Quantifying greenhouse gas emission risks from natural gas pipeline incidents
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».