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Citation: Güngör, E.; ¸Sen, G.
Sustainable Afforestation Strategies:
Hybrid Multi-Criteria
Decision-Making Model in
Post-Mining Rehabilitation. Forests
2024,15, 783. https://doi.org/
10.3390/f15050783
Academic Editors: Yanmin Teng,
Chao Wang and Jinyan Zhan
Received: 22 March 2024
Revised: 25 April 2024
Accepted: 26 April 2024
Published: 29 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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4.0/).
Article
Sustainable Afforestation Strategies: Hybrid Multi-Criteria
Decision-Making Model in Post-Mining Rehabilitation
Ersin Güngör 1, * and Gökhan ¸Sen 2
1Faculty of Forestry, Bartın University, 74100 Bartın, Turkey
2Faculty of Forestry, Kastamonu University, 37100 Kastamonu, Turkey; gsen@kastamonu.edu.tr
*Correspondence: egungor@bartin.edu.tr; Tel.: +90-532-628-33-18
Abstract: This article describes an effective approach for selecting suitable plant species for afforesta-
tion 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.
Keywords: post-mining rehabilitation; reforestation; multi-criteria decision-making; SWARA-AHP
hybrid approach
1. Introduction
Terrestrial ecosystems, covering 31% of the Earth’s surface [
1
], have an important
position in achieving sustainable development and combating climate change. Terrestrial
ecosystems and forests, which cover 30% of the world’s land and are the source of livelihood
for about 1.6 billion people, including 70 million indigenous peoples, are among the most
studied topics in achieving sustainable development goals, particularly in attaining Goal
13 (climate action) and Goal 15 (terrestrial life) [
2
]. Mature forests are utilized as carbon
stores. Deforestation leads to a reduction in biodiversity, damage to human utilization,
the destruction of carbon sinks, and thus increased climate change impacts. One of the
factors that causes the destruction of forests is the mining sector. Especially wild mining
causes permanent damage to the forest ecosystem. The number of abandoned mines
worldwide is hundreds of thousands [
3
]. Although mining is a temporary activity, its
environmental impact continues long after the mine site is closed. Mining is a sector that is
constantly developing and growing in our age. While trying to meet raw material needs,
the mining sector is also taking measures to address increasing environmental and social
concerns. For this reason, the rehabilitation of mine sites is recognized as an important
part of sustainable development strategies in many countries [
4
]. Rehabilitation efforts are
aimed at eliminating the environmental damage caused by mining sites [5,6].
Post-mined land rehabilitation is defined as the restoration of mining sites to their
original condition. Planting, land management, spatial planning, physical restoration, land
use planning, species selection, soil characteristics, and pre-mining land use status are
Forests 2024,15, 783. https://doi.org/10.3390/f15050783 https://www.mdpi.com/journal/forests
Forests 2024,15, 783 2 of 21
taken into account [
7
,
8
]. Rehabilitation works at mining sites start when mining activities
start and continue with the activities. Post-mined land rehabilitation works should be
completed when the mining permit period ends [9]. For this reason, mine closure and the
subsequent rehabilitation process is the last stage of mining activities. Rehabilitation works
should be compatible with the final land use and morphology of the area [
10
]. Afforestation
is one of the best methods to minimize damage to the environment during mining activities
and prepare the land for subsequent use [
11
]. The plant species selection process is the
main factor affecting the success of rehabilitation works.
Post-mining afforestation contributes to improving air quality and protecting bio-
diversity by sequestering carbon. It plays a critical role in mitigating climate change.
Afforestation offers many benefits for the environment and local communities. Trees act as
natural air purifiers, filtering pollutants and improving air quality. Afforestation provides
aesthetics to the site and contributes to creating an ecosystem on site, increasing the trees’
resistance to plant diseases and insects. Tree roots stabilize soils, reducing erosion and
preventing further soil degradation. Afforestation with species that are suitable for the
region increases success and reduces the cost of additional planting or completion. Af-
forestation initiatives give local communities a sense of ownership. It also enables them to
participate in environmental protection efforts [
12
,
13
]. In addition to all these, the fact that
a site has negative qualities, such as barrenness and inefficiency in the pre-use period, is
not an excuse for not rehabilitating the area [14].
In post-mining afforestation, determining the appropriate criteria and selecting or
prioritizing tree species based on these criteria is a multi-criteria decision-making (MCDM)
process. Many studies in the literature have been undertaken using the MCDM method [
15
].
Due to its flexibility and high efficiency in analyzing decision problems, MCDM has
been used in many prioritization studies for forest creation [
16
–
19
]. On the other hand,
it can be stated that many post-mining rehabilitation and afforestation projects utilize
the MCDM process. Several studies focus on limiting factors for plant growth [
20
,
21
]
and different land use preferences in post-mining rehabilitation [
11
,
22
–
37
]. Some post-
mining land use studies, as represented by References [
3
–
7
], begin with a structured
problem and apply a combination of methods or methodologies. In the study conducted by
Soltanmohammadi et al. [
3
–
5
], a framework is established for the suitability of the mined
area for new use. Evaluations are based on 50 attributes across four main criteria and are
used to assess 23 specific alternatives categorized into eight land use categories. These
referenced studies employ a hybrid approach: the analytic hierarchy process (AHP) is
utilized for criterion prioritization, while other techniques, such as the technique for order
preference by similarity to ideal solution (TOPSIS), elimination and choice expressing reality
(ELECTRE), and the preference ranking organization method for enrichment evaluation
(PROMETHEE), are employed as alternative assessments.
Studies on MCDM methods in the mining sector are utilized in various domains.
However, there exists a research gap in the literature regarding the evaluation of tree
species alternatives for post-mining rehabilitation using hybrid MCDM techniques, such
as SWARA and the AHP. Filling this gap constitutes the motivation behind this research.
Therefore, this study focuses on post-mining afforestation. The forest is a crucial step in
some post-mining land use types. Selecting suitable plant species is pivotal in achieving the
goals of mine reclamation. As previously mentioned, afforestation is the optimal alternative
in this context, primarily aimed at forest formation in reclamation. Hence, the research was
conducted in the Gürgenpınar–Topluca mining fields in Bartın province, Turkey, which
have significant mining areas. The selection of the best plant species for forestry purposes
was made in this study. The study aims to apply the AHP and SWARA methods to analyze
plant species selection using integrated multi-attribute decision-making. A hybrid approach
of the AHP and SWARA methods was employed to weigh the criteria and prioritize the
species. Thus, decision-makers (DMs) could objectively evaluate both criterion weights
and species priorities.
The main objectives of this article can be outlined as follows:
Forests 2024,15, 783 3 of 21
(i)
Development of an integrated SWARA-AHP hybrid method for tree species selection
in post-mining afforestation. This method is referred to as the “WPA framework”;
(ii)
Determining the weights and importance rankings of the main and sub-criteria in
selecting alternative species for rehabilitation afforestation;
(iii)
Conducting a case study in surface mining areas to demonstrate the feasibility of this
method;
(iv)
Creating a resource to aid decision-makers in similar problems using the proposed
method;
(v)
Raising awareness about the use of MCDM methods in post-mining afforestation
decision problems.
DM opinions were utilized in the evaluations. In this context, it is demonstrated that
it is feasible to determine criterion weights and the most suitable tree species for mine site
afforestation using the SWARA-AHP hybrid approach. Furthermore, the study indicates
that optimal benefits are achieved in the results of afforestation decision problems evaluated
by MCDM methods.
2. Materials and Methods
2.1. Case Study Area
This research was conducted in Bartın province and is considered a case study (Figure 1).
Bartın is located in the Western Black Sea region. Bartın is surrounded by the Kure Mountains.
The geology of the region is based on limestones and coal mudstones. Due to its climate and
geological structure, the mining areas in Turkey’s Western Black Sea region have favorable
conditions for the growth of many tree species. Summers are hot, and winters are mild in the
region. In 2023, the average temperature in the region was 25
◦
C. The highest temperature
was 41
◦
C in July, and the lowest temperature was
−
24
◦
C in January. Annual precipitation in
the region is 393 mm. Candidate plants should be able to adapt to these climatic conditions.
