sustainability
Article
Research on Potential Evaluation and Sustainable
Development of Rural Biomass Energy in Gansu
Province of China
Sheng Zhong 1, *, Shuwen Niu 1,2 and Yipeng Wang 1
1
2
*
College of Earth and Environmental Sciences, Lanzhou University, 222 Tianshui S Rd, Chengguan Qu,
Lanzhou 730000, China; shuwenn@lzu.edu.cn (S.N.); ypwang2016@lzu.edu.cn (Y.W.)
Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University,
222 Tianshui S Rd, Chengguan Qu, Lanzhou 730000, China
Correspondence: wywybz@163.com; Tel.: +86-150-0250-8523
Received: 20 September 2018; Accepted: 17 October 2018; Published: 20 October 2018
Abstract: The development and utilization of renewable energy is an important way to solve the
environmental dilemma. Biomass energy is a kind of renewable energy and one of the most widely
distributed and easily accessible energy forms. It has currently become a main direction of renewable
energy development. This paper took Gansu Province of China as the research object to calculate its
theoretical reserves of biomass energy resources and then evaluate its potential of biomass energy
development by using TOPSIS method under different agricultural development and geographical
environmental conditions. Spatial autocorrelation analysis was also performed to reveal the spatial
distribution and temporal evolution of the potential of biomass energy development in Gansu
Province. The results show that: (1) The total reserves of biomass energy resources from agricultural
wastes in Gansu Province reach 7.28 × 107 t/year, with equivalent biogas production of about
1.95 × 1010 m3 /year. (2) In most counties of Gansu Province, the Ci value is smaller than 0.5000,
indicating that the potential of biomass energy development is relatively low in Gansu Province.
(3) The spatial agglomeration of biomass energy development potential occurs mainly in the Hexi area,
the Gannan area and the Loess Plateau area of East Gansu Province. (4) There is an area with obvious
high-low (H-L) agglomeration of biomass energy development potential to the north-west side of the
Gannan area with low-low (L-L) agglomeration of biomass energy development potential. It is a key
zone to help drive biomass energy development in the Gannan area. (5) The spatial range of positive
correlation (high-high and low-low agglomeration) areas shrunk during the evaluation period.
Keywords: biomass energy; rural energy; potential evaluation; spatial-temporal analysis; growth pole;
Gansu province; the northwest region of China
1. Introduction
The development and utilization of renewable energy is an important way to solve the
environmental dilemma [1]. It could help promote economic diversification, raise productivity and
enhance environmental quality and energy justice [2]. Biomass energy is a kind of renewable energy
among many other renewable energy forms (wind, solar, hydraulic, geothermal, etc.) [3] and is also
one of the most widely distributed and easily accessible energy forms. Biomass is almost the most
important source of renewable energy in rural areas. Therefore, biomass energy has become a main
direction of renewable energy development.
Sustainability 2018, 10, 3800; doi:10.3390/su10103800
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Biomass energy is one of the oldest energy resources used by humankind [4]. As humans just
learned how to use fire, biomass energy began to be used for cooking and lighting. After the rise of
agriculture, agricultural residues played an important role in energy supply [5]. To date, in many
remote areas, biomass energy still dominates the energy sector, particularly as the main source of energy
for cooking and heating [6]. For example, in Sub-Saharan Africa, people relying on the traditional
use of biomass to obtain energy account for 80% of total population [6,7]. Biomass energy mainly
comes from surplus agricultural land, agricultural residues and wastes and forestry residues [8]. The
main processes for obtaining energy from biomass include direct combustion, pyrolysis, gasification,
hydro gasification, liquefaction, anaerobic digestion, alcoholic fermentation and trans-esterification [9].
Developing biomass energy is conducive to the sustainable development of human society as it can
solve problems such as environmental degradation and resource depletion [10]. Europe is one of the
regions where biomass energy was developed relatively earlier than in other areas. AEBIOM estimated
a potential in the EU at about 78 billion Nm3 of biomethane, of which 58.9 billion Nm3 derived from
agriculture (27.2 billion Nm3 from crops, 10 billion Nm3 from straw, 20.5 billion Nm3 from manure
and 1.2 billion Nm3 from landscape management) and 19 billion Nm3 from waste (10.0 billion Nm3
from MSW, 3.0 billion Nm3 from industrial waste and 6.0 billion Nm3 from sewage sludge). From this
potential, 46 billion Nm3 could be used until 2020 [11]. A spatial information system-based approach
was used to evaluate biogas potential in Europe and spatial data on European-wide livestock and
poultry were used for analysis. Results showed that the theoretical biogas potential of manure was
estimated at 26 billion m3 biomethane in Europe (23 billion m3 biomethane in the EU) and the realistic
biogas potential, counting on collectible manure, was assessed at 18 billion m3 biomethane in Europe
(16 billion m3 biomethane in the EU) [12]. Fuel wood from forestry residues plays an important role in
the development of biomass energy in Europe. If household fuelwood is included in energy wood, then
wood biomass could satisfy 2–18% of world’s primary energy needs in 2050 [13]. In Turkey, fuel wood
is considered the most noteworthy biomass energy because its share of the total energy production
is as high as 14% in Turkey [14]. In Southeast Asia, especially Malaysia, oil palm waste is the most
important source of biomass energy. It is estimated that Malaysia has the potential to generate around
15 billion m3 of biogas annually [15] and oil palm waste accounts for around 98.7% of total biomass
energy potential in Sabah of Malaysia [16]. China is also a country with high biomass energy potential.
In China, annual biogas potential from agricultural wastes is approximately (3350.58 ± 669.28) ×
108 m3 (equal to 239.22 ± 47.78 million tons of standard coal) and such potential was underutilized in
the past [17]. Among them, only livestock and poultry dung are expected to provide 110 billion m3
biogas equivalent by 2020 [18]. Besides, the total potential from crop residues (30%) and energy crops
(70%) is equivalent to 1/4 of the total annual oil consumption of China [19].
Northwest China is an economically underdeveloped and ecologically fragile region, where the
development of biomass energy is urgently needed to improve residents’ quality of life. At present,
however, little attention has been paid to the development and utilization of biomass energy in this
region. Some scholars have explored the extension and environmental impact of biomass energy
development projects in this region [20]. However, there is almost no research on the assessment
of biomass energy resource reserves and exploitation potential in this region, which is an obvious
research gap that needs to be filled. Moreover, most scholars tend to calculate the specific value
of theoretical biomass energy resource reserves in a region and then take this value as the actual
potential of biomass energy development in the corresponding region. There are also some scholars
who use spatial data to evaluate biomass energy development potential [12] but they only calculate
the theoretical reserves of biomass energy resources in different areas and then explore the spatial
distribution of biomass energy resources reserves. Current research rarely considers the influence of
the agricultural development and geographical conditions on the actual potential of biomass energy
development in a region. The calculated theoretical biomass resource reserves cannot reflect the actual
potential of biomass energy development in a certain area, thus the actual biomass energy development
potential should be evaluated. Therefore, this paper is devoted to filling above research gaps.
