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Carbon Sequestration Capacity after Ecological Restoration of Open-Pit Mines: A Case Study in Yangtze River Basin, Jurong City, Jiangsu Province

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Open-pit mining seriously damages the original vegetation community and soil layer and disturbs the carbon cycle of vegetation and soil, causing instability in the mining ecosystem and decrease in the carbon sequestration capacity of the mining area. With the deepening of environmental awareness and the influence of related policies, the ecological restoration of open-pit mines has been promoted. The mining ecosystem is distinct owing to the disperse distribution of mines and small scale of single mines. However, the carbon sequestration capability of mines after ecological restoration has not been clearly evaluated. Therefore, this study evaluated the carbon sequestration capacity of restoration mines, taking the mines of the Yangtze River Basin in Jurong City, Jiangsu Province as the research objects. Firstly, the visual effects of the vegetation and soil in their current status were determined through field investigation, the methods for sampling and data collection for the vegetation and soil were selected, and the specific laboratory tests such as the vegetation carbon content and soil organic carbon were clarified. Meanwhile, the evaluation system consisting of three aspects and nine evaluation indexes was established by using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The process of evaluation included the following: the establishment of the judgment matrix, calculation of the index weight, determination of the membership function, and establishment of the fuzzy membership matrix. Finally, the evaluation results of the restoration mines were determined with the ‘excellent, good, normal and poor’ grade classification according to the evaluation standards for each index proposed considering the data of the field investigation and laboratory tests. The results indicated that (1) the evaluation results of the mines’ carbon sequestration capacity were of excellent and good grade at a proportion of 62.5% and 37.5%, which was in line with the field investigation results and demonstrated the carbon sequestration capacity of all the restored mines was effectively improved; and (2) the weights of the criterion layer were ranked as system stability > vegetation > soil with the largest value of 0.547, indicating the stability of the system is the main factor in the carbon sequestration capacity of the mines and the sustainability of the vegetation community and the stability of soil fixation on the slope. The proposed evaluation system effectively evaluates the short-term carbon sequestration capability of the restoration mining system according to the visual effects and the laboratory testing results, objectively reflecting the carbon sequestration capacity via qualitative assessment and quantitative analysis. The evaluation method is relatively applicable and reliable for restoration mines and can provide a reference for similar ecological restoration engineering.
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Citation: Zhou, S.; Li, X.; Zhang, P.;
Lu, G.; Zhang, X.; Zhang, H.; Zhang, F.
Carbon Sequestration Capacity after
Ecological Restoration of Open-Pit
Mines: A Case Study in Yangtze River
Basin, Jurong City, Jiangsu Province.
Sustainability 2024,16, 8149. https://
doi.org/10.3390/su16188149
Academic Editors: Rajesh Kumar
Jyothi, Francis F. Pavloudakis,
Christos Roumpos and Philip-Mark
Spanidis
Received: 24 July 2024
Revised: 14 September 2024
Accepted: 17 September 2024
Published: 18 September 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Carbon Sequestration Capacity after Ecological Restoration of
Open-Pit Mines: A Case Study in Yangtze River Basin,
Jurong City, Jiangsu Province
Shenli Zhou 1, Xiaokai Li 2, *, Pengcheng Zhang 1, Gang Lu 1, Xiaolong Zhang 2, Huaqing Zhang 2
and Faming Zhang 2
1Institute of Geochemical Exploration and Marine Geological Survey, ECE, Nanjing 210007, China;
13357816148@163.com (S.Z.); 15952019058@163.com (P.Z.); njlg@hotmail.com (G.L.)
2School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China;
231309080031@hhu.edu.cn (X.Z.); 231309080030@hhu.edu.cn (H.Z.); zhangfm@hhu.edu.cn (F.Z.)
*Correspondence: 221309080014@hhu.edu.cn; Tel.: +86-159-0564-7682
Abstract: Open-pit mining seriously damages the original vegetation community and soil layer and
disturbs the carbon cycle of vegetation and soil, causing instability in the mining ecosystem and
decrease in the carbon sequestration capacity of the mining area. With the deepening of environmental
awareness and the influence of related policies, the ecological restoration of open-pit mines has
been promoted. The mining ecosystem is distinct owing to the disperse distribution of mines and
small scale of single mines. However, the carbon sequestration capability of mines after ecological
restoration has not been clearly evaluated. Therefore, this study evaluated the carbon sequestration
capacity of restoration mines, taking the mines of the Yangtze River Basin in Jurong City, Jiangsu
Province as the research objects. Firstly, the visual effects of the vegetation and soil in their current
status were determined through field investigation, the methods for sampling and data collection
for the vegetation and soil were selected, and the specific laboratory tests such as the vegetation
carbon content and soil organic carbon were clarified. Meanwhile, the evaluation system consisting
of three aspects and nine evaluation indexes was established by using the analytic hierarchy process
(AHP) and fuzzy comprehensive evaluation (FCE). The process of evaluation included the following:
the establishment of the judgment matrix, calculation of the index weight, determination of the
membership function, and establishment of the fuzzy membership matrix. Finally, the evaluation
results of the restoration mines were determined with the ‘excellent, good, normal and poor’ grade
classification according to the evaluation standards for each index proposed considering the data
of the field investigation and laboratory tests. The results indicated that (1) the evaluation results
of the mines’ carbon sequestration capacity were of excellent and good grade at a proportion of
62.5% and 37.5%, which was in line with the field investigation results and demonstrated the carbon
sequestration capacity of all the restored mines was effectively improved; and (2) the weights of the
criterion layer were ranked as system stability > vegetation > soil with the largest value of 0.547,
indicating the stability of the system is the main factor in the carbon sequestration capacity of the
mines and the sustainability of the vegetation community and the stability of soil fixation on the slope.
The proposed evaluation system effectively evaluates the short-term carbon sequestration capability
of the restoration mining system according to the visual effects and the laboratory testing results,
objectively reflecting the carbon sequestration capacity via qualitative assessment and quantitative
analysis. The evaluation method is relatively applicable and reliable for restoration mines and can
provide a reference for similar ecological restoration engineering.
Keywords: open-pit mines; ecological restoration; carbon sequestration capacity; analytic hierarchy
process; fuzzy comprehensive evaluation; comprehensive evaluation model; index system
Sustainability 2024,16, 8149. https://doi.org/10.3390/su16188149 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 8149 2 of 22
1. Introduction
Open-pit mining seriously damages the ecological environment by destroying orig-
inal vegetation and excavating soil mass and rock mass, leading to issues such as soil
erosion, land pollution, and geological hazards [
1
3
]. Mining significantly disrupts the
‘vegetation-soil carbon pool’ in the mining area, leading to a decrease in soil organic carbon
and the photosynthetic carbon fixation of vegetation [
4
,
5
]. As a result, the carbon seques-
tration capacity of the mining ecosystem rapidly decreases and gradually transforms into
a carbon source. In recent years, the United Nations has put forward the need to restore
ecosystems (including mining ecosystems) to improve the stability and sustainability of
ecosystems [
6
]. Additionally, with the influence of the carbon peak and carbon neutrality
goals and other policies in China, the ecological restoration of open-pit mines has been
accelerated. Therefore, establishing an evaluation system for the carbon sequestration
capacity of open-pit mines after ecological restoration is of great significance to assess the
effect of the restoration of ecosystems and achieve the dual carbon goals.
The ecological restoration of mines mainly relies on measures and technologies via
human intervention to gradually restore the mining ecosystem [
7
,
8
]. Measures and tech-
nologies that have been studied and developed include soil remediation and vegetation
rebuilding. Soil remediation can be divided into physical remediation, chemical remedi-
ation, and biological remediation [
9
12
], and the effect of soil restoration is studied via
factors such as the soil microbial indicators, the accumulation of soil organic carbon, and
water-holding capacity [
13
15
]. The vegetation rebuilding techniques applied in instances
mainly include vegetation-growing concrete, vegetative net, and vegetation bag [
16
18
],
and the vegetation restoration effect is evaluated by indicators such as remote sensing data
and images, soil seed bank, and vegetation diversity [
19
21
]. Therefore, the soil quality
and vegetation growth state of the restoration mines have been greatly improved.
Although the ecological restoration effect of mines is verified by studies, the capacity
and mechanism of carbon sequestration has not been clearly evaluated. Some studies
reveal that the key approaches of the carbon sink after ecological restoration are related to
vegetation and soil [
5
,
22
] and discuss the interrelationship between them in the process
of carbon sequestration [
23
,
24
]. Meanwhile, a computation model, modeling, and testing
are utilized to quantify or forecast the carbon sequestration of the mining site or the soil
organic carbon [
25
28
]. Therefore, the analysis provides guidelines for evaluating the
carbon sequestration capability of open-pit mines after ecological restoration.
Choosing an appropriate evaluation method can enhance the objectivity and reliability
of the evaluation results. The combination of the analytic hierarchy process (AHP) and the
analytic hierarchy process (FCE) can evaluate problems via qualitative and quantitative
methods according to the relationship among the indexes [
29
,
30
]. An evaluation model, by
using the AHP-FCE method, has been applied in assessing ecosystems. For instance, to
enhance the sustainability of the distinct ecosystem called Mulberry-Dyke and Fish-Pond
System, the AHP-FCE method is used to assess the provision, regulation, and cultural
services from the viewpoint of ecosystem services [
31
]. For the restoration mines, some
studies use the AHP-FCE method to establish different evaluation systems for various
evaluation goals. Owing to the fact that many evaluation goals related to restoration mines
have been effectively evaluated, such as the ecological restoration effects of slopes [
32
,
33
]
and difficulty of limestone mine restoration [
34
], the AHP-FCE method can be introduced
to evaluate the carbon sequestration capability of restoration mines.