Bartın is one of the richest regions of Turkey in terms of forest and mineral wealth. The study
area is located between the towns of Gürgenpınar and Topluca, between 41
◦
17
′
and 41
◦
44
′
north longitude and 32
◦
05
′
and 32
◦
46
′
east latitude. There are several mining operations in
the area, processing limestone, marl, cement, limestone, clayey limestone, and mudstone,
reaching a size of 1300 ha. There is a 28.4 ha area that has ceased production and is undergoing
rehabilitation [38].
Forests 2024, 15, x FOR PEER REVIEW 3 of 23
(i) Development of an integrated SWARA-AHP hybrid method for tree species selection
in post-mining afforestation. This method is referred to as the “WPA framework”;
(ii) Determining the weights and importance rankings of the main and sub-criteria in
selecting alternative species for rehabilitation afforestation;
(iii) Conducting a case study in surface mining areas to demonstrate the feasibility of
this method;
(iv) Creating a resource to aid decision-makers in similar problems using the proposed
method;
(v) Raising awareness about the use of MCDM methods in post-mining afforestation
decision problems.
DM opinions were utilized in the evaluations. In this context, it is demonstrated that
it is feasible to determine criterion weights and the most suitable tree species for mine site
afforestation using the SWARA-AHP hybrid approach. Furthermore, the study indicates
that optimal benefits are achieved in the results of afforestation decision problems evalu-
ated by MCDM methods.
2. Materials and Methods
2.1. Case Study Area
This research was conducted in Bartın province and is considered a case study (Fig-
ure 1). Bartın is located in the Western Black Sea region. Bartın is surrounded by the Kure
Mountains. The geology of the region is based on limestones and coal mudstones. Due to
its climate and geological structure, the mining areas in Turkey’s Western Black Sea region
have favorable conditions for the growth of many tree species. Summers are hot, and win-
ters are mild in the region. In 2023, the average temperature in the region was 25 °C. The
highest temperature was 41 °C in July, and the lowest temperature was −24 °C in January.
Annual precipitation in the region is 393 mm. Candidate plants should be able to adapt to
these climatic conditions. Bartın is one of the richest regions of Turkey in terms of forest
and mineral wealth. The study area is located between the towns of Gürgenpınar and
Topluca, between 41°17′ and 41°44′ north longitude and 32°05′ and 32°46′ east latitude.
There are several mining operations in the area, processing limestone, marl, cement, lime-
stone, clayey limestone, and mudstone, reaching a size of 1300 ha. There is a 28.4 ha area
that has ceased production and is undergoing rehabilitation [38].
Figure 1. Case study area.
Figure 1. Case study area.
Forests 2024,15, 783 4 of 21
The area is located on the 10 km long Bartın–˙
Inkumu Highway and is close to residen-
tial areas. Dust, noise, and explosions during mining activities have a negative impact on
the lives of people in the surrounding area. Excavation pits, waste dumping sites, and high
slopes disrupt aesthetics and damage the existing green texture. Loss of flora and fauna
also occurs in the forested areas damaged during mining. During the extraction, processing,
and transportation of minerals, there is pressure on the land, resulting in soil compaction.
As a result of the change in topography and drainage patterns, rivers and groundwater are
adversely affected. The damage caused by mining in the region is intensely perceived by
the local population. Natural balance is completely disrupted in such areas, with sediments
carried by wind and water erosion filling lakes, rivers, and dams and polluting drinking
water sources, resulting in visual pollution and damage to aquatic life, agricultural areas,
and settlements.
2.2. Criteria for Calculating Weights
To determine the weighting of criteria and tree species in post-mining afforestation,
10 people participated as DMs in this research. Information on DMs is given in Table A1. It
is recommended that the number of DMs should be three or more in group studies [
39
].
Their selection process was based on their expertise, practical experience in relevant fields,
and general knowledge of post-mined land rehabilitation afforestation. The information
about the decision-makers is detailed. Due to the literature review and the opinions of the
decision-makers, ten criteria were deemed appropriate for the study (see Table A2). The
relevant criteria were categorized under three headings. According to the criteria groups,
the criteria are defined as ecological (C1: Resistance, C2: Compatibility, C3: Pollution
Prevention, C4: Erosion Prevention, C5: Growth Type and Strength), social (C6: Aesthetic
Appearance, C7: Access to Plant Species), and economic (C8: Carbon Stock and Credit, C9:
Reducing Afforestation Cost, C10: Economic Efficiency).
2.3. Tree Species to Prioritize
Bartın, which is considered a case study, is one of the richest regions of Turkey in terms
of both forest and mining assets. Due to its climate and geological structure, the mining
areas in the Western Black Sea region of Turkey have favorable conditions for the growth
of many tree species. There are more than ten plant species suitable for rehabilitation
afforestation. The main species are Acer negundo,Acer campestre,Alnus glu-tinosa subsp.
glutinosa,Ailanthus altissima,Carpinus betulus,Gleditsia triacanthos,Juglans regia,Pinus nigra,
Pinus pinea,Pseudotsuga menziesii,Populus nigra subsp. nigra,Quercus robur subsp. robur,
Robinia pseudoacacia L., Salix alba, and Ulmus minor [
40
]. Many of these species can be
used in post-mining afforestation in quarries. Tree species with a C/N ratio below 20 are
prominent in rehabilitation plantations. For example, Robinia pseudoacacia,Alnus glutinosa,
and Alnus incana have proven useful for biological soil remediation due to the nitrogen
fixation of their roots and the favorable C/N ratio of their fallen leaves [
41
,
42
]. In the WPA
framework, the AHP is used together with SWARA. When the number of alternatives in
the AHP is high, it becomes difficult to construct comparison matrices. In cases with more
than one decision-maker, comparisons can take a long time, as the number of alternatives
increases [
43
]. As the number of comparisons increases, it is extremely difficult to keep
the consistency ratio (CR) value within 0.1 [
44
]. For this reason, considering the AHP
constraints, the number of tree species to be considered in the WPA calculations was
limited to five.
In this research, a list of 15 tree species that have the potential to be used in post-
mining afforestation was created. In this context, literature information [
41
,
42
] was also
used. DMs evaluated these tree species according to the ecological, social, and economic
criteria. Evaluations were made on a 5-point Likert-type scale according to the Delphi
technique. The Delphi method is one of the basic tools for forecasting values in various
types of issues. It uses the knowledge of experts, which is properly aggregated (e.g., in the
form of descriptive statistics measures), and returns to the previous group of experts again,
Forests 2024,15, 783 5 of 21
thus starting the next round of forecasting [
45
]. This scale was developed by Mancuso
et al. [
46
]. On the scale, the following statements are included: “very important (5 points)”,
“somewhat important (4 points)”, “somewhat important (3 points)”, “I do not expect this
(2 points)”, and “this does not apply to me (1 point)”. At the end of the two-stage process,
the arithmetic mean of the DM scores was calculated (see Table A3). Tree species scoring
above five (Robinia pseudoacacia L., Alnus glutinosa subsp. Glutinosa,Populus nigra subsp.
Nigra,Quercus robur subsp. Robur, and Salix alba L.) were taken into account in the WPA
framework calculations. Brief characteristics of the tree species considered in this context
are given in Table 1.
Table 1. Brief characteristics of the tree species [47].
Brief
Characteristic
A1 A2 A3 A4 A5
Robinia pseudoacacia L.
Alnus glutinosa subsp.
glutinosa
Populus nigra subsp.
nigra
Quercus robur subsp.
robur Salix alba L.
Plant height [m]: 19.69 19.19 26.13 27.64 21.04
Life span: Perennial Perennial Perennial Perennial Perennial
Life form: Phanerophyte, Tree Phanerophyte, Tree Phanerophyte, Tree Phanerophyte, Tree Phanerophyte, Tree
Origin:
Neophyte
Germany,
Hungary,
Bulgaria,
Turkey
Native
Europe,
Turkey
Native
Southern Europe,
Mediterranean,
Central Asia,
Turkey
Native
Europe,
Western Caucasus,
Turkey
Native
United Kingdom,
Caucasus,
China,
Turkey
Humidity relationship: Dry Wet Wet Mesic Wet
Reaction relationship: Slightly acidic to
near-neutral Slightly acidic to
near-neutral Alkaline Slightly acidic to
near-neutral Alkaline
Nutrient relationship: Eutrophic Eutrophic Eutrophic Mesotrophic Eutrophic
Salinity relationship: Non-saline Slightly saline or
brackish Non-saline Non-saline Non-saline
Broad habitat: Scrub, Forest
Aquatic, Wetland, Scrub,
Forest, Sparsely
vegetated (incl. rock and
scree)
Wetland, Scrub, Forest Grassland (non-alpine,
non-saline), Scrub,
Forest
Aquatic, Wetland, Scrub,
Forest, Sparsely
vegetated (incl. rock and
scree)
Post-mining
afforestation
relationship:
Successful in preventing
erosion and post-mining
afforestation [
48
,
49
]. The
wood is valuable.
