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Carbon footprint (CF) estimation has emerged as an integral tool for greenhouse gas (GHG) management, providing direction for emission reduction strategies and verification processes. Since capturing the full lifecycle emissions of goods and services at the city level can be methodologically challenging due to the fact that cities have multiple sources of emissions (e.g. transportation, waste, buildings), our study focused on evaluating the carbon footprint of various types of urban buildings across six sectors. This evaluation was conducted in one of the most rapidly urbanized cities in the Middle East, Doha City, Qatar, and was based on electricity consumption data from 2017 to 2020. Using multiregional input–output life cycle assessment (MRIO-LCA) and hot/coldspot analysis, this study identified CF emission spatiotemporal patterns and the major buildings responsible for the highest emissions. The results indicated that residential villas and the commercial sector had the highest electricity consumption and steady increases in CF emissions during the study period, whereas hotels had the lowest annual electricity consumption among all sectors. The study also identified significant CF emission hotspots from Doha residential buildings in the southwestern and northeastern areas. In contrast, the CF hotspots from commercial buildings were concentrated in the industrial area in the southern part of the city and in the West Bay area in the eastern part. Overall, the findings of this study provide important policy implications and a comprehensive assessment of dynamic changes in CF emissions from different building types.

This study offers a comprehensive analysis of the distribution, evolution, and driving factors of CO2 emissions from 1990 to 2016 at multiple spatial scales. Utilizing 26 indicators encompassing various facets of CO2 emissions, it is employed principal component analysis (PCA) and empirical orthogonal functions (EOFs) to identify the dominant characteristics of global CO2 emissions. This model retained three core components, accounting for 93% of the global CO2 variation, reflecting emission trajectories and associated economic metrics, such as Gross domestic product (GDP). The analysis differentiated the effects of these components based on countries' economic standings. Using a novel aggregated index, significant national contributors to global CO2 emissions are pinpointed. Notably, the leading contributors are found among developed nations (e.g., the United States, Canada, Japan), Gulf states (e.g., Saudi Arabia, Qatar), and emerging economies (e.g., China, Brazil, Mexico). Furthermore, these results highlight that shifts in global CO2 emissions over the past 30 years are predominantly influenced by factors like industrial emissions and GDP. Results also demonstrate a distinct relationship between a country's CO2 emissions and its physical and socioeconomic factors. Specifically, the nation's coastline length, population density in coastal regions, and the diversity of its climatic conditions significantly influence its carbon footprint.

Migrants are among the groups most vulnerable to infection with viruses due to the social and economic conditions in which they live. Therefore, spatial modeling of virus transmission among migrants is important for controlling and containing the COVID-19 pandemic. This research focused on modeling spatial associations between COVID-19 incidence rates and migrant workers. The aim was to understand the spatial relationships between COVID-19 infection rates of migrants and the type of workplace at the subnational level in Oman. Using empirical Bayes smoothing as well as local indicators of spatial associations, six work sectors (health, agriculture, retail and business, administrative, manufacturing, and mining) were investigated as risk factors for disease incidence. The results indicated that the six work sectors each had a significant spatial association with cases of COVID-19. High rates of COVID-19 cases in relation to the workplace were clustered in the densely populated areas of Muscat. Similarly, high rates of COVID-19 cases were located in the northern part of the country, along the Al-Batnah plain, where migrants are often employed in the agricultural sector. Further, the rate of COVID-19 in migrants employed in the health sector was higher than that for the other sectors. Therefore, working in the health sector can be considered a hot spot for the spread of COVID-19 infections. Due to a paucity of studies addressing the spatial analysis of COVID-19 associations with workplaces, the findings of this research are useful for decision makers to set the necessary policies and plans to control the outbreak of the virus not only in Oman or the Gulf Cooperation Council but also in other developing societies.

Migrants are among the groups most vulnerable to infection with viruses due to the social and economic conditions in which they live. Therefore, spatial modeling of virus transmission among migrants is important for controlling and containing the COVID-19 pandemic. This research focused on modeling spatial associations between COVID-19 incidence rates and migrant workers. The aim was to understand the spatial relationships between COVID-19 infection rates of migrants and the type of workplace at the subnational level in Oman. Using empirical Bayes smoothing as well as local indicators of spatial associations, six work sectors (health, agriculture, retail and business, administrative, manufacturing, and mining) were investigated as risk factors for disease incidence. The results indicated that the six work sectors each had a significant spatial association with cases of COVID-19. High rates of COVID-19 cases in relation to the workplace were clustered in the densely populated areas of Muscat. Similarly, high rates of COVID-19 cases were located in the northern part of the country, along the Al-Batnah plain, where migrants are often employed in the agricultural sector. Further, the rate of COVID-19 in migrants employed in the health sector was higher than that for the other sectors. Therefore, working in the health sector can be considered a hot spot for the spread of COVID-19 infections. Due to a paucity of studies addressing the spatial analysis of COVID-19 associations with workplaces, the findings of this research are useful for decision makers to set the necessary policies and plans to control the outbreak of the virus not only in Oman or the Gulf Cooperation Council but also in other developing societies.