According to the literature review, it can be concluded that most of the studies re-
lated to mine ecological restoration focus on the technologies for vegetation rebuilding
and soil reconstruction, while there are some studies assessing the environmental bene-
fit and restoration effect of the ecological restoration engineering and other ecosystems
using the AHP-FCE method. Although there are some studies on carbon sequestration
that mainly focus on large-scale ecosystems via quantitative calculation, the evaluation of
carbon sequestration on small-scale ecosystems and the establishment of the evaluation
system is relatively little. Owing to the fact that the restoration mining ecosystem has the
Sustainability 2024,16, 8149 3 of 22
characteristics of the disperse distribution of mines and the small scale of single mines
and traditional restoration measures concentrate on soil reconstruction and vegetation
rebuilding, the establishment of the evaluation system for the carbon sequestration ca-
pability within the short term (3–5a) is of practical significance considering the current
status of the vegetation community and soil layer. Therefore, taking specific mines in the
Yangtze River Basin, Jurong City, Jiangsu Province as the research objects, the evaluation
system including three aspects and nine evaluation indexes was proposed considering
the visual effects and carbon content properties of the vegetation and soil by combining
the content, experience, and research direction of the existing studies. The evaluation
results were comprehensively obtained from the field investigation for the current status
of restored mines, laboratory tests about the carbon content level of the vegetation and
soil, and the evaluation system with different indexes. The research not only presents an
evaluation system to evaluate the ecological restoration effect from the viewpoint of the
carbon sequestration capability of restored mines by combining the qualitative assessment
and quantitative analysis from the field investigation and indoor work, but also provides a
reference for the similar evaluation of ecological restoration engineering and the carbon
sequestration capability of other small-scale ecosystems.
2. Method
2.1. Brief Introduction of Study Area
The study area is in the northern part of Jurong City, Jiangsu Province with rich mineral
resources. The open-pit mines are located within 10 km of the Yangtze River Basin, mainly
concentrated near Baohua Mountain (Figure 1). The terrain of the research area mainly
consists of low mountains, hills, downlands, gentle slopes, and mountain valleys. The
water system in the research area is well developed, and the climate has the characteristics
of abundant rainfall and ample sunshine.
Sustainability 2024, 16, 8149 3 of 22
sequestration on small-scale ecosystems and the establishment of the evaluation system is
relatively lile. Owing to the fact that the restoration mining ecosystem has the
characteristics of the disperse distribution of mines and the small scale of single mines
and traditional restoration measures concentrate on soil reconstruction and vegetation
rebuilding, the establishment of the evaluation system for the carbon sequestration
capability within the short term (3–5a) is of practical signicance considering the current
status of the vegetation community and soil layer. Therefore, taking specic mines in the
Yange River Basin, Jurong City, Jiangsu Province as the research objects, the evaluation
system including three aspects and nine evaluation indexes was proposed considering the
visual eects and carbon content properties of the vegetation and soil by combining the
content, experience, and research direction of the existing studies. The evaluation results
were comprehensively obtained from the eld investigation for the current status of
restored mines, laboratory tests about the carbon content level of the vegetation and soil,
and the evaluation system with dierent indexes. The research not only presents an
evaluation system to evaluate the ecological restoration eect from the viewpoint of the
carbon sequestration capability of restored mines by combining the qualitative assessment
and quantitative analysis from the eld investigation and indoor work, but also provides
a reference for the similar evaluation of ecological restoration engineering and the carbon
sequestration capability of other small-scale ecosystems.
2. Method
2.1. Brief Introduction of Study Area
The study area is in the northern part of Jurong City, Jiangsu Province with rich
mineral resources. The open-pit mines are located within 10 km of the Yange River Basin,
mainly concentrated near Baohua Mountain (Figure 1). The terrain of the research area
mainly consists of low mountains, hills, downlands, gentle slopes, and mountain valleys.
The water system in the research area is well developed, and the climate has the
characteristics of abundant rainfall and ample sunshine.
Figure 1. Location of the study area and the open-pit mines after ecological restoration.
During the rapid economic development period, quarrying and mining activities led
to the formation of uneven rock slopes, making it dicult for ecological restoration due
to a total damaged area of 246.88 hm2. With the awareness of environmental protection
and the enforcement of relevant protection policies, large-scale ecological restoration
measures were carried out on the open-pit mining areas in the study area during the
period of 2018–2020, which led to great improvement in the ecological environment.
Figure 1. Location of the study area and the open-pit mines after ecological restoration.
During the rapid economic development period, quarrying and mining activities led
to the formation of uneven rock slopes, making it difficult for ecological restoration due to a
total damaged area of 246.88 hm
2
. With the awareness of environmental protection and the
enforcement of relevant protection policies, large-scale ecological restoration measures were
carried out on the open-pit mining areas in the study area during the period of 2018–2020,
which led to great improvement in the ecological environment.
2.2. Ecological Restoration Effect on Mines
The field investigation was conducted on mines in the study area to find out the
restoration status of vegetation and soil. The mining objects in the study area are mostly
building materials and stone materials (such as limestone, sandstone, dolomite, etc.),
leading to the formation of rock slopes. The selection of the restoration measures is
Sustainability 2024,16, 8149 4 of 22
influenced by the current status and geological conditions of the slope, such as the height
and angle of the slope, lithology, weathering degree, etc. The investigation results indicated
that the restoration measures could be divided into slope reinforcement, soil reconstruction,
and vegetation rebuilding.
1. Slope reinforcement
The excavation process and long-term exposure lead to the decline in rock mass quality
and the tendency to form geological hazards such as landslides, debris flow, etc. Therefore,
to improve the slope stability and ensure the fixed carbon stored in the stable strata, the
slope reinforcement measures area mainly includes slope cutting, anchor cables, protective
nets, and a drainage–interception system.
2. Soil reconstruction
The original soil is detached via excavation and transportation, resulting in a lower
content and quality of soil remaining in the mining area. To ensure the soil fertility and the
survival rate of vegetation, related treatments are taken to improve the organic content of
soil used for reconstruction.
3. Vegetation rebuilding
Vegetation rebuilding is a significant method to improve vegetation coverage after
soil reconstruction. Although the growth state and the dominant type of vegetation are
different in the study area, the shrub is the most selected type due to the growth advantage.
The schemes and distribution characteristics of vegetation are shown in Table 1.
Table 1. Main schemes and distribution characteristics of vegetation.
Main Combination Patterns Distribution Characteristics
Scheme Vegetation Types
Scheme 1 Herbs The scheme usually occurs in mines with small areas, low slope heights, and
gentle slopes. The vegetation is distributed in all areas of the mine.
Scheme 2 Shrubs
Scheme 3 Herbs + Shrubs
The scheme is the most common and occurs in small to medium-sized mines.
The dominant plants are usually shrubs with good survival ability, distributing
on slopes, slope platforms, and bottom of the mines, while the herbs are in a
relatively small living space.
Scheme 4 Herbs + Trees
The scheme usually occurs in abandoned mines or mines with small changes
on the terrain. Trees are planted at intervals of length and width at a certain
distance, with herbs under the trees.
Scheme 5 Herbs + Shrubs + Trees
The scheme is relatively common while the distribution range of various
vegetation is relatively fixed. The shrubs are usually located on the slopes,
slope platforms, and the bottom of the mines; the trees are usually on the
bottom of the mines; the herbaceous plants are under the trees and shrubs or in
a specific area as the dominant species. Some mines with large-area slope
platforms choose to plant trees in the platforms.
According to the field investigation results of the restoration mines and the data
collected in the mines of the study area, 8 mines after ecological restoration were selected
for vegetation and soil sampling (Figure 2). The description and the information of the
8 specific restoration mines are shown in Table 2.
Sustainability 2024,16, 8149 5 of 22
Sustainability 2024, 16, 8149 5 of 22
Figure 2. Location of the selected restoration mines in the sampling area.
Table 2. The description and the information of the selected restoration mines.
No. Location Mining
Object Soil Type Restoration Measures Finish
Time
Size
(hm2)
Slopes
Characteristics
Dominant
Vegetation
M1
The southeast of the Xianglu
Mountain, adjacent to Road
X202
Limestone Sandy Soil
Slope cuing, soil spray sowing,
interception and drainage
system, and irrigation system
2020.12 2.83
4 slopes and 3 slope
pathways with the
slope gradient of 25
40°
Indigofera
tinctoria L.
M2
The south of the Shangmeng
Village and the west of the
Hongshi Village
Limestone Sandy Soil
Slope cuing, passive prevention
net, soil spray sowing,
interception and drainage
system, and irrigation system
2020.12 20.34
2–3 slopes and 1–2
slope pathways with
the slope gradient of
35–4
A
morpha
f
ruticosa L.
M3
The south of the Nijia
Mountain and the west of the
Hanjiabian Village, adjacent to
Langya Road
Limestone Cohesive
Soil
Slope cuing, soil spray sowing,
interception and drainage
system, and irrigation system
2020.8 3.24
3 slopes and 2 slope
pathways with the
slope gradient of 35
40°
Indigofera
tinctoria L. and
Solidago
canadensis L.
M4 The southeast of the Zhujiabian
Village, adjacent to Road G312
Limestone
and
dolomite
Cohesive
Soil
Slope cuing, soil spray sowing,
interception and drainage
system, and irrigation system
2020.12 1.88
1–2 slopes and 1 slope
pathway with the
slope gradient of 30
40°
Solidago
canadensis L.
and Indigofera
tinctoria L.
M5
The south of Huangzhixin auto
shop, adjacent to Jiangcheng
Road
Limestone Sandy Soil
Slope cuing, soil spray sowing,
interception and drainage
system, and irrigation system
2019.12 11.25
2–6 slopes and 1–5
slope pathways with
the slope gradient of
30–7
Robinia
p
seudoacacia L.
and Solidago
canadensis L.
M6
The west side of Zhuli village,
adjacent to left side of
Zhouwang Road
Clay Cohesive
Soil Land leveling and sowing 2021.1 2.16
Wastelands of the
mining area, with the
slope gradient of 5–10
°
Solidago
canadensis L.
M7
The northeast side of Sujiabian
village and the Shenkeng
Reservoir, adjacent to Road
G312
Limestone Gravelly
Soil
Slope cuing, retaining wall, soil
spray sowing, interception and
drainage system, and irrigation
system
2021.4 2.57
2 slopes and 1 slope
pathway with the
slope gradient of 25
35°
Solidago
canadensis L.
and Indigofera
tinctoria L.
M8
The east side of Jinlong
Community, adjacent to Road
G312
Limestone Cohesive
Soil
Slope cuing, soil spray sowing,
interception and drainage
system, and irrigation system
2021.3 7.23
3 slopes and 2 slope
pathways with the
slope gradient of 20
35°
Solidago
canadensis L.
and Indigofera
tinctoria L.
2.3. Approaches for Carbon Sequestration of Mines after Ecological Restoration
2.3.1. Carbon Sequestration Approaches Analysis of Ecological Restoration Mines
It can be concluded that the main measures causing the obvious visual eect of
ecological restoration are soil reconstruction and vegetation rebuilding, which eciently
promote a transforming role in the carbon cycle of the mining ecosystem from the carbon
source to the carbon sink. The main approaches for the carbon sequestration of the
restoration mines are as follows (Figure 3).