High aesthetic value.
Successful in
post-mining
afforestation.
Biomass source.
It produces nitrogen
nodules in its roots.
To enrich the soil in
terms of plant nutrients
[50,51].
Successful in
post-mining
afforestation
Important in the fight
against climate change
[52].
Cleans heavy metal
pollution in the soil [53].
Successful in post-fire
afforestation and
post-mining
afforestation [54].
Suitable for continental
climate.
Flood resistant [55].
Successful in
post-mining
afforestation.
High phytoremediation
efficiency [56].
2.4. WPA Framework
The overall objective of the analysis of weighting criteria and prioritization of species
(WPA) is to weight plant species selection criteria and prioritize plant species in a hierarchi-
cal structure. This objective is placed at the first level of the hierarchy. At the second level,
plant species selection criteria are ranked and weighted. At the third level, plant species
are prioritized based on the weight of each criterion (Figure 2).
SWARA was preferred for criteria weighting (Level 2) due to its ease of calculation
and application and ranking of the criteria according to their superiority. In species
prioritization (Level 3), the AHP was easily used in the calculations since the number
of variables evaluated, i.e., the number of species, was three. The SWARA-AHP hybrid
approach was preferred for this context, which consists of three stages: objective, criteria
weights, and type priorities.
In this paper, post-mined afforestation for the restoration of the Gürgenpınar–Topluca
mine site, which was a forest before mining activities, is discussed. The aim is to determine
the criteria and tree species to be used in this context. According to the hierarchy in
Figure 2, 10 criteria that serve the purpose in Level 1 and the ecological, social, and
economic characteristics in Level 2 were weighted. Then, the prioritization of the species
Forests 2024,15, 783 6 of 21
given in Level 3 was carried out. In the assessments, ranking, weighting, and prioritization
processes were carried out in a hierarchical order.
Forests 2024, 15, x FOR PEER REVIEW 6 of 23
Figure 2. Hierarchical structure of WPA.
SWARA was preferred for criteria weighting (Level 2) due to its ease of calculation
and application and ranking of the criteria according to their superiority. In species prior-
itization (Level 3), the AHP was easily used in the calculations since the number of varia-
bles evaluated, i.e., the number of species, was three. The SWARA-AHP hybrid approach
was preferred for this context, which consists of three stages: objective, criteria weights,
and type priorities.
In this paper, post-mined afforestation for the restoration of the Gürgenpınar–Top-
luca mine site, which was a forest before mining activities, is discussed. The aim is to de-
termine the criteria and tree species to be used in this context. According to the hierarchy
in Figure 2, 10 criteria that serve the purpose in Level 1 and the ecological, social, and
economic characteristics in Level 2 were weighted. Then, the prioritization of the species
given in Level 3 was carried out. In the assessments, ranking, weighting, and prioritization
processes were carried out in a hierarchical order.
2.5. A Hybrid SWARA-AHP Method Integrated into WPA Framework
DMs select the best alternative under many criteria. MCDM techniques have been
developed using iterative numerical techniques to assist the DM [57]. Evaluation criteria
often try to achieve conflicting objectives simultaneously.
The problem is solved with an integrated SWARA-AHP hybrid approach developed
on the WPA framework. SWARA was preferred for weighting the criteria, and the AHP
was preferred for prioritizing the species. In this context, the SWARA-AHP hybrid ap-
proach was preferred for its short-time results and simplicity of calculations. With a sam-
ple application realized in this way, the study is different from other studies and has a
unique structure.
Figure 2. Hierarchical structure of WPA.
2.5. A Hybrid SWARA-AHP Method Integrated into WPA Framework
DMs select the best alternative under many criteria. MCDM techniques have been
developed using iterative numerical techniques to assist the DM [
57
]. Evaluation criteria
often try to achieve conflicting objectives simultaneously.
The problem is solved with an integrated SWARA-AHP hybrid approach developed
on the WPA framework. SWARA was preferred for weighting the criteria, and the AHP was
preferred for prioritizing the species. In this context, the SWARA-AHP hybrid approach
was preferred for its short-time results and simplicity of calculations. With a sample
application realized in this way, the study is different from other studies and has a unique
structure.
On the other hand, many hybrid methods, such as SWARA-TOPSIS, SWARA-VIKOR,
SWARA-COPRAS, SWARA-PROMETHEE, and SWARA-MOORA, are suitable to be used
together [58–60].
A group decision-making procedure was designed to integrate the SWARA-AHP
hybrid approach (Figure 3). In the goal-oriented calculations, ranking, weighting, and
prioritization processes were carried out in a hierarchical order. In the analyses, group deci-
sions were combined using the geometric mean approach, which is an accepted technique
in the relevant field. In addition, expert opinions could be obtained consistently due to the
ease of data evaluation [61].
Forests 2024,15, 783 7 of 21
Forests 2024, 15, x FOR PEER REVIEW 7 of 23
On the other hand, many hybrid methods, such as SWARA-TOPSIS, SWARA-VI-
KOR, SWARA-COPRAS, SWARA-PROMETHEE, and SWARA-MOORA, are suitable to
be used together [58–60].
A group decision-making procedure was designed to integrate the SWARA-AHP hy-
brid approach (Figure 3). In the goal-oriented calculations, ranking, weighting, and prior-
itization processes were carried out in a hierarchical order. In the analyses, group deci-
sions were combined using the geometric mean approach, which is an accepted technique
in the relevant field. In addition, expert opinions could be obtained consistently due to the
ease of data evaluation [61].
Figure 3. WPA framework of proposed SWARA-AHP-integrated MCDM methodology.
Figure 3. WPA framework of proposed SWARA-AHP-integrated MCDM methodology.
In the SWARA-AHP hybrid approach, the criteria required for the evaluations were
brought together with the help of the literature review and expert opinions. In this re-
search, ten DMs were involved in the decision-making for mine site afforestation. Hybrid
approaches that utilize more than one expert opinion are not uncommon for group applica-
tions of MCDM. In fact, these hybrid applications enhance the quality of the study. On the
other hand, hybrid approaches improve the quality of the findings by capturing as many
opposing views as possible. The individual opinions of the experts are handled according
to the group decision-making rules, and the solution method of the hybrid approach is
used. In the SWARA-AHP hybrid approach, expert judgments are combined by taking the
geometric mean of the data.
Forests 2024,15, 783 8 of 21
The AHP and SWARA methods used in the study are briefly introduced theoretically
in this section, and the application processes are explained.
2.5.1. SWARA Method
The stepwise weight assessment ratio analysis (SWARA) method is one of the MCDM
methods introduced into the literature by Keršuliene, Zavadskas, and Turskis in 2010. In
the SWARA method, the criteria of alternatives are ranked from the most important to the
least important [
62
]. First, the DM ranks the criteria in descending order of importance. In
the case of multiple DMs, each DM ranks the criteria in descending order of importance.
Accordingly, there are as many criteria rankings as the number of DMs. In group
decision-making, the overall ranking is determined by taking the geometric mean of the
criteria rankings determined by the DMs. Based on the overall ranking, the DMs compare
the criteria with the previous criterion starting from criterion 2. Each DM performs the
comparison of the criteria in the overall ranking individually. The weights of the criteria
are determined according to the SWARA method after the comparison of the DMs. As a
result, the number of DMs results in priority vectors showing the weights of the criteria.
The final overall priority values are obtained by taking the geometric mean of the priority
value of each criterion [61–64].
The SWARA method was preferred because it supports group decision-making, has
given good results in past applications, is easy to use, and gives DMs more opportunities
to set priorities.
