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The Collision of Climate Narratives and Green Food Production Cost

気候変動ナラティブとグリーン食品生産コストの衝突 (AI 翻訳)

V. D. Smirnov, I. D. Tyukhmeneva

Review of Business and Economics Studies📚 査読済 / ジャーナル2026-04-22#炭素価格Origin: Global
DOI: 10.26794/2308-944x-2026-14-1-19-36
原典: https://doi.org/10.26794/2308-944x-2026-14-1-19-36
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🤖 gxceed AI 要約

日本語

本論文は、グリーン製品への大量需要を実現するには、従来製品との価格パリティが不可欠と論じる。その手段として、内部炭素価格(ICP)を戦略的財務計画ツールとして捉え、将来の環境リスクコストを事前に統合し、内部イノベーション資金を創出する手法を提案する。このアプローチにより、企業は戦略的リスクを低減しつつ、競争優位を獲得できる。

English

This paper argues that mass demand for green products requires cost parity with conventional alternatives. It proposes using internal carbon pricing (ICP) as a strategic financial planning tool to integrate future environmental risks into project calculations, fund innovation, and incentivize price parity. The approach reduces strategic risks and creates competitive advantage through proactive adaptation.

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

本論文はICPを規制リスクのヘッジではなく戦略的計画に位置づけ、日本のGX政策における企業の脱炭素投資判断に示唆を与える。SSBJや有価証券報告書での気候関連開示実務において、ICPは将来炭素コストの内部化手段として注目される。

In the global GX context

This paper reframes internal carbon pricing from a risk management tool to a strategic driver of innovation and cost reduction. It aligns with global disclosure frameworks (TCFD, ISSB) that encourage integration of climate risks into financial planning, and offers a practical pathway for companies under CSRD and SEC climate rules to achieve price parity for green products.

👥 読者別の含意

🔬研究者:Provides a conceptual framework linking ICP to operational efficiency and price parity, opening avenues for empirical testing across industries.

🏢実務担当者:Corporate sustainability teams can use this to reposition ICP as a tool for funding innovation and achieving cost competitiveness, not just compliance.

🏛政策担当者:Highlights how carbon pricing can be embedded in corporate strategy to accelerate the green transition, informing policies that encourage ICP adoption.

📄 Abstract(原文)

Key factors in limiting the negative impact of human society on the planet’s climate include the availability of cost-effective low-carbon technologies and manufacturers’ ability to shift mass consumer demand from traditional to environmentally friendly products. Private companies, focused on maximizing profits, face a dilemma: follow a reactive logic of minimizing regulatory risks or develop proactive strategies that turn climate challenges into a source of competitive advantage. The aim of the study is to find a way to improve the internal operational efficiency of green goods producers to achieve price parity with conventional analogs, that, all other things being equal, will provide the mass consumer with the opportunity to realize an environmentally sound purchasing model. This work is conceptual and analytical in nature and focuses on developing a methodology for managing value creation in the production of green products, applicable under conditions of confidentiality of cost data specific to each company. Results. The article argues that mass demand for green products can only be ensured by increasing a company’s internal operating efficiency to bring their production costs closer to those of traditional counterparts. The use of an internal carbon price (ICP) is proposed as a tool for achieving this goal. Conclusions. Unlike the traditional approach focused on hedging regulatory risks, the authors position the ICP as a tool for strategic financial planning. This allows for the a priori integration of future environmental risks’ cost in target markets into project performance calculations, creating internal sources of innovation funding and establishing economic incentives to achieve price parity with traditional counterparts. The article demonstrates that this approach not only reduces strategic risks but also creates conditions for market share and company capitalization growth through proactive adaptation to changing market terms.

🔗 Provenance — このレコードを発見したソース

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。