Climate Change, Multinationals, and the Energy Transition: Insights into this Global Grand Challenge
Bibliographic record
Abstract
Firms and governments across the world face pressure to address climate change concerns. The International Energy Agency’s latest report suggests that global economic recovery after the Covid-19 pandemic, the war in Ukraine and the associated energy concerns in a fragile geopolitical context, and increased regulations to curb climate change have heightened the central role of energy as an important issue in its own right. Moreover, it is apparent that the projected energy demands are unsustainable for the global climate goals, which makes the shift towards renewable energy sources and low carbon solutions one of the most important global grand challenges of our time. Energy producers—from large multinational enterprises to clean tech startups—are at the center of this energy transition, but must make the transition from fossil fuels to renewables in the context of changing regulations, geopolitical tensions, and increasing energy demand. This panel symposium brings together leading scholars on this topic to share a lively discussion on management theory and practice related to the energy transition. The purpose of this panel symposium is to encourage diverse perspectives on the energy transition and stimulate research on this global grand challenge.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".