Using Social, Economic and Land-Use Indices to Build a Local Sustainability Index in a Mining Region of the Sierra Tarahumara, Mexico

Abstract

Ore mining has served as a predictor of economic wellbeing since it brought development to countries. However, these benefits do not always extend to all localities that comprised the center of this industry. This paper examined the contribution of mining to local communities. An index of local sustainability was constructed based on economic, social, and land-use data from twelve localities where mining and forestry are their major economic activities. Land-use variables were obtained from Landsat Thematic Mapper (TM 5) images for 2000, and Landsat Operational Land Imager (OLI8) for 2014, while the socioeconomic variables were collected in twelve localities with an 85-question survey. A sustainability index was developed for each group of variables—economic (ESI), social (SSI) and land-use sustainability index (LUSI)—to further build a local sustainability index (LSI). Three localities showed the highest ESI (0.61, 0.53 and 0.43) and SSI (0.90, 0.79 and 0.78), while two localities had the lowest values in the ESI and SSI. In contrast, the highest value of LUSI was found in two other different localities and in one with lower SSI. Income from mining activities is positively associated with the ESI and SSI, but there was no evidence of linear association with the LUSI. A local index of sustainability provides useful information for planning and development strategies.

Full text

resources
Article
Using Social, Economic and Land-Use Indices to
Build a Local Sustainability Index in a Mining Region
of the Sierra Tarahumara, Mexico
Carmelo Pinedo-Álvarez, Karla Ozuki Chacón-Chumacero, Alfredo Pinedo-Álvarez,
Martín Martínez-Salvador, Marusia Rentería-Villalobos, Eduardo Santellano-Estrada
and Sandra Rodríguez-Piñeros *
Facultad de Zootecnia y Ecologia, Universidad Autonoma de Chihuahua, Periferico Francisco R. Almada Km 1,
Chihuahua 31110, Mexico; cpinedo@uach.mx (C.P.-Á.); karlaozuki@gmail.com (K.O.C.-C.);
apinedo@uach.mx (A.P.-Á.); msalvador@uach.mx (M.M.-S.); mrenteria@uach.mx (M.R.-V.);
esantellano@uach.mx (E.S.-E.)
*Correspondence: spineros@uach.mx; Tel.: +52-614-496-6334
Received: 13 July 2017; Accepted: 21 August 2017; Published: 28 August 2017
Abstract:
Ore mining has served as a predictor of economic wellbeing since it brought development
to countries. However, these benefits do not always extend to all localities that comprised the center of
this industry. This paper examined the contribution of mining to local communities. An index of local
sustainability was constructed based on economic, social, and land-use data from twelve localities
where mining and forestry are their major economic activities. Land-use variables were obtained
from Landsat Thematic Mapper (TM 5) images for 2000, and Landsat Operational Land Imager (OLI8)
for 2014, while the socio-economic variables were collected in twelve localities with an 85-question
survey. A sustainability index was developed for each group of variables—economic (ESI), social
(SSI) and land-use sustainability index (LUSI)—to further build a local sustainability index (LSI).
Three localities showed the highest ESI (0.61, 0.53 and 0.43) and SSI (0.90, 0.79 and 0.78), while two
localities had the lowest values in the ESI and SSI. In contrast, the highest value of LUSI was found in
two other different localities and in one with lower SSI. Income from mining activities is positively
associated with the ESI and SSI, but there was no evidence of linear association with the LUSI. A local
index of sustainability provides useful information for planning and development strategies.
Keywords: Chihuahua; sustainability; rural development
1. Introduction
Metal ore extraction has been important for the economic development of nations even before
industrialization. More recently, low- and middle-income countries have increased their Gross
Domestic Product (GDP) [
1
] and consequently higher human development indices have been achieved
due to mining industry [
2
]. However, there is debate about the economic benefits offered by mining
and the high underlying costs of the environmental degradation produced by its operations [
3
–
6
].
The extraction of mineral resources, especially surface mining, causes numerous environmental and
social impacts [
7
–
9
]. The most common is degradation to some landscapes [
10
], which, along with
the increasing land-use change patterns and forest fragmentation, drastically affects equilibrium in
ecosystems [
11
–
13
]. Consequently, there is a loss of soil productivity [
14
] that further impacts the
livelihood of rural localities [
15
]. Other forms of social impacts are due to changes in infrastructure
networks, non-balanced industrial development, resettlement and changes in the economic and social
structure of the local population, family disruption, schooling drop offs [
16
], and loss of cultural
heritage [16].
Resources 2017,6, 42; doi:10.3390/resources6030042 www.mdpi.com/journal/resources

Resources 2017,6, 42 2 of 15
The large economic benefits associated with large foreign investment, exports and fiscal
revenues [
2
] of mining have made possible the development of polices and technologies to mitigate
and remediate those impacts [
17
]. Mining companies are committed to reducing the amount of water
and energy needed for their operations and research has improved the treatment of acid rock drainage
and wastewater; in addition, land reclamation is included into their business plans as suggested by
international standards [
18
]. While these sustainable practices have been steadily implemented around
the world, the degree of the impacts and contribution of mining depends on different factors that
are more relevant at the local level [
19
]. Consequently, assessments at the local level are important
to better allocate resources and solutions that will enhance the quality of life of inhabitants around
mining operations. In this sense, measuring sustainability as a metric that encompasses economic,
social, and land use variables, of areas under ore extraction should be a requirement to develop
strategies that favor production and natural resource management [
20
]. Sustainability indicators and
composite indices are instrumental to communicate complex information [
21
] and serve to monitor
and evaluate sustainable strategies [
22
,
23
]; in addition, they provide guidance to decision makers [
24
]
when developing public policies [
25
]. Furthermore, indicators and indices can also be used to make
comparisons among countries and regions [
26
]. At the regional and local level sustainability indices
serve to assess the contribution of mining to small rural and indigenous localities, which are frequently
the owners of the intervened land [27].
Land-use planning implies the evaluation of forest landscape impacts to further understand the
dynamic of the social, economic, and ecological component associated to it [
28
]. Land-use indices
based on GIS are good predictors of changes in landscape patterns [
29
] and help to design strategies for
restoration [
30
]; while socioeconomic indices reflect the contribution of natural resources to wellbeing.
Incorporating socioeconomic indices to land-use patterns at the local level is critical for decision-making
in areas under mining extraction. Recently, most of the rural projects to restore or ameliorate the
environmental impacts of mining highly rely on community participation; communities will be prompt
to participate according to the degree of the benefits and impacts perceived [
31
]. Although there is
a large body of research that has studied the impact and benefits of mining, research using GIS to
explore land use changes and its connection to socioeconomic wellbeing at the local level is exiguous.
Most of the studies that address the social component of mining has revolved around the assessment
of socioeconomic benefits and costs, and the effects to development if closing the mines [
32
] partially
reflecting the real impacts and benefits to stakeholders.
Mining is one of the oldest economic activities developed in Mexico after the Spanish conquest.
In recent decades, Mexican gold and silver have substantially reached the international markets [
11
]
occupying first and seventh rank of silver and gold production with an estimated market value of
2.4 and 4.3 thousand millions United States Dollars, respectively [
33
]. The state of Chihuahua is
characterized for its forestry and mineral richness, in particular along the Sierra Tarahumara; those
two activities comprise more than 50% of the state’s income [
34
]. While forest harvesting has stagnated
lately, mining has expanded. In 2015 the extraction of gold in the municipality of Ocampo was of 9640
tons, around 7.2% of the total production of Mexico [
33
]; similarly, this municipality is known for its
large areas of forest cover.
In Mexico, most of the studies related to mining have been done at the state level; there are not
studies at the local level that provide information of the real contribution/impacts of mining to society.
This study aimed to evaluate the impacts of mining to the landscape at the local level. To accomplish
this goal, we constructed indices of sustainability for the social, economic, and land-use dimensions to
further develop an index of local sustainability. Building local indices would help policy makers and
industry to develop mitigation and adaptation strategies that directly benefit the affected localities.

