Elements and Enablers of Low-Emissions Pathways
Bibliographic record
Abstract
Abstract Meeting the challenge of climate change requires worldwide action from all sectors of society. Throughout this transition, oil and gas will have a role to play within the mix of energy sources to meet the need for affordable and clean energy products and services. IPIECA has identified a list of common elements and enablers of future pathways that most projected low-emissions pathways shared. The resulting paper considers the near- and long-term aims of the Paris Agreement, along with the challenges to address climate change whilst meeting the UN Sustainable Development Goals. Further to this, the current energy system is explored in order to provide a basis to evaluate the common elements of the multiple pathways to a low-emissions future. The three common elements of an energy system transition are: improving efficiency; reducing emissions from the power generation; and deploying alternative low-emission options in end-use sectors. The common enablers of a low-emissions pathways identified are: collaboration, effective policy and the availability of finance. This paper explores in detail these elements and examines key technologies to support this transition. The role of the oil and gas industry in meeting the long-terms aims of the Paris Agreement is also outlined. Furthermore, it addresses the challenges on addressing climate change in the context of the Paris Agreement and the UN Sustainable Development Goals and provides a perspective on the role of the oil and gas industry as enablers of pathways to meet a low-emissions future.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".