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Confronting climate change, this study quantifies the embodied carbon emissions from the building sector—an overlooked yet significant contributor to urban carbon emissions. Through a novel empirical framework, the embodied carbon of Chicago's building stock was assessed and visualized, providing a scalable template for other cities with comparable datasets. The analysis encompasses 1,010,840 buildings and identifies 157 architectural archetypes. This methodology facilitates the granular evaluation of embodied carbon, guiding strategic urban carbon mitigation planning. establishes visual analytics tools for informed policymaking. The geospatially resolved findings identify emissions-intensive zones showing that majority of emissions are concentrated in specific archetypes and geographic areas, delivering actionable data for urban development stakeholders. The findings reveal that increasing the building lifespan to 80 years with a 20% reduction in building sizes can decrease carbon emissions to one-third of the current value. The research harnesses the Excel 3D Map tool for emissions visualization, offering an intuitive understanding of urban emissions' spatial dynamics and highlighting key areas for intervention. Furthermore, a sensitivity analysis explores the repercussions of end-life thresholds and building sizes on projected emissions. This multifaceted approach enhances the capacity for data-driven, low-carbon urban planning, paving the way for cities to align with global decarbonization goals and laying the groundwork for a versatile embodied carbon assessment methodology. This study contributes to the existing literature by creating a transferable framework for measuring embodied carbon in building stocks, providing detailed geo-accurate data for urban planners and policymakers, and offering benchmarks and visualizations for assessing and understanding embodied carbon performance and mitigation in urban environments, exemplified by the city of Chicago. [ABSTRACT FROM AUTHOR]

This study evaluates the current scope of smart technology applications that support aging in place and identifies potential avenues for future research. The global demographic shift towards an aging population has intensified interest in technologies that enable older adults to maintain independence and quality of life within their homes. We conducted a systematic review of the scientific literature from Web of Science, PubMed, and ProQuest, identifying 44 smart technologies across 32 publications. These technologies were classified into three categories: nonmobile technologies for individual monitoring, nonmobile technologies for home environment monitoring, and wearable technologies for health and activity tracking. Notably, the research in this area has grown significantly since 2018; yet, notable gaps persist, particularly within the traditional disciplines related to aging and in the use of quantitative methodologies. This emerging field presents substantial opportunities for interdisciplinary research and methodological advancement, highlighting the need for well-developed research strategies to support the effective integration of smart technology in aging in place. [ABSTRACT FROM AUTHOR]

Monitoring microclimate variables within cities with high resolution and accuracy is crucial for enhancing urban resilience to climate change. Assessing intra-urban characteristics is essential for ensuring satisfactory living standards. This paper presents a comprehensive methodology for studying urban heat islands (UHIs) on a university campus, emphasizing the importance of multi-modal and multi-temporal data collection. The methodology integrates mobile surveys, stationary sensor networks, and drone-based thermal imaging, providing a detailed analysis of temperature variations within urban microenvironments. The preliminary findings confirm the presence of a UHI on the campus and identify several hotspots. This comprehensive approach enhances the accuracy and reliability of UHI assessments, offering a cost-effective, fine-resolution approach that facilitates more effective urban planning and heat mitigation strategies. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) is a crucial factor in developing sustainable cities and societies. Appropriate data collection, analysis, and prediction are essential first steps in studying the effects of the UHI. This research systematically reviewed the papers related to the UHI that have used on-site data collection in the United States and Canada and the papers related to predicting and analyzing this effect in these regions. To achieve this goal, this study extracted 330 articles from Scopus and Web of Science and, after selecting the papers, reviewed 30 papers in detail from 1998 to 2023. The findings of this paper indicated a methodological shift from traditional sensors and data loggers towards more innovative and customized technologies. Concurrently, this research reveals a growing trend in using machine learning, moving from supportive to direct predictive roles and using techniques like neural networks and Bayesian networks. Despite the maturation of UHI research due to these developments, they also present challenges in technology complexity and data integration. The review emphasizes the need for future research to focus on accessible, accurate technologies. Moreover, interdisciplinary approaches are crucial for addressing UHI challenges in an era of climate change. [ABSTRACT FROM AUTHOR]

Passive design characteristics (PDC) play a pivotal role in reducing the energy burden on households without imposing additional financial constraints on project stakeholders. However, the scarcity of PDC data has posed a challenge in previous studies when assessing their energy-saving impact. To tackle this issue, this research introduces an innovative approach that combines deep learning-powered computer vision with machine learning techniques to examine the relationship between PDC and energy burden in residential buildings. In this study, we employ a convolutional neural network computer vision model to identify and measure key indicators, including window-to-wall ratio (WWR), external shading, and operable window types, using Google Street View images within the Chicago metropolitan area as our case study. Subsequently, we utilize the derived passive design features in conjunction with demographic characteristics to train and compare various machine learning methods. These methods encompass Decision Tree Regression, Random Forest Regression, and Support Vector Regression, culminating in the development of a comprehensive model for energy burden prediction. Our framework achieves a 74.2 % accuracy in forecasting the average energy burden. These results yield invaluable insights for policymakers and urban planners, paving the way toward the realization of smart and sustainable cities. • Employing Convolutional Neural Networks (CNN) to extract passive design features. • Passive design characteristics are associated with average energy burden. • Machine Learning-based methods provide the best energy burden prediction. • Support Vector Regressor (SVR) predicts energy burden with 74.2 % accuracy. • Passive design characteristics are essential for the prediction models. [ABSTRACT FROM AUTHOR]