Figure 2. Location of the selected restoration mines in the sampling area.
Table 2. The description and the information of the selected restoration mines.
No. Location
Mining Object
Soil Type
Restoration Measures
Finish Time Size
(hm2)
Slopes
Characteristics
Dominant
Vegetation
M1
The southeast of
the Xianglu
Mountain,
adjacent to Road
X202
Limestone Sandy Soil
Slope cutting, soil
spray sowing,
interception and
drainage system, and
irrigation system
2020.12 2.83
4 slopes and
3 slope pathways
with the slope
gradient of
25–40
Indigofera
tinctoria L.
M2
The south of the
Shangmeng
Village and the
west of the
Hongshi Village
Limestone Sandy Soil
Slope cutting, passive
prevention net, soil
spray sowing,
interception and
drainage system, and
irrigation system
2020.12 20.34
2–3 slopes and
1–2 slope
pathways with
the slope
gradient of
35–40
Amorpha
fruticosa L.
M3
The south of the
Nijia Mountain
and the west of
the Hanjiabian
Village, adjacent
to Langya Road
Limestone Cohesive Soil
Slope cutting, soil
spray sowing,
interception and
drainage system, and
irrigation system
2020.8 3.24
3 slopes and
2 slope pathways
with the slope
gradient of
35–40
Indigofera
tinctoria L. and
Solidago
canadensis L.
M4
The southeast of
the Zhujiabian
Village, adjacent
to Road G312
Limestone and
dolomite Cohesive Soil
Slope cutting, soil
spray sowing,
interception and
drainage system, and
irrigation system
2020.12 1.88
1–2 slopes and
1 slope pathway
with the slope
gradient of
30–40
Solidago
canadensis L.
and Indigofera
tinctoria L.
M5
The south of
Huangzhixin
auto shop,
adjacent to
Jiangcheng Road
Limestone Sandy Soil
Slope cutting, soil
spray sowing,
interception and
drainage system, and
irrigation system
2019.12 11.25
2–6 slopes and
1–5 slope
pathways with
the slope
gradient of
30–70
Robinia
pseudoacacia L.
and Solidago
canadensis L.
M6
The west side of
Zhuli village,
adjacent to left
side of
Zhouwang Road
Clay Cohesive Soil Land leveling and
sowing 2021.1 2.16
Wastelands of the
mining area,
with the slope
gradient of 5–10
Solidago
canadensis L.
M7
The northeast
side of Sujiabian
village and the
Shenkeng
Reservoir,
adjacent to Road
G312
Limestone Gravelly Soil
Slope cutting,
retaining wall, soil
spray sowing,
interception and
drainage system, and
irrigation system
2021.4 2.57
2 slopes and
1 slope pathway
with the slope
gradient of
25–35
Solidago
canadensis L.
and Indigofera
tinctoria L.
M8
The east side of
Jinlong
Community,
adjacent to Road
G312
Limestone Cohesive Soil
Slope cutting, soil
spray sowing,
interception and
drainage system, and
irrigation system
2021.3 7.23
3 slopes and
2 slope pathways
with the slope
gradient of
20–35
Solidago
canadensis L.
and Indigofera
tinctoria L.
Sustainability 2024,16, 8149 6 of 22
2.3. Approaches for Carbon Sequestration of Mines after Ecological Restoration
2.3.1. Carbon Sequestration Approaches Analysis of Ecological Restoration Mines
It can be concluded that the main measures causing the obvious visual effect of ecolog-
ical restoration are soil reconstruction and vegetation rebuilding, which efficiently promote
a transforming role in the carbon cycle of the mining ecosystem from the carbon source to
the carbon sink. The main approaches for the carbon sequestration of the restoration mines
are as follows (Figure 3).
Sustainability 2024, 16, 8149 6 of 22
Figure 3. Main approaches for carbon sequestration of the restoration mines.
1. Veg etat ion
The biomass and carbon content of vegetation in the mines are key to the carbon
storage of mining vegetation. The vegetation can absorb CO2 from the atmosphere and
convert it into O2 via photosynthesis and photosynthetic products through accumulation
carbon compounds, which are released into the atmosphere and, respectively, stored in
branches, stems, leaves, and roots [5]. Vegetation roots can also transport organic carbon
materials to the soil, indirectly aecting soil organism activities and the soil carbon
sequestration capacity. What is more, the young vegetation used for restoration with the
characteristic of fast growth ensures the short-term carbon sequestration of vegetation.
2. Soil
Soil is the largest carbon pool in terrestrial ecosystems, which can be divided into
organic carbon and inorganic carbon. Owing to the low activity and diculty of the
transformation of inorganic carbon, soil carbon sequestration is mainly measured by the
organic carbon content. Soil absorbs CO2 from the atmosphere mainly through the
vegetation root and soil organism [22]. The organic maer brought by dead maer or lier
is the main source of nutrients in the soil, increasing the content of organic maer and soil
organism activity. Soil aggregates can convert inorganic carbon and organic carbon into a
steady state, enhancing the xation capacity of carbon in soil [35]. In addition, vegetation
plays a role in the maintenance of soil aggregates and soil organism activities.
3. Soil Organisms
Soil organisms are the link between the vegetation and soil for carbon sequestration,
playing an important role in facilitating carbon sequestration on a global scale [36]. The
organism activities and secretions of soil organisms can participate in the carbon cycle
such as the synthesis, decomposition, and xation of carbon materials by directly xing
CO2 with the natural pathways.
2.3.2. Selection of Carbon Pools of Ecological Restoration Mines
At present, studies on the carbon sequestration capability of terrestrial ecosystems
mainly categorize or quantify from 5 carbon pools: aboveground biomass, underground
biomass, dead maer, lier, and soil organic carbon [37,38]. The selection of carbon pools
Figure 3. Main approaches for carbon sequestration of the restoration mines.
1. Vegetation
The biomass and carbon content of vegetation in the mines are key to the carbon
storage of mining vegetation. The vegetation can absorb CO
2
from the atmosphere and
convert it into O
2
via photosynthesis and photosynthetic products through accumulation
carbon compounds, which are released into the atmosphere and, respectively, stored
in branches, stems, leaves, and roots [
5
]. Vegetation roots can also transport organic
carbon materials to the soil, indirectly affecting soil organism activities and the soil carbon
sequestration capacity. What is more, the young vegetation used for restoration with the
characteristic of fast growth ensures the short-term carbon sequestration of vegetation.
2. Soil
Soil is the largest carbon pool in terrestrial ecosystems, which can be divided into
organic carbon and inorganic carbon. Owing to the low activity and difficulty of the trans-
formation of inorganic carbon, soil carbon sequestration is mainly measured by the organic
carbon content. Soil absorbs CO
2
from the atmosphere mainly through the vegetation root
and soil organism [
22
]. The organic matter brought by dead matter or litter is the main
source of nutrients in the soil, increasing the content of organic matter and soil organism
activity. Soil aggregates can convert inorganic carbon and organic carbon into a steady
state, enhancing the fixation capacity of carbon in soil [
35
]. In addition, vegetation plays a
role in the maintenance of soil aggregates and soil organism activities.
3. Soil Organisms
Soil organisms are the link between the vegetation and soil for carbon sequestration,
playing an important role in facilitating carbon sequestration on a global scale [
36
]. The
Sustainability 2024,16, 8149 7 of 22
organism activities and secretions of soil organisms can participate in the carbon cycle such
as the synthesis, decomposition, and fixation of carbon materials by directly fixing CO
2
with the natural pathways.
2.3.2. Selection of Carbon Pools of Ecological Restoration Mines
At present, studies on the carbon sequestration capability of terrestrial ecosystems
mainly categorize or quantify from 5 carbon pools: aboveground biomass, underground
biomass, dead matter, litter, and soil organic carbon [
37
,
38
]. The selection of carbon pools
should follow conservative principles according to the actual situation of the mines after
ecological restoration.
Based on the results of the field investigation and the analysis of the main approaches
for carbon sequestration, the vegetation and soil are the important approaches. Therefore,
the vegetation including the aboveground part and underground roots and soil are the
non-ignorable carbon pools. The remaining carbon pools should be reasonably judged
based on the restoration status. The carbon pools for the mines are shown in Table 3.
Table 3. The types and selection reason of carbon pools for the mines in the study area.
Carbon Pool Selection Result Selection Reason
Vegetation Aboveground biomass Yes The growth of vegetation coverage and the increase in vegetation
biomass are the main changes in mines after ecological restoration.
Underground biomass
Soil organic carbon Yes
Soil reconstruction enhances the activities of soil organisms, vegetation
roots, and soil respiration, as an important pathway for the carbon
sequestration of the mining ecosystem.
2.4. Sampling, Data Collection, and Laboratory Test
The analysis indicates that vegetation and soil are the main approaches for the restora-
tion mines. Based on the general description and information of the 8 specific mines
selected for sampling and further studies, it can be concluded that most of the selection
mines have the dominant vegetation of shrubs and have a certain slope gradient, which
can influence the effect of ecological restoration.
To ensure the representativeness of the quadrats, 3–4 quadrats were set in each sample
area for the laboratory test and data collection. The guidelines for the quadrats setting are
as follows: at least one quadrat should be set in each terrain area of the mine (the bottom of
the mine, slope, and slope pathway) to determine the restoration status of each terrain area;
the specific quadrat location of each area should be set according to the type of dominant
vegetation; and the vegetation within the quadrat can represent the average growth status
of the vegetation.
2.4.1. Sampling
The location of the quadrats should be determined by on-site installation of PVC pipes
and recorded by GPS. The types of quadrat were divided according to the vegetation types.
The quadrat description and the sampling requirements are shown in Table 4.
Table 4. The size of the vegetation quadrats and the requirements of sampling.
Vegetation Quadrats Requirements of Sampling
Type Size Sampling Method
Herb 1 m ×1 m
Clear cutting method
Cut down all herbs in the quadrat and separate the aboveground
and underground parts for collection.
Shrubs 2 m ×2 m Cut down 3–5 shrubs representing the average growth status and
separate the aboveground and underground parts for collection.
Trees 10 m ×10 m Standard wooden method Measure the diameter at breast height, height, crown width of the
all the trees in the quadrat for biomass calculation.
