Evidence map›Paper›PMID 38839820›Full record

ArticleScientific reports2024

Urban 3D building morphology and energy consumption: empirical evidence from 53 cities in China.

Yang Wang, Guiquan Sun, Yingmei Wu, Mark W Rosenberg

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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.

Yang WangFaculty of Geography, Yunnan Normal University, Kunming, 650500, China.
Guiquan SunFaculty of Geography, Yunnan Normal University, Kunming, 650500, China. sgq@ynnu.edu.cn.
Yingmei WuFaculty of Geography, Yunnan Normal University, Kunming, 650500, China.
Mark W RosenbergDepartment of Geography, Queen's University, Kingston, Ontario, K7L 3N6, Canada.

Funding

Yunnan Fundamental Research Projects grant No. 202301AT070062Yunnan Fundamental Research Projects Grant No. 202401AS070037"Yunnan Revitalization Talent Support Program" in Yunnan Province grant No. XDYC-WHMJ-2022-0016"Yunnan Revitalization Talent Support Program" in Yunnan Province XDYC-QNRC-2022-0740Yunnan Science and Technology Program Grant No. 202305AP350016
6 · The paper itself

Abstract

The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model's ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

Indexed as

3D building morphologyBuilding energy consumptionElectricity consumptionEnergy conservationLarge cities in ChinaSpatial Durbin model

Identifiers

PMID38839820
PMCPMC11153520

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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.