The renewable energy industry in Massachusetts as a complex system: Developing a shared understanding for policy making

A model-based field study was conducted to understand the mental models of participants in the photovoltaic industry in Massachusetts, with the purpose of understanding of how that industry works as a complex system. Mental models of industry participants are important, both as the holders of the best system information and as the critical actors in any policy solution. Experts from manufacturing, installation, development, policy, and advocacy sectors were interviewed. The knowledge they conveyed was expressed as a set system dynamics models; these models were characterized, compared, and combined in order to answer the following research questions: What are the mental models of participants? How widely are mental models shared among participants? What is the combined model of the system? How accurate are these models? Given these models, what policies would lead to success?;The system described by informants is revealed as one of distributed and embedded agency—actors have the ability to take meaningful action, but that action and its effects are limited by the complexity of the system and by the actions of other actors. Both the growth of the industry and constraints on the growth occur through dynamic processes, many however outside local control. Mental models are shared in clusters of informants, with some differences between these groupings. Informants vary on the level of aggregation needed to express their descriptions and on the most important dynamic force. However, many processes are commonly perceived across informants, they perceive the same system trajectories, and the behavior of the simulation models constructed from their mental models was similar.;A combined model was constructed which included a full range of potential feedback loops within an abstracted version of the described system. Testing for policy using the combined model reveals that the structures necessary for growth are present, as expected. Under several reasonable conditions, growth can be halted before reaching its potential: if reinforcing feedback processes saturate; if industry capacity grows too slowly; or if goals are too low or if they erode. Coordinated actions—multiple small interventions—are more effective than isolated large actions as market growth policy.