'Containerized' infrastructure solutions have the potential to power the needs of under-resourced communities at the Food/Water/Health nexus, particularly for off-grid, underserved, or remote populations. Drawing from a uniquely large sample of identical containerized solar photovoltaic energy deployments in Rwanda ("Boxes" from OffGridBox), we estimate the potential reach and impact that a massive scale-up of such a flexible, modular approach could entail for fast-growing yet resource-constrained communities around the world. This analysis combines modeled and in-the-field data to consider three use cases (water, food, and health), across optimistic and realistic scenarios. We estimate pollution externalities and compare this solution to incumbent technologies, incorporating uncertainties. In our optimistic scenarios, this containerized solution could provide for either 2083 individuals' daily drinking water needs, 1674 individuals' daily milk consumption, or 100% of a health clinic's energy demand. We then quantify the added benefit of providing these loads using solar energy instead of the incumbent non-renewable diesel generator in terms of cost and air quality, and incorporate the sensitivity of results to uncertainties using Monte Carlo Analysis simulations. For water purification and milk chilling uses, we find that solar has a lower lifecycle cost of energy; 0.39 and 0.38 USD/kWh respectively compared to 0.63 [range: 0.52, 0.80] USD/kWh and 0.59 [range: 0.48, 0.76] USD/kWh for diesel. Additionally, solar has lower cost variability and avoids pollutant and greenhouse emissions (e.g., 85,799.08 kgs [range: 66,830.49, 115,491.30] of carbon dioxide over the 20-year system lifetime). Moving beyond the standard energy modeling of previous literature, this analysis is uniquely able to inform future sustainable energy systems at the Food/Water/Health nexus.
A growing body of evidence suggests that digital literacy is an important barrier constraining adoption and use of Internet and digital technologies in the developing world. By enabling people to effectively find valuable information online, digital literacy can play a crucial role in expanding economic opportunities, thereby leading to human development and poverty reduction. Unfortunately, there is a dearth of validated survey measures for capturing digital literacy of populations who have limited prior exposure to technology. We present a novel approach for measuring digital literacy of low literacy and new Internet users, an important segment of users in developing countries. Using a sample of 143 social media users in Pakistan, which includes a significant fraction of low literacy individuals, we measure digital literacy by observing the effectiveness of participants in completing a series of tasks and by recording a set of self-reported survey responses. We then use machine learning methods (e.g., Random Forest) to identify a parsimonious set of survey questions that are most predictive of ground truth digital literacy established through participant observation. Our approach is easily scalable in low-resource settings and can aid in tracking digital literacy as well as designing interventions and policies tailored to users with different levels of digital literacy.
Global banks play intermediary and even direct roles in achievement of the UN Sustainable Development Goals (SDGs). However, developmental progress is complex to measure due to siloed, varied and even non-transparent, ambiguous, non-standardized ways of calibration and reporting. This research explores public disclosures (sustainability, CSR, and annual reports, press releases, website, and others) of fifty banks across nine geo-segments over five calendar years (2018–2023). Inductive methods, co-word assessments, content analysis are deployed to develop qualitative commentaries indicating geo-specific performances of the banks. The research findings indicate goal-specific attribution and discovery of motives and initiatives. This validates how motives in embracing the SDGs vary and relate to achievement of – (A) core business objectives; (B) support and financing of other industries, organizations, and governments in sustainable initiatives; and (C) corporate citizenship, altruistic and ethical considerations. Methodological approaches to calibrate findings across seventeen SDGs help identify benchmark practices, understand complementary actions, and required focus for banks and other industries. This is relevant to geo-specific sustainability challenges, trade-offs, and requirements. The findings can guide public policies and regulations, empower banks and other institutions to accelerate awareness and evaluate effectiveness towards sustainable developments.
