Scaling Deep Decarbonization Technologies
Bibliographic record
Abstract
Abstract The 2020s represent an unprecedented time to address climate change in the United States given the opportunities provided by its large renewable resource base and decreasing technology costs, and the threat to humans and ecosystems due to rapidly emerging climate impacts. This study addresses technical, societal, and policy issues for scaling deep decarbonization technologies, describing and extending the analysis presented at a National Academy of Sciences workshop convening modeling, industry, and other experts. Prominent modeling studies estimate that solar and wind electricity generation technologies must be deployed at more than four times their current highest annual rates, and solutions are needed for low‐carbon firm generation and energy storage. These studies also show electric vehicle sales needing to increase from 300,000 vehicles per year to annual sales in the tens of millions. Scaling these technologies poses substantive challenges. Other more difficult emissions reductions will need to come from decarbonizing corporate value chains and the heavy industry sector. Negative emissions technologies will compete as mitigation options but scaling these pose substantial resource requirements. Further, decarbonization is not simply an issue of technologies. Reaching net‐zero emissions is a transformation of society occurring over decades, not years. The societal aspects of decarbonization involves developing a social compact around the need to decarbonize and benefits that will accrue. Policy elements encompass federal, sub‐national, economic, and regulatory approaches. Because technologies, political priorities, and societal opinions will change over this time, consensus‐building scientific institutions including the National Academies will enable the independent technical and policy advice needed.
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) | 0.002 | 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".