Industry’s Path to a Greener Future: A Perspective on Current Sustainable Practices and Areas of Opportunity
Bibliographic record
Abstract
Chemistry is directly and indirectly related to some of the most polluting industries, making it exceptionally critical for chemists to act and develop novel solutions toward more sustainable industrial practices. In this perspective, participants of the 2023 ACS Summer School on Green Chemistry & Sustainable Energy describe state-of-the-art developments that the chemical industry has spearheaded to reduce greenhouse gas (GHG) emissions and contribute toward achieving the 2030 Agenda for Sustainable Development. Herein, we illustrate a variety of methods that the chemical industry has employed, ranging from technological factors, such as using catalysis, implementing AI to reduce energy-intensive processes, and developing carbon capture technology and sustainable fuels, to socioeconomic factors─incorporating circularity, society targeted innovation and education, and developing successful collaborations between the private and public sectors. This perspective aims to trigger discussions and highlight how multifaceted approaches are necessary to support the transition to a greener industrial sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".