ニューサウスウェールズ州2026–2030年における車両更新・再エネ電力・道路排出にわたる統合的確率的移行リスク
Joint probabilistic transition risk across fleet turnover, renewable electricity, and road emissions in New South Wales, 2026–2030 (原題)
Vijay Raj Giri
🤖 gxceed AI 要約
日本語
豪NSW州の2026–2030年を対象に、低排出車普及・再エネ電力・道路排出等の指標をコピュラ仮定とモンテカルロで連動させ、移行リスクを評価。ガソリン・ディーゼル車が全仮定で支配的で、悪性ストレス下でCVaR95が最大となる。普及・再エネ・排出の協調改善でCVaR95は0.438から0.299へ低下するが、普及加速単独ではテールリスクが減らない。
English
This study assesses transition risk for New South Wales 2026–2030, linking low-emission vehicle uptake, renewable electricity, and road emissions via copula assumptions and Monte Carlo simulation. Petrol/diesel vehicles remain dominant under all dependence assumptions, with the adverse stress tail producing the greatest downside risk. Coordinated improvement across uptake, renewables, and emissions cuts CVaR95 from 0.438 to 0.299, while faster uptake alone lowers average loss but not tail risk.
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
日本の運輸部門脱炭素・EV普及政策やSSBJのシナリオ分析実務に対し、複数指標を連動させた確率的移行リスク評価の手法例を提供する。
In the global GX context
Adds a probabilistic, multi-indicator transition-risk method linking fleet turnover, power decarbonization, and road emissions—relevant to TCFD/ISSB scenario analysis and transport transition finance.
👥 読者別の含意
🔬研究者:EV普及と電力脱炭素を連動させた移行リスクの確率的モデリング手法として参考になる。
🏢実務担当者:自動車・電力・運輸企業がシナリオ分析で複数指標を統合評価する際の枠組みとして活用できる。
🏛政策担当者:EV普及策と再エネ拡大を協調させないとテールリスクが残る点を政策設計に反映すべき。
📄 Abstract（原文）
Planning for the transition to low emission vehicles requires more than a forecast of vehicle uptake. This study develops an assessment of transition risk for New South Wales (NSW), Australia, over 2026–2030. Annual indicators describe low emission vehicle uptake, petrol and diesel shares, renewable electricity, road energy use, road emissions, and passenger activity. Forecast ranges for these indicators are linked through independent, Gaussian copula, and Student-t copula assumptions and propagated through Monte Carlo simulation. The results are summarised using event probabilities, a composite transition loss index, and conditional value at risk at the 95 percent tail (CVaR 95 ), supported by robustness checks and bounded scenarios. Petrol and diesel vehicles remain dominant across all dependence assumptions, while the adverse stress tail case produces the greatest downside risk in every robustness variant. A coordinated improvement in uptake, renewable electricity, the decline of petrol and diesel vehicles, and emissions reduces CVaR 95 from 0.438 to 0.299. Faster uptake alone lowers average loss but does not reduce tail risk because the transition loss index identifies cases in which uptake advances without a corresponding improvement in renewable electricity. This result partly reflects the construction of the index and should not be interpreted as evidence that faster uptake is harmful. The findings show that NSW transition monitoring should consider changes in the vehicle fleet, electricity generation, and road emissions together.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.cstp.2026.101971first seen 2026-10-17 04:39:25
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