異なるトリップ目的別に見た建築環境・知覚的ユーザビリティと低炭素交通手段選択
Built Environment, Perceived Usability, and Low-Carbon Mode Choice Across Different Trip Purposes (原題)
Ting Li, Xiao Dong, Jiaping Liu, Qingze Li, Peizeng Huang
🤖 gxceed AI 要約
日本語
中国西安の855名を対象に、ランダムフォレスト・SHAP・PDP・PCA・SEMを組み合わせ、6つのトリップ目的別に建築環境（BE）と低炭素交通手段選択の関係を分析。BEの重要次元や非線形応答、知覚を介した経路は目的ごとに大きく異なり、通勤ではバス停距離約200mに転換点が見られた。知覚的BEは一部経路のみを部分媒介し、完全媒介は確認されなかった。
English
Using 855 respondents in Xi'an, China, this study combines random forest, SHAP, PDPs, PCA, and SEM to examine how built-environment (BE) associations with low-carbon mode choice vary across six trip purposes. BE importance, nonlinear response patterns, and perception-mediated pathways differed substantially by purpose, with a turning point near 200 m to the nearest bus stop for commuting. Perceived BE only partially mediated specific pathways, suggesting purpose-specific planning priorities.
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
日本では都市部の脱炭素交通政策（コンパクトシティ、LRT・バス再編）や自治体の環境基本計画と接続しうる。ただしSSBJ・有報・TCFDなど企業開示との直接関係は薄く、主に自治体・都市計画実務への示唆にとどまる。
In the global GX context
Globally, this contributes to the transport-decarbonization evidence base relevant to city climate action plans and Scope 3 commuting emissions, though it does not directly engage TCFD/ISSB/CSRD disclosure frameworks. Its ML-based heterogeneity analysis offers a methodological template for purpose-specific sustainable mobility policy.
👥 読者別の含意
🔬研究者:トリップ目的別にBE効果の非線形性と知覚媒介を分解するML+SEM統合手法が参考になる。
🏢実務担当者:通勤・業務移動の低炭素化施策を検討する企業の通勤手当・拠点立地戦略に示唆。
🏛政策担当者:バス停距離200m等の閾値を踏まえた目的別の都市・交通計画設計に活用できる。
📄 Abstract（原文）
Existing studies often examine associations between the built environment (BE) and low-carbon mode choice using aggregated travel outcomes, with limited attention to how these relationships vary across trip purposes. Using a final analytical sample of 855 respondents from Xi’an, China, this study integrates random forest (RF), SHapley Additive exPlanations (SHAP), partial dependence plots (PDPs), principal component analysis (PCA), and structural equation modeling (SEM) to examine purpose-specific BE importance, nonlinear response patterns, and perception-mediated pathways across six trip purposes. These include commuting, education, maintenance activities involving shopping and routine daily services, healthcare, recreation involving cultural, fitness, and entertainment activities, and outdoor leisure involving parks, squares, and scenic areas. The results show substantial heterogeneity across trip purposes. RF models achieved area under the receiver operating characteristic curve (ROC-AUC) values ranging from 0.742 to 0.861, while the most important BE dimensions differed across purposes. Nonlinear associations also varied in form and range across trip purposes, including an apparent turning point at approximately 200 m for the distance to the nearest bus stop in commuting trips, beyond which the predicted probability of low-carbon travel declined. SEM results further showed that perceived BE mediated only specific BE–travel pathways, with partial mediation identified for PCA3 in maintenance trips and PCA2 in recreation trips after accounting for community-level clustering. No full mediation was identified. These findings show that BE–travel relationships vary in environmental importance, nonlinear response, and perception-mediated pathways across daily activities. The results can help planners identify purpose-specific environmental priorities and develop more targeted community-scale interventions for sustainable transportation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su181910055first seen 2026-10-03 04:54:40
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