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The soil samples were taken within the vegetation quadrats. The soil samplings with a
weight of 500–1000 g were taken from 5 points in the quadrat (Figure 4) while the ring-knife
samples were collected for the density measurement. In order to test the soil properties at
different depths, the soil samples should be taken at the depths of 0–10 cm and 10–20 cm.
Sustainability 2024, 16, 8149 8 of 22
(a) (b) (c) (d)
Figure 4. Soil sampling from 5 points in the quadrat: (a) schematic diagram of soil sampling points;
(b) schematic diagram of actual sampling from 5 points; (c) schematic diagram of the ring-knife
sampling points at the depths of 0–10 cm; (d) schematic diagram of the ring-knife sampling points
at the depths of 10–20 cm.
2.4.2. Field Investigation Data Collection
The data of the vegetation and soil can reect the vegetation growth status and the
characteristics of the soil, which should be collected within the quadrats. The
requirements for data collection in the eld investigation are shown in Table 5.
Table 5. The main data and requirements of vegetation and soil data collection.
Object Data Collection Requirement
Herb Herbal type, coverage rate of unit area, average height, and number of herbaceous plants in the quadrat; connectivity
of herbaceous growth in the sample area.
Shrubs Shrub type, coverage rate of unit area, average height, tuft size, and number of shrubs in the quadrat; connectivity o
f
shrub growth in the sample area.
Trees Tree type, canopy density, average height, crown diameter, diameter at breast height and number of trees in the
quadrat; connectivity of shrub growth in the sample area.
Soil Soil type, average thickness, gravel content, and basic particle size of soil.
2.4.3. Laboratory Test
The laboratory tests were conducted to obtain the results of the carbon content of the
vegetation samples, soil total nitrogen (TN), soil total phosphorus (TP), and soil organic
carbon (SOC). All the specic details of each test process and quality control of the testing
results should reach the requirements and guidelines of the relevant test methods and
technology standards. All the vegetation and soil samples were tested after the pre-
treatments of dryness, grind, sift, storage, etc. The specic analytical technique or method
and the equipment (Figure 5) used for each test are illustrated.
(a) (b) (c)
Figure 5. Equipment used for laboratory tests: (a) carbon element analyzer; (b) automatic Kjeldahl
nitrogen analyzer; (c) spectrophotometer.
The carbon content of the vegetation sample was measured using a carbon element
analyzer (EA4000-FS125, Jena GmbH, Jena, Germany) according to the guidelines of the
elemental analyzer method [39] and the component determination of agricultural biomass
Figure 4. Soil sampling from 5 points in the quadrat: (a) schematic diagram of soil sampling points;
(b) schematic diagram of actual sampling from 5 points; (c) schematic diagram of the ring-knife
sampling points at the depths of 0–10 cm; (d) schematic diagram of the ring-knife sampling points at
the depths of 10–20 cm.
2.4.2. Field Investigation Data Collection
The data of the vegetation and soil can reflect the vegetation growth status and the
characteristics of the soil, which should be collected within the quadrats. The requirements
for data collection in the field investigation are shown in Table 5.
Table 5. The main data and requirements of vegetation and soil data collection.
Object Data Collection Requirement
Herb Herbal type, coverage rate of unit area, average height, and number of herbaceous plants in the
quadrat; connectivity of herbaceous growth in the sample area.
Shrubs
Shrub type, coverage rate of unit area, average height, tuft size, and number of shrubs in the quadrat;
connectivity of shrub growth in the sample area.
Trees
Tree type, canopy density, average height, crown diameter, diameter at breast height and number of
trees in the quadrat; connectivity of shrub growth in the sample area.
Soil Soil type, average thickness, gravel content, and basic particle size of soil.
2.4.3. Laboratory Test
The laboratory tests were conducted to obtain the results of the carbon content of the
vegetation samples, soil total nitrogen (TN), soil total phosphorus (TP), and soil organic
carbon (SOC). All the specific details of each test process and quality control of the testing
results should reach the requirements and guidelines of the relevant test methods and
technology standards. All the vegetation and soil samples were tested after the pre-
treatments of dryness, grind, sift, storage, etc. The specific analytical technique or method
and the equipment (Figure 5) used for each test are illustrated.
The carbon content of the vegetation sample was measured using a carbon element
analyzer (EA4000-FS125, Jena GmbH, Jena, Germany) according to the guidelines of the
elemental analyzer method [
39
] and the component determination of agricultural biomass
raw materials (NYT3498-2019 [
40
]). The content of the soil total nitrogen (TN) was deter-
mined by the Kjeldahl method [
41
] and the nitrogen determination of forest soils (LY/T
1228-2015 [
42
]) with the Automatic Kjeldahl Nitrogen Analyzer (K1100-FS305, Shandong
Haineng Co., Ltd., Shandong, China). A spectrophotometer (UV2600-FS119, Shimadzu
Co., Ltd., Suzhou, China) was used to measure the soil total phosphorus (TP) and soil
organic carbon (SOC). The soil total phosphorus (TP) was determined according to the
Sustainability 2024,16, 8149 9 of 22
acid soluble method [
43
] and the phosphorus determination methods of forest soils (LY/T
1232-2015 [
44
]). The soil organic carbon was measured to the requirements of the potassium
dichromate oxidation spectrophotometric method [
45
] and the soil determination of organic
carbon (HJ615-2011 [46]).
f
Figure 5. Equipment used for laboratory tests: (a) carbon element analyzer; (b) automatic Kjeldahl
nitrogen analyzer; (c) spectrophotometer.
2.4.4. Data Analysis
Excel 2021 was used to calculate and process the data, such as mean and standard
deviation, and MATLAB 2018 was used for calculation in the process of the evaluation.
2.5. Establishment of Evaluation System for the Carbon Sequestration Capacity of Mines
The carbon sequestration capacity of restoration mines should be evaluated by sci-
entific, systematic, hierarchical, independent indexes. The AHP model decomposes the
targeted goal into the goal layer, criterion layer, and scheme layer and clusters the indexes
according to the interrelationships, while the FCE method provides a quantitative and
qualitative approach for evaluation based on the fuzzy transformation and maximum mem-
bership principle, solving the ambiguity and uncertainty in the judgment process [2931].
Therefore, the evaluation system for the carbon sequestration capacity of mines was
established by using the AHP-FCE method. The weight of each index was obtained by the
judgment matrix; then, the relationship matrix of the indexes was calculated according
to the membership function of fuzzy mathematics, and the final evaluation result was
determined by the comprehensive. The specific steps of the evaluation are as follows.
2.5.1. Establishment of Evaluation Indexes and Evaluation Criteria
The analysis of the main approaches and carbon pools indicated that the vegetation
and soil are the main indicators for the evaluation of the ecological restoration in view
of the carbon sequestration. Owing to the fact that the vegetation is the driving force
of the mining ecosystem and soil is important for the restoration and improvement of
the ecosystem, the vegetation, soil, and slope form a common whole via the restoration
measures to enhance the stability of the system.
Therefore, this study took the carbon sequestration capacity of the restored mining
ecosystem as the main evaluation goal, and the criterion layer was divided into vegetation,
soil, and the coherent stability of the vegetation–soil–slope system (the system stability),
which contained 9 indexes selected according to the approaches of carbon sequestration,
expert advice, and data collected (Figure 6).
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Figure 6. Structure of the evaluation system.
2.5.2. Determination of Index Weight [33,47]
1. Establishment of Judgment Matrix
Based on the structure of the evaluation system shown in Figure 6, the judgment
matrix is established according to the domination relationship between the adjacent
layers. The 1–9 scale method is used to compare the important values. The judgment
matrix A is shown as Equation (1):
𝐴
=
𝑎 𝑎 ⋯𝑎

𝑎 𝑎 ⋯𝑎

⋮⋮
𝑎 𝑎 ⋯𝑎

(1)
where 𝑎 is the comparison value of the importance of i to j (𝑎>0, 𝑎=1, and 𝑎=
1𝑎
, and i,j = 1,2,n).
2. Calculation of Index Weight
The weighted vector 𝑤 is calculated as Equation (2), and the index weight is
calculated after the normalization of the weighted vector shown as Equation (3):
𝒘𝒊=
󰇧
 
𝑎
󰇨
(2)
𝑊=𝑤
𝒘𝒊
 (3)
3. Consistency Check of Judgment Matrix
The consistency check of the judgment matrix is used for verifying the objectivity of
data, which can be calculated with Equations (4)(6):
𝜆=1
𝑛(
𝐴
𝑊)
𝑤
 (4)
𝐶=𝜆−𝑛
𝑛−1 (5)
𝐶=𝐶
𝑅 (6)
where 𝜆 is the largest eigenvalue of the judgment matrix, n is the judgment matrix
order, 𝐶 is the consistency index, 𝐶 is the consistency ratio, 𝑅 is the average
consistency index.
Figure 6. Structure of the evaluation system.
2.5.2. Determination of Index Weight [33,47]
1. Establishment of Judgment Matrix
Based on the structure of the evaluation system shown in Figure 6, the judgment
matrix is established according to the domination relationship between the adjacent layers.
The 1–9 scale method is used to compare the important values. The judgment matrix A is
shown as Equation (1):
A=
a11 a12 · · · a1n
a21 a22 · · · a2n
.
.
..
.
.....
.
.
an1an2· · · ann
(1)
where
aij
is the comparison value of the importance of ito j(
aij >
0,
aii =
1, and
aji =1
aij
,
and i,j= 1,2,. . .n).
2. Calculation of Index Weight
The weighted vector
wi
is calculated as Equation (2), and the index weight is calculated
after the normalization of the weighted vector shown as Equation (3):
wi=n
j=1aij 1
n(2)
Wi=wi
n
i=1wi
(3)
3. Consistency Check of Judgment Matrix
The consistency check of the judgment matrix is used for verifying the objectivity of
data, which can be calculated with Equations (4)–(6):
λmax =1
n
n
i=1
(AW)i
wi
(4)
CI=λmax n
n1(5)
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CR=CI
RI
(6)
where
λmax
is the largest eigenvalue of the judgment matrix, nis the judgment matrix order,
CI
is the consistency index,
CR
is the consistency ratio,
RI
is the average consistency index.