Resources 2017,6, 42 3 of 15
2. Methodology
2.1. Study Site
The Ocampo mining region is located at the heart of Sierra Tarahumara in the state of Chihuahua,
Mexico. This region is comprised of three municipalities Ocampo, Temosachi, and Moris with a
GPS coordinates of 28.49 N, 108.49 E and 28.08 N, 107.91 E (Figure 1). About 80% of the region
is composed of steep mountains, while the other 20% is covered by plateaus and valleys [
34
] with
elevations between 458 and 2911 meters above the sea level (masl). Three rivers (Balloreca, Concheño
and Apituychi) flow through the mountains systems, irrigating the agricultural land of the state of
Sonora to finally discharge into the Pacific Ocean [
35
]. Due to biological and physical characteristics
of the region, there are two Natural Protected Areas (Parque Nacional Basaseachi and the Área de
Protección de Flora y Fauna Tutuaca) that are home to a large number of species of flora and fauna.
Resources 2017, 6, 42 3 of 15
The Ocampo mining region is located at the heart of Sierra Tarahumara in the state of
Chihuahua, Mexico. This region is comprised of three municipalities Ocampo, Temosachi, and Moris
with a GPS coordinates of 28.49 N, 108.49 E and 28.08 N, 107.91 E (Figure 1). About 80% of the region
is composed of steep mountains, while the other 20% is covered by plateaus and valleys [34] with
elevations between 458 and 2911 meters above the sea level (masl). Three rivers (Balloreca, Concheño
and Apituychi) flow through the mountains systems, irrigating the agricultural land of the state of
Sonora to finally discharge into the Pacific Ocean [35]. Due to biological and physical characteristics
of the region, there are two Natural Protected Areas (Parque Nacional Basaseachi and the Área de
Protección de Flora y Fauna Tutuaca) that are home to a large number of species of flora and fauna.
Figure 1. Location of the 12 study sites in the municipalities of Temosachic, Ocampo and Moris in the
mining region of Ocampo.
In economic terms, the region produced 35% of the gold extracted from the Sierra
Tarahumara [36]. In 2015, despite of the historical importance of forest production in the Sierra
Tarahumara, the value of gold production was three times higher than the forest production [36,37].
At national level, the Ocampo region occupies 3rd place for gold production with 451 million USD
and 2nd place of forest production with an estimate of 157 million USD. The region is currently facing
Figure 1.
Location of the 12 study sites in the municipalities of Temosachic, Ocampo and Moris in the
mining region of Ocampo.
In economic terms, the region produced 35% of the gold extracted from the Sierra Tarahumara [
36
].
In 2015, despite of the historical importance of forest production in the Sierra Tarahumara, the value
of gold production was three times higher than the forest production [
36
,
37
]. At national level,

Resources 2017,6, 42 4 of 15
the Ocampo region occupies 3rd place for gold production with 451 million USD and 2nd place of
forest production with an estimate of 157 million USD. The region is currently facing a fast process of
degradation and fragmentation due to the extraction of gold and timber harvesting principally; this
situation is aggravated by forest clear cut for agriculture and cattle, and forest fires [34,38].
2.2. Data Collection
2.2.1. Land Use Indices
The land-use change, number of patches, Shannon and Simpson indices, Normalized Difference
Vegetation Index (NDVI) and the rate of erosion were obtained after processing four scenes of Landsat
Images TM 5 for the year 2000 and Landsat OLI for 2014 under the path/row 33/40, 33/41, 34/40 and
34/41 with their respective radiometric and atmospheric correction [
39
]. The images were acquired for
free from the United States Geological Survey and registered under the Universal Transversal Mercator
(UTM) on the zone 12 N with datum World Geodesic System of 1984. Each band has a resolution of
30m. To compare images of different years the Top Atmosphere (TOA) process was used to convert
digital numbers (DN) to values of reflectance. Differences of atmospheric conditions were corrected
with the dark object subtraction method (DOSM) [
40
]. To adjust the localization error a geometric
re-sampling with a linear polynomic of first grade was used until obtaining the square root of the
medium error at 1.0 pixel.
In addition, the land use classification and vegetation was determined through the supervised
analyzing method using the Euclidian distance to measure pixels’ similarity [
41
]. Using the Gaussian
probabilistic model we obtained maps for 2000 and 2014. Once we had land use, a Patch Analysis
module Arcgis
®
(Redlands, CA, USA) was conducted to determine fragmentation [
42
]. Landscape
fragmentation was represented by the number of patches in the area (NumP). Patches were then
analyzed as individual polygons or as group of nearby neighborhood. The two images were converted
to vectors before the calculation of de indices.
2.2.2. Socioeconomic Data
Data were collected at the household level in 12 localities of three municipalities comprising the
Ocampo mining region. A questionnaire of 85 questions was designed and divided in 10 sessions
to obtain information on: general household information, sources of income, expenditure, type of
house, water availability, way to dispose waste, agricultural, livestock and forestry production, and
perceptions about food security, from which we derived the final 12 economic and the 10 social
indicators (Table 1). The sample population was randomly selected using the reference of [
43
]. A total
of 130 questionnaires were administered in the localities. The number of questionnaires varied in each
locality due to the total number of households for each of them. Four graduate and undergraduate
students were trained to serve as interviewers.
Table 1. Indicators selected for the economic, social and environmental components.
Indicators Components Unit of Measurement
Annual income per mining activity Economic $
Annual income from forestry activities Economic $
Annual income from government programs Economic $
Income from agricultural activities Economic $
Income from other wages Economic $
Other sources of income Economic $
Costs per dress Economic $
Food expenses Economic $