High quality user requirements are positively correlated with successful design outcomes, but engaging stakeholders within low-income contexts can present financial and time-related challenges to product developers from non-local industrial and academic institutions with limited knowledge of the context. Existing literature provides guidance for engaging stakeholders during the early stages of product design in high-income country contexts, but few studies have examined the effectiveness of these methods in low-income country contexts. This study evaluated three user requirements elicitation and prioritization methods including open-ended, clustering, and discrete choice. Ghanaian healthcare delivery stakeholders with varying types of expertise, years of experience, and from various types of healthcare facilities were recruited to allow for diversity of responses. Participants included physicians (n = 10), nurses/midwives (n = 16), biomedical technicians (n = 14), and public health officers (n = 7). A hypothetical mechanical device for managing and treating postpartum hemorrhage was chosen to characterize each method's ability to elicit and prioritize user requirements. The open-ended method captured general requirements of a design concept, yet resulted in predominantly generic requirements. The results from the open-ended method were used to inform the clustering and discrete choice methods. The clustering and discrete choice methods were useful for inferring in-depth user requirements and eliciting stakeholder priorities. The clustering method revealed that usability and affordability were high-priority requirements among all four stakeholder groups. An individual difference scaling analysis was performed using the clustering method outcomes, which indirectly identified ease-of-use, availability, and effectiveness as the priority user requirements categories. Stakeholders ranked ease-of-use as the highest-priority user requirement, followed by performance, cost, and place-of-origin requirements, using the discrete choice method. Given the significance of the ease-of-use requirement, an analytical framework based on sub-requirements was developed for quantifying stakeholder needs. Lastly, the relative merits of the three elicitation approaches and their implications for use with different stakeholder groups were examined.
User surveys alone do not accurately measure the actual use of improved cookstoves in the field. We present the results of comparing survey-reported and sensor-recorded cooking events, or durations of use, of improved cookstoves in two monitoring studies, in rural Maharashtra, India. The first was a free trial of the Berkeley-India Stove (BIS) provided to 159 households where we monitored cookstove usage for an average of 10 days (SD = 4.5) (termed “free-trial study”). In the second study, we monitored 91 households' usage of the BIS for an average of 468 days (SD = 153) after they purchased it at a subsidized price of about one third of the households' monthly income (termed “post-purchase study”). The studies lasted from February 2019 to March 2021. We found that 34% of households (n = 88) over-reported BIS usage in the free-trial study and 46% and 28% of households over-reported BIS usage in the first (n = 75) and second (n = 69) surveys of the post-purchase study, respectively. The average over-reporting in both studies decreased when households were asked about their usage in a binary question format, but this method provided less granularity. Notably, in the post-purchase study, sensors showed that most households dis-adopted the cookstove even though they purchased it with their own money. Surveys failed to detect the long-term declining trend in cookstove usage. In fact, surveys indicated that cookstoves’ adoption remained unchanged during the study. Households tended to report nominal responses for use such as 0, 7, or 14 cooking events per week (corresponding to 0, 1, or 2 times per day), indicating the difficulty of recalling exact days of use in a week. Additionally, we found that surveys may also provide misleading qualitative findings on user-reported cookstove benefits without the support of sensor data, causing us to overestimate impact. Some households with zero sensor-recorded usage reported cookstove fuel savings, quick cooking, and less smoke. These findings suggest that surveys may be unreliable or insufficient to provide solid foundational data for subsidies based on the ability of a stove to reduce damage to health or reduce emissions in real-world implementations.
Housing in remote Australia's Indigenous communities has remained an unsolved challenge after many years of effort. Factors to be considered in remote housing have been researched broadly but rarely taking a holistic design point of view. This requires the inclusion of all factors that affect the success of a design project (eg resources, physical and social environment and processes). This study is a response to the question: which factors should engineers consider in their remote Indigenous communities building projects? In this study, these factors were extracted from a literature review. Special emphasis was put on resources related to the Northern Territory remote housing. Ten key factors which thus form goals for the establishment of any project were found after filtering and organising the findings from different publications. Experts in remote housing from the government, private sector and academia were then interviewed to gather their opinion on the solutions with respect to each factor. The results of this study will provide designers with a practical to-do list for planning and implementing their projects in remote communities. Further, the results could be used by decision-makers in developing policies.
Household air pollution from biomass cooking is the most significant environmental health risk in the Global South. Interventions to address this risk mostly promote less-polluting stoves and clean fuels, but their diffusion has proven difficult. This paper assesses the potentially complementary role of ventilation in reducing household air pollution. Using state-of-the-art measurements of kitchen concentrations of particulate matter (PM2.5) and personal exposure from around 250 households in rural Senegal, we show that higher ventilation is strongly related to lower kitchen concentration, though absolute pollution levels remain high. This association is robust to controlling for a comprehensive set of potential confounders. Yet, these reductions in concentration do not clearly translate into lower pollution exposure among cooks, probably due to avoidance behaviour. Our findings indicate that ventilation interventions may reduce smoke concentration nearly as much as many real-world clean stove interventions and can hence be an important complement to existing strategies. However, a more holistic approach is needed in order to reduce personal exposure in line with international health standards.
