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Conserving Water, Conserving Cash: Agency Specific Strategies for Water Conservation

Megan Connors
Jackie Fielder
Corey Garcia
Kate Hassey
Josh Jones
Bay Area Water Supply and Conservation Agency
2016

The challenge to maintain access to a clean and reliable water supply has been of particular concern for water distributors in California due to the current drought, one of the worst in 500 years. Agencies such as the Bay Area Water Supply and Conservation Agency (BAWSCA) anticipate being able to meet their member agencies’ reliability need until 2040 partially through a series of passive and active water conservations strategies. However, there is still concern over BAWSCA’s ability to meet the necessary water demand during drought years (Sandkulla et al. 2015). As such, renewed attention is being placed on water conservation efforts that mitigate the effects of water shortages and reduce water purchases in future times of need.

Our team worked with BAWSCA to evaluate their water conservation and efficiency programs and deliver recommendations that will help them conserve water in the most cost-effective and targeted manner.

Our investigation aimed to answer the following questions:

  • Which of the existing conservation programs are most cost-effective (measured in dollars per acre-foot)?
  • How does participation in conservation programs vary with income level, race, and other demographic factors?
  • What do agency administrators at the agencies that conserve the most and the agencies that conserve the least think about demographic factors’ effects on participation in conservation programs?

For our cost-effectiveness analysis, we calculated how much it cost each agency to conserve one acre-foot of water with each program. By comparing this to the avoided cost of buying an acre-foot of water from the San Francisco Public Utilities Commission (SFPUC), we determined whether it was cost-effective to run the various BAWSCA initiatives. We then ranked the programs based on the average cost per acre-foot of water conserved for participating agencies across all years in order to determine the relative cost-effectiveness of the initiatives.

We found that on average, all but the School Education Program and the Turf Replacement Program were cost-effective in the years studied. Even these two programs were run in a cost-effective manner by some of the agencies in some years. This reveals an opportunity for BAWSCA to make these initiatives more cost-effective as a whole. The ET Controller Rebate and the Pre-Rinse Spray Nozzle Program were discovered to be the most cost-effective water conservation initiatives.

The second component of our report studies demographic impact on water conservation programs through a series of Ordinary Least Squares (OLS) regressions. Our regression results suggest that Spanish speakers, the unemployed, and those with high incomes generally participate less in conservation programs. Primary participants are renters, college graduates, and those citizens living in areas hardest hit by the drought.

The last component of our report used a qualitative approach to give us information that the quantitative approach could not. We interviewed managers at American Water (City of East Palo Alto) and Purissima Hills Water District. We found that conservation managers at these agencies perceive language and income to greatly influence conservation behaviors and water demand, further confirming our demographic analysis.

Bringing together these findings, we recommend that BAWSCA scale our qualitative research findings to a standardized, BAWSCA-wide study. The results would help BAWSCA understand the specific mechanisms through which demographics affect program participation. With these findings, BAWSCA should then encourage water agencies to reduce barriers to entry for demographics with historically low participation rates and reach these consumers through targeted advertising campaigns. We further recommend that BAWSCA revamp its data collection training and mechanisms to ensure more thorough and accurate data analyses in the future.

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