The analysis steps of the SWARA method are listed below [63]:
Step 1: Each DM prioritizes the criteria according to their importance. The most
important criterion is usually given a score of 1.00 points, while the other criteria are given
scores in multiples of 0.05 points. In a model with lth DM and n criteria, the priority
assigned to criterion jby DM kis denoted as pk
j, where j=1, 2, . . . , n;k=1, 2, . . . , l.
Step 2: The individual evaluations of all DMs are combined according to the geometric
mean given in relation (1). Here,
pj
denotes the combined relative importance score for
each criterion.
pj= l
∏
k=1
pk
j!1
l
,∀j. (1)
Step 3: All criteria are ranked in descending order according to their relative impor-
tance scores. Then, starting with the second criterion, the relative importance (comparative
importance) of the following criteria is calculated as the value of criterion j relative to
the previous criterion
(j−1)
, denoted as
sj
. According to this order, the comparative
importance values of the geometric means are shown in Equation (2).
sj=pj−1−pj,j=2, . . . n. (2)
Step 4: The coefficients of each criterion are obtained by pairwise comparison and
denoted as
cj
. This coefficient indicates how important criterion
j+
1 is relative to criterion
j. The cjvalues are calculated, as in Equation (3):
cj=1, j=1;
sj+1, j=2, . . . n.(3)
Step 5: The adjusted weights s′jare calculated for all criteria in Equation (4):
s′
j=
1, j=1;
s′j−1
cj
,j=2, . . . n.(4)
Forests 2024,15, 783 9 of 21
Step 6: The final criteria weights (wi)are calculated in Equation (5):
wi=s′j
∑n
j=1s′j
,j=1, 2, . . . n. (5)
2.5.2. AHP Method
The analytic hierarchy process (AHP) is one of the most widely used MCDM tech-
niques in identifying, weighting, and prioritizing the types of criteria. This method, devel-
oped by Saaty [
65
], solves many problems, such as equipment selection in mining, mine
site selection, post-mining land use type selection, and species selection in afforestation [
25
].
This interactive method allows the DM or group of DMs to express their preferences and
discuss the results. In general, the AHP is based on the principle of decomposition, a series
of “pair-wise comparisons”, i.e., comparing criteria and alternatives against each other. It
is based on the principle of synthesizing and prioritizing preferences. This method is also
used to assign priorities to criteria and sub-criteria [66,67].
In the AHP, DMs evaluate their judgments about criteria and alternatives by consider-
ing qualitative and quantitative elements together. In addition, this method is frequently
used to solve complex decision problems by considering multiple criteria. The AHP exam-
ines the components of complex problems in a hierarchical structure, and qualitative and
quantitative information can be evaluated together in the analysis. The scores obtained at
each level of the hierarchy are combined to reach a conclusion. The AHP reaches the result
by multiplying the weight scores in the hierarchy [68].
The stages of AHP analysis are stated below in order:
Step 1: A hierarchical structure is created. Thus, DMs can easily compare criteria and
alternatives. At the top of the hierarchical structure is the purpose of the model;
Step 2: DMs compare the criteria through pairwise comparisons. “Pairwise compari-
son matrices” are used in comparisons. In these matrices, the values on the prime diagonal
are one. The relative importance of n and the superiority of each objective in terms of
criteria are determined according to the importance scale consisting of numerical values
between 1 and 9 in pairwise comparisons through judgments [68];
Step 3: The weights of the values in the benchmark matrix are determined. Each
element of the matrix is divided by the sum of its column. Thus, vectors belonging to
the columns are formed, and the column vectors are combined to form a normalized
comparison matrix. The arithmetic mean of the row elements of the normalized matrix is
taken. The column vector defined as the eigenvector is obtained, and a
(n×n)
pairwise
comparison matrix is formed in Equation (6) [69].
A=
a11 a12 · · · a1n
a21 a22 · · · a2n
.
.
..
.
.....
.
.
an1an2· · · ann
aiz =1
aiz ,aii =1, z=1, 2, . . . , n
(6)
Here,
aiz
expresses the preference level of attribute
i
over attribute
z
and vice versa. The
comparison matrix is then normalized by dividing each column of the pairwise comparison
matrix by the sum of the entries of the corresponding column. The relative weight of
attribute
i
results from the eigenvalue
λI
in this matrix. The resulting relative weight vector
is multiplied by the element weight coefficients at higher levels to reach the hierarchy apex.
The global weight vector Wof the attributes is the result according to Equation (7).
W=
w1
w2
.
.
.
wn
(7)
Forests 2024,15, 783 10 of 21
The DM team considers Saaty’s importance scale (Table 2) in pairwise comparisons.
The pairwise comparison matrix is obtained by calculating the weighted geometric scores
of the DMs in Equation (8).
ag
iz =
X
∏
x=1
(ax
iz )wx (8)
Table 2. Importance scale in AHP [67].
Intensity of Importance Description
1 Equal Importance
3 Moderate Importance
5 Strong Importance
7 Very Strong Importance
9 Extreme Importance
2, 4, 6, 8 Intermediate Values
In Equation (8), the term
ag
iz
represents the collective assessment of DMs on the relative
importance of attributes
i
and
z
. The term
ax
iz
represents
x
’s DM’s assessment of the relative
importance of attributes
i
and
z
. The terms
wx
and
x
represent the normalized weight and
the number of DMs by DMx, respectively;
Step 4: The consistency ratio and consistency indicator of the criteria whose eigenvectors
are created are calculated. The consistency ratio (CR) is calculated, as in Equations (9) and (10).
The consistency ratio is an indicator of whether the comparisons made by DMs about the criteria
are consistent. The consistency ratio should be less than 0.1. If it is higher, the calculations
should be checked by re-evaluating the pairwise comparisons [
70
]. The CR value is obtained by
calculating the largest eigenvector (λmax) of the matrix in Formula (9).
λmax =∑n
i=1di
wi
n(9)
The Randomness Index (RI) is used to calculate the consistency indicator (CR). The RI
is the value needed to calculate the consistency ratio. Table 3shows the RI values, which
consist of fixed numbers and are determined according to the value of
n
[
68
]. The CR value
is calculated according to Equation (10).
CR =λmax −n
(n−1)·RI (10)
Table 3. Randomness Index (RI).
n1 2 3 4 5 6 7 8 9 10
RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49
3. Results
3.1. Weighting of Criteria
In the study, SWARA was calculated according to the steps (steps 1–6) specified in the
relevant method. In this context, the opinions of ten decision-making experts (DMs) were
considered within the scope of SWARA. The combined relative importance scores for each
criterion were calculated using individual evaluations. The SWARA calculations are given
in Tables A4–A6 and Figure 4. This section is divided into subheadings. It should provide
a concise and precise description of the experimental results and their interpretation, as
well as the experimental conclusions that can be drawn.
Forests 2024,15, 783 11 of 21
Forests 2024, 15, x FOR PEER REVIEW 11 of 23
Table 3. Randomness Index (RI).
n 1 2 3 4 5 6 7 8 9 10
RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49
3. Results
3.1. Weighting of Criteria
In the study, SWARA was calculated according to the steps (steps 1–6) specified in
the relevant method. In this context, the opinions of ten decision-making experts (DMs)
were considered within the scope of SWARA. The combined relative importance scores
for each criterion were calculated using individual evaluations. The SWARA calculations
are given in Table A4-A6 and Figure 4. This section is divided into subheadings. It should
provide a concise and precise description of the experimental results and their interpreta-
tion, as well as the experimental conclusions that can be drawn.
Figure 4. Global weights of criteria calculated by SWARA method.
3.2. Prioritization of Species
The AHP was calculated according to the steps (steps 1–4) specified in the relevant
method (see Tables A7 and A8). Within the scope of the AHP, pairwise comparison opin-
ions from ten DMs were considered. In this way, the priority values of the alternatives for
each tree species were determined.
3.3. SWARA-AHP Results
Within the scope of the study, the SWARA and AHP results were combined under
one roof. According to the results of the SWARA-AHP hybrid approach, A1 alternative
was selected as the most suitable tree species for rehabilitation plantations since it received
the highest priority value (Table 4). The ranking order is A1: Robinia pseudoacacia L. (0456)
> A2: Alnus glutinosa subsp. glutinosa (0248) > A3: Populus nigra subsp. nigra (0146) > A4:
Salix alba L. (0103) > A5: Quercus robur subs. robur (0048) (Figure 5).
