DOI: 10.7910/dvn/33jjxt
title: EnvClim Grids: Global Urban Environmental Vulnerability and Climate Risk, 500m Resolution (circa 2020–2050)

description: OVERVIEW:
EnvClim Grids is a global geospatial dataset characterizing current environmental vulnerability and future climate risk for urban populations, with a focus on lower- and middle-income countries (LMICs).

The dataset covers 11,422 urban centers worldwide, as defined by the Global Human Settlement Urban Centres Database (GHS-UCDB 2024), including all cities with ≥50,000 residents. A novel socioeconomic vulnerability layer is included for cities in 91 LMICs, identifying neighborhood-scale “low SES” areas based on higher building density and lower road connectivity relative to similar-sized cities nationally.

The dataset integrates 17 global public datasets into 7 indicators of current environmental vulnerability (unsafe air pollution, warm spells, extreme heat, water scarcity, hurricane-force winds, storm surge flooding, seasonal river/surface flooding) and 6 indicators of projected future climate risk (2050, SSP2-RCP4.5) (extreme heat, water scarcity, hurricane-force winds, storm surge flooding, seasonal river flooding, and landslide susceptibility).

All indicators are harmonized to a ≈500×500 m grid for comparability across cities, enabling analysis of intersecting socioeconomic and environmental vulnerabilities at the sub-city level where data coverage allows. City-level and national summaries can be generated to support adaptation planning, risk prioritization, and investment targeting.

GEOGRAPHIC COVERAGE: 
Global — 190 countries, 11,422 urban centers (GHS-UCDB 2024 boundaries)

SPATIAL RESOLUTION:
~500 × 500 meters

TEMPORAL COVERAGE:
Current environmental vulnerability: 2010–2022 (indicator-specific time ranges)
Future climate risk projections: circa 2050 (SSP2-RCP4.5 scenario)

DATA SOURCES:
Global Human Settlement Urban Centres Database (GHS-UCDB, 2024); 
OpenStreetMap; 
Overture Maps Foundation Building Footprints; 
Williams et al. (2024) Extreme Heat and WBGT projections; 
He et al. (2021) Water Scarcity; 
Gahtan et al. (2024) Cyclone Winds (IBTrACS); 
Dullaart et al. (2022) Storm Surge Flooding; 
Pekel et al. (2016) Surface Water Occurrence; 
Dottori et al. (2016) Riverine Flooding; 
Shen et al. (2024) PM2.5 Air Pollution; 
Emberson et al. (2020) Landslide Susceptibility; 
Additional datasets listed in the Methods section of the related publication.

FILE CONTENTS
cities_fishnet_final.gpkg – Geopackage with all indicators at 500 m resolution
EnvClim-Grids-data-dictionary.pdf – Documentation of indicators, sources, and processing steps

METHODOLOGICAL SUMMARY:
City Boundaries: Defined using GHS-UCDB 2024.
Socioeconomic Vulnerability: Derived from City Segments Layer v1, classifying segments with higher building density and lower road connectivity as low SES.
Current Environmental Vulnerability: Seven binary indicators derived from best-available global datasets.
Future Climate Risk: Six binary indicators based on downscaled projections for 2050 under SSP2-RCP4.5.
Integration: Indicators harmonized to 500 m grids and overlaid to identify populations exposed to multiple, compounding vulnerabilities and risks.

LIMITATIONS:
Designed for global and national comparisons; local application requires in-country validation.
Some indicators (e.g., flash flood risk) are not globally available and may underestimate certain hazards.
SES vulnerability layer is only available for 91 LMICs with sufficient road and building data coverage.

RECOMMENDED CITATION: Thomson, Dana R., Martinez, Juan F., Liu, E Jen, and de Sherbinin, Alex. 2025. EnvClim Grids: Global Urban Environmental Vulnerability and Climate Risk, 500m Resolution (circa 2020–2050). Harvard Dataverse. https://doi.org/10.7910/DVN/[DOI-TBD].
description: Other contributors to this work: Lindsay Barbieri, Laura M. Hammett, Anna Brown, Monica Bansal, Kara Reeve, Antoinette Wannebo, Susana B. Adamo, Alejandro Blei, Cascade Tuholske, Matthew Heaton, Sylwia Trzaska, Kytt MacManus, Carolynne Hultquist, and Xiaoshi Xing.