Resources 2017,6, 42 5 of 15
Table 1. Cont.
Indicators Components Unit of Measurement
Expenses for services Economic $
Recreation expenses Economic $
Household expenses Economic $
Expenditure on productive activities Economic $
Households with water availability Social %
Level of overcrowding Social %
Homes with electricity Social %
House with earthen floor Social %
Households with waste collection Social %
Household drainage Social %
Egg consumption per week Social None
Chicken consumption per week Social None
Consumption of beef per week Social None
Milk consumption per week Social None
Number of patches Environmental No
Erosion rate Environmental Ton/ha−1
Shannon Index Environmental Index
Simpson Index Environmental Index
Land-use change Environmental %
Differential normalized vegetation index (NDVI)
Environmental Index
2.2.3. Developing the Sustainability Indices (IS)
Data from the questionnaires were processed in Excel to further standardized measuring units for
each variable. A relative index was created for each component (Equation (1)), [22].
R. I. =1−(Xi−Xmin)/(Xm ax −Xmin)(1)
where: R. I. Relative index, X
i
value of variable iin the locality X; while, X
max
and X
min
are maximum
and minimum values for the variable iin all localities.
Thus, values range from 0 to 1, being 1 the locality in better condition. However, due the
characteristics of some variables, in which 1 does not imply positive connotation such: level of housing
crowding, type of flooring in the house, number of patches, Shannon and Simpson indices, % of
land-use change, and vegetation index, a factor correction was needed to be able to standardize the
meaning of the values 0 and 1 (see Equation (2)).
R.I. =(Xi−Xmax )/(Xmax −Xmin)(2)
For example, if the house flooring is of dirt, the index should have a value close to 0 to show
that this kind of flooring is less desirable. Then, economic (ESI), social (SSI) and land use (LUSI)
sustainability indices were computed to then comprised them in an index of local sustainability (LSI).
The following equation (Equation (3)) was used to calculate the LSI.
LSI =∑n
i=1RIE
12 +∑n
i=1RIS
10 +∑n
i=1RIL
6(3)
where: LSI = Local sustainability index, RI = Relative index.
2.3. Assessment of Sustainability Indices
A principal component analysis (PCA) was used to observe relations between social, economic,
and land use variables. PCA showed the contribution of each variable to each of the sustainability
indices and explained the total observed variable; it also helped to observe contrasts among the
localities. SAS
®
CORR (SAS, Cary, NC, USA) procedure was run to find the lineal association

Resources 2017,6, 42 6 of 15
between pair of components [
44
]. A cluster analysis for the localities was used to estimate the
closest sustainability index and to validate the PCA procedure [
45
]. Finally, data was represented with
the radar diagram to ease understanding.
3. Results and Discussion
3.1. Land Use Sustainability Index (LUSI)
Maps showed 10 different types of land use and vegetation (Figure 2): Mining; Open Lands;
Oak Forest; Pine Forest; Oak-pine Forest; Pine-oak Forest; Shrubs; Waters Bodies; Deciduous Forest,
and Crops Lands. Land-use change, Shannon and Simpson indices, NDVI, and erosion were correlated
to landscape fragmentation. The number and average size of patches of forest cover also showed
forest fragmentation. Ocampo presented a number of patches of 115, a Shannon index of 1.34 and a
Simpson index of 0.67 indicating the presence of fragmentation. The oak forest (OF) showed greater
number of patches (537) and pine forest (PF) the lowest (see Table 2). There was a decreased of the
average size of patches as a response of the number of patches. From 2000 to 2014 land-use changed
as gold mining increased in the localities of Ocampo and Moris. A steady increase of fragmentation
could be the response of continuous fuelwood harvesting, opening of roads and changes in land use
by expansion of the mining industry, and human settlements [45].
Resources 2017, 6, 42 6 of 15
3.1. Land Use Sustainability Index (LUSI)
Map s sho wed 1 0 diff eren t type s of l and u se an d vege tati on (Fi gure 2): M inin g; Ope n Lan ds; O ak
Forest; Pine Forest; Oak-pine Forest; Pine-oak Forest; Shrubs; Waters Bodies; Deciduous Forest, and
Crops Lands. Land-use change, Shannon and Simpson indices, NDVI, and erosion were correlated
to landscape fragmentation. The number and average size of patches of forest cover also showed
forest fragmentation. Ocampo presented a number of patches of 115, a Shannon index of 1.34 and a
Simpson index of 0.67 indicating the presence of fragmentation. The oak forest (OF) showed greater
number of patches (537) and pine forest (PF) the lowest (see Table 2). There was a decreased of the
average size of patches as a response of the number of patches. From 2000 to 2014 land-use changed
as gold mining increased in the localities of Ocampo and Moris. A steady increase of fragmentation
could be the response of continuous fuelwood harvesting, opening of roads and changes in land use
by expansion of the mining industry, and human settlements [45].
Figure 2. Mapping of land use classification with Landsat OLI8 images from year 2014.
Higher values of LUSI were present in the localities of Yepachi (0.74), La Batería de Rodríguez
(0.74) and Moris (0.66), this can be explained due to low levels of erosion and drastic changes in the
land-use. In contrast, Gasachi (0.47), Jesús del Monte (0.40) and Ocampo (0.21) showed the lowest
Figure 2. Mapping of land use classification with Landsat OLI8 images from year 2014.
Higher values of LUSI were present in the localities of Yepachi (0.74), La Batería de Rodríguez
(0.74) and Moris (0.66), this can be explained due to low levels of erosion and drastic changes in the
land-use. In contrast, Gasachi (0.47), Jesús del Monte (0.40) and Ocampo (0.21) showed the lowest