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2. Overview of Study Region
2. Overview of Study Region
Gansu Province is located at northwest China, near Shaanxi Province in the east, Qinghai Province
Gansu Province
at Xinjiang
northwestProvince
China, near
Province
inProvince
the east,and
Qinghai
and Sichuan
Province is
in located
the south,
in theShaanxi
west and
Ningxia
Inner
Province
and
Sichuan
Province
in
the
south,
Xinjiang
Province
in
the
west
and
Ningxia
Province
andof
Mongolia Autonomous region in the north (Figure 1). Gansu Province has a total land area
Inner Mongolia
Autonomous region in the north (Figure 1). Gansu Province has a total land area of
4.56
× 105 km2 , mostly
located on the second ladder of China’s terrain, with an average elevation of
4.56 × 105 km2, mostly located on the second ladder of China’s terrain, with an average elevation of
1500–2000 m. Its length from east to west was more than 1600 km. Its shape is like a bone, on the
1500–2000 m. Its length from east to west was more than 1600 km. Its shape is like a bone, on the
northwest border of the Qinghai-Xizang Plateau. Since ancient times, it has been a main route from the
northwest border of the Qinghai-Xizang Plateau. Since ancient times, it has been a main route from
Western region to the Central Plains. With obvious geographical advantages, it is now an important
the Western region to the Central Plains. With obvious geographical advantages, it is now an
route for the implementation of “Belt and Road” strategy.
important route for the implementation of “Belt and Road” strategy.
Figure 1.
1. Geographical
Geographical location
location map
Figure
map of
of Gansu
Gansu Province.
Province.
GansuProvince
Provinceisisin
in aa region
region where
where three
three major plateaus
plateaus (the
Gansu
(the Loess
Loess Plateau,
Plateau,the
theQinghai-Tibet
Qinghai-Tibet
Plateau and
and the
the Inner
Inner Mongolia
Mongolia Plateau)
Plateau) meet, thus
Plateau
thus it
it has
has complex
complex and
and diverse
diverse terrain
terrain and
and
geomorphology. From
From east
east to
to west,
west, it can be roughly divided
geomorphology.
divided into
into four
four distinct
distinctgeographical
geographicalunits.
units.
(1)The
TheLongdong
LongdongLoess
LoessPlateau
Plateau area,
area, located
located in
in the
the central
central and
(1)
and eastern
eastern part
partof
ofGansu
GansuProvince,
Province,west
west
theShaanxi-Gansu
Shaanxi-Gansu border
border and
and east
east of
of Wushaoling,
Wushaoling, is
a typical
typical loess
ofofthe
is a
loess hilly
hilly area.
area. (2)
(2)The
TheLongnan
Longnan
Mountainarea,
area,located
located
south
of Gansu
Province
of Weihe
of the
Mountain
in in
thethe
south
of Gansu
Province
and and
southsouth
of Weihe
River, River,
is part is
of part
the western
western
extension
of
the
Qinling
Mountains.
There
are
dense
mountains
in
this
area,
the
mountains
extension of the Qinling Mountains. There are dense mountains in this area, the mountains are high
are the
high
and the
areThe
deep.
(3) ThePlateau
Gannan
Plateau
close
to the Qinghai-Tibet
Plateau
and
valleys
arevalleys
deep. (3)
Gannan
area,
closearea,
to the
Qinghai-Tibet
Plateau (called
the
(called
the
roof
of
the
world),
is
a
typical
plateau
area
with
a
high
topography
and
high
altitude.
(4)
roof of the world), is a typical plateau area with a high topography and high altitude. (4) The Hexi
The Hexiarea,
Corridor
with Wushaoling
its east
and Gansu-Xinjiang
at its west
Corridor
witharea,
Wushaoling
at its eastat end
andend
Gansu-Xinjiang
borderborder
at its west
end, end,
is an
is
an
elongated
area
with
length
of
1000
km.
The
terrain
is
flat,
about
1000–1500
m
above
the
elongated area with length of 1000 km. The terrain is flat, about 1000–1500 m above thesea
sealevel.
level.
Themain
mainterrain
terrainconsists
consistsof
offloodplain
floodplainin
in front
front of
of mountains,
mountains, floodplain
The
floodplain in
in the
the river
riverand
anddesert
desertterrain.
terrain.
Its
southern
part
is
the
Qilian
Mountains,
most
of
which
are
over
3500
m
above
the
sea
level.
Its southern part is the Qilian Mountains, most of which are over 3500 m above the sea level.
Gansu Province’s economic development lags behind that in many other regions of China. The
Gansu
Province’s economic development lags behind that in many other regions of China.
GDP of Gansu Province was 679.03 billion CNY in 2015, ranking 27th out of 31 provinces,
The GDP of Gansu Province was 679.03 billion CNY in 2015, ranking 27th out of 31 provinces,
autonomous regions and municipalities in the country. Its per capita GDP was only 26,165.30 CNY
in 2015 and was the lowest in China. The average urbanization rate in Gansu Province was 46.37% in
Sustainability 2018, 10, 3800
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autonomous regions and municipalities in the country. Its per capita GDP was only 26,165.30 CNY in
2015 and was the lowest in China. The average urbanization rate in Gansu Province was 46.37% in
2015, which was lower than the national average of 56.10%. The per capita disposable income of urban
residents was 23,767.10 CNY in 2015, which was far below the national average of 31,195.00 CNY.
The primary industry output value was 95.41 billion CNY in 2015, ranking 23rd in the country. The per
capita disposable income of rural residents was 6936.20 CNY in 2015, which was also far below the
national average of 11,421.70 CNY.
3. Materials and Methods
3.1. Data Sources
The primary data of agricultural development, agricultural production and physical geography
in each county and district of Gansu Province in 1997–2015 were obtained. The main data sources are
as follows: (1) Statistical yearbooks of provinces and cities, such as “Gansu Development Yearbook”
(1998–2016), “Lanzhou Statistical Yearbook” and so forth. (2) Statistical bulletins of cities, districts
and counties, such as the Wuwei Municipal Statistical Bulletin on National Economic and Social
Development, the Liangzhou District Statistical Bulletin and so forth. (3) Authoritative literatures.
Some of the data used in this study were from authoritative literatures, such as the “Atlas of China”
published by China Map Publishing House and so forth.
3.2. Index System Establishment
The index system for evaluating biomass energy development potential was constructed
(Table 1). The index system consists of an evaluation content system (agricultural development
foundation and biomass energy resource endowment) and an evaluation impact system (physical
geographical elements).
Table 1. Index system.
Index System
Items
Indexes
Units
Agricultural
development
foundation
Primary industry output value
Agricultural population
Per capita disposable income in rural areas
Agricultural acreage
Total power of agricultural machinery
CNY
People
CNY
mu
kw
Biomass energy
resource
endowment
Grain total output
Cash crops total output
Pig total output
Sheep total output
Cow total output
t
t
Pigs
Sheep
Cows
Physical
geographical
elements
Unit altitude difference
Annual precipitation
Annual mean temperature
Annual sunshine duration
m
mm
◦C
h
Evaluation content
system
Evaluation impact
system
Unit altitude difference = Relief Amplitude/total land area, Relief Amplitude = local highest elevation-local
lowest elevation.
In this index system, the evaluation of agricultural development foundation and biomass energy
resource endowment enables to obtain the theoretical potential of biomass energy development in
various regions of Gansu Province. Note, however, that this theoretical potential is limited by the
physical geography of corresponding region. Therefore, to more accurately evaluate the actual potential
of biomass energy development in a region, the influence of physical geographical elements should be
considered. The physical geographical elements do not change significantly in a short period; thus,
they were assumed here to be constant during the evaluation period.
