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Mapping the knowledge roadmap of low carbon building: A scientometric analysis

Abstract Due to global climate change, carbon reduction has become a critical issue for the construction industry. Low carbon building has been adopted as a strategic objective, and its implementation demonstrates the enormous potential of reducing carbon emissions. Despite much research, some important research areas or gaps have not been identified, while simultaneously, few studies describe the knowledge roadmap for low carbon building research necessary to guide scholars and practitioners. The purpose in this paper is to provide a comprehensive and objective analysis of research areas regarding low carbon building using the scientometrics method. A total of 378 low carbon building-related publications were reviewed. Based on the keyword co-word analysis, document co-citation analysis and clustering analysis, the results show that 100 research keywords, 3 research hotspots and 5 research domains are extremely important for the advancement of low carbon building. Based on the in-depth analysis of the findings, the knowledge structure with different prominences was developed, which grouped LCB research into five themes and various sub-topics. Significantly, this paper provides the knowledge roadmap needed to facilitate further research and applications for both academia and industry stakeholders to improve low carbon building.
- Hong Kong Polytechnic University China (People's Republic of)
- RMIT University Australia
- Guangzhou University China (People's Republic of)
- Harbin Institute of Technology China (People's Republic of)
- Guangzhou University China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).68 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
