Evidence map›Paper›PMID 40045007›Full record

ArticleScientific reports2025

Prefabricated building construction in materialization phase as catalysts for hotel low-carbon transitions via hybrid computational visualization algorithms.

Gangwei Cai, Xiaoting Guo, Yuguang Sun

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Gangwei CaiCollege of Architecture and Urban Planning, Tongji University, Shanghai, 200092, China. 11712014@zju.edu.cn.
Xiaoting GuoSchool of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou, 310023, China.
Yuguang SunBaoye Group Company Limited, Shaoxing, 312030, China. syguang@yeah.net.

Funding

China Postdoctoral Science Foundation 2023M730024Huzhou Science and Technology Plan Project 2023ZD2054Zhejiang Provincial Construction Research Project 2023K219
6 · The paper itself

Abstract

This study examines the carbon emissions of star-rated hotels in Hangzhou, comparing the environmental impact of prefabricated construction (PC) and conventional construction (CC) methodologies. The research reveals that PC generally results in lower carbon emissions during the materialization phase, with notable variations across different hotel star levels and administrative regions. Higher-star hotels exhibit higher total emissions, primarily due to larger scale and reliance on conventional construction methods. In contrast, lower-tier hotels benefit more consistently from the adoption of prefabricated construction, leading to significant reductions in carbon emissions. Regional analysis shows that the impact of the COVID-19 pandemic on hotel turnover and carbon decoupling trends varies, with core urban areas experiencing a more pronounced decoupling effect, while suburban regions exhibited slower recovery. The findings underscore the potential for prefabricated construction to reduce carbon footprints, particularly in mid-tier and lower-tier hotels. This study contributes to the understanding of sustainable construction practices in the hotel industry and provides a foundation for future research focused on refining carbon emission assessments, incorporating real-world data, and exploring the integration of renewable energy and lifecycle emissions.

Indexed as

Carbon decouplingMachine learning spatio-temporal analysisMaterialization phasePrefabricated buildingStar-rated hotelSustainable development goals (SDGs)Sustainable tourism

Identifiers

PMID40045007
PMCPMC11882787

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.