Resources 2017,6, 42 7 of 15
values of LUSI. Ocampo is a locality where both mining and forest harvesting are the main sources of
income; which highly exceeds the income of the localities nearby.
Table 2. Number of patches by coverage classification for scenes 2000 and 2014.
Municipality Coverage Number of Patches
2000 2014
Temósachi Mining 0 0
Open land 7 11
Oak forest 12,850 13,387
Pine forest 100 105
Oak-pine forest 1041 1063
Pine-oak forest 2476 2543
Shrubs 131 183
Water bodies 0 0
Deciduous forest 0 0
Crops lands 23 23
Moris Mining 2 4
Open land 2 11
Oak forest 3726 3945
Pine forest 2 2
Oak-pine forest 930 1073
Pine-oak forest 207 230
Shrubs 0 0
Water bodies 0 0
Deciduous forest 687 710
Crops lands 0 0
Ocampo Mining 0 37
Open land 24 30
Oak forest 3723 3913
Pine forest 57 57
Oak-pine forest 1108 1250
Pine-oak forest 1688 1748
Shrubs 0 0
Water bodies 0 3
Deciduous forest 192 283
Crops lands 0 0
3.2. Economic Sustainability Index (ESI)
The localities of Huajumar (0.61), Gasachi (0.53) and Ocampo (0.43) showed the highest values for
this index, due principally to their higher income compared to the rest of the localities. Huajumar and
Ocampo are closer to mining camps and also have a good road system; therefore, more than 50% of
the population’s income comes from mining activities. As opposed to El Pilar (0.23), Huevachi (0.20)
and Tutuaca (0.03) that showed lower values for this index, operation camps are farther down.
3.3. Social Sustainability Index (SSI)
This index showed a similar tendency of ESI, localities with higher incomes registered higher
SSI values, Gasachi (0.91), Huajumar (0.79) and Ocampo (0.78). Higher income, as expected allows
people to have better houses with faucet water and more access to food. In contrast, the localities of
Huevachi (0.44), La Batería de Rodríguez (0.42) and Tutuaca (0.28) showed the lowest values of the
12 localities of this study. Variables associated to housing were more relevant for the SSI. A decent
housing that allows a person to satisfy their basic needs should have faucet water, electrical energy,
sewer and solid waste system; concrete flooring material, and enough number of rooms to harbor each
member of the family [
46
,
47
]. The localities that exhibited low social index are consistent with those

Resources 2017,6, 42 8 of 15
localities considered under extreme poverty [
47
], meaning that besides decent housing, there is also a
lack of education or health services.
In regard to food security as an indicator of wellbeing, availability and consumption of animal
products such, chicken, beef, poultry, dairy products and eggs the following data was found.
The localities of Gasachi, Huajumar and Ocampo showed a major consumption of these products
with a frequency of “at least once a week”, eggs and milk were products that are consumed 4 days of
the week. In contrast, the localities of Huevachi, Batería de Rodríguez, and Tutuaca showed lower
consumption of these products due to their lower income that limited the access to these products.
3.4. Local Sustainability Index (LSI)
We constructed a composite index using land-use and socioeconomic variables. The LSI for the
localities of El Pilar, Huevachi and Tutuaca showed the lowest values (0.39, 0.35 and 0.24 respectively).
The large distance from the mining activities limits the access to basic needs and food, it also has
influence on income; which is reflected on lack of education, and precarious housing for these
localities. In contrast, Huajumar, Gasachi and Las Estrellas exhibited higher values (0.67, 0.65 and 0.53
respectively). These localities presented higher incomes and better housing condition; which imply
that income from mining is expended in goods and services for household [27].
3.5. Assessment of Sustainability Indices
The values of the sustainability indices are presented in Table 3. Income was a determinant
for the ESI (p= 0.0057) and for SSI (p= 0.001); however, there was not an empirical evidence of its
impact to LUSI (p= 0.8121). Figure 3shows the sensibility of indices to changes in income from
mining. The ESI showed an increase of 0.017% per year for every 10 thousand Mexican pesos;
meanwhile, the SSI increase in 0.022%. Although the LUSI showed a negative value (
−
0.002), this
was not statistically significant (p= 0.8121). A similar trend was observed when comparing localities.
Mining as a major activity had an impact on the ESI (p= 0.0150) and the SSI (p= 0.0012) but no for
the
LUSI (p= 0.8881)
. We also analyzed the impact of natural protected areas on the indices due to
the fact that some localities are within the limits of natural protected areas. There was not significant
evidence for the
ESI (p= 0.2183)
and the LUSI (p= 0.313); however, there is a significant correlation on
the SSI (p= 0.0392).
Table 3.
Indices composed of economic, social and land use sustainability determined in the mining
region of Ocampo, Chihuahua, Mexico.
Locality ESI SSI LUSI LSI
Basaseachi 0.42 0.59 0.57 0.51
El Pilar 0.23 0.45 0.58 0.39
Gasachi 0.53 0.91 0.47 0.65
Huajumar 0.61 0.79 0.58 0.67
Huevachi 0.20 0.44 0.51 0.35
Jesús del Monte 0.30 0.51 0.40 0.40
La Batería de Rodríguez 0.26 0.42 0.74 0.42
Las Estrellas 0.30 0.74 0.64 0.53
Moris 0.30 0.68 0.66 0.51
Ocampo 0.43 0.78 0.21 0.51
Tutuaca 0.03 0.28 0.60 0.24
Yepachi 0.27 0.53 0.74 0.47
ESI = Economic Sustainability Index. SSI = Social Sustainability Index. LUSI = Land use Sustainability Index.
LSI = Local Sustainability Index.

