Closing the Loop: How Regenerative Robust Gasification Enhances Recycling and Supply Chain Resilience
Bibliographic record
Abstract
Municipal solid waste (MSW) recycling is constrained by contamination, heterogeneity, and infrastructure built around material-specific pathways. We introduce effectiveness-normalized greenhouse gas (GHG) emissions as a system-level metric that adjusts reported process burdens by feedstock eligibility (Effectiveness Fraction, EF) and carbon recovery efficiency (CRE) to reflect real-world MSW conditions. Using published LCA data and engineering estimates, we benchmark six pathways, mechanical recycling, PET depolymerization, enzymatic depolymerization, pyrolysis, supercritical water gasification (SCWG), and Regenerative Robust Gasification (RRG), at the scale of mixed MSW. Normalizing for EF and CRE reveals large differences between process-level and system-level performance. Mechanical recycling and PET depolymerization show low process intensities yet high normalized impacts because they can treat only a small share of plastics in MSW. SCWG performs well at broader eligibility. RRG, a plasma-assisted molten-bath approach integrated with methanol synthesis, maintains the lowest normalized impact (~1.6 t CO2e per ton of recycled polymer) while accepting virtually all organics in MSW and vitrifying inorganics. Modeled methanol yields are ~200–300 gal·t−1 without external hydrogen and up to ~800 gal·t−1 with renewable methane reforming. The metric clarifies trade-offs for policy and investment by rewarding technologies that maximize diversion and carbon retention. We discuss how effectiveness-normalized results can be incorporated into LCA practice and Extended Producer Responsibility (EPR) frameworks and outline research needs in techno-economics, regional scalability, hydrogen sourcing, and uncertainty analysis. Findings support aligning infrastructure and procurement with robust, scalable routes that deliver circular manufacturing from heterogeneous MSW.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".