Active Public Health & Healthcare Climate, Earth & Environment

Participatory Analytics for Climate-Health Adaptation in Disadvantaged Urban Communities in Brazil (PACHA)

In plain English

AI plain-English summary

Favela residents in three Brazilian cities will turn their lived experience of heatwaves, floods, and respiratory illness into data that city officials cannot ignore. The PACHA project tackles a double injustice: the same communities that suffer most from climate change are also invisible in the official datasets that guide adaptation spending. Without this evidence, municipal adaptation plans risk bypassing the people who need them most. The project brings together community leaders, climate scientists, health researchers, and policymakers to co-create a digital platform and methodological toolkit. In Participatory Urban Living Labs in Curitiba, Natal, and Niterói, residents will contribute their own observations—where flooding blocks access to clinics, which neighbourhoods lack shade, how heat affects elderly caregivers—and combine them with climate and health records. The analysis uses an intersectional lens, tracking how impacts differ by gender, race, and age. If successful, the platform and toolkit will give other cities across Brazil and Latin America a replicable way to make disadvantaged communities visible in adaptation planning. The immediate change is not a new vaccine or a carbon capture device—it is a shift in who gets to define the problem and whose data counts.

View original technical description
Disadvantaged urban communities face compounded climate and data injustices: while disproportionately affected by the impacts of climate change, they remain largely invisible in official data and decision-making processes. In response, the PACHA project aims to inform city adaptation implementations by making visible and reducing the climate change impacts on the health and well- being of Brazilian favela urban communities. We address this through a transdisciplinary partnership between urban community leaders, policymakers, social scientists, climate scientists and health researchers to reveal and reduce climate-health impacts using a place-based focus on favela communities, and a gender, race and age-sensitive intersectional lens. Using a mixed- methods approach to participatory analytics, we combine climate and health data, intersectional multi-level analysis of impacts and vulnerabilities, and participatory action research with affected communities to translate lived experiences into citizen data. Participatory Urban Living Labs in three Brazilian cities (Curitiba, Natal, and Niterói) will engage multiple stakeholders in using our evidence to inform the implementation of municipal climate adaptation towards equitable health outcomes for disadvantaged communities. We will co-create an innovative digital platform and a methodological toolkit that can be adapted to support other cities in Brazil and Latin America, strengthening the capacity for enabling equitable, transformative climate-health adaptation.

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Researchers

Cláudio Santos e Silva (EPMC Awardee)Eduardo Diniz (EPMC Awardee)Gervasio Santos (EPMC Awardee)Járvis Campos (EPMC Awardee)Maria Yury Ichihara (EPMC Awardee)Paulo Nascimento Neto (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

ADAPTA-Mossoro: Collaborative Asset Planning for Urban Climate Change Adaptation in Mossoro, Brazil
The CIDACS Climate and Environmental Platform (CIDACS-Clima): A data resource to study climate and health
Place-Based Engagement Strategies with Local Communities for better Climate Resilience Governance in Disaster Situations
GCRF_NF345 Tackling Covid19 through co-production: engaging Brazilian vulnerable communities in facing the consequences of pandemics
Healthy urban living and ageing in place: Physical activity, built environment and knowledge exchange in brazilian cities (hulap)

Original classification

Climate Impacts Award

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