Socio-Economic Impact of the Brumadinho Landslide: A Hybrid MCDM-ML Approach

Aline Menezes (), Peter Wanke, Jorge Antunes, Roberto Pimenta, Irineu Frare, André Andrade, Wallace Oliveira and Antonio Mamede
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Aline Menezes: Fundação Getulio Vargas, Brazilian School of Public and Business Administration, Edifício Roberto Campos, Jornalista Orlando Dantas Street, 30—Botafogo, Rio de Janeiro CEP 22231-010, Brazil
Peter Wanke: COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme, 355, Rio de Janeiro CEP 21949-900, Brazil
Jorge Antunes: COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme, 355, Rio de Janeiro CEP 21949-900, Brazil
Roberto Pimenta: Fundação Getulio Vargas, Brazilian School of Public and Business Administration, Edifício Roberto Campos, Jornalista Orlando Dantas Street, 30—Botafogo, Rio de Janeiro CEP 22231-010, Brazil
Irineu Frare: Fundação Getulio Vargas, Brazilian School of Public and Business Administration, Edifício Roberto Campos, Jornalista Orlando Dantas Street, 30—Botafogo, Rio de Janeiro CEP 22231-010, Brazil
André Andrade: Instituto Brasileiro de Ensino, Desenvolvimento e Pesquisa (IDP), SGAS Quadra 607, Módulo 49, Via L2 Sul, Brasilia CEP 70200-670, Brazil
Wallace Oliveira: Fundação Getulio Vargas, Brazilian School of Public and Business Administration, Edifício Roberto Campos, Jornalista Orlando Dantas Street, 30—Botafogo, Rio de Janeiro CEP 22231-010, Brazil
Antonio Mamede: Fundação Getulio Vargas, Brazilian School of Public and Business Administration, Edifício Roberto Campos, Jornalista Orlando Dantas Street, 30—Botafogo, Rio de Janeiro CEP 22231-010, Brazil

Sustainability, 2024, vol. 16, issue 18, 1-32

Abstract: Most humanitarian logistics research focuses on immediate response efforts, leaving a gap regarding the long-term socio-economic impacts of post-tragedy financial aid. Our research investigates the Brumadinho landslide tragedy in Minas Gerais, Brazil, analyzing the effectiveness of financial aid in fostering sustainable recovery and resilience in affected communities. We employ a hybrid multi-criteria decision-making (MCDM) and machine learning model to quantitatively assess the socio-economic impact on affected municipalities. Using social responsibility indices from official state government datasets and data from the PTR transparency initiative—a financial aid program determined by the Judicial Agreement for Full Reparation and operationalized by FGV Projetos, which allocates USD 840 million for the reparation of damages, negative impacts, and socio-environmental and socio-economic losses—our analysis covers all municipalities in Minas Gerais over 14 years (10 years before and 4 years after the tragedy). We determine a final socio-economic performance score using the max entropy hierarchical index (MEHI). Additionally, we assess the efficiency of the PTR financial aid in affected municipalities through examining MEHI changes before and after the transfers using a difference-in-differences (DiD) approach. Our findings reveal both direct and indirect impacts of the tragedy, the efficacy of financial aid distribution, and the interplay of various socio-economic factors influencing each municipality’s financial health. We propose policy recommendations for targeted and sustainable support for regions still coping with the long-term repercussions of the Brumadinho landslide.

Keywords: Brumadinho landslide tragedy; socio-economic impact; multi-criteria decision making (MCDM); financial aids analysis; machine learning; policy decision support (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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