This study presents an innovative integrated approach for smart cities, aimed at promoting environmentally sustainable economies through novel technological and socio-economic transitions. The proposed model determines the smart city index (SCI) by aggregating 32 distinct performance indicators that significantly transform the environment, economy, energy, social, governance, and transportation sectors. This model is inherently multidisciplinary and is methodologically processed using multi-criteria decision analysis, which is aggregated using four distinct weighting schemes. The model results reveal that based on the equal weighting scheme, Sydney emerges as the city with the highest SCI score of 0.72, whereas Lima is identified as the city with the lowest SCI score of 0.26. On the other hand, based on the sustainability triad scheme, Toronto tops the list with an SCI score of 0.77, whereas Abuja scores the lowest with an SCI score of 0.31. Interestingly, Toronto, Vancouver, and Montreal continue to maintain their position among the top 5 cities across all three schemes: equal weighting, sustainability triad, and energy-focused schemes. Furthermore, the energy-focused scheme identifies Montreal as the top-performing city, scoring 0.7, followed by Oshawa at 0.67, and four Canadian cities top the SCI scores in this scheme. In contrast, Lima still remains at the bottom of the list with an SCI score of 0.27. Finally, based on a smart health-focused scheme, Sydney, Osaka, and Hämeenlinna rank highest in SCI scores. Overall, the proposed approach and model provide valuable insights and guidelines for policy-makers and urban planners to design and implement smart city initiatives that can significantly enhance sustainable development and improve quality of life in urban settings.

Rising average sea levels and increasingly extreme conditions pose serious threats to low-lying coastal areas due to various coastal hazards. These include the permanent marine submersion of land due to higher average sea levels, more frequent or intense coastal flooding, and increased coastal erosion. Climate drivers such as future Sea Level Rise (SLR) and marine storm events will significantly increase the damage on coastal energy infrastructure. This research assesses coastal energy infrastructure and consumption of the population at risk under the different SLR scenarios. These scenarios are modeled using Coupled GIS and Machine Learning models, utilizing elevation data points and monthly energy data of 2019. Preliminary result shows that in 2030, The coastal inundation maps will cover 6.6 km², and the total affected population will be 78,000, which means that 5% of the residential power units in Qatar will be under high climatic risk in Doha. By 2050, SLR could increase the exposure of residential electric meters to marine submersion by 20%, affecting an additional 8.5% of the population. By the year 2100, modeled results show that approximately 60% of Doha’s land surface may be at risk of submersion due to rising sea levels, potentially impacting an estimated 1,876,200 individuals as a result of accelerated SLR and increasingly frequent storm events. However, the number of residential power units exposed to extreme coastal marine events will increase by 40%. This research offers crucial insights into population and infrastructure at risk from future SLR, emphasizing the need for targeted mitigation strategies. We advocate for integrating Nature-Based Solutions (NBS) into coastal management policies to provide sustainable, cost-effective protection for vulnerable areas, while safeguarding infrastructure and supporting local communities.