Active Climate, Earth & Environment

Driving Cleaner Air Through Climate Mitigation Actions: A case study for Mexico City and electromobility

Funder Wellcome Charity-funded
Associated organisations Centro de Transporte Sustentable de México, A.C. · International Institute for Applied Systems Analysis · Norwegian Meteorological Institute · Universidad Nacional Autónoma de México Europe PMC affiliations are not treated as award recipients or mapped locations.
Funding £1.8M
Period Nov 2025 — Nov 2028

In plain English

AI plain-English summary

Mexico City’s air is among the most polluted in the world, and this project will calculate exactly how many fewer people would get sick or die if the city switched to electric vehicles. The problem is that low- and middle-income cities face a double burden: they must cut greenhouse gas emissions while also protecting residents from toxic air. Current policies often treat climate and health separately, so governments lack the tools to see which actions deliver the biggest health gains for the most vulnerable people. This project fills that gap by building a single modelling framework that links emissions, air pollution concentrations, and health outcomes. If successful, the research will produce a web-based tool that city officials and policymakers can use to test different scenarios—such as accelerating the shift to electric buses and taxis—and see the predicted reductions in PM₂.₅, nitrogen dioxide, and ozone. The analysis will also overlay the World Resources Institute’s Urban Inequality Index to show whether the poorest neighbourhoods, which typically breathe the dirtiest air, would benefit most. For Mexico City, the case study could directly inform its next air quality and decarbonisation plans. The same modelling approach could later be adapted for other cities in the global south.

View original technical description
Many cities in LMIC face significant health challenges due to air pollution and play a vital role in reducing GHG emissions. This project aims to develop an integrated modeling and policy analysis framework to quantify the health benefits of reducing air pollution and GHG emissions with a focus on vulnerable populations. While the project will address local and regional sources contributing to urban pollution, a case study for Mexico City will emphasize mitigation actions for transport sector. Atmospheric chemical transport modelling and downscaling air pollutant concentrations (PM₂.₅, NO₂, ozone) will be simulated with EMEP/uEMEP chemical transport models, enabling high-resolution spatial assessment. A dedicated version of the GAINS model will be developed to estimate emissions of GHGs, SLCPs, and criteria pollutants under baseline and mitigation scenarios including the main contributing sectors. A pilot web-based tool will be developed allowing stakeholders to further analyze the results and explore other scenarios. The analysis will also use the WRI’s Urban Inequality Index to understand inequalities in health and socioeconomic benefits. A case study on the benefits of accelerating electromobility in Mexico City will be developed to inform Mexico City´s air quality and decarbonization policies, while engaging key stakeholders through a participatory science exercise.

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Researchers

Andrea Calderon (EPMC Awardee)Beatriz Cardenas (EPMC Awardee)Gregor Kiesewetter (EPMC Awardee)Hilde Fagerli (EPMC Awardee)Patricia Segura-Medina (EPMC Awardee)Rodrigo Diaz (EPMC Awardee)ZBIGNIEW KLIMONT (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Evaluating Climate and Air Pollution Mitigation Strategies in the metropolitan area of Monterrey, Nuevo León: Public Health Benefits of Black Carbon Reduction
Economic Assessment of Health Co-benefits of Policies to Reduce Greenhouse Gas Emissions
Co-Benefits of Climate Actions for Air and Health in India
Impact of hybrid/electric vehicles on urban air pollution
Climate, Economics and Health: Adding air quality impacts into social cost of carbon estimates

Original classification

Climate mitigation solutions in LMICs

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.