Research on energy consumption structure evolution trend prediction and low-carbon transition path optimization
エネルギー消費構造の進化傾向予測と低炭素転換経路最適化に関する研究 (AI 翻訳)
Tao Chen
🤖 gxceed AI 要約
日本語
本論文は、エネルギー消費構造の進化における多因子結合、時系列変動、転換経路制約の問題に対処するため、予測と最適化を組み合わせた計算フレームワークを構築。複数データを統合し、石炭・石油・天然ガス・非化石エネルギーの割合変化を予測するモデルと、転換コスト・排出削減・エネルギー安全保障を考慮した低炭素経路最適化モデルを開発。実験では予測精度が高く（RMSE 0.021、MAPE 3.84%）、最適化後の累積炭素排出量は継続経路比13.4%削減、2035年に非化石エネルギー比率43.0%を達成。エネルギー構造調整と低炭素転換の意思決定に計算支援を提供。
English
This paper constructs a computational framework combining trend prediction and path optimization for energy consumption structure evolution. It integrates multi-source data to build time-series prediction models for coal, oil, natural gas, and non-fossil energy shares, and an optimization model considering transition costs, emission reductions, and energy security. Results show high prediction accuracy (RMSE 0.021, MAPE 3.84%) and a 13.4% reduction in cumulative carbon emissions, with non-fossil energy reaching 43.0% by 2035. The framework supports decision-making for energy structure adjustment and low-carbon transition.
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
本論文の手法は日本のエネルギー政策（第6次エネルギー基本計画やGX実現に向けた基本方針）における非化石エネルギー比率向上や排出削減目標達成のための定量的分析に活用可能。特に複数制約下での最適化手法は日本の地域特性や政策変数を考慮したシナリオ分析に応用できる。
In the global GX context
This paper offers a robust computational framework for energy transition path optimization, relevant to global GX efforts such as national decarbonization strategies and energy mix planning. The integration of multi-source data and optimization under constraints aligns with ISSB and TCFD's emphasis on scenario analysis, providing tools for assessing transition risks and opportunities.
👥 読者別の含意
🔬研究者:This paper provides a methodology for energy structure prediction and optimization that can be applied to various contexts and extended with additional variables.
🏢実務担当者:The framework can be used by energy companies and sustainability teams to model transition pathways and assess carbon reduction targets.
🏛政策担当者:The optimization model offers quantitative support for designing energy policies and setting emission reduction targets.
📄 Abstract（原文）
Aiming at the problems of multi-factor coupling, significant time series fluctuation and complex transition path constraints in the evolution of energy consumption structure, this paper constructs a computational research framework combining trend prediction and path optimization. By integrating multi-source data such as energy consumption, economic growth, industrial structure, carbon emission constraints and policy variables, a time-series prediction model for the changes in the proportion of coal, oil, natural gas and non-fossil energy is established, and a low-carbon path optimization model considering transition costs, carbon emission reduction effects and energy security is further constructed. On this basis, a joint intelligent solution strategy is introduced to improve the optimization efficiency in complex constraint scenarios. Experimental results show that the prediction accuracy of the proposed model is high, the RMSE is reduced to 0.021, and the MAPE is 3.84%. After optimization, the cumulative carbon emissions are reduced by 13.4% compared with the continuation path, and the proportion of non-fossil energy will increase to 43.0% in 2035. The research results can provide computational support for energy structure adjustment and low-carbon transition decision-making.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.65102/is2026251first seen 2026-05-17 05:23:10
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