1⃣ Modeling Climate Extremes and Their Impacts
Building a Global Climate Extremes Database
- Developed a high-resolution global dataset covering 1881-2100, incorporating thirteen types of extreme weather events (heatwaves, droughts, windstorms, etc.).
- Data is based on ISIMIP3b bias-adjusted projections and ERA5 reanalysis, ensuring high accuracy and spatial detail.
- The dataset enables detailed assessments of how climate extremes will affect human populations, ecosystems, and economic sectors.
AI-Based Prediction of Climate Extremes
-Designed a probabilistic deep learning framework capable of predicting heatwaves, floods, and droughts based on simple climate inputs (e.g. temperature, precipitation).
-Uses a conditional generative adversarial network (GAN) architecture, improving prediction accuracy and enabling better uncertainty quantification.
-Enhances reliability in areas of high socioeconomic exposure, allowing policymakers to identify future risk hotspots.
Economic and Social Impacts of Climate Change
-Analyzed the historical and projected impacts of climate extremes on GDP, inequality, and human development (HDI) using econometric fixed-effects models.
-Assessed regional economic damages by linking extreme weather events to life expectancy, education, and income levels.
-Provided first-of-its-kind quantifications of economic losses due to extreme climate events under different Shared Socioeconomic Pathways (SSPs).
2⃣ Climate Mitigation and Policy Evaluation
AI-Driven Emission Prediction Models
-Developed a deep learning-based system to forecast CO2 and CH₄ emissions from land-use changes, including wetlands and forestry.
-Uses Generative Adversarial Networks (GANs) and reinforcement learning techniques to simulate future emissions pathways.
-Aims to improve projections beyond traditional IAMs by incorporating data-driven uncertainty quantification.
Integrated Market and Policy Modeling
-Designed a Multi-Agent Reinforcement Learning (MARL) framework to simulate electricity markets under net-zero scenarios.
-The model helps identify regulatory gaps, evaluate different policy mechanisms, and assess market stability under different climate targets.
-Provides quantitative insights for policymakers on market designs that can accommodate large-scale renewable energy and carbon pricing.
Robust Transition Pathways for Net-Zero
-Developed a small-scale IAM with technological richness, integrating robust control methods to assess transition risks.
-Applied deep-learning solutions for high-dimensional partial differential equations (DGM-PIA method) to improve policy resilience analysis.
-Developed a global sensitivity analysis (GSA) methodology using Optimal Transport solvers, allowing better uncertainty assessments in climate-economic modeling.
3⃣ Advancing Decision-Making Under Uncertainty
Climate Emulators for Fast Policy Assessment
-Built a machine-learning-based climate emulator to generate high-resolution climate projections from simple temperature inputs.
-Enables policymakers and researchers to quickly assess regional climate risks without running computationally expensive full-scale climate models.
Uncertainty Quantification and Model Misspecification Analysis
-Developed new uncertainty quantification techniques for climate policy cost-benefit analysis.
-Investigated the impact of model misspecification and deep uncertainty on economic evaluations of climate mitigation pathways.
-Demonstrated how different assumptions about climate damages can significantly alter policy recommendations, emphasizing the need for robust decision-making.
Developing a Foundation Model for Climate-Economy Interactions
-Launched efforts to build the first deep-learning foundation model that integrates climate and socioeconomic interactions.
-Uses multimodal AI architectures, diffusion models, and explainable AI techniques to capture complex feedback loops between climate and economy.
-Provides a groundbreaking tool for forecasting climate-economic dynamics, making it a potential state-of-the-art solution for climate policy analysis.
