For example, in the wind energy example we have been using, consider the strategy to engage utilities in driving better wind infrastructure siting.
In the first step of the results chain, utilities only purchase from providers doing good siting. Which producers do the utilities purchase from and where are they likely to expand wind infrastructure? These are the areas in which we can expect improved siting if the strategy works. Then the chain notes that fewer permitting problems speed wind development. How much will the potential wind footprint grow because of this? If all this wind development is well sited, where will it be placed?
Answering these questions will identify the potential wind footprint and the areas avoided.
These questions could be answered simply by using existing projections or systematically collected expert opinion and local knowledge, and overlaying relevant data sets. Alternatively, they may be answered through complex modeling that includes spatial optimization of data identifying relative importance of many sensitive areas, energy generation potential, distance to transmission infrastructure and other factors that make wind development more or less profitable.