Research on Potential Evaluation and Sustainable Development of Rural Biomass Energy in Gansu Province of China

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.


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.
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 Nm 3 of biomethane, of which 58.9 billion Nm 3 derived from agriculture (27.2 billion Nm 3 from crops, 10 billion Nm 3 from straw, 20.5 billion Nm 3 from manure and 1.2 billion Nm 3 from landscape management) and 19 billion Nm 3 from waste (10.0 billion Nm 3 from MSW, 3.0 billion Nm 3 from industrial waste and 6.0 billion Nm 3 from sewage sludge).From this potential, 46 billion Nm 3 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 m 3 biomethane in Europe (23 billion m 3 biomethane in the EU) and the realistic biogas potential, counting on collectible manure, was assessed at 18 billion m 3 biomethane in Europe (16 billion m 3 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 m 3 (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 m 3 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.

Overview of Study Region
Gansu Province is located at northwest China, near Shaanxi Province in the east, Qinghai Province and Sichuan Province in the south, Xinjiang Province in the west and Ningxia Province and Inner Mongolia Autonomous region in the north (Figure 1).Gansu Province has a total land area of 4.56 × 10 5 km 2 , 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 northwest border of the Qinghai-Xizang Plateau.Since ancient times, it has been a main route from the Western region to the Central Plains.With obvious geographical advantages, it is now an important route for the implementation of "Belt and Road" strategy.

Overview of Study Region
Gansu Province is located at northwest China, near Shaanxi Province in the east, Qinghai Province and Sichuan Province in the south, Xinjiang Province in the west and Ningxia Province and Inner Mongolia Autonomous region in the north (Figure 1).Gansu Province has a total land area of 4.56 × 10 5 km 2 , 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 northwest border of the Qinghai-Xizang Plateau.Since ancient times, it has been a main route from the Western region to the Central Plains.With obvious geographical advantages, it is now an important route for the implementation of "Belt and Road" strategy.Gansu Province is in a region where three major plateaus (the Loess Plateau, the Qinghai-Tibet Plateau and the Inner Mongolia Plateau) meet, thus it has complex and diverse terrain and geomorphology.From east to west, it can be roughly divided into four distinct geographical units.Gansu Province is in a region where three major plateaus (the Loess Plateau, the Qinghai-Tibet Plateau and the Inner Mongolia Plateau) meet, thus it has complex and diverse terrain and geomorphology.From east to west, it can be roughly divided into four distinct geographical units.
(1) The Longdong Loess Plateau area, located in the central and eastern part of Gansu Province, west of the Shaanxi-Gansu border and east of Wushaoling, is a typical loess hilly area.(2) The Longnan Mountain area, located in the south of Gansu Province and south of Weihe River, is part of the western extension of the Qinling Mountains.There are dense mountains in this area, the mountains are high and the valleys are deep.(3) The Gannan Plateau area, close to the Qinghai-Tibet Plateau (called the roof of the world), is a typical plateau area with a high topography and high altitude.(4) The Hexi Corridor area, with Wushaoling at its east end and Gansu-Xinjiang border at its west end, is an elongated area with length of 1000 km.The terrain is flat, about 1000-1500 m above the sea level.The main terrain consists of floodplain in front of mountains, floodplain in the river and desert terrain.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 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 26,165.30CNY 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 23,767.10CNY in 2015, which was far below the national average of 31,195.00CNY.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 11,421.70CNY.

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.

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).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.The research ideas are shown as a flow chart (Figure 2)

Methodology (1) IEW & TOPSIS
Decisions can be made in mathematical language to obtain the best solution to problems.However, in actual analysis and decision-making process, conflicting situations often need to be considered.For example, in this study, rich reserves of biomass energy resources and good agricultural development foundation can promote the potential of biomass energy development but which can be restricted by geographical environmental elements at the same time.This situation is called a multi-criteria decision-making problem, which needs to be solved by multi-criteria decisionmaking method.Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Analytic Hierarchy Process (AHP) and so on are available methods.These methods have been widely used in project planning, content evaluation and risk assessment in different fields [21][22][23][24].In the field of energy research, these methods are also used to evaluate energy development potential [25], as well as energy consumption and performance [26].The feasibility of these methods in this field has been widely confirmed.Notably, VIKOR is a multi-criteria decision-making method with compromise scheme, which is inconsistent with the objective of this paper.Compared with AHP, TOPSIS method is more operable and it is easier to understand the quantization results of this approach.Therefore, TOPSIS method was used here to evaluate the potential of biomass energy development in rural areas of Gansu Province at county scale.The basic principle of TOPSIS method is to carry out systematic evaluation of different projects based on different attributes by constructing the original index matrix and making use of the multi-scheme decision-making method [27,28].The aim is to find out the best solution and to solve the system problem optimally [29].The specific steps of the TOPSIS method are as follows.
① Raw data standardization.The raw data of biomass energy evaluation indexes of the counties of Gansu Province in each year were obtained and the evaluation matrix of n rows (samples) and m columns (indexes) was formed: . The raw data were standardized by the Spannweite

