Climate Informatics and Biomimetic Policy Framework: A New Interdisciplinary Research Paradigm for Global Energy Transition

This article proposes an interdisciplinary research framework integrating climate informatics, post-classical computing infrastructure, and biomimetic policy pathways, aiming to address the insufficient ability of traditional planning methods to cope with uncertainties in global energy transition and provide a new paradigm for energy governance. The framework combines multi-spatiotemporal scale data and distributed computing, reconstructs policy logic by drawing on the self-organization characteristics of ecosystems, and emphasizes the core values of resilience and interdisciplinary collaboration.

The global energy system is in a critical period of transitioning to distributed low-carbon systems, but faces cognitive and governance challenges beyond technology: How to make robust decisions in uncertain nonlinear systems? Traditional planning relies on deterministic models and static scenario analysis, which are inadequate in responding to extreme climate events, technological mutations, and policy shocks. As an interdisciplinary field, climate informatics combines machine learning, big data, and earth system science to provide refined information support for energy decision-making.

The technical architecture is based on multi-spatiotemporal scale datasets such as NASA MERRA-2 reanalysis data, the National Solar Radiation Database (NSRDB), and the Regional Energy Deployment System (ReEDS); it emphasizes post-classical computing infrastructure (distributed and decentralized) to improve data processing speed and system resilience; and advocates a community-driven data curation mechanism. Methodologically, it is necessary to solve the problem of multi-scale coupling (integration of hourly meteorological fluctuations and decades-long transition trajectories), develop modular open-source platforms across disciplinary boundaries, and focus on the interpretability of machine learning models.

Innovatively introducing biomimetic thinking to reconstruct policy logic: Traditional policies pursue optimal solutions, while biomimetic pathways draw on the self-organization and adaptive characteristics of ecosystems, treating the energy system as a "metabolic system" and shifting from a static "planning-execution" model to a dynamic "perception-response" model (e.g., rapid mobilization of reserve resources under extreme weather). It requires matching decentralized decision-making mechanisms and granting more autonomy to local communities and distributed energy operators.

Analysis of extreme events found that high-variability renewable energy sources (wind and solar) may experience synchronous output declines under abnormal climate scenarios (compound risks are often underestimated); it is recommended to use large-sample ensemble simulations to stress-test the resilience boundaries of the system. At the same time, attention should be paid to the gradual impacts of long-term climate change (such as rising cooling water temperatures reducing thermal power plant efficiency, and changes in precipitation patterns affecting hydropower dispatching), which need to be incorporated into infrastructure investment decisions.

Practical implications include: 1. Energy transition is a co-evolutionary process of technology, institutions, and social cognition, so policies need to retain flexibility; 2. Data infrastructure is as important as physical infrastructure, and investment should be made in open and traceable data curation and sharing mechanisms; 3. In the face of deep uncertainty, resilience is more important than efficiency; market design needs to be re-examined to externalize the value of system resilience.

The integration of climate informatics and the biomimetic policy framework represents an important leap in energy research methodology, balancing refined analysis and systematic thinking, and providing policy guidance while acknowledging complexity. Although it is in the early stages of development, the interdisciplinary multi-scale perspective provides an inspiring approach to addressing the governance challenges of energy transition.