Date of Award

Fall 12-2017

Level of Access

Open-Access Thesis

Degree Name

Master of Science (MS)

Department

Ecology and Environmental Sciences

Advisor

Sandra de Urioste-Stone

Second Committee Member

Jean MacRae

Third Committee Member

Caroline Noblet

Abstract

As society’s need for metals increases more mining locations will likely be sought. Maine contains 10 known significant metal deposits but there are currently no active metal mines. Interest in developing one of these deposits prompted legislative changes to the metallic mineral mining (MMM) law and rules to be pursued. Social license to operate (SLO) or the acceptance of mining activities by communities plays an increasing role in the siting and profitability of mining activities. This study broadens the application of SLO to the context of a statewide policy debate. Appropriate policy development for MMM needs to consider the views of residents and their risk perceptions toward this type of mining activity being conducted in the state. This thesis aims to measure Maine residents’ risk perception and acceptance levels of MMM in order to inform a current statewide policy debate. Using a mixed methods approach, this study implemented a qualitative case study and a quantitative resident mail survey (N = 501). The case study dove into the context of the debate and used qualitative content analysis (QCA) to identify the positional stances of stakeholders and the major themes that have been most prominent throughout the debate. Opposition to the proposed rules has been the principal stance from stakeholders. The QCA resulted in four prominent themes from this debate: water permeates everything, using experiences and examples, inadequate rules, and mistrust. The qualitative results show that, counter intuitively, pushing to get a bill passed can actually hinder the fulfillment of the bill’s purpose. The quantitative study investigated the risk perceptions of Maine residents to MMM in their state and explored the social-psychological constructs that explain risk perception levels. This study also examined the utility of a risk perception model originally developed for the topic of climate change on an additional natural resource topic. Results from the hierarchal regression analysis show that the full risk perception model is able to explain over 80% of the variance in risk perceptions with significant predictors being knowledge of impacts to local assets, normative factors, biospheric value orientations, and level of trust in certain information sources. This thesis concludes with a convergence of the findings from both the qualitative and quantitative components. Predominantly congruent with each other these findings demonstrate the advantage of a mixed methods approach in studying contemporary social-natural resource issues.

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