Closing the Loop: How Regenerative Robust Gasification Enhances Recycling and Supply Chain Resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it