As the global climate crisis intensifies, cities face the dual challenge of meeting housing demands and reducing environmental impacts such as carbon emissions. This tension presents both challenges and opportunities for reducing emissions in the building sector. One critical obstacle in urban energy and carbon analysis is the scarcity of building characteristic data which hinders effective urban analysis. This research aims to provide an automated, scalable data-driven approach to extract building form data from Google Street View Images and help construct necessary building characteristic datasets for urban analysis. In this paper, we illustrate this concept with a focus on the building Window-to-Wall Ratio.

We first collected geometric and non-geometric building data from various publicly available data sources of Baltimore and applied object detection techniques and zero-shot machine learning models on Google Street View images to complement some missing data items. We then integrated the data through spatial join techniques. The scalable approach used in this research addresses the challenge of missing data and scattered datasets, which leads to incomplete building characteristic data needed for energy and carbon modeling at the urban scale. This method also enables automated extraction of certain urban form data (including WWR) for an entire city. We used the constructed dataset to develop housing archetypes representing Baltimore’s housing stock. The output data of this research was then used to simulate energy use and carbon emissions associated with buildings in urban blocks. Key findings include achieving a mean squared error (MSE) of 0.0418, confirming the method’s reliability. This research offers researchers, urban planners, and policymakers insights into data-driven methods to improve urban data for environmental and carbon-efficient analysis. The results also contribute to developing an urban carbon emission modeling framework to predict building-related greenhouse gas emissions and guide urban planning, using Baltimore as a case study.

ABSTRACT: Urban heat stress is a growing concern for construction workers, particularly in densely packed urban areas. This study uses the Physiological Equivalent Temperature (PET) to evaluate heat stress across six New York City districts: Midtown Manhattan, Downtown Brooklyn, Harlem, Bedford-Stuyvesant, Brooklyn, and St. Albans. By examining different metabolic rates—light (180 W/m²), moderate (300 W/m²), and heavy (415 W/m²)—we analyze how physical activity and urban morphology impact workers' thermal stress. Combined with ENVI-met simulations, PET comprehensively explains the interplay between environmental factors like air temperature, humidity, wind speed, solar radiation, and metabolic heat generation. OSHA standards suggest metabolic rates for outdoor labor to mitigate heat stress but neglect to consider urban heat island (UHI) impacts and the variability of metabolic rates in different locales. This study addresses that gap by concentrating on construction workers performing duties ranging from light sedentary to strenuous physical activity. The findings indicate that regions with high building densities, such as Downtown Brooklyn and Midtown Manhattan, continuously have greater PET levels, intensifying thermal stress, especially at higher temperatures. Conversely, more vegetated areas such as North Harlem and St. Albans exhibit reduced PET values, signifying superior thermal conditions at all activity levels. These findings underscore the significant influence of urban layout and external environmental elements, including restricted green spaces, in exacerbating heat exposure. The study emphasizes the importance of flexible urban design concepts to reduce heat stress using more plants, water features, reflecting materials, and shaded work locations. Emphasizing drinking, shaded rest spaces and acclimatization improves OSHA's heat safety standards. Integrating occupational health and urban planning is crucial to protect employees from heat-related hazards, particularly in high-density urban settings where customized mitigating measures can significantly increase worker safety.

Riyadh, the capital of Saudi Arabia, is at a critical phase, shaped by multiple forces, including rapid urbanization, a hot-arid climate, and national sustainability goals. Under the umbrella of Vision 2030 and a recent pledge toward net-zero emissions by 2060, the city must grapple with extreme heat, rapidly increasing energy demands, corresponding greenhouse gas (GHG) emissions, and a growing housing sector. While global frameworks lead the decarbonization efforts, the unique conditions of local contexts complicate the translation of policies into action. More specifically, cultural preferences for large, standalone villas, the historical reliance on subsidized energy, and infrastructural constraints hinder decarbonization in Saudi Arabia's building sector and pose challenges for integrating advanced technologies, passive measures, and energy-efficient building practices.

This paper presents a comprehensive literature review of international best practices, regional policies, and existing research on Saudi Arabia's building sector. This review identifies persistent barriers across cultural, geographical, historical, economic, political, environmental, infrastructural, technological, and behavioral domains—factors that intensify the urban heat island effect and hinder efforts to reduce GHG emissions and energy use in Saudi Arabia's building sector. It highlights how these intertwined factors intensify the urban heat island effect, reduce incentives for renewable energy adoption, and limit the enforcement of building codes.

This study gathers lessons in policymaking, community engagement, and market-based incentives by examining examples from hot-arid and other regions—such as Phoenix's adaptation strategies or Copenhagen's integrated planning. This study underscores that climate action is most effective when policies align with local cultural values and are supported by targeted incentives. Such alignment can help reduce housing-related GHG emissions and energy intensity in the long run. Ultimately, the study informs policymakers, urban planners, and researchers on navigating the complex path from intent to sustainable implementation, ensuring Riyadh's growth aligns with environmental stewardship, economic rationality, and cultural integrity.