Background

In the context of accelerating global climate change, the increasing frequency of extreme weather events, rising sea levels, and the degradation of ecosystems pose severe challenges not only to the natural ecological balance, public health, and the sustainable development of the global economy (Battiston et al., 2021). More profoundly, these impacts transmit risks to the financial sector through their effects on the real economy. The dual compounding of physical risks and transition risks is progressively evolving into credit, market, and operational risks for financial institutions. This constitutes a threat to the stable operation of the financial system, becoming a significant potential source of financial risk (Mao et al., 2023). In the face of this complex systemic challenge - shaped by the interplay of climate change, economic systems, and financial networks - traditional reactive approaches and analytical methods based on static assumptions are no longer sufficient to meet real-world needs. Addressing this challenge fundamentally requires the development and application of advanced computational methods, including numerical techniques, simulation models, and machine learning algorithms, to capture the dynamic, nonlinear, and high-dimensional nature of climate-economy-finance interactions. Meanwhile, there is an urgent need for governments, international organizations, and market entities to collaboratively advance proactive climate strategy management and construct a scientific, efficient, and resilient climate risk governance system. Such a system is essential for effectively identifying, assessing, mitigating, and adapting to the multifaceted impacts brought about by climate change (Chenet et al., 2021). The establishment of this system relies on advanced computational tools to model the dynamic evolution of complex systems, simulate the nonlinear feedback of policy interventions, and quantify risk transmission pathways under deep uncertainty.

In the process of driving the economic and social transition towards low-carbon and resilient models, technological innovation and financial innovation are emerging as two core drivers. On one hand, cutting-edge technologies represented by artificial intelligence, big data, cloud computing, the Internet of Things, and blockchain are profoundly reshaping our capacity to monitor, assess, and manage climate risks. These technologies have transformed climate change simulations and forecasts from static and subjective models into data-driven, real-time, dynamic systems capable of supporting complex system modeling, intelligent analysis, and optimized decision-making. Such shift greatly enhances the precision of risk early warnings and the flexibility of governance strategies. On the other hand, innovative financial instruments such as green credit and bonds, climate index insurance, and carbon emissions trading markets are providing indispensable financial support and risk-sharing mechanisms for climate adaptation infrastructure construction, low-carbon technology research and development, and resilience building in vulnerable regions (Ardia et al., 2023). However, despite significant progress in their respective fields, how to effectively integrate the power of cutting-edge technology with the vitality of financial markets to construct a collaborative and efficient climate risk governance framework - covering the entire process of ex-ante prevention, in-event response, and ex-post recovery - remains a major and pressing issue for academia and policymakers alike. Against this backdrop, the various methodological tools offered by computational economics, such as numerical techniques, simulation methods, machine learning methods, or computational analysis of economic models, provide a crucial analytical foundation for addressing this integration challenge. These computational methods are not only capable of handling high-dimensional nonlinear complex systems but also, through rigorous numerical experiments, can reveal the causal mechanisms and policy effect boundaries under different innovation combinations.

Although technology holds great potential in enhancing monitoring and early warning capabilities, and finance plays an increasingly prominent role in resource allocation, current governance practices still face multiple challenges. First, there is a gap between technological innovation and its application. Many advanced climate models and data analysis tools remain in the experimental stage and have not been effectively translated into actionable, implementable governance strategies and public policies. From a computational perspective, this gap reflects a lack of robust, validated numerical frameworks that can bridge high-dimensional climate data with decision-support tools for policymakers. Second, financial innovation suffers from lag and mismatch. Existing financial instruments often struggle to accurately quantify complex and highly uncertain climate risks, leading to capital concentration while mismatching the most urgent emission reduction needs or the most vulnerable adaptation regions (Javadi & Masum, 2021). Computational methods such as stochastic simulation, Bayesian inference, and non-parametric uncertainty quantification are urgently needed to improve the pricing and allocation of climate-related financial products. Third, the inclusivity of governance models is severely inadequate. Traditional frameworks often neglect the specific needs of developing countries, marginalized groups, and less-developed regions. Technological change may exacerbate the digital divide, while financial exclusion leaves vulnerable groups even more fragile when facing climate shocks, trapping them in a predicament of “growth without development” or “adaptation without equity”. Finally, there is a lack of effective coupling between technological solutions and financial incentive mechanisms. Monitoring data is difficult to directly translate into pricing bases for financial products, and technological early warning signals cannot smoothly trigger automatic responses from financial resources, resulting in a lack of systemic synergy. Overcoming these challenges demands novel computational frameworks—for instance, integrated assessment models with high spatial-temporal resolution, machine learning-based early warning systems, and multi-agent simulations of heterogeneous adaptation behaviors. The core value of these computational frameworks lies in their ability to quantify path dependence, critical transitions, and feedback loops that traditional simplified models cannot capture, thereby providing substantial guarantees for the scientific validity and robustness of governance strategies.

