Development Engineering最新文献

This paper presents a geospatial mapping model for assessing spatial distribution and demand of biomass sources for household energy use in Nepal. In the context of rural households, correlation between supply and demand of biomass is crucial for designing effective rural energy programs. Three districts were considered to represent the country's main topographical regions: lowlands, hills, and mountains, where geospatial distribution and demand of biomass are different. The supply potential of fuelwood was assessed using Geographical Information System (GIS) tool, and the potential of crop residues and dung and household energy demands were determined by field surveys and experiments. The results showed that households with secure access to biomass sources in lowlands, hills and mountains were 57%, 50% and 3% respectively. In lowlands, crop residues and dung were extensively used due to lack of forest biomass, whereas forest biomass was extensively used in hills and mountains, with negligible use of crop residues and animal dung. The results indicate that use of improved cooking stoves and biogas was negligible and thus cleaner biomass energy conversion and cooking technologies are needed to achieve universal target of clean cooking for all. The GIS model provided better estimation of biomass energy supply potential in the communities, which is crucial in the design of energy policies for sustainable clean cooking solutions. It is anticipated that this geospatial mapping model is also applicable to the cases of other developing countries, which have dominant biomass consumption for household energy use.

With recent advances in high-resolution satellite imagery and machine vision algorithms, fine-grain geospatial data on population are now widely available: kilometer-by-kilometer, worldwide. In this paper, we showcase how researchers and policymakers in developing countries can leverage these novel data to precisely identify “education deserts” – localized areas where families lack physical access to education – at unprecedented scale, detail, and cost-effectiveness. We demonstrate how these analyses could valuably inform educational access initiatives like school construction and transportation investments, and outline a variety of analytic extensions to gain deeper insight into the state of school access across a given country. We conduct a proof-of-concept analysis in the context of Guatemala, which has historically struggled with educational access, as a demonstration of the utility, viability, and flexibility of our proposed approach. We find that the vast majority of Guatemalan population lives within 3 km of a public primary school, indicating a generally low incidence of distance as a barrier to education in that context. However, we still identify concentrated pockets of population for whom the distance to school remains prohibitive, revealing important geographic variation within the strong country-wide average. Finally, we show how even a small number of optimally-placed schools in these areas, using a simple algorithm we develop, could substantially reduce the incidence of education deserts in this context. We make our entire codebase available to the public – fully free, open-source, heavily documented, and designed for broad use – allowing analysts across contexts to easily replicate our proposed analyses for other countries, educational levels, and public goods more generally.

Conjoint experiments (CEs) provide designers with insights into consumer preferences and are one of several user-based design approaches aimed at meeting users’ needs. Traditional CEs require participants to evaluate products based on two-dimensional (2D) visual representations or written lists of attributes. Evidence suggests that product representations can affect how participants perceive attributes, an effect that might be exacerbated in a Low- and Middle-Income Country setting where CEs have seldom been studied.

This study examined how physical three-dimensional (3D) prototypes and 2D renderings with written specifications of attribute profiles generated differences in estimated utilities of a CE about a hypothetical new tool for electronic-waste recycling, among workers in North-Eastern Thailand. Two independent CEs were performed with each representation form. Ninety participants across both experiments each ranked three sets of five alternative tool concept solutions from most to least preferred. The results of the conjoint analysis guided the design of a tool optimized for user preferences, which was then distributed to half of the sample through a Becker-DeGroot-Marschak auction experiment. One month after the auction, participants completed an endline survey.

The results point toward potential differences in relative importance of different product attributes based on product representation. Price was found to have no significant impact on the valuation of tools in either experiment. The differences in relative importance of product attributes may have been explained by the limitations of 2D renderings for conveying sizes.

Further research is needed to understand the impact of product representation on preferences in this context. We recommend careful consideration for product representations – specifically, how well the representations convey all product attributes being evaluated – in CEs. Using a combination of 2D renderings and 3D product features might have satisfied both the speed and low-cost advantages of renderings while enabling participants to have a better sense of product features.

To design energy access solutions for rural households in developing countries it is important to have an accurate estimation of what their electricity consumption is. Studies reveal that they mainly use electricity to meet their lighting needs, as they cannot afford high power-consuming appliances. However, the scarce data availability and modeling complexity are a challenge to compute correctly the load profiles without collecting data on-site. This paper presents a methodology that computes the hourly lighting load profiles of rural households in East Africa requiring a small amount of publicly available input data. Combining data from household surveys, climate, and satellite imagery, the methodology applies machine learning for determining occupant behavior patterns, and lamps ownership for indoor and outdoor usage. For this, an average prediction accuracy of 80% is reached. After applying lighting requirement functions, load profiles are generated and then validated using measured data from 13 households in Kenya. Results show that the methodology is able to compute the load profiles with an average normalized root mean squared error of 0.7%, which is less compared to existing simulation approaches using on-site data. To demonstrate a broad application, the monthly lighting consumption is computed and projected geospatially for households in Kenya.