Emission-Reduction Decision-Making in a Shipping Logistics Service Supply Chain Under Carbon Cap-And-Trade Mechanisms: Based on Two-Way Cost Sharing of AI Technology
カーボンキャップアンドトレード制度下における海運物流サービスサプライチェーンの排出削減意思決定：AI技術の双方向コストシェアリングに基づいて (AI 翻訳)
Guangsheng Zhang, Ran Yan, Zhaomin Zhang, Shiguan Liao, Tianlong Luo
🤖 gxceed AI 要約
日本語
この論文は、カーボンキャップアンドトレード制度下で、海運物流サービスサプライチェーンにおける排出削減のためのAI技術導入の意思決定を分析する。サービス提供者と統合者の間でのコストシェアリング契約（単方向・双方向）が排出削減レベル、サービス量、利益に与える影響をモデル化し、政府の炭素価格規制の効果も検討した。結果、コストシェアリングは削減と利益を向上させるが、双方向契約は限定的な範囲でのみ有効である。
English
This paper analyzes emission reduction decision-making in a shipping logistics service supply chain under carbon cap-and-trade, focusing on AI technology adoption. It models cost-sharing contracts (one-way and two-way) between service provider and integrator, examining impacts on reduction levels, service volume, and profits. Findings show cost-sharing enhances reduction and profits, but two-way contracts are only effective within a narrow cost-sharing ratio range. Government carbon price regulation can incentivize reduction but may affect low-carbon logistics volume.
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
日本の海運業界ではGX推進が急務であり、カーボンプライシング導入が検討されている。本モデルはAI技術とコストシェアリングの効果を示し、日本企業の実務や政策設計に示唆を与える。
In the global GX context
Globally, carbon pricing mechanisms are expanding, and logistics firms face pressure to decarbonize. This paper provides a modeling framework for AI-driven emission reduction with cost-sharing contracts, offering insights for supply chain coordination under transition finance and regulatory constraints.
👥 読者別の含意
🔬研究者:Game-theoretic model for emission reduction decisions with AI and cost-sharing in logistics supply chains under cap-and-trade.
🏢実務担当者:Shows how cost-sharing contracts can improve emission reduction and profits when adopting AI in shipping logistics.
🏛政策担当者:Highlights that carbon price regulation must balance emission reduction and logistics service volume to avoid adverse effects.
📄 Abstract（原文）
Under the background of the carbon cap and trading mechanism, the shipping logistics service supply chain faces pressure to reduce carbon emissions, and artificial intelligence technology provides a new technological path for emission reduction. In the context of a carbon cap-and-trade system, this study examines a shipping logistics service supply chain comprising a service provider and a service integrator, where the provider adopts AI technologies for direct emission reduction and the integrator contributes indirectly. It investigates optimal decision-making under two models: a single emission-reduction model (only provider uses AI) and a joint-emission-reduction model (both adopt AI), while also exploring one-way and two-way cost-sharing contracts between them. The study establishes these models to analyze the impact of cost-sharing contracts on emission reduction levels, total service volume, and profits, and further examines how government regulation of carbon trading prices can promote reduction. Findings reveal that cost-sharing contracts effectively enhance emission reduction, output, and member benefits; one-way contracts are conducive to operations, while two-way contracts are effective only within a small cost-sharing ratio range. The joint model outperforms the single model under specific parameter thresholds, and cost-sharing ratios influence decentralized versus centralized decision-making. Government carbon price regulation can encourage reduction but must consider its effects on low-carbon logistics volume and profits.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/systems14040401first seen 2026-05-15 17:11:05
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