Figure 4. Global weights of criteria calculated by SWARA method.
3.2. Prioritization of Species
The AHP was calculated according to the steps (steps 1–4) specified in the relevant
method (see Tables A7 and A8). Within the scope of the AHP, pairwise comparison opinions
from ten DMs were considered. In this way, the priority values of the alternatives for each
tree species were determined.
3.3. SWARA-AHP Results
Within the scope of the study, the SWARA and AHP results were combined under one
roof. According to the results of the SWARA-AHP hybrid approach, A1 alternative was
selected as the most suitable tree species for rehabilitation plantations since it received the
highest priority value (Table 4). The ranking order is A1: Robinia pseudoacacia L. (0456) >
A2: Alnus glutinosa subsp. glutinosa (0248) > A3: Populus nigra subsp. nigra (0146) > A4:
Salix alba L. (0103) > A5: Quercus robur subs. robur (0048) (Figure 5).
Table 4. Summary representation of SWARA ×AHP calculations.
SWARA Results
Criteria
(c) C1C2C3C4C5C6C7C8C9C10
0.0767 0.0748 0.0799 0.0909 0.0866 0.1024 0.0998 0.1288 0.1229 0.1374
Alternative AHP Results
A1 0.424 0.505 0.431 0.505 0.459 0.487 0.450 0.427 0.425 0.425
A2 0.243 0.207 0.243 0.234 0.289 0.275 0.240 0.247 0.260 0.260
A3 0.180 0.134 0.160 0.115 0.083 0.087 0.158 0.185 0.180 0.180
A4 0.110 0.105 0.120 0.093 0.123 0.104 0.083 0.095 0.093 0.093
A5 0.042 0.050 0.045 0.052 0.046 0.047 0.068 0.045 0.042 0.042
SWARA ×AHP Results
ACC1C2C3C4C5C6C7C8C9C10 wi
A1 0.058 0.065 0.053 0.052 0.046 0.044 0.039 0.034 0.033 0.032 0.456
A2 0.033 0.027 0.030 0.024 0.029 0.025 0.021 0.020 0.020 0.019 0.248
A3 0.025 0.017 0.020 0.012 0.008 0.008 0.014 0.015 0.014 0.013 0.146
A4 0.015 0.013 0.015 0.009 0.012 0.009 0.007 0.008 0.007 0.007 0.103
A5 0.006 0.006 0.006 0.005 0.005 0.004 0.006 0.004 0.003 0.003 0.048
Forests 2024,15, 783 12 of 21
Forests 2024, 15, x FOR PEER REVIEW 12 of 23
Table 4. Summary representation of SWARA × AHP calculations.
SWARA Results
Criteria
(c)
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
0.0767
0.0748
0.0799
0.0909
0.0866
0.1024
0.0998
0.1288
0.1229
0.1374
Alternative
AHP Results
A1
0.424
0.505
0.431
0.505
0.459
0.487
0.450
0.427
0.425
0.425
A2
0.243
0.207
0.243
0.234
0.289
0.275
0.240
0.247
0.260
0.260
A3
0.180
0.134
0.160
0.115
0.083
0.087
0.158
0.185
0.180
0.180
A4
0.110
0.105
0.120
0.093
0.123
0.104
0.083
0.095
0.093
0.093
A5
0.042
0.050
0.045
0.052
0.046
0.047
0.068
0.045
0.042
0.042
SWARA × AHP Results
C
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
wi
A
A1
0.058
0.065
0.053
0.052
0.046
0.044
0.039
0.034
0.033
0.032
0.456
A2
0.033
0.027
0.030
0.024
0.029
0.025
0.021
0.020
0.020
0.019
0.248
A3
0.025
0.017
0.020
0.012
0.008
0.008
0.014
0.015
0.014
0.013
0.146
A4
0.015
0.013
0.015
0.009
0.012
0.009
0.007
0.008
0.007
0.007
0.103
A5
0.006
0.006
0.006
0.005
0.005
0.004
0.006
0.004
0.003
0.003
0.048
Figure 5. Prioritization of plant species.
4. Discussion
The main objective of this research was to select suitable plant species for mine rec-
lamation. However, the author also endeavored to implement a robust MCDM approach,
specifically SWARA-AHP, in selecting the plant types as an additional objective of this
work.
4.1. Criteria and Sub-Criteria
In this context, 10 criteria under three criteria groups were ranked and weighted by
SWARA. The criteria with the highest degree of importance in rehabilitation afforestation
for DMs were included in the “economic criteria” group and constituted the first three
places in the overall ranking (C10: 0.1374, C8: 0.1288, and C9: 0.1229). It is stated in many
studies [71–73] that economic criteria have an important place in mine rehabilitation fea-
sibility studies. For this reason, plants that are intended to reduce afforestation costs
Figure 5. Prioritization of plant species.
4. Discussion
The main objective of this research was to select suitable plant species for mine reclamation.
However, the author also endeavored to implement a robust MCDM approach, specifically
SWARA-AHP, in selecting the plant types as an additional objective of this work.
4.1. Criteria and Sub-Criteria
In this context, 10 criteria under three criteria groups were ranked and weighted by
SWARA. The criteria with the highest degree of importance in rehabilitation afforestation
for DMs were included in the “economic criteria” group and constituted the first three
places in the overall ranking (C10: 0.1374, C8: 0.1288, and C9: 0.1229). It is stated in
many studies [
71
–
73
] that economic criteria have an important place in mine rehabilitation
feasibility studies. For this reason, plants that are intended to reduce afforestation costs
should be preferred when selecting plant species in rehabilitation afforestation. On the
other hand, productivity should also be adopted as a principle in plant species selection.
This will contribute to the sustainability of rehabilitation projects. Effective projects will
also encourage the efficient use of resources. The carbon sequestration capacity of plant
species in rehabilitation afforestation should be another important criterion to be considered
in afforestation investments. Afforestation with species with high carbon sequestration
capacity will effectively combat climate change and reduce carbon emissions.
Studies involving species contributing significantly to the aesthetic value in afforesta-
tion areas will enhance visual appeal. On the other hand, ease of access to the selected
plant species is another factor affecting the success of afforestation. For this reason, the
cultural kit will provide practicality to the decision-maker in the process of initiating and
maintaining afforestation projects.
The plant species selected for good afforestation should help reduce soil and water pollu-
tion. In particular, plants that can contribute to reducing problems such as industrial pollution
should be preferred. Plant species should be selected with root systems and soil retention
properties that help prevent or reduce soil erosion. This improves soil fertility and reduces the
effects of environmental erosion. The growth rate, size, and overall robustness of the selected
plant species are important for the success of the rehabilitation process. The rehabilitation
process can be accelerated by choosing fast-growing, strong, and durable plants.
In the selection of plant species in rehabilitation plantations, it is important to prefer
species that are resistant to plant diseases and pests. This ensures the healthy and sustainable
development of plant populations and can reduce maintenance costs. The selected plant
species should be compatible with the climate, soil, and other environmental conditions of
the rehabilitation area. Regional compatibility is critical to ensure successful plant growth and
ecosystem stability. The compatibility and interactions of plant species with other species in
Forests 2024,15, 783 13 of 21
the rehabilitation area should be considered. Adaptation should be encouraged, and negative
interactions should be avoided. Thus, ecosystem balance can be maintained.
In fact, the criteria groups considered in this publication and all the criteria under them
have an important place in mine rehabilitation afforestation. However, in rehabilitation
investments and feasibility studies, economic criteria have an important place in making
afforestation decisions. Likewise, rehabilitation works for mines mean the closure of a
mining operation. Such activities are considered a cost element rather than a return for the
enterprise. For this reason, rehabilitation costs should be included in feasibility reports at
the stage of deciding to mine a site, that is, at the very beginning of the project. In this way,
it will be possible to return mines that have completed their economic life back to nature.
