Can we interpret the evolution of coastal land use conflicts? Using Artificial Neural Networks to model the effects of alternative development policies

Environmental conflicts in coastal areas are determined by the interaction of global and local phenomena. Identifying the factors characterising the evolution of conflicts in relation to spatial dynamics is complex. Analysing related data and interpreting the results necessitate the use of methods that take this complexity into account. Artificial Neural Networks (ANN) have been used to accomplish this task. Although ANN have been widely implemented in physics, natural science and engineering, their application in spatial and social science is still in an early stage. We present the results of a study concerning land use conflict in the area of Civitavecchia, the main harbour of the Rome metropolitan area. Local environmental issues are air pollution from a large thermal power plant, the movement of ferries, cruise ships, and increased individual commuting. We simulate alternative policy scenarios for the conflict under study in a wider context involving 27 cases. Results indicate that only an environment-led policy is capable of reducing the intensity of the conflict. The other two proposed development tracks focussing on economic efficiency and social equity would slightly aggravate the conflict.