<b>High-Resolution Urban Carbon Footprint Maps of the Metropolitan Area of Mexico City (MCMA)</b>

<p dir="ltr">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, <i>Sustainable Cities and Society</i>, Volume 150, 107933, ISSN 2210-6707, doi.org/10.1016/j.scs.2026.107933.(https://www.sciencedirect.com/science/article/pii/S2210670726008176)</p><p dir="ltr"><br></p><p dir="ltr">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 <b>100-m spatial resolution</b>. To ensure robust statistical transparency for environmental analysis and spatial planning, each mapping product includes the <b>mean, standard deviation (SD), and coefficient of variation (CV)</b> derived from the underlying predictive modeling framework.</p><p dir="ltr">All emission values are expressed in <b>metric tons of CO2-equivalent per year per person (tCO2eq / year / person)</b>.</p><h4 dir="ltr"><b>Associated Publication Reference</b></h4><p dir="ltr">The data and modeling frameworks contained within this repository are associated with the following study, currently under consideration in the journal <i>Sustainable Cities and Society</i>:</p><ul><li><b>Manuscript Title:</b> Urban Structural and Infrastructural Inequalities Shape Fine‑Scale Carbon Footprints of Diet, Housing, and Transportation: Evidence from the Metropolitan Area of Mexico City.</li><li><b>Status:</b> Under review / consideration in <i>Sustainable Cities and Society</i>.</li></ul><h4 dir="ltr"><b>Methodological Summary & Data Generation</b></h4><p dir="ltr">The spatial carbon models were constructed by integrating primary household survey data with high-resolution environmental, socioeconomic, and infrastructural predictors using machine learning spatial modeling:</p><ol><li><b>Sampling & Survey Design:</b></li><li><ul><li>Primary data collection was conducted via a face-to-face household survey across 992 randomly approached dwellings (houses and apartments) within the MCMA.</li><li>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.</li><li>Exact GPS coordinates were logged for each surveyed dwelling to enable spatial modeling.