Goutte Stephane, Université Paris Saclay

This special issue addresses the fragmentation between environmental sciences, energy systems, public health, and finance by promoting integrated forecasting methodologies capable of anticipating cascading systemic risks. Research communities need to develop common predictive frameworks that capture complex feedback mechanisms linking environmental governance, ecosystem services, financial markets and societal resilience under unprecedented uncertainty.

The special issue welcomes innovations in econometric, statistical, machine learning and artificial intelligence approaches using novel data sources such as satellite imagery and alternative data. Key focus areas include electricity system resilience under renewable integration, carbon market forecasting, natural capital accounting, sustainable finance, and the distributional impacts of energy transitions.

Contributions are sought globally with emphasis on emerging and developing economies perspectives. The collection will comprise 10-12 peer-reviewed articles demonstrating methodological diversity and practical relevance for policymakers, regulators, investors and sustainability managers.

Appel publié par Journal of Forecasting. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Forecasting climate physical risk impacts on asset prices, sovereign credit ratings, and insurance markets
• Climate transition risk scenario modeling and stress testing for banks and institutional investors
• Predicting the financial materiality of climate policy shocks (carbon taxes, border adjustment mechanisms)
• Nowcasting and forecasting climate-related litigation and regulatory enforcement risks
• Volatility forecasting under compound climate extremes and cascading natural hazards
• Predictive models linking air/water pollution exposure to healthcare costs, labor productivity, and insurance claims
• Forecasting the economic and financial spillovers of zoonotic disease outbreaks and pandemics
• Ecosystem degradation leading indicators for public health expenditure forecasting
• Early warning systems for environmental health emergencies using multi-source data fusion
• Forecasting the macroeconomic impact of environmental regulatory tightening in emerging economies
• Carbon credit and emissions allowance price forecasting (spot, futures, and options markets)
• Green bond pricing, yield spread determinants, and default risk prediction
• ESG rating dynamics: forecasting rating changes, rating disagreement, and market impact
• Predicting the performance of climate-themed investment portfolios and low-carbon equity indices
• Forecasting the diffusion of green financial innovations (sustainability-linked loans, transition bonds)
• Forecasting the economic value of biodiversity loss and ecosystem service degradation
• Predictive models for natural capital accounting and sovereign wealth fund risk exposure
• Land-use change and deforestation risk forecasting for supply chain finance and commodity markets
• Water scarcity prediction and its implications for agricultural commodity pricing and sovereign risk
• Forecasting the insurance and reinsurance implications of ecosystem tipping points
• Forecasting the differential impact of climate policies on developed vs. emerging economies
• Predicting capital flow reversals triggered by environmental shocks in frontier markets
• Sovereign debt sustainability forecasting under climate and ecological stress
• Forecasting international trade disruptions from environmental regulations and carbon border adjustments
• Cross-border spillover effects of environmental governance changes: a global forecasting perspective
• Machine learning and deep learning architectures for environmental-financial data
• Graph neural networks for forecasting interconnected climate-financial networks
• Mixed-frequency and nowcasting approaches for environmental indicators and market variables
• Uncertainty quantification and probabilistic forecasting under climate deep uncertainty
• Conformal prediction, Bayesian nonparametrics, and ensemble methods for environmental-financial prediction
• Large language models (LLMs) and alternative data for environmental risk nowcasting
• Forecasting electricity demand, renewable generation and flexibility needs in low-carbon power systems
• Electricity price forecasting under renewable intermittency, extreme weather events and geopolitical disruptions
• Early warning systems for power-system vulnerability, cascading failures and blackout risks
• Forecasting resilience indicators for transmission and distribution networks
• Forecasting flexibility resources including storage, demand response and data centers
• AI-driven forecasting for smart grids and decentralized electricity systems
• Forecasting electricity markets under high renewable penetration and climate uncertainty
• Forecasting critical mineral demand for the energy transition
• Energy poverty, energy justice and vulnerability forecasting
• Multi-energy forecasting integrating electricity, hydrogen and storage systems

Timeline

• September 30, 2027: Submission deadline
• April 30, 2028: Final decisions

Associate editors

• Xiang Gao, Shanghai Business School
• Jingjia Zhang, Nankai University