4.2. Alternatives
For the plant species considered, it should be noted that the selection of suitable
plant species for mine reclamation is closely related to edaphic and topographic factors as
well as climatic conditions. Among soil properties, saturation moisture, organic matter,
limestone, and the C/N ratio greatly influence the distribution and selection criteria of
plants. Furthermore, the relationship of plants with environmental factors was the basis
for the selection of plant species [
74
]. Plant species were selected based on the mentioned
criteria. The identification of plant species adapted to the environmental conditions in a
particular area will also provide guidance for the remediation and regeneration of similar
mine sites. In this context, the selected species will be adapted to the mining conditions in
the Gürgenpınar–Topluca region and will be suitable for the regeneration of the mine.
In the SWARA-AHP hybrid approach, analyses were carried out with ten DMs to
determine the weights of the criteria and determine the priorities of the species. In the
Gürgenpınar–Topluca area, Robinia pseudoacacia L. was found to be the most suitable species
for mine site rehabilitation afforestation, with 0.502 in line with the regional conditions and
criteria. On the other hand, the results of the study show that Alnus glutinosa subsp. glutinosa
with 0.288 and Populus nigra subsp. nigra with 0.210 were also suitable for afforestation.
In mine rehabilitation afforestation, it is important to select suitable plant species and
determine the afforestation criteria to be used in the selection process. Robinia pseudo-acacia
L. is considered an important plant because it is the most common plant in the region. It
was considered an alternative plant in terms of erosion prevention in the rehabilitation
plan, especially due to its suitability to the conditions of the site. This species may also
have some advantages, such as its C/N ratio and carbon accumulation. Alnus glutinosa
subsp. glutinosa is highly compatible with the climatic conditions of the Western Black
Sea region, and this species is also important for aesthetic value and carbon sequestration.
Populus nigra subsp. nigra is very good at wastewater absorption and is considered to have
a high potential for Gürgenpınar–Topluca mine rehabilitation. Likewise, the chance of
afforestation success and carbon accumulation in rehabilitation works to be carried out
with these species is high, and the afforestation costs are low compared to other species.
Furthermore, it is difficult to ignore the selection of preferences due to the role that plant
presence plays in the rehabilitation of the mine; for this reason, only existing species were
considered in the assessments.
A study conducted by Ebrahimabadi (2016) [
73
] in the Chadormaloo iron mine of Iran
identified key criteria for plant species selection in mine reclamation, including landscape
characteristics, resistance to pests and diseases, growth consistency and method, avail-
ability, economic viability, soil protection, water storage, and pollution prevention. Four
alternatives—Artemisia sieberi,Zygophyllum,Salsola yazdiana, and Halophytes types—were as-
sessed in the Chadormaloo iron ore mine. Subsequently, utilizing the fuzzy AHP approach,
Artemisia sieberi was determined as the optimal plant species for mine rehabilitation.
Furthermore, Ebrahimabadi et al. (2018) [
75
] conducted a comparative analysis of the
PROMETHEE and fuzzy TOPSIS methods for selecting the most suitable plant species
to reclaim the Sarcheshmeh copper mine. Six vegetation types, such as pistachio, wild
almond, ephedra, astragalus, salsola, and tamarix, were considered and adapted to the
Forests 2024,15, 783 14 of 21
mine’s conditions. The PROMETHEE and fuzzy TOPSIS methods were then employed
to assess these alternatives based on seven criteria, including appearance, resistance to
pests and diseases, growth characteristics, accessibility, economic feasibility, soil-water
conservation, and pollution control. As a result, wild almond was identified as the optimal
choice according to both methods. These findings highlight the robustness of the MCDM
approaches in selecting appropriate plant species for mine rehabilitation efforts.
4.3. Comparison to Other MCDM and Group Decision-Making Studies
Within the scope of the study, the SWARA-AHP hybrid approach, which is a robust
MCDM approach, was applied and the opinions of DMs, i.e., experts, were analyzed.
SWARA-AHP is a suitable method for selecting criteria, plant species, or other multi-
criteria in decision-making problems.
In fact, both criteria and tree species can be prioritized using the AHP only. However,
since 10 criteria are considered in this survey, it is not easy to compare the criteria one by
one with the others using the AHP. In comparisons of seven or more criteria, the AHP
consistency rates are often not among the desired levels. Therefore, in problems with more
than seven variables, the AHP alone is insufficient as a solution [
67
]. In such problems, the
ranking method should be preferred instead of comparison in criteria weighting. For this
purpose, many methods, such as SWARA, TOPSIS, and VIKOR, are used together with the
AHP [60].
SWARA has been successfully applied to the solution of many MCDM problems.
SWARA is used in various fields due to its suitability for experts to work together and
its simple application [
61
–
63
,
76
–
81
]. For this reason, SWARA is also defined as an expert-
oriented method in the literature [
81
]. The number of comparisons required in SWARA
is significantly lower compared to other methods. In the data obtained through ques-
tionnaires, the consistent responses of the participants make the SWARA method more
successful. In the SWARA method, participants evaluate the criteria freely, without em-
ploying any scale [82].
4.4. Limitations of the Study and Future Improvements
The SWARA-AHP approach stands as a suitable tool for the selection of plant species
or addressing other multi-criteria decision-making challenges. Nonetheless, this approach
presents certain constraints and drawbacks, as elucidated below.
In this approach, the DM is only asked to make a judgment based on the criteria
specified in this context. At this point, the DM is required to indicate the relative importance
of one criterion over another or to prefer one alternative to another. However, when the
number of alternatives and criteria increases, the pairwise comparison process becomes
cumbersome, and the risk of inconsistency arises. For this reason, instead of using a
single method, such as the AHP, there is a need for hybrid approaches where more than
one method is considered together. Researchers or mine site managers should create an
appropriate hybrid approach according to the nature of the problem they have. In this
context, many methods, such as COPRAS, PROMETHEE, and MOORA, which are among
the methods of MCDM, are suitable to be used together with both SWARA and the AHP.
5. Conclusions
Multi-criteria decision-making (MCDM) approaches, especially hybrid methods such
as SWARA-AHP, are considered appropriate methods for species selection for post-mining
afforestation. This approach has contributed to making the right decisions in a rational
decision-making process. MCDM effectively reflects the experience of DMs.
As previously mentioned, criteria weighting and species prioritization for rehabilita-
tion afforestation were performed to determine the environmental, social, and economic
use possibilities of post-mining sites. Establishing a decision-supported WPA framework is
important for the adoption of an analytical method with the following benefits:
Forests 2024,15, 783 15 of 21
•
DMs’ opinions and preferences are taken into account in identifying plant species
that can be used in rehabilitation afforestation. The attributes of DMs’ preferences are
integrated into the MCDM approach;
•
The mathematical operations on the WPA framework are designed in a hierarchical
order to understand various and contradictory attributes. This facilitates a more
comprehensive and accurate decision-making process in criteria prioritization and
plant species selection;
•
Results for post-mining afforestation are presented to all stakeholders through an
understandable algorithm. The data are accessible, and analyses and calculations can
be audited.
In post-mining afforestation, it is important to determine appropriate criteria to reduce
environmental impacts and select appropriate plant species for mining areas. In this context,
the Bartın region, which includes important mining areas of Turkey, was selected as a case
study. The Gürgenpınar–Topluca mine site was analyzed to determine the compatibility
of local plants with the soil, water, and climatic conditions. In this paper, the forest was
selected as the post-mining land use type for rehabilitation afforestation, and criteria and
candidate tree species were identified for this purpose. The SWARA-AHP hybrid approach
is suitable for weighting criteria and prioritizing plant species. Through this approach,
criteria, such as resistance compatibility, pollution prevention, erosion prevention, growth
type and strength, aesthetic appearance, access to plant species, carbon stock and credit,
reducing afforestation cost, and economic efficiency, can be effectively ranked and weighted
under the group of ecological, social, and economic criteria.
The analyses show that economic criteria play an important role in rehabilitation
investments and feasibility studies. This is because post-mining afforestation is considered
a cost rather than a return for the enterprises in the Gürgenpınar–Topluca mining region.
The results demonstrate that economic criteria are important in the decision-making process.