Resources 2017,6, 42 9 of 15
Resources 2017, 6, 42 9 of 15
Table 3. Indices composed of economic, social and land use sustainability determined in the mining
region of Ocampo, Chihuahua, Mexico.
Locality ESI SSI LUSI LSI
Basaseachi 0.42 0.59 0.57 0.51
El Pilar 0.23 0.45 0.58 0.39
Gasachi 0.53 0.91 0.47 0.65
Huajumar 0.61 0.79 0.58 0.67
Huevachi 0.20 0.44 0.51 0.35
Jesús del Monte 0.30 0.51 0.40 0.40
La Batería de Rodríguez 0.26 0.42 0.74 0.42
Las Estrellas 0.30 0.74 0.64 0.53
Moris 0.30 0.68 0.66 0.51
Ocampo 0.43 0.78 0.21 0.51
Tutuaca 0.03 0.28 0.60 0.24
Yepachi 0.27 0.53 0.74 0.47
ESI = Economic Sustainability Index. SSI = Social Sustainability Index. LUSI = Land use Sustainability
Index. LSI = Local Sustainability Index.
Figure 3. Values of the B1 estimator for the region's sustainability indices miner of Ocampo.
In this study, the SSI followed the same pattern since the indicators of human wellbeing are
associated to income. Consumption of beef per week, chicken consumption per week and annual
income per mining activities showed the highest values of contribution to main component 1 (Table 4).
In contrast, the main component 2 is more influenced by the number of patches, annual income from
forestry activities and NDVI. In this way we can associate the main component 1 as an indicator of
economic development, and the main component 2 as an indicator of ecological development. The
analysis of LSI confirmed the tendency of localities of Huajumar and Gasachi accordingly to the
results obtained by the PCA. Therefore, it can be concluded that the extraction of gold contributed to
economic benefits that enhance the quality of life of people in terms of housing, water availability,
education, and road network. Although there was evidence of landscape fragmentation, this was not
significant to those documented at regional level [48].
Figure 3. Values of the B1 estimator for the region's sustainability indices miner of Ocampo.
The principal component analysis showed contrast within the localities of Gasachi and Tutuaca
(Figure 4); the latter, observed the lowest value of the component 1 (
−
5.07), which is consistent with
the lowest value for the LSI (0.24). The localities of Huajumar and Ocampo showed similar trends
with high values on ESI in comparison with Tutuaca and Huevachi that presented the lowest values.
Low income in these localities is considered a barrier to access goods and services such food and
decent housing. Long distance to mining operations is one of the major problems for these localities,
as observed by [
19
] as well. Meanwhile, Huajumar showed a medium value in the component 1 (2.39)
and a high value on component 2 (3.97) indicating that there was a balance among the three indices,
which in turn, was exhibited with a highest value in the LSI (0.67).
Resources 2017, 6, 42 10 of 15
Figure 4. PCA ordination showing the localities along the first and the second principal component
axes. These axes explained 47% of the total variation.
Mining in the Ocampo region was the first predictor of erosion and fragmentation; a previous
study reported high levels of trace elements at the runoff water due to mining [34]. Although there
were degrees of land disturbance at the local level, these were not significant to those documented at
the surface scale of the Sierra Tarahumara [48].
The extraction of mineral resources, especially surface mining, causes numerous negative
environmental externalities and socio-economic impacts, i.e., land use changes, ecosystem
disturbances, watercourse relocation, and a decrease in ground water level [8], and changes in the
economic and social structure of the local population. In recent year public controversies about
mining and sustainability have arose, due to the contribution of mining to socioeconomic
development versus the environmental degradation [49]. Gold mining, in particular, has a negative
image due to low contribution to local localities [50] that has been a determinant of illegal mining
which in turn, has detrimental consequences to landscape such high levels of fragmentation, water
pollution and loss of aquatic biodiversity [12].
Figure 4.
PCA ordination showing the localities along the first and the second principal component
axes. These axes explained 47% of the total variation.

Resources 2017,6, 42 10 of 15
The radar diagram compiles and summarizes the relationship among the indices obtained from
the PCA (Figure 5). It can be seen how the localities identified with the highest incomes showed the
values closest to unity in the economic and social indices, while the land-use index behaved differently.
As an example, localities such as Ocampo and Basaeachi, that presented high values of ESI and SSI
showed low values in the LUSI, due to the fact that they presented changes of forestland use.
Resources 2017, 6, 42 11 of 15
Figure 5. Radar diagram showing the distribution of assessed sustainability indices.
Despite mining seeming to enhance quality of life in economic terms, there is a level of inequality
of income among the localities and households. Although there are groups of people who live in the
mining operation centers, their income is not necessary a product of their jobs on the mine, leaving
them more vulnerable to access goods and services in economic terms [19]. The same situation arises
for localities a little further from the mining operation centers; in these cases, localities do not have
both physical and economic access to those services [20].
The accessibility dimension of food security is associated with income and a good road system
principally; in this regard, our study corroborated what [19] also found, that localities far from
production centers have limited access to animal products due to the lack of income and distance.
Local sustainability indices are important to understand regional dynamics in the three
dimensions of sustainability. In localities with an indigenous population, where culture and
community are intrinsically linked to the management of forest and mining resources, social and
economic indicators become more important. The challenge for natural resource management is to
balance ecological functioning of natural systems with an increasingly diverse set of demands
imposed on those systems by human needs [51]. These include employment, subsistence goods,
recreational opportunities, tourism-based economic development, as well as spiritual connections,
heritage values, social meanings and aesthetics [52]. Our study confirmed what was previously found
by [53] in 71 localities in Australia.
Figure 5. Radar diagram showing the distribution of assessed sustainability indices.
In this study, the SSI followed the same pattern since the indicators of human wellbeing are
associated to income. Consumption of beef per week, chicken consumption per week and annual
income per mining activities showed the highest values of contribution to main component 1 (Table 4).
In contrast, the main component 2 is more influenced by the number of patches, annual income from
forestry activities and NDVI. In this way we can associate the main component 1 as an indicator
of economic development, and the main component 2 as an indicator of ecological development.
The analysis of LSI confirmed the tendency of localities of Huajumar and Gasachi accordingly to the
results obtained by the PCA. Therefore, it can be concluded that the extraction of gold contributed
to economic benefits that enhance the quality of life of people in terms of housing, water availability,
education, and road network. Although there was evidence of landscape fragmentation, this was not
significant to those documented at regional level [48].

