2025 – Advancing the diagnosis of poverty traps: A regional application of Emergy Theory and the 5SEnSU model in Brazil

Resumo (EN)

Poverty traps constitute significant impediments to sustainable development, particularly in structurally unequal nations such as Brazil. This study presents an Emergy-based Five-Sector Sustainability (5SEnSU) Model as an innovative framework for diagnosing regional poverty traps, integrating environmental, economic, and social dimensions within a systemic, biophysical perspective. Unlike traditional models, the proposed approach quantifies poverty-related imbalances through a unified metric—solar emergy joules (sej)—facilitating direct comparison across sectors and regions. Application of this model to Brazil’s macro-regions revealed substantial spatial heterogeneities: the North Region exhibited the highest Intensity of Poverty Traps (IPT = 23.0), primarily driven by external trade dependency and labor undervaluation, while the Southeast faced severe ecological degradation risks (IPT = 7.1). In contrast, the Northeast and Midwest demonstrated entrenched labor inequalities (IPT = 4.8 and 5.7, respectively), despite more favorable economic indicators. Nationally, key challenges were underscored by an unfavorable Labor Income Ratio (LIR = 2.7) and Export Money Ratio (XMR > 1), indicating widespread undervaluation of labor and inequitable external trade. Nevertheless, strengths such as a relatively favorable Money Import Ratio (MIR < 1) and significant carbon neutrality potential were identified. The emergybased 5SEnSU Model provides a robust diagnostic tool for policymakers, offering high-resolution insights to
design targeted interventions for resource redistribution, ecosystem restoration, and social inclusion. This work advances sustainability assessment by offering a replicable and quantitatively consistent method focused on the biophysical perspective of poverty traps within socioecological systems. The model does not directly address social and political power relations, but it provides complementary insights grounded in resource flows.