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2. Decision-Making Approach to Urban Energy Retrofit—A Comprehensive Review

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   Shu, Lei; Zhao, Dong (June 2023, Buildings)

   This research presents a comprehensive review of the research on smart urban energy retrofit decision-making. Based on the analysis of 91 journal articles over the past decade, the study identifies and discusses five key categories of approaches to retrofit decision-making, including simulation, optimization, assessment, system integration, and empirical study. While substantial advancements have been made in this field, opportunities for further growth remain. Findings suggest directions for future research and underscore the importance of interdisciplinary collaboration, data-driven evaluation methodologies, stakeholder engagement, system integration, and robust and adaptable retrofit solutions in the field of urban energy retrofitting. This review provides valuable insights for researchers, policymakers, and practitioners interested in advancing the state of the art in this critical area of research to facilitate more effective, sustainable, and efficient solutions for urban energy retrofits. 

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5. Building energy consumption and urban form: A multi-temporal empirical investigation

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   Ho, Chi On; Miotti, Marco; Jain, Rishee K (August 2025, Environment and Planning B: Urban Analytics and City Science)

   Buildings are a main contributor to global energy consumption, with urban form significantly affecting their energy use. To quantify these effects, many studies relied on simulated data for limited buildings or empirical data at coarse temporal scales (monthly or yearly). However, few studies conducted large-scale empirical analyses of hourly consumption. In this paper, we empirically evaluate the impacts of urban form on energy consumption across multiple temporal scales (hourly, daily, monthly, and daily peak) for residential and commercial buildings in Santa Clara, CA. Using population density as a proxy for urban form, we built regression models and analyzed non-linear relationships and interaction effects. Results show that energy use intensity (EUI) varies non-linearly and significantly with population density, building size, and building use type. By increasing population density by one level , hourly EUI could drop by up to 32%, 36%, and 74% for single-family residential, multi-family residential, and commercial buildings respectively. The trends and magnitudes are consistent across temporal scales for the same building use type and population density level. These findings can inform decision-making regarding land use and urban planning for decarbonization goals and demand-side management programs. 

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