Classifying Critical Factors That Influence Community Acceptance of Mining Projects for Discrete Choice Experiments in the United States
Local community acceptance is a key indicator of the socio-political risk associated with a mining project. Discrete choice modeling could enhance stakeholder analysis, a critical step in community engagement. This paper seeks to identify and classify key mining project characteristics and demographic factors that influence individual acceptance of mining projects for discrete choice experiments. Six demographic factors were selected and project characteristics were classified into 16 characteristics, based on the literature. A survey of residents of mining and non-mining communities was used to test the hypothesis that these mine characteristics and demographic factors will influence respondents' decision to accept a proposed mining project. Four (age, gender, income and education) of the six demographic factors were confirmed to be significantly (p < 0.05) correlated to respondent's ranking of the importance of the mine characteristics. These demographic factors are likely to be important explanatory variables of an individual's decision to support a mining project. All sixteen project characteristics are identified as important factors. The most important mining project characteristics were found to be job opportunities, water shortage or pollution, air pollution, and land pollution. Both groups of respondents reported similar opinions on 12 of the mining characteristics and differed, marginally, on infrastructure improvement, labor shortage for other businesses, noise pollution, and mine life. This result serve as a starting point for efficient choice experiment (survey) design and effective discrete choice modeling. These models can provide a viable framework for data-driven community engagement and sustainable mine management.
S. Que et al., "Classifying Critical Factors That Influence Community Acceptance of Mining Projects for Discrete Choice Experiments in the United States," Journal of Cleaner Production, vol. 87, no. 1, pp. 489-500, Elsevier, Jan 2015.
The definitive version is available at https://doi.org/10.1016/j.jclepro.2014.09.084
Mining and Nuclear Engineering
Mathematics and Statistics
Keywords and Phrases
Acceptance tests; Mining; Noise pollution; Pollution; Population statistics; Water pollution; Community acceptance; Community consultation; Discrete choice experiments; Discrete choice models; Explanatory variables; Individual acceptance; Infrastructure improvements; Project characteristics; Surveys; Discrete choice modeling
International Standard Serial Number (ISSN)
Article - Journal
© 2015 Elsevier, All rights reserved.
01 Jan 2015