Tip O’Neill famously said that “all politics is local.” Since local issues are defined in large measure by local institutions, geography, and demographics, this implies that all politics are spatial. This is especially the case when it comes to issues of environmental justice (EJ), where geospatial factors influence both the location of pollution and the “social geography” of demographics (Cutter, 1995).

Integrating geospatial data generated by geographic information systems (GIS) with Agent-Based Modeling (ABM) techniques opens up new vistas for theoretical research and especially for applied analysis. This is particularly the case when the interactions of agents in an ABM, and subsequent emergent behaviors, are conditional on properties that vary by location. For the analyst or researcher, GIS-ABM models can improve the empirical validity of analysis and explanations and also provide decision-support to policymakers.

In this paper, we present a spatially explicit multi-agent simulation, which we use to simulate two scenarios and make some inferences regarding EJ concerns over the siting of locally unwanted land use (LULU) facilities. The Sustainable Energy Modeling Program (SEMPro) allows us to simulate how decisions get made responding to different levels of citizen power. It simulates the complexity of infrastructure siting by fusing GIS data for a specific locale with an agent-based model of citizen attitude and behavior diffusion, along with spatial bargaining models of stakeholder and regulatory decision-making using game-theoretic models. Our results indicate that, despite all the money spent on assessing the engineering aspects of major infrastructure projects, citizen participation and political power are more important to outcomes than the level of local disruption that a project causes. Low levels of individual income and education reduce public participation in energy facilities siting. Less-powerful individuals are less influential in influencing project outcomes. We also find that the effect of the project’s disruption on the number of citizen messages is contingent on citizen attributes that vary geographically. Our findings may be of use to several debates in the EJ research.

First, we find support for socio-political explanations that argue that poor and/or uneducated communities have more difficulty developing effective opposition to disruptive projects (Mohai et al., 2009). A second implication relates to the temporal debate about which came first, locally unwanted land uses, or poor and minority communities. Our results are also consistent with research that finds that unwanted facilities are imposed on existing communities with a low ability to oppose them (see Hamilton, 1995).

There are also several policy implications from the findings of our ABM-GIS model. The first policy implication is that a more egalitarian process for siting infrastructure would result in more citizen opposition, fewer highly disruptive projects near citizens and, possibly, greater social justice in the long run. The second policy implication is that ABM-GIS models can be an effective way to integrate social justice issues into project planning.