In the Gürgenpınar–Topluca mining area, prioritizing species with low afforestation costs
and high productivity and carbon sequestration potential can be adopted as a strategy for
sustainable land management and successful forest establishment. This strategy contributes
to the efforts to combat climate change and reduce carbon emissions. For successful forest
restoration in the region, environmental, economic, and social uses of the post-mining land
use type should be considered. Based on these criteria, the best candidates for revegetation
were prioritized as follows: Robinia pseudoacacia L., Alnus glutinosa subsp. glutinosa,Populus
nigra subsp. nigra, Salix alba L., and Quercus robur subs. robur.
Author Contributions: Conceptualization, E.G. and G.¸S.; methodology, E.G.; software, E.G.; vali-
dation, E.G. and G.¸S.; formal analysis, E.G.; investigation, E.G. and G.¸S.; resources, E.G. and G.¸S.;
data curation, E.G. and G.¸S.; writing—original draft preparation, E.G.; writing—review and editing,
E.G.; visualization, E.G. and G.¸S.; supervision, E.G. and G.¸S.; project administration, E.G.; funding
acquisition, E.G. All authors have read and agreed to the published version of the manuscript.
Funding: This research was found by Zonguldak Technology Development Zone, within BTSB
(Republic of Türkiye Ministry of Industry and Technology), grant number [BTSB-2021-70974], project
title “Strategies for Using Waste Beech Mushroom Compost in Mine Site Afforestation”.
Data Availability Statement: Data are contained within the article.
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
WPA Analysis of weighting criteria and prioritization of species
MCDM Multiple-criteria decision-making
AHP Analytic hierarchy process
Forests 2024,15, 783 16 of 21
SWARA Stepwise weight assessment ratio analysis
CR Consistency ratio
DMs Decision-makers
Appendix A
Table A1. Information about DMs.
Education Level Specializations Experience Department
PhD Forestry 25 years Planning
PhD Forestry 20 years
Pollution and Resistance
PhD Forestry 21 years Water
PhD Forestry 18 years Aesthetics
PhD Forestry 15 years Carbon
PhD Environmental 28 years Erosion
MSc Environmental 15 years Afforestation
MSc Soil 16 years Solid Waste
BSc Civil 30 years Planning
BSc Civil 30 years Planning
Table A2. Assessment criteria.
Criteria Group
Label Criterion Description
Ecological
C1 Resistance Plants fight both diseases and insect pests in nature. In rehabilitation plantations,
species that do not cause direct and indirect losses and are resistant to diseases and
insects should be preferred.
C2 Compatibility
In rehabilitation, compatibility with the region and other species is important in
species selection. Plants with high adaptability make good use of nutrients, water, heat,
and light in the environment. They develop protection against drought, parasites, or
extreme temperature changes.
C3 Pollution Prevention
Mining waste is one of the most undesirable pollutants for the environment. Each
plant has the ability to eliminate different pollutants at different levels. For this reason,
afforestation should be undertaken with plants that have a high potential to eliminate
pollution.
C4 Erosion Prevention Rehabilitation sites should be afforested with species that have high wind resistance
and water and soil retention capacity, as well as fast and well-growing species. In this
way, the risk of soil, water, and wind erosion can be reduced.
C5 Growth Type and Strength In afforestation, the growth type and strength of the plant are important for its
attachment to the soil, its growth, and its continuity in the field.
Social
C6 Aesthetic Appearance Aesthetic appearance forms the basis of human beings’ view of nature. Prioritizing
species with high aesthetic value in rehabilitation has a positive effect on the
appearance of the site and human psychology [83].
C7 Access to Plant Species
In rehabilitation works, access to and procurement of plant species to be planted on the
site is important. In afforestation, saplings that are easy to procure and adapt to local
conditions should be selected from regional nurseries.
Economic
C8 Carbon Stock and Credit
Carbon stock refers to the process that prevents the release of carbon into the
atmosphere over a certain period of time [84]. Prioritizing species with a high carbon
storage capacity in afforestation is important for reducing the amount of carbon
dioxide in the atmosphere and for the enterprise to receive carbon credits. This will
also contribute to the prevention of global warming [85].
C9 Reducing Afforestation Cost
In rehabilitation feasibility studies, many cost items, such as surveys, land preparation,
fencing, and planting, should be well defined. Methods with low-cost items and
species suitable for these methods reduce the cost of afforestation. Likewise, candidate
species for afforestation should not only be ecologically and technically healthy but
also socially acceptable and economically cost-effective.
C10 Economic Efficiency
Economic efficiency is an important criterion affecting costs and investment decisions.
In mining investments, the economic return of the ore to be obtained and the damage
to nature should be analyzed well. In fact, the cost of reclamation works to be carried
out on lands devastated after mining should be included in investment calculations in
the initial feasibility studies. Likewise, the tree species to be used in rehabilitation
afforestation is an important variable in calculating economic efficiency.
Forests 2024,15, 783 17 of 21
Table A3. Species suitable for rehabilitation afforestation in the study area and Delphi score.
Species Delphi Score Mean
Included in WPA Framework
1 A1—Robinia pseudoacacia L. 4.35 Yes
2 A2—Alnus glutinosa subsp. glutinosa 4.20 Yes
3 A3—Populus nigra subsp. nigra 3.82 Yes
4 A4—Quercus robur subsp. robur 3.22 Yes
5 A5—Salix alba L. 3.22 Yes
6Pinus pinea 2.65 No
7Pseudotsuga menziesii 2.64 No
8Acer negundo 2.60 No
9Acer campestre 2.56 No
10 Ailanthus altissima 2.54 No
11 Carpinus betulus 2.50 No
12 Gleditsia triacanthos 2.48 No
13 Juglans regia 2.42 No
14 Pinus nigra 2.41 No
15 Ulmus minör 2.40 No
Table A4. Evaluations of DMs for criteria and merged relative importance SWARA scores.
Criteria
Individual Evaluations of DMs
pk
jMerged Relative Importance Score
¯
pj
DM1DM2DM3DM4DM5DM6DM7DM8DM9DM10
C10.40 0.15 0.20 0.50 0.60 0.35 0.20 0.25 0.50 0.60 0.337013
C20.50 0.20 0.30 0.20 0.40 0.45 0.25 0.30 0.20 0.40 0.302801
C30.60 0.25 0.50 0.35 0.25 0.55 0.30 0.35 0.35 0.25 0.356503
C40.65 0.40 0.40 0.40 0.75 0.70 0.55 0.55 0.40 0.75 0.536673
C50.35 0.50 0.65 0.25 0.65 0.40 0.40 0.65 0.25 0.65 0.446138
C60.80 0.65 0.60 0.55 0.55 0.80 0.45 0.60 0.55 0.55 0.601182
C70.95 0.45 0.55 0.65 0.50 1.00 0.65 0.55 0.65 0.50 0.623497
C81.00 0.70 1.00 0.90 0.80 0.95 0.70 1.00 0.95 0.95 0.887299
C90.70 1.00 0.80 0.80 0.85 0.75 0.95 0.90 0.70 0.90 0.829290
C10 0.90 0.90 0.90 1.00 1.00 0.85 0.90 0.95 1.00 1.00 0.938450
Table A5. Calculation of final criteria weights using the SWARA method.
Criteria
Merged Relative
Importance Score
(Ordered)
¯
pj
Comparative
Importance
sj
Coefficient
Value
cj
Corrected
Weight Value
s’j
Final Weight
Value
wj
Rank
C10 0.938450 - 1.000000 1.000000 0.1365 1
C80.887299 0.051151 1.051151 0.951338 0.1298 2
C90.829290 0.058009 1.058009 0.899178 0.1227 3
C70.623497 0.205793 1.205793 0.745714 0.1018 4
C60.601182 0.022315 1.022315 0.729437 0.0995 5
C40.536673 0.064509 1.064509 0.685234 0.0935 6
C50.446138 0.090535 1.090535 0.628346 0.0857 7
C30.356503 0.089635 1.089635 0.576657 0.0787 8
C10.337013 0.019490 1.019490 0.565633 0.0772 9
C20.302801 0.034211 1.034211 0.546922 0.0746 10
Table A6. Summary representation of SWARA calculations.
Criteria SWARA Results
C1C2C3C4C5C6C7C8C9C10
0.0772 0.0746 0.0787 0.0935 0.0857 0.0995 0.101898 0.1298 0.1227 0.1365
Forests 2024,15, 783 18 of 21
Table A7. Weight values of tree species found by AHP calculations.
