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Abstract Climatic extremes have historically been seen as univariate; however, recent international reports have highlighted the potential for an increase in compound climate events (e.g., hot and dry events, recurrent flooding). Despite the projected increase in the frequency of compound climate events and the adoption of compound event terminology, few studies identify climate extremes as compound climate events and little evidence exists on the societal impacts of these compound climate events. This scoping review summarizes key findings and knowledge gaps in the current state of empirical studies that focus on the societal impacts of compound climate events. We identified 28 eligible studies published in four databases reporting on the societal impacts of compound climate events in four sectors: agriculture, public health, the built environment, and land use. Overall, we found the need for more research explicitly linking compound climate events to societal impacts, particularly across multiple compound climate events, rather than single case study events. We also noted several key findings, including changes in agricultural productivity, loss of habitat, increased fire risk, poor mental health outcomes, decreased health care access, and destruction of homes and infrastructure from these events. Additional research is needed both globally and locally to understand the implications of compound climate events across different geographic regions and populations to ensure responsive adaptation policies in a compound climate event framework. 

Recent advances in construction automation increased the need for cooperation between workers and robots, where workers have to face both success and failure in human-robot collaborative work, ultimately affecting their trust in robots. This study simulated a worker-robot bricklaying collaborative task to examine the impacts of blame targets (responsibility attributions) on trust and trust transfer in multi-robots-human interaction. The findings showed that workers’ responsibility attributions to themselves or robots significantly affect their trust in the robot. Further, in a multi-robots-human interaction, observing one robot’s failure to complete the task will affect the trust in the other devices, aka., trust transfer.