1. Introduction
Climate change pressures and tightening environmental regulations have led many governments to adopt carbon cap-and-trade systems as a key mechanism.
These schemes set emission caps for firms, while enabling trading of carbon allowances to ensure cost-efficient abatement (Xu et al. 2017). Within this context, the adoption of Artificial Intelligence (AI) has emerged as a promising enabler for two critical aspects: (i) From micro perspective, AI helps monitoring and verifying real-time emissions at the firm and supply chain level (Yin et al. 2024), eliminating the potential information asymmetry in supply chains. (ii) From macro perspective, AI can forecast carbon policies and carbon allowances to mitigate market uncertainties (Jazairy et al. 2025).
Based on the above background, the motivation for this study stems from several important challenges. First, while monitoring AI (micro) can eliminate information asymmetries in system, the high-emission firm is less incentivized to implement as benefit might be hurt. Second, forecasting AI (macro) can reduce uncertainties, but its effectiveness depends on model accuracy, which may incur a significant cost. Firms must balance this cost against the benefits of improved accuracy. Third, in competitive supply chains, rivals may adopt AI at different stages, creating strategic asymmetries that influence both market outcomes and environmental performance. These considerations highlight the need for a rigorous analytical study of AI adoption strategies in carbon markets.
Existing literature has studied carbon cap-and-trade in supply chains extensively (Kroes et al. 2012, Huang et al. 2021). There is also growing work on AI adoption in supply chains, primarily focusing on predictive analytics and operational efficiency (Mithas et al. 2022, Jazairy et al. 2025). However, the intersection of AI adoption and carbon market dynamics remains underexplored, particularly with regard to enhancing environmental sustainability in supply chains.
2. Research Aim and Questions
This project seeks to examine how firms in competing supply chains should strategically adopt AI under a carbon cap-and-trade system. The key research questions (RQs) include:
RQ1. What is the optimal AI adoption strategy, i.e., what to adopt (monitoring vs. forecasting), when to adopt (timing), and to what extent (accuracy level)?
RQ2. How does AI adoption change firms’ strategic decisions (e.g., pricing, quantity) in carbon trading and supply chain competition?
RQ3. How is the effectiveness of AI adoption on carbon reduction and supply chain performance under cap-and-trade? What are the implications of AI adoption for the long-term stability of carbon markets?
3. Methodology
The project will adopt game-theoretical approaches to model competing supply chains under cap-and-trade regulation. Firms’ strategies will include whether, when, and how to adopt AI for emission monitoring and policy forecasting. Unique features such as information asymmetry, policy uncertainty, and consumers’ environmental concerns will be captured in model settings. Equilibrium analysis will reveal the conditions under which AI adoption is optimal, and comparative statics will show its impact on emissions, profits, and social welfare.
4. Expected Outcomes
This research contributes by bridging AI adoption and carbon cap-and-trade in the context of competitive supply chains. It advances theory in sustainable operations and provides insights for firms and policymakers on leveraging AI for emissions reduction. The project is expected to yield the following outcomes:
(i) Publishing/submitting 3 research articles that develop innovative analytical models to identify optimal AI adoption strategies under varying considerations (e.g., different roles of AI, market contexts, and consumer behaviors).
(ii) Attending at least 1 international academic conference in operations management domain to present the major findings.
(iii) Producing a policy brief for policy-makers to highlight key policy implications and practical recommendations derived from the study.
References:
[1] Huang, X., Tan, T., & Toktay, L. B. (2021). Carbon leakage: The impact of asymmetric regulation on carbon‐emitting production. Production and Operations Management, 30(6), 1886-1903.
[2] Jazairy, A., Shurrab, H., & Chedid, F. (2025). Impact pathways: walking a tightrope—unveiling the paradoxes of adopting artificial intelligence (AI) in sales and operations planning. International Journal of Operations & Production Management, 45(13), 1-27.
[3] Kroes, J., Subramanian, R., & Subramanyam, R. (2012). Operational compliance levers, environmental performance, and firm performance under cap and trade regulation. Manufacturing & Service Operations Management, 14(2), 186-201.
[4] Mithas, S., Chen, Z. L., Saldanha, T. J., & De Oliveira Silveira, A. (2022). How will artificial intelligence and Industry 4.0 emerging technologies transform operations management? Production and Operations Management, 31(12), 4475-4487.
[5] Xu, X., He, P., Xu, H., & Zhang, Q. (2017). Supply chain coordination with green technology under cap-and-trade regulation. International Journal of Production Economics, 183, 433-442.
[6] Yin, Y., Wang, H., & Deng, X. (2024). Real-time logistics transport emission monitoring-integrating artificial intelligence and internet of things. Transportation Research Part D: Transport and Environment, 136, 104426.