Publication Type

PhD Dissertation

Publication Date



Policy analytics are essential in supporting more informed policy-making in en-vironmental management. This dissertation employs a fusion of machine methods and explanatory empiricism that involves data analytics, math programming, opti-mization, econometrics, geospatial and spatiotemporal analysis, and other ap-proaches for assessing and evaluating current and future environmental policies.
Essay 1 discusses household informedness and its impact on the collection and recycling of household hazardous waste (HHW). Household informedness is the degree to which households have the necessary information to make utility-maxim-izing decisions about the handling of their waste. Such informedness seems to be influenced by HHW public education and environmental quality information. This essay assesses the effects of household informedness on HHW collection and recy-cling using public agency data, community surveys, drinking water compliance re-ports, and census data for California from 2004 to 2012. The results enable the cal-culation of the elasticity of the output quantities of HHW collected and recycled for differences in household informedness at the county level.
Essay 2 considers the pro-environmental spatial spillovers, based on agency actions and waste collection behavior that is occurring in other counties, that repre-sent the influence of HHW-related practices in close-by regions. Using county-level spatio-temporal datasets that consist of economic, demographic, and HHW data in California from 2004 to 2015, I evaluate the impact of grants on the HHW collection activities using a research design that emphasizes spatial variations and controls for confounding factors. A random effects panel data model with instrumental variables is then developed to measure the effects of HHW grant on HHW collection activi-ties while considering the spatial effects from the influence of the waste collection activities among close-by counties or regions.
Essay 3 assesses transition pathways in electricity generation and their future water impacts using an electricity generation capacity expansion model. Scenarios that do or do not comply with the U.S. Environmental Protection Agency's proposed carbon pollution standards – the New Source Performance Standards and Clean Power Plan – are considered. Using the Electric Reliability Council of Texas region as an illustration, the scenarios with the carbon regulations are shown to have lower water use from the power sector than the continuation of the status quo with more electricity generation from coal than natural gas. This is due to an increase in elec-tricity generation from renewable sources and natural gas combined cycle plants that is influenced by the CO2 allowance price. Water withdrawal limits affect elec-tricity generation, decreasing it from power plants with once-through cooling, but this will increase water consumption.
These essays demonstrate the use of a variety of data analytics and management science methods that represent advances in policy analytics to overcome the re-search challenges, such as the data limitations, the uncertainties associated with the analysis of energy futures, and best practices establishing causal estimates in em-pirical research designs. This dissertation contributes to the growing body of re-search on policy analytics for environmental sustainability and improves our under-standing of how to craft policies that enhance sustainability for the future


policy analytics, data analytics, informedness, household hazardous waste, sustainability, water impacts

Degree Awarded

PhD in Information Systems


Databases and Information Systems | Environmental Policy | Information Security


KAUFFMAN, Robert John

Copyright Owner and License

Singapore Management University

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Wednesday, April 10, 2019