When the
CR
is less than 10, it indicates that the consistency of the judgment matrix is
very good. Therefore, the weight set can be obtained, which is shown as Equation (7):
W=(W1,W2, . . . , Wn)(7)
4. Determination of Membership Function
The membership function is designed based on the distribution characteristics of
the evaluation index, combined with the features of the trapezoidal distribution function,
shown as Equations (8)–(11):
Yν1=
1(xie1)
e2xi
e2e1(e1<xie2)
0(xi>e2)
(8)
Yν2=
0(xie1,xie3)
xie1
e2e1(e1<xie2)
e3xi
e3e2(e2<xie3)
(9)
Yν3=
0(xie2,xie4)
xie2
e3e2(e2<xie3)
e4xi
e4e3(e3<xie4)
(10)
Yν4=
0(xie3)
xie3
e4e3(e3<xie4)
1(xi>e4)
(11)
where
xi
is the actual value of each index,
xi
is the upper and lower limit of the evalua-
tion value.
2.5.3. Fuzzy Comprehensive Evaluation [33,47]
1. Establishment of Fuzzy Set of Evaluation
According to the evaluation structure, the fuzzy sets of evaluation U are established
with the evaluation index, shown as Equation (12):
U=(B1,B2,B3)=(C1,C2,C3, . . . , . . . , C9)(12)
2. Establishment of Comments Set of Evaluation
According to the classification of the evaluation level in the relevant studies, the
evaluation levels of the carbon sequestration capacity for the mines were divided into
excellent capacity, good capacity, normal capacity, and poor capacity, as shown in Table 6.
Each evaluation level reflects the carbon sequestration capability of restoration mines via
the vegetation, soil properties, and the system stability. The evaluation levels stand for
different restoration statuses of mines with practical significance, for instance, excellent
capacity demonstrates the vegetation with almost full coverage in the mining area and
higher carbon content, the soil properties close to the primitive soil around the mining area,
and the coherent system with little deformation on the vegetation community, soil layer,
and slope. The evaluation level standards for each index are shown in Table 7.
Sustainability 2024,16, 8149 12 of 22
Table 6. Comments set of evaluation.
V V1V2V3V4
Comment Level 1 Level 2 Level 3 Level 4
Capacity level Excellent Good Normal Poor
Table 7. The evaluation level standards for each index.
Evaluation Index V1V2V3V4
Carbon Content (C1)/% 40–55 30–40 20–30 5–20
Connectivity Rate (C2)/% >85 70–90 70–50 <50
Coverage of Unit Area (C3)/% >85 70–85 70–45 <45
Biomass (C4)/g >250 180–250 90–180 <90
Soil Organic Carbon (C
5
)/(g
·
kg
1
)
>25 16–25 9–16 <9
Soil Total Nitrogen (C6) /(g·kg1)>2 1.4–2 0.8–1.4 <0.8
Soil Total Phosphorus
(C7)/(g·kg1)>2 1.4–2 0.8–1.4 <0.8
Proportion of Soil Rainfall Erosion
Area on Slope (C8)/%
No Soil Rainfall
Erosion Area
Soil Rainfall
Erosion Area < 12%
Soil Rainfall
Erosion Area < 15%
Soil Rainfall
Erosion Area
15%
Proportion of Slope Local
Instability Area (C9)/%
No Slope Instability
Area
Slope Instability
Area < 12%
Slope Instability
Area < 15%
Slope Instability
Area 15%
3. Establishment of Fuzzy Membership Matrix
The establishment of the fuzzy membership matrix R is based on the classification
criteria of each evaluation index level, shown as Equation (13):
R=
r11 r12 · · · r1k
r21 r22 · · · r2k
.
.
..
.
.....
.
.
rn1rn2· · · rnk
(13)
where
rij
is the grade of membership of the ievaluation index in the jevaluation level
(i= 1, 2, . . ., n;j= 1, 2, . . ., k).
4. Establishment of Fuzzy Comprehensive Evaluation Matrix
The fuzzy comprehensive evaluation matrix S is calculated by synthesizing the
weighted vector w and membership matrix R, shown as Equation (14):
S=wi×Ri={b1,b2, . . . , bn}(14)
where biis the grade of membership of the evaluated goal to the fuzzy subset.
According to the maximum membership principle, it is known that the evaluation re-
sult is the evaluation level of the carbon sequestration capacity of the mines after ecological
restoration corresponding to the maximum value.
3. Results of Case Study
The limestone quarry (M
1
) is taken as the specific case to demonstrate the process of
calculation and evaluation using the evaluation system for the evaluation of the carbon
sequestration capacity.
3.1. Analysis of the Landscape Changes in the Case Study
The general description and information of the limestone quarry (M
1
, Figure 7) are
displayed in Table 1, including the location, size, mining object, soil type, restoration
measures, etc. Based on the growth status of the vegetation (Figure 7c,d), the data that
are obtained from the field investigation and laboratory test and used for comprehensive
evaluation are shown in Table 8.
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(a) (b)
(c) (d)
Figure 7. Surface landscape of the case study in dierent periods: (a) before ecological restoration
(2019.5); (b) completion of ecological restoration (2020.4); (c) through a period of time after
ecological restoration (2023.7); (d) schematic diagram of vegetation on the slope (2023.7).
Table 8. The evaluation index data for each case study.
C1 C2 C3 C4 C5 C6 C7 C8 C9
% % % g g·kg1 g·kg1 g·kg1 % %
36.36 80 90 1002.24 6.10 0.68 0.62 14 0
3.2. Comprehensive Evalution of the Carbon Sequestration Capacity of the Case Study
3.2.1. Calculation of Index Weight of the Case Study
According to the assignment of relative importance values of each of the evaluation
indicators in dierent layers, the judgment matrix and consistency check of the case study
are shown in Table 9.
Therefore, the weight set of the criterion layer is
𝑊=W󰇝𝐵,𝐵,𝐵󰇞=W󰇝0.345,0.108,0.547󰇞
and the weight sets of the scheme layer are
𝑊=󰇝𝐶,𝐶,𝐶,𝐶󰇞=W󰇝0.540,0.279,0.129,0.052󰇞
𝑊=󰇝𝐶,𝐶,𝐶󰇞=W󰇝0.517,0.359,0.124󰇞
𝑊=󰇝𝐶,𝐶󰇞=W󰇝0.800,0.200󰇞
Figure 7. Surface landscape of the case study in different periods: (a) before ecological restoration
(2019.5); (b) completion of ecological restoration (2020.4); (c) through a period of time after ecological
restoration (2023.7); (d) schematic diagram of vegetation on the slope (2023.7).
Table 8. The evaluation index data for each case study.
C1C2C3C4C5C6C7C8C9
% % % g g·kg1g·kg1g·kg1% %
36.36 80 90 1002.24 6.10 0.68 0.62 14 0
3.2. Comprehensive Evalution of the Carbon Sequestration Capacity of the Case Study
3.2.1. Calculation of Index Weight of the Case Study
According to the assignment of relative importance values of each of the evaluation
indicators in different layers, the judgment matrix and consistency check of the case study
are shown in Table 9.
Therefore, the weight set of the criterion layer is
WAB=W{B1,B2,B3}=W{0.345, 0.108, 0.547}
and the weight sets of the scheme layer are
WB1C={C1,C2,C3,C4}=W1{0.540, 0.279, 0.129, 0.052}
WB2C={C5,C6,C7}=W1{0.517, 0.3594, 0.124}
WB3C={C7,C8}=W1{0.800, 0.200}
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Table 9. The judgment matrix and consistency check of the case study.
Adjacent
Layers Judgment Matrix Index Weight Consistency Check
A–B
BiB1B2B3Wiλmax = 3.054
CI= 0.027
CR= 0.047 < 0.100
B11 4 1/2 0.345
B21/4 1 1/4 0.108
B32 4 1 0.547
B1–C
B1CjC1C2C3C4W1j
λmax = 4.249
CI= 0.083
CR= 0.092 < 0.100
C11 2 6 7 0.540
C21/2 1 3 4 0.279
C31/6 1/3 1 5 0.129
C41/7 1/4 1/5 1 0.052
B2–C
B2CjC5C6C7W2jλmax = 3.108
CI= 0.054
CR= 0.093 < 0.100
C51 2 3 0.517
C61/2 1 4 0.359
C71/3 1/4 1 0.124
B3–C
B3CjC8C9W3jλmax = 2.000
CI= 0
CR= 0.000 < 0.100
C81 3 0.800
C91/3 1 0.200
3.2.2. Determination of Fuzzy Membership Matrix of the Case Study
The fuzzy membership matrixes were established via the grade of the evaluation
indexes, evaluation level standards, and the specific evaluation index data in Table 7. The
fuzzy membership matrixes of the indexes in the criterion layer the case study are
R1=
0 0.6 0.4 0
0 0.5 0.5 0
0.5 0.5 0 0
1 0 0 0
R2=
0.5 0.5 0
0 0.3 0.7
0 0.8 0.2
0
0
0
R3=0 0.7 0.3
0.8 0.2 0
0
0
3.2.3. Multi-Level Fuzzy Comprehensive Evaluation of the Case Study
Based on the weight sets and fuzzy membership matrixes of the scheme layers, the
fuzzy comprehensive evaluation matrix of the criterion layer was calculated and the
evaluation results of each aspect of the criterion layer were obtained according to the
maximum membership principle. The fuzzy membership matrixes of the goal layer are
calculated via the same method and the results of the case study are shown in Table 10.
Table 10. The calculation results and evaluation results of index in different layers.
Layer Index Calculation Process Excellent Good Normal Poor Evaluation Result
Criterion Layer
B1SB1C=WB1C×R10.117 0.528 0.356 0 Good
B2SB2C=WB2C×R20.259 0.391 0.351 0 Good
B3SB3C=WB3C×R30.160 0.600 0.240 0 Good
Goal Layer A SAB=WAB×R10.156 0.553 0.292 0 Good
3.2.4. Analysis of Evaluation Results
According to the calculation results of the index weight of the criterion layer, it can
be seen that the weight value of the system stability had the highest proportion of 0.547;
next was the vegetation community with the proportion of 0.345. Therefore, the stability of
Sustainability 2024,16, 8149 15 of 22
the system is the main factor contributing to the carbon sequestration capacity of mines.
Owing to the fact that mining excavation causes changes in the stress state of the mining
slope and loosens the soil and rock mass, the primary goal of ecological restoration is the
stability of the mining ecosystem including the improvement in the slope stability and the
reduction in soil erosion. Therefore, the coherent system represents the sustainability of the
vegetation community, and the stability of the soil fixed on the slope. Secondly, by combing
the weight sets of the scheme layer, it can be known that the vegetation community is
the key factor in the carbon sequestration effect. Not only does the vegetation reduce the
soil erosion by intercepting rainfall and improve the environmental and physicochemical
properties of the soil but quick carbon storage in the vegetation can be achieved in the short
term due to the fact that the vegetation seedlings used for planting have a strong growth
ability at their young stage.
