| Title |
A comprehensive model for socially responsible rehabilitation of mining sites using Q-rung orthopair fuzzy sets and combinative distance-based assessment |
| ID_Doc |
67000 |
| Authors |
Deveci, M; Gokasar, I; Brito-Parada, PR |
| Title |
A comprehensive model for socially responsible rehabilitation of mining sites using Q-rung orthopair fuzzy sets and combinative distance-based assessment |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.eswa.2022.117155 |
| Abstract |
Mining companies play a critical role in developing mineral wealth across the globe. Interacting effectively with local communities is yet another potential source of long-term profitability, because of the opportunities that are not accessible if community engagement is not achieved. The financial advantages of a positive company image can be linked to attracting and retaining employees, as well as sustaining or even enhancing the capacity to do business with local suppliers. The socially responsible rehabilitation of a site after mine closure can facilitate access to new or former jobs for the mine workers. This study focuses on how to identify the best rehabilitation strategy after the closure of a mining site. In particular, a q-rung orthopair fuzzy sets (q-ROFSs) based CODAS (COmbinative Distance-based ASsessment) model is developed to support the evaluation of socially responsible rehabilitation activities in mining sites. To test and validate the model, the proposed methodology is compared to the ARAS (Additive Ratio Assessment) method. The results show that rehabilitation and social transition subsidy is the best alternative among those considered. Implementation of this alternative benefits the mining companies and also brings social benefits to the mine workers and the wider communities within the mining site. |
| Author Keywords |
Sustainable mining; Rehabilitation; Social responsibility; Q-rung orthopair fuzzy sets; multi-criteria decision making (MCDM) |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000792401800008 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science |
| Research Area |
Computer Science; Engineering; Operations Research & Management Science |
| PDF |
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