Modeling Residential Rainwater Harvesting Potential in the USA
CitationBrodt, Shiran. 2020. Modeling Residential Rainwater Harvesting Potential in the USA. Master's thesis, Harvard Extension School.
AbstractWater scarcity and issues with water quality have made constant headlines during the last decade. For example, consider the crisis in Flint, MI and the attention that water as a commodity has gotten from top investment banks (Goldman Sachs, n.d.). Scarcity has been driven by a depletion of freshwater resources, which are also increasingly at risk of pollution across the continental US (Lerner, 2018). These issues are reflected in the constant escalation of water utility prices during that same time period (Water and Wastewater, 2017; Walton & Lafond, 2018). If those catalysts aren’t enough to continue driving water prices at a rate that handily outpaces inflation, then the impending investment of $1T USD needed to replace a majority of the underground water infrastructure over the next 25 years should be enough of a catalyst to extend this trend deep into the future (Buckley, Gunnion, & Sarni, 2016; Deloitte, 2016).
Rain falls on roofs every year and is not collected, which puts pressure on public water infrastructure in the form of stormwater runoff. Rainwater harvesting (RWH) is a method used throughout history to take advantage of rain to provide water at the household level. Some municipalities have begun to subsidize RWH systems, but it is not prevalent. The main questions I addressed in this thesis were: Can RWH prove to be economically viable investments for households over the next 20 years in terms of return on investment (ROI)? Can RWH provide enough of a public benefit, in terms of reduced pressure on public water infrastructure, to entice local governments to provide incentives for households to adopt such systems?
My main objective was to conduct a cost benefit analysis (CBA) to evaluate the conditions under which RWH is a viable investment at the household level. The foundational data sets of this model include aggregated water pricing data for 30-cities across the US and 30-year precipitation averages compiled by NOAA. The tiered structure of the water pricing data set allowed me to shift my focus to aggregate consumption rather than specific end uses. I also showed how one can gauge reliability of RWH systems through a sensitivity analysis, adjusting key variables such as water prices, precipitation, system costs, and roof size.
The results indicated that in potable scenarios there were only two cities that featured positive NPVs, ranging from $952 to $13,586 depending on usage, out of the 30-city sample size. In contrast, there were nine such instances in the non-potable analysis, with NPVs ranging from $287 to $18,869. IRRs ranged from 2.6% to 9% and 2.3% to 12% for potable and non-potable, respectively, within the set of cities which produced positive NPVs. Furthermore, when considering potable potential with a subsidy equivalent to the existing legislation in Austin, TX, there were nine occurrences of positive NPVs. When adjusting parameter values for key variables I found that the most influential variables on profitability, in order, were water price escalation rates, roof size and precipitation, and system cost. Ongoing maintenance and electricity costs were the least influential.
This type of model is generalizable to RWH systems anywhere, substituting parameter values. This holds implications for policy makers in their decision-making with respect to water infrastructure planning. Determining the effectiveness of subsidies is one of the outputs of this model, which can prove to be a catalyst for RWH adoption.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365617