A review of international eco-industrial parks for implementation success in the United States

Eco-industrial parks (EIP) are an organization of businesses grouped around material needs and outputs. Functional synergies need to be formed that benefit both or multiple companies in these grouped organizations. Such synergies may be in the form of sharing resources, materials, infrastructure, information, or industrial ecology principles in the form of one entity using the by-product of another entity as input. There are environmental, economic as well as societal gains to be realized through eco-industrial parks. A meta-analysis was conducted to assess EIP success to date, as well as to report experienced advantages of EIPs in practice. Many EIP projects failed to come to fruition or have transformed and fallen back on traditional industrial practices. Close examination of such cases provides valuable lessons for future EIP projects and provides insight into why eco-industrial parks have historically high failure rates in the United States. The study offers a summary and critical analysis of success factors for EIP development (e.g., geographic requirements, stakeholder involvement and dedication, community involvement, and regulatory system/agency support). In addition, the strategies and methods for future success of eco-industrial parks (e.g., agent-based modeling, optimization modeling, non-competitive waste stream selection) are discussed. Agent-based modeling can identify true costs and benefits and enable monitoring of EIPs during their operation. Use of optimization techniques may be applied to overcome the complexity of multi-objective mathematical models aiming to balance the needs of multiple firms and multiple resources being allocated among them. Non-competitive waste streams can alleviate various social concerns between firms in an EIP conglomerate, due to reduced competition and mutual benefit such as re-utilizing waste that is traditional expensive to eliminate, reducing disposal costs, and raw material sourcing costs.