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Discrete Choice Experiment Consideration: A Framework for Mining Community Consultation with Case Studies

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  • Sisi Que

    (Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Yu Huang

    (Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Kwame Awuah-Offei

    (Mining and Explosives Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA)

  • Liang Wang

    (State Key Lab of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

  • Songlin Liu

    (State Key Lab of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

Abstract

Local community acceptance, a key indicator of the socio-political risk of a project, is addressed through good stakeholder (community) engagement. Discrete choice modeling (DCM) enhances stakeholder analysis and has been widely applied to encourage community engagement in energy projects. However, very little detail is provided on how researchers design discrete choice experiments (DCEs). DCE design is the key step for effective and efficient data collection. Without this, the discrete choice model may not be meaningful and may be misleading in the local community engagement effort. This paper presents a framework for mining community engagement DCE design in an attempt to determine (1) how to identify the optimum number of factors and (2) how to design and validate the DCE design. Case studies for designing discrete choice experiments for community acceptance of mining projects are applied to accommodate these two objectives. The results indicate that the four-factor design, which seeks to reduce cognitive burden and costs, is the optimal choice. A survey was used to examine the difficulty of the survey questions and the clarity of the instructions for the designs. It has, therefore, been proven that the DCM design is of reasonable cognitive burden. The results of this study will contribute to a better design of choice experiments (surveys) for discrete choice modeling, leading to better policies for sustainable energy resource development.

Suggested Citation

  • Sisi Que & Yu Huang & Kwame Awuah-Offei & Liang Wang & Songlin Liu, 2023. "Discrete Choice Experiment Consideration: A Framework for Mining Community Consultation with Case Studies," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13070-:d:1228907
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    References listed on IDEAS

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