Special Issue: Climate Transition and Operational Risk Modelling: Implications for Supply Chains and Financial Decision-Making

Climate-related risks, viewed through a systems operation’s lens, can disrupt the coordination, flow, and stability of financial and economic processes. Although their importance is increasingly acknowledged by academic, regulatory, and practitioner communities, quantifying environmental externalities and designing effective mitigation policies remain open to debate and subject to contention. A key source of uncertainty stems from the transition to a low-carbon economy, particularly concerning its timing and pace. This transition may induce substantial effects on firms, especially those in carbon-intensive sectors, posing systemic challenges to their stability. This underscores the urgent need for rigorous analytical solutions to model climate-related risks and to study their transmission mechanisms within interconnected economic systems.

From a firm’s perspective, the regulatory push towards decarbonization and more sustainable economic models necessitates an optimal trade-off between transforming production processes and maintaining competitive market performance. This reflects a fundamental conflict between short-term objectives, such as profitability and stakeholder satisfaction, and long-term strategic goals related to sustainability and risk mitigation; transitioning to low-carbon operations demands a long-term planning horizon and a comprehensive assessment of associated risks. However, firms must also remain resilient to sudden exogenous shocks that can destabilize the delicate balance between stakeholder interests and shareholder value maximization. As a result, ensuring business continuity and profit stability may, at times, require a short-term focus. Importantly, even when a firm or sector is well positioned to absorb a direct shock, significant vulnerabilities may persist through indirect exposures, particularly through interdependencies in global supply chains. Recent developments have shown how external disruptions can propagate through production networks, amplifying uncertainty and operational risk (Hallegatte, 2019; Guan et al., 2020). There is therefore a need for integrated modelling approaches that jointly capture carbon emissions, operational disruptions, and related risks across multiple supply chain tiers, framing the problem as a multi-objective optimization task (Kumar et al., 2017).

Due to the progressive integration of financial markets, such interdependencies can occur through, for example, asset price movements, volatility changes, or liquidity shocks, generating non-trivial aggregate patterns (Hommes, 2002; Gabaix et al., 2003; Sornette, 2009; Scheffer et al., 2012) that may ultimately lead to market instability (Durlauf, 2005; Scheffer et al., 2009; Diks et al., 2019). However, how such instability may emerge from supply chain relationships remains largely unexplored. Recent contributions have begun to address this gap, for example, by modelling the effects of carbon pricing on asset values and portfolio risks (Benedetti et al., 2021), examining how optimal credit portfolio realignment can facilitate low-carbon transitions (Gobet & Lage, 2024), and investigating how investment horizons influence transition outcomes under uncertainty (Semmler et al., 2024).

The mechanisms by which climate and environmental risks are transmitted to financial and economic systems constitute a multidisciplinary research agenda (Battiston et al., 2021). Robust stochastic optimization techniques play a critical role in managing credit, counterparty, and supply chain risks that are amplified by the climate transition. Concurrently, advanced machine learning methods, such as supervised learning and natural language processing, offer promising tools for forecasting carbon stranded risks and classifying complex climate policy scenarios, with significant implications for both operational and investment decisions. This call for papers invites contributions that examine how financial institutions are responding to growing uncertainty in portfolio allocation and risk management, driven by evolving climate regulations and the inherent unpredictability of low-carbon transition pathways. We particularly welcome applied and methodological operations research studies that address these challenges in an innovative and rigorous manner. Topics of interest include, but are not limited to:

- Robust stochastic optimization methods for climate transition risks

- Portfolio optimization and risk-adjusted return modelling under climate policy uncertainty

- Credit and counterparty risk assessment under low-carbon transition scenarios

- Risk-sharing mechanisms and insurance models for climate-related disruptions

- Supply chain risk management in climate transition

- Operational decision-making in emission trading schemes and carbon pricing

- Predictive modelling of carbon stranding risk via supervised learning

- Machine learning and big data analytics for climate risk scenario classification

- Natural language processing applications in climate policy risk analysis

- Bayesian network approaches to modelling climate transition risk propagation

- Agent-based models of climate transition dynamics and systemic effects.

Authors should submit a cover letter and a manuscript by 31 December 2026, via the Journal’s online submission site, Editorial Manager. See the Author Instructions on the website if you have not yet submitted a paper through Springer’s web-based system. When prompted for the article type, select “Original Research.” On the Additional Information screen, you will be asked if the manuscript belongs to a special issue, choose yes and the special issue’s title, Climate Transition and Operational Risk Modelling: Implications for Supply Chains and Financial Decision-Making, to ensure that it will be reviewed for this special issue. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred, if accepted, to a regular issue.

Papers will be subject to a strict review process under the supervision of the Guest Editors, and accepted papers will be published online individually, before print publication.

References

Battiston, S., Y. Dafermos, & I. Monasterolo (2021). Climate risks and financial sta-bility. Journal of Financial Stability 54, 100867.

Benedetti, D., E. Biffis, F. Chatzimichalakis, L. L. Fedele, & I. Simm (2021). Climate change investment risk: optimal portfolio construction ahead of the transition to a lower-carbon economy. Annals of Operations Research 299(1), 847–871.

Diks, C., C. Hommes, & J. Wang (2019). Critical slowing down as an early warning signal for financial crises? Empirical Economics 57, 1201–1228.

Durlauf, S. N. (2005). Complexity and empirical economics. The Economic Journal 11(504), F225–F243.

Gabaix, X., P. Gopikrishnan, V. Plerou, & H. E. Stanley (2003). A theory of power-law distributions in financial market fluctuations. Nature 423(6937), 267–270.

Gobet, E. & C. Lage (2024). Optimal ecological transition path of a credit portfolio distribution, based on multidate Monge–Kantorovich formulation. Annals of Operations Research 336 (1), 1161–1195.

Guan, D., D. Wang, S. Hallegatte, S. J. Davis, J. Huo, S. Li, Y. Bai, T. Lei, Q. Xue, D. Coffman, et al. (2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour 4(6), 577–587.

Hallegatte, S. (2019). Disasters’ impacts on supply chains. Nature Sustainability 2(9), 791–792.

Hommes, C. H. (2002). Modeling the stylized facts in finance through simple nonlinear adaptive systems. Proceedings of the National Academy of Sciences 99(suppl 3), 7221–7228.

Kumar, R. S., A. Choudhary, S. A. I. Babu, S. K. Kumar, A. Goswami, & M. K. Tiwari (2017). Designing multi-period supply chain network considering risk and emission: a multi-objective approach. Annals of Operations Research 250(2), 427–461.

Scheffer, M., J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter, V. Dakos, H. Held, E. H. Van Nes, M. Rietkerk, & G. Sugihara (2009). Early-warning signals for critical transitions. Nature 461(7260), 53–59.

Scheffer, M., S. R. Carpenter, T. M. Lenton, J. Bascompte, W. Brock, V. Dakos, J. Van de Koppel, I. A. Van de Leemput, S. A. Levin, E. H. Van Nes, et al. (2012). Anticipating critical transitions. Science 338(6105), 344–348.

Semmler, W., K. Lessmann, I. Tahri, & J. P. Braga (2024). Green transition, investment horizon, and dynamic portfolio decisions. Annals of Operations Research 334(1), 265–286.

Sornette, D. (2009). Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press.

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