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Towards a hierarchical optimization framework for spatially targeting incentive policies to promote green infrastructure amidst multiple objectives and uncertainty
Citation:
Barnhart, B., P. Mayer, M. Papenfus, M. Bostian, K. Deb, AND Z. Wu. Towards a hierarchical optimization framework for spatially targeting incentive policies to promote green infrastructure amidst multiple objectives and uncertainty. Salish Sea Ecosystem Conference 2018, Seattle, Washington, April 04 - 06, 2018.
Impact/Purpose:
Prioritizing restoration and urban water infrastructure funding requires balancing objectives related to cost and anticipated outcomes. This work presents an optimization framework to prioritize incentive policies to promote green infrastructure in urban watersheds. This abstract will be submitted to the 30th Salish Sea Ecosystem Conference, which will be held April 4-6, 2018 in Seattle, Washington and will serve to connect local policy makers and urban watershed managers with an optimization framework to prioritize projects in order to reduce costs while ensuring project effectiveness. .
Description:
We introduce a hierarchical optimization framework for spatially targeting green infrastructure (GI) incentive policies in order to meet objectives related to cost and environmental effectiveness. The framework explicitly simulates the interaction between multiple levels of policy makers (e.g., local, regional, national) and policy followers (e.g., landowners, communities) and is especially useful for constructing and evaluating environmental and ecological policy. Using the framework with a hypothetical urban watershed, we present trade-offs between policy cost and environmental benefits (e.g., water usage, nutrient run-off) using GI incentive policies. In addition, we introduce uncertainties related to policy budget, compliance, and GI effectiveness and show that robust policies (with respect to each uncertainty type) are possible at the expense of reductions in overall objective performance. Overall, we demonstrate the utility of hierarchical optimization as a framework for targeting incentives to promote effective GI that ensures robust policies amidst conflicting objectives and uncertainty.