For strategies that aim to find efficient spatial solutions for multiple goals, the process may be more complex.

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.