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3.3. Research Ideas and Methods
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2018,
10, x FOR PEER REVIEW
3.3.1.
Research
Ideas
5 of 21
The
research
The
researchideas
ideasare
areshown
shownas
asaaflow
flow chart
chart (Figure 2)
Figure2.
2. The
The flow
flow chart
Figure
chart of
ofresearch
researchideas.
ideas.
3.3.2.
Methodology
3.3.2.
Methodology
IEW
TOPSIS
(1)(1)
IEW
&&
TOPSIS
Decisionscan
canbe
bemade
made in
in mathematical
mathematical language
to to
problems.
Decisions
language totoobtain
obtainthe
thebest
bestsolution
solution
problems.
However,ininactual
actualanalysis
analysisand
anddecision-making
decision-making process,
need
to to
be be
However,
process, conflicting
conflictingsituations
situationsoften
often
need
considered.
example,
in study,
this study,
rich reserves
of biomass
energy resources
good
considered.
ForFor
example,
in this
rich reserves
of biomass
energy resources
and goodand
agricultural
agricultural foundation
developmentcan
foundation
theofpotential
biomass
energy development
butcan
development
promotecan
thepromote
potential
biomassofenergy
development
but which
which
can
be
restricted
by
geographical
environmental
elements
at
the
same
time.
This
situation
is a
be restricted by geographical environmental elements at the same time. This situation is called
called
a
multi-criteria
decision-making
problem,
which
needs
to
be
solved
by
multi-criteria
decisionmulti-criteria decision-making problem, which needs to be solved by multi-criteria decision-making
making method. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS),
method. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska
VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Analytic Hierarchy Process (AHP)
Optimizacija I Kompromisno Resenje (VIKOR), Analytic Hierarchy Process (AHP) and so on are
and so on are available methods. These methods have been widely used in project planning, content
available methods. These methods have been widely used in project planning, content evaluation and
evaluation and risk assessment in different fields [21–24]. In the field of energy research, these
risk assessment in different fields [21–24]. In the field of energy research, these methods are also used
methods are also used to evaluate energy development potential [25], as well as energy consumption
to and
evaluate
energy development
potential
well as energy
performance
[26].
performance
[26]. The feasibility
of [25],
theseasmethods
in this consumption
field has beenand
widely
confirmed.
The
feasibility
of these
methods in this
field has been
widely
confirmed.
Notably,
is a
Notably,
VIKOR
is a multi-criteria
decision-making
method
with
compromise
scheme,VIKOR
which is
multi-criteria
decision-making
method
with
compromise
scheme,
which
is
inconsistent
with
inconsistent with the objective of this paper. Compared with AHP, TOPSIS method is more operablethe
objective
this paper.
Compared
with AHP, TOPSIS
is moreTherefore,
operable TOPSIS
and it ismethod
easier to
and it isofeasier
to understand
the quantization
results ofmethod
this approach.
understand
results
of this
Therefore,
TOPSIS in
method
was used
here to
was used the
herequantization
to evaluate the
potential
ofapproach.
biomass energy
development
rural areas
of Gansu
evaluate
the
potential
of
biomass
energy
development
in
rural
areas
of
Gansu
Province
at
county
scale.
Province at county scale. The basic principle of TOPSIS method is to carry out systematic evaluation
The
principle
of TOPSIS
method
is toattributes
carry outby
systematic
evaluation
of different
based
ofbasic
different
projects
based on
different
constructing
the original
index projects
matrix and
use
of the multi-scheme
decision-making
method
[27,28].
aim is
toof
find
the best
onmaking
different
attributes
by constructing
the original index
matrix
andThe
making
use
theout
multi-scheme
solution and to solve
the system
optimally
[29].
The
steps of the
method
are
decision-making
method
[27,28].problem
The aim
is to find
out
thespecific
best solution
andTOPSIS
to solve
the system
as
follows.
problem optimally [29]. The specific steps of the TOPSIS method are as follows.
Raw
data
standardization.The
Theraw
raw data
data of
of biomass
ofof
thethe
counties
1①
Raw
data
standardization.
biomassenergy
energyevaluation
evaluationindexes
indexes
counties
of
Gansu
Province
in
each
year
were
obtained
and
the
evaluation
matrix
of
n
rows
(samples)
and
mm
of Gansu Province in each year were obtained and the evaluation matrix of n rows (samples) and
columns (indexes) was formed: X = ( Xij ) m  n . The raw data were standardized by the Spannweite
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columns (indexes) was formed: X = ( Xij )m×n . The raw data were standardized by the Spannweite
Standardization to eliminate the impact of dimensions and magnitudes of indexes on the final results.
The obtained matrix was X 0 = ( X 0 ij )m×n .
2 Weight determination. IEW (Information Entropy Weight) method was used to determine the
weight of index [30]. The information entropy values of the standardized indexes were calculated with
the following formula:
n
eij = −K ∑ f ij ln f ij
(1)
i =1
where K (K = 1/ln n) is a constant, which is related to the number of evaluation objects n.
fij is calculated as follows:
1+ X 0 ij
f ij = m
∑ (1 + X 0 ij )
(2)
i =1
Index information utility value G is the difference between 1 and the index information
entropy value:
Gij = 1 − eij
(3)
Then, the weight of the index can be calculated as follows:
W ij =
Gij
1 − eij
=
n
∑ Gij
j =1
n
(4)
n − ∑ eij
j =1
This objective weighting process is purely based on unbiased data and is thus able to overcome
the deficiencies of subjective weighting methods [31,32].
3 Ideal reference solution and anti-ideal reference solution. The Ideal reference solution and
anti-ideal reference solution are determined as follows [29]:


 maxvij ( f or stimulant)
i
Vi+ =
(5)

 minvij ( f or destimulant)
i
Vi− =



minvij ( f or stimulant)
i

 maxvij ( f or destimulant)
(6)
i
4 Ci value calculation. The weighted Euclidean distance was used to measure the difference
between the normalized value of each index and the worst (optimal) solution.
v
u n
u
2
d− = t ∑ w j ( xij − x − )
(7)
v
u n
u
2
d+ = t ∑ w j ( xij − x + )
(8)
i
i
j
j =1
j
j =1
The smaller the di+ value is, the closer the evaluation object is to the optimal solution and the better
the development condition is, whereas the smaller the di− value is, the closer the evaluation object is to
the worst solution and the worse the development condition is [33,34]. and can be integrated as Ci [35].
ci =
di−
di− + di+
(9)
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The value of Ci ranges between 0 and 1. The closer the Ci value is to 1, the higher the evaluation
level is; the closer the Ci value is to 0, the lower the evaluation level is.
