The role of electrification and the power sector in U.S. carbon neutrality
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".