Road traffic jams are a major problem in most cities of the world, resulting in massive delays, increased fuel wastage, and monetary and productivity losses. Unlike conventional computer networks, which experience congestion due to excessive traffic, road transportation networks can experience traffic jams over prolonged periods due to traffic bursts over short time scales that push the traffic density beyond a threshold jam density. We observe that the emergence of such jams can happen over a very short duration, hence we term them as sudden traffic jams. We provide a formalism for understanding the phenomena of sudden traffic jams and show evidence of its existence using loop detector data from New York City. Further, we show the signature of sudden jams when observed at hourly resolution. We also provide a method to compute the traffic curve in a situation where we do not have access to fine-grained flow and density information. With this method, using only hourly speed data from Uber, we compute traffic curves for the road segments in Nairobi, São Paulo, and New York City, which is, by our knowledge, the first attempt to do so for signalized road networks. Running our analysis on the Uber movement speed data for the three cities, we show numerous instances of jams that last several hours, and sometimes as long as 2–3 days. Empirically, we find that Nairobi experiences 3x the mean jam time per road segment as compared to São Paulo and New York City. Based on key development metrics, we find that the ratio of traffic load per road segment for São Paulo, New York City, and Nairobi is approximately 1:2:3. We propose that chaotic driving patterns and traffic mismanagement in the developing world cities lead to tighter traffic curves, more intense jams and overall lower road capacity utilization, which explains the observed data. We posit that the problem of traffic congestion in developing countries cannot be solved entirely by building new infrastructure, but also requires smart management of existing road infrastructure.
Although some progress has been made in recent years, ensuring universal access to electricity remains a major challenge in many countries in sub-Saharan Africa, particularly in rural areas. In light of this challenge, solar photovoltaic (PV) mini-grid systems have emerged as a promising solution for off-grid electrification. However, little is known about their actual performance and reliability when used in real-world applications. Using real-time monitored data and IEC's evaluation standard, this paper examines the performance and reliability of a 375 kWp off-grid PV mini-grid system installed in a remote small town in Ethiopia. The findings showed that the mini-grid produced 1182 kWh/day of electricity compared to the estimated generation of 2214 kWh/day, a difference of 1032 kWh/day (46.6% less). In contrast, 87% of the average daily electricity generated was delivered to the load. The discrepancies can be attributed to average PV capture losses of 2.75 kWh/kWp/day and system losses of 0.40 kWh/kWp/day. The performance evaluation results revealed that the mini-grid system is performing poorly, with average on-site module efficiency (ηpc), temperature corrected performance ratio (PRcorr), capacity factor (CF) and overall system efficiency (ηsys) of 9.85%, 42%, 13%, and 8.76%, respectively. It was found that the daily PV energy output could not meet the daily demand. As a result, the load is shed off from the power supply for 13 h a day; between 17:00 and 19:00 and again between 21:00 and 08:00. The study demonstrated that accurate demand assessment and robust system sizing, taking into account the impact of local weather conditions and prospective electricity demand growth is critical to ensure high performance and reliability of off-grid PV mini-grid systems.
Using a novel large-scale dataset that links thousands of expenditure programs to the Sustainable Development Goals for over a decade, we analyze the impact of public expenditure on more than 100 different development indicators. Contrary to the single-dimensional view of evaluating expenditure in terms of overall economic growth, we take a multi-dimensional approach. Then, we assess the effectiveness of three quantitative methods for capturing expenditure effects on development: (1) regression analysis, (2) machine learning techniques, and (3) agent computing. We find that, under the existing data and for this particular task, approaches (1) and (2) have difficulties disentangling sector-specific effects (i.e., target effects in the SDG semantics), which is consistent with results in previous empirical research. In contrast, by applying a micro-founded agent-computing model of policy prioritization, we can provide empirical evidence about potential impacts and bottlenecks across a high-dimensional policy space. Our findings suggest that, in the discussion of budgeting for SDGs, one should carefully evaluate the data available, the suitability of data-driven approaches, and consider alternative methods that are richer in terms of incorporating explicit causal mechanisms and scalable to a large set of indicators.
