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<p>Climate change exacerbates poverty dynamics in many agrarian economies, particularly in Sub-Saharan Africa, where climate-sensitive livelihoods are central to rural development systems. This study explores the spatial interaction between climate variability, infrastructure development, and poverty dynamics in Tanzania, employing an integrated geospatial and spatial econometric framework. The analysis combines two decades of infrastructure and socio-economic data (2002–2022), recent climatic observations (2018–2023), and satellite-derived vegetation indicators (2017–2021) to assess regional disparities and environmental vulnerability. Three spatial econometric models—Spatial Autoregressive (SAR), Geographically Weighted Regression (GWR), and the Spatial Durbin Model (SDM)—are applied to capture both spatial dependence and spatial spillover effects across 26 administrative regions and selected representative study areas. Model comparison reveals that the SDM offers the strongest explanatory performance (AIC = −362.05), highlighting significant spatial interactions between climatic stressors, infrastructure access, and poverty outcomes. The results show strong spatial clustering of poverty and demonstrate that environmental shocks, particularly rainfall variability and deforestation, are positively associated with poverty incidence. Meanwhile, infrastructure access and education contribute to poverty reduction. Further, the findings indicate that infrastructure investments generate measurable spatial spillover effects, where improvements in one region can promote poverty reduction in neighboring areas. While vegetation indicators such as NDVI and SIF provide key environmental insights, their direct statistical association with poverty was comparatively weak, suggesting that socio-economic and institutional factors mediate the impacts of climate on livelihoods. These results underscore the importance of spatially targeted, climate-resilient infrastructure investments and region-specific development strategies. The study contributes a transferable analytical framework that integrates remote sensing, climate data, and spatial econometric modeling to support evidence-based policy design for climate adaptation and poverty reduction in developing economies.</p>

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Climate change and poverty dynamics in Tanzania: geospatial analysis of the interaction between infrastructure, climate impact, and regional disparities

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Kizito August Ngowi,
  • Min Ji,
  • Hanyu Ji,
  • Zequn Liu,
  • Pengfei Song

摘要

Climate change exacerbates poverty dynamics in many agrarian economies, particularly in Sub-Saharan Africa, where climate-sensitive livelihoods are central to rural development systems. This study explores the spatial interaction between climate variability, infrastructure development, and poverty dynamics in Tanzania, employing an integrated geospatial and spatial econometric framework. The analysis combines two decades of infrastructure and socio-economic data (2002–2022), recent climatic observations (2018–2023), and satellite-derived vegetation indicators (2017–2021) to assess regional disparities and environmental vulnerability. Three spatial econometric models—Spatial Autoregressive (SAR), Geographically Weighted Regression (GWR), and the Spatial Durbin Model (SDM)—are applied to capture both spatial dependence and spatial spillover effects across 26 administrative regions and selected representative study areas. Model comparison reveals that the SDM offers the strongest explanatory performance (AIC = −362.05), highlighting significant spatial interactions between climatic stressors, infrastructure access, and poverty outcomes. The results show strong spatial clustering of poverty and demonstrate that environmental shocks, particularly rainfall variability and deforestation, are positively associated with poverty incidence. Meanwhile, infrastructure access and education contribute to poverty reduction. Further, the findings indicate that infrastructure investments generate measurable spatial spillover effects, where improvements in one region can promote poverty reduction in neighboring areas. While vegetation indicators such as NDVI and SIF provide key environmental insights, their direct statistical association with poverty was comparatively weak, suggesting that socio-economic and institutional factors mediate the impacts of climate on livelihoods. These results underscore the importance of spatially targeted, climate-resilient infrastructure investments and region-specific development strategies. The study contributes a transferable analytical framework that integrates remote sensing, climate data, and spatial econometric modeling to support evidence-based policy design for climate adaptation and poverty reduction in developing economies.