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CONDJUST

Financiado

Conservation Data Justice

CONDJUST will create a new research field, Conservation Data Justice, that bridges three distinct areas of enquiry: conservation prioritisation, political ecology and Data Justice. The former uses data which risk marginalising rur... ver más

30/11/2027

UAB

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITAT AUTÒNOMA DE BARCELONA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 1265

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-07-18

ERC-2021-ADG: ERC ADVANCED GRANTS Scope:Objectives

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Cerrada hace 3 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UAB

2.49M€ | Lider

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Duración del proyecto: 64 meses Fecha Inicio: 2022-07-18
Fecha Fin: 2027-11-30

Líder del proyecto

UNIVERSITAT AUTÒNOMA DE BARCELONA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 1265

Presupuesto del proyecto 2M€

Descripción del proyecto CONDJUST will create a new research field, Conservation Data Justice, that bridges three distinct areas of enquiry: conservation prioritisation, political ecology and Data Justice. The former uses data which risk marginalising rural peoples. The latter does not yet examine conservation data. Meanwhile political ecologists do not yet consider Data Justice approaches when tackling conservation prioritisation. CONDJUST will interrogate conservation data and models, and explore the epistemic communities producing them, to develop new theories of socially just, data-driven conservation. It will challenge the colonising tendencies of prioritisation work and seek decolonising alternatives. CONDJUST is timely because ambitious new global targets seek to safeguard 30% of the planet for conservation by 2030 (and more afterwards). These plans pose risks for rural people because the data and modelling they use can contain diverse forms of bias, exclusion and omission. These risks will grow as more social media data are used in conservation prioritisation. We need insights from Data Justice to understand these dangers, and how they might be counter-acted. This project has four objectives, each with a corresponding work package. These are: 1. Systematically examine the sources of bias and distortion in conservation data used in global prioritisation work. 2. Use Data Justice thinking in new analyses of biodiversity conservation, and increase our understanding of socially just conservation prioritisation. 3. Critically explore the construction of different epistemic communities in conservation prioritisation, and political ecology, to understand what inhibits and enhances learning between them. 4. Examine how policies responding to prioritisation are shaped by, or resist, the new measures proposed. These work packages will be pursued by an interdisciplinary team led by the PI and composed of three post-doctoral researchers, two PhDs, an administrator and an advisory board.

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