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.

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.

‘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.

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.

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.

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.

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.