Towards a hierarchical optimization framework for spatially targeting incentive policies to promote green infrastructure amidst multiple objectives and uncertainty
Abstract
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.
Authors: Brad Barnhart, Paul M. Mayer, Michael Papenfus, Moriah Bostian, Kalyanmoy Deb, Zhichao Wu
Published in: Western CEDAR (Western Washington University) (2018)