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<p>The Amazon rainforest, a critical global carbon sink and biodiversity haven, faces accelerating deforestation driven by agricultural expansion and infrastructure development. This study integrates exploratory spatial data analysis (ESDA) with spatial panel models to quantify direct and indirect effects of key drivers across 753 municipalities (2008–2019). Results reveal that a 1% increase in pasture area induces 0.33% local deforestation and 0.34% in neighboring areas, while soy expansion exhibits weaker yet significant spillovers (0.058% local, 0.060% neighboring). Heavy machinery (crawler tractors) exacerbates forest loss (0.29% total effect per 1% increase in use), contrasting with moderate mechanization (wheeled tractors), which reduces deforestation (− 0.066% local, − 0.068% neighboring). Additionally, higher population density is associated with lower deforestation rates, both locally (− 0.4081) and in adjacent areas (− 0.4193), suggesting a protective effect linked to urbanization and economic diversification. Cluster analysis identifies the shift of deforestation hotspots from Mato Grosso to Amazonas, potentially linked to infrastructural expansion and regulatory gaps. We conclude that curbing Amazonian deforestation requires spatially targeted policies: (1) stringent regulation of heavy machinery in primary forests, (2) economic-ecological zoning to redirect agricultural expansion to degraded Cerrado biome frontiers, and (3) fiscal incentives for sustainable intensification via crop-livestock-forest integration (CLFI). These measures, combined with real-time monitoring, could mitigate emissions while bolstering Brazil’s agricultural competitiveness.</p>

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Deforestation in the Brazilian Amazon: an analysis of spatial interactions between economic and demographic factors

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • Luan Marca,
  • Augusto Mussi Alvim

摘要

The Amazon rainforest, a critical global carbon sink and biodiversity haven, faces accelerating deforestation driven by agricultural expansion and infrastructure development. This study integrates exploratory spatial data analysis (ESDA) with spatial panel models to quantify direct and indirect effects of key drivers across 753 municipalities (2008–2019). Results reveal that a 1% increase in pasture area induces 0.33% local deforestation and 0.34% in neighboring areas, while soy expansion exhibits weaker yet significant spillovers (0.058% local, 0.060% neighboring). Heavy machinery (crawler tractors) exacerbates forest loss (0.29% total effect per 1% increase in use), contrasting with moderate mechanization (wheeled tractors), which reduces deforestation (− 0.066% local, − 0.068% neighboring). Additionally, higher population density is associated with lower deforestation rates, both locally (− 0.4081) and in adjacent areas (− 0.4193), suggesting a protective effect linked to urbanization and economic diversification. Cluster analysis identifies the shift of deforestation hotspots from Mato Grosso to Amazonas, potentially linked to infrastructural expansion and regulatory gaps. We conclude that curbing Amazonian deforestation requires spatially targeted policies: (1) stringent regulation of heavy machinery in primary forests, (2) economic-ecological zoning to redirect agricultural expansion to degraded Cerrado biome frontiers, and (3) fiscal incentives for sustainable intensification via crop-livestock-forest integration (CLFI). These measures, combined with real-time monitoring, could mitigate emissions while bolstering Brazil’s agricultural competitiveness.