Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2017. ; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. ; Cataloged from student-submitted PDF version of thesis. ; Includes bibliographical references (pages 99-102). ; Waste management is a significant challenge for India. The Indian waste landscape is changing rapidly as the population grows, the composition of the waste generated evolves, the extent of waste segmentation changes and the technologies available to collect and process waste improve. Many solutions have been proposed for dealing with the mixed waste but the most appropriate solution for a particular context is difficult to quantify. Thus, decisions are often made without considering the long-term economic, environmental or social consequences. The present work focuses on helping Indian cities improve collection, transportation and treatment of waste by developing a GIS-based decision support tool that assesses the cost effectiveness and efficiency of collection strategies, treatment technologies and system configurations. The tool considers the unique elements of a city including the demographics, waste composition, scale, existing infrastructure for waste collection and treatment and potential for implementing new technologies. Understanding the prevailing waste management architecture of these cities is vital in designing systems which adapt to meet the needs of the growing population with changing aspirations and consumer behavior. There is a lack of bottom-up data on the composition and volumes of waste in India. Our data-driven decision-making approach combines baseline data collection through waste audits with a systems optimization modeling approach. By using the tool to evaluate the economic, environmental and social impact of different technology configurations at varying scales, we are able to quantify the expected performance associated with different architectures. The decision support tool can be used to find the minimum cost waste configuration that considers both environmental GHG emissions and employment, by constructing trade-off graphs between competing goals. A compromise solution that satisfies competing goals is obtained at the turning point of the trade-off graphs. We also test the feasibility of improving the segregation rate in Muzaffarnagar and the impact segregation policies have on the metrics of the waste system. From the waste audits, we see that Indian households have a high composition of organic waste and waste generation increases with income level. By implementing a weekly feedback social incentive mechanism, we see that the segregation rate of organic waste by households increases to nearly twice than those households that were given no feedback. The tool shows that as the segregation rate of the city increases, the costs and GHG emissions reduce, while the employment of the waste system increases. The level of centralization of the system reduces as the level of segregation of waste increases, that is, the system moves towards smaller scale processing plants instead of large scale centralized plants. ; by Dhivya Ravikumar. ; S.M. in Technology and Policy