Greenhouse gas mitigation for U.S. plastics production: energy first, feedstocks later
Bibliographic record
Abstract
Plastics production is responsible for 1% and 3% of U.S. greenhouse gas (GHG) emissions and primary energy use, respectively.Replacing conventional plastics with bio-based plastics (made from renewable feedstocks) is frequently proposed as a way to mitigate these impacts.Comparatively little research has considered the potential for green energy to reduce emissions in this industry.This paper compares two strategies for reducing greenhouse gas emissions from U.S. plastics production: using renewable energy or switching to renewable feedstocks.Renewable energy pathways assume all process energy comes from wind power and renewable natural gas derived from landfill gas.Renewable feedstock pathways assume that all commodity thermoplastics will be replaced with polylactic acid (PLA) and bioethylene-based plastics, made using either corn or switchgrass, and powered using either conventional or renewable energy.Corn-based biopolymers produced with conventional energy are the dominant near-term biopolymer option, and can reduce industry-wide GHG emissions by 25%, or 16 million tonnes CO 2 e/year (mean value).In contrast, switching to renewable energy cuts GHG emissions by 50%-75% (a mean industry-wide reduction of 38 million tonnes CO 2 e/year).Both strategies increase industry costs-by up to $85/tonne plastic (mean result) for renewable energy, and up to $3000 tonne À1 plastic for renewable feedstocks.Overall, switching to renewable energy achieves greater emission reductions, with less uncertainty and lower costs than switching to corn-based biopolymers.In the long run, producing bio-based plastics from advanced feedstocks (e.g.switchgrass) and/or with renewable energy can further reduce emissions, to approximately 0 CO 2 e/year (mean value).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".