(2) Spatial autocorrelation
Spatial autocorrelation analysis is a common spatial analysis method that has been widely used
in the study of geography. It is used to explore the temporal and spatial variation of geographical
elements and it can effectively solve problems that traditional spatial statistical methods cannot directly
solve. In this study, the spatial autocorrelation method was used to analyze the potential of biomass
energy development in Gansu province. The specific steps of this method are as follows:
1 Spatial weight matrix construction.
Spatial weight matrix is the basis of the spatial
autocorrelation method. In this study, a two-dimensional symmetric spatial weight matrix based on
the domain rule was established as follows:
(
1 County I is adjacent to county j
Wij =
(10)
0 County I is not adjacent to county j

w11
 w
 21

 ···
wn1
w12
w22
···
wn2

· · · w1n
· · · w2n 


··· ··· 
· · · wnn
(11)
2 Global autocorrelation
Global autocorrelation analysis reflects the total spatial concentration and distribution of biomass
energy development potential in Gansu Province, which is usually measured by global Moran’s I.
The formula is as follows [36]:
n
I=
n
n ∑ ∑ wij ( xi − x )( xi − x )
i =1 j =1
!
n
n
n
i =1 j =1
i =1
∑ ∑ wij ∑ (xi − x)
!2
2
(12)
where xi and xj are the Ci values of i and j samples in the study area, respectively. I value is between
[−1, 1]. The closer the I value is to 1, the more positive the correlation of Ci value is in space. The closer
the I value is to −1, the more negative the correlation of Ci value is in space. If I value is close to 0, this
indicates that Ci value has no correlation in space [37].
Standardized Z value can be used to determine whether the index I has significant autocorrelation
in space.
I − E( I )
Z(I) = p
(13)
VAR( I )
where E(I) and VAR(I) are theoretical expectation and theoretical variance of I value, respectively. For a
given level of significance, if Z is positive and significant, there is a positive spatial autocorrelation.
If Z is negative and significant, there is a negative spatial autocorrelation. If Z value is close to 0, then
the sample shows a random independent distribution.
3 Local autocorrelation
Local autocorrelation analysis focuses on the correlation of research object in local space and
clearly identifies the location where spatial agglomeration occurs and changes. Moran scatter plot and
LISA cluster map can be used for local autocorrelation analysis.
The Moran scatter plot is a two-dimensional presentation of data z and its spatial lag factor Wz,
with (Wz, z) as the coordinate [38]. It distributes the data points of Ci into four different quadrants.
The first quadrant to the fourth quadrant correspond to four different spatial patterns: high-high
(HH) agglomeration, low-high (LH) agglomeration, low-low (LL) agglomeration and high-low (HL)
agglomeration, respectively. H (L) indicates that the observed value is higher (lower) than their
average value.
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The LISA cluster map generated by the local Moran’ I can reflect the spatial agglomeration
characteristics more intuitively and measure the correlation degree between cities. The local Moran’ I
is calculated as follows:
n
Ii = ( xi − x ) ∑ Wij x j − x
(14)
j =1
4. Results
4.1. Biomass Energy Resources
By collecting relevant data of agricultural production in Gansu Province, we calculated the total
reserves of biomass energy resources from agricultural wastes by using the ratio of grain to straw, the
coefficient of discharge of livestock and poultry effluents and so forth. The resource composition and
regional distribution of biomass energy in Gansu Province were preliminarily analyzed. This provides
a basis for the research of biomass energy development and utilization in Gansu province.
RGS (the ratio of grain to straw) was derived from the value published in 2015 by the National
Development and Reform Commission of China and the Ministry of Agriculture of China in “Notice
on the Final Evaluation of Comprehensive Utilization Planning of Crop Straw” (2015, 3264). In this
notice, according to the current situation and characteristics of agricultural development in different
regions of China, different RGS were determined and used. Among them, the RGS for all kinds of
crops in the northwest farming area of China were (kg/kg): corn 1.52, wheat 1.23, beans 1.07 and
potato 1.22. DCLP (the discharge coefficient of livestock and poultry effluents) was obtained from the
published “The Manual of the First National Survey of Pollution Sources of Livestock and Poultry
Breeding.” The specific values were (kg/day): pigs 2.37, sheep 2.60, beef cattle 15.01 and cow 32.86.
The DCLP of cow and beef cattle are different, thus the coefficient was determined to be 20.00 kg/day
in this paper.
The results show that the total reserves of biomass energy resources from agricultural
wastes in Gansu Province reach 7.28 × 107 t/year, with equivalent biogas production of about
1.95 × 1010 m3 /year (Table 2). The amount of equivalent biogas production per capita in Gansu
Province is 748.80 m3 /year. The biogas consumption of a family of three is generally in the range of
200–250 m3 /year. Clearly, the biomass energy resources in Gansu Province are relatively abundant.
If they could be effectively exploited, they are theoretically sufficient to meet the energy demands in
rural areas of Gansu Province.
Table 2. Total reserves, per capita reserves and geographical distribution of biomass energy resources
in Gansu Province.
Total reserves
(t)
Biogas equivalent
(m3 )
Per capita reserves
(m3 )
The Hexi
Area *1
The Longzhong
Area *2
The Longdong
Area *3
The Longnan
Area *4
The Gannan
Area *5
Total or
Average
2.57 × 107
1.55 × 107
1.30 × 107
1.06 × 107
8.15 × 106
7.28 × 107
6.81 × 109
3.89 × 109
3.50 × 109
2.92 × 109
2.34 × 109
1.95 × 1010
1398.11
484.57
700.32
447.26
784.36
748.77
*1 Including Jiayuguan city, Jinchang city, Wuwei city, Zhangye city, Jiuquan city. *2 Including Lanzhou city, Baiyin
city, Dingxi city. *3 Including Pingliang city, Qingyang city. *4 Including Tianshui city, Longnan city. *5 Including
Linxia prefecture, Gannan prefecture.
The reserves of biomass energy resources in the Hexi area are 2.57 × 107 t/year, accounting
for more than 35% of the total reserves of biomass energy resources in Gansu Province. The total
population of this area is only about 4.8 million, thus the per capita biomass energy reserves can reach
about 1400 m3 /year. Clearly, the Hexi area is the region with the most abundant reserves of biomass
energy resources in Gansu Province. The reserves of biomass energy resources in the Longzhong
area are second only to those in the Hexi area and are 1.55 × 107 t/year, accounting for 20% of the
total biomass energy resource reserves in Gansu Province. The total population in the Longzhong
Sustainability 2018, 10, 3800
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area is 8.04 million, far more than that in the Hexi area. Therefore, the per capita biomass energy
reserves in the Longzhong area is only about 485 m3 /year and far lower than that in the Hexi area.
The total reserves of biomass energy resources in the Longdong area are slightly less than those in the
Longzhong area, accounting for about 18% of the total reserves in Gansu Province. However, the total
population in the Longdong region is 5 million, much less than that in the Longzhong area. Therefore,
the per capita biomass energy reserves in the Longdong region reaches 700 m3 /year and is more
than that in the Longzhong area. The biomass energy resources reserves in the Longnan area and the
Gannan area account for 15% and 12% of the total reserves in Gansu Province, respectively. The total
population in Longnan area is second only to that in the Longzhong area and is about 6.5 million. Its
per capita biomass energy resource reserves is the lowest in Gansu Province, only about 447 m3 /year.
In contrast, although the biomass energy resource reserves in the Gannan area are the least in Gansu
Province, its total population is less than 3 million and thus its per capita biomass energy resource
reserves is relatively high, about 750 m3 /year, which is second only to that of the Hexi area.
