The Dual Impact of Organic Farming Support: Evaluating Greenhouse Gas Emissions Abatement and Private Payoffs
有機農業支援の二重の影響：温室効果ガス排出削減と私的利得の評価 (AI 翻訳)
Alessandro Varacca, Silvia Coderoni, Roberto Esposti
🤖 gxceed AI 要約
日本語
本論文は、EU共通農業政策（CAP）の有機農業支援が温室効果ガス排出削減に効果的かを、イタリアの農場パネルデータと因果機械学習を用いて評価。有機農業の採用は農家の所得向上に寄与する一方、GHG削減効果は限定的であり、政策設計の改善が必要と結論付ける。
English
This study evaluates the effectiveness of EU Common Agricultural Policy (CAP) organic farming support in reducing greenhouse gas emissions. Using panel data from Italian farms and causal machine learning, it finds that while organic adoption boosts farm income, its GHG abatement impact is limited, suggesting policy design improvements.
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
EUのCAPを対象とするが、日本の農業環境政策や有機農業支援制度の効果検証に同様の因果推論手法が応用可能。日本の農業分野におけるGHG削減政策の効率性評価に示唆を与える。
In the global GX context
This paper provides a rigorous causal evaluation of agricultural subsidies for climate mitigation, relevant to global debates on CAP reform, carbon farming, and the design of results-based payments for ecosystem services.
👥 読者別の含意
🔬研究者:Use the causal machine learning framework to assess heterogeneous treatment effects in agricultural policy evaluation.
🏢実務担当者:Limited direct applicability, but agribusinesses can understand the trade-off between farm income and emissions reduction under subsidy schemes.
🏛政策担当者:Key evidence that current organic farming support may not efficiently achieve GHG abatement, suggesting redesign towards performance-based measures.
📄 Abstract（原文）
ABSTRACT This study evaluates whether financial support for organic farming under the EU Common Agricultural Policy (CAP) effectively contributes to reducing greenhouse gas (GHG) emissions in agriculture. Using data from a panel of Italian farms between 2015 and 2022, we examine both the private benefits (farm income) and societal outcomes (GHG emission reductions) associated with the adoption of organic practices. To account for differences in farm‐level responses, we develop an analytical framework of farmers' decision‐making and apply causal machine learning techniques that allow for heterogeneous treatment effects. Our findings reveal that while many farmers benefit economically from adopting organic farming, the impact in terms of GHG abatement is often limited. These results raise concerns about the overall effectiveness and efficiency of current CAP support and suggest directions for improving policy design.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1002/bse.71010first seen 2026-06-10 05:45:20
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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。