Conventional infrastructure appraisal in Africa prioritizes short-term economic performance while insufficiently accounting for the environmental conditions that govern long-term sustainability, spatial equity, and development resilience. To address this gap, this study develops an explicitly SDG-oriented spatial–ecological framework to examine how environmental quality conditions the economic returns of large-scale infrastructure investments under corridor-based development. The primary objective is to quantify infrastructure–environment complementarity and identify ecological thresholds regulating spatial spillovers and investment effectiveness along Tanzania’s Belt and Road Initiative (BRI) corridors. High-resolution remote sensing and spatially explicit socioeconomic data for 2012–2023 are integrated within a spatial econometric design. A Spatial Durbin Model (SDM) incorporating the Normalized Difference Vegetation Index (NDVI) is estimated to capture non-linear interaction effects, with economic activity proxied by Night-Time Light (NTL) intensity across 2680 corridor grid cells. The results identify a statistically robust ecological threshold at NDVI = −0.8σ, beyond which infrastructure investments shift from low to high economic effectiveness. A strong positive infrastructure–environment interaction (β = 6.44, p < 0.001) indicates that environmental quality functions as a productive modulating factor rather than a passive constraint. Spatial classification shows that 63% of corridor areas are investment-ready, while 15% require ecological restoration prior to effective infrastructure deployment. Although institutional quality and long-term post-construction dynamics are not explicitly modeled, the framework provides a replicable and policy-relevant decision-support tool, offering actionable guidance for aligning corridor development with SDGs 9, 11, and 13 and advancing sustainable infrastructure planning in the Global South.

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.

Conventional infrastructure evaluation in Africa emphasizes economic performance while overlooking environmental conditions that govern long-term sustainability. This study develops a spatial–ecological framework to quantify how environmental quality modifies the developmental returns of infrastructure investments along Tanzania's Belt and Road Initiative (BRI) corridors. A spatial econometric approach integrating remote sensing and socioeconomic indicators captures infrastructure–environment interactions. Using a Spatial Durbin Model (SDM) with the Normalized Difference Vegetation Index (NDVI), the analysis identifies environmental thresholds and estimates complementarity effects on economic activity, measured through Night-Time Lights (NTL). Results reveal a critical threshold at NDVI = –0.8σ, delineating zones where investments generate high returns from areas where environmental degradation limits effectiveness. A significant NTL–NDVI interaction coefficient (6.44, p < 0.001) indicates that higher environmental quality substantially enhances infrastructure-driven growth. Spatial classification shows 63% of the corridor as high-priority zones with optimal returns and positive spillovers, while 15% requires ecological restoration prior to investment. The model demonstrates high explanatory power (pseudo R² = 0.882). The findings provide a replicable decision-support tool for optimizing investment allocation, aligning infrastructure strategies with SDGs 9, 11, and 13.