| Yazarlar |
GÜNGÖR ERSİN
Bartın Üniversitesi, Türkiye |
Doç. Dr. Gökhan ŞEN
Kastamonu Üniversitesi, Türkiye |
| Özet |
| This article describes an effective approach for selecting suitable plant species for afforestation in post-mining rehabilitation. The research was conducted in the Western Black Sea region of Turkey. The aim of the research is to perform accurate criteria weighting and species prioritization for afforestation in post-mining degraded areas. This helps to ensure consistent conditions for the future use of the site as a forest, sustainability of nature, and selection of appropriate species adapted to the difficult post-mining conditions. In this study, which is a multi-criteria decision-making problem (MCDM), the weights of the criteria were determined by stepwise weight assessment ratio analysis (SWARA), and the priority ranking of the species was determined by the analytic hierarchy process (AHP). Analyses were carried out with 10 afforestation criteria and five tree species. According to the analysis, the top three ranked criteria are Economic Efficiency > Carbon Stock and Credit > Reducing Afforestation Cost. The five species’ priority ranking is Robinia pseudoacacia L. (0.456) > Alnus glutinosa subsp. glutinosa (0.248) > Populus nigra subsp. nigra (0.146) > Salix alba L. (0.103) > Quercus robur subs. robur (0.048). The hybrid approach is expected to increase the effectiveness of post-mining rehabilitation works. |
| Anahtar Kelimeler |
| multi-criteria decision-making | post-mining rehabilitation | reforestation | SWARA-AHP hybrid approach |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | FORESTS |
| Dergi ISSN | 1999-4907 |
| Dergi Tarandığı Indeksler | SCI |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 05-2024 |
| Cilt No | 15 |
| Sayı | 5 |
| Doi Numarası | 10.3390/f15050783 |
| Makale Linki | http://dx.doi.org/10.3390/f15050783 |
| Atıf Sayıları | |
| WoS | 2 |
| SCOPUS | 2 |
| Google Scholar | 3 |
