A spatial decision support system for environmentally sustainable communities

In an effort to move toward sustainable development, many communities are considering the possibilities of industrial ecology strategies and eco-industrial development. Drawing an analogy from ecology and ecosystems modeling, industrial ecology is a theoretical structure that considers the various industries and industrial sectors in a particular place as a linked system. By focusing on interactions and interrelationships within an integrated landscape, industrial ecology involves understanding not only what is happening at a particular location but also the connections between places and locations. In such a comprehensive framework for decision-making, immense levels of information need to be collected, analyzed, and synthesized. Scenarios are difficult to forecast, and interactions are full of complexity and uncertainty. While over 40 communities in the United States claim to be planning or initiating planning on an eco-industrial park or network, progress is slow and the demand for analytical decision support tools is high. The central goal of the research presented here is to develop a spatially-oriented decision support system (SDSS) to aid communities in this process of planning for sustainability through the design and development of eco-industrial parks and networks. With its focus on decision-making for environmentally sustainable communities, this research explores the methodological development of a decision support tool for modeling interactions and linkages across the urban environmental landscape. The research is also an investigation into the ways in which communities and decision makers may best address the complex information needs of place-based environmental decision-making. In particular, the design of the SDSS explores how geographic issues such as scale, networks, and spatial interactions can best be modeled and understood in a community-based collaborative decision-making process.