The acceleration of low-carbon transitions: Insights, concepts, challenges, and new directions for research
Bibliographic record
Abstract
Given that several low-carbon transitions are now accelerating, what can we say about the drivers, conditions, mechanisms, and dynamics of acceleration? This question is widely discussed in policy and academic circles, but so far there is little attempt to develop a more comprehensive answer that considers the pluralistic and heterogeneous nature of what acceleration is, how it comes about, and how it can be studied moving forward. To provide a more comprehensive approach to the phenomenon of acceleration, this paper offers a prismatic perspective that mobilizes insights from several social science disciplines and fields that have engaged with acceleration and links them to sustainability transitions. The objectives of the paper are threefold: to map out concepts or tools that are useful for better understanding or interpreting acceleration; to reflect on prominent themes and topics; and to identify research gaps and fruitful directions. Written by an interdisciplinary and authoritative team of authors, the paper draws from a wide range of concepts including but not limited to feedback theory from political science, incumbent reorientation and innovation races from business and management literature, cultural theory and public acceptance from socio-cultural studies, along with insights from consumption studies and sociology. It draws on this corpus to identify five singular dimensions of acceleration (economics, technology, business, policy, and behavior/culture) as well as four multi-dimensional mechanisms (tipping points, multi-system interactions, threshold dynamics and deep leverage points). It then examines underlying drivers and understandings of acceleration before synthesizing perspectives and charting directions for future research.
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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.020 | 0.056 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".