4.2. Evaluation of Biomass Energy Development Potential
4.2.1. Weight Calculation Results
The index weight was determined by IEW. Physical geography is only a factor affecting the
potential of biomass energy but not a decisive factor. Therefore, during calculation, the weight of
physical geographical elements should be calculated separately and then included into the calculation
results of evaluation content system. Then, the final results were obtained.
In the evaluation content system (Figure 3), the indexes that have the greatest impact on the
potential of biomass energy development are x10 , x9 and x3 , with weights of 0.1063, 0.1031 and 0.1002,
respectively. This is due to the following two aspects. First, livestock and poultry manure are the
most available resources to develop biomass energy, thus the related indexes should have large weight
values. Second, the development of biomass energy and its market-oriented supply depend on certain
economic conditions. As the income of rural residents reaches a certain level, they will choose to
purchase biogas or biomass molding fuel (BMF) with relatively high prices. In other words, we should
promote the comprehensive development and marketization of biomass energy only when most of
the residents have the financial ability to purchase biomass energy. In the evaluation impact system,
y2 and y1 have the greatest impact on the potential of biomass energy development, with weights
of 0.2702 and 0.2460, respectively. These two indexes, one affecting the agricultural production and
the other affecting the large-scale exploitation of biomass energy, are important factors that limit the
development
of local biomass energy.
Sustainability 2018, 10, x FOR PEER REVIEW
10 of 21
Figure
Figure3.3.Weight
WeightValues
Valuesof
ofEach
EachIndex.
Index.
In the evaluation content system (Figure 3), the indexes that have the greatest impact on the
potential of biomass energy development are x10, x9 and x3, with weights of 0.1063, 0.1031 and 0.1002,
respectively. This is due to the following two aspects. First, livestock and poultry manure are the
most available resources to develop biomass energy, thus the related indexes should have large
weight values. Second, the development of biomass energy and its market-oriented supply depend
Sustainability 2018, 10, 3800
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4.2.2. Ci Values Calculation and Sorting Results
The Ci value of each county was determined by TOPSIS method. Taking the year of 2015 as an
example, we calculated the Ci values and obtained their rankings (Table 3).
Table 3. Ci values of counties in Gansu province and their rankings at 2015.
County
Ci
Rank
County
Ci
Rank
County
Ci
Rank
Liang Z
Gan Z
Hui N
Su Z
Min Q
Zhen Y
Jing Y
Jin T
Kong T
Huan
Ning
An D
Lin T
Jing T
Ling T
Jing C
Qin A
Gu L
Gan G
Wu S
Wu D
Mai J
Qing C
Yu Z
Yong C
Tong W
Min L
Qin Z
Long X
Dun H
0.7385
0.5702
0.5557
0.5207
0.5152
0.5111
0.4995
0.4956
0.4951
0.4905
0.4834
0.4812
0.4796
0.4730
0.4694
0.4625
0.4607
0.4568
0.4551
0.4509
0.4472
0.4439
0.4426
0.4410
0.4409
0.4404
0.4396
0.4354
0.4342
0.4316
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Jing N
Zhuang L
Shan D
Xi F
Lin Z
Yong D
Zheng N
Qing S
Gua Z
Gao T
Li
Ma Q
He S
Hui
Min
Yu M
Hua C
Wei Y
Chong X
Kang
Cheng
Dong X Z
Kang L
Hua T
Su N
Wen
Lu Q
Zhang J C
Dang C
Hong G
0.4316
0.4296
0.4292
0.4266
0.4242
0.4240
0.4234
0.4218
0.4217
0.4207
0.4191
0.4160
0.4144
0.4142
0.4125
0.4096
0.4015
0.4009
0.4006
0.3970
0.3929
0.3902
0.3859
0.3857
0.3843
0.3794
0.3784
0.3757
0.3753
0.3735
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Xi H
Lin X (co-)
Gao L
Jin C
Xia H
Cheng G
Guang H
Yong J
Die B
Su B
Zhuo N
Liang D
Tian Z
Bai Y
He Z
Jia Y G
Ji S S
Ping C
Xi G
AKS
Zhou Q
Zhang
He Z
Lin T
Qi L H
Lin X (ci-)
An N
----
0.3725
0.3724
0.3721
0.3720
0.3698
0.3632
0.3595
0.3570
0.3546
0.3541
0.3519
0.3505
0.3501
0.3480
0.3479
0.3463
0.3405
0.3378
0.3361
0.3346
0.3343
0.3342
0.3268
0.3232
0.3168
0.2990
0.1934
----
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
----
The Ci values of most counties were smaller than 0.5000 in 2015, indicating that the potential of
rural biomass energy development was generally low in Gansu Province. This agrees with the fact that
the ecological environment is poor in Gansu Province and agricultural development lags behind that in
many other regions of China. Among all regions of Gansu Province, Liangzhou District had the highest
Ci value, reaching 0.7385, far higher than those of other counties. In addition, most of the high-value
counties/districts were concentrated in the Hexi Corridor area, such as Liangzhou District, Ganzhou
District, Suzhou District, Minqin County and so forth. Among the low-value counties/districts, many
such as Linxia city, Lintan county and the Hezuo city (except Anning District, Qilihe District and Xigu
District, where the Ci value was too low due to the high level of urbanization) were concentrated in the
Gannan area. Therefore, Gannan area can be preliminarily seen as a low potential concentration area.
4.2.3. Ci Value Interannual Variation
The average Ci value of Gansu Province at each year was calculated to observe the temporal
variation of Ci values during the evaluation period (Figure 4).
urbanization) were concentrated in the Gannan area. Therefore, Gannan area can be preliminarily
seen as a low potential concentration area.
4.2.3. Ci Value Interannual Variation
average
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10, 3800Ci value of Gansu Province at each year was calculated to observe the temporal
11 of 20
variation of Ci values during the evaluation period (Figure 4).
Figure
4. Interannual
averageCCi value
(1997–2015).
Figure
4. Interannualvariation
variation of average
(1997–2015).
i value
The average
Ci value
GansuProvince
Province fluctuated
fluctuated aalot
time
butbut
it was
in ain
general
trendtrend
The average
Ci value
of of
Gansu
lotwith
with
time
it was
a general
of
decreasing
first
and
then
rising.
Specifically,
the
entire
evaluation
period
can
be
divided
into
two into
of decreasing first and then rising. Specifically, the entire evaluation period can be divided
phases:
two phases:
(1) 1997–2006 is a period of Ci value fluctuating and generally declining. During this period, the
(1) 1997–2006
is a period of Ci value fluctuating and generally declining. During this period, the
Ci value fluctuated greatly and was in a downward trend. This is because during this period Gansu
Ci value fluctuated greatly and was in a downward trend. This is because during this period Gansu
Province generally paid more attention to the development of the second and third industries and
Province generally paid more attention to the development of the second and third industries and
the agricultural development was relatively lagged behind, leading to a generally declining trend of
agricultural production.
(2) 2007–2015 is a period of Ci value steadily increasing. At around 2008, the Chinese government
began to make great efforts to solve the “three rural problems” and invested many financial and
material resources to solve the rural and agricultural development problems. The government of
Gansu Province also gradually recognized the importance of agricultural development and began to
pay more attention to agricultural production; thus, the agricultural production capacity of Gansu
Province began to rise.
