Improvements in sustainable energy utilization in global urban areas: Insights from remote sensing surveys.

Wen Song, Shisong Cao, Linlin Lu, Mingyi Du, Meizi Yang, Jingyi Chen, Jie Jiang

Abstract read

In one paragraph

Article in iScience, 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

7 authors.

Wen SongSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Shisong CaoSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Linlin LuKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

Mingyi DuSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Meizi YangSchool of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Jingyi ChenSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Jie JiangSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the global status of sustainable energy use in urban built-up areas (UBAs), addressing a critical data gap for achieving Sustainable Development Goals. By integrating multi-source remote sensing and gridded data from 2001 to 2019 regarding GDP, population, electricity, and carbon emissions, the researchers constructed a Sustainable Utilization Energy in Urban Built-up Areas (SUEUBA) index for 41 countries. The analysis reveals a steady global improvement, characterized by consistent progress in developed nations and a polarized performance among developing countries, where some show rapid growth while others stagnate. Major economies face distinct challenges due to complex socioeconomic conditions. These findings underscore the urgent need for differentiated policy strategies-ranging from infrastructure upgrades in high-income regions to expanded renewable access in rapidly urbanizing areas-and provide a scalable framework for monitoring and guiding sustainable urban energy transitions.

Indexed as

Applied sciencesEnergy sustainabilityEnvironmental science

Identifiers

PMID41858614

DOI10.1016/j.isci.2026.115129

PMCPMC12995879

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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.