Alternative
Ranking Values Obtained Normalized Value of Decision Matrix Weight
C1 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S1
A1 1.000 3.000 7.000 3.000 5.000 0.498 0.634 0.525 0.246 0.217 0.424
A2 0.333 1.000 5.000 3.000 5.000 0.166 0.211 0.375 0.246 0.217 0.243
A3 0.143 0.200 1.000 5.000 7.000 0.071 0.042 0.075 0.410 0.304 0.180
A4 0.333 0.333 0.200 1.000 5.000 0.166 0.070 0.015 0.082 0.217 0.110
A5 0.200 0.200 0.143 0.200 1.000 0.100 0.042 0.011 0.016 0.043 0.042
CR < 0.05 2.010 4.733 13.343 12.200 23.000 1.000 1.000 1.000 1.000 1.000 1.000
C2 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S2
A1 1.000 7.000 7.000 3.000 5.000 0.550 0.789 0.600 0.290 0.294 0.505
A2 0.143 1.000 3.000 3.000 5.000 0.079 0.113 0.257 0.290 0.294 0.207
A3 0.143 0.333 1.000 3.000 3.000 0.079 0.038 0.086 0.290 0.176 0.134
A4 0.333 0.333 0.333 1.000 3.000 0.183 0.038 0.029 0.097 0.176 0.105
A5 0.200 0.200 0.333 0.333 1000 0.110 0.023 0.029 0.032 0.059 0.050
CR < 0.05 1.819 8.867 11.667 10.333 17.000 1.000 1.000 1.000 1.000 1.000 1.000
C3 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S3
A1 1.000 3.000 5.000 3.000 5.000 0.484 0.616 0.524 0.294 0.238 0.431
A2 0.333 1000 3.000 3.000 5.000 0.161 0.205 0.315 0.294 0.238 0.243
A3 0.200 0.333 1.000 3.000 5.000 0.097 0.068 0.105 0.294 0.238 0.160
A4 0.333 0.333 0.333 1.000 5.000 0.161 0.068 0.035 0.098 0.238 0.120
A5 0.200 0.200 0.200 0.200 1.000 0.097 0.041 0.021 0.020 0.048 0.045
CR < 0.05 2.067 4.867 9.533 10.200 21.000 1.000 1.000 1.000 1.000 1.000 1.000
C4 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S4
A1 1.000 5.000 7.000 5.000 5.000 0.574 0.734 0.574 0.349 0.294 0.505
A2 0.200 1.000 3.000 7.000 3.000 0.115 0.147 0.246 0.488 0.176 0.234
A3 0.143 0.333 1.000 1.000 5.000 0.082 0.049 0.082 0.070 0.294 0.115
A4 0.200 0.143 1.000 1.000 3.000 0.115 0.021 0.082 0.070 0.176 0.093
A5 0.200 0.333 0.200 0.333 1.000 0.115 0.049 0.016 0.023 0.059 0.052
CR < 0.05 1.743 6.810 12.200 14.333 17.000 1.000 1.000 1.000 1.000 1.000 1.000
C5 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S5
A1 1.000 5.000 7.000 3.000 5.000 0.533 0.764 0.488 0.246 0.263 0.459
A2 0.200 1.000 5.000 7.000 5.000 0.107 0.153 0.349 0.574 0.263 0.289
A3 0.143 0.200 1.000 1.000 3.000 0.076 0.031 0.070 0.082 0.158 0.083
A4 0.333 0.143 1.000 1.000 5.000 0.178 0.022 0.070 0.082 0.263 0.123
A5 0.200 0.200 0.333 0.200 1.000 0.107 0.031 0.023 0.016 0.053 0.046
CR < 0.05 1.876 6.543 14.333 12.200 19.000 1.000 1.000 1.000 1.000 1.000 1.000
C6 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S6
A1 1.000 7.000 7.000 3.000 5.000 0.550 0.812 0.568 0.243 0.263 0.487
A2 0.143 1.000 3.000 7.000 7.000 0.079 0.116 0.243 0.568 0.368 0.275
A3 0.143 0.333 1.000 1.000 3.000 0.079 0.039 0.081 0.081 0.158 0.087
A4 0.333 0.143 1.000 1.000 3.000 0.183 0.017 0.081 0.081 0.158 0.104
A5 0.200 0.143 0.333 0.333 1.000 0.110 0.017 0.027 0.027 0.053 0.047
CR < 0.05 1.819 8.619 12.333 12.333 19.000 1.000 1.000 1.000 1.000 1.000 1.000
C7 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S7
A1 1.000 7.000 5.000 5.000 3.000 0.533 0.807 0.436 0.246 0.231 0.450
A2 0.143 1.000 5.000 7.000 3.000 0.076 0.115 0.436 0.344 0.231 0.240
A3 0.200 0.200 1.000 7.000 3.000 0.107 0.023 0.087 0.344 0.231 0.158
A4 0.200 0.143 0.143 1.000 3.000 0.107 0.016 0.012 0.049 0.231 0.083
A5 0.333 0.333 0.333 0.333 1.000 0.178 0.038 0.029 0.016 0.077 0.068
CR < 0.05 1.876 8.676 11.476 20.333 13.000 1.000 1.000 1.000 1.000 1.000 1.000
C8 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S8
A1 1.000 3.000 7.000 3.000 5.000 0.498 0.634 0.525 0.243 0.238 0.427
A2 0.333 1.000 5.000 3.000 5.000 0.166 0.211 0.375 0.243 0.238 0.247
A3 0.143 0.200 1.000 5.000 7.000 0.071 0.042 0.075 0.405 0.333 0.185
A4 0.333 0.333 0.200 1.000 3.000 0.166 0.070 0.015 0.081 0.143 0.095
A5 0.200 0.200 0.143 0.333 1.000 0.100 0.042 0.011 0.027 0.048 0.045
CR < 0.05 2.010 4.733 13.343 12.333 21.000 1.000 1.000 1.000 1.000 1.000 1.000
C9 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S9
A1 1.000 3.000 7.000 3.000 5.000 0.498 0.642 0.525 0.243 0.217 0.425
A2 0.333 1.000 5.000 3.000 7.000 0.166 0.214 0.375 0.243 0.304 0.260
A3 0.143 0.200 1.000 5.000 7.000 0.071 0.043 0.075 0.405 0.304 0.180
A4 0.333 0.333 0.200 1.000 3.000 0.166 0.071 0.015 0.081 0.130 0.093
A5 0.200 0.143 0.143 0.333 1.000 0.100 0.031 0.011 0.027 0.043 0.042
CR < 0.05 2.010 4.676 13.343 12.333 23.000 1.000 1.000 1.000 1.000 1.000 1.000
C10 A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 S10
A1 1.000 3.000 7.000 3.000 5.000 0.498 0.642 0.525 0.243 0.217 0.425
A2 0.333 1.000 5.000 3.000 7.000 0.166 0.214 0.375 0.243 0.304 0.260
A3 0.143 0.200 1.000 5.000 7.000 0.071 0.043 0.075 0.405 0.304 0.180
A4 0.333 0.333 0.200 1000 3.000 0.166 0.071 0.015 0.081 0.130 0.093
A5 0.200 0.143 0.143 0.333 1000 0.100 0.031 0.011 0.027 0.043 0.042
CR < 0.05 2.010 4.676 13.343 12.333 23.000 1.000 1.000 1.000 1.000 1.000 1.000
Forests 2024,15, 783 19 of 21
Table A8. Summary representation of AHP calculations.
Alternative
AHP Results
C1C2C3C4C5C6C7C8C9C10
A1 0.424 0.505 0.431 0.505 0.459 0.487 0.450 0.427 0.425 0.425
A2 0.243 0.207 0.243 0.234 0.289 0.275 0.240 0.247 0.260 0.260
A3 0.180 0.134 0.160 0.115 0.083 0.087 0.158 0.185 0.180 0.180
A4 0.110 0.105 0.120 0.093 0.123 0.104 0.083 0.095 0.093 0.093
A5 0.042 0.050 0.045 0.052 0.046 0.047 0.068 0.045 0.042 0.042
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