Optimal carbon abatement under climate change and uncertainty
気候変動と不確実性の下での最適炭素削減 (AI 翻訳)
Xin Li, Zhuo Jin
🤖 gxceed AI 要約
日本語
本論文は、気候変動とモデルの不確実性を統合した動学的確率的一般均衡モデルを開発し、社会的厚生を最大化する社会計画者の観点から炭素削減戦略を分析。気候モデルの不確実性が温度上昇を抑制し、クリーンな投入物の生産を増加させ、炭素削減支出や社会的炭素コスト、炭素税を引き上げることを示した。また、炭素削減が温度リスクの価格を高めることを発見。
English
This paper develops a dynamic stochastic general equilibrium model integrating climate change, damages, abatement, and model uncertainty. It shows that climate model uncertainty leads to lower temperature increases, higher clean-input production, higher abatement expenditures, and higher social cost of carbon and carbon tax. Carbon abatement effectively reduces temperature change and increases the price of temperature risk.
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
日本では炭素価格付けの議論が進む中、本論文は気候モデルの不確実性が炭素税や社会的炭素コストに与える影響を定量的に示しており、カーボンプライシング政策設計に示唆を与える。SSBJやTCFDのシナリオ分析における不確実性考慮にも関連。
In the global GX context
This paper provides insights for global carbon pricing design by demonstrating how climate model uncertainty affects optimal abatement, SCC, and carbon taxes. Relevant to TCFD scenario analysis and ISSB's climate resilience approach, as it quantifies the impact of uncertainty on carbon pricing metrics.
👥 読者別の含意
🔬研究者:GX researchers should note the DSGE framework integrating model uncertainty into optimal abatement, extending standard climate-economics literature.
🏢実務担当者:Corporate sustainability teams can use insights on how climate uncertainty affects SCC and carbon tax projections, informing internal carbon pricing.
🏛政策担当者:Policymakers can leverage findings on the impact of model uncertainty on optimal carbon tax trajectories and the need for robust climate policies.
📄 Abstract（原文）
We develop a dynamic stochastic general equilibrium model that integrates climate change, climate damages, carbon abatement strategies, and model uncertainty. We adopt the perspective of a social planner who maximizes social welfare, balancing preferences for consumption and emissions while accounting for aversion to model misspecification. Through a comprehensive numerical analysis, we examine the dynamics of temperature change, the allocation between clean and dirty inputs, abatement expenditures, the social cost of carbon (SCC), the carbon tax, and the market price of risk. Our key findings indicate that climate model uncertainty leads to lower temperature changes, increased clean-input production, higher carbon abatement expenditures, and higher social costs of carbon and the carbon tax. Furthermore, we demonstrate that carbon abatement effectively reduces temperature change; it also leads to distinct temporal patterns in the SCC and the carbon tax, and to a higher price of temperature risk.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.econmod.2026.107755first seen 2026-07-16 05:27:59
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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。