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Alghamdi, Abdulaziz Saeed M.

University of British Columbia

Canadian higher education institutions (HEIs) consume 60% of the total energy supplied to the educational sector, which ultimately contributes to significant greenhouse gas (GHG) emissions. In addition, HEIs consume a large amount of water for indoor use and research purposes. Existing tools developed for the sustainability assessment of HEIs, cover generic aspects such as operations, engagement, and innovation but often overlook key environmental accounting aspects like water, energy, and carbon (WEC) flows. Furthermore, academic buildings are the largest infrastructure units in HEIs and are sub-grouped under the commercial and institutional (C&I) buildings in Canada. HEIs buildings are intrinsically different from other C&I buildings in terms of energy and water consumption patterns. There is a dire need to assess the sustainability performance of HEIs and academic buildings as critical infrastructure units. This research aims to develop a framework for sustainability performance benchmarking of HEIs in terms of WEC flows. First, an inter-university benchmarking method was proposed to classify and benchmark 34 HEIs across Canada. The results indicate that small-sized HEIs perform better on average than their medium and large-sized counterparts. Furthermore, the size-based classification of the HEIs better justifies the relationship between the flows of energy and GHGs. A strong correlation coefficient of 0.97 between energy consumed and associated GHG emissions was observed for small-and-medium-sized HEIs. Later, an intra-university benchmarking was performed on 71 academic buildings using fuzzy clustering techniques to classify academic buildings. To account for the uncertainties in partitioning, the fuzzy c-mean algorithm was employed to classify the buildings based on the normalized WEC performances. The findings show that building activities such as laboratory intensive buildings are among the least sustainable as a cluster of buildings. The fuzzy clustering technique assists in highlighting the intrinsic characteristics and activities associated with the least performing buildings. Finally, a probabilistic fuzzy synthetic evaluation method was proposed to address aleatory and fuzziness (or vagueness) uncertainties raised in the intra-university benchmarking. The findings show that 32% of the buildings were classified into the most sustainable class during the summer, while in the winter season, none of the buildings were classified as most sustainable, indicating that heating significantly affects the energy performance of buildings. The proposed spatiotemporal analysis helps identify external variations such as the seasonal and occupant variations that affect the environmental performance in HEIs. The uncertainty analysis contributes to addressing commonly overlooked uncertainties associated with benchmarking techniques. The deliverables of this research identify the least performing HEIs in terms of WEC flows. The methodology proposed will improve communicating, setting targets, and improving the sustainability performance of HEIs.

Text

2021-11-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80144

Civil Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/