Based on the evaluation results of the limestone quarry (M
1
) as the case study, by
using the multi-level fuzzy comprehensive evaluation, it is indicated that the status of
the vegetation community and soil is good while the stability of the coherent system is
excellent. The evaluation results are consistent with the visual effect and conclusions of the
field investigation (Figure 7c,d).
4. Discussion
4.1. Analysis of Carbon Sequestration Capacity of the Mines after Ecological Restoration in the
Study Area
Although the restoration mines in the study area are of great number, the ecological
restoration has also been well and orderly carried out. The types of mines and the dominant
vegetation in the research area are relatively uniform, but the slope conditions vary greatly.
Therefore, using the evaluation system to assess the carbon sequestration capacity of
the restoration mining ecosystem can provide a reference for engineering experience for
ecological restoration. The above calculation and evaluation processes were extended
to the eight selected sample areas. To verify the effectiveness of the evaluation results,
the comparison of the situation before and after the restoration of each mine is shown in
Figure 8. The test results of the vegetation and soil samples of each selected mine (including
M1) are shown in Table 11 and the results are shown in Table 12.
Sustainability 2024, 16, 8149 15 of 22
of the system is the main factor contributing to the carbon sequestration capacity of mines.
Owing to the fact that mining excavation causes changes in the stress state of the mining
slope and loosens the soil and rock mass, the primary goal of ecological restoration is the
stability of the mining ecosystem including the improvement in the slope stability and the
reduction in soil erosion. Therefore, the coherent system represents the sustainability of
the vegetation community, and the stability of the soil xed on the slope. Secondly, by
combing the weight sets of the scheme layer, it can be known that the vegetation
community is the key factor in the carbon sequestration eect. Not only does the
vegetation reduce the soil erosion by intercepting rainfall and improve the environmental
and physicochemical properties of the soil but quick carbon storage in the vegetation can
be achieved in the short term due to the fact that the vegetation seedlings used for planting
have a strong growth ability at their young stage.
Based on the evaluation results of the limestone quarry (M1) as the case study, by
using the multi-level fuzzy comprehensive evaluation, it is indicated that the status of the
vegetation community and soil is good while the stability of the coherent system is
excellent. The evaluation results are consistent with the visual eect and conclusions of
the eld investigation (Figure 7c,d).
4. Discussion
4.1. Analysis of Carbon Sequestration Capacity of the Mines after Ecological Restoration in the
Study Area
Although the restoration mines in the study area are of great number, the ecological
restoration has also been well and orderly carried out. The types of mines and the
dominant vegetation in the research area are relatively uniform, but the slope conditions
vary greatly. Therefore, using the evaluation system to assess the carbon sequestration
capacity of the restoration mining ecosystem can provide a reference for engineering
experience for ecological restoration. The above calculation and evaluation processes were
extended to the eight selected sample areas. To verify the eectiveness of the evaluation
results, the comparison of the situation before and after the restoration of each mine is
shown in Figure 8. The test results of the vegetation and soil samples of each selected mine
(including M1) are shown in Table 11 and the results are shown in Table 12.
(a)
(b)
Figure 8. Cont.
Sustainability 2024,16, 8149 16 of 22
Sustainability 2024, 16, 8149 16 of 22
(c)
(d)
(e)
(f)
(g)
Figure 8.: Comparison of the situation before and after restoration of each mine: (a) M2 (2017.6 and
2023.12); (b) M3 (2020.8 and 2023.12); (c) M4 (2020.12 and 2023.8); (d) M5 (2020.1 and 2023.12); (e)
M6 (2020.10 and 2023.7); (f) M7 (2019.10 and 2023.9); (g) M8 (2019.5 and 2023.10).
Figure 8. Comparison of the situation before and after restoration of each mine: (a) M
2
(2017.6 and
2023.12); (b) M
3
(2020.8 and 2023.12); (c) M
4
(2020.12 and 2023.8); (d) M
5
(2020.1 and 2023.12); (e) M
6
(2020.10 and 2023.7); (f) M7(2019.10 and 2023.9); (g) M8(2019.5 and 2023.10).
Sustainability 2024,16, 8149 17 of 22
Table 11. The laboratory testing results of sampling areas.
Sample
Areas
Quadrat
Number
Carbon Content
of Vegetation (%)
Soil Organic Carbon (g·kg1) Soil Total Nitrogen (g·kg1)
Soil Total Phosphorus (g
·
kg
1
)
0–10 cm 10–20 cm 0–10 cm 10–20 cm 0–10 cm 10–20 cm
M1
Q1–1 36.26 6.80 5.90 0.613 0.657 0.634 0.608
Q1–2 36.03 6.50 6.00 0.634 0.772 0.653 0.592
Q1–3 36.78 5.90 5.50 0.698 0.714 0.623 0.601
M2
Q2–1 35.71 7.10 5.80 0.561 0.545 0.423 0.439
Q2–2 33.94 6.80 7.20 0.548 0.539 0.366 0.358
Q2–3 34.38 7.80 7.20 0.684 0.742 0.408 0.460
M3
Q3–1 37.76 4.80 4.30 0.578 0.651 0.477 0.486
Q3–2 37.39 6.30 5.60 0.645 0.606 0.430 0.426
Q3–3 39.08 5.40 5.80 0.612 0.551 0.392 0.429
M4
Q4–1 38.71 4.50 4.70 0.412 0.453 0.374 0.371
Q4–2 37.46 4.30 5.20 0.444 0.366 0.534 0.525
Q4–3 38.82 4.50 5.30 0.472 0.398 0.444 0.461
M5
Q5–1 44.91 5.20 5.60 0.458 0.582 0.589 0.638
Q5–2 44.79 5.10 5.40 0.382 0.477 0.616 0.622
Q5–3 44.84 4.70 4.20 0.550 0.473 0.606 0.586
M6
Q6–1 42.94 4.80 4.30 0.753 0.681 0.830 0.795
Q6–2 43.67 5.60 4.20 0.795 0.612 0.737 0.766
Q6–3 42.49 6.30 5.70 0.863 0.781 0.791 0.819
M7
Q7–1 44.36 6.10 5.00 0.578 0..453 0.252 0.251
Q7–2 44.63 6.50 5.50 0.598 0.462 0.265 0.263
Q7–3 44.27 5.50 4.90 0.529 0.486 0.237 0.250
M8
Q8–1 42.18 5.10 6.20 0.469 0.433 0.550 0.543
Q8–2 43.97 5.00 5.80 0.510 0.483 0.578 0.523
Q8–3 44.51 6.20 7.40 0.535 0.479 0.567 0.555
Table 12. The results of selected sample areas.
Sample Areas Criterion Layer Goal Layer
Calculation Result Evaluation Result Calculation Result Evaluation Result
M2
{0.272, 0.728, 0.000, 0.000} Good
{0.384, 0.573, 0.041, 0.000} Good
{0.259, 0.355, 0.386, 0.000} Normal
{0.480, 0.520, 0.000, 0.000} Good
M3
{0.075, 0.761, 0.164, 0.000} Good
{0.288, 0.558, 0.138, 0.015} Good
{0.000, 0.103, 0.757, 0.139} Normal
{0.480, 0.520, 0.000, 0.000} Good
M4
{0.542, 0.416, 0.042, 0.000} Excellent
{0.668, 0.209, 0.081, 0.041} Excellent
{0.000, 0.000, 0.617, 0.383} Normal
{0.880, 0.120, 0.000, 0.000} Excellent
M5
{0.558, 0.416, 0.026, 0.000} Excellent
{0.674, 0.212, 0.067, 0.047} Excellent
{0.000, 0.025, 0.537, 0.438} Normal
{0.880, 0.120, 0.000, 0.000} Excellent
M6
{0.373, 0.498, 0.000, 0.129} Good
{0.621, 0.320, 0.014, 0.045} Excellent
{0.103, 0.763, 0.134, 0.000} Good
{0.880, 0.120, 0.000, 0.000} Excellent
M7
{0.302, 0.624, 0.075, 0.000} Good
{0.323, 0.566, 0.094, 0.018} Good
{0.000, 0.207, 0.629, 0.164} Normal
{0.400, 0.600, 0.000, 0.000} Good
M8
{0.300, 0.700, 0.000, 0.000} Good
{0.278, 0.637, 0.065, 0.019} Good
{0.000, 0.217, 0.603, 0.180} Normal
{0.320, 0.680, 0.000, 0.000} Good
The evaluation results of the eight selected mines for sampling showed that the
proportion at the excellent level of the carbon sequestration capacity of the mines is 62.5%
Sustainability 2024,16, 8149 18 of 22
and the proportion at the good level is 37.5% while there are none at the normal level
and poor level. By combing through the changes in the vegetation coverage and the soil
layer in Figure 8and the evaluation results, it can be concluded that the stability of the
coherent system is key to improving the restoration mining ecosystem, which stands for
the sustainability of the vegetation community development and the adaptability of the
soil layer fixed in the slope as a matrix. For example, the mines M
4–6,
with results at
the excellent level, have the advantage of a low slope gradient, which can encounter less
difficulty in soil construction to provide nutrients for vegetation and more areas for the
improvement in the soil quality. The results indicated that all the abandoned open-pit
mines that have undergone ecological restoration can meet the requirements for the carbon
sequestration capacity.
4.2. Analysis of Ecological Restoration Effect for the Open-Pit Mines
Although the effect of the ecological restoration and the carbon sequestration capability
of the open-pit mines are presented and obviously improved through the qualitative
evaluation via the visual effects and quantitative analysis of the evaluation results, more
analysis is needed as a guideline for similar ecological restoration mines.
4.2.1. Effect of Ecological Restoration Measures on the Mining Ecosystem
Ecological restoration measures have improved the ecological environment of open-pit
mines. The landscape of the mine gradually becomes compatible with the undamaged
landscape of the surrounding area as a whole. According to the coverage from Figure 8
and the evaluation results, the vegetation mainly maintains in the range of 80–95% under
the impact of the slope gradient. The capability of vegetation growth should be considered
for the vegetation combination scheme, which can influence the distribution of different
vegetation and further affect the total vegetation growth status in the mines. For instance,
among the selected mines, the slopes of M
2–4
are steep, but the shrub is selected for
restoration in the lower part of the slope while the whole slope of M
3
and the upper
slope of M
4
are planted with herbs, with a larger area exposed and a weaker fixation
of the soil layer. Therefore, the selection of the appropriate scheme for the vegetation
combination of vegetation rebuilding on the slope plays a significant role in reducing soil
erosion and geological disasters and ensuring the stability of the soil layer on the slope and
the sustainability of the vegetation community’s survival.
