This research presents key factors influencing smart building integration into smart cities considering the city as a technological system. This paper begins with an overview of the concept of smart buildings, defining their features and discussing the technological advancements driving their development. The frameworks for smart buildings are presented, emphasizing energy efficiency, sustainability, automation, and data analytics. Then, the concept of a smart city and the role of digitalization in its development is explored. The conceptual framework of smart building into a smart city is presented, contributing to understanding the complex process of integrating smart buildings into smart cities. Further research delves into the factors influencing the integration of smart buildings into smart cities, focusing on energy, mobility, water, security systems, and waste management infrastructure domains. Each thematic area is examined, highlighting the importance of integration and the associated challenges and opportunities, based on research in the literature and the analysis of case studies. This enables the identification of 26 factors influencing integration and the synthesis of findings. The findings indicate that the successful integration of smart buildings into smart cities requires attention to multiple factors related to smart energy, smart mobility, smart water, smart security, and smart waste management infrastructures. The results obtained from this research provide valuable insights into the factors influencing smart building integration into a smart city from a technological perspective, enabling stakeholders to make informed decisions and develop strategies paving the way for sustainable, resilient, and efficient urban environments.

Smart and resilient cities are on the same path to long-term sustainability and need the creation of an ecosystem that can withstand an increasing number of hazards in the future. One of the primary goals of the smart city movement was to address many issues that have arisen as a result of growing urbanization, including the scarcity of energy, traffic congestion, and pollution. The way urban dwellers live, work, and travel due to more often natural disasters and political issues is changing, and this has an impact on the economy and business models, forming a new trend in urban studies. Smart cities play a critical role in the worldwide fight against Covid-19 in terms of tracking and tracing instances of coronavirus using smart technologies. The integration of smart buildings into a smart city also plays an important role in this scenario, providing advanced infrastructure and greater comfort for their residents as well as improved safety control, increased energy efficiency, accessibility to services, and overall satisfaction with a higher quality of life. The findings of this study are based on the review of papers selected from high-impact research journals, as well as an analysis of the most important industry tendencies and widely referenced literature on smart cities and resilient cities. As a result, this study examines the concepts of smart and resilient cities, as well as the relationship between smart city technologies, and resilient city indicators; the impact of technologies applicable in different smart city domains on city resilience is discussed. Finally, the conclusions on how proposed integration toward smart and resilient cities could be implemented for future cities’ development are presented.

Smart buildings’ role is crucial for advancing smart cities’ performance in achieving environmental sustainability, resiliency, and efficiency. The integration barriers continue due to technology, infrastructure, and operations misalignments and are escalated due to inadequate assessment frameworks and classification systems. The existing literature on assessment methodologies reveals diverging evaluation frameworks for smart buildings and smart cities, non-uniform metrics and taxonomies that hinder scalability, and the low use of machine learning in predictive integration modelling. To fill these gaps, this paper introduces a novel machine learning model to predict smart building integration into smart city levels and assess their impact on smart city performance by leveraging data from 147 smart buildings in 13 regions. Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. The SVR-trained model substantially outperformed other models, achieving an R-squared of 0.81, Root Mean Square Error (RMSE) of 0.33 and Mean Absolute Error (MAE) of 0.27, enabling precise integration prediction. Case studies revealed that low-integration buildings gain significant benefits from progressive target upgrades, whilst those buildings that have already implemented some integrated systems tend to experience diminishing marginal benefits with further, potentially disruptive upgrades. The conclusion of this study states that by utilising the developed machine learning model, owners and policymakers are capable of significantly improving the integration of smart buildings to build better, more sustainable, and resilient urban environments.