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Retrofitting existing multi-story buildings (MSBs) to improve energy efficiency is a crucial strategy for achieving urban net-zero building objectives. The large-area characteristic of MSBs increases both the potential impact of building energy retrofits and the complexity of decision-making, introducing significant uncertainties that remain insufficiently addressed in existing research. Therefore, this study aims to explore retrofits applied in MSB and critically review current knowledge regarding evaluation criteria for retrofits, with a particular focus on the uncertainty associated with retrofitting projects. The retrofits are classified into three dimensions: envelope (77 studies), renewable energy technologies (93 studies), and indoor environment (81 studies). The findings reveal that the current research hotspot involves integrating passive, active, and renewable-energy-based retrofit strategies, and highlight a research gap in broader social dimensions. This review also examined three core dimensions of uncertainty: input variables (36 studies), analytical methods (32 studies), and fuzzy data processing techniques (6 studies). Under input variables, the application scenarios of 13 variable distribution types used in Monte Carlo are summarized, along with empirical recommendations for selecting appropriate distribution shapes. The insights from five commonly used uncertainty analysis methods are compiled for retrofitting contexts. Finally, fuzzy logic is found to play a control-oriented role in quantifying fuzzy behaviors data within indoor environmental control strategies. Overall, this review integrates fragmented knowledge on MSB retrofitting and provides a structured foundation, along with systematic research approaches and recommendations, to advance future research by synthesizing retrofit strategies, evaluation criteria, and uncertainty analysis, thereby supporting robust and inclusive decision-making for urban net-zero transitions.
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Ma et al. (2026) studied this question.
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