Bibliographic record
Abstract
Purpose Efforts to address climate change need to focus on curtailing carbon emissions to the atmosphere, and these efforts need to focus in large part on energy processes and activities, given the significant contributions to carbon emissions of the energy sectors of countries. This paper aims to describe key energy‐related steps needed to address climate change. Design/methodology/approach The key energy‐related steps needed to address climate change are identified, discussed and illustrated. Findings Several key energy‐related steps are identified that need to be addressed to combat climate change. These include: use non‐carbon‐based energy sources; use non‐carbon‐based energy carriers and/or energy carriers that facilitate the use of non‐carbon‐based energy sources; remove and sequester carbon‐based atmospheric emissions; and increase efficiency. Originality/value Given the major contributions of energy processes to climate change, it is anticipated that initiatives to address these steps will allow major advances to be achieved regarding addressing greenhouse gas emissions and climate change. The key energy‐related steps constitute a logical and pragmatic approach that provides a coherent overall structure to guide efforts to address climate change. It is expected that addressing these steps should assist broader efforts to achieve sustainable development.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".