Therefore, the evaluation and investigation results provide experience for the selection
of mine treatment measures, thereby enhancing the potential for the self-restoration of
mining ecosystems and the sustainability of carbon sequestration.
4.2.2. Effect of Ecological Restoration on Approaches of Carbon Sequestration of Mines
The vegetation and soil are the main carbon pools in the restoration mining ecosys-
tem and are related to the sustainability of carbon sequestration, which forms the whole
interaction with the medium between the vegetation and soil. Owing to the fact that the
ecosystem converts CO
2
from a free into a steady state through a process such as vegetation
photosynthesis and the soil organism, a greater proportion of vegetation and soil coverage
can offer a larger area for the absorption, transformation, and storage of CO2.
So, the restoration status can be monitored via the data from the laboratory testing
results, and also be effectively evaluated via the changes obtained from the visual effect to
determine if there are additional areas for more carbon sequestration. So, it broadens the
mind regarding the selection of the quantitative evaluation data for the establishment of
the evaluation system for the carbon sequestration capacity.
4.2.3. Evaluation of Effect on the Carbon Sequestration of Mining Ecosystem
Based on the changes in the landscape and the approaches for the carbon sequestration
of mines, the evaluation system quantitatively evaluates the carbon sequestration capability
considering three aspects and nine evaluation indexes.
Sustainability 2024,16, 8149 19 of 22
The results of the calculation and evaluation not only assess the effect of the stability
of the soil layer, the sustainability of the vegetation community’s survival, and the stability
of the system, but also determine the contribution of each index to the carbon sequestration
effect according to the weight value. The fact that the evaluation results are consistent
with the reality via the qualitative analysis, which also compared the restoration effects,
provides theoretical data and experience reference for restoration measures.
4.3. Limitations and Future Work of the Study
In the research, the carbon sequestration capacity of the restoration mines was studied
based on a field investigation, laboratory tests, and an evaluation system. Although the
ecological restoration effects were studied from the view of carbon sequestration via the
evaluation system regarding the influencing factors, there are some existing limitations.
In the evaluation system, the aspects of the criterion layer and evaluation indexes are
relatively few. For instance, the restoration measures influence the carbon sequestration
capacity and other soil physical and chemical properties may reflect the restoration effect.
So, the aspects and indexes of the evaluation system can be enriched and deepened with
more collected materials and laboratory tests.
Also, the data collection heavily relies on the field investigation and laboratory tests.
However, the number of sampling areas and samples in a single mine are not enough, and
all the mines are from a single region. In a subsequent study, more sampling areas and
sampling quadrats should be set, and other regions will be taken into consideration to
improve the accuracy and reliability.
Moreover, although the evaluation system can analyze the targeted goals, it relies on
the investigation of the restoration state and the AHP-FCE method heavily, which is not
suitable for long-term evaluation. Therefore, other data collection methods such as remote
sensing and suppled investigation and other evaluation methods will be introduced for
mutual verification in the future research. The development of software tools can also
facilitate the data processing and evaluation process much easier and faster.
5. Conclusions
The assessment of restoration mines is to evaluate the influence and effect of ecological
restoration on the mining ecosystem. However, there are few studies on the evaluation
of the carbon sequestration capability of open-pit mines after ecological restoration. In
the process, we concluded that the vegetation and soil in restoration mines are the main
media for fixing and storing CO
2
. Therefore, the evaluation system with nine indexes was
proposed from the aspects of the vegetation, soil, and coherent stability of the vegetation–
soil–slope system based on the AHP-FCE method. The evaluation was performed via the
data obtained from the field investigation and laboratory tests.
The calculation results of the weights showed that the system stability was the most
important influencing factor with the largest value of 0.547, followed by vegetation and
soil; the weight values of the vegetation carbon content (C
1
), soil organic carbon (C
5
), and
proportion of soil erosion area on the slope (C
8
) were relatively higher. Therefore, the
system stability and the restoration state of the vegetation and soil were closely related to
the carbon sequestration capability, ensuring fixed CO
2
was stored in the stable system.
Additionally, the evaluation results of the mines in the study area and all aspects of the
evaluation system are at the excellent and good levels, which are consistent with the
qualitative evaluation results of the field investigation. It revealed the improvement in the
mining ecosystem and the carbon sequestration capability due to the ecological restoration.
Therefore, according to the results of the weight calculation and evaluation, it can be
concluded that the evaluation system has strong applicability and effectiveness, which can
provide a reference for the evaluation of the carbon sequestration capability of mines or
other similar small-scale projects after ecological restoration. In the subsequent evalua-
tion work, the evaluation indexes can be further refined and improved according to the
secondary data sources such as remote sensing and complement field investigations. In
Sustainability 2024,16, 8149 20 of 22
summary, the research and findings offer a new approach for the sustainable development
of the mining ecosystem and the relevant guidelines for the evaluation of the ecological
restoration effect.
Author Contributions: Conceptualization, S.Z. and P.Z.; methodology, F.Z. and G.L.; software, X.L.,
X.Z. and H.Z.; validation, G.L., P.Z. and S.Z.; investigation, F.Z. and G.L.; writing—original draft
preparation, X.L. and X.Z.; writing—review and editing, X.L. and H.Z. All authors have read and
agreed to the published version of the manuscript.
Funding: The research was funded by the Jiangsu Geological Exploration Project Fund [2022-No.27-
Item.38].
Data Availability Statement: The data presented in this study are not available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Yang, T.H.; Sun, D.D.; Xu, X.C.; Xie, L.K.; Lin, T.F. Problems and countermeasures in green, safe and efficient mining of large-scale
open-pit mines in Xinjiang. J. Min. Saf. Eng. 2022,39, 1–12. (In Chinese) [CrossRef]
2.
Zhang, L.J.; Hu, Z.Q.; Yang, D.Z.; Li, H.H.; Liu, B.; Gao, H.; Cao, C.J.; Zhou, Y.; Li, J.F.; Li, S.C. Land Use Dynamic Evolution and
Driving Factors of Typical Open-Pit Coal Mines in Inner Mongolia. Int. J. Environ. Res. Public Health 2022,19, 9723. [CrossRef]
3.
Timsina, S.; Hardy, N.G.; Woodbury, D.J.; Ashton, M.S.; Cook-Patton, S.C.; Pasternack, R.; Martin, M.P. Tropical surface gold
mining: A review of ecological impacts and restoration strategies. Land Degrad. Dev. 2022,33, 3661–3674. [CrossRef]
4.
Ranjan, A.K.; Parida, B.R.; Dash, J.; Gorai, A.K. Evaluating Impacts of Opencast Stone Mining on Vegetation Primary Production
and Transpiration over Rajmahal Hills. Sustainability 2023,15, 8005. [CrossRef]
5.
Chen, F.; Zhu, Y.F.; Ma, J.; Dong, W.X.; You, Y.N.; Yang, Y.J. Mechanism, potential and regulation of carbon sequestration and
sink enhancement in ecological restoration of mining areas in the Loess Plateau. Coal Sci. Technol. 2023,51, 502–513. (In Chinese)
[CrossRef]
6.
Shao, Y.; Xu, Q.X.; Wei, X. Progress of Mine Land Reclamation and Ecological Restoration Research Based on Bibliometric
Analysis. Sustainability 2023,15, 10458. [CrossRef]
7.
Ma, X.; Zhang, A.W.; Huang, L.; Zhao, S.Y.; Zhang, W.X. Research on measures for reducing emissions and increasing sinks in
mine ecological restoration under the goals of carbon peak and carbon neutrality. China Coal 2023,49, 109–115. [CrossRef]
8.
Chen, T.Y.; Qu, N.; Wang, J.X.; Liu, Y.C.; Feng, J.; Zhang, S.L.; Xu, C.Y.; Cao, Z.Q.; Pan, J.; Li, C.L. Effects of different ecological
restoration methods on the soil physicochemical properties and vegetation community characteristics of the Baotou light rare
earth tailings pond in Inner Mongolia, China. Environ. Sci. Pollut. Res. 2024,31, 19725–19737. [CrossRef]
9.
Chakraborty, P.; Singh, S.; Hazra, B. Soil pollution in Indian coal mines, available remediation techniques and rock dust application:
A review. J. Earth Syst. Sci. 2023,132, 147. [CrossRef]
10.
Guo, K.J.; Zhao, J.J.; Li, Y.F.; Liu, X.; Liu, T.; Niu, Y.H.; Li, X. Research progress on remediation technology for heavy metal-
contaminated soil in mines. J. Agric. Resour. Environ. 2023,40, 249–260. (In Chinese) [CrossRef]
11.
Sharma, N.; Singh, G.; Sharma, M.; Mandzhieva, S.; Minkina, T.; Rajput, V.D. Sustainable Use of Nano-Assisted Remediation for
Mitigation of Heavy Metals and Mine Spills. Water 2022,14, 3972. [CrossRef]
12.
Song, P.P.; Xu, D.; Ma, Y.C.; Dong, S.J.; Feng, J. Recent advances in soil remediation technology for heavy metal contaminated
sites: A critical review. Sci. Total Environ. 2022,838, 156417. [CrossRef]
13.
Couic, E.; Alphonse, V.; Livet, A.; Giusti-Miller, S.; Bousserrhine, N. Influence of Ecological Restoration on Mercury Mobility and
Microbial Activities on Former Guyanese Mining Sites. Appl. Sci. 2021,11, 2231. [CrossRef]
14.
Misebo, A.M.; Pietrzykowski, M.; Wos, B. Soil Carbon Sequestration in Novel Ecosystems at Post-Mine Sites-A New Insight into
the Determination of Key Factors in the Restoration of Terrestrial Ecosystems. Forests 2022,13, 63. [CrossRef]
15.
Singh, P.D.; Klamerus-Iwan, A.; Pietrzykowski, M. Water Retention Potential in Novel Terrestrial Ecosystems Restored on
Post-Mine Sites: A Review. Forests 2022,14, 18. [CrossRef]
16.
