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The process of estimating the carbon footprint (CF) has become a key method for managing greenhouse gas (GHG) emissions, guiding strategies for emission reduction and validating those strategies. Given the complexity of quantifying total lifecycle emissions in residential buildings, this study delves into assessing the CF focusing on water and electricity consumption in two types of residential buildings: mainly residential villas and residential flats. This assessment was carried out in Doha City, Qatar, using data on water and electricity consumption from 2017 to 2020. The study employs the Multi-Regional Input-Output Life Cycle Assessment (MRIO-LCA) model to calculate and convert the water and electricity consumption data into the CF of these buildings. Further, the study employs various methods for statistical and spatial statistical analysis of CF emissions, including geographically weighted regression (GWR), Ordinary Least Squares (OLS), and hotspot and cold spot assessments. The annual electricity CF emissions from these buildings are approximately 7 MtCO 2 equivalent, with residential villas contributing about 83% of this total. Concurrently, the annual average water CF in residential buildings is around 0.06 MtCO 2 equivalent, predominantly attributed to villas. The findings highlight the substantial impact of residential structures, particularly villas, on the city's overall CF emissions. Furthermore, the findings underscore the significant impact of residential structures, especially villas, on Doha's CF emissions, revealing a marked seasonal increase, particularly during the summer months and a notable spike in 2020. The spatial analysis of CF emissions reveals consistent spatial clustering patterns across different seasons in 2020. Elevated CF emissions from villas are concentrated in Doha's central, northern, and northeastern regions, while cold spots are predominantly in the eastern and southern areas. Understanding CF in residential settings is crucial for developing strategies to reduce emissions, enhance energy efficiency, and address climate change. This research helps inform targeted interventions for more sustainable residential energy and water use, aligning with broader environmental and climate goals. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 marks a transformative era for businesses and industries, characterized by advanced technologies like automation, Internet of Things (IoT), artificial intelligence (AI), smart factories, and cyber-physical systems. This revolution promises significant advantages, including enhanced productivity, sustainable progress, and heightened resilience. However, the integration of Industry 4.0 is challenged by the need for a skilled workforce with expertise in areas such as information technology and data analytics. Higher education institutions (HEIs) play a vital role in equipping future professionals with these skills, necessitating curriculum updates and infrastructure enhancements. Simultaneously, the importance of education for sustainable development (ESD) has been underscored by global initiatives like the Sustainable Development Goals (SDGs). ESD instills a sense of responsibility for economic, ecological, and equitable well-being. As digital technologies blur the lines between industries, education faces the challenge of adapting to evolving demands. The integration of AI tools in education has emerged as a catalyst for reshaping learning experiences, fostering innovation, and preparing individuals for the digital age. AI chatbots such as ChatGPT have garnered widespread attention and possess the potential to revolutionize various aspects of education. However, their integration raises ethical concerns, necessitates curriculum redesign, requires strategies for continuous learning, and demands alignment with industry standards. While the potential of AI integration in education is promising, there is a notable gap in the existing literature when it comes to exploring the ethical implications, the influence of AI on ESD, the impact on the structure of Blooms Taxonomy, collaboration between academia and industry, strategies for continuous learning, and the effective integration of AI tools for personalized learning. This paper aims to critically examine the integration of AI tools, with a specific emphasis on ChatGPT, in education within the context of ESD. It delves into the transformative potential, ethical considerations, imperatives for continuous learning, and the role of industry partnerships. By providing insights and strategies, this paper contributes to the ongoing discussion about the evolving nature of education in a technologically driven world, equipping academic institutions to navigate the complexities and opportunities associated with AI integration in education more effectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • There is an association between water and electricity consumption. • Lockdown period increased electricity and water consumption by 30% and 6% in 2020 compared to 2019. • The electricity consumption in the industrial and commercial sectors dropped due to the pandemic. • The water and electricity consumption in the residential sectors increased during the summer. • The spatial impact shows variation across the six sectors. This study investigates the water – electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors. Due to inadequate research on spatial modelling of water – electricity consumption in the context of the COVID-19 pandemic, this study investigated geographical block-level variation in water and electricity consumption in Doha city of Qatar. Spatial analyses were performed to investigate the spatial differences in each sector. Five geospatial techniques in a Geographical Information System (GIS) context were used in the study. Moran's I , Anselin Local Moran's I , and Getis-Ord G i ∗ statistics tools were used to identify the hot spots and cold spots of water and electricity consumption in each sector. Furthermore, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed to investigate the spatial relationship between water and electricity consumption during the pandemic year. The findings show that there is a distinction in water and electricity consumption at the block level across all sectors and over time. Hot spot and spatial regression analysis reveal spatial and temporal heterogeneities in the study area across the six socioeconomic sectors. The intensity of hot spots of water and electricity consumption are found in the southern and western parts of the city due to high population density and the concentration of the commercial and industrial areas. Furthermore, analyzing the spatiotemporal correlation between the water and electricity consumption across the six sectors shows variation within and between these sectors over space and time. The results show a positive relationship between water and electricity consumption in some blocks and over time of each sector. During the lockdown phase, strong positive correlation between water and electricity consumption have exist in the residential sector due to extra water and electricity footprints in this sector. Conversely, the water and electricity consumption were positively correlated but declined in the industrial and commercial sector due to the curtailment in production, economic activities, and reduction in people's mobility. Mapping the hot spot blocks and the blocks with high relationship between water and electricity consumption could provide useful insight to decision-makers for targeted interventions. [ABSTRACT FROM AUTHOR]

Hydrocarbon-contaminated soils are considered as one of the major environmental issues that harm human well-being, particularly in arid regions of the world. Phytoremediation is a possible mitigation measure for this issue and has been suggested as it is cost-effective compared with other remediation technologies for soil clean-up, such as soil thermal treatment and soil washing. However, there are still gaps in the literature regarding the behavior of annual and perennial desert plants and their ability to survive in hydrocarbon-contaminated soils in arid ecosystems. Therefore, this study aims to develop an integrated approach using remote sensing techniques to understand the behavior of annual and perennial desert plants over different types of oil-contaminated soils (oil tarcrete, wet-oil lake, bare soil, and vegetation cover) in the Kuwait Desert and to explore the impact of climate and physical soil properties on the regrowth of native desert plants. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and ferrous iron (Fe2+) index (FI) were used to determine the changes in oil contamination and vegetation cover from 1992 to 2002, and 2013–2020. Subsequently, statistical tests were performed to determine the influence of climatic and soil physical characteristics on changes in hydrocarbon contamination and desert plant behavior. The results showed that hydrocarbon contamination was high at the study sites in the first six years (1992–1997) after contamination, and then decreased in the following years. However, vegetation cover was low in the first six years but significantly increased after 1998, reaching >65%. It was also found that annual plants had the highest distribution rate compared to perennial plants, which mainly depended on the soil type. We concluded that certain annual and perennial plants could successfully grow over tarcrete-contaminated sites, making these sites more suitable for the restoration of native desert plants than hydrocarbon-contaminated sites. We also observed that the succession process of vegetation growth over hydrocarbon-contaminated soils could be associated with vegetation growth on a clean sediment layer covering the oil layer. Additionally, we observed that the remobilization of aeolian sediment over many contaminated sites in Kuwait resulted in the accumulation of organic matter, plant seeds, and dust particles that create layers of nutrient-rich soil for the initial growth of plants. [ABSTRACT FROM AUTHOR]

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too. [ABSTRACT FROM AUTHOR]