Mid-Century Strategies: Pathways to a Low-Carbon Future?
Bibliographic record
Abstract
Since the Paris Agreement was adopted in 2015, both national and subnational governments have been encouraged to submit Mid-Century Strategies, outlining how they would reach their deep decarbonization goals. However, research on the design and potential of these strategies has been very limited. To address this shortcoming, here we assess 13 such strategies – six national, seven subnational – in a comparative fashion. We find that the energy-economy-climate models underpinning these strategies are generally of high quality, though national jurisdictions generally performed better. However, most strategies are not plausible without significant changes to policy, and the industrial sector in particular presents a major limitation. The strategies are helpful in revealing this gap, but much works remains to be done for plausible mid-century decarbonization trajectories to become a reality. We also find that public input and societal participation in strategy building were a double-edged sword depending on the constellation of domestic preferences. • Governmental Mid-Century Strategies for deep decarbonization are underpinned by high-quality energy-economy-climate models • Governments’ proposed strategies require significant new policies, as even among jurisdictions that have an MCS, extant policies are insufficient to achieve deep decarbonization • No jurisdiction studied has yet put forward a plausible decarbonization policy for the industrial sector • Public input and societal participation can be a double-edged sword: they can increase durability of the strategy but also enable opposing forces to mobilize against ambitious changes
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".