Bi-level optimization of carbon emission pricing for urban road traffic and environmental impacts
都市道路交通と環境影響に対する炭素排出価格の二段階最適化 (AI 翻訳)
Yingke Yang, Zhiyun Zou, Congjian Liu, Yongjia Wang, Zehao Jiang, Qixuan Cui
🤖 gxceed AI 要約
日本語
本研究は、交通渋滞料金と排出削減を統合した二段階最適化モデル（ERCM）を提案。ガソリン車と電気自動車の排出係数をMOVESと電力変換モデルで別途推定し、中国東莞市で検証した結果、全都市でCO₂排出量3.01%、課金区域内で13.06%の削減効果を確認。理論と実証の両面から都市交通管理者に示唆を与える。
English
This study proposes a bi-level emission reduction congestion pricing model (ERCM) that integrates traffic pricing with environmental outcomes. Using MOVES-based gasoline emission factors and a power-energy conversion model for electric vehicles, a case study in Dongguan, China shows total CO₂ reduction of 3.01% citywide and 13.06% within the pricing zone. It provides theoretical and empirical support for urban traffic managers in designing effective carbon pricing strategies.
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
本論文は中国の都市を対象とするが、日本でも都市部での渋滞課金やカーボンプライシングの検討が進む中、料金設定と交通・環境の相互作用をモデル化する手法は参考になる。特にSSBJやTCFDに関連する運輸部門の排出削減策として、実務的な示唆を提供する。
In the global GX context
This paper contributes to the global discourse on carbon pricing in the transportation sector by modeling the endogenous relationship between pricing strategies, traffic flow, and emission factors. The bi-level optimization framework and calibration of electric vehicle emissions are relevant for cities worldwide considering congestion pricing for climate mitigation.
👥 読者別の含意
🔬研究者:Provides a novel bi-level optimization model integrating congestion pricing with vehicle-specific emission factors, offering a methodological contribution for transport-environment modeling.
🏢実務担当者:Urban traffic managers can use the ERCM framework and findings to estimate emission reductions from congestion pricing strategies in their own cities.
🏛政策担当者:Offers empirical evidence on the effectiveness of congestion pricing for carbon reduction, supporting policy design for urban transport decarbonization.
📄 Abstract（原文）
Abstract Limiting carbon emissions from the transportation sector has become increasingly critical due to the intensification of global climate change. Congestion pricing strategy has received widespread attention as a measure to reduce emissions. However, the pricing affects both traffic conditions and environmental outcomes, which in turn influences the effectiveness of such pricing strategies. To capture these endogenous interactions and derive effective pricing strategy designs, this study proposes a bi-level programming emission reduction congestion pricing model (ERCM), considering the endogenous relationship between pricing strategies, road network operating conditions and vehicle emission factors. The upper-level model represents traffic managers optimizing emission reduction pricing strategies, while the lower-level model represents road users and establishes a congestion-pricing-based user equilibrium model (CSUEM) that incorporates emission reduction charges. In order to distinguish the power sources of different vehicles, gasoline emission factors were calibrated using Motor Vehicle Emission Simulator (MOVES) -based speed bins, while electric vehicle factors were estimated via a power-energy conversion model. A case study conducted in Dongguan, China, evaluated the carbon reduction effects of various congestion pricing strategies. The results indicate that, compared to a no-pricing scenario, the proposed pricing scheme reduced total CO₂ emissions by 3.01% across the city and by 13.06% within the pricing zone, respectively, within the congestion pricing demonstration area. This study provides both a theoretical foundation and empirical evidence for urban traffic managers to formulate effective carbon emission reduction pricing strategies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1093/tse/tdag009first seen 2026-05-05 08:02:35 · last seen 2026-05-05 19:14:28
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