Methodology (1) IEW & TOPSIS
Decisions can be made in mathematical language to obtain the best solution to problems.However, in actual analysis and decision-making process, conflicting situations often need to be considered.For example, in this study, rich reserves of biomass energy resources and good agricultural development foundation can promote the potential of biomass energy development but which can be restricted by geographical environmental elements at the same time.This situation is called a multi-criteria decision-making problem, which needs to be solved by multi-criteria decision-making method.Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Analytic Hierarchy Process (AHP) and so on are available methods.These methods have been widely used in project planning, content evaluation and risk assessment in different fields [21][22][23][24].In the field of energy research, these methods are also used to evaluate energy development potential [25], as well as energy consumption and performance [26].The feasibility of these methods in this field has been widely confirmed.Notably, VIKOR is a multi-criteria decision-making method with compromise scheme, which is inconsistent with the objective of this paper.Compared with AHP, TOPSIS method is more operable and it is easier to understand the quantization results of this approach.Therefore, TOPSIS method was used here to evaluate the potential of biomass energy development in rural areas of Gansu Province at county scale.The basic principle of TOPSIS method is to carry out systematic evaluation of different projects based on different attributes by constructing the original index matrix and making use of the multi-scheme decision-making method [27,28].The aim is to find out the best solution and to solve the system problem optimally [29].The specific steps of the TOPSIS method are as follows.columns (indexes) was formed: X = (X ij ) 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 = (X 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: where K (K = 1/ln n) is a constant, which is related to the number of evaluation objects n. f ij is calculated as follows: Index information utility value G is the difference between 1 and the index information entropy value: Then, the weight of the index can be calculated as follows: This objective weighting process is purely based on unbiased data and is thus able to overcome the deficiencies of subjective weighting methods [31,32]. 3Ideal reference solution and anti-ideal reference solution.The Ideal reference solution and anti-ideal reference solution are determined as follows [29]: 4 C i value calculation.The weighted Euclidean distance was used to measure the difference between the normalized value of each index and the worst (optimal) solution.
The smaller the d + i value is, the closer the evaluation object is to the optimal solution and the better the development condition is, whereas the smaller the d − i 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 C i [35].
The value of C i ranges between 0 and 1.The closer the C i value is to 1, the higher the evaluation level is; the closer the C i 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 0 County I is not adjacent to county j (10) 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]: where x i and x j are the C i 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 C i value is in space.The closer the I value is to −1, the more negative the correlation of C i value is in space.If I value is close to 0, this indicates that C i value has no correlation in space [37].
Standardized Z value can be used to determine whether the index I has significant autocorrelation in space.
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. 3Local 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 C i 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.
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:

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 × 10 7 t/year, with equivalent biogas production of about 1.95 × 10 10 m 3 /year (Table 2).The amount of equivalent biogas production per capita in Gansu Province is 748.80 m 3 /year.The biogas consumption of a family of three is generally in the range of 200-250 m 3 /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.The reserves of biomass energy resources in the Hexi area are 2.57 × 10 7 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 m 3 /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 × 10 7 t/year, accounting for 20% of the total biomass energy resource reserves in Gansu Province.The total population in the Longzhong 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 m 3 /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 m 3 /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 m 3 /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 m 3 /year, which is second only to that of the Hexi area.

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 x 10 , x 9 and x 3 , 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, y 2 and y 1 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.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

C i Values Calculation and Sorting Results
The C i value of each county was determined by TOPSIS method.Taking the year of 2015 as an example, we calculated the C i values and obtained their rankings (Table 3).The C i 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 C i 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 C i 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.

C i Value Interannual Variation
The average C i value of Gansu Province at each year was calculated to observe the temporal variation of C i 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.

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).The average Ci value of Gansu Province fluctuated a lot with time but it was in a general trend of decreasing first and then rising.Specifically, the entire evaluation period can be divided into two phases: (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 Province generally paid more attention to the development of the second and third industries and The average C i value of Gansu Province fluctuated a lot with time but it was in a general trend of decreasing first and then rising.Specifically, the entire evaluation period can be divided into two phases: (1) 1997-2006 is a period of C i value fluctuating and generally declining.During this period, the C i 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 the agricultural development was relatively lagged behind, leading to a generally declining trend of agricultural production.
(2) 2007-2015 is a period of C i 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.

