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<p>This paper investigates the impact of artificial intelligence (AI) investment on demand prediction accuracy and its implications for emission reduction and operational decisions in a manufacturer-retailer supply chain under carbon peaking and neutrality goals, considering both parties’ risk-averse behaviors. By embedding demand forecast precision into mean–variance utility functions, we model three scenarios: manufacturer-led AI, retailer-led AI, and no AI investment, to derive equilibrium strategies. Results reveal that risk aversion exacerbates demand volatility’s adverse effects on utilities and emission reductions without AI. Retailer-led AI enhances demand signal accuracy, enabling manufacturers to optimize emission efforts and pricing, yielding mutual benefits within fixed and variable AI cost thresholds. Manufacturer-led AI improves production-side analytics and supply chain synergy but risks inflating wholesale prices, potentially eroding retailer profits. Furthermore, we design a vertical profit-risk cross-shareholding (VPRS) contract to coordinate the AI-driven supply chain, demonstrating how Nash bargaining and transfer payments align incentives, achieving Pareto-efficient outcomes under technological and budgetary constraints. Theoretical and computational analyses provide actionable insights for balancing AI adoption costs, emission reduction efficiency, and risk-averse preferences, advancing sustainable decision-making in low-carbon supply chains.</p>

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Artificial intelligence investment and vertical cross-shareholding contractual coordination for low-carbon supply chains with forecast inaccuracy and risk concerns

  • 期刊论文

  • 发表日期：

    2026年1月26日

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  • Longfei He,
  • Yujiang Li,
  • Xiaolu Song,
  • Baiyun Yuan,
  • Xiaohang Yue,
  • Runzhu Han

摘要

This paper investigates the impact of artificial intelligence (AI) investment on demand prediction accuracy and its implications for emission reduction and operational decisions in a manufacturer-retailer supply chain under carbon peaking and neutrality goals, considering both parties’ risk-averse behaviors. By embedding demand forecast precision into mean–variance utility functions, we model three scenarios: manufacturer-led AI, retailer-led AI, and no AI investment, to derive equilibrium strategies. Results reveal that risk aversion exacerbates demand volatility’s adverse effects on utilities and emission reductions without AI. Retailer-led AI enhances demand signal accuracy, enabling manufacturers to optimize emission efforts and pricing, yielding mutual benefits within fixed and variable AI cost thresholds. Manufacturer-led AI improves production-side analytics and supply chain synergy but risks inflating wholesale prices, potentially eroding retailer profits. Furthermore, we design a vertical profit-risk cross-shareholding (VPRS) contract to coordinate the AI-driven supply chain, demonstrating how Nash bargaining and transfer payments align incentives, achieving Pareto-efficient outcomes under technological and budgetary constraints. Theoretical and computational analyses provide actionable insights for balancing AI adoption costs, emission reduction efficiency, and risk-averse preferences, advancing sustainable decision-making in low-carbon supply chains.