Strategic Frameworks for Global Energy Transitions: An Integrated Analysis of Climate Informatics, Post-Classical Compute Infrastructures, and Biomimetic Policy Pathways

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🔬研究者:エネルギー転換の統合モデリングや気候情報学に興味がある研究者にとって、概念的な枠組みとデータ活用の方向性を提供する。

🏢実務担当者:実務的な開示や炭素会計には直接活用しにくいが、長期戦略立案の参考になる可能性がある。

🏛政策担当者:エネルギー政策の長期的ビジョンやデータ駆動型計画の重要性を認識する上で示唆に富む。

Strategic Frameworks for Global Energy Transitions: An Integrated Analysis of Climate Informatics, Post-Classical Compute Infrastructures, and Biomimetic Policy Pathways The global energy architecture is currently undergoing a structural phase transition of unprecedented scale and complexity. Historically defined by centralized extraction, linear transmission mechanisms, and deterministic demand forecasting, the modern energy paradigm is rapidly evolving into a highly decentralized, stochastic, and metabolically complex network. This transition is being driven by the intersecting vectors of extreme climate volatility, the exponential energy demands of advanced computational infrastructures, and the urgent necessity for deep decarbonization across emerging and developed economies. As global energy demand scales non-linearly alongside the proliferation of artificial intelligence and hyperscale computing, classical models of energy deployment, infrastructure planning, and ecological mitigation are proving fundamentally inadequate. To bridge the widening gap between legacy energy systems and future planetary requirements, the analytical frameworks utilized to model generation, transmission, and environmental impact must undergo a profound ontological shift. This comprehensive report investigates the multi-dimensional vectors of this transition. By synthesizing granular climate data sets, paleoclimatic baseline modeling, post-classical computational infrastructure proposals, advanced machine-learning-driven safety protocols, hydro-ecological constraints, and regional policy simulation engines, the analysis constructs a unified architecture for the future of global energy. The findings indicate that the energy systems of the coming decades will not merely respond to anthropogenic demand; they must act as integrated, self-regulating biological systems that co-optimize computational throughput, environmental homeostasis, and regional socio-economic development. The Epistemological Foundation: Open Data Infrastructures and "Research as Living" To effectively navigate the extreme complexity of synthesizing high-resolution climate data, metabolic artificial intelligence architectures, ecological safety constraints, and regional macroeconomics, the global energy sector must adapt its underlying approach to scientific research and institutional metacognition. A structural shift is required, conceptualizing the process of research and development not as a static, linear accumulation of data, but as a dynamic, interconnected living system.1 The Biological Ontology of Inquiry The "Research as Living" framework postulates that scientific inquiry satisfies the core invariants of biological living systems.1 In the context of global energy, the research apparatus metabolizes inputs—such as anomalies in grid load, newly processed atmospheric temperature datasets, and tooling innovations—and maintains its organization through autopoiesis via standardized methodologies, peer review, and robust archival systems.1 Furthermore, it evolves through variation and selection, driving conceptual mutations from classical terrestrial power grids toward decentralized, biomimetic compute reefs.1 By treating energy research as a self-maintaining organism, scientific progress is reframed as an "adaptive expansion" rather than linear accumulation.1 This substrate-neutral account of inquiry integrates philosophy of science, systems theory, and evolutionary dynamics, positioning technologies—including generative AI and automated telemetry algorithms—not merely as passive tools, but as co-agents within the evolving ecology of the energy sector.1 Community Curation and Software Sustainability For this living system of research to survive, its central nervous system—the open dataset repositories—must be impeccably maintained. The increasing concern for the availability and transparency of scientific data has resulted in initiatives promoting the archival and curation of datasets as legitimate, citable research outcomes.2 Repositories support a massive variety of use cases, often implementing minimal top-down control to allow organic growth. To tackle quality control, platforms rely on community curation, where communities of users self-organize to filter relevant resources, providing decentralized trust and effective organization.2 The traceability of these components is paramount. Tracking software and data citations ensures that the complex models used for global energy forecasting are reproducible and accountable. Analyses of citation dynamics reveal that researchers frequently cite specific "concept DOIs".4 The "citation speed"—the time elapsed between the publication of the cited object and the citing object—serves as a critical metric for estimating the velocity