Air pollution co-benefits from strengthening electric transmission and distribution systems
Bibliographic record
Abstract
Inefficiencies in the transmission and distribution (T&D) of electricity between suppliers and customers can lead to higher compensatory electricity generation and unanticipated air pollution. Using both life cycle assessments and uncertainty analyses, we estimate the compensatory air pollutants – CO 2 eq , SO x , NO x , and PM 2.5 – associated with aggregate and non-technical T&D losses at national and subnational scales. Our global analysis estimates that 1 Gigatonne of CO 2eq and 1.3 Megatonnes (Mt) NO x , 1.6 Mt SO x , and 2 Mt PM 2.5 are associated with annual aggregate T&D losses. We also find that approximately 274 Mt CO 2eq , 367 kilotonnes (kt) NO x , 486 kt SO x , and 535 kt PM 2.5 are emitted due to non-technical T&D losses. Our subnational analysis in the United States demonstrates the variation of emissions savings across regulatory jurisdictions. We present an initial deployment cost analysis for CO 2 eq reduction which compares deploying smart meters (i.e., reducing non-technical T&D losses) to renewable energy generation expansion. Investments in T&D infrastructure are beneficial in a completely decarbonized system because improvements in the T&D grid can make investments in renewable energy more cost-effective. • We investigate air pollution emissions from inefficiencies in electric transmission and distribution systems. • Approximately 274 Mt CO 2eq , 367 kt NO x , 486 kt SO x , and 535 kt PM 2.5 result from non-technical transmissions losses. • Subnational analysis in the United States demonstrates the variation of emissions savings across regulatory jurisdictions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".