Describing local community acceptance with discrete choice theory for enhanced community engagement

<p>This research sought to facilitate improved community (stakeholder) analysis by providing further insight on the determinants of local community acceptance using discrete choice theory. Specifically, the goals were to: (1) Identify, classify, and verify the important project characteristics and key demographic factors which affect local community acceptance of a mining project; (2) Account for the large number of relevant factors inherent in discrete choice experiments for mining community acceptance evaluation; and (3) Examine discrete choice models to select the most appropriate model for mining community consultation. The research will test the hypotheses that various discrete choice models can describe the local community’s acceptance of mining projects.</p><p>Surveys were used to validate a classification of important mining project characteristics and demographic factors. Sixteen project characteristics and four demographic factors were identified as important for individual preferences for mining projects. A mixed style, blocking scheme, fractional factorial without interaction discrete choice experiment was proposed to overcome the challenge posed by the large number of relevant factors. The design was validated, revised, and implemented in Salt Lake City, UT to illustrate the usefulness of discrete choice theory in mining stakeholder analysis. Three candidate discrete choice models were evaluated to select the best model for mining stakeholder analysis. The results show that the conditional logit model, stratified by question, is the most suitable. The proposed approach has been demonstrated to answer three important questions for enhanced stakeholder analysis: (1) what are the factors that affect stakeholders’ decision and how do these affect their preferences? (2) what is the effect of demographics on individual preferences? (3) what is the value of environmental and social impacts to individuals in the community?</p>"--Abstract, page iii.</p>

Name thesis:degree_name

Ph. D. in Mining Engineering

Grantor

Missouri University of Science and Technology

Year dc:date.available

2016

Author dc:creator

• Que, Sisi

• Mining Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2416

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3418