Novel Incorporation of Environmental Social Governance Ratings for Premarket and Established Chemical Process Technologies
Bibliographic record
Abstract
For sustainable and innovative premarket manufacturing technologies, there is only limited data available from the typically small companies that operate them. This limitation does not allow for the application of conventional environmental social governance (ESG) rating methodologies in such cases. This puts emerging technologies at a disadvantage to investors and shareholders given that ESG reporting is an eminent financial growth instrument. To compensate for this gap, a novel ESG evaluation technique is proposed and designed to assess both traditional (TRL 7–9) and emerging chemical process technologies (TRL 4–6). This approach challenges present economic arguments against adopting new technologies with future, yet uncertain, prospects for economical gain. Historically, ESG methodologies have focused on evaluating organizations, including companies. We propose an alternative approach, evaluating technologies and their applications in an entrepreneurial context. This study developed principles to modify criteria from the established commercial ESG framework of Morgan Stanley Capital Investment (MSCI). These principles were applied to relevant environmental and social criteria derived from MSCI’s primary ESG grading methodology. The ESG Industry Materiality Map and the Global Industry Classification Standard reveal that critical issues within the governance pillar are not deemed highly significant for the chemical and materials sectors, while pivotal issues under the environmental and social pillars are prioritized in ESG evaluations. A case study exemplified and demonstrated the proposed approach in the context of ammonia manufacturing in Australia. It compared traditional centralized ammonia production using steam methane reforming and Haber–Bosch (HB) conversion with the alternative regional, premarket production using high-temperature plasma (HTP) and green, electric mini-Haber–Bosch processes (e-mini-HB). The premarket technology is superior in environmental rating and inferior in social rating, which can be used to provide commercial advice to emerging HTP/e-mini-HB companies. As the environmental score has a higher MSCI-ESG weight factor, a superior overall ESG rating is determined for the premarket HTP/e-mini-HB technology.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| 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".