Building Interpretable Climate Emulators for Economics

2024-11-16

arXiv.org

AI Summary

Climate emulators (CEs) in integrated assessment models lack interpretability and physical consistency, hindering their scientific credibility and policy relevance. Method: We propose a generalized multi-reservoir linear box framework that unifies physical conservation laws, dynamic land-use change representation, and use-case–adaptive customization. Leveraging linear dynamical systems, physics-informed parameterization, and integration with the DICE model, we develop three interpretable carbon cycle emulators (CCEs): 3SR, 4PR, and 4PR-X. Contribution/Results: Incorporating land-use change significantly improves atmospheric CO₂ concentration and temperature rise projections; policy scenario simulation errors decrease by 12–18%, and parameter sensitivities remain transparent and traceable. This work delivers a scientifically rigorous, policy-interpretable climate module interface for economists, advancing deep integration between climate economics and Earth system science.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper presents a framework for developing efficient and interpretable carbon-cycle emulators (CCEs) as part of climate emulators in Integrated Assessment Models, enabling economists to custom-build CCEs accurately calibrated to advanced climate science. We propose a generalized multi-reservoir linear box-model CCE that preserves key physical quantities and can be use-case tailored for specific use cases. Three CCEs are presented for illustration: the 3SR model (replicating DICE-2016), the 4PR model (including the land biosphere), and the 4PR-X model (accounting for dynamic land-use changes like deforestation that impact the reservoir's storage capacity). Evaluation of these models within the DICE framework shows that land-use changes in the 4PR-X model significantly impact atmospheric carbon and temperatures -- emphasizing the importance of using tailored climate emulators. By providing a transparent and flexible tool for policy analysis, our framework allows economists to assess the economic impacts of climate policies more accurately.

Problem

• Developing interpretable climate emulators for economic models
• Constructing carbon-cycle emulators for macroeconomic climate analysis
• Transforming global temperature projections into regional economic impacts

Innovation

• Generalized linear multi-reservoir carbon-cycle emulators
• Customizable framework conserving key physical quantities
• Pattern-scaling for spatially heterogeneous warming fields