Using Mental Modeling to Systematically Build Community Support for New Coal Technologies for Electricity Generation

Chapter
Part of the Risk, Systems and Decisions book series (RSD)

Abstract

Organizations that wish to successfully implement technologies that might affect local communities and that are of long-term strategic importance are wise to work with those communities to ensure that proposed projects are acceptable. In fact, organizations increasingly are required to work with communities to ensure that projects are mutually acceptable (Gregory et al., Energ Pol 31(12):1291–1299, 2003). Achieving projects that are broadly acceptable increasingly requires engaging with community stakeholders early in the process and adapting the project design to align with the values, interests, and priorities of the community. It also calls for developing effective risk communications to address misunderstandings or gaps in perceptions about the project and its potential impacts on the community.

This chapter presents a case study of how Mental Modeling was used to develop and implement a science-informed, systematic approach to stakeholder engagement in a rural community in Alberta, Canada. Mental Modeling was used to discover community members’ values and perceptions about a potential demonstration project using Integrated Gasification Combined Cycle (IGCC) generation technologies combined with Carbon Capture and Storage (CCS) in the area. This was the first integration of proven technologies applied to significantly reducing greenhouse gas emissions in a coal-fired power plant in North America. The insight gained from the mental models research was then used to develop a comprehensive Host Community Engagement Strategy and Plan based on leading risk communications principles and practices.

Keywords

Environmental Impact Assessment Environmental Impact Assessment Stakeholder Engagement Power Corporation Integrate Gasification Combine Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This chapter is based on reports from each of the key activities discussed here. Special thanks to Martin Kennedy, Megan Young, Dr. Dan Kovacs, Megan Young, Dr. Laurel Austin, and Anne Papmehl for their contributions to this chapter.

References

  1. Alberta’s Reserves 2003 and Supply/Demand Outlook 2004-2013. Retrieved from http://www.aer.ca/documents/sts/ST98/st98-2004.pdf.
  2. Capital Power Corporation (EPCOR). (2004). Consultation Process and procedures.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Decision PartnersMississaugaCanada
  2. 2.Decision PartnersCalgaryCanada

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