4.3. Spatial and Temporal Analysis
4.3.1. Global Autocorrelation Analysis
(1) Global Moran’s I and significance test
The global Moran’s I of Ci value from 1997 to 2015 was calculated with GeoDa 0.95i software and
significance test was also carried out.
The global Moran’s I passed the significance test under the level of 0.01. This shows that there is a
significant positive spatial autocorrelation of Ci value in Gansu province at county scale and there is a
strong spatial dependence between counties in terms of Ci value (Table 4).
Table 4. Global Moran’s I and its significance test results (1997–2015).
Year
Global Moran’s I
E[I]
SD
p-Value
Z-Score
1997
1998
1999
2000
2001
2002
2003
2004
2005
0.2240
0.2383
0.2188
0.2430
0.2183
0.1903
0.1918
0.1945
0.1827
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
0.0446
0.0464
0.0436
0.0455
0.0455
0.0437
0.0454
0.0439
0.0445
0.003
0.003
0.002
0.002
0.002
0.003
0.002
0.001
0.002
5.3215
5.3795
5.3017
5.5394
5.1219
4.6302
4.4970
4.6523
4.3858
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Table 4. Cont.
Year
Global Moran’s I
E[I]
SD
p-Value
Z-Score
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
0.1804
0.1805
0.1668
0.1767
0.1720
0.1742
0.1718
0.1708
0.1621
0.1726
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
−0.0116
0.0446
0.0427
0.0431
0.0438
0.0454
0.0460
0.0444
0.0450
0.0447
0.0454
0.001
0.001
0.002
0.001
0.002
0.003
0.002
0.003
0.002
0.003
4.3255
4.4982
4.1407
4.2892
4.0990
4.0056
4.1692
3.9575
3.9047
3.9679
(2) Interannual variation of global Moran’s I
Although the global Moran’s I fluctuated slightly during the evaluation period, it showed a
downward trend in general (Figure 5). Especially after 2002, there was an obvious declining trend.
This shows that the spatial correlation of biomass energy potential in Gansu Province decreased from
1997 to 2015 and the spatial dependence of various regions in terms of biomass energy development
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2018, 10, x FOR PEER REVIEW
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potential
also decreased.
Figure
5. Interannual
Moran’sI (1997–2015).
I (1997–2015).
Figure
5. Interannualvariation
variation of
of global
global Moran’s
(3) Spatial
agglomeration
characteristics(Moran
(Moranscatter
scatter plot)
(3) Spatial
agglomeration
characteristics
plot)
The characteristics
of spatial
agglomeration
the trend
of evolution
canintuitively
be intuitively
The characteristics
of spatial
agglomeration
and and
the trend
of evolution
can be
reflected
reflected
by
Moran
scatter
plot.
In
this
paper,
we
chose
six
time
points
(1997,
2000,
2004,
2007,and
20112015)
by Moran scatter plot. In this paper, we chose six time points (1997, 2000, 2004, 2007, 2011
and 2015) to observe the spatial agglomeration characteristics and evolution of the potential of
to observe the spatial agglomeration characteristics and evolution of the potential of biomass energy
biomass energy development in rural areas of Gansu Province (Figure 6).
development in rural areas of Gansu Province (Figure 6).
First, there are more counties in the first and third quadrants in the Moran scatter plot than in
First,
there
are
more counties
in the
first(Figure
and third
in high-high
the Moran(H-H)
scatter
plot than in the
the other
two
quadrants
at each time
point
6). Inquadrants
other words,
agglomeration
other and
twolow-low
quadrants
at each
time pointpatterns
(Figureare
6). very
In other
words,
(H-H)
agglomeration
(L-L)
agglomeration
common.
In high-high
contrast, less
counties
are in the and
low-low
(L-L)
agglomeration
patterns
are
very
common.
In
contrast,
less
counties
are
in
the
second and fourth quadrants of the Moran scatter plot (Figure 6). This means the high-lowsecond
(H-L) and
fourthagglomeration
quadrants ofand
the low-high
Moran scatter
plot
(Figure
6).
This
means
the
high-low
(H-L)
agglomeration
(L-H) agglomeration patterns are relatively less common in Gansu
Province. (L-H)
In general,
there is a significant
spatial correlation
of biomass
energy
development
and low-high
agglomeration
patternspositive
are relatively
less common
in Gansu
Province.
In general,
in Gansu
Province.
Second,
the number
countiesenergy
in the first
and third quadrants
there potential
is a significant
positive
spatial
correlation
of of
biomass
development
potentialofinthe
Gansu
Moran
scatter
plot
tend
to
decrease
with
time
and
that
in
the
third
quadrant
seems
to
decrease
even
Province. Second, the number of counties in the first and third quadrants of the Moran scatter plot
more. In comparison, the number of counties in the second and fourth quadrants tends to increase
tend to decrease with time and that in the third quadrant seems to decrease even more. In comparison,
with time. This indicates the decrease of the global Moran’s I of Ci value and the weakening of the
positive spatial correlation of biomass energy development potential in Gansu Province.
Sustainability 2018, 10, 3800
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the number of counties in the second and fourth quadrants tends to increase with time. This indicates
the decrease of the global Moran’s I of Ci value and the weakening of the positive spatial correlation of
Sustainability
10, x FOR PEER
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14 of 21
biomass
energy2018,
development
potential
Figure 6. Moran scatter plot at each time point.
Figure 6. Moran scatter plot at each time point.
4.3.2. Local Autocorrelation Analysis
4.3.2.Moran’s
Local Autocorrelation
Analysis
(1) Local
I (LISA cluster
map)
(1)order
Local to
Moran’s
cluster
In
furtherI (LISA
highlight
themap)
spatial heterogeneity of biomass energy development potential at
county scale
in Gansu
Province,
LISA
used to clearly
identify
thedevelopment
location where
spatial
In order
to further
highlight
the index
spatialwas
heterogeneity
of biomass
energy
potential
at county scale
in Gansu
Province,
LISA indexpotential
was usedoccurs
to clearly
the location
where
spatial of
agglomeration
of biomass
energy
development
andidentify
intuitively
reflect the
evolution
agglomeration
of biomass energy
development
potential
and Province
intuitively(Figure
reflect the
biomass
energy development
potential
in different
regionsoccurs
of Gansu
7). evolution
of
biomass
energy
development
potential
in
different
regions
of
Gansu
Province
(Figure
7). in the Hexi
The spatial agglomeration of biomass energy development potential occurred mainly
The
spatial
agglomeration
of
biomass
energy
development
potential
occurred
mainly
the Hexi
area, the Gannan area and the Loess Plateau area of East Gansu Province (Figure 7). TheinHexi
area is
area, the Gannan area and the Loess Plateau area of East Gansu Province (Figure 7). The Hexi area is
a traditional agricultural area in Gansu Province. Its agricultural development leads to richness of
a traditional agricultural area in Gansu Province. Its agricultural development leads to richness of
biomass energy resources reserves, thus it is one of the main areas where biomass energy development
biomass energy resources reserves, thus it is one of the main areas where biomass energy
potential shows a H-H agglomeration pattern. The Loess Plateau of East Gansu Province has a
development potential shows a H-H agglomeration pattern. The Loess Plateau of East Gansu
flat terrain,
abundant
large area
of cultivated
landofand
rich rural
resources,
which
Province
has a flatsunshine,
terrain, abundant
sunshine,
large area
cultivated
landlabor
and rich
rural labor
provide
a
good
foundation
for
agricultural
development.
