Agent-Based Modeling of Green Hydrogen Industry Scale-Up in Russia: Critical Thresholds, Phase Dynamics, and Investment Requirements
ロシアにおけるグリーン水素産業のスケールアップのエージェントベースモデリング：臨界閾値、フェーズダイナミクス、投資要件 (AI 翻訳)
K. Gomonov, Svetlana V. Ratner, Arsen A. Petrosyan, S. Revinova
🤖 gxceed AI 要約
日本語
この研究は、エージェントベースモデリングを用いて2024～2050年のロシアのグリーン水素産業のスケールアップをシミュレーション。3つのフェーズと2つの臨界閾値を特定し、電解槽の学習率が最も重要なパラメーターであることを示した。また、ロシアの2030年生産目標（0.55 Mt）は構造的に達成不可能であると結論付けた。
English
This study uses agent-based modeling to simulate the scale-up of Russia's green hydrogen industry from 2024 to 2050. It identifies three phases and two critical thresholds, with the electrolyzer learning rate as the most influential parameter. It concludes that Russia's 2030 production target (0.55 Mt) is structurally infeasible under all scenarios.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
ロシアのグリーン水素戦略は、日本の水素政策（水素基本戦略など）とも間接的に関連。本論文のフェーズダイナミクスや投資要件分析は、日本の水素事業者や政策担当者にとって参考となる。ただし、ロシア固有の条件に依存している点に注意。
In the global GX context
This paper provides a rigorous agent-based modeling framework for understanding the non-linear dynamics of green hydrogen scale-up, which is globally relevant as many countries pursue hydrogen strategies. The identification of critical thresholds and the role of the learning rate offers insights for policy design and investment decisions in emerging hydrogen markets beyond Russia.
👥 読者別の含意
🔬研究者:Useful for researchers studying hydrogen market dynamics and using agent-based modeling for energy transition analysis.
🏢実務担当者:Corporate sustainability teams can learn about the importance of electrolyzer learning rates and the need for strategic investment losses as catalysts.
🏛政策担当者:Policymakers should note that ambitious production targets may be structurally infeasible without sufficient renewable capacity and willingness to pay for green hydrogen.
📄 Abstract（原文）
The development of a green hydrogen industry is a strategic priority for Russia’s energy transition, yet the dynamics of scaling up this nascent sector remain poorly understood. This study uses agent-based modeling (ABM) to simulate the co-evolution of Russia’s electricity, hydrogen, and electrolyzer sectors over 2024–2050. The model incorporates three types of heterogeneous agents (power producers, hydrogen producers, and electrolyzer manufacturers) operating under bounded rationality. Four scenarios are examined across 50 Monte Carlo runs each, varying the electrolyzer learning rate (10–25%), willingness to pay for green hydrogen (2–6 $/kg), and government support intensity. The results reveal an endogenous three-phase development pattern: Phase I (2024–2028) dominated by renewable capacity build-up reaching ~30 GW; Phase II (2029–2040) characterized by rapid electrolyzer deployment scaling to 14.5 GW; and Phase III (2041–2050) marked by stabilization at approximately 30 GW producing 1.12 Mt/year at 3.1 $/kg. Two critical thresholds are identified: renewable capacity exceeding 30–38 GW and low-cost electricity above 4–7 TWh/year. The electrolyzer learning rate emerges as the most influential parameter, while the pessimistic scenario confirms market failure without a green premium (WTP < 2 $/kg). Strategic investment losses of 2–6 billion USD are necessary catalysts for industry emergence. Russia’s 2030 production target (0.55 Mt) is found structurally infeasible under all scenarios.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/hydrogen7020053first seen 2026-05-15 19:29:08
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。