Currently, climate risk governance stands at a critical juncture where opportunities and challenges coexist (Gavriilidis, 2021). On one hand, breakthroughs in cutting-edge technology and the expansion of green finance provide unprecedented momentum for systemic transformation. On the other hand, if the aforementioned contradictions are not resolved in a timely manner, they will severely constrain governance effectiveness. Faced with these intricate challenges, relying solely on piecemeal technological improvements or singular financial policies can no longer meet the demands of a complex real-world system. There is an urgent need to construct a comprehensive framework integrating data-driven decision-making and computational modeling. This framework is essential to deeply reshape the synergistic mechanisms between technology and finance in climate risk governance, explore feasible implementation pathways, and assess their far-reaching socio-economic impacts.

This special issue focuses on the application of computational economics methods to climate risk governance, with particular attention to how cutting-edge technologies reshape the capabilities of climate risk monitoring, assessment, and management, as well as how financial innovation can guide resource allocation to address the challenges of climate change. We seek papers that not only address climate-risk questions but also develop, extend, or rigorously apply computational economics methods—including, but not limited to, agent-based modeling, machine learning, dynamic system simulation, optimal control, network analysis, nonparametric techniques, and numerical solution methods. In particular, authors should clearly explain why the specific computational techniques used are suitable for the research problem at hand, and how the computational approach critically affects the reliability, accuracy, interpretability, or policy insights of the obtained results. Submissions should clearly articulate their computational and methodological innovations, demonstrating how these advances enable new insights into the coupled climate-economy-finance system that would be unattainable with traditional analytical or reduced-form approaches. By doing so, the special issue will help bridge the theoretical divide between technical sciences and social sciences in current interdisciplinary research, providing cutting-edge computational tools for building a more resilient and inclusive climate governance system. Furthermore, by bringing together cutting-edge research from diverse fields, it can offer a solid theoretical basis and practical guidance for policymakers designing climate strategies that balance efficiency and equity, and for practitioners developing more targeted financial instruments and technological solutions. In addition, the research findings presented in this special issue will contribute valuable theoretical foundations and practical experience for international cooperation and dialogue on climate change, particularly for developing countries exploring climate risk governance pathways that align with their specific national circumstances.

Topics covered in this special issue include (but are not limited to):

• Climate risk transmission and financial stability: computational modeling and dynamic simulation
• Climate stress testing and scenario analysis: applications of machine learning and numerical techniques
• Climate resilience and adaptive governance: dynamic optimization and multi-agent simulation
• Climate finance instrument design: computational methods for pricing and risk assessment
• Uncertainty quantification in climate-finance coupled systems: Bayesian methods and non-parametric techniques
• Corporate climate transition strategies: optimal control and machine learning for pathway analysis
• Climate-resilient corporate operations: AI-driven and data-driven dynamic models
• Climate risk assessment: computational early warning systems and real-time monitoring frameworks
• Climate-resilient infrastructure: digital twins, simulation, and optimization techniques
• Integrated assessment models and decarbonization pathways: numerical solution and sensitivity analysis
• Global climate governance, green finance, and green development: network analysis and complex systems modeling
• Scalability of climate finance and policy instruments: computational experiment-based evaluation and validation

Tentative timeline

Submission starts from: 1st July, 2026

Submission deadline: 31th December, 2026

Fully reviewed manuscript ready for production: As soon as possible

Guest Editors

Prof. Malin Song, Anhui University of Finance and Economics, China

Email: songml@aufe.edu.cn; songmartin@163.com

Prof. Marilen Gabriel Pirtea, Department of Finance, West University of Timisoara, Timisoara, Romania

Email: marilen.pirtea@e-uvt.ro

Prof. Santiago Budría, Department of Business Administration, Universidad Antonio de Nebrija, Madrid, Spain

Email: sbudria@nebrija.es

References

Ardia, D., Bluteau, K., Boudt, K., & Inghelbrecht, K. (2023). Climate Change Concerns and the Performance of Green vs. Brown Stocks. Management Science, 69(12), 7607-7632.

Battiston, S., Monasterolo, I., Riahi, K., & van Ruijven, B. J. (2021). Accounting for finance is key for climate mitigation pathways. Science, 372(6545), 918-920

Chenet, H., Ryan-Collins, J., & van Lerven, F. (2021). Finance, climate-change and radical uncertainty: Towards a precautionary approach to financial policy. Ecological Economics, 183, 106957.

Gavriilidis, K. (2021). Measuring Climate Policy Uncertainty. SSRN Scholarly Paper, 3847388.

Javadi, S., & Masum, A.-A. (2021). The impact of climate change on the cost of bank loans. Journal of Corporate Finance, 69.

Mao, X., Wei, P., & Ren, X. (2023). Climate risk and financial systems: A nonlinear network connectedness analysis. Journal of Environmental Management, 340.