Therefore,
it
is
also
an
area
showing
resources, which provide a good foundation for agricultural development. Therefore, it is also an
H-H area
agglomeration
ofagglomeration
biomass energy
development
potential. Inpotential.
contrast,In
the
Gannan
shows
showing H-H
of biomass
energy development
contrast,
thearea
Gannan
area shows significant L-L agglomeration of biomass energy development potential and low Ci values
Sustainability 2018, 10, 3800
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significant L-L agglomeration of biomass energy development potential and low Ci values are not
only concentrated but also spread to cover a wide spatial range. The reason is that although animal
husbandry in this area is relatively developed, its agricultural development is poor, labor resources
are scarce, the topography is uneven and the rural residents live in a scattered way. It is thus difficult
to achieve the centralized utilization of biomass energy. It is worth noting that there is an area with
obvious H-L agglomeration of biomass energy development potential to the north-west side of the
Gannan area. It is a typical transition zone, including several counties with high Ci values, together
with the counties with low Ci values in the Gannan area to form a H-L agglomeration pattern of
biomass energy development potential. This transition zone should be identified as a key zone to help
develop biomass energy in the Gannan area characterized by L-L agglomeration of biomass energy
development
in theREVIEW
future.
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Figure7.7.LISA
LISAcluster
clustermap.
map.
Figure
The spatial range of positive correlation (H-H and L-L agglomeration) regions shrunk during the
(2) Local Geary’s coefficient
evaluation period, while that of negative correlation (H-L and L-H agglomeration) regions fluctuated.
local Moran’s
I, the local
Geary’s
coefficient
canto
detect
agglomeration
The Compared
number of with
H-H the
agglomeration
regions
decreased
from
15 in 1997
10 inspatial
2015 and
the number
more
[32]. regions decreased even more, from 30 in 1997 to 23 in 2015. It shows that with
of L-Laccurately
agglomeration
At each of
thetechnological
six time points,
no region had
Z valueProvince,
significant
at difference
0.01 and 0.0001
levels (Figure
agricultural
and
development
inaGansu
the
in biomass
energy
8).
The Z valuepotential
of most regions
significant
at the
level of 0.001.
These regions were characterized
development
betweenwas
rural
regions was
gradually
decreasing.
by strong
spatial
agglomeration
of migration
biomass energy
development
potential
but theenergy
spatialdevelopment
range of the
There
was no
obvious spatial
of agglomeration
pattern
of biomass
agglomeration
pattern
shrunk during
evaluation
It showspattern
that with
the agricultural
potential during
the evaluation
period.the
Only
the H-Hperiod.
agglomeration
experienced
a small
development in Gansu Province, the regional difference in the potential of biomass energy
development is decreasing. In addition, some regions had a Z value significant at 0.05 level. The
degree of agglomeration of these regions was not so strong and the spatial distribution of these
regions was relatively scattered. Although there has been a decrease in number of regions with
agglomeration of biomass energy development potential, the decrease is not significant. By
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change. This change is mainly reflected in the migration of H-H agglomeration pattern from the
eastern part to the central part of Gansu Province. The eastern part of Gansu Province is an important
high potential area of biomass energy development. H-H agglomeration pattern is prevalent in this
region but gradually weakens. In recent years, the H-H agglomeration pattern has gradually appeared
in the central part of Gansu Province and this trend is continuing. This reflects a spatial migration of
agglomeration pattern of biomass energy development potential.
(2) Local Geary’s coefficient
Compared with the local Moran’s I, the local Geary’s coefficient can detect spatial agglomeration
more accurately [32].
At each of the six time points, no region had a Z value significant at 0.01 and 0.0001 levels
(Figure 8). The Z value of most regions was significant at the level of 0.001. These regions were
characterized by strong spatial agglomeration of biomass energy development potential but the spatial
range of the agglomeration pattern shrunk during the evaluation period. It shows that with the
agricultural development in Gansu Province, the regional difference in the potential of biomass energy
development is decreasing. In addition, some regions had a Z value significant at 0.05 level. The degree
of agglomeration of these regions was not so strong and the spatial distribution of these regions was
relatively scattered. Although there has been a decrease in number of regions with agglomeration of
biomass energy development potential, the decrease is not significant. By comparing the local Moran’s
I with the local Geary’s coefficient, we can see that the spatial agglomeration revealed by the latter
is more accurate than that revealed by the former at different significance levels [32]. On the whole,
however,
the 2018,
difference
isPEER
insignificant.
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Figure
coefficientgraph.
graph.
Figure8.
8. Geary’s
Geary’s coefficient
5. Conclusions and Recommendations
5.1. Conclusions
On the basis of the calculation of biomass energy resources, this paper considers, for the first
time, the influence of agricultural development and geographical environmental factors on biomass
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5. Conclusions and Recommendations
5.1. Conclusions
On the basis of the calculation of biomass energy resources, this paper considers, for the first
time, the influence of agricultural development and geographical environmental factors on biomass
energy development and evaluates the actual potential of biomass energy development in Gansu
Province. The results indicate that the biomass energy development potential is generally low in
Gansu Province. But this paper find that the low development potential in the Gannan area is not due
to the low reserves of biomass energy resources but due to the comprehensive influence of factors such
as poor agricultural development, scarce labor resources, uneven topography and so forth. In addition,
the existence of a transitional zone for biomass energy development is also found for the first time in
Gansu Province. This zone is significant for developing the abundant biomass energy resources and
improving the biomass energy development potential in the Gannan area. This is the main innovation
and contribution of this paper. On this basis, the conclusions of this paper are as follows:
(1) In most counties of Gansu Province, the Ci value is smaller than 0.5000, indicating that the
biomass energy development potential is generally low.
(2) The spatial agglomeration of biomass energy development potential occurs mainly in the
Hexi area, the Gannan area and the Loess Plateau area of East Gansu Province. The central region
of the Hexi area and the Loess Plateau area of East Gansu Province are the main areas with H-H
agglomeration of biomass energy development potential. The Gannan area shows a very significant
L-L agglomeration of biomass energy development potential.
(3) There is an area with obvious H-L agglomeration of biomass energy development potential
to the north-west side of the Gannan area. It is a typical transition zone, including several counties
with high Ci values, together with counties with low Ci values in the Gannan area to form a H-L
agglomeration pattern. This transition zone can play a key role in driving the development of biomass
energy in the Gannan area.
(4) The spatial range of positive correlation (H-H and L-L agglomeration) regions shrunk during
the evaluation period. The number of H-H agglomeration regions decreased from 15 in 1997 to 10 in
2015 and the number of L-L agglomeration regions decreased even more, from 30 in 1997 to 23 in 2015.
It shows that with the agricultural and technological development in Gansu Province, the difference in
biomass energy development among rural areas is gradually decreasing.
