Risk profiles of scenarios for the low-carbon transition
Bibliographic record
Abstract
Providing energy to an economy through fuel supply chains incurs risks which can be identified and quantified by systematic analysis. Scenario analysis and risk analysis are complementary tools for assessing possible changes to socio-technical systems. Applying a risk evaluation method to published future energy scenarios shows how risk in the energy system might vary with time. In a UK case study six scenarios to 2050 are analysed, focusing on installed electricity generating capacity. Of the seven categories of risk, political risk scored the highest over the whole period. Despite the installed capacity increasing by a factor of up to three by 2050 with reductions in GHG emissions, our analysis projects a reduction in risk and shows how significantly the pathways differ. To indicate the difficulty of such an expansion of the electricity system, we propose the use of a new metric – the Scale of Challenge (SoC) – equal to the total risk score times the installed capacity. The key to achieving a low-carbon transition may lie in moderating exposure to risk. Identifying the origin and type of risk can inform policy since net-zero is not zero risk.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".