Resources 2017,6, 42 11 of 15
Table 4. Contribution of the indicators to the main components PC1 and PC2.
Indicator PC1 PC2
Annual income per mining activity 0.29 0.09
Annual income from forestry activities 0.08 0.29
Annual income from government programs 0.11 0.15
Income from agricultural activities 0.01 0.27
Income from other wages 0.07 −0.06
Other sources of income 0.21 −0.14
Costs per dress 0.19 −0.22
Food expenses 0.27 −0.25
Expenses for services 0.04 0.12
Recreation expenses 0.25 0.11
Household expenses 0.27 0.11
Expenditure on productive activities 0.24 0.03
Households with water availability 0.03 0.29
Level of overcrowding −0.18 0.2
Homes with electricity 0.2 0.25
House with earthen floor −0.1 −0.1
Household drainage 0.19 −0.22
Households with waste collection 0.23 0.07
Egg consumption per week 0.15 0.22
Chicken consumption per week 0.32 −0.15
Consumption of beef per week 0.32 −0.08
Milk consumption per week 0.23 −0.06
Number of patches 0.04 −0.38
Erosion rate 0.1 −0.14
Shannon Index 0.09 −0.11
Simpson Index 0.19 0.13
Land-use change 0.14 0.21
Differential normalized vegetation index (NDVI)
0.12 0.27
Mining in the Ocampo region was the first predictor of erosion and fragmentation; a previous
study reported high levels of trace elements at the runoff water due to mining [
34
]. Although there
were degrees of land disturbance at the local level, these were not significant to those documented at
the surface scale of the Sierra Tarahumara [48].
The extraction of mineral resources, especially surface mining, causes numerous negative
environmental externalities and socio-economic impacts, i.e., land use changes, ecosystem disturbances,
watercourse relocation, and a decrease in ground water level [
8
], and changes in the economic and social
structure of the local population. In recent year public controversies about mining and sustainability
have arose, due to the contribution of mining to socioeconomic development versus the environmental
degradation [
49
]. Gold mining, in particular, has a negative image due to low contribution to local
localities [
50
] that has been a determinant of illegal mining which in turn, has detrimental consequences
to landscape such high levels of fragmentation, water pollution and loss of aquatic biodiversity [12].
Despite mining seeming to enhance quality of life in economic terms, there is a level of inequality
of income among the localities and households. Although there are groups of people who live in the
mining operation centers, their income is not necessary a product of their jobs on the mine, leaving
them more vulnerable to access goods and services in economic terms [
19
]. The same situation arises
for localities a little further from the mining operation centers; in these cases, localities do not have
both physical and economic access to those services [20].
The accessibility dimension of food security is associated with income and a good road system
principally; in this regard, our study corroborated what [
19
] also found, that localities far from
production centers have limited access to animal products due to the lack of income and distance.
Local sustainability indices are important to understand regional dynamics in the three
dimensions of sustainability. In localities with an indigenous population, where culture and community

Resources 2017,6, 42 12 of 15
are intrinsically linked to the management of forest and mining resources, social and economic
indicators become more important. The challenge for natural resource management is to balance
ecological functioning of natural systems with an increasingly diverse set of demands imposed on
those systems by human needs [
51
]. These include employment, subsistence goods, recreational
opportunities, tourism-based economic development, as well as spiritual connections, heritage values,
social meanings and aesthetics [
52
]. Our study confirmed what was previously found by [
53
] in
71 localities in Australia.
4. Conclusions and Recommendations
Sustainable development in the Sierra Tarahumara is becoming more important in particular
for the localities that depend on the forest and mining industry. To respond to many sustainability
challenges, these localities must be able to measure their progress towards sustainable development.
The framework of local-level sustainability indicators generated in this study could be used as
baseline information to assess the level of sustainability of localities dependent on mining in temperate
forests and deciduous forests of the Sierra Tarahumara. Nine of the twelve localities had the lowest
ESI (<0.40) implying that their level of sustainability is low or unsustainable. The remaining three
localities had ESI values of 0.61, 0.53 and 0.43 suggesting a medium level of sustainability. This could
be explained by the annual income from economic activities, food expenses and housing costs. The SSI
showed the same trend for the same localities (0.90, 0.79 and 0.78). These values indicate that income
from mining activity is associated with housing affordability, access to communication and food, in
particular animal products as a source of protein.
Localities with low sustainability indices in ESI and SSI showed better behavior in LUSI (>0.66).
This is explained by the fact that they did not present a significant change in soil use and their
erosion rate was low. Three others localities with intermediate values of economic and social
sustainability also presented intermediate values in the LUSI. This is due to the fact that, in addition
to participating in mining activities, towns and villages are developing alternative activities such as
self-consumption agriculture.
The LSI integrated by ESI, SSI and LUSI showed that three of the twelve localities presented
the lowest sustainability indices (<0.39). This may be due to the fact that these localities are far from
large population centers and therefore they lack on fixed and well-paid jobs, which is reflected in the
precarious housing conditions, poor access to basic services and healthy food.
Although there is a dynamic in the change of coverage of forestland to the use of mining land,
there was still insufficient evidence to affirm that the activity implies a process of forest degradation in
the region studied, perhaps due to its surface area. We also concluded that mining activity did not have
a significant impact on the LUSI component in the two natural protected areas. The condition of being
within an area with a protected natural area category had only impacts on the social sustainability
index. This is probably due to the distance from the localities to the mining centers. This implies that
conservation may halt social development in the region, as an expression of decent housing, water
availability, and access to food. Then, there is a dilemma that needs to be resolved: while mining
provides immediate social benefits in terms of jobs, income, and infrastructure, conservation runs
short on it, and therefore, mining would be more attractive for people under poverty. This dilemma
urges the finding of sustainable programs for those localities, such as ecotourism or payments for
environmental services to compensate for those benefits that mining could not bring in the short run.
Although mining wealth appears to improve the quality of life for localities in the Ocampo
mining region, it is necessary to extend these scales of study to the other mining regions of the
Sierra Tarahumara where indigenous workers prevail. It is also necessary to obtain information
related to the perception of the local residents in relation to their opinion by the economic, social and
environmental impacts.

