Evidence map›Paper›PMID 42793883›Full record

ArticleEntropy (Basel, Switzerland)2026

Energy-Mix Entropy and the Scale-Composition Divide in Urban Renewable Transitions: Evidence from Chinese Cities.

Niwei Ma, Qing Mo, Fenglie Xi, Ying Liang, Yuan Li

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 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.

Niwei MaCollege of Tourism & Urban Rural Planning, Xichang University, Xichang 615000, China.
Qing MoCollege of Tourism & Urban Rural Planning, Xichang University, Xichang 615000, China.
Fenglie XiCollege of Tourism & Urban Rural Planning, Xichang University, Xichang 615000, China.
Ying LiangCollege of Tourism & Urban Rural Planning, Xichang University, Xichang 615000, China.
Yuan LiCollege of Tourism & Urban Rural Planning, Xichang University, Xichang 615000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cities can consume more renewable energy without achieving an equivalent shift in their energy mix. We examine this scale-composition divide in a balanced panel of 284 Chinese cities from 2007 to 2019 and use normalized Shannon entropy to characterize the state of each city's energy portfolio. The analysis distinguishes absolute renewable consumption, total renewable share, and wind plus solar share; it also separates digital infrastructure, digital financial inclusion, and environmental digital governance. Total energy use is strongly positively associated with absolute renewable consumption, negatively associated with total renewable share, and positively associated with wind plus solar share. Energy-mix entropy has a nonlinear association with renewable composition that persists across lagged and alternative diversity measures. By contrast, upper-lower quantile differences, formal subgroup contrasts, and capability interactions based on entropy excluding renewable carriers do not survive multiple testing adjustment. The digital measures also diverge in a common sample: digital financial inclusion is negative, environmental digital governance is positive, and digital infrastructure is imprecisely estimated. Urban renewable transition is therefore better understood as a compositional process associated with portfolio state than as the uniform accumulation of city capabilities.

Indexed as

Chinese citiesdigital infrastructuredistributional heterogeneityenergy-mix diversitygreen innovationrenewable energy transitionShannon entropyurban capabilities

Identifiers

PMID42793883
PMCPMC13605522

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.