Evidence map›Paper›PMID 41851186›Full record

ArticleScientific reports2026

Artificial intelligence adoption for advancing energy justice: a multidimensional perspective.

Yong Ye, Tingye Huang, Ziyi Shi, Yixuan Luo, Xiaojun Zhang

Abstract read
In one paragraph

Article in Scientific reports, 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

5 authors.

Yong YeSchool of Economics and Management, Fuzhou University, No.2 Wulongjiang Road, Fuzhou, 350108, China.
Tingye HuangSchool of Economics and Management, Fuzhou University, No.2 Wulongjiang Road, Fuzhou, 350108, China.
Ziyi ShiSchool of Economics and Management, Fuzhou University, No.2 Wulongjiang Road, Fuzhou, 350108, China.
Yixuan LuoSchool of Economics and Management, Fuzhou University, No.2 Wulongjiang Road, Fuzhou, 350108, China. lois_luo@fzu.edu.cn.
Xiaojun ZhangSchool of Economics and Management, Fuzhou University, No.2 Wulongjiang Road, Fuzhou, 350108, China. xiaojun@fzu.edu.cn.

Funding

National Social Science Fund of China 24CJY088
6 · The paper itself

Abstract

While the global energy transition increasingly centers on the Just Transition principle, the role of technologies like artificial intelligence (AI) in achieving equitable outcomes remains an empirical black box. This study interrogates the premise of technological neutrality by empirically examining the relationship between AI adoption and energy justice. Using a panel dataset for 30 Chinese provinces from 2008 to 2022, we construct a multidimensional energy justice index to analyze AI’s net effects, pathways, and institutional dependencies. Our findings reveal a complex reality: while AI adoption significantly enhances overall energy justice, the effect is neither universal nor unconditional. This positive effect is mediated by improved energy efficiency, green innovation, higher energy prices, and reduced industrial density, and is amplified by stricter environmental regulations and better digital infrastructure. However, the benefits are concentrated in China’s advanced eastern region. Furthermore, AI’s contribution is uneven across justice dimensions: it markedly improves procedural and recognition justice but can initially exacerbate distributional injustice. These results demonstrate that AI is not an inherent instrument of justice but a malleable socio-technical force whose equitable outcomes depend on policy design and institutional context. To ensure AI serves a just transition, policy must shift from simply promoting technology to proactively shaping the regulatory and infrastructural ecosystems that govern its deployment.

Indexed as

Artificial intelligenceEnergy justiceEnvironmental regulationGreen innovationThreshold effect

Identifiers

PMID41851186
PMCPMC13106770

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.