This paper analyzes users’ willingness to pay (WTP) for safe drinking water in a resource-poor region in West Bengal, India, with dangerously high groundwater arsenic concentrations. The study was carried out during the installation of an Electro Chemical Arsenic Remediation (ECAR) water treatment plant at the site. Using a contingent valuation method, the study elicits WTP, based on a stratified random sample of 1003 households. Arsenic is invisible and odorless, and related health risks have a prolonged latency period. As a result, awareness about arsenic and the perceived benefits of any arsenic remediation technology are low. In the study area, only 21% of respondents were aware of the danger of high arsenic concentrations in groundwater, however, a large number of the respondents reported irregularity of drinking water supply and a lack of quality assurance. About 64% of the respondents were willing to pay for ECAR-treated safe drinking water. Participants opting for home delivery were willing to pay more than those willing to collect water from the plant. The average WTP was high enough to recover the operational cost of the plant. Households with higher income and educational attainment, more awareness about arsenic contamination, and living in proximity to the plant were willing to pay more than the others. Regular interaction with the community, maintaining transparency, and interacting closely with the local administration created a sense of local ownership for the technology that was found to be crucial for the societal embedding of the technology.
The positive deviance approach in international development scales practices and strategies of positively-deviant individuals and groups: those who are able to achieve significantly better development outcomes than their peers despite having similar resources and challenges. This approach relies mainly on traditional data sources (e.g. surveys and interviews) for identifying those positive deviants and for discovering their successful solutions. The growing availability of non-traditional digital data (e.g. from remote sensing and mobile phones) relating to individuals, communities and spaces enables data innovation opportunities for positive deviance. Such datasets can identify deviance at geographic and temporal scales that were not possible before. But guidance is needed on how this new data can be employed in the positive deviance approach, and how it can be combined with more traditional data to gain deeper, more meaningful, and context-aware insights. This paper presents such guidance through a data-powered method that combines both traditional and non-traditional data to identify and understand positive deviance in new ways and domains. This method has been developed iteratively through six development projects covering five different domains – sustainable cattle ranching, agricultural productivity, rangeland management, research performance, crime control – with global and local development partners in six countries. The projects combine different types of non-traditional data with official statistics, administrative data and interviews. Here, we describe a structured method for data-powered positive deviance developed from the experience of these projects, and we reflect on lessons learned. We hope to encourage and guide greater use of this new method; enabling development practitioners to make more effective use of the non-traditional digital datasets that are increasingly available.
High-performance wastewater treatment technologies suited to the urban environment remain largely inaccessible to developing countries due to financial constraints. Instead, inadequate technologies are being used that adversely affect the quality of water resources and limit their sustainability. One high performing technology that offers possible solution is a packaged version of the integrated fixed-film activated sludge (IFAS) system consisting of a 20 m3 aerobic reactor and a 4.2 m3 settlement tank. The present work has investigated aspects of this typically-expensive solution that can be economized to improve its uptake in these countries. To achieve this a life cycle cost analysis (LCCA) was performed and potential savings identified. The results obtained show that the life cycle cost is $0.31/m3 and that costs primarily occurred at the construction stage (11.9%) and the operation and maintenance stage (88.1%) with negligible disposal costs. A reduction of up to 42.4% in construction costs were shown to be accessible by adopting other materials such as high-density polyethylene (HDPE) or to a lesser extent glass-fibre reinforced polymer (GFRP). The greatest single cost in the life cycle was found to be incurred by aeration (48.9%), requiring expenditure of $0.15/m3, however the use of intermittent aeration (IA) could reduce this further to $0.08/m3. Further work is suggested to investigate the broader sustainability of the different aeration strategies in light of these economic results.
