Evidence map›Paper›PMID 41372187›Full record

ArticleScientific data2025

Multi-resolution dataset of electricity consumption in Chinese cities.

Kaile Zhou, Rong Hu, Xinhui Lu, Ziwei Yang, Yaxuan Gao

Abstract read
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Kaile ZhouSchool of Management, Hefei University of Technology, Hefei, China.
Rong HuSchool of Management, Hefei University of Technology, Hefei, China. hurong@mail.hfut.edu.cn.ORCID http://orcid.org/0000-0003-0752-7377
Xinhui LuSchool of Management, Hefei University of Technology, Hefei, China.
Ziwei YangSchool of Management, Hefei University of Technology, Hefei, China.
Yaxuan GaoSchool of Management, Hefei University of Technology, Hefei, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 72242103National Natural Science Foundation of China (National Science Foundation of China) 72271071
6 · The paper itself

Abstract

Timely and spatially refined electricity consumption data are essential for supporting urban energy transition, electricity demand response, and supply-demand balancing strategies. However, publicly accessible datasets with high temporal resolution and city-level granularity remain scarce, particularly in developing countries such as China. In this study, we generate electricity consumption estimate for 296 Chinese cities in 2022 at both daily and monthly resolutions. In addition, this study presents a top-down framework for estimating city-level daily and monthly electricity consumption by integrating open-access multi-source data, including nighttime light imagery and high-resolution meteorological variables. The validation results demonstrate a strong alignment between the estimated multi-resolution values and officially reported annual statistics. This dataset addresses critical data gaps in urban-scale electricity studies by providing a publicly accessible, lower-cost, and scalable proxy for capturing electricity consumption dynamics at both daily and monthly scales. It is particularly valuable for academic research, exploratory analysis, and understanding spatiotemporal patterns of urban electricity consumption.

Identifiers

PMID41372187
PMCPMC12701008

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.