A multi-criteria decision framework for the selection of low carbon building measures for office buildings in Hong Kong

Abstract

Access to this document was granted through an Emerald subscription provided by emerald-srm:412925 [] For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -This paper aims to develop a multi-criteria decision framework (MCDF) for the selection of appropriate low carbon building (LCB) measures for office buildings in Hong Kong. Design/methodology/approach -The research was carried out through a critical literature review and a case study with a low carbon office building project. Findings -In total, 26 LCB measures were identified, under the five groups of building envelope, heating, ventilating and air conditioning system, lighting and elevators, renewable energy and appliances. Also identified were 16 decision criteria, centred on the implementation-related, economic, environmental and production-related aspects. The identified measures and criteria, coupled with the information and business processes of office building project delivery, formed the conceptual MCDF. The MCDF was also verified using an office building project. Research limitations/implications -The limitation of this research was the absence of the energy bill which could help to further verify the model in the case study. Originality/value -The developed framework should add value to knowledge of the use of multi-criteria decision-making methods and support the design decision-making of selecting LCB measures for office building projects in Hong Kong. The findings should also inform LCB design in other hot and humid subtropical urban environments.

BIM-aided variable fuzzy multi-criteria decision making of low-carbon building measures selection

“…The delivery process of LCBs can be simplified into three phases, namely, planning, designing, and managing and retrofitting [3]. Previous research has identified and categorized LCB measures.…”

“…Energyefficient lighting was added to LCB measures by Malatji et al [12]. Based on the framework of Malatji et al and Zhang et al [3,12], 26 LCB measures in five groups were identified for commercial buildings in Hong Kong as an example of hot and humid subtropical climate.…”

“…Geldermann et al [16] made improvements and used fuzzy preference and fuzzy weights to obtain fuzzy scores. The fuzzy PROMETHEE algorithm is briefly introduced with similar equations as follows [3,16,27]:…”

“…The construction industry is a key sector expected to support achieving carbon emission reductions, as 33% of greenhouse gas (GHG) emissions, 40% of energy consumption and 36% of energy related carbon emissions are concerned with buildings [3]. Also, GHG emissions from buildings in cities are even more serious due to high urban density [1].…”

Multicriteria Decisions in Urban Energy System Planning: A Review

“…Zhang et al (2014) for example pointed out that depending on the expected outcome, DMs could choose either ELECTRE I when aiming for a choice problematic or ELECTRE II for a ranking problematic. noted that DMs seeking to learn about worst performing alternatives should avoid resorting to MEW, as this method relies on multiplicative aggregation and tends to rank alternatives at 0, although these might not be the worst performing over all criteria.…”