Just Transition: A whole-systems approach to decarbonisation
Bibliographic record
Abstract
Transition to a post-carbon economy implies changes that are both far-reaching and unprecedented. The notion that a decarbonization transition must encompass multiple forms of justice is gaining ground. In response, the concept of Just Transition has become ever more popular – and confusion about its meaning ever greater. We argue in this paper that the term Just Transition needs a rigorous updating to develop its full conceptual power for the analysis and evaluation of the rapid and extensive energy transitions already underway. After reviewing the different uses of Just Transition in practice and scholarship, we propose that the term be used as an analytical concept for an ongoing process of transition. The Just Transition concept can provide an integrated, whole-system perspective on justice (procedural, distributive, recognition, and restorative) that can help in identifying systemic solutions to address environmental and socio-economic concerns. This would differ from reductionist approaches that derive from legacy silo-sectoral or technologically driven approaches; these too often overlook negative side-effects and wider justice implications of reorganizing economic practice. An examination of COVID-19 pandemic responses illustrates our operationalization of the Just Transition concept, highlighting the importance of designing whole-system policies that are equitable, as well as the pitfalls of pursuing a narrow sectoral approach. Taking seriously the implications of complex systems with hard-to-predict effects also has concrete implications for policy interventions at all levels of governance. In particular, we highlight the importance of attending to multiple social inequalities for ensuring the resilience of whole-system decarbonization in the face of instability, unpredictability, and unprecedented change.Key policy insights The transition to net-zero will be neither sustainable nor credible if it creates or worsens social inequalities; a backlash is likely if the transition is not perceived to be just.Pathways forward may only emerge through observation, experimentation, and experience.A range of policy tools exist to address Just Transition concerns. These include addressing social and environmental aspects of economic policy; making sure that interventions are adapted to local contexts; building democratic engagement platforms; and open and transparent communication.Job creation does not guarantee just outcomes, as justice goes beyond employment conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".