5.2. Recommendations
(1) AAA and SMC
To develop biomass energy in different regions, we should follow two principles: Act According
to One’s Adaptability (AAA) and Suit One’s Measures to Local Conditions (SMC). AAA refers to
considering the local agricultural development foundation and the income level of rural residents and
then making appropriate choice regarding development scale and development model. SMC refers to
taking into account the influence of natural geographical conditions on the development of biomass
energy, respecting nature and rationally making plans.
We need to consider whether a region has enough natural resources and financial ability to develop
biomass energy, which is the premise of the implementation of biomass energy development projects.
First, if a region has abundant reserves of biomass energy resources and at the same time the
income levels of rural residents enable them to purchase relatively expensive commodity energy, then
biomass energy development projects can be carried out in this region.
Second, if a region has abundant biomass energy resources but the income level of rural residents
is low, commercial biomass energy development projects, such as agricultural recycling project,
compressed biomass fuel production project and so forth, are suitable for implementation. These
projects can not only help exploit the rich biomass energy resources in the region but also help
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increase the income of local farmers. When the income level of local farmers enables then to purchase
commodity energy, medium and large-scale biogas projects (MLBPs) can then be carried out.
Third, if a region has no abundant biomass energy resources but the income level of rural
residents is high, then there is an urgent need for concentrated supply of energy. Based on the good
local economic development, modern rural communities can be built to make residents live in a
centralized way and then farms and plantations can be built near the communities. By doing these,
the daily living needs of community residents can be satisfied. More importantly, organic wastes from
farms and plantations can be used to produce biomass energy through MLBPs so that residents can be
supplied with stable and clean energy.
Fourth, if a region has no abundant biomass energy resources and the income level of rural
residents is low, then biomass energy development projects should not be considered in such situation.
Furthermore, we must take into account the influence of local natural geographical conditions on
biomass energy development and then rationally make plans. If the local area is dominated by flat
terrain and the rural residents live in a concentrated way (a large rural community), then a large-scale
biomass energy development project might be selected and carried out. If the terrain of the local
area is relatively uneven, the residents live in a scattered way and the land for construction is very
limited, then it is almost impossible to construct a large-scale rural community and thus medium-sized
biomass energy development projects should be selected and carried out. In addition to topography,
temperature, precipitation and sunshine duration are also important physical geographical factors
that should be considered. These factors will not only affect agricultural production and further affect
reserves of biomass energy resources but also affect the development of biomass energy. For example,
biogas production (anaerobic fermentation) requires appropriate temperature and adequate sunlight.
(2) Growth pole drives biomass energy development in the Gannan area
This study reveals that the Gannan area is the largest and most typical area with L-L agglomeration
of biomass energy development potential in Gansu Province. However, this is not a result of a lack
of reserves of biomass energy resources in this area but a result of its uneven terrain and sparsely
distributed population. In other words, the theoretical potential of biomass energy development in
the Gannan area is considerable but the actual potential is low due to physical geographical factors.
Meanwhile, it is also found that there is an area with obvious H-L agglomeration of biomass energy
development potential to the northwestern side of the Gannan area. This is an important and key zone
(growth pole) to help transform the theoretical potential of biomass energy development in the Gannan
area into actual potential. Therefore, it is necessary to give full play to the growth pole function of this
key zone, specifically from the following two aspects:
First, biomass energy development projects can be tentatively carried out in this zone. This zone is
adjacent to the Gannan area and they have similar physical geographical features. Pilot-scale biomass
energy development projects can be first laid out in this zone to find potential problems and accumulate
relevant experience. On this basis, the projects can be gradually popularized to the Gannan area.
Second, efforts should be made to improve the biomass energy development potential in the
Gannan area. Counties with rich biomass energy resources, concentrated distribution of population
and high-income level of rural residents should be identified. Then, these regions can be cultivated
into areas with high biomass energy development potential. On this basis, the development of biomass
energy in the whole Gannan area can be gradually promoted. In combination with the above analysis,
it was found that Linxia County, Yongjing County and Zhouqu County all have the potential to become
new growth poles of biomass energy development in the Gannan area.
(3) Agricultural modernization
The development and utilization of biomass energy and agricultural modernization are inseparable.
With the progression of agricultural modernization in Gansu Province, agricultural productivity
has been improved and many agricultural biomass resources need to be utilized. At the same
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time, by concentrated utilization of agricultural biomass resources, agricultural organic wastes can
be reduced, which is conducive to the modernization of agriculture. In addition, biomass energy,
represented by biogas and BMF, is clean, renewable, stable and affordable, thus its wide application
can promote the transformation of rural energy structure in Gansu Province. Furthermore, the use of
biomass energy can improve the living and production environment and the quality of life in rural
areas. Therefore, the development and utilization of biomass energy must be closely related to the
modernization of agriculture. Specifically, we can start from the following aspects.
First, Gansu Province should actively introduce modern agricultural production technologies,
promote intensive agricultural production and improve the level of agricultural mechanization.
Modern agricultural production technologies can not only improve agricultural production capacity
and increase the reserves of biomass energy resources but also facilitate the collection and centralized
utilization of biomass energy resources so as to easily reduce agricultural organic wastes.
Second, Gansu Province should develop recycling agriculture and promote the construction
of ecological agriculture (eco-agriculture). Eco-agriculture is a modern and efficient agricultural
development model that considers economic, environmental and social benefits. The recycling
agriculture is one of the important contents of eco-agriculture. For example, the typical “pig-bog-fruit”
project is a relatively common model of ecological recycling agriculture. This model can give full
play to the advantages of biomass energy and achieve the goal of “low mining, high utilization, low
emission and recycling.” It not only improves the efficiency of biomass energy utilization but also
produces good economic, environmental and social benefits.
Third, Gansu Province should advocate and popularize modern lifestyles. Modernization of rural
lifestyle is also one of the important contents of agricultural modernization and mainly includes three
aspects: urbanization of life, community-based living and environmental cleanliness. The realization
of these three aspects must be based on a centralized supply of stable and clean commodity energy.
Since natural gas can hardly be popularized in rural areas and electricity is expensive, biomass energy,
represented by biogas, has become the best choice for centralized supply of commodity energy in
rural areas. Therefore, advocating the modernization of lifestyles in rural areas of Gansu Province
can not only improve the quality of life but also provides a suitable way to utilize biomass energy.
It is beneficial to promote the market-oriented biomass energy development in Gansu Province in an
all-round way.
This section is not mandatory but can be added to the manuscript if the discussion is unusually
long or complex.
Author Contributions: Conceptualization, S.Z. and S.N.; methodology, S.Z. and S.N.; software, S.Z.; validation,
S.Z., S.N. and Y.W.; formal analysis, S.Z.; investigation, S.Z., S.N. and Y.W.; resources, S.N.; data curation, S.Z.
and S.N.; writing—original draft preparation, S.Z.; writing—review and editing, S.Z. and S.N.; visualization, S.Z.;
supervision, S.N.; project administration, S.Z. and S.N.; funding acquisition, S.Z. and S.N.
Funding: This resertch was funded by The "Belt and Road" special project, Lanzhou university, China. The grant
number is 236000/841040.
Conflicts of Interest: No conflict of interest exits in the submission of this manuscript and manuscript is approved
by all authors for publication. I would like to declare on behalf of my co-authors that the work described was
original research that has not been published previously and not under consideration for publication elsewhere,
in whole or in part. All the authors listed have approved the manuscript that is enclosed.
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