experts check the findings for completeness and accuracy.
- 3Stakeholder validation — present the list to key stakeholders for feedback and anything that has been missed.
- 4Cross-reference with policy objectives — make sure the prioritised gaps line up with national policy goals and international commitments, not only with what is easiest to measure.
- 5Reality check — weigh the prioritised list against the resources genuinely available, to confirm it is achievable and not only correct in principle.
6A gap analysis that has been through this validation gives Step 5 a list that stakeholders have already had a chance to see and shape, which makes the targeted engagement in that step more effective.