What Do Government And Nonprofit Stakeholders Want To Know About Nuclear Fuel Cycle? A Semantic Network Analysis

Approach

Nan LiDominique Brossard*

Dietram ScheufeleDepartment of Life Sciences Communication

College of Agricultural and Life SciencesUniversity of Wisconsin – Madison, USA

SRA Annual Meeting, Baltimore, MD December 8-11 2013

NUCLEAR ENERGY IS MORE AND MORE POLITICIZED

Friedrichs, J. (2011). Peak energy and climate change: The double bind of post-normal science. Futures, 43(4), 469–477. .

NUCLEAR FUEL CYCLES: COMPLEX RISKS AND REGULATORY UNCERTAINTY

• Cost- Capital cost, operating cost,

maintenance cost, carbon emission credits

• Waste management- Feasibility of geological disposal,

reprocessing and recycling

- Safety- Power plant safety, safety of the

overall fuel cycle

• Nuclear proliferation

MIT. (2003). The future of nuclear power (pp. 1–26). Cambridge, MA.

STAKEHOLDERS APPLY DIFFERENT MENTAL MODELS WHEN MAKING DECISIONS

Skarlatidou, A., Cheng, T., & Haklay, M. (2012). What do lay people want to know about the disposal of nuclear waste? A mental model approach to the design and development of an online risk communication. Risk Analysis, 32(9), 1496–511.

OUR PROJECT

• Identify U.S. federal agencies and nonprofit organizations as two important stakeholders involved in making policy decisions

• Understand and compare how they perceive the areas associated with the risks of nuclear fuel cycle

• Apply a semantic network analysis approach to examining the stakeholders’ mental models

METHODS

• Conduct one-hour-long cognitive interviews with 6 government and 6 nonprofit stakeholders between April and June 2012

• Ask questions about different dimensions of the risks associated with nuclear fuel cycle (e.g., economics, safety, waste, proliferation etc.)

• Use the artificial neural network software CATPAC II to analyze the transcripts (9,929 words for government and 12,130 words for nonprofit stakeholders)

• Describe the contents of mental models using Hierarchical Cluster

Analysis (HCA) and Multidimensional Scaling (MDS)

HOW THE SOFTWARE WORKS

• Read through the text with a window of n (n=5) words (e.g., 1 to 6, 2 to 7, 3 to 8) and document the co-occurring patterns of words

• Hierarchical Cluster Analysis to analyze the word covariance matrix- The clustered words represent the frequently co-occurring concepts

• Multidimensional Scaling analysis helps draw the “conceptual maps”

and visualize the contents of mental models- The grouped words represent the emergent meaning and dominant themes of

the text

HCA RESULTS: GOVERNMENT STAKEHOLDERS

Yucca Mountain

Environment, Transportation and Local Impact

Recycling

Proliferation

Economic and Waste Management

C1 C2 C3 C4 C5

MDS MAP: GOVERNMENT STAKEHOLDERS

Notes: Dark points represent the concepts that do not fit the results of hierarchical cluster analysis

Cluster 2: Proliferation

Cluster 3: Economic and waste management

Cluster 4: Recycling

Cluster 5: Environment,

transportation and local impact

HCA RESULTS: NONPROFIT STAKEHOLDERS

ProliferationAlternative energy

Economic

Yucca MountainReprocessing

Uranium and waste storage

C1 C2 C3 C4 C5 C6

MDS MAP: NONPROFIT STAKEHOLDERS

Notes: Dark points represent the concepts that do not fit the results of hierarchical cluster analysis

Cluster 1: Proliferation

Cluster 2: Alternative energy Cluster 3: Economic

Cluster 4: Yucca

Mountain

Cluster 6: Uranium and waste storage

SUMMARY

Note: Bolded words are unique to the particular group in each column.

DISCUSSION AND CONCLUSION

• Mental models vary for different stakeholders because they are closely related to institutional responsibilities and tasks

• Semantic network analysis approach is appropriate for describing

and comparing the contents of stakeholder mental models

• Should not assume the consistency in cognitions and risk-related beliefs between stakeholders with distinct expertise and interests

• Risk communication efforts are needed to promote stakeholder participation in policymaking

Thanks for your attention

dbrossard@wisc.eduTwitter: @brossardd

This material is based on work supported by grants from the U.S. Department of Energy (Contract No. 120341). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Department of Energy.