Evidence map›Paper›PMID 39866469›Full record

ArticleHeliyon2025

A novel index to predict the cost of green resilient buildings.

Muhammad Ali, Ayesha Zubair, Jahanzaib Israr, Wasim Abbass, Zubair Masoud, Abdullah Mohamed

Abstract read
In one paragraph

Article in Heliyon, 2025. 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

6 authors.

Muhammad AliDepartment of Civil Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
Ayesha ZubairDepartment of Civil Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
Jahanzaib IsrarDepartment of Civil Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
Wasim AbbassDepartment of Civil Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
Zubair MasoudDepartment of Civil Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
Abdullah MohamedResearch Center, Future University in Egypt, New Cairo, 11835, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change impacts the demand of the construction industry to reduce its carbon footprint while increasing the resilience of the buildings. This twofold need emphasises better understanding and cost prediction of green resilient buildings based on their 'resilience' and 'sustainability', which is very limited or based on obsolete perceptions and stereotypes. This study presents a novel index to predict the cost of converting a conventional building to a green-resilient building. In this study; twenty factors based on comprehensive market research have been analysed both quantitatively and qualitatively. An economic impact model has been developed for the cost prediction of green resilient buildings through a novel Green Resilient Building Index, based on sustainability and resilience factors, and the cost of conventional buildings. It has been observed that on-ground construction cost and predicted values fall within 10 % of the 1:1 line validating the precision and confidence level of the research data. Single-factor analysis of variance showed the acceptable precision of the results at a confidence level of 95 %. Nevertheless, this study envisions supporting the professionals and policymakers to develop a sustainable construction industry.

Indexed as

Cost predictionEconomic impactGreen resilientSustainable construction

Identifiers

PMID39866469
PMCPMC11761301

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.