Spatiotemporal Carbon Emission Characteristics and Sustainable Reduction Strategies for Road Networks: A Simulation of Targeted Road-Segment Control and Vehicle Electrification
道路ネットワークにおける時空間的炭素排出特性と持続可能な削減戦略：対象路線制御と車両電動化のシミュレーション (AI 翻訳)
Kun Xie, Peixin Guo, Jiayu Bao, Honghui Dong, Zhihua Xiong, Chunjiao Dong
🤖 gxceed AI 要約
日本語
本研究は、北京のGPS軌跡データ（5699台、2019年9月）とCOPERT排出モデルを用いて、道路ネットワークの車両炭素排出の時空間特性を分析した。さらに、ライフサイクルアセスメント（LCA）を考慮した電動車両の排出量を組み込み、ランダム選択とランキング最適化に基づく排出削減戦略を提案した。シミュレーションの結果、ランキング最適化方式はランダム選択よりも高い削減効果を示し、電動化促進と精密な削減制御の統合が有効であることが示された。
English
Using GPS trajectory data from 5,699 vehicles in Beijing (September 2019) and the COPERT emission model, this study analyzes the spatiotemporal characteristics of vehicle carbon emissions on road networks. Incorporating Life Cycle Assessment (LCA) emissions for electric vehicles, carbon reduction strategies based on stochastic selection and ranking-based optimization are proposed from two dimensions: road-segment control and vehicle electrification. Simulation results show that under specified scenario assumptions, the ranking-based optimization scheme yields approximately 2 to 3 times greater carbon reductions than stochastic selection, indicating that integrating EV promotion policies with precise carbon reduction control strategies can effectively mitigate urban road network emissions.
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
本研究成果は、日本の都市交通におけるCO2削減施策（EV補助金や渋滞課金など）の効果検証に応用可能である。特に、LCAを考慮したEVの排出評価や、路線ごとの最適制御手法は、日本国内のスマートシティ構想や交通脱炭素政策において参考となる。
In the global GX context
This study provides a data-driven framework for urban transport decarbonization that is relevant globally. The integration of real-world trajectory data with LCA-based EV assessment and segment-level optimization offers actionable insights for cities worldwide that are developing EV promotion and traffic management policies to meet climate targets under TCFD/ISSB frameworks.
👥 読者別の含意
🔬研究者:Validates emission reduction potential of EVs and road segment control using real trajectory data and LCA, providing a replicable methodology for urban carbon accounting.
🏢実務担当者:Urban planners and transportation authorities can use the ranking-based optimization approach to design effective carbon reduction measures targeting specific road segments or vehicle electrification.
🏛政策担当者:Supports the design of integrated policies that combine EV adoption incentives with traffic management to maximize carbon reductions, offering simulation evidence for policy targets.
📄 Abstract（原文）
Global climate change poses a critical challenge to sustainable urban development. The construction of low-carbon transportation systems is therefore a core strategy for enhancing the sustainability of mega-city road networks. Combining the characteristics of urban road traffic networks, this paper establishes a method for vehicle trip segmentation and carbon emission estimation based on GPS trajectory data (5699 vehicles, Beijing, September 2019) and the COPERT emission model, analyzing the spatiotemporal distribution characteristics of vehicle emissions. By incorporating the Life Cycle Assessment (LCA) emissions of electric vehicles, this study proposes carbon reduction strategies based on stochastic selection and ranking-based optimization from two dimensions: road-segment and vehicle electrification. Simulation methods are employed to evaluate the effectiveness of different strategies, as well as road network carbon emissions, under four vehicle electrification structures: Pyramid, Inverted Pyramid, Olive, and Dumbbell. Results indicate that carbon emission intensity rises significantly due to traffic congestion during peak hours. Under the LCA framework, Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) show significantly lower emissions than traditional Internal Combustion Engine Vehicles (ICEVs). Under the specified scenario assumptions, the ranking-based optimization scheme is estimated to yield carbon reductions approximately 2 times (segment control) and 3 times (electrification) those of the stochastic selection scheme, respectively. The study concludes that integrating EV promotion policies with precise carbon reduction control strategies can effectively mitigate urban road network carbon emissions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18136773first seen 2026-07-05 05:14:22
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