A study on the spatial distribution of the renewable energy industries in China and their driving factors

Renewable Energy 139 (2019) 161e175

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Renewable Energy journal homepage: www.elsevier.com/locate/renene

A study on the spatial distribution of the renewable energy industries in China and their driving factors Qiang Wang a, b, Mei-Po Kwan c, d, Jie Fan e, f, *, Kan Zhou e, f, Ya-Fei Wang e, f a State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 35007, PR China b School of Geographical Sciences, Fujian Normal University, Fuzhou 35007, PR China c Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA d Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands e Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100191, PR China f College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 July 2018 Received in revised form 6 January 2019 Accepted 12 February 2019 Available online 16 February 2019

Examining the spatial distribution of the renewable energy industries (REIs) at the county-level and their driving factors is critical for appropriate future policy-making. However, there have been very few studies on this issue to date in China. From a geographical perspective, this study uses the data of the total number and generating capacity of renewable energy plants at the county-level to investigate the spatial distribution patterns of China's REIs, and analyzes their driving factors. The results show that the past decade has witnessed unprecedented development in renewable energy technologies in China, and REIs are constantly springing up in the southwestern and northwestern China, exhibiting a “double-core” spatial pattern. Furthermore, this distribution pattern significantly exhibits resource-dependent and policy-led characteristics, namely, environmental conditions, the abundance of renewable resources and supportive policies are the crucial factors in the clustering of the REIs in China. In addition, as a powerful stimulus to the development of renewable energy economy, the effect of governmental policy significantly varies among different REIs, and a spatial mismatch between the abundance of renewable resources and local governmental development policies is identified, especially for the wind and solar power industries. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Renewable energy industries Regional variations Spatial distribution County-level China

1. Introduction As a crucial driving force for socio-economic development, the energy industry has been widely linked to issues of sustainable development [1e6], energy security [7,8], climate change [9,10], and even international relations [11e13]. To address the pressure caused by energy issues, exploiting renewable energy resources has been widely regarded as one of the most effective approaches to enhance national energy independence and mitigate climate change while ensuring economic growth [14e16]. As the largest energy consumer and CO2 emitter in the world,

* Corresponding author. Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, NO.11 Datun Road, Chaoyang District, Beijing 100101, China. E-mail address: [email protected] (J. Fan). https://doi.org/10.1016/j.renene.2019.02.063 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

China has witnessed spectacular growth in energy demand during the last three decades, and the Chinese government has increasingly recognized the importance of the transition towards renewable energy systems [17,18]. Since 2005, three national Five Year Plans (FYPs) d the 11th (2006e2010), 12th (2011e2015), and 13th (2016e2020) d have proposed the goals of energy saving and emission mitigation, and the country has aimed to reduce energy consumption per unit of gross domestic production (GDP) by approximately 20% by 2010, 32.8% by 2015, and 42.9% by 2020 [19e21] when compared to the 2005 level. In addition, the government also planned to reduce CO2 emissions per unit of GDP by 28.6% by 2020 when compared to 2010, and to gradually raise the share of non-fossil fuels in the energy mix from 8.3% in 2010 to about 15% by 2020. To realize these goals, the government has implemented various supportive policies to foster renewable energy deployment [22e26], including renewable power purchase