Internet-connected sensor technologies have recently been used to monitor water service infrastructure in remote settings. In this study, 397 groundwater pumps were observed in Plateau State, Nigeria over 12 months in 2021. Two hundred of these sites were instrumented with remotely reporting electronic sensors, including 100 hand-pump sensors, 50 electrical pump sensors, and 50 cistern water-level sensors. Every two months, phone calls and site visits were used to collect a ground-truth of pump functionality: whether the pump was capable of delivering water, regardless of actual use. Our study examined: (1) What are the operating characteristics and trends of these different kinds of water pumps?; (2) Can water-point functionality be predicted with electronic sensors?; and (3) Does the instrumented water-point sample accurately represent average water-system functionality across the region? An automated classifier generated functional/non-functional diagnostics for instrumented pumps on a weekly basis. Classifier diagnostics were compared to ground-truth data, showing an overall accuracy of 91.7% (96.1% for hand-pumps, 63.9% for cisterns, and 93.2% for electrical boreholes), with high fleet-wide sensitivity in correctly identifying a functional pump (94.4%), but poor overall specificity in correctly identifying a non-functional, broken pump (25.0%). This discrepancy is attributable to the sensors’ difficulty in distinguishing between a broken pump and an unused pump. Varied patterns were seen in pump usage as a function of rainfall, with hand-pump use decreasing significantly, electrical pump usage decreasing to a lesser degree, and cistern use increasing in response to local rainfall. A comparison of the 200 instrumented to 197 non-instrumented sites showed statistically similar repair and failure rates. The high overall accuracy of the sensor–diagnostic system—and the demonstration that sensor-instrumented sample sites can represent population-level breakdown and repair frequencies—suggests this technology’s utility in supporting sample-based monitoring of overall water pump functionality and water volume delivery. However, the poor performance of the system in distinguishing between broken and unused pumps will limit its ability to trigger repair activities at individual pumps.
Modern energy services are essential to replace the extensive use of traditional biomass fuels driving several environmental, health, and social issues affecting the welfare of low-income citizens. Particularly, in Colombia, 11% of the households rely on inefficient firewood cooking systems, while two million people have either intermittent access or no access to electricity. This is particularly important in the department of Cordoba, where an average of 32% of the households relies on firewood for cooking, increasing to 66% of the households in rural areas. Furthermore, 20% of the rural population lack access to electricity. Therefore, this study aims at defining the biogas-based energy potential of the available agricultural and manure wastes in the department. To this end, governmental data is used to estimate the demand for firewood for cooking, the resulting GHG emissions, and the available agricultural and manure wastes. Overall, there are around 1.2 million t of agricultural wastes and 2.2 million t of manure yearly available in the department, representing an energy potential of 6687 TJ. Using 26% of the biogas-based energy potential identified suffices to support the 1334 TJ of biogas needed to replace cooking firewood and to supply the 390 TJ needed for household electricity generation. The use of biogas can reduce GHG emissions to 11% of the emissions resulting from cooking firewood. Polyethylene tubular digesters appear as the most indicated household technology, contrasted to geomembrane tubular digesters that need 2.4 times the initial capital investment while fixed dome digesters need 7.9 times the initial capital investment. Implementing household digesters to support the energy demand for cooking in the department, necessitates a minimum of 18 million USD, while the implementation of ‘digester + electric generator’ needs between 1.7 and 5.7 million USDdepending on the monthly demand of electricity of 60 kWh or 187 kWh.
We present a case study of successful uptake of a productive use of electricity (PUE) co-located at an off-grid clinic powered by OffGridBox in Rwanda. We develop a techno-economic analysis of the standardized, modular, and redeployable power supply technology, characterizing cost components, revenue considerations, and key challenges. We present a technical characterization of system utilization based on remote monitoring of electricity consumption, power reliability, and power quality at a PUE intervention site, estimating system reliability at 81% over the study period. Lastly, we characterize socio-economic costs and benefits from the productive user's perspective drawing on mixed-method interviews. We find that relatively low amounts of electricity consumption (10–30 kWh per month) command a high revealed willingness to pay (∼$3 per kWh) for the solar-powered displacement of diesel-based welding, significantly improving the unit economics of the deployed system. This analysis and data provides a resource model for the standardization of mini-grid hardware, performance and cost frameworks, and metrics to assess off-grid, under-grid and ultimately grid interactive distributed generation systems. These models are urgently needed to meet the UN Sustainable Development Goal SDG 7 commitment to achieve universal energy access by 2030.
