Probabilistic Identification of Technology Tipping Points in Deeply Decarbonised Energy Systems
深い脱炭素化されたエネルギーシステムにおける技術転換点の確率的同定 (AI 翻訳)
Gian Müller, Thomas Schöb, Jann M. Weinand, Iain Staffell
🤖 gxceed AI 要約
日本語
本論文は、エネルギーモデルにモンテカルロサンプリングを組み合わせ、欧州2カ国の電力システムにおける技術転換点を確率的に特定する。風力・太陽光・CCS・水素などの競争力がシステム条件に応じて大きく変動することを示し、頑健なネットゼロ戦略のためのコスト閾値を提示する。
English
This paper couples a national energy optimization model with Monte Carlo sampling (10,000 runs) across German and British power systems to probabilistically identify technology tipping points. It reveals wide uncertainty in the roles of wind, solar, gas with CCS, and hydrogen, and provides robust cost thresholds for net-zero strategies.
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
日本でも2050年カーボンニュートラルに向けたエネルギー構成の不確実性が課題となっている。本手法は、電源計画や水素導入の判断に確率的リスク管理を導入する枠組みとして参考になる。
In the global GX context
The paper reframes scenario analysis as risk management, linking uncertainty to cost targets. This is directly relevant to TCFD/ISSB scenario requirements and transition planning for energy-intensive sectors globally.
👥 読者別の含意
🔬研究者:Methodology for probabilistic energy system modeling with explicit tipping points; applicable to uncertainty quantification in net-zero pathways.
🏢実務担当者:Cost thresholds for key technologies (e.g., CCS, hydrogen) can inform investment decisions and procurement strategies.
🏛政策担当者:Provides a framework for setting robust policy targets under uncertainty, highlighting the need for flexible, adaptive strategies.
📄 Abstract（原文）
Energy policy is often guided by a small set of least-cost pathways to net-zero emissions, despite wide uncertainty in technology performance, fuel prices, demand and weather. To avoid overstating confidence in any single pathway, we quantify the likelihood of alternative technology pathways and identify the assumptions driving divergence, including the conditions under which technologies reach critical tipping points in competitiveness. We couple a sector-linked national optimisation model with Monte Carlo sampling (10,000 runs) across two European power systems (Germany and Great Britain) to generate probability distributions of capacity expansion and robust cost thresholds for key technologies. Results reveal substantial ambiguity in the future roles of wind versus solar, gas with carbon capture, and negative-emissions options. Tipping points vary widely with system conditions, while cross-country differences highlight the role of institutional constraints and resource endowments. Britain exhibits an either-or decision around nuclear power, investing if costs in 2035 fall below EUR 4700/kW, otherwise favouring offshore wind. Germany's uncertainty centres on dispatchable low-carbon options: gas with carbon capture (below EUR 2100/kW), biomass with carbon capture (below EUR 4200/kW), or hydrogen if electrolysis is below EUR 560/kW. We reframe scenario analysis as risk management by linking uncertainty to cost targets and minimum deployment requirements for robust net-zero strategies.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2606.16469first seen 2026-06-16 04:11:28
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