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agreements, financial incentives, and carbon taxes on fossil energy sources [27e34]. By 2016, the renewable power capacity in China has reached 570 GW (GW), ranking 1st in the world and accounting for about 31.4% of the world's installed renewable power generation capacity (see Fig. 1) [35,36]. So far, as one of the seven national strategic emerging industries, the renewable energy industries (REIs) in China have been developing rapidly and have received considerable academic attention [5,20,22,37]. Additionally, there is an extensive literature on the rationales behind the development of the REIs from the geographical, physical, social, and economic perspectives (See Table 1). However, existing studies paid much attention to the related issues at the national and provincial levels and mainly focused on a single renewable energy industry, while few studies discussed one practical problem d how to coordinate top-down renewable energy development policies with various local conditions in different regions [56,57], especially at the county-level, given that there are vast regional disparities in geographical features and economic development levels among the counties [58e60]. Against this background, this paper defines the REIs as the energy production industries that apply new technologies to deploy renewable resources for electricity generation. Then, we analyze the current status of China's REIs and investigate their spatial distribution patterns at the county-level and their dominant driving factors. Based on these analyses, we propose some policy implications for improving the development of the REIs in China with considerations of regional features. To the best of our knowledge, this paper is the first to analyze the spatial heterogeneity of China's REIs and their development mechanisms at the county-level. This paper is structured as follows. After this introduction, Section 2 describes the methodology and data. The present situation of the REIs' development will be generally analyzed in Section 3. Section 4 presents the spatial heterogeneity and distribution patterns of the REIs at the county-level and the rationales behind these patterns, then points out several policy implications. Section 5 concludes this study with a discussion of the future works. 2. Data and methodology This research seeks to examine the spatial patterns of China's

REIs at the county-level and analyze their regional features and disparities. To realize these research objectives, the analytical framework is presented as follows. First, based on local enterprises and plants data, the geographical distribution of the REIs in China and their clustering patterns are investigated through the Cluster and Outlier analysis method. Then, the GeoDetector method is applied to reveal the driving factors involved in the development of the REIs in China and derive pertinent policy implications. 2.1. Cluster and Outlier analysis To identify significant hot spots, cold spots and spatial outliers, the Cluster and Outlier analysis tool is adopted. In this study, Cluster and Outlier analysis is performed using Anselin's Local Moran's I statistic [61]. The calculation is given as follows:


Ii ¼

x¼

 n xi  x X mo

  wij xj  x

n 1X x n i¼1 i

Pn mo ¼

(1)

j¼1;jsi

(2)

j¼1;jsi

2  xj  x

n1

 x2

(3)

where xi is an attribute for feature i, x is the mean of the corresponding attribute, wij is the spatial weight between features i and j, and n is the total number of features. The possible outcomes of this analysis are presented in Table 2. 2.2. GeoDetector GeoDetector is a new tool for measuring the spatial heterogeneity of geographic elements and revealing the driving factors behind spatial distribution [62e64]. This method has been applied in many fields, including physical and human geography, climate change, environmental science, and population studies [65e68]. The rationale of this tool is that variable Y is associated with

Fig. 1. Renewable power capacity in the top seven countries worldwide, 2015.

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Table 1 Summary of existing empirical studies on the rationales behind the development of the REI. Authors

Scale

Theme

Methodology

Indicators

Pahle et al. [3]

Country-level

Policy cycle

Yoon and Sim [4]

Country-level

Dong et al. [29]

Province-level

The important role of governmental action for green economy The causes of the South Korean government's failure to successfully deploy its renewable energy policy The distribution and cluster pattern of China‫׳‬s REI

Yi and Liu [30]

City-level

Spatial error model

Schuman and Lin [38]

Country-level

Qualitative analysis

Renewable energy policy

Xu et al. [39]

Province-level

Review and discussions

Biodiesel production and consumption

Yu et al. [40]

Enterprise-level

The fixed effect model and the random effect model

Private R&D investment of enterprise

Onifade [41]

Country-level

Policy cycle

Objectives driven by hybrid and traditional policies.

Hills and Michalena [42]

Country-level

Questionnaire, statistical and mathematical approach

Policy reform, and renewable energy experts.

Yu et al. [43]

Region-level

Data analysis and discussion

Chang and Lee [44]

Province-level

Rate relief, maintaining savings, pursuing cost-effective and robust action. Energy consumption, environmental protection and economic development

Yu et al. [45]

State-level

Strunz et al. [46]

Country- level

Atalay et al. [47]

Country- level

Romano et al. [48]

Country- level

Zhao and Luo [49]

Country- level

Thapar et al. [50]

Country- level

Karatayev et al. [51]

Country- level

Zamfir et al. [52]

Country-level

Bujang et al. [53]