Resources 2017,6, 42 13 of 15
Acknowledgments:
Authors want to thank local communities for their valuable contribution, we also want to
thank to graduate and undergraduate students Victor Manuel Aguilar, Maria Angelina Gutierrez, y Maria Esther
Salas for helping us with data collection. We are also thankful to the inhabitants of the communities of this study
without their willingness to participate this study could not have been possible.
Data collection for this study was funded by SEP-PROMEP (Programa al Mejoramiento Académico)
September 2014 Convenio OF-14-7427, project “The contribution of forest to food security: perception of rural
communities of the Sierra Tarahumara, Chihuahua. Mexico”.
Author Contributions:
Carmelo Pinedo and Alfredo Pinedo contributed to the geospatial processing and spatial
analysis of environmental indicators; Karla Ozuki Chacón contributed in the data collection and organization of the
database, and data analysis. Martin Martínez wrote and discussed the level 1 and 2 indicators;
Marusia Renter
ía
revised language editing and organization of the manuscript; Eduardo Santellano, helped with instrument
designed to collect socioeconomic data, and statistical analysis. Sandra Rodríguez constructed the socioeconomic
indicators and helped with the design of the instrument and structured the English manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Hilson, G. The environmental impact of small-scale gold mining in Ghana: Identifying problems and possible
solutions. Geogr. J. 2002,168, 57–72. [CrossRef]
2.
McMahon, G.; Moreira, S. The Contribution of the Mining Sector to Socioeconomic and Human Development;
Extractive Industries for Development Series 30; World Bank: Washington, DC, USA, 12 June 2014.
3.
Garvin, T.; McGee, T.K.; Smoyer-Tomic, K.E.; Aubynn, E.A. Community–company relations in gold mining
in Ghana. J. Environ. Manag. 2009,90, 571–586. [CrossRef] [PubMed]
4.
Larsen, J.E.; Kassibo, B.; Lange, S.; Samset, I. Socio-Economic Effects of gold Mining in Mali; CMI Report; CHR
Milchensen Institute: Bergen, Norway, 2006.
5.
Petkova, V.; Lockie, S.; Rolfe, J.; Ivanova, G. Mining developments and social impacts on communities:
Bowen Basin case studies. Rural Society 2009,19, 211–228. [CrossRef]
6.
Kumah, A. Sustainability and gold mining in the developing world. J. Clean. Prod.
2006
,14, 315–323.
[CrossRef]
7.
Solomon, F.; Katz, E.; Lovel, R. Social dimensions of mining: Research, policy and practice challenges for the
minerals industry in Australia. Resour. Policy 2008,33, 142–149. [CrossRef]
8.
Spasi´c, N.; Stojanovi´c, B.; Nikoli´c, M. Environmental impact of the mining activity and revitalization of
degraded space. Arhit Urban 2005,16–17, 75–85.
9.
Mensah, A.K.; Mahiri, I.O.; Owusu, O.; Mireku, O.D.; Wireko, I.; Kissi, E.A. Environmental Impactos of
Mining: A Study of Mining Localities in Ghana. Appl. Ecol. Environ. Sci. 2015,3, 81–94.
10.
Schueler, V.; Kuemmerle, T.; Schröder, H. Impacts of Surface Gold Mining on Land Use Systems in Western
Ghana. AMBIO 2011,40, 528–539. [CrossRef] [PubMed]
11.
Hilson, G.; Haselip, J. The environmental and socioeconomic performance of multinational mining companies
in the developing world economy. Miner. Energy 2004,3, 25–47. [CrossRef]
12.
Hun-Bok, J.; Seong-Tack, Y.; Bernhard, M.; Soon-Oh, K.; Seong-Sook, P.; Pyeong-Koo, L. Transport and
sediment-water partitioning of trace metals in acid mine drainage: An example from the abandoned
Kwangyang Au–Ag mine area, South Korea. Environ. Geol. 2005,48, 437–449.
13.
Matejicek, L.; Kopachova, V. Changes in croplands as a result of large scale mining and the associated impact
on food security studied using time-series Landsat images. Remote Sens. 2010,2, 1463–1480. [CrossRef]
14.
Rahman, S.; Rahman, M. Impact of land fragmentation and resource ownership on productivity and
efficiency: The case of rice producers in Bangladesh. Land Use Policy 2009,26, 95–103. [CrossRef]
15.
Bridge, G. Mapping the bonanza: Geographies of mining investment inan era of neoliberal reform.
Prof. Geogr. 2004,56, 406–421.
16.
Kitula, A.G.N. The environmental and socio-economic impacts of mining on local livelihoods in Tanzania:
A case study of Geita District. J. Clean. Prod. 2006,14, 405–414. [CrossRef]
17.
Rajaram, R. Issues in Sustainable Mining Practices, Chapter 3. In Sustainable Mining Practices A Global
Perspective; Rajaram, V., Dutta, S., Eds.; A.A. Balkema Publishers: Leiden, The Netherlands, 2005; pp. 45–89.
18.
IFC—International Finance Corportation. Sustainable and Responsible Mining in Africa; A Getting Started
Guide; IFC Africa: Nairobi, Kenya, 2014.

