Name: Kevin Halsey
Address: 700 NE Multnomah, Suite 1000, Portland, OR 97232
Phone: +1 503.416.6166
To create a decision support system (DSS) to be used by federal, state, and regional siting programs for evaluating marine renewable energy project proposals. The DSS will integrate oceanographic, ecological, human use data, stakeholder input, and cumulative impacts using probabilistic statistical methods to identify the optimal course of action regarding siting of marine renewable energy projects in the context of coastal and marine spatial planning. This system is expected to increase social and economic benefits by reducing uncertainty regarding the impacts of ocean renewable energy projects on marine ecosystems and coastal communities in the Pacific Northwest, improving stake-holder and community involvement in siting decisions, and assessing the cumulative impacts of ocean renewable energy projects.
Broad Agency Announcement: Bureau of Ocean Energy Management (BOEM), US Department of Energy (DOE), and the National Oceanic and Atmospheric Administration (NOAA)
Develop a software system designed for decision analysis and support for siting of marine renewable energy devices, or other marine spatial planning tasks.
The system is designed to use stakeholder data as an input to evaluate potential conflicts that can only be understood subjectively (e.g., visual impacts). Early stakeholder testing has reinforced the need for simplified survey approaches to ensure proper stakeholder input into the system.
The Bayesian Assessment for Spatial Siting (BASS) tool was developed in 2011-2013. BASS is a multi-criteria decision analysis system that functions with uncertain information and stakeholder input to evaluate ocean renewable energy project proposals.
- Ullmann, D., Halsey, K., Goldfinger, C., 2013, Managing Eco-System Services Decisions, BASS system white paper, 11 p
- Erhardt, M.W., 2013, A Bayesian Approach to Marine Spatial Planning, [M.S. thesis], Oregon State University, Corvallis, Oregon, 338 pp
- Lockett, D., 2012, A Bayesian approach to habitat suitability prediction, [M.S. thesis]: Corvallis, Oregon, Oregon State University. 85 pp