Evidence map›Paper›PMID 42821660›Full record

ArticlePloS one2026

Energy infrastructure construction and enterprises' Artificial Intelligence development: Evidence from China's ultra-high voltage transmission projects.

Wenhe Lin, Yumin Guo, Bingxuan Wang, Qiuwang Cheng

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Wenhe LinCollege of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, China.ORCID https://orcid.org/0000-0002-8190-0224
Yumin GuoCollege of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, China.
Bingxuan WangCollege of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, China.
Qiuwang ChengCollege of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, China.ORCID https://orcid.org/0009-0004-2640-9207

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultra-high voltage (UHV) transmission projects are a key component of China's energy supply-side reforms, improving the efficiency and reliability of the power system. While their macro-level importance is well recognized, evidence on their impact on firm-level artificial intelligence (AI) development remains limited. This study addresses this gap by exploiting the staggered rollout of UHV transmission projects across prefecture-level cities as a quasi-natural experiment. Using a multi-period difference-in-differences (DID) approach on a panel of Chinese listed enterprises from 2007 to 2023, this study finds that UHV transmission projects significantly promote enterprises' AI development. The effect is stronger for non-state-owned enterprises, enterprises located in inland regions, enterprises with CEO-chair duality, and high-tech enterprises. Mechanism analyses show that UHV transmission projects enhance enterprises' AI development by strengthening technological innovation, expanding enterprises' organizational resource base, and improving regional financial development. Further analyses indicate that UHV transmission projects also improve resource allocation efficiency and alleviate development bottlenecks. By linking physical energy infrastructure with firm-level AI development, this study highlights the importance of reliable electricity supply for technology adoption and digital transformation.

Indexed as

Artificial IntelligenceElectric Power SuppliesChina

Identifiers

PMID42821660
PMCPMC13630311

What OpenQuestion holds

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Registered trials

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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.