Browsing by Author "Gurung, Tashi"

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    Alternative Energy Opportunities in Rosemount, MN
    (Resilient Communities Project (RCP), University of Minnesota, 2015) Bodene-Yost, Zizanie; Gurung, Tashi; Oldham, Mary
    This project was completed as part of a year-long partnership between the City of Rosemount and the University of Minnesota’s Resilient Communities Project (http://www.rcp.umn.edu). City of Rosemount facilities, including wells, fire stations, and city offices are powered with traditional electrical service. The City is interested in switching to alternative energy sources that prove to be cost-effective and environmentally friendly. The goal of this project was to assess and make recommendations for the use of solar, wind, and other alternative energy sources to power municipal facilities. In collaboration with city project lead Christine Watson, Public Works Coordinator for the City of Rosemount, a team of students in PA 5242: Environmental Planning, Policy, and Decision Making researched case studies of alternative-energy-powered municipal facilities throughout Minnesota and analyzed funding sources and lessons learned from each case. A final report and presentation from the project are available.
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    A Prioritization Model for Country Selection
    (Hubert H. Humphrey School of Public Affairs, 2016) Abdiwahab, Ali; Brailsford, Elizabeth; Gurung, Tashi; Kenney, Brandon; Yamoah, Evans
    The USDA Foreign Agricultural Service’s Office of Capacity Building and Development manage the Food for Progress Program (FFPr) that is designed to assist developing countries around the world. To enhance and improve prioritization of countries for funding, this project aims to identify comparable, cross-country sources of objective and quantifiable data and indicators to identify potential countries for receiving USDA FFPr funds. After a rigorous literature review on theory of prioritization and a comprehensive look of several organizations, this project focused on a few organizations and based the work largely on the Millennium Challenge Corporation (MCC). MCC provides detailed information about their country selection process, indicators used, criteria used to select indicators, and country eligibility. The first step in the process is to narrow the list of countries potentially available for funding by including only Low Income and Lower-Middle Income countries, as determined by the Gross National Income. Several best-fitting, high quality indicators were chosen that were relevant to the goal of this project. The list of indicators can be broken down either politically or by the three key outcomes of the USDA. The list of indicators was narrowed to thirteen based on a set of strict criteria. These criteria were chosen in order to eliminate all but the highest-quality indicators. Indicators were given different weights depending on its importance and relevancy. The data for the selected indicators were extracted from different sources and aggregated for analysis. Then the countries were ranked, and this ranking process involved a multistep process. To make the data comparable, the data were rescaled and weighted before it was ranked. Out of eighty-one applicable countries, the pool was narrowed to the top fifteen most appropriate countries per the table of indicators. We have two different results: 1) when the indicators were given individual weights in order of importance to the USDA’s goals, and 2) when the indicators were equally weighted. We recommend the individualized weights. These fifteen countries are the result of the model. The top fifteen countries were assessed in terms of validity, and in terms of countries that the FFPr and MCC are currently or have worked with in the past, the prioritization model works well well. However, the prioritization model is a purely mathematical, quantitative model and does not consider agency-specific criteria. Therefore, it is highly recommended that organizations use this model only as a starting point and then apply practical world knowledge and agency-specific criteria to formulate a final list of priority countries. A few indicators have been highlighted that were not feasible due to not meeting the criteria but could be instrumental for future consideration if the data does improve. Additionally, for future work, other qualitative sources of information not used in this project could help guide country selection. It is the expectation of this project that the model will prove to be instrumental in prioritizing countries for purposes of aid beyond the confines of USDA, across borders, and for many organizations working to help developing nations.