Evidence map›Paper›PMID 33748315›Full record

ArticleEarth's future2021

Potential for Electric Vehicle Adoption to Mitigate Extreme Air Quality Events in China.

J L Schnell, D R Peters, D C Wong, X Lu, H Guo, H Zhang, P L Kinney, D E Horton

Open access · goldAbstract read
In one paragraph

Article in Earth's future, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

5 citing papers in PubMed, 39 citations in OpenAlex.

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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

8 authors at 6 institutions in 4 countries.

J L SchnellDepartment of Earth and Planetary Sciences and Institute for Sustainability and Energy at Northwestern University, Evanston, IL, USA.ORCID 0000-0002-4072-4033
D R PetersProgram in Environmental Sciences, Northwestern University, Evanston, IL, USA.ORCID 0000-0002-4111-689X
D C WongUS Environmental Protection Agency, Research Triangle Park, NC, USA.
X LuSchool of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, China.
H GuoDepartment of Earth System Science, University of California Irvine, Irvine, CA, USA.
H ZhangDepartment of Environmental Science and Engineering, Fudan University, Shanghai, China.ORCID 0000-0002-1797-2311
P L KinneyDepartment of Environmental Health, Boston University School of Public Health, Boston, MA, USA.
D E HortonDepartment of Earth and Planetary Sciences and Institute for Sustainability and Energy at Northwestern University, Evanston, IL, USA.ORCID 0000-0002-2065-4517
Northwestern University · USBoston University · USEnvironmental Protection Agency · USFudan University · CNState Key Joint Laboratory of Environment Simulation and Pollution Control · CNUniversity of California, Irvine · US

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

Electric vehicle (EV) adoption promises potential air pollutant and greenhouse gas (GHG) reduction co-benefits. As such, China has aggressively incentivized EV adoption, however much remains unknown with regard to EVs' mitigation potential, including optimal vehicle type prioritization, power generation contingencies, effects of Clean Air regulations, and the ability of EVs to reduce acute impacts of extreme air quality events. Here, we present a suite of scenarios with a chemistry transport model that assess the potential co-benefits of EVs during an extreme winter air quality event. We find that regardless of power generation source, heavy-duty vehicle (HDV) electrification consistently improves air quality in terms of NO

Identifiers

PMID33748315
PMCPMC7970456
OpenAlexW3089332797

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.