Optimizing Urban Passenger Transportation Modes to Reduce Carbon Emissions
都市旅客輸送モードの最適化による炭素排出削減 (AI 翻訳)
Xuntao Qiu, Yuanwen Lai, Jianzheng Lin, Yuxia He, Hongxin He, Said M. Easa, Ziye Lan, Hangyu Liang, S Y Wang
🤖 gxceed AI 要約
日本語
本研究は、都市旅客交通において炭素排出を最小化しつつ関係者のコストバランスを図る多目的最適化モデルを開発した。福州市でのケーススタディでは、自家用車の利用を4.1%削減し、バス・タクシー・ライドシェアを増加させることで、システム全体の炭素排出を6.7%、旅行者コストを2.4%削減できることを実証した。政策シナリオ分析により、脱炭素化に向けた効果的な経路も特定している。
English
This study develops a multiobjective optimization model to determine the optimal urban passenger transport modal structure that minimizes carbon emissions while balancing costs for stakeholders. Using a life-cycle assessment, it shows fuel-cycle operations are the main emission source for ICVs, PHEVs, and BEVs. A case study in Fuzhou City achieved a 6.7% reduction in system carbon emissions and a 2.4% decrease in traveler costs by shifting 4.1% of private car trips to buses, taxis, and ride-hailing. Policy scenarios further identify effective pathways for decarbonizing urban transport.
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
本論文は中国福州市をケースに、都市旅客交通のモーダルシフトによる炭素排出削減の最適化モデルを提示。日本でも都市交通のカーボンニュートラル達成に向けた政策立案に応用可能な知見を提供する。
In the global GX context
This paper provides a validated optimization framework for decarbonizing urban passenger transport, applicable to cities globally. It demonstrates significant emission reductions through modal shift, informing urban transport policy and planning for carbon neutrality.
👥 読者別の含意
🔬研究者:Provides a multiobjective optimization methodology with LCA for transport modal structure, useful for urban decarbonization research.
🏢実務担当者:Can be used by city planners and transport authorities to evaluate modal shift strategies and design low-carbon transport systems.
🏛政策担当者:Offers evidence for policy interventions promoting public transit and reducing private car usage to meet emission reduction targets.
📄 Abstract（原文）
The transition toward carbon neutrality requires transformative pathways for urban passenger transport, a sector currently dominated by private vehicles. To address this challenge, this study develops a multiobjective optimization model to determine the optimal modal structure that minimizes carbon emissions while balancing costs for key stakeholders. A life-cycle assessment of carbon emissions revealed that fuel-cycle operations constitute the primary emission source across conventional internal combustion engine vehicles (ICVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). The core optimization model simultaneously pursues three objectives: maximizing the cost-effectiveness of carbon reduction per unit investment, minimizing travelers’ costs (including time and monetary expenses), and minimizing operators’ costs (covering vehicle ownership, maintenance, and fuel). The model framework incorporates costs borne by authorities for promoting low-carbon transport and key constraints such as per capita carbon intensity limits and supply-demand equilibrium. An improved Nondominated Sorting Genetic Algorithm (NSGA-II) algorithm was employed to solve this complex model. A case study in Fuzhou City demonstrated the model’s practical effectiveness. The optimized transportation structure showed a 4.1% decrease in private car usage, offset by increases in conventional bus (2.8%), taxi (0.3%), and ride-hailing (1.0%) shares. This restructuring achieved a 6.7% reduction in system carbon emissions and a 2.4% decrease in traveler costs while maintaining operator viability. Policy scenario analysis further identified effective pathways for structural evolution toward decarbonization. The study provides a validated framework for designing efficient urban transport strategies that align with carbon neutrality objectives.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1061/jtepbs.teeng-9466first seen 2026-06-02 05:00:18 · last seen 2026-06-03 05:06:11
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