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Record W4386461632 · doi:10.3389/fpsyg.2023.1272343

Editorial: Discrete emotions in environmental decision-making

2023· editorial· en· W4386461632 on OpenAlexaff
Eugene Y. Chan, Katharine Howie, Felix Septianto

Bibliographic record

VenueFrontiers in Psychology · 2023
Typeeditorial
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPsychologyFront (military)Oceanography

Abstract

fetched live from OpenAlex

For the past few decades, emotion research has demonstrated how emotional valence differentially influences individuals' decision making (Peters et al. 2006). However, there is still very limited understanding regarding how discrete emotions can influence environmental behavior, potentially due to the complex nature of climate change (Pihkala 2022). Understanding the role of emotions is vital in this setting as research has demonstrated strong predictive power for outcomes like climate mitigation (Xie et al., 2019), preference for energy technologies (Jobin and Siegrist, 2018), and support for policy (Smith and Leiserowitz, 2014). Environmental decisions often involve complex and multifaceted issues with long-term consequences (Larson et al., 2015), and people's emotional responses can heavily influence their attitudes and actions (Davidson and Kecinski, 2022). Positive emotions like empathy and concern for nature can motivate individuals to engage in pro-environmental behaviors, such as recycling or supporting conservation efforts (Berenguer, 2007). Conversely, negative emotions like fear or denial can hinder environmental action or lead to unsustainable practices (Bostrom et al., 2018). This special issue of Frontiers recognizes the emotional underpinnings of environmental decision-making, policymakers, educators, and advocates can tailor their messages and strategies to appeal to people's emotions in ways that inspire positive environmental actions. The first paper in our special issue, Shipley et al. (2023) focus on two discrete emotions-pride and guilt. The authors how place attachment influence these two discrete emotions, thereby influencing pro-environmental behavior. Then, Sanford et al. (2023) focus on social media-specifically, the authors examine Twitter "tweets" and the emotional content they contain as they relate to environmental awareness. The concern for protecting the planet continues to be examined with Seibt et al. (2023) addressing how communal sharing relationships evoke an emotion termed "kama muta," thereby expanding an understanding of how sympathy, compassion, and care influence environmental decision-making. Myers et al. (2023) then shift focus to communication strategies. Specifically, the authors examine how emotions regarding climate change information (not climate change more generally) influence their support for policy measures, focusing on the five emotions of guilt, anger, hope, fear, and sadness. Recognizing the global scope and complexity of climate concerns, Bohm et al. (2023) use Appraisal Theory to examine cross-cultural differences in emotional reactions to climate change and climate related actions. Zhang et al. (2023) also bring an international scope studying how adolescents' happiness may influence their willingness to protect the environment using data from eight countries. Our penultimate article, Geiger et al. ( 2023) use meta-analysis to investigate the effectiveness of hope in promoting sustainable decisions.Future research on the role of individuals' emotions in environmental decision-making should further explore how emotional responses vary across different environmental issues and contexts beyond culture (Bohm et al., 2023) and age (Zhang et al., 2023). Investigating whether emotions differ in intensity and directionality depending on the type of environmental concern (e.g., climate change, deforestation, pollution) and the geographic, cultural, or socio-economic context in which individuals are situated can provide valuable insights for tailoring communication and policy approaches.Understanding the nuanced emotional landscape surrounding diverse environmental challenges can inform targeted interventions that resonate with individuals' emotional realities, fostering more profound connections to nature and driving meaningful pro-environmental actions. Additionally, research could delve into the interplay between emotions and cognitive processes in online environments as communication these days are largely done using social media (Sanford et al., 2023). Ultimately, these investigations can empower policymakers, educators, and activists to effectively harness emotions as a force for positive environmental change. Indeed, recognizing emotions not toward climate change itself but toward communication (Myers et al., 2023) will further expand how emotions play a role in sustainable decision-making. Policy-makers can design incentives and rewards that trigger positive emotions for adopting eco-friendly behaviors, reinforcing the link between personal well-being and environmental responsibility. Finally, new methods such as meta-analyses (Geiger et al., 2023) can better assess the effectiveness of strategies and interventions beyond traditional psychological methods of surveys and experiments. Indeed, policy-makers can strategically leverage people's emotions to promote environmentally friendly choices and decisions by employing targeted communication and policy interventions, as Myers et al. ( 2023) has shown. By crafting compelling narratives that evoke empathy and concern for the environment, policy makers can raise awareness about pressing environmental issues and their potential impact on communities and future generations. Utilizing positive emotional appeals, such as hope and optimism (Myers et al., 2023), or guilt, pride and even sympathy (Shipley et al., 2023;Seibt et al., 2023) policy makers can highlight success stories and the transformative potential of sustainable practices, inspiring individuals to take action. By tapping into the emotional dimensions of decision-making, policy makers can foster a sense of shared responsibility and collective action, empowering individuals to become agents of positive environmental change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0040.002
Science and technology studies0.0040.004
Scholarly communication0.0110.006
Open science0.0060.003
Research integrity0.0180.020
Insufficient payload (model declined to judge)0.0320.017

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.308
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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