of innovation and self-citation rates within computational energy modeling.4 Furthermore, systems supporting software sustainability and comprehensive research data management 5 enable the analysis of large-volume, multi-institute climate model outputs. The development of centralized analysis facilities, such as the PRIMAVERA Data Management Tool, provides the requisite infrastructure for handling the petabytes of data generated by global climate ensembles.6 Without this curated, community-driven data backbone, the high-fidelity capacity expansion models required for the energy transition would collapse under their own computational weight. Additionally, initiatives like Bionomia help complete the high-quality curation loop by linking natural history specimen records to the specific researchers who collected them, improving digital annotations and taxonomic crediting within open infrastructures.7 This rigorous attribution is essential when mapping the ecological impacts of new energy infrastructures across diverse biomes. High-Resolution Climate Informatics and Spatiotemporal Grid Modeling The physical foundation of any advanced energy transition strategy is the accuracy, granularity, and longitudinal depth of its climate informatics. Because variable renewable energy (VRE) sources—specifically wind turbines and solar photovoltaics—are fundamentally tethered to atmospheric physics, the capacity expansion models utilized to plan national and continental grids must ingest massive spatiotemporal climatic datasets to ensure operational resilience and avoid catastrophic shortfalls. Atmospheric Datasets in Capacity Expansion Models The reliance on historical, short-term weather averages for energy forecasting has historically resulted in high-variance capacity deficits during extreme weather events. To mitigate this vulnerability, advanced grid planning now necessitates decades of hourly, highly granular atmospheric data. A prime example of this paradigm shift is the integration of high-resolution climate modeling into the Regional Energy Deployment System (ReEDS). Recent data architectures provide hourly modeled surface air temperature distributions, measured in degrees Celsius, for the 48 contiguous states in the United States spanning a continuous 26-year period from 1998 through 2024.8 Retrieved initially from the National Solar Radiation Database (NSRDB) and originally generated utilizing the NASA MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) model, this dataset represents a profound upgrade in grid simulation fidelity.8 The integration of continuous hourly datasets allows capacity expansion models to transcend deterministic planning and embrace stochastic resilience. By capturing extreme, low-probability weather events—such as unprecedented heat domes, sustained polar vortexes, and anomalous cloud cover durations—the ReEDS framework can stress-test simulated grid architectures under historical worst-case scenarios.8 The mathematical representation of capacity expansion within such a framework relies heavily on the modeled surface air temperature () at a given hour and region , which dictates both the thermodynamic efficiency of thermal generation plants and the volumetric surge in HVAC (Heating, Ventilation, and Air Conditioning) electrical loads: In this optimization function, the expected systemic cost is minimized over time and region , where represents capital expenditures, represents installed capacity, represents marginal operational costs, is the power generated, and represents the lost load penalty governed by temperature-dependent demand spikes. By utilizing the comprehensive MERRA-2 dataset, the ReEDS model optimizes the precise geographic distribution of energy assets, ensuring that solar arrays, wind farms, and utility-scale storage capacities are deployed where they can maximally offset the thermodynamic vulnerabilities of the grid.8 Deep Time Analogs: Paleoclimate Fields for Extreme Scenario Testing While historical data spanning a quarter-century is critical for near-term grid resilience, the accelerating pace of anthropogenic climate forcing requires energy systems architects to look beyond the Anthropocene for reliable thermodynamic analogs. Modern climate informatics is increasingly leveraging paleoclimatic data to understand the behavioral dynamics of global energy systems under high-greenhouse-gas concentrations. Reconstructed temperature and hydroclimate fields from deep time slices—specifically the mid-Pliocene (approximately 3.25 million years ago) and the early Pliocene (4.75 million years ago)—serve as critical boundary conditions for future energy models.9 These reconstructions provide annual, summer (JJA), and winter (DJF) mean values for fundamental thermodynamic variables: sea-surface temperature (tos), 2-meter air temperature (tas), precipitation (pr), evaporation (ev), and sea ice concentration (siconc).9 The mid-Pliocene represents a geological epoch where atmospheric carbon dioxide concentrations were comparable to modern trajectories, yet the Earth system had reached a state of thermal e

• openalex https://doi.org/10.5281/zenodo.19483131first seen 2026-05-05 19:12:26