Global Autocorrelation Analysis (1) Global Moran's I and significance test
The global Moran's I of C i 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 C i value in Gansu province at county scale and there is a strong spatial dependence between counties in terms of C i value (Table 4).(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 potential also decreased.(3) Spatial agglomeration characteristics (Moran scatter plot) The characteristics of spatial agglomeration and the trend of evolution can be intuitively reflected 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 biomass energy 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 the other two quadrants at each time point (Figure 6).In other words, high-high (H-H) agglomeration 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-low (H-L) agglomeration and low-high (L-H) agglomeration patterns are relatively less common in Gansu Province.In general, there is a significant positive spatial correlation of biomass energy development potential in Gansu Province.Second, the number of counties in the first and third quadrants of the Moran scatter plot tend to decrease with time and that in the third quadrant seems to decrease even more.In comparison, 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 biomass energy development potential in Gansu Province.(3) Spatial agglomeration characteristics (Moran scatter plot) The characteristics of spatial agglomeration and the trend of evolution can be intuitively reflected 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 biomass energy 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 the other two quadrants at each time point (Figure 6).In other words, high-high (H-H) agglomeration 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-low (H-L) agglomeration and low-high (L-H) agglomeration patterns are relatively less common in Gansu Province.In general, there is a significant positive spatial correlation of biomass energy development potential in Gansu Province.Second, the number of counties in the first and third quadrants of the Moran scatter plot tend to decrease with time and that in the third quadrant seems to decrease even more.In comparison, 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 C i value and the weakening of the positive spatial correlation of biomass energy development potential in Gansu Province.

Local Autocorrelation Analysis
(1) Local Moran's I (LISA cluster map) In order to further highlight the spatial heterogeneity of biomass energy development potential at county scale in Gansu Province, LISA index was used to clearly identify the location where spatial agglomeration of biomass energy development potential occurs and intuitively reflect the evolution of biomass energy development potential in different regions of Gansu Province (Figure 7).
The spatial agglomeration of biomass energy development potential occurred mainly in the Hexi 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 biomass energy resources reserves, thus it is one of the main areas where biomass energy development potential shows a H-H agglomeration pattern.The Loess Plateau of East Gansu Province has a flat terrain, abundant sunshine, large area of cultivated land and rich rural labor resources, which provide a good foundation for agricultural development.Therefore, it is also an area showing H-H agglomeration of biomass energy development potential.In contrast, the Gannan area shows significant L-L agglomeration of biomass energy development potential and low Ci values In order to further highlight the spatial heterogeneity of biomass energy development potential at county scale in Gansu Province, LISA index was used to clearly identify the location where spatial agglomeration of biomass energy development potential occurs and intuitively reflect the evolution of biomass energy development potential in different regions of Gansu Province (Figure 7).
The spatial agglomeration of biomass energy development potential occurred mainly in the Hexi 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 biomass energy resources reserves, thus it is one of the main areas where biomass energy development potential shows a H-H agglomeration pattern.The Loess Plateau of East Gansu Province has a flat terrain, abundant sunshine, large area of cultivated land and rich rural labor resources, which provide a good foundation for agricultural development.Therefore, it is also an area showing H-H agglomeration of biomass energy development potential.In contrast, the Gannan area shows significant L-L agglomeration of biomass energy development potential and low C i 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 C i values, together with the counties with low C i 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 potential in the future.(

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 There was no obvious spatial migration of agglomeration pattern of biomass energy development potential during the evaluation period.Only the H-H agglomeration pattern experienced a small 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 difference is insignificant.

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

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 C i 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 C i values, together with counties with low C i 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.

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 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 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 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.

Figure 1 .
Figure 1.Geographical location map of Gansu Province.

( 1 )
The Longdong Loess Plateau area, located in the central and eastern part of Gansu Province, west of the Shaanxi-Gansu border and east of Wushaoling, is a typical loess hilly area.(2) The Longnan Mountain area, located in the south of Gansu Province and south of Weihe River, is part of the western extension of the Qinling Mountains.There are dense mountains in this area, the mountains are high and the valleys are deep.(3) The Gannan Plateau area, close to the Qinghai-Tibet Plateau (called the roof of the world), is a typical plateau area with a high topography and high altitude.(4) The Hexi Corridor area, with Wushaoling at its east end and Gansu-Xinjiang border at its west end, is an elongated area with length of 1000 km.The terrain is flat, about 1000-1500 m above the sea level.The main terrain consists of floodplain in front of mountains, floodplain in the river and desert terrain.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 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 26,165.30CNY in 2015 and was the lowest in China.The average urbanization rate in Gansu Province was 46.37% in

Figure 1 .
Figure 1.Geographical location map of Gansu Province.

3. 3 .
Research Ideas and Methods 3.3.1.Research Ideas The research ideas are shown as a flow chart (Figure 2) Sustainability 2018, 10, x FOR PEER REVIEW 5 of 21

Figure 2 .
Figure 2. The flow chart of research ideas.

Figure 2 .
Figure 2. The flow chart of research ideas.

Figure 3 .
Figure 3. Weight Values of Each Index.

Figure 7 .
Figure 7. LISA cluster map.The spatial range of positive correlation (H-H and agglomeration) regions shrunk during the evaluation period, while that of negative correlation (H-L and L-H agglomeration) regions fluctuated.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 agricultural and technological development in Gansu Province, the difference in biomass energy development potential between rural regions was gradually decreasing.There was no obvious spatial migration of agglomeration pattern of biomass energy development potential during the evaluation period.Only the H-H agglomeration pattern experienced a small

Table 2 .
Total reserves, per capita reserves and geographical distribution of biomass energy resources in Gansu Province.

Table 3 .
C i values of counties in Gansu province and their rankings at 2015.