Abstract

Sub-Saharan African countries consistently rank low on human development indicators despite decades of international aid and domestic investment. To address these challenges, governments across the region are allocating substantial portions of their national budgets to infrastructure expansion, viewing it as a catalyst for accelerated socio-economic development. However, given severely constrained budgets, these nations often face trade-offs between infrastructure development and investment in other important sectors such as healthcare and education. This highlights the need for infrastructure investments to deliver their anticipated benefits. This dissertation examines the relationship between infrastructure development and socio-economic development in Sub-Saharan Africa, with a particular focus on electricity and paved road access as enablers of development. The research specifically investigates how the expansion of these infrastructure influence two important aspects of development: (1) the adoption of irrigation in agricultural systems and (2) patterns of land use and land cover change. A key methodological contribution of this work is its approach to addressing the region’s data accessibility challenges. Previous research examining infrastructure’s role in development has been constrained by this data scarcity, forcing studies to either focus narrowly on individual villages, communities, or specific projects, or to rely on aggregated national-level statistics that conceal local variations. This dissertation bridges this gap by leveraging remote sensing and machine learning techniques to develop medium- to high-resolution, multitemporal maps of infrastructure access. This multi-scale approach enables assessments across household, community, district, and national scales. The resulting datasets enable the tracking of spatiotemporal changes in electricity and all-weather road infrastructure coverage, providing an understanding of when a particular community got access to the infrastructure. Beyond its research applications in this dissertation, this methodological framework offers a cost-effective, replicable approach for monitoring infrastructure development in data-scarce environments that can be adapted by researchers and policymakers. The findings reveal that electricity and paved road access can be mapped reliably with freely available nighttime light and surface reflectance satellite images, achieving balanced accuracies as high as 86% and 95% respectively. We found a weak correlation between infrastructure access and irrigation adoption in farmlands in northern Ethiopia. Instead, irrigation efficiency measures and crop types emerged as stronger determinants of irrigation adoption. Lastly, we found that access to electricity significantly increased cropland at the expense of rangeland and forest in Rwanda. The dissertation is structured into five main interconnected chapters, each addressing specific research questions while contributing to a deeper understanding of the relationship between infrastructure and development. The results offer insights for policymakers seeking to maximize returns on infrastructure investments, particularly by developing approaches for targeting interventions to improve agricultural processes.

Library of Congress Subject Headings

Infrastructure (Economics)--Developing countries--Remote sensing; Infrastructure (Economics)--Africa, Sub-Saharan--Remote sensing; Land use--Africa, Sub-Saharan--Remote sensing

Publication Date

5-2025

Document Type

Dissertation

Student Type

Graduate

Degree Name

Sustainability (Ph.D.)

Department, Program, or Center

Sustainability, Department of

College

Golisano Institute for Sustainability

Advisor

Nathan Williams

Advisor/Committee Member

Amitrajeet A. Batabyal

Advisor/Committee Member

Jay Taneja

Campus

RIT – Main Campus

Plan Codes

IMGS-PHD

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