Digitalization prompts Chinese firms to team up on green innovation, boosting firm value and easing financing constraints; the effect operates by building interfirm trust and lowering collaboration costs and is amplified by subsidies, regulation and disclosure.

Main Finding

Digitalization encourages firms to engage in supply chain green collaborative innovation (SCGCI). Theoretical modeling and empirical evidence (Chinese listed firms, 2007–2023) show that digital transformation reshapes firms’ profit-maximizing decisions and strategic interactions so that collaboration along the supply chain becomes the optimal approach to balance green innovation costs and competitive advantage. The positive effect of digitalization on SCGCI operates mainly by strengthening interfirm trust and lowering collaboration barriers, is amplified by government subsidies, digital regulation, and environmental information disclosure, spills over to upstream and downstream partners, and yields measurable financial benefits (higher firm value and reduced financing constraints).

Key Points

• Strategic result: Supply chain collaboration is an optimal strategy under competition for coordinating green innovation investments and sharing benefits/costs.
• Role of digitalization: Firm-level digital transformation materially increases the likelihood and intensity of SCGCI.
• Mechanisms (mediators):
  • Enhanced interfirm trust (better information sharing, coordination).
  • Reduced collaboration barriers (transaction costs, search/matching frictions).
• Moderators (strengthen the effect):
  • Government subsidies for green/technology projects.
  • Digital regulation that standardizes or legitimizes digital collaboration.
  • Greater corporate environmental information disclosure.
• Spillovers: A firm’s digitalization produces positive effects not only for itself but also for upstream and downstream supply-chain partners, altering strategic incentives across the chain.
• Economic outcomes: SCGCI translates into higher firm value and alleviated financing constraints, supporting long-term competitiveness.
• Heterogeneity: The magnitude of effects varies with market environment and firm-specific characteristics (industry, size, competitive intensity, etc.).

Data & Methods

• Theoretical component: A dynamic game model embedding digitalization as a strategic capability to analyze how digital transformation affects firms’ profit-maximizing choices and strategic interactions with supply-chain partners in competitive markets.
• Empirical component:
  • Sample: Listed firms over 2007–2023.
  • Approach: Panel empirical tests linking firm digitalization to indicators of SCGCI, with mediation analysis for trust and collaboration barriers, moderation tests for subsidies/regulation/disclosure, and additional analyses for spillover effects and firm-level financial outcomes.
  • Outcomes reported: evidence supporting the theoretical predictions (digitalization → greater SCGCI; mediation and moderation patterns; positive spillovers; improvements in firm value and financing constraints).
• Note: The abstract does not specify exact variable constructions (e.g., digitalization index, SCGCI measures such as collaborative green patents or joint projects) or estimation techniques; the study combines formal modeling with econometric tests on firm panel data.

Implications for AI Economics

• Digitalization as an AI-relevant capability: The findings imply that investments in digital technologies (including AI) can reduce coordination frictions, improve trust via better information processing/sharing, and thereby facilitate cooperative innovation across firms—especially for public-good–type projects like green innovation.
• Strategic complementarities and spillovers: AI adoption by one firm can generate positive externalities along the supply chain, creating potential coordination failures (underinvestment) but also opportunities for policies that internalize spillovers (subsidies, standards, regulation).
• Policy design:
  • Targeted subsidies and digital regulation amplify private incentives to use AI/digital tools to coordinate green innovation—policymakers can leverage these levers to scale socially valuable collaborative projects.
  • Transparency mandates (environmental disclosure) strengthen collaboration incentives—regulatory regimes that improve observable information can magnify AI’s coordination benefits.
• Measurement and identification challenges: Empirical work on AI economics should carefully measure digitalization/AI intensity, collaboration outcomes, and causal links (instrumental designs, natural experiments, or firm-level randomized interventions could strengthen identification).
• Directions for research:
  • Quantify welfare impacts of AI-driven coordination in supply chains (consumer prices, environmental externalities).
  • Model heterogeneous adoption incentives and strategic interactions when AI platforms or data-sharing ecosystems exist.
  • Study optimal policy mixes (subsidies, standards, data-sharing rules) to internalize AI-related spillovers and encourage joint investments in green innovation.
• Managerial takeaway: Firms can treat AI/digital transformation not only as a productivity tool but as a strategic lever to coordinate costly collective investments (e.g., green R&D) with partners, yielding firm-level financial gains and system-level benefits.