A Temporal Dendritic Neural Model for Carbon Emission Forecasting
炭素排出予測のための時間的樹状ニューラルモデル (AI 翻訳)
T Zhang, Ting Jin, Kang Wu, Yue Wang
🤖 gxceed AI 要約
日本語
本論文は、時系列データの複雑な非線形関係を捉えるため、時間的樹状ニューラルモデル（TDNM）と樹状適応学習（DAL）アルゴリズムを提案。中国54都市の1999～2023年のパネルデータを用い、経済・社会・エネルギー13指標から炭素排出を予測。従来手法より高い精度と安定性を示し、蘇州市のシナリオ分析により産業都市の低炭素発展経路を提示。
English
This paper proposes a Temporal Dendritic Neural Model (TDNM) with a Dendritic Adaptive Learning algorithm for multivariate carbon emission forecasting. Using panel data from 54 Chinese cities (1999-2023) with 13 driving factors, it outperforms conventional ML and deep learning models. Scenario analysis for Suzhou City illustrates future emission trends under different development pathways, offering methodological support for low-carbon transitions in industrial cities.
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」目標を背景に、都市レベルでの炭素排出予測手法を提供。日本ではSSBJ対応や自治体の排出量把握に応用可能性があるが、中国のデータ構造に依存しており、直接的な導入には調整が必要。
In the global GX context
This study advances carbon emission forecasting with a novel neural architecture, relevant to global climate disclosure and transition planning. While focused on Chinese cities, the methodology could be adapted for other regions. The scenario analysis approach is useful for corporate and municipal decarbonization pathway design under ISSB/TCFD frameworks.
👥 読者別の含意
🔬研究者:Presents a temporal dendritic neural model for emission prediction, outperforming standard baselines; useful for carbon accounting methodology research.
🏢実務担当者:Scenario analysis for future emission trends aids corporate and city-level decarbonization planning; model can be adapted for regional footprint estimation.
🏛政策担当者:Provides a forecasting tool for local carbon peak analysis; relevant for shaping low-carbon development policies in industrial regions.
📄 Abstract（原文）
Accurate carbon emission prediction is critical for regional low-carbon transitions and the realization of China’s “dual carbon” goals. However, carbon emission systems exhibit significant complex nonlinear relationships and time-dependent characteristics, making it difficult for traditional statistical methods and conventional neural networks to fully capture their dynamic evolution. To address this challenge, this paper proposes a multivariate carbon emission prediction method that integrates a Temporal Dendritic Neural Model (TDNM) with a Dendritic Adaptive Learning (DAL) algorithm. Based on panel data from 54 prefecture-level cities in China spanning 1999 to 2023, this study systematically constructs a carbon emission driving factor system comprising 13 indicators across economic, social, and energy dimensions. Empirical results indicate that the proposed model achieves competitive predictive performance and improved stability compared with several conventional machine learning models and deep learning baselines. Furthermore, taking the carbon emission predictions of Suzhou City as a starting point, a detailed scenario analysis is conducted to clarify the evolution trends of future carbon emissions under different development pathways, providing methodological references for analyzing carbon emission evolution and low-carbon development pathways in industrial cities.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/math14142648first seen 2026-07-27 05:07:51
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