Country-level

Regional variations and policy drivers of REI's development Mechanisms established by the Renewable Energy Law and its implementing regulations Development of biodiesel industry in China The influence of government subsidies on enterprises' R&D investment behavior in China Contextualizing and conceptualizing the hybrid renewable energy support policy and industry development The diversity of perceptions of energy actors relates to the balance of Market Pull and Policy Push and its influence Allocating the economic benefits of renewable energy between stakeholders What kinds of industries should be encouraged or discouraged for Taiwan's future development Assessing the potential impacts of different policies on their considered electricity markets. Development of policies for renewable energy sources Adoption of renewable energy technologies and its striking variation driven by policy transfer and political leadership Impact of green policies on renewable energy investments. Driving force of rising renewable energy in China Economic and environmental effectiveness of renewable energy policy instruments The barriers to uptake of renewable energy in the context of the electricity sector Public policies supporting the renewable energy development Current and future energy demands.

Energy access policies, and renewable energy technologies Policy environment, policy design, policy implementation, and monitoring and feedback Industrial output value, the number and location of key companies/industrial bases Numbers of green jobs and firms

Hua et al. [54]

Country- level (China and Australia) Country level (the globe)

Comparing renewable energy deployment in Australia and China Policies encouraging renewable energy integration

Abdmouleh et al. [55]

Policy cycle

Analytic Network Process

Data analysis and discussion

Stepwise regression model, and simulation model

Economic, social, technological, and environmental factors

Review and discussion

Politico-economic incentives.

Data analysis and discussion

Share of renewable energy within total energy mix, total and per capita installed renewable energy.

Panel corrected standard error model EKC model; cointegration test

Regulatory policies, fiscal incentives, and public investments. Income, unemployment rate and regulation Policy instruments

Data analysis and discussion

Analytical Hierarchy Process methodology

Political and regulatory instruments.

Data analysis and discussion

Policy changes.

Data analysis and discussion

Current energy supply, hydropower, Biomass/biogas, municipal solid waste, solar PV, wind. Renewable energy deployment scale

Comparison analysis and discussion Review and comparison

All applied mechanisms

Table 2 Output feature class of the cluster and outlier analysis. Attribute

Indication

HH (High-High) HL (High-Low) LH (Low-High) LL (Low-Low)

Statistically Statistically Statistically Statistically

significant significant significant significant

cluster of high values spatial outlier with high value surrounded by features with low values spatial outlier with low value surrounded by features with high values cluster of low values

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variable X if their spatial distributions tend to be consistent. The association between Y and X is measured by:

q¼1

L 1 X N s2 Ns2 h¼1 h h

(4)

where the value of the q-statistic indicates the degree of spatial association of Y with indicator X; N is the number of units in the study area; s2 stands for the variance of Y; L represents theL total P number of subcategories of Y and X (h ¼ 1, 2, …, L), and N ¼ Nh ; h¼1that s2h is the variance of subarea Yh. With q2[0, 1], q ¼ 0 indicates Y is not spatially heterogeneous, suggesting that there is no association between Y and X; q ¼ 1 indicates that Y is perfectly spatially heterogeneous and completely controlled by X. q-statistic can help us to determine the dominant factors and analyze the interactive relationships between variables. REIs generally present themselves spatially with components of the human-environment interactions embedded in particular settings [69]. As mentioned in Section 1, we assume that there are three categories of factors influencing the spatial distribution of the REIs (see Table 3), including environmental conditions, the abundance of natural resources, and economic environment. In this study, environmental conditions refer to the local features of landform, climate, and average terrain elevation, which provide the wide-open space and favorable climate for REIs' development. Natural resources are the abundant supplies to REIs, including hydropower, solar, wind, and biomass resource. The term economic development environment in this study, refers to the external economic factors that influence REIs development, including economic development level, economic structure, and supportive policies.

