The transformation path of energy and power systems and carbon emission prediction models under the dual carbon goals
デュアルカーボン目標下におけるエネルギー・電力システムの転換経路と炭素排出予測モデル (AI 翻訳)
Yan Li, Xin Zhao, Wanlei Xue, Zhifan Liu
🤖 gxceed AI 要約
日本語
本論文は、中国の「ダブルカーボン」目標達成のために、源・網・荷・蓄の協調に基づく低炭素転換経路設計手法を構築し、多時間スケールの技術進化と時系列最適化アルゴリズムを統合した炭素排出予測モデル（SNES）を開発した。短期予測でRMSE 125千トン、MAPE 3.8%、R² 0.973と高精度であり、長期シミュレーションでは政策強化と技術加速経路で2030年頃にピークアウトし、2060年に75%以上削減またはほぼゼロ排出達成可能と示した。エネルギーシステムの低炭素転換に科学的根拠を提供する。
English
This paper designs a low-carbon transformation path for energy systems under China's dual carbon goals, integrating source-grid-load-storage coordination and multi-time-scale technology evolution. It develops the SNES carbon emission prediction model achieving RMSE 125k tons, MAPE 3.8%, R² 0.973 in short-term backtesting. Long-term scenarios show emissions peaking around 2030 and reduction exceeding 75% or near-zero by 2060 under policy strengthening and technology acceleration paths.
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
中国の「ダブルカーボン」目標に焦点を当てたものであるが、日本の2050年カーボンニュートラル目標との共通点も多い。特に電力システムのモデリング手法や政策強化シナリオの考え方は、日本のエネルギー基本計画やGX推進戦略の検討にも示唆を与える。ただし、中国特有の政策枠組みに基づくため、直接適用には注意が必要。
In the global GX context
This paper addresses China's dual carbon goals but its modeling approach for carbon emission prediction and energy system transformation pathways has global relevance. The integration of source-grid-load-storage and multi-time-scale optimization can inform similar efforts under TCFD/ISSB frameworks for transition planning. The scenario analysis demonstrating peaking by 2030 and deep decarbonization by 2060 provides a benchmark for emerging economies.
👥 読者別の含意
🔬研究者:Provides a validated carbon emission prediction model (SNES) and scenario analysis for energy system transformation, useful for researchers in energy modeling and carbon accounting.
🏢実務担当者:Energy companies and grid operators can reference the path design and prediction model for their own low-carbon transition planning and disclosure.
🏛政策担当者:Policymakers can learn from the dual carbon goal scenario analysis, especially the role of policy strengthening and technology acceleration in achieving emissions peaking and deep reduction.
📄 Abstract（原文）
To achieve the "dual carbon" goals, this paper constructs a low-carbon transformation path design method for energy and power systems based on the collaboration of source-grid-load-storage, and integrates multi-time-scale technology evolution and time series optimization algorithms to establish a carbon emission prediction model. Model verification shows that the proposed SNES model has a root mean square error (RMSE) of 125,000 tons, an mean absolute percentage error (MAPE) of 3.8%, and a goodness of fit (R²) of 0.973 in short-term historical backtracking. The prediction accuracy is significantly better than that of traditional models. Long-term scenario simulations show that under the path of policy strengthening and technological acceleration, carbon emissions can peak around 2030 and achieve a reduction of over 75% and near-zero emissions respectively by 2060, providing a scientific decision-making basis and path reference for the low-carbon transformation of the energy system.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1117/12.3115757first seen 2026-06-17 07:33:35
🔔 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。