Quantifying greenhouse gas emission risks from natural gas pipeline incidents
Bibliographic record
Abstract
Natural gas pipelines are key energy infrastructures worldwide. Pipeline incidents frequently result in greenhouse gas (GHG) emissions that remain unaccounted for in carbon inventories. This study analyzed natural gas pipeline incidents in the United States, finding that such incidents released 14.71–18.20 million tCO 2 e during 2010–2021, representing an additional 2.67%–3.30% of total emissions. Presently, the US Environmental Protection Agency inventories record emissions during routine normal operations while excluding the incident-based releases. Regional patterns show that US Gulf Coast and South Central states have substantially higher emission risks than other regions. Pipeline age analysis reveals a non-monotonic risk pattern, with dual peaks driven by distinct failure mechanisms. Early-life pipelines (0–10 years) have elevated risks resulting from equipment failures, while long-life serviced pipelines experience degradation-related risks. The top 10% of incidents generate 57% of total emissions. Targeting the high-emitting incidents could reduce cumulative emissions by over 40%, highlighting substantial mitigation opportunities through improved monitoring and management strategy. • Pipeline incidents caused additional emissions of 2.67%–3.30% beyond EPA inventories • Emission risks follow a bimodal age pattern, with peaks in pipelines aged 0–10 and 41–50 years • Top 10% of incidents drive 57% of emissions, offering over 40% reduction potential Natural gas pipelines represent critical energy infrastructure spanning 500,000 km across the United States, yet their contribution to greenhouse gas emissions through operational incidents remains poorly quantified and often overlooked in climate mitigation strategies. As natural gas continues to serve as a bridge fuel in the energy transition, accurately accounting for all emission sources becomes essential for meeting climate commitments and ensuring infrastructure resilience. This research addresses a critical gap not in conventional accounting but in quantifying emission risks and emission factors from pipeline incidents. While routine operational emissions receive considerable attention, incident-related emissions can be large in scale despite being less frequent. Our findings show that US pipeline incidents emit as much as 4 to 5 coal-fired power plants annually, yet they are excluded from official inventories. The identification of regional disparities and age-related risks provides actionable insights for pipeline safety and emissions mitigation. This work supports data-driven policy, guides infrastructure investments, and underscores the need to incorporate incident emissions into future climate strategies. US natural gas pipeline incidents emitted 14.71–18.20 million tCO 2 e between 2010 and 2021, representing 2.67%–3.30% beyond official inventories that exclude such events by design. This study reveals regional and age-related emission risks and shows that curbing the top 10% of incidents could reduce emissions by over 40%.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".