Each data point received a score connected to each category, with a higher score for higher ranked aspects such as environmental relevance and a lower score for lower ranked aspects such as harmonisation potential. Environmental relevance was ranked highest due to its direct link to circular economy goals and urgent policy needs. By contrast, metrics only reflecting current policy mentions were ranked lower to avoid reinforcing existing reporting biases in the benefit scores (such as waste management). By focusing on the benefits of the underlying environmental impact and potential, and establishing an overview of plastic flows, Sweco applies a forward-striving approach.
Scoring dimensions
Cost scoring: Cost levels were determined based on:
Labour intensity, such as the need for coordination or new data collection
Technical complexity, e.g. alignment of definitions or data models
Data quality issues, including gaps, inconsistencies, or the need for expert validation
Each data point was assigned a cost grade from 1 (minimal effort, e.g. single-source, standardised data) to 5 (very high effort, e.g. new surveys or waste composition analyses).
Benefits scoring: Benefit scores were assigned across four strategic dimensions:
Environmental relevance, whether the data point represent a stream with high plastic leakage or low circularity
Analytical relevance, whether the data point is needed for high-level material flow analysis (MFA)
Policy relevance, whether the data supports monitoring of goals and targets
Harmonisation potential, whether the data exists across countries and enables joint analysis
These dimensions were weighted according to strategic importance and normalised to ensure comparability. Scores were summed to generate a single benefit score for each data point. This allowed all data gaps to be ranked by total benefit, enabling clear prioritisation when paired with the cost scores.