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Data and code for "Spatial mismatch between public EV charging supply and travel-derived decarbonisation opportunity: a diagnose–attribute–site framework for central Shanghai"

上海中心部における公共EV充電供給と移動由来の脱炭素機会の空間的不一致：診断・属性・サイト決定フレームワークのデータとコード (AI 翻訳)

Anonymous

Zenodoデータセット2026-07-07#EV・輸送Origin: CN経営インパクト: 資金調達対象セクター: transport
DOI: 10.5281/zenodo.21245284
原典: https://zenodo.org/records/21245284

🤖 gxceed AI 要約

日本語

上海中心部の1,494のグリッドセルを対象に、公共EV充電供給と移動由来の脱炭素機会の空間的不一致を分析。2SFCAアクセシビリティ、二変量LISA、GeoShapley属性分析、炭素加重MCLP最適配置などの手法を用い、充電インフラの効率的な配置のための枠組みを提供する。

English

This study examines the spatial mismatch between public EV charging supply and travel-derived decarbonization opportunity across 1,494 grid cells in central Shanghai. It employs 2SFCA accessibility, bivariate LISA, GeoShapley-style attribution, and carbon-weighted MCLP siting to provide a framework for optimizing EV charging infrastructure placement.

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

本論文は上海市中心部を対象とするが、その分析枠組みは日本の都市にも応用可能。日本ではEV充電インフラ整備が進む中、脱炭素効果を最大化する配置戦略に示唆を与える。

In the global GX context

While focused on Shanghai, the diagnose–attribute–site framework is transferable to other cities. It offers a data-driven approach to align EV charging infrastructure with decarbonization potential, relevant for global sustainable transport planning.

👥 読者別の含意

🔬研究者:Provides a replicable spatial analysis methodology for evaluating EV charging infrastructure and decarbonization linkages.

🏢実務担当者:Offers insights for optimal siting of EV charging stations to maximize carbon reduction per unit of investment.

🏛政策担当者:Highlights the importance of spatial targeting in EV charging policy to achieve transport decarbonization goals.

📄 Abstract（原文）

This repository contains the processed data and analysis code to reproduce the results reported in the associated manuscript, which examines the spatial mismatch between public electric-vehicle charging supply and travel-derived decarbonisation opportunity across 1,494 500-m grid cells in central Shanghai. The package includes: grid-level data (charging point-of-interest, travel-derived carbon field, built-environment indicators), analysis code (2SFCA accessibility, bivariate LISA/Lee's L, GeoShapley-style attribution, carbon-weighted MCLP siting, and robustness checks), and figure-generation scripts. Note: Raw taxi-GPS trajectories are not included due to privacy and licensing restrictions; the derived grid-level carbon field is provided as an input. A full reproduction guide is in README.md. This dataset is currently restricted for double-blind peer review. Author information will be added upon publication.

🔗 Provenance — このレコードを発見したソース

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。