Co-creating socio-technical scenarios for net-zero emission pathways: Comparison of five national case studies
Bibliographic record
Abstract
The extent to which modelled future pathways support effective policymaking for sustainability transitions has been questioned for a long time, with one major issue being the insufficient integration with the perspectives of policymakers and other stakeholders. One proposal to address this issue has been to set up facilitative dialogues with stakeholders to extend model-based pathways to socio-technical scenarios. This paper presents the results of a first series of such co-creation workshops, where stakeholders discussed bottlenecks for model-based decarbonisation pathways and ways to overcome these bottlenecks through tailored policy mixes. The workshops took place in five countries: Brazil, Canada, Greece, Germany, and the UK, each with a specific sector focus. In all five workshops, it became clear that substantial tensions exist between the “ideal” modelled decarbonisation pathways and the real-world situation on the ground. Also, adverse political framework conditions, uncertainty of future policies and resistance of powerful actors were emphasised as overarching bottlenecks in most workshops. At the same time, in several instances stakeholders pointed out important aspects of transformative trajectories that are not covered by the models. Some challenges and solutions stand out in all countries in spite of the strong diversity of contexts: allocation of capital towards massive investments into low-carbon solutions; infrastructure development for generation and transport of hydrogen, capture and use of CO2 as well as electricity grid and storage adapted to renewable energy solutions; stakeholder and citizen dialogues, where agreement is reached on cornerstones of long-term decarbonisation trajectories; and demand-side measures complementing investments into low-carbon processes.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".