The role of electrification and the power sector in U.S. carbon neutrality
Why this work is in the frame
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Bibliographic record
Abstract
The United States has pledged to achieve net-zero greenhouse gas emissions by 2050. We examine a series of net-zero CO 2 scenarios to investigate the impact of advanced electrification of end-use sectors on the dynamics of America's net-zero transition through 2050. Specifically, we use an integrated assessment model, GCAM-USA, to explore how advanced electrification can influence the evolution of the electricity system in pursuit of net-zero. State-level resolution for end-use demand sectors and energy transformation is a key feature of GCAM-USA that allows for elucidation of the variation in end-use electrification across states. All scenarios in this study are designed to be consistent with the modeling protocol for the Energy Modeling Forum Study 37 model inter-comparison project. Our scenarios show the scale of transformation in the power sector with average annual capacity additions reaching 121-143 GW/year and 172-190 GW/year in 2050 net-zero CO 2 scenarios and 2045 net-zero CO 2 scenarios, respectively, in the 2040s — approximately three to five times the 2021-2023 average. In 2050 net-zero CO 2 scenarios, electrification rates in 2050 range from 15-48 % for transportation, 65-83 % for buildings, and 20-38 % for industry. If net-zero CO 2 is achieved in 2045, transportation, buildings, and industry are 27-53 %, 78-84 %, and 41-53 % electrified by 2050, respectively. Advanced electrification of end-use sectors can reduce the magnitude of reliance on negative emissions by driving down residual positive emissions by mid-century. Altogether, our results demonstrate that a net-zero transition in the United States will require deep and rapid structural changes to the energy system .
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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 | 0.001 | 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 it