Abstract
Complex social-ecological systems—such as cities and regions—change in time whether or not we intervene through plans and policies. This is due in part to the numerous individual and organizational actors who make self-interested, unilateral decisions. Public decision makers are expected to act in the public interest and are accountable to constituents. They need the ability to explore alternatives, select ones that are likely to benefit the public, and avoid or mitigate negative outcomes. Predicting processes and outcomes in the context of complex systems is risky, however, and mistakes can be costly. Switching from prediction of specific future states to anticipation of possible ranges of futures may help contend with the uncertainties inherent in these systems. We propose here a dynamic network model for generating ranges of possible futures for employment location in an economic region. The model can be used to anticipate employment location effects of various policies. First, using historical (2002–2015) number and location of jobs in two rather different metropolitan areas, we calibrate the model for each and validate it against actual data. Having found that the model performs well, we show how policy makers can use it to ask what-if questions regarding proposed policies to either attract businesses to specific locations or discourage them from locating in parts of the region.
Highlights
Complex social-ecological systems—such as cities and regions—change in time whether or not we intervene through plans and policies
Is the normative scenario described in current terms—needs, technologies, constraints which may no longer have currency by the target year—but successfully reaching the target is contingent on specific futures and non-robust if these favorable conditions fail to materialize (e.g. Kaufman 2012, Simmie & Martin 2010)
From 2002 to 2015, the average market potentials of both regions grew at an annual rate of about 3%, slightly higher for Dallas-Fort Worth Combined Statistical Areas (CSAs) (DFW) than for Northeast Ohio (NEO)
Summary
To represent the qualitative difference among the two regions, we have computed the respective Shannon entropies (eq 6 below) of their spatial employment distributions from 2002 to 2015 (Fig. 3). The higher the entropy value, the more distributed/graduated the regional structure is between large and small cities, which is the case for NEO, Fig. 3 Entropy of the regional spatial employment distribution for Dallas-Fort Worth and Northeast Ohio, 2002–2015 where the entropy hovers close to 0.9 for the entire time period considered. Decision makers need the ability to anticipate consequences on other places in a region when, for example, they use policies such as tax instruments to attract businesses to a location or set aside undeveloped land for conservation purposes. This requires the ability to capture region-wide interactions among municipalities following a policy intervention. We apply the model to the annual spatial distribution of number of jobs in both the Dallas-Forth Worth region and the North East Ohio regions, from 2002 to 2015
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