Resources 2017,6, 42 14 of 15
19.
Tonts, M.; Plummer, P.; Lawrie, M. Socio-economic wellbeing in Australian mining towns: A comparative
analysis. J. Rural Stud. 2012,28, 288–301. [CrossRef]
20.
Lawson, E.T.; Bentil, G. Shifting sands: Changes in community perceptions of mining in Ghana. Environ.
Dev. Sustain. 2014,16, 217–238. [CrossRef]
21.
Singh, R.K.; Murty, H.R.; Gupta, S.K.; Dikshit, A.K. An overview of sustainability assessment methodologies.
Ecol. Indic. 2009,9, 189–212. [CrossRef]
22.
Martínez-Salvador, M.; Beltrán-Morales, L.; Valdez-Cepeda, R.; Arias, H.R.; Troyo-Dieguez, E.;
Murillo-Amador, B.; Galindo, J.J.; Ortega-Rubio, A. Assessment of sustainibility performance on the
utilization of agave (Agave salmiana ssp crassispina) in Zacatecas, Mexico. Int. J. Sustain. Dev.
2007
,14,
1–10.
23.
Hák, T.; Janoušková, S.; Moldan, B. Sustainable Development Goals: A need for relevant indicators.
Ecol Indic.
2016,60, 565–573. [CrossRef]
24.
Morse, S.; McNamara, N.; Acholo, M. Soils, souls and agricultural sustainability: The need for connection.
Int. J. Sustain. Dev. 2004,7, 410–432. [CrossRef]
25. Beroya-Eitner, M.A. Ecological vulnerability indicators. Ecol. Indic. 2016,60, 329–334. [CrossRef]
26.
Hens, L.; De Wit, J. The development of indicators and core indicators for sustainable development: A state
of the art review. Int. J. Sustain. Dev. 2003,6, 436–459. [CrossRef]
27.
Ritter, R.M. Archibald. Canada: From fly-In, Fly out to mining metropolis. In Large Mines and the Community:
Socioeconomic and Environment Effects in Latin America, Canada and Spain; McMahon, G., Remy, F., Eds.;
International Development Research Center, The World Bank: Washington, DC, USA, 2001.
28.
Bateman, I.J.; Harwood, A.R.; Mace, G.M.; Watson, R.T.; Abson, D.J.; Andrews, B.; Binner, A.; Crow, A.;
Day, B.H.; Dugdale, S.; et al. Bringing ecosystem services into economic decision-making: Land use in the
United Kingdom. Science 2013,341, 45–50. [CrossRef] [PubMed]
29.
Menegaki, M.E.; Kaliampakos, D.C. Evaluating mining landscape: A step forward. Ecol. Eng.
2012
,43, 26–33.
[CrossRef]
30.
Lei, K.; Pan, H.; Lin, C. A landscape approach towards ecological restoration and sustainable development
of mining areas. Ecol. Eng. 2016,90, 320–325. [CrossRef]
31.
Damigos, D.; Menegaki, M.; Kaliampakos, D. Monettizing the social benefits of landfill mining: Evidence
from a Contingent Valuation survey in a rural area in Greece. Waste Manag.
2016
,51, 119–129. [CrossRef]
[PubMed]
32.
Andrews-Speed, P.; Ma, G.; Shao, B.; Liao, C. Economic responses to the closure of small scale coal mines in
Chongging, China. Resour. Policy 2005,30, 39–54. [CrossRef]
33.
CAMIMEX. Cámara Minera de Mexico. Mexico Minero Bicentenario. 2016. Available online: https:
//www.camimex.org.mx/files/5314/3917/9156/sup_2010-10.pdf (accessed on 16 September 2016).
34.
Chacón, K.O.; Rentería, M.; Pinedo, C. Assessment of trace elements in surface water from gold mining area
located to West in Chihuahua, Mexico. In Proceedings of the 4th IWA Mexico YWP Conference, Guanajuato,
Mexico, 25–27 April 2015.
35.
Villanueva, J.; Fulé, P.; Paredes, C.; Estrada, J.; Sánchez, I. Reconstrucción de la precipitación estacional
para el barlovento de la Sierra Madre Occidental con anillos de crecimiento de Pseudotsuga menziesii (Mirb.).
Cienc. For. Mex. 2009,34, 37–69.
36.
SGM (Servicio Geològico Mexicano). Anuario Estadìstico de la Minerìa en Mèxico, 2015. Coordinaciòn
General de Minerìa. Pacuca, Hidalgo, Mex. 2015. Available online: https://www.gob.mx/sgm (accessed on
16 June 2016).
37.
SEMARNAT (Secretaria del Medio Ambiente y Recursos Natuales). Anuario estadìstico de la porduccion
forestal 2015. Dirección General de Gestión Forestal y de Suelos. Dirección del Registro y del Sistema
Nacional de Gestión Forestal. Mèxico, Mex. Consulta en. 2016. Available online: https://www.gob.mx/
cms/uploads/attachment/file/181383/ANUARIO_FORESTAL_2015.pdf (accessed on 23 June 2016).
38.
EyéKawi, Grupo Integral de Servicios Ecosistémicos A.C. Diagnóstico Sobre Determinación y Degradación
Forestal en Zonas Prioritarias en el Estado de Chihuahua; Technical Report; EyéKawi A.C.: Chihuahua, Chi,
Mexico, 2014. (In Spanish)
39.
Jensen, J.R. Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed.; Prentice-Hall:
Upper Saddle River, New Jersey, 1996.

Resources 2017,6, 42 15 of 15
40.
Song, C.; Woodcock, C.E.; Seto, K.C.; Lenney, M.P.; Macomber, S.A. Classification and change detection using
Landsat TM data: When and how to correct atmospheric effects? Remote Sens. Environ.
2001
,75, 230–244.
[CrossRef]
41.
Richards, J.A. Remote Sensing Digital Image Analysis: An Introduction; Springer: Berlin/Heidelberg,
Germany, 1999.
42.
McGarigal, K. Fragstats Help. Massachusetts, US. University of Massachusetts, 2015. Available
online: http://www.umass.edu/landeco/research/fragstats/documents/fragstats.help.4.2.pdf (accessed
on 29 May 2016).
43.
Costello, A.B.; Osborne, J.W. Best practices in exploratory factor analysis: Four recommendations for getting
the most from your analysis. Pract. Assess. Res. Eval. 2005,10, 1–9.
44.
Chacón, K.O. Impacto de la Actividad Minera y Forestal en el Desarrollo Sustentable de la Región Minera
Ocampo, Chihuahua. Ph.D. Thesis, Universidad Autónoma de Chihuahua, Chihuahua, Mexico, 2016.
45. Jollitte, I.T. Principal Component Analysis; Springer Science and Business Media: New York, NY, USA, 2014.
46.
Azapagic, A. Developing a framework for sustainable development indicators for the mining and minerals
industry. J. Clean. Prod. 2004,12, 639–662. [CrossRef]
47.
Consejo Nacional de Población. Indíce de Marginación por Localidad. Available online: http://www.
conapo.gob.mx/es/CONAPO/Indice_de_Marginacion_por_Localidad_2010 (accessed on 20 March 2015).
48.
Pinedo. A.A.; Prieto A.J.A., Morales C.M.C., Villarreal G.F., Pinedo A.C. Atlas del Sistema de Monitoreo de Datos
e Información de Indicadores Ambientales de la Sierra Tarahumara; Proyecto Tarahumara Sustentable; Universidad
Autónoma de Chihuahua: Chihuahua, Mexico, 2016.
49.
Mudd, G.M. The environmental sustainability of mining in Australia: Key mega-trends and looming
constraints. Resour. Policy 2010,35, 98–115. [CrossRef]
50.
Kuma, J.S. Hydrogeological studies on the Tarkwa gold mining district, Ghana. Bull. Eng. Geol. Environ.
2007,66, 89–99. [CrossRef]
51.
Beckley, T.M. Sustainability for Whom? Social Indicators for Forest-Dependant Localities in Canada; Sustainable
Forest Management Network; Project Report 2000-34; University of Alberta: Edmonton, Albert, 2000.
52.
Sherry, E.; Halseth, R.; Fondahl, G.; Karjala, M.; Leon, B. Local-Level Criteria and Indicators: An Aboriginal
Perspective on Sustainable Forest Management. Forestry
2005
,78, 513–539. Available online: http://forestry.
oxfordjournals.org/ (accessed on 14 October 2015).
53.
Hajkowicz, S.A.; Heyenga, S.; Moffat, K. The relationship between mining and socio-economic well being in
Australia’s regions. Resour. Policy 2011,36, 30–38. [CrossRef]
©
2017 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 (http://creativecommons.org/licenses/by/4.0/).