Niyomukiza, J.B.; Eisazadeh, A.; Tangtermsirikul, S. Recent advances in slope stabilization using porous vegetation concrete in
landslide-prone regions: A review. J. Build. Eng. 2023,76, 107129. [CrossRef]
17.
Dai, Y.Q.; Xiao, L.; Liu, W.H.; Liu, Y.M.; Zhang, Q.; Huang, C.M. Benefit evaluation of runoff and sediment reduction measures on
the earth slopes in mountainous areas, Southwest China. Sci. Soil. Water Conserv. 2023,21, 73–82. [CrossRef]
18.
Liu, K.J.; Liu, W.; Liu, M.X.; Fan, X. Performance test and stability analysis of jute planting bag on subgrade slope. Sci. Soil Water
Conserv. 2022,20, 67–73. [CrossRef]
19.
Wang, W.; Liu, R.Y.; Gan, F.P.; Zhou, P.; Zhang, X.W.; Ding, L. Monitoring and Evaluating Restoration Vegetation Status in Mine
Region Using Remote Sensing Data: Case Study in Inner Mongolia, China. Remote Sens. 2021,13, 1350. [CrossRef]
Sustainability 2024,16, 8149 21 of 22
20.
Fonseca, W.D.; Martins, S.V.; Fioresi, E.M.; Villa, P.M. Complementing seedling planting with nucleation techniques increases
forest restoration potential in areas around bauxite mining. Land Degrad. Dev. 2024,35, 3075–3089. [CrossRef]
21.
Riviera, F.; Renton, M.; Dobrowolski, M.P.; Veneklaas, E.J.; Mucina, L. Patterns and drivers of structure, diversity, and composition
in species-rich shrublands restored after mining. Restor. Ecol. 2021,29, e13360. [CrossRef]
22.
Lei, S.G.; Wang, W.Z.; Li, Y.Y.; Yang, X.C.; Zhou, Y.L.; Duan, Y.T.; Zhao, X.T.; Cheng, W. Study on disturbance and resto-ration of
soil organic carbon pool in large-scale open-pit mining areas in Northern China. Coal Sci. Technol. 2023,51, 100–109. [CrossRef]
23.
You, Y.N.; Zhu, Y.F.; Chen, F.; Cheng, Y.J.; Dong, W.X.; Ma, J. Effects of Vegetation Types on the Potential and Pathway of Microbial
Carbon Sequestration in Reclaimed Soil of Open-pit Mine. J. Ecol. Rural Environ. 2023,39, 1170–1179. [CrossRef]
24.
Soria, R.; Rodriguez-Berbel, N.; Sanchez-Canete, E.; Villafuerte, A.B.; Ortega, R.; Miralles, I. Organic amendments from recycled
waste promote short-term carbon sequestration of restored soils in drylands. J. Environ. Manag. 2023,327, 116873. [CrossRef]
25.
Kowalska, A.; Singh, B.R.; Grobelak, A. Carbon Footprint for Post-Mining Soils: The Dynamic of Net CO
2
Fluxes and SOC
Sequestration at Different Soil Remediation Stages under Reforestation. Energies 2022,15, 9452. [CrossRef]
26.
Fox, J.E.; Campbell, J.E.; Acton, P.M. Carbon Sequestration by Reforesting Legacy Grasslands on Coal Mining Sites. Energies 2020,
13, 6340. [CrossRef]
27.
Han, J.Z.; Hu, Z.Q.; Mao, Z.; Li, G.S.; Liu, S.G.; Yuan, D.Z.; Guo, J.X. How to Account for Changes in Carbon Storage from Coal
Mining and Reclamation in Eastern China? Taking Yanzhou Coalfield as an Example to Simulate and Estimate. Remote Sens. 2022,
14, 2014. [CrossRef]
28.
Bandyopadhyay, S.; Novo, L.A.B.; Pietrzykowski, M.; Maiti, S.K. How to Assessment of Forest Ecosystem Development in Coal
Mine Degraded Land by Using Integrated Mine Soil Quality Index (IMSQI): The Evidence from India. Forests 2022,11, 1310.
[CrossRef]
29.
Singh, A.; Agarwal, S.; Prabhat, A. A multi-criteria decision framework to evaluate sustainable alternatives for repurposing of
abandoned or closed surface coal mines. Front. Earth Sci. 2024,12, 1330217. [CrossRef]
30.
Wang, H.W.; Yan, M.; Gao, Y.; Wang, Y.Q.; Dai, X.H. An Evaluation System for Assessing the Operational Efficiency of Urban
Combined Sewer Systems Using AHP-Fuzzy Comprehensive Evaluation: A Case Study in Shanghai, China. Water 2023,15, 3434.
[CrossRef]
31.
Tang, S.Y.; Liu, Z.W.; Li, Y.M.; Zhou, M.Q. Enhancing Sustainability through Ecosystem Services Evaluation: A Case Study of the
Mulberry-Dyke and Fish-Pond System in Digang Village. Sustainability 2024,16, 1875. [CrossRef]
32.
Yang, Y.S.; Liu, D.X.; Xiao, H.; Chen, J.G.; Yu, D.; Dong, X.; Xia, Z.Y.; Xu, W.N. Evaluating the Effect of the Ecological Restoration
of Quarry Slopes in Caidian District, Wuhan City. Sustainability 2019,11, 6624. [CrossRef]
33.
Jiang, Y.Y.; Wang, X.W.; Tan, C.M.; Sun, D.Y.; Li, J.Z. Effect Analysis and Evaluation on Ecological Restoration of Post-earthquake
Slopes along Chuanzhusi-Jiuzhaigou Highway. Sustainability 2023,40, 208–215. [CrossRef]
34.
Hao, J.; Li, H.T.; An, C.L.; Zhao, Z.W.; Han, L.; Wang, H.W. Difficulty Evaluation Method and Application Study of Geological
Environment Restoration in Open-Pit Limestone Mines. Min. Res. Dev. 2024,44, 175–184. [CrossRef]
35.
Chen, X.X.; Liang, A.Z.; Zhang, X.P. Research methods of carbon sequestration by soil aggregates: A review. J. Appl. Ecol. 2012,23,
1999–2006. [CrossRef]
36.
Hirt, H.; Boukcim, H.; Ducousso, M.; Saad, M.M. Engineering carbon sequestration on arid lands. Trends Plant Sci. 2023,28,
1218–1221. [CrossRef]
37.
Saklaurs, M.; Karklina, A.; Liepa, L.; Jansons, A. The Evaluation of Small- and Medium-Stream Carbon Pools in the Riparian
Forests in Latvia. Forests 2022,13, 506. [CrossRef]
38.
Sione, S.M.J.; Wilson, M.G.; Ledesma, S.G.; Gabioud, E.A.; Oszust, J.D.; Rosenberger, L.J. Driving factors of tree biomass and soil
carbon pool in xerophytic forests of northeastern Argentina. Ecol. Process. 2023,12, 64. [CrossRef]
39.
Ribeiro, F.P.; Gatto, A.; de Oliveira, A.D.; Pulrolnik, K.; Valadao, M.B.X.; Araujo, J.B.C.N.; de Carvalho, A.M.; Ferreira, E.A.B.
Carbon Storage in Different Compartments in Eucalyptus Stands and Native Cerrado Vegetation. Plants 2023,12, 2751. [CrossRef]
40.
NY/T 3498-2019; Determination of Ingredients in Agricultural Biomass Materials Elemental Analyzer Method. Chinese Standard:
Beijing, China, 2019.
41.
Wang, Z.R.; Hasi, E.; Han, X.J.; Qingda, M. Fractal characterization of soil particle size distribution under different land use
patterns on the north slope of Wula Mountain in China. J. Min. Saf. Eng. 2024,24, 1148–1164. [CrossRef]
42. LY/T 1228-2015; Nitrogen Determination Methods of Forest Soils. Chinese Standard: Beijing, China, 2015.
43.
Hu, Y.F.; Liu, X.; Xiong, S.C.; Zhang, L.Y.; Li, J.H.; Yuan, C.Y.; Xu, Z.F.; You, C.M.; Tao, B.; Xu, H.W.; et al. Patterns of soil
phosphorus fractions across a chronosequence of Cryptomeria japonica var. sinensis in rainy area of western China. Atca Ecol. Sin.
2024,44, 686–698. [CrossRef]
44. LY/T 1232-2015; Phosphorus Determination Methods of Forest Soils. Chinese Standard: Beijing, China, 2015.
45.
Uddin, M.J.; Hooda, P.S.; Mohiuddin, A.S.M.; Haque, M.E.; Smith, M.; Waller, M.; Biswas, J.K. Soil organic carbon dynamics in
the agricultural soils of Bangladesh following more than 20 years of land use intensification. J. Environ. Manag. 2022,305, 114427.
[CrossRef] [PubMed]
Sustainability 2024,16, 8149 22 of 22
46.
HJ 615-2011; Soil. Determination of Organic Carbon. Potassium Dichromate Oxidation Spectrophotometric Method. Chinese
Standard: Beijing, China, 2011.
47.
Liu, L.W.; Zhang, Y.L.; Zhao, L.; Zhan, C.; Liang, C. An Attempt to Evaluate the Green Construction of Large-Scale Hydropower
Projects: Taking Wudongde Hydropower Station on the Jinsha River, China as an Example. Sustainability 2022,14, 194. [CrossRef]
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... Shang, Shen, and Lopez-Marcos et al. discussed the impact of slope conditions on vegetation restoration, such as the slope dip and slope aspect [20][21][22]. Zou, Mounsey, and Zhou et al. evaluated the ecological restoration effect of vegetation on mine slopes using remote sensing, environmental driver analysis, and the AHP-FCE method [23][24][25]. ...
... The interrelationship of vegetation biomass and soil properties in mining areas is discussed through qualitative analysis [33], soil micromorphological techniques [34], and vegetation ecological indexes [35,36]. Further, research related to the carbon sequestration capability and carbon storage provides different methods to evaluate vegetation's carbon sequestration potential, using quadrat surveys and field studies [25,37,38], LiDAR and hyperspectral data [39], particle swarm optimization algorithms [40], etc. ...
... As the main approaches for the carbon sequestration of the restored mines, vegetation biomass and carbon content are key to improving vegetation carbon storage via photosynthesis [25,51,52]. Therefore, the prediction model of vegetation carbon storage is established according to the vegetation biomass and carbon content. ...
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