This paper reviews the diverse applications of drone technologies in the built environment and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs), have emerged as valuable tools for environmental scientists, offering new possibilities for data collection, monitoring, and analysis in the urban environment. The paper begins by providing an overview of the different types of drones used in the built environment, including quadcopters, fixed-wing drones, and hybrid models. It explores their capabilities and features, such as high-resolution cameras, LiDAR sensors, and thermal imaging, which enable detailed data acquisition for studying climate change impacts in urban areas. The paper then examines the specific applications of drones in the built environment and their contribution to climate change research. These applications include mapping urban heat islands, assessing the energy efficiency of buildings, monitoring air quality, and identifying sources of greenhouse gas emissions. UAVs enable researchers to collect spatially and temporally rich data, allowing for a detailed analysis and identifying trends and patterns. Furthermore, the paper discusses integrating UAVs with artificial intelligence (AI) to derive insights and develop predictive models for climate change mitigation and adaptation in urban environments. Finally, the paper addresses drone technologies’ challenges and the future directions in the built environment. These challenges encompass regulatory frameworks, privacy concerns, data management, and the need for an interdisciplinary collaboration. By harnessing the potential of drones, environmental scientists can enhance their understanding of climate change impacts in urban areas and contribute to developing sustainable strategies for resilient cities.

Energy is a key ingredient to facilitate economic development in the Middle East. Expectations for a rapidly growing economy in the next decade will likely cause an increase in the fraction of energy consumed domestically limiting what is available for export. These challenges are the biggest for resource-rich countries, since their economy is heavily dependent on fossil fuel exports alongside an energy-intensive economy. Thus, the paper addresses the question of how the development of energy systems among resource-rich countries has changed over the past three decades and what role can they play in the sustainable development of the region’s energy system and emission reduction goals? To address this question, we present an overview on energy trends in four resource-rich countries in the Middle East, which nearly account for 76% of the region’s energy-related emissions and about 77% of total energy consumption. These countries are namely, Iran, Saudi Arabia, Kuwait, and the UAE. Accordingly, we present a comparative energy analysis between the four countries through examining historical and current energy trends, the structure of energy supply, the status of renewable deployment and energy-related carbon dioxide (CO₂) emissions. Results from the analysis showed that inefficient energy production and consumption have played a role in the deterioration of the energy landscape of the four countries compared to the global energy system. Thus, this highlights the necessity for suitable energy strategies and effective policies that will be central to sustainable energy development. The analysis presented here could be used to better understanding of the impacts of current gaps and inefficiencies in large energy consumers in the Middle East.

One of the major climate threats is extreme heat events, as they pose significant risks to public health that are well documented in the epidemiologic literature. The effects of extreme heat events have been evident over the past years by several extreme heat events worldwide. With the growing concerns of future heat exposure, numerous studies in the literature have developed heat vulnerability indices based on determinants that have heat-related impacts. However, there has been limited guidance on heat vulnerability assessment that accounts for the impacts of the characteristics of the built environment and changes in population dynamics over time. This paper focuses on developing the methodology for heat vulnerability assessment in urban areas using System Dynamics (SD) based on integrating three levels of the physical urban environment: the urban level, the building level, and the human adaptive capacity to heat exposure. We examine the viability of using SD modeling as an approach to examine the key drivers in heat vulnerability assessment in urban areas. Thus, the paper assesses the dynamic relationship between heat vulnerability components, namely, Susceptibility, Exposure, Coping Capacity, and Adaptive Capacity, and their effect on increased or decreased vulnerability under extreme heat events. The paper concludes with an applied case study in Cairo, Egypt, to test the use of the SD approach in assessing heat vulnerability in urban settings. Results from the proposed SD model confirm the underlying hypothesis that vulnerability from heat exposure is dynamically linked to the coping and adaptive capacity of the surrounding built environment with the urban population’s socioeconomic characteristics. The main contribution of this approach is that it allows for parallel examination of the effect of the human system that simulation models cannot include and the performance of the built environment system that epidemic heat vulnerability studies cannot capture.

According to IPCC (2014), the global mean temperature is expected to increase from 1.4 °C to 5.8 °C by 2100. The implications will be particularly significant in urban areas as indoor and outdoor comfort levels will be disrupted, leading to significant health impacts. One of the expected impacts is indoor overheating, as it has been identified as one of the major causes of thermal discomfort and is directly linked to the potential increase in mortality levels in the future. This paper focuses on the potential implications of increased overheating hours on human health in an old low-income residential neighborhood. We study the effect of three main factors: population coping capacity, building thermal performance, and human physiological response to heat exposure. This is achieved by examining an old low-income neighborhood in Cairo, Egypt, whose residents have limited cooling systems access. Results indicate higher overheating risks in older buildings with a projected increase of 18% in indoor temperature and higher health risks, especially for elderly residents. The study's findings can be considered a starting point to examine the relationship between exposure duration, indoor air temperature range, and potential health risks for vulnerable urban communities with limited access to cooling mechanisms such as AC units.

With the escalating impact of climate change coupled with increased urbanization, many cities will experience extreme heat events and intense flooding. Current modeling approaches often fail to incorporate high-resolution, frequently updated data sources, such as aerial imagery from web mapping platforms, limiting their effectiveness in identifying areas at risk. To address this gap, the paper presents CLIM-SEG, a novel framework for high-resolution urban heat and flood risk assessment, addressing critical gaps in current climate risk modeling. This framework integrates semantic segmentation of aerial imagery with a weighted sum approach that integrates environmental, socioeconomic, and building data to provide comprehensive risk evaluations at the census tract level. CLIM-SEG synthesize land cover data with hazard and vulnerability factors, producing risk scores ranging from 0 to 1. This low-cost and efficient framework can enable urban planners to prioritize resources for flood mitigation and heat adaptation, addressing the limitations of current approaches and contributing to the field of urban planning and climate change adaptation. The propoosed methodology incorporates a custom-curated dataset of 545 aerial images, including 145 manually annotated segmentation maps, to fine-tune advanced semantic segmentation models. The optimized Segmenter model achieves a pixel accuracy of 97.85% and an Intersection over Union (IoU) of 0.9578 for key urban features, significantly outperforming baseline models. Boston is selected to represent an ideal representation for both heat and flood risk, as the city experiences severe urban heat islands, and is susceptible to coastal and riverine flooding, with over 11,000 structures expected to be affected by 2070 due to sea level rise and increased precipitation. Results from flood and heat risk models indicate that census tracts in South End have the highest flood risk, with a weighted score value of 0.825, while census tracts in the Fenway-Kenmore neighborhood show the highest heat risk, with a score of 0.991. Both of these results have also been verified with other heat and flood risk mapping sources for Boston. The proposed framework of CLIM-SEG not only addresses the challenges faced by Boston but also has the potential to be scaled to other urban areas dealing with the impacts of climate change, providing a valuable tool for risk assessment and decision-making in the face of a changing climate.