Evidence map›Paper›PMID 37620343›Full record

ArticleScientific data2023

Greenhouse gas emissions from U.S. crude oil pipeline accidents: 1968 to 2020.

Hongfang Lu, Zhao-Dong Xu, Kaihui Song, Y Frank Cheng, Shaohua Dong, Hongyuan Fang, Haoyan Peng, Yun Fu, Dongmin Xi, Zizhe Han and 5 more

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 8 institutions in 4 countries.

Hongfang Lu *China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China.
Zhao-Dong XuChina-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China. zhdxu@163.com.ORCID http://orcid.org/0000-0003-2226-9871
Kaihui Song *Data-Driven EnviroLab, School of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
Y Frank Cheng *Department of Mechanical Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
Shaohua DongSchool of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing, 102249, China.
Hongyuan FangYellow River Laboratory, Zhengzhou University, Zhengzhou, 450001, China.
Haoyan PengChina-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China.
Yun FuSchool of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing, 102249, China.
Dongmin XiChina-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China.
Zizhe HanChina-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China.
Xinmeng JiangChina-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China.
Yao-Rong DongSchool of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
Panpan GaiSchool of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, 212013, China.
Zhiwei ShanChina-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China.
Yuli ShanSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK. y.shan@bham.ac.uk.ORCID http://orcid.org/0000-0002-5215-8657
Southeast University · CNChina University of Petroleum, Beijing · CNJiangsu University · CNUniversity of Birmingham · GBUniversity of Calgary · CAUniversity of North Carolina at Chapel Hill · USXi'an University of Architecture and Technology · CNZhengzhou University · CN

Funding

Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation) BK20220848Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation) BK20222006
6 · The paper itself

Abstract

Crude oil pipelines are considered as the lifelines of energy industry. However, accidents of the pipelines can lead to severe public health and environmental concerns, in which greenhouse gas (GHG) emissions, primarily methane, are frequently overlooked. While previous studies examined fugitive emissions in normal operation of crude oil pipelines, emissions resulting from accidents were typically managed separately and were therefore not included in the emission account of oil systems. To bridge this knowledge gap, we employed a bottom-up approach to conducted the first-ever inventory of GHG emissions resulting from crude oil pipeline accidents in the United States at the state level from 1968 to 2020, and leveraged Monte Carlo simulation to estimate the associated uncertainties. Our results reveal that GHG emissions from accidents in gathering pipelines (~720,000 tCO

Identifiers

PMID37620343
PMCPMC10450021
OpenAlexW4386127240

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.