2.3. Data sources The data for 2198 counties in this study were assembled from various sources. Renewable energy plants data were collected from the Renewable Energy Database of the China National Renewable Energy Center [70]. Data on GDP, population, the share of nonagricultural industry were obtained from the China Statistical Yearbooks (County-level) in 2016 [71]. Data on the divisions of landform, climate, and terrain elevation, and hydropower resources are collected from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences [72]. Records of wind and solar measurements for counties are obtained from the China Meteorological Data Service Center of the China Meteorological Administration [73]. In addition, to empirically evaluate the effect of supportive policies on the development of REIs, it is crucial to collect a reliable data set on the quantity and content of policies and instruments, including province-level, prefecture-level, and county-level policies. In this study, we collect national policies related to the REIs during 2005e2015 from China Renewable Energy Information Portal [74], while the policies issued by the local governments on the prefecture- and county-level 2005e2015 were collected from Polaris Power Grid Portal [75]. It is worth noting that, we just focus on the supportive policies for REIs development, then it can be hypothesized that these policies at different levels promote REIs development to varying degrees, namely, policies targeting at REIs development on the county-level should be more practical and feasible than those on province- and prefecture-level, which can effectively enhance the efficiency of supportive policies. Hence, we measure the presence of renewable energy policy using a dummy variable that equals to ‘‘1” on the province-level if a province has adopted one supportive policy by 2015, and using a dummy variable that equals to ‘‘2” on

Table 3 Potential determinants influencing the spatial distribution of REI. Determinants

Factors

Environmental conditions Abundance of natural resources Economic development environment

Landform, climate, terrain Wind resource, Hydro resource, Solar resource, biomass resource Per capita GDP, proportion of non-agricultural industry, supportive policies

Fig. 2. Share of electricity production in China by sources, 1990e2016.

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Fig. 3. Number of policies and measures to promote REIs development on the national level during 1997e2015.

Fig. 4. Spatial distribution of renewable power capacity on the provincial level in China, 2015.

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the prefecture-level if a prefecture has developed and implemented one supportive policy, and using dummy variable that equals to ‘‘300 on the county-level if a county has developed and implemented one supportive policy. Since our analysis was focused on the county-level, it can be assumed that counties located in a province or prefecture are exposed to the same provincial or prefectural policy. Then, a county's total score of policy instruments is calculated as follows:

P¼

3 X n X

psi

(5)

s¼1 i¼1

where P represents a county's total score; s implies the administrative hierarchy level, including the provincial (s ¼ 1), prefectural (s ¼ 2), and county level (s ¼ 2); i denotes the total amount of policies at s level in a county; psi is the policy score on the s level. 3. The present situation of the REIs in China With double-digit economic growth rates and rapidly increasing energy demand, China is now building the world's largest REIs,

which are of strategic importance in the context of upgrading the country's existing industrial infrastructure as well as for national energy security, energy conservation and emissions reductions. Therefore, the installed capacity and production from all renewable technologies had increased substantially over the last decade. By 2016, China has had the world's largest installed capacity and investment in renewable energy (including hydro, solar and wind power) for five years. However, although the share of renewable energy in electricity production has been gradually rising, renewable sources just accounted for a little over one-quarter of the total electricity generation in China in 2015, with most of the remainder provided by coal power plants (Fig. 2). With regard to the supportive policies for promoting REIs development, China has been running without a government agency effectively managing the country's energy sector since the dissolution of the Energy and Industry Department in 1993. Energy-related issues were supervised by multiple organizations such as the National Development and Reform Commission, the Ministry of Commerce, and the State Electricity Regulatory Commission. Until 2008, the National Energy Administration was founded under the National Development and Reform Commission.

Fig. 5. Clustering patterns of the REIs on the county-level, 2015.

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However, its work has been inefficient. In January 2010, the State Council decided to set up a National Energy Commission, headed by former Chinese Premier Jiabao Wen. The commission is responsible for drafting a national energy development plan, reviewing energy security and major energy issues, and coordinating domestic energy development and international cooperation. Meanwhile, the Chinese Government has been accelerating the implementation of multiple policies to promote renewable energy. A package of national laws, policies and programs had been promulgated since 1997 (Fig. 3). In general, industry subsidy has been one of the most important policies supporting REIs development in China. However, compared with developed countries, subsidies in China are less diverse and represent “government obligations” d the government requires that provinces and regions develop certain amounts of renewable energy according to national and provincial programs, and the prices of feed-in tariffs are set by policy-makers rather than by the green electricity market. Meanwhile, the regional considerable disparities in renewable energy resources, levels of economic development, supporting

