The urban heat island (UHI) is a crucial factor in developing sustainable cities and societies. Appropriate data collection, analysis, and prediction are essential first steps in studying the effects of the UHI. This research systematically reviewed the papers related to the UHI that have used on-site data collection in the United States and Canada and the papers related to predicting and analyzing this effect in these regions. To achieve this goal, this study extracted 330 articles from Scopus and Web of Science and, after selecting the papers, reviewed 30 papers in detail from 1998 to 2023. The findings of this paper indicated a methodological shift from traditional sensors and data loggers towards more innovative and customized technologies. Concurrently, this research reveals a growing trend in using machine learning, moving from supportive to direct predictive roles and using techniques like neural networks and Bayesian networks. Despite the maturation of UHI research due to these developments, they also present challenges in technology complexity and data integration. The review emphasizes the need for future research to focus on accessible, accurate technologies. Moreover, interdisciplinary approaches are crucial for addressing UHI challenges in an era of climate change.

Purpose: Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.Design/methodology/approach: The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.Findings: The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.Practical implications: Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.Originality/value: For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

This study evaluates the current scope of smart technology applications that support aging in place and identifies potential avenues for future research. The global demographic shift towards an aging population has intensified interest in technologies that enable older adults to maintain independence and quality of life within their homes. We conducted a systematic review of the scientific literature from Web of Science, PubMed, and ProQuest, identifying 44 smart technologies across 32 publications. These technologies were classified into three categories: nonmobile technologies for individual monitoring, nonmobile technologies for home environment monitoring, and wearable technologies for health and activity tracking. Notably, the research in this area has grown significantly since 2018; yet, notable gaps persist, particularly within the traditional disciplines related to aging and in the use of quantitative methodologies. This emerging field presents substantial opportunities for interdisciplinary research and methodological advancement, highlighting the need for well-developed research strategies to support the effective integration of smart technology in aging in place.