A Comprehensive Framework for Energy Decision‐Making Toward Achieving Net Zero by 2050 Using Machine Learning
2050年ネットゼロ達成に向けた機械学習を用いたエネルギー意思決定の総合的フレームワーク (AI 翻訳)
Muhammad Zubair, Yumna Akram, Polina Matesha
🤖 gxceed AI 要約
日本語
本研究は、UAEを対象に機械学習を用いて2050年までの電力構成の変化を予測する。化石燃料の割合は94%から49%に減少し、再生可能エネルギーと原子力が増加。CO2排出量は92%削減されるが、完全な脱炭素化には至らず、更なる政策強化が必要であることを示す。
English
This study uses machine learning to forecast the UAE's electricity mix transition to 2050, showing fossil fuels decline from 94% to 49%, renewables and nuclear rise. CO2 emissions drop 92%, but a gap remains, highlighting need for deeper systemic change.
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
日本はエネルギー多消費国であり、UAEの事例は脱炭素経路策定に参考となる。ただし、日本の電源構成や政策目標は異なるため、直接適用には注意が必要。
In the global GX context
This paper provides a reproducible ML framework for national energy planning, relevant for countries pursuing net-zero. The UAE case demonstrates potential of data-driven policy evaluation, but the gap analysis underscores the challenge of full decarbonization.
👥 読者別の含意
🔬研究者:ML-based forecasting approach offers a template for long-term energy modeling.
🏢実務担当者:Emissions gap analysis can inform corporate transition planning.
🏛政策担当者:Framework highlights insufficiency of current policies; deeper systemic change needed.
📄 Abstract（原文）
Achieving net‐zero emissions by mid‐century is essential to limit global warming within internationally endorsed thresholds and avert the most acute consequences of climate change. This study presents a policy‐anchored, machine learning (ML)–based forecasting framework to project the long‐term evolution of a national electricity mix under decarbonization commitments. Using the United Arab Emirates (UAE) as a representative case of an energy‐intensive economy pursuing diversification, we integrate historical data (1985–2021) with supervised ML models to forecast shifts in generation sources, CO 2 emissions, and temperature trends through 2050. Results reveal a structural transformation: fossil fuels decline from 94% of electricity generation in 2021 to 49% by 2049, while renewables and nuclear rise to 39% and 6.1%, respectively. These transitions drive a projected 92% reduction in energy‐related CO 2 emissions and a 32% slowdown in the rate of surface temperature increase—from ~0.0415°C/year to ~0.0283°C/year. However, comparison with an idealized net‐zero pathway highlights a persistent gap: emissions levels by 2050 remain nearly 30 million tonnes above the target benchmark. This divergence underscores that while current efforts yield significant climate benefits, they are insufficient for full decarbonization. The findings emphasize the need for deeper systemic change, including accelerated clean technology deployment, grid modernization, and enhanced demand‐side efficiency. By embedding national policy signals into a quantitative and reproducible framework, the study offers actionable insights for strategic energy planning and long‐term climate mitigation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1155/er/6692535first seen 2026-05-15 17:46:07
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