Evidence map›Paper›PMID 38818239›Full record

ArticlePNAS nexus2024

Quantifying US air pollution policy: How political and regional factors influence pollutant mitigation.

Guoxing Zhang, Zhanglei Chen, Jiexun Li, Bin Su, Yang Gao, Lean Yu

Abstract read
In one paragraph

Article in PNAS nexus, 2024. 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
–field-weighted citation impact
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.

  1. PMScience advances · 2026
    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

6 authors.

Guoxing ZhangSchool of Management, Lanzhou University, Lanzhou 730000, China.ORCID https://orcid.org/0000-0003-0548-2865
Zhanglei ChenSchool of Management, Lanzhou University, Lanzhou 730000, China.ORCID https://orcid.org/0009-0000-0646-0807
Jiexun LiDepartment of Decision Sciences, Western Washington University, Bellingham, WA 98225, USA.ORCID https://orcid.org/0000-0002-5259-5363
Bin SuEnergy Studies Institute, National University of Singapore, 119620, Singapore, Singapore.ORCID https://orcid.org/0000-0003-4273-7054
Yang GaoSchool of Management, Lanzhou University, Lanzhou 730000, China.ORCID https://orcid.org/0009-0004-6034-9631
Lean YuBusiness School, Sichuan University, Chengdu 610065, China.ORCID https://orcid.org/0000-0002-8035-4938

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Air pollution control in the United States has evolved into a comprehensive policy system spanning from the federal to the state level over time. A unified quantitative analysis of policy intensity can shed light on the policy evolution across different levels, the influence of partisan and regional factors on policy, and the relationships with emissions of major pollutants. By harnessing the policy text of the Clean Air Act (CAA) at the federal level and State Implementation Plans (SIPs) at the state governments (1955-2020), we deployed a Natural Language Processing approach to define a policy intensity index to systematically quantify the US air policy landscape. Our findings highlight that the 1970 CAA amendment carries the most vigorous intensity as it established a holistic control system for the first time. Subsequent years witnessed a general trend of partisan polarization, eventually leading to a graduate convergence between red and blue states. Blue states demonstrated a closer alignment with federal directives and a superior efficacy in pollutant reduction. Regionally, the Northeast displays the highest overall policy intensity, and the West exhibits the highest coordination with the federal benchmarks, making these regions outperform others in air pollution control. Our study not only discusses policy implications for air pollutant reductions considering partisan and regional differences but also provides a novel measurement tool to quantify policies for assessing disparities and synergies.

Indexed as

Clean Air Actnatural language processingpolicy effectivenesspolicy intensitypolitical polarization

Identifiers

PMID38818239
PMCPMC11138116

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.