We analyze the impact of public water infrastructure and water handling technologies on the water quality and water handling behavior of households in rural Benin using both quasi-experimental and experimental household-level panel data. We find that the installation of improved village-level water sources induces households to reduce water disinfection efforts at home, indicating that households perceive improved public water infrastructure as a substitute for improved water handling to obtain safe drinking water. Consequently, point-of-use drinking water quality does not change. A reduction of contamination with E. coli at points of use can only be achieved if interventions providing drinking water technologies at the water source are complemented by household-level interventions and efforts to teach households how to maintain good water quality.
The India Mark II/III hand pump system is now over 40 years old and has become a staple in how water is retrieved in many rural areas across India and Africa. With over 4 million installations, it is estimated that 10% of the world’s population is using one on a daily basis. One of the components that fails most frequent is the nitrile cup seal found in the under-ground pump cylinder, often causing the pump to become derelict for undesirably long periods of time. This paper’s focus is on this cup seal and how the wear and life of the seal can be predicted. A test rig was created to evaluate and test cup seals. From this, seal performance and the nitrile cup seal wear coefficient were found. A two-dimensional finite element model of the seal and the pump cylinder was then developed and used to calculate the pressure distribution and wear of the seal. Archard’s wear law was used to simulate the wear. From this, models for pump performance and seal wear prediction were created. This data was then used to explore possible design improvements to the cup seal. By combining this data with engineering tools such as ANSYS, MATLAB, and JMP, a new seal design was generated that has the potential to last 12% longer than the original cup seal.
Developers and users situated in low-resource settings are faced with unique contextual and infrastructure challenges when accessing and consuming cloud-based services. In low-resource settings, access to cloud services and platforms is usually characterized by low-end computing devices and often unreliable and slow mobile broadband Internet connections. In this paper, we discuss key challenges for developing for and accessing cloud services in resource constrained settings, namely, (1) Frequent Internet partitions and bandwidth constraints, (2) Data jurisdiction restrictions, (3) Vendor lock-in, and (4) Poor quality of service. Inspired by these challenges, we propose a set of important design considerations and properties for a resilient multi-cloud service layer, that includes: (1) Containerization and orchestration of applications, (2) Application placement and replication, (3) Portability and multi-cloud migration, (4) Resilience to network partitions and bandwidth constraints, (5) Automated service discovery and load balancing, (6) Localized image registry, and (7) Support for platform monitoring and management. We present an implementation and validation case study, Crane Cloud, an open source multi-cloud service abstraction layer built on-top of Kubernetes that is designed with inherent support for resilience to network partitions, microservice orchestration (deployment, scaling and management of containerized applications), a localized image registry, support for migration of services between private and public clouds to avoid vendor lock-in issues and platform monitoring. We evaluate the performance and user experience of Crane Cloud by implementing and deploying a computational and bandwidth intensive machine learning system. The results show lower response times of the system on Crane Cloud compared with hosting on other public clouds. The Crane Cloud platform is serving as a cloud-service for students and developers in low-resource settings and also as an education platform for cloud computing.
Despite decades of global development programming, poverty persists in the low-and-middle-income countries targeted by these efforts. Training approaches to global development must change and the role of engineers in these efforts must evolve to account for structural and systemic barriers to global poverty reduction. Rapid growth in Global Engineering graduate programs in the United States and Canada creates an opportunity to unify efforts between academic institutions and ensure that programs align with the sector's needs as identified by practitioners. To build consensus on how to equip engineering students with the knowledge, skills and attitudes necessary, we convened practitioners, faculty and graduate students for a two-day workshop to establish an agreed-upon Global Engineering body of knowledge. The workshop was informed by a pre-event survey of individual participants and representatives of participating academic institutions with graduate programs in Global Engineering or a related field. Through the workshop breakout sessions and post-event work by the authors, we developed the following priority learning objectives for graduate education in global engineering: Contextual Comprehension and Analysis; Cross-cultural Humility; Global Engineering Ethics; Stakeholder Analysis and Engagement; Complex Systems Analysis; Data Collection and Analysis; Data-driven Decision Making; Applied Engineering Knowledge; Project Design; Project Management; Multidisciplinary Teamwork and Leadership; Communication; Climate Change, Sustainability, and Resilience; Global Health; and Development Economics. Although technical skills are central to preparing the next generation of Global Engineers, transversal and interdisciplinary skills are equally important in equipping students to work across sectors and account for barriers to global development and equity.
