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 – CO2eq, SOx, NOx, and PM2.5 – associated with aggregate and non-technical T&D losses at national and subnational scales. Our global analysis estimates that 1 Gigatonne of CO2eq and 1.3 Megatonnes (Mt) NOx, 1.6 Mt SOx, and 2 Mt PM2.5 are associated with annual aggregate T&D losses. We also find that approximately 274 Mt CO2eq, 367 kilotonnes (kt) NOx, 486 kt SOx, and 535 kt PM2.5 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 CO2eq 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.
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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) | 0.000 | 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 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".