What do we mean when we say climate change is urgent?

Recent discussions of climate change in multiple domains—the academic literature, the popular press, political movements, and international climate policy regime—have increasingly framed the phenomenon as a “crisis,” an “emergency,” or an “urgent” situation. In this paper, we contextualize the time pressure of climate change in the broader social science literature, perform bibliometric and discourse analyses of this framing, and explore potential implications of this trend for climate decision making. While the increased prevalence of time pressure terms is arguably part and parcel of modernity, these terms are in general not synonymous. In the context of climate decision making, we find that “urgency” functions as a boundary object relaying the internalization of time pressure between (1) the academic literature and the international climate change policy regime and (2) political movements and the popular press; especially as construed in these latter domains, “crisis” and “emergency” connote time pressure but so too generate a constellation of other affective and cognitive states. A review of a set of related literatures suggests that the time pressure framing of climate change affects the quantity and quality of information and the range of options (e.g., geoengineering) considered in choice processes for mitigation and adaptation actions, as well as the sequencing and timing of chosen plan elements; furthermore, these effects likely vary in both direction and magnitude with characteristics of the individuals or organizations in which they manifest. Taken as a whole, the crisis framing of climate change is likely to polarize beliefs and actions, especially in the absence of accompanying information about self-efficacy and hope

Assessment of students’ cognitive–affective states in learning within a computer-based environment: Effects on performance

Students’ cognitive-affective states are human elements that are crucial in the design of computer-based learning (CBL) systems.This paper presents an investigation of students’ cognitiveaffective states (i.e., engaged concentration, anxiety, and boredom) when they learn a particular course within CBL systems.The results of past studies by other researchers suggested that certain cognitive-affective states; particularly boredom and anxiety could negatively influence learning in a computer-based environment.This paper investigates the types of cognitive-affective state that students experience when they learn through a specifi c instance of CBL (i.e., a content sequencing system). Further, research was carried to understand whether the cognitive-affective states would infl uence students’ performance within the environment.A one-way between-subject-design experiment was conducted utilizing four instruments (i) CBL systems known as IT-Tutor for learning computer network, (ii) a pre-test, (iii) a post-test, and (iv) self-report inventory to capture the students’ cognitive-affective states. A cluster analysis and discriminant function analysis were employed to identify and classify the students’ cognitiveaffective states.Students were classifi ed according to their prior knowledge to element the effects of it on performance.Then,non-parametric statistical tests were conducted on different pairs of cluster of the cognitive-affective states and prior knowledge to determine differences on students’ performance. The results of this study suggested that all the three cognitive-affective states were experienced by the students. The cognitive-affective states were found to have positive effects on the students’ performance.This study revealed that disengaged cognitive-affective states, particularly boredom can improve learning performance for lowprior knowledge students

ATM automation: guidance on human technology integration

© Civil Aviation Authority 2016Human interaction with technology and automation is a key area of interest to industry and safety regulators alike. In February 2014, a joint CAA/industry workshop considered perspectives on present and future implementation of advanced automated systems. The conclusion was that whilst no additional regulation was necessary, guidance material for industry and regulators was required. Development of this guidance document was completed in 2015 by a working group consisting of CAA, UK industry, academia and industry associations (see Appendix B). This enabled a collaborative approach to be taken, and for regulatory, industry, and workforce perspectives to be collectively considered and addressed. The processes used in developing this guidance included: review of the themes identified from the February 2014 CAA/industry workshop1; review of academic papers, textbooks on automation, incidents and accidents involving automation; identification of key safety issues associated with automated systems; analysis of current and emerging ATM regulatory requirements and guidance material; presentation of emerging findings for critical review at UK and European aviation safety conferences. In December 2015, a workshop of senior management from project partner organisations reviewed the findings and proposals. EASA were briefed on the project before its commencement, and Eurocontrol contributed through membership of the Working Group.Final Published versio

Information, Experience, and Willingness to Mitigate Mental Health Consequences From Flooding Through Collective Defence

Demand for reducing mental health impacts from flooding through collective flood defence is elicited using a contingent valuation method with a sequential hypothetical scenario, which accounts for human resilience and experience. A two-step model fits the survey data: it combines a binary sample selection rule to distinguish protesters and participants with a Tobit model to accommodate true zero responses among participants. Results show that non-symptoms-specific information on mental health risk may bias the willingness to pay downward. Risk-averse individuals who have taken self-insurance protection measures are willing to pay for additional protection through collective defence. Feelings, such as worries and anxiety related to flooding, drive the demand, which supports the risk-as-feelings hypothesis for mental health protection from flooding. Inexperience rather than experience of flooding is found to increase demand, which indicates that individual mental resilience to flooding may increase after an event as posited by the inoculation hypothesis. © 2022. American Geophysical Union. All Rights Reserved.This paper is supported by María de Maetzu excellence accreditation 2018–2022 (Ref. MDM‐2017‐0714), funded by MCIN/AEI/10.13039/501100011033/; and by the Basque Government through the BERC 2022–2025 program

The Influence of Optimism Bias on Time and Cost on Construction Projects

The unresolved scholarly debate to curtail cost and time performances in projects has led to alternate solutions, departing from the dominant technical school of thought to include concepts from behavioural sciences. In this paper, we consider the psychological effect, namely optimism bias, as one of the root causes for delays in cost overruns on projects. The research objectives were to determine the level of bias among project participants, rank time and cost overrun causes according to the participants’ bias score and establish a mitigation strategy to curb potential delays and cost overrun impacts based on the bias scores obtained. A literature survey was conducted to determine causal factors contributing to delays and cost overruns linked to optimism bias. Through a pilot survey of three semi-structured interviews, eighty factors obtained from the literature survey were reduced to 24 critical delay and cost overrun factors relevant to Trinidad and Tobago. A questionnaire was subsequently developed seeking construction professionals to rate their bias scores based on an 11-point Likert scale. The research confirms that project planners and decision-makers exhibit moderate levels of optimism bias; however, participants lacked awareness of the impact of optimism bias on projects outcomes. Project location, environmental impacts and historic preservation, and labour disputes are the top three critical factors where project professionals displayed increased optimistic tendencies. It is proposed that contingency “time window” and reference class forecasting be implemented as control mechanisms to mitigate the impacts of time and cost overruns on projects. This research introduces a novel method to account for and measure optimism bias on construction projects. This study adds knowledge into delays and cost overruns causation and provides a foundation for future studies on quantifying psychological effects on projects and enhancing overall project management practices. Doi: 10.28991/esj-2021-01287 Full Text: PD

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge