From Opinion Polarization to Climate Action: A Social–Climate Model of the Opinion Spectrum
意見の分極化から気候行動へ: 意見スペクトラムの社会-気候モデル (AI 翻訳)
Athira Satheesh Kumar, Krešimir Josić́, Chris T. Bauch, Madhur Anand
🤖 gxceed AI 要約
日本語
気候変動に対する意見を連続スペクトラムとして捉え、意見と行動のフィードバックを結合した社会-気候ネットワークモデルを開発。行動変革への抵抗、緩和コストの上昇、気候イベントへの反応の遅れが2°C超の気温上昇をもたらす可能性を示す。しかし、緩和コストの低減や異なる意見を持つ個人間の社会的学習の促進により回避可能。意見の分極化は気温変化への感受性が高い場合に解消され、緩和行動が普及することを予測。
English
We developed a coupled social-climate network model where opinions on climate change form a continuum, linking opinion dynamics with actions and temperature trajectories. Results show that resistance to behavior change, high mitigation costs, and slow response to climate events can lead to >2°C warming, but lowering costs and promoting social learning can avoid this. The model demonstrates emergence of opinion polarization and suggests it can be extinguished with sensitive temperature response. Policy implications include leveraging peer influence and reducing individualism.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX政策において、国民の意見形成と行動変容のメカニズム理解は重要。本モデルは社会的学習の促進や個人の固執低減が気候目標達成に寄与することを示し、日本におけるパブリック・エンゲージメント戦略や地域レベルでの緩和策に示唆を与える。
In the global GX context
This paper contributes to understanding the social dynamics of climate action globally. It shows that peer influence and reducing mitigation costs are crucial for achieving international climate goals. The framework integrates social and natural sciences, offering insights for designing effective climate policies and communication strategies, particularly relevant for TCFD and ISSB's focus on transition risk and public acceptance.
👥 読者別の含意
🔬研究者:GX researchers studying the intersection of social science and climate modeling should examine how opinion dynamics affect mitigation outcomes.
🏢実務担当者:Sustainability practitioners can use insights on social learning and cost reduction to design internal climate engagement programs.
🏛政策担当者:Policymakers should note that reducing mitigation costs and fostering cross-opinion interactions can accelerate climate action and reduce polarization.
📄 Abstract（原文）
Abstract. We developed a coupled social–climate network model that links opinion dynamics and the climate system, where opinions directly translate into actions and actions reflect underlying opinions, to examine how this feedback shapes collective behavior and global temperature trajectories. In contrast to previous social–climate models that discretized opinions, we assumed opinions on climate change form a continuum, and were thereby able to capture more nuanced interactions. The model shows that resistance to behavior change, elevated mitigation costs, and slow response to climate events can result in a global temperature anomaly in excess of 2[Formula: see text]C. However, this outcome could be avoided by lowering mitigation costs and increasing the rate of interactions between individuals with differing opinions (social learning). Our model is one of the first to demonstrate the emergence of opinion polarization in a human-environment system. We predict that polarization of opinions in a population can be extinguished, and the population will adopt mitigation practices, when the response to temperature change is sensitive, even at higher mitigation costs. It also indicates that even with polarized opinion, an average promitigative opinion in the population can reduce emissions. Finally, our model underscores how frequent and unexpected social or environmental changes, such as policy changes or extreme weather events, can slow climate change mitigation. This analysis helps identify the factors that support achieving international climate goals, such as leveraging peer influence and decreasing stubbornness in individuals, reducing mitigation costs, and encouraging climate-friendly lifestyles. Our model offers a valuable new framework for exploring the integration of social and natural sciences, particularly in the domain of human behavioral change.
🔗 Provenance — このレコードを発見したソース
- openalex http://arxiv.org/abs/2503.04689first seen 2026-07-06 04:35:38
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。