## 2 Materials and Methods
### Evaluation Metrics and Data Collection  
During the simulations, we collect data to evaluate both technical performance and traceability outcomes. Below, we summarize key metrics and how they are derived (to be presented in Section 3):  
* Throughput (TX/s): Number of blockchain transactions executed per second during peak load. This helps assess if adding circular transactions (returns, recycle events) significantly impacts system throughput. * Latency (s per TX): average time from submitting a transaction to it being committed on the ledger. We measure this for different transaction types (e.g., adding a production record vs. querying history) to see if any new function is a bottleneck. * Traceability Completeness: A qualitative but also quantitative measure: we will verify that for each final product produced, we can query and retrieve its full lineage (including if it contains recycled content, the lineage of that recycled content). We might report that, e.g., \"100% of products had complete lineage recorded\" as a success criterion. If any gaps occurred (e.g., a recycled lot not linked correctly), that would indicate an issue. * Recycled Content Traceability (%): As introduced earlier, we compute the fraction of new products that contain recycled material and for which the exact source (prior product) is known via the ledger. In an ideal case, this is 100% for all recycled content used, meaning every bit of recycled material in a new product is traced to its origin. * Return Audit Rate: Fraction of returned items that have a corresponding recycle or reuse record on the chain. Ideally 100% if all returns are processed correctly. * Performance across Scenarios: Metrics like average transactions per product, or time to process one product's lifecycle, can be compared. This highlights how the framework scales with complexity. We anticipate, for example, that the furniture scenario might have, say, 50 transactions per product (including components and recycling) vs. 10 per product in apparel, but that the Fabric network can still handle it within a similar time frame with no critical failures. * Scalability Tests: In the original work, we varied order size from 200 to 20,000. In the extended study, we can similarly vary the number of products or returns to see how it affects the above metrics. The proposed circular economy metrics align closely with established measurement frameworks and industry standards, providing robust validation for their applicability in blockchain-enabled supply chains. Our Recycled Content Traceability (%) metric directly corresponds to the Ellen MacArthur Foundation's \"Material Circularity Indicator\" [32] and supports compliance with emerging regulations such as the EU's Single-Use Plastics Directive, which mandates 25% recycled content in plastic bottles by 2025 [33]. The metric's granular tracking capability exceeds current industry standards by providing immutable provenance documentation rather than relying on supplier declarations. Similarly, the Return/Reuse Audit Rate aligns with the OECD's \"Policy Guidance on Resource Efficiency\" framework [34], which em phasizes the importance of measuring material recovery rates to assess circular economy effectiveness. Our blockchain-based approach provides superior auditability compared to traditional self-reporting mechanisms, addressing the OECD's concern about data reliability in circular economy metrics. The Cycle Closure Rate metric operationalizes key principles from the ISO 14040 series on Life Cycle Assessment by quantifying material flow loops within defined system boundaries [35]. This approach resonates with the European Environment Agency's \"Circular Economy Monitoring Framework,\" which tracks material flow indicators but lacks the transaction-level granularity that blockchain systems can provide [36].