人工知能と気候リスクショック
Artificial Intelligence and Climate Risk Shocks (原題)
Pengyu Chen, Zhongzhu Chu, Sarula Bai, QianYing Chen
🤖 gxceed AI 要約
日本語
51カ国・1996〜2023年のクロスナショナルパネルデータを用い、AIが物理的気候リスクを統治する効果を二方向固定効果モデルで検証した。効果はリスク感知・捕捉・行動統合の経路を通じ、AI機能応用が技術・応用分野より影響が大きい。政治経済統合同盟で効果が強く、リスクが高まるほど統治効果も増大する。
English
Using cross-national panel data for 51 countries (1996–2023), this study applies a two-way fixed effects model to test whether AI governs physical climate risks. Effects operate through risk sensing, seizing, and action integration, with AI functional applications mattering more than technologies or fields. Governance effects are stronger in political-economic integration alliances and intensify as climate risks rise.
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
日本ではSSBJ・有報での気候リスク開示が進む中、AIをリスク感知・統合に活用する視点は、企業のシナリオ分析やTCFD対応の高度化に示唆を与える。政策面でもAI活用による気候ガバナンス強化の根拠となりうる。
In the global GX context
Amid TCFD/ISSB-driven climate risk disclosure, this paper offers empirical evidence that AI strengthens physical climate risk governance, relevant to scenario analysis and risk-sensing infrastructure. It adds a cross-national, policy-oriented angle to the AI-for-climate literature, though it stops short of disclosure/accounting applications.
👥 読者別の含意
🔬研究者:AIと物理的気候リスクの因果関係をマクロパネルで示した点が、AI×気候ガバナンス研究の実証基盤となる。
🏢実務担当者:AIによるリスク感知・統合の枠組みは、TCFDシナリオ分析や適応策立案のツール選定に応用できる。
🏛政策担当者:AI普及政策が気候ガバナンスを強化しうるという根拠を提供し、同盟・統合枠組みの設計に示唆を与える。
📄 Abstract（原文）
ABSTRACT Artificial intelligence (AI) has been widely applied across various fields and has demonstrated effectiveness to some extent. However, some scholars have raised concerns about its ethical implications and potential rebound effects, particularly in the context of climate issues. To address these debates, we obtained cross‐national panel data for 51 countries from 1996 to 2023 through the ISETS Energy Finance Network, WIPO, and World Bank databases. A two‐way fixed effects model was used to examine the relationship between AI and physical climate risks. The findings are as follows: (1) AI can effectively govern physical climate risks. (2) The governance effect of AI on physical climate risks comes from risk sensing, risk seizing, and action integration. (3) Compared to AI technologies and application fields, AI functional applications have a greater governance impact. (4) Political and economic integration alliances show a stronger AI governance effect compared to purely economic alliances. (5) The governance effect of AI intensifies as physical climate risks increase. These results provide theoretical support and empirical evidence for government efforts to promote AI applications and achieve climate governance. These findings provide theoretical support and empirical evidence for government initiatives to promote AI applications in climate governance.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1111/risa.70371first seen 2026-09-28 05:23:58
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