The knowns and unknowns in our understanding of how plastics impact climate change: a systematic review
Bibliographic record
Abstract
Plastic pollution and climate change are two major environmental issues of this century, with implications for ecosystem health, the economy, and humankind. Plastics have the potential to affect the climate in multiple ways, yet we lack a thorough understanding of what data we have on this phenomenon and where the knowledge gaps are. Here, we conducted a systematic review to assemble knowledge and answer the question: How do plastics impact climate through three major mechanisms–emissions of greenhouse gases across the plastics lifecycle, interference with Earth’s carbon sinks, and interference with Earth’s radiation budget? We searched through all 14 databases in Web of Science for relevant articles, and amended this pool with articles from manual reference searching and expert elicitations. Using rigorous inclusion and exclusion criteria, including the exclusion of non-peer reviewed studies to minimize risk of bias, we ultimately selected 143 articles for our review - 36 lifecycle greenhouse gas emissions papers, 83 carbon sink papers, and 24 radiation budget papers. Based on current available data, we found that the plastics lifecycle can emit up to two gigatonnes of carbon dioxide equivalents per year, with the most emissions being produced at the primary production and product manufacture stages. From existing carbon sink studies, we identified more instances of plastics negatively affecting carbon sequestration than vice versa. From the radiation budget papers, we found that radiative impacts are predominantly cooling in nature. The body of evidence is incomplete and more research is needed to confirm these findings and fill in existing knowledge gaps. Future work should prioritize quantifying greenhouse gas emissions from the transportation, consumption, and unmanaged waste stages of the plastics lifecycle. We need more studies that examine the impact of plastics on coastal blue carbon ecosystems and marine carbon sequestration endpoints, and more studies examining the impact of plastics on direct radiative forcing via aerosols, cloud properties, and the albedo/melting rate of surfaces and ice/snow. Immediate action is required to decarbonize the plastics lifecycle, and full accounting of the climate impact of plastics is needed in emissions scenarios, inventories, and climate models across geographies and sectors.
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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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".