Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed MSW (ODD-MSW) via crowdsourced UAV imagery, trained and evaluated across 29 regions in 10 Sub-Saharan African countries, encompassing diverse environmental contexts. A deep learning model trained on manually annotated image tiles achieved an overall accuracy of 92.87% (F1 = 0.927) in detecting ODD-MSW across all study regions. Applied without any site-specific retraining to 25 independent OpenAerialMap scenes from 21 cities in 19 countries on five continents, the frozen model retained an F1 score of 0.867, providing direct evidence of transferability beyond the training domain. Predicted distributions reveal heterogeneous accumulation patterns, ranging from localized hotspots - often along waterways, where waste can exacerbate flood and public health risks - to more dispersed litter across urban areas. Waste accumulation is most strongly associated with population density (Spearman rho = 0.674, p < 0.001 ) and indicators of lack of local infrastructure access ( rho = 0.700, p < 0.001 ), whereas its relationship with broader measures of regional development is weaker and not statistically significant ( rho = 0.215, p = 0.26 ), highlighting the importance of fine-scale data for understanding localized waste dynamics. By releasing the model, this study provides a ready-to-use tool for UAV imagery collected by municipalities and local mapping communities, enabling ODD-MSW monitoring without extensive technical expertise. This approach empowers local practitioners to convert UAV imagery into actionable insights, supporting targeted interventions and improved municipal solid waste management across Sub-Saharan Africa.
Informal transit serves the majority of urban travelers in many low- and middle-income countries, yet systematic data on safety and transit quality remain limited. Big data sources like telematics provide granular information on a limited set of indicators, while traditional passenger surveys offer comprehensive insights but are expensive and administered infrequently. We test lightweight, phone-based surveys to elicit volunteered passenger feedback across 77 Kenyan matatus (minibuses), combined with telematics data from 25 vehicles. Passengers rated driving speed, perceived safety, and provided open-ended comments. We assess whether such feedback provides information beyond existing sources like telematics and how it should be interpreted given its volunteered nature and subjective passenger perceptions. Passenger reports of high speeds align closely with telematics-measured speed, indicating crowdsourced feedback accurately captures observable driving behavior despite self-selection. In contrast, perceived safety shows weak association with telematics indicators; joint analysis of ratings and comments highlights the subjective nature of safety perceptions. Open-ended comments add value by revealing context telematics miss: distracted driving, alcohol use, and unsafe pickup practices. Though comments primarily reflect the survey’s safety emphasis, some address COVID-19 compliance, service quality, and vehicle condition, suggesting broader informational value. Large language models accurately classify comments in zero-shot settings, enabling scalable analysis. While challenges remain in consistently eliciting feedback, results demonstrate that short, low-burden phone-based surveys can generate actionable information on transit safety and quality.
Uncertain rainfall onset complicates maize establishment in eastern Kenya, where both grain and stover contribute to farm livelihoods. We evaluated pre-rain dry-soil sowing, rainfall-triggered wet sowing and delayed sowing over five seasons at water-limited Machang'a and wetter Kiamaogo. Sowing treatment was assigned to split-plot main plots and locally predominant maize varieties to subplots, with three replications and repeated observation of the same physical plots. Site-specific mixed models quantified grain yield, stover yield, harvest index, establishment, productive-cob formation and grain-yield failure, while sowing-specific rainfall exposure was reconstructed for all ten site-season environments. At Machang'a, dry sowing increased grain yield by 51-86% relative to wet or delayed sowing in the 2009 short rains and 2010 long rains. In three other seasons, 94.4-100% of plots failed to produce grain despite established plants and measurable stover, showing that vegetative biomass could persist under conditions that prevented grain production. At Kiamaogo, sowing responses varied among variety-season combinations. Delayed sowing increased stover by 1.96-3.69 Mg ha-1 in six of 15 combinations and reduced harvest index by 0.086-0.125 in four of 15 combinations, indicating shifts in biomass allocation rather than a consistent grain-yield advantage. Across sites, treatment responses reflected strong interactions among environment, season and variety. The study shows that sowing-time strategies can substantially alter both grain production and residue supply in rainfed maize systems and provide a field basis for developing climate-informed sowing strategies under variable rainfall.
This article proposes a theoretical framework that systematically integrates human rights and economic analysis, addressing gaps in previous attempts linking theory and policy. Rather than developing a new theoretical approach, we work within a standard social planner setting in which a social welfare function embeds both fiscal constraints and rights-based valuations, ensuring Pareto-efficient outcomes while guaranteeing minimum rights. Conceptually, we link principles, policy instruments, and outcomes, highlighting shared structures across disciplines and enabling policy design that balances efficiency, equity, and transparency. Empirically, a case study of universal social protection strategies in South Africa illustrates how economic, human rights, and integrated approaches produce different priorities: targeted basic income maximizes fiscal efficiency, the universal transfer maximizes equality and rights, and the integrated perspective favors the expansion of the existing system as the second-most rights-consistent and most cost efficient option. This fully integrated framework demonstrates that human rights economics is not a slogan, nor merely the economics of human rights or rights within economics, but a true integration of both—practically applicable, context-sensitive, and offering a pathway to fiscally sustainable, socially inclusive, and administratively feasible policies.
