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The impacts of climate change and development present significant challenges and complexities that require new solutions, wise choices, and multi-disciplinary integration. In this context, emotional intelligence (EI) plays a crucial role. However, traditional engineering education and practice overlook the importance of understanding and managing emotions. This research aims to determine the impact of EI as a tool to enhance proactive decision-making and implement sustainable measures within the engineering profession.The study makes three main research contributions. First, it confirms a positive relationship between EI and proactive sustainable decision-making among engineers. This means that engineers with high EI are more likely to consider the impacts of their decisions on various stakeholders and dimensions of sustainability. Second, it suggests that EI can enhance creativity and innovative thinking in engineering, helping engineers to develop effective solutions for challenges related to climate change. Third, the study advocates for incorporating EI training and assessment into engineering curriculums to foster a sustainable and ethical engineering culture. By improving EI, engineers can enhance their interpersonal skills, self-awareness, and emotional management, which in turn can significantly improve teamwork in addressing challenges related to climate change. • Innovative Approach: The paper introduces emotional intelligence (EI) as a novel factor in engineering decision-making for climate change adaptation, which provides a fresh perspective within the realm of sustainable practices. • Relevance to Sustainability: It emphasizes the role of EI in fostering proactive and strategic decision-making, which is crucial for mitigating and adapting to a changing climate, aligning with the journal's focus on sustainable solutions1. • Multidisciplinary Integration: The study bridges engineering, psychology, and environmental science, reflecting the journal's interdisciplinary approach to addressing sustainability challenges. • Practical Implications: By exploring the attitudes of engineers and their decision-making tendencies in relation to EI, the paper offers insights that can lead to targeted interventions, contributing to the journal's aim of promoting sustainable practices. • This paper does not constitute a bibliometric study; rather, it provides empirical research on the impact of EI on sustainable decision-making among engineers. [ABSTRACT FROM AUTHOR]

This paper investigates the relative importance and effect of conventional and unconventional Canadian monetary policy surprises on housing prices. Using a credible approach to identify structural monetary policy shocks in Canada and a comprehensive Bayesian VAR model to analyze their effects on financial and macroeconomic variables, we find that both conventional monetary policy and quantitative easing shocks have a significant and persistent effect on housing prices in Canada. However, the inflationary effect of a quantitative easing shock is more pronounced than that of an easing target monetary policy surprise. Specifically, the peak effect of a 25-basis point expansionary conventional monetary policy shock is a 2.30% increase in real housing prices while a comparable quantitative easing surprise leads to a peak of 4.56% increase in real housing prices. We conclude that expansionary monetary policy in various forms has had significant inflationary effects on Canadian housing prices. But quantitative easing surprises stand out as the most prominent contributory factor to the escalating Canadian real estate price. Quantitative easing impact consistently outweighs the effects of forward guidance and conventional monetary policy shocks. • Identifies Canadian monetary policy shocks, both conventional and unconventional. • Analyzes impact on housing prices using Bayesian VAR for new insights. • Quantitative easing (QE) impacts housing prices more than conventional shocks. • A 25bps conventional shock raises housing prices by 2.30%; QE by 4.56%. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring has the potential to enhance the safety and serviceability of our aging infrastructures by detecting anomalies at an early stage. Bayesian Dynamic Linear Models (BDLM) have been shown to be effective at detecting anomalies by extracting structural patterns and latent variables from complex and noisy time series. However, the autoregressive component modelling the stationary prediction errors in most BDLM has a tendency to wrongfully capture patterns that should be attributed to anomalies, and thus hinders their detectability. This paper proposes a new bounded autoregressive (B A R) component, which imposes constraints on the autoregressive latent process with a new mixture Rectified Linear activation Unit. The B A R component is probabilistically verified on synthetic data using a new F1t metric, and is validated using real observations collected on a bridge and on a dam located in Canada. The experimental results demonstrate that the B A R model surpasses the performance of the existing autoregressive component with (1) an improved accuracy at estimating hidden states, (2) an early detection of anomalies, (3) a capacity to detect smaller anomaly magnitudes, and (4) the ability to control the tradeoff between the anomaly detectability and the false alarm rate. [ABSTRACT FROM AUTHOR]