Prioritization of Mining Companies’ CSR Programs in Local Communities using the AHP Method: A Python-Based Multi-Criteria Decision-Making Approach

Abstract

Corporate Social Responsibility (CSR) programs enable mining companies to address the concerns of local communities affected by their operations. In the mining sector, members of a local community often hold differing views regarding the priority that should be assigned to CSR programs implemented by mining companies. Understanding local communities’ preferences when selecting CSR programs is essential for effectively addressing their concerns and expectations. However, research on decision-support tools that integrate local community perspectives into the selection of CSR programs in Guinean mining companies remains largely unexplored. Using the Analytic Hierarchy Process (AHP) method implemented in Python, this study developed a structured, transparent, and consistent decision-support framework for prioritizing CSR programs according to participants’ preferences. The framework incorporates eight (08) indicators related to the economic dimension of CSR in the Guinean mining sector. Data were collected through a pairwise comparison questionnaire administered to thirteen (13) participants, including representatives of local communities, local authorities, and company employees. The empirical application of the framework to the case of Kouroussa Gold Mining (KGM) revealed that the highest priorities were assigned to income-generating activities (weight = 0.366), vocational training for young people (weight = 0.150), and adequate remuneration of local workers (weight = 0.410). The results also showed acceptable consistency levels in participants’ judgments, with consistency indices satisfying the recommended threshold (CI ≤ 0.1).

Classification JEL : M14; Q56; C44.

Paper type : Empirical Research