167

facilities, and development strategies, likely affect REIs development, as well the efficiency of supporting policy. At the provincial level, with great support from government policies, southwest China, including Sichuan, Yunnan, Guizhou, Guangxi, Hunan and Hubei, has become the national renewable power production center, accounting for 60.1% of the nation's total production (Fig. 4). Six provinces are rich in hydropower resources, and form one of the important power supply bases for China's “Transmission of Power in West to East Program,” with a 73.3% share of the nation's hydropower generation. In addition, with the exploitation of solar power and wind power, northwest China, including Xinjiang, Gansu, Qinghai and Inner Mongolia, also plays an important role in national REIs development, accounting for 37.1% of the nation's wind power generation and 64.0% of solar power generation. On contrary, east China and central China are the regions that import and consume the most energy in the country, with less power generation from renewable energy sources when compared to the western regions of the country.

Fig. 6. Clustering patterns of the wind power industry on the county-level, 2015.

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4. Results, discussion and implications 4.1. Spatial patterns of the REIs As shown in Fig. 5, by 2015, the REIs have an uneven distribution throughout the country. Most enterprises are concentrated in 81 counties in southwest and northwest China, exhibiting a “doublecore” feature, where the total installed electricity generation capacity accounts for 41.7% of the country's total renewable power generation capacity. In southwest China, the REIs are mainly distributed in 71 counties along the Lancang River (the Chinese part of the Mekong River) and Chin-sha River, where hydropower is the major REI. In northwest China, the REIs are concentrated in national wind power generation bases, such as Hami in the Xinjiang, Jiuquan in Gansu Province, and western Inner Mongolia, they accounts for almost one-fifth of the national wind power generation capacity. In terms of the differences in the spatial patterns between different kinds of REIs (Figs. 6e9), the spatial clusters of the wind power industry take up wider spaced109 counties with high wind power generation capacity are significantly clustered in grassland or the Gobi Desert in the northern and northwestern China, as well as in coastal areas in Liaoning, Shandong and Jiangsu. The solar power industry also closely clusters in northwestern China, where has about 90 counties with high solar power generation capacity.

The distribution of hydropower industry has the smallest clustering pattern, and 81 counties with high hydropower generation capacity are located along the Lancang River and Chin-sha River. Unlike other kinds of REIs, spatial clustering of the biomass power industry is not statistically significant. As a whole, the REIs in China have been developed in relatively less populated western China whereas the major electricityconsuming areas are in eastern China, this may cause technical difficulty and extra cost in transferring renewable energy between regions. Consequently, electricity generated by renewable sources is mainly consumed locally, even a large proportion goes unused or wasted because of insufficient transmission capacity. In 2015, the total electricity generation from renewable sources reached 1.42 million gigawatt hours (GWH), but only 23.0% of it was transferred to the east China and absorbed by net electricity importers; 72.5% of total generated electricity was consumed locally, whereas 4.5% was abandoned. In addition, according to the National Renewable Energy and Electricity Development Monitoring and Evaluation Report published in 2016, the total abandoned wind power and solar power increased to 33,900 GWH and 49,00 GWH respectively, which took the share of 19.2% and 18.7% in the corresponding total power generation from wind or solar. Regionally, the proportions of abandoned wind power and solar power were even higher in northwestern China. In particular, the proportion of abandoned

Fig. 7. Clustering patterns of the solar power industry on the county-level, 2015.

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Fig. 8. Clustering patterns of the hydropower industry on the county-level, 2015.

wind power in Gansu, Xinjiang and Jilin reached over 30%, as well the abandoned solar power in Gansu and Xinjiang accounted for 25% of total solar power generation [35]. China's target to “effectively alleviate” renewable power waste remains a challenge. 4.2. Analysis of the influencing factors Because the spatial clustering of the biomass power industry is not significant, the factors affecting its distribution pattern will not be discussed in this study. Based on information obtained from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences, the counties are divided into arid, semiarid, semi-humid, humid