メキシコシティ都市圏（MCMA）の高解像度都市カーボンフットプリントマップ

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:機械学習と世帯調査を組み合わせた空間排出推計手法の実証例として、都市炭素会計研究に有用。

🏢実務担当者:自治体・都市計画担当者が地域脱炭素計画の基礎データとして活用できる高解像度マップの手法を提供。

🏛政策担当者:都市インフラ格差と排出の関係を示し、公平な脱炭素政策設計の根拠を提供する。

Rogelio O. Corona-Núñez, Isela Jasso-Flores, Francisco E. Ramas Arauz, Adriana Larralde, Salomón González, Irmene Ortíz. (2026), Urban structural and infrastructural inequalities shape fine‑scale carbon footprints of diet, housing, and transportation: Evidence from the metropolitan area of Mexico City, Sustainable Cities and Society, Volume 150, 107933, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2026.107933.(https://www.sciencedirect.com/science/article/pii/S2210670726008176) This dataset provides high-resolution spatial models of household carbon footprints across the Metropolitan Area of Mexico City (MCMA), structured for open-access sharing. The repository contains raster surfaces mapping annual per capita greenhouse gas emissions at a 100-m spatial resolution. To ensure robust statistical transparency for environmental analysis and spatial planning, each mapping product includes the mean, standard deviation (SD), and coefficient of variation (CV) derived from the underlying predictive modeling framework.All emission values are expressed in metric tons of CO2-equivalent per year per person (tCO2eq / year / person).Associated Publication ReferenceThe data and modeling frameworks contained within this repository are associated with the following study, currently under consideration in the journal Sustainable Cities and Society:Manuscript Title: Urban Structural and Infrastructural Inequalities Shape Fine‑Scale Carbon Footprints of Diet, Housing, and Transportation: Evidence from the Metropolitan Area of Mexico City.Status: Under review / consideration in Sustainable Cities and Society.Methodological Summary & Data GenerationThe spatial carbon models were constructed by integrating primary household survey data with high-resolution environmental, socioeconomic, and infrastructural predictors using machine learning spatial modeling:Sampling & Survey Design:Primary data collection was conducted via a face-to-face household survey across 992 randomly approached dwellings (houses and apartments) within the MCMA.The survey captured the full metropolitan density gradient, ranging from low peripheral densities (1.6 inhabitants/ha) to dense urban cores (362 inhabitants/ha), mapped across a 100-m population density grid spanning up to 461 inhabitants/ha.Exact GPS coordinates were logged for each surveyed dwelling to enable spatial modeling. Questionnaires were adapted from the Greenhouse Gas Protocol for Project Accounting.Emission Domains:Dietary Emissions: Estimated using a food-frequency approach capturing weekly consumption across major food groups and retail outlet types. Local and international life-cycle assessment emission factors were applied. Production-phase impacts dominate (>94% of variance), while transport distances were excluded due to product-origin data limitations and to prevent spatial bias.Housing Component: Captured direct and indirect household consumption of electricity (via billing data and water distribution energy requirements) and liquefied petroleum gas (LPG) for cooking and water heating, evaluated using official Mexican government emission factors for 2022.Transportation Emissions: Calculated from routine mobility tracking (work, education, and daily travel) across public transport, private vehicles (incorporating vehicle types and fuel specifications), and point-to-point commercial aviation distances.Predictive Modeling & Spatial Mapping:To scale survey responses across the MCMA, predictive spatial models were trained using the Random Forest algorithm on 683 spatially aggregated observations (70% training, 30% independent validation).Random Forest was selected for its capacity to handle high-dimensional urban data, accommodate multicollinearity, and model complex non-linear relationships.Model explainability and feature evaluation were performed using the DALEX library, applying permutation-based variable importance and Ceteris paribus partial dependence profiles to map how urban structure, accessibility, and socioeconomic drivers influence household carbon footprints across the metropolitan gradient.

• openalex https://doi.org/10.6084/m9.figshare.33260739.v2first seen 2026-09-28 05:22:55