複数シナリオにおける低炭素交通モード転換が炭素排出に与える影響
[Impact of Low-carbon Transport Mode Conversion on Carbon Emissions in Multiple Scenarios]. (原題)
Shuhong Ma, Yuxuan Deng, Chao-Jie Duan, Yu-Fei Han
🤖 gxceed AI 要約
日本語
本研究は、タクシーGPS、地下鉄カード、シェア自転車のデータを用いて、深圳の交通機関の炭素排出を推定し、低炭素交通への転換の削減可能性を評価した。その結果、「シェア自転車＋鉄道」の組み合わせで最大20.34%削減でき、駅の乗り換え能力が重要であることが分かった。また、電動シェア自転車の導入で最大22.01%削減できると示した。
English
Using multi-source data from Shenzhen, this study models urban transport carbon emissions and evaluates low-carbon mode shift scenarios. Results show that combining shared bikes with rail transit can cut emissions by up to 20.34%, while station transfer capacity affects adoption. Deploying shared e-bikes could achieve up to 22.01% reduction, offering insights for optimizing transfer facilities and reducing 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
日本の都市交通政策や次世代モビリティ推進に参考となる。特に、駅の乗り換え能力向上やシェアサイクル導入の効果を定量的に示しており、自治体の交通計画やカーボンニュートラル施策に活用できる。
In the global GX context
This study provides empirical evidence on low-carbon transport transitions, relevant to global urban decarbonization efforts. It demonstrates the potential of integrating shared mobility with public transit, offering insights for cities aiming to meet climate targets under frameworks like C40 or ICLEI.
👥 読者別の含意
🔬研究者:Provides a bottom-up carbon accounting method for urban transport and scenario analysis of mode shifts.
🏢実務担当者:Useful for urban planners and transport operators to design low-carbon mobility strategies and optimize transfer facilities.
🏛政策担当者:Informs policies promoting shared mobility and transit integration to achieve urban carbon reduction targets.
📄 Abstract（原文）
With the increasing demand for urban transportation， green travel has become one of the main ways for cities to reduce carbon emissions. By utilizing multi-source transportation data such as taxi GPS data， subway card usage data， and bike-sharing order data， we aim to reveal the carbon emission reduction potential of the transformation of urban public transportation travel patterns. Firstly， by extracting the driving mileage of different transportation modes， a "bottom-up" urban transportation carbon emission calculation model is established to fit the spatial distribution characteristics of carbon emissions from different travel modes. Combined with the implementability of carbon reduction policies， three low-carbon combined travel scenarios are proposed to evaluate the carbon reduction potential of urban public transportation in differentiated scenarios. Taking Shenzhen City as an example for verification， the results showed that taxi travel， subway travel， and shared bike travel in urban transportation had a high degree of spatial overlap， and taxi travel could be replaced by combined low-carbon travel modes. The combined travel of "shared bikes + rail transit" could reduce carbon emissions by up to 20.34%. The connection and transfer capacity of subway stations will affect the willingness to choose low-carbon modes. Under the travel alternative scenarios considering the differentiation of station transfer capabilities， combined travel could reduce carbon emissions by up to 13.57%. Increasing the deployment of shared electric bikes could reduce carbon emissions by up to 22.01%， which would be a further decrease of 11.21% compared with the substitution of shared bikes. The research results can provide references for optimizing transfer facilities and reducing carbon emissions in areas with higher demand.
🔗 Provenance — このレコードを発見したソース
- openalex https://pubmed.ncbi.nlm.nih.gov/42670111first seen 2026-09-03 05:07:58
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