Climate Decision-Making

Abstract

Climate change decision-making has emerged in recent decades as an area of research and practice, expanding on an earlier focus on climate policy. Defined as the study of decisions relevant for climate change, it draws on developments in decision science, particularly advances in the study of cognitive and deliberative processes in individuals and organizations. The effects of climate, economic, social, and other framings on decision-making have been studied, often showing that nonclimate frames can be as effective, or more effective, than climate frames in promoting decision-making and action. The concept of urgency, linked to the ideas of climate crisis and climate emergency, has taken on importance in recent years. Research on climate decision-making has influenced numerous areas of climate action, including nudges and other behavioral interventions, corporate social responsibility, and Indigenous decision-making. Areas of transformational change, such as strategic retreat in the face of sea-level rise, are emerging. Expected final online publication date for the Annual Review of Environment and Resources, Volume 45 is October 19, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Emotional pathways to climate change responses

“…Several studies indicate that negative emotions, particularly fear appeals, elevate climate risk perceptions, and motivation to act (Leiserowitz, 2006;Lu & Schuldt, 2015;Skurka et al, 2018;Wong-Parodi & Feygina, 2021), but overall, the empirical research record is quite mixed (Reser & Bradley, 2017). Other research indicates that positive emotions such as hopefulness, pride, and gratefulness have a greater propensity to motivate pro-climate responses (Carlson et al, 2020;Orlove et al, 2020). Taking action in and of itself can generate positive emotional responses, and communications that generate the anticipation of such positive emotions has been identified as a potentially more effective strategy than the use of negative emotions (Schneider et al, 2017;Shiota et al, 2021).…”

Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis

“…Many researchers have conducted in-depth research on the keyword extraction algorithm and cluster center point selection algorithm in the process of text clustering so as to optimize the clustering results. There are many existing keyword extraction algorithms, which can be divided into three categories: algorithms based on statistical models, algorithms based on semantics, and algorithms based on machine learning [ 10 – 12 ]. The traditional clustering algorithm in Euclidean space selects the initial cluster center point in a random way and iteratively updates the cluster and the cluster center point so that the cluster center point is getting closer and closer to the class center point, but due to the initial cluster center point.…”

Internet of Things Device Identification Algorithm considering User Privacy

“…In terms of home furnishing, people are looking forward to having an automated and intelligent home environment, such as moving target intrusion detection, gas leakage monitoring and alarming, temperature monitoring and automatic adjustment, and a series of electrical appliances that are automatically turned on and off according to the owner's living habits; in medical care, people hope to carry monitoring nodes with them, monitor the health of the human body, and automatically call medical assistance in case of emergency; in intelligent door locks, users do not need to carry keys after leaving and only need facial recognition or passwords to open the door locks. These ideas are nothing short of unrealistic in the past, and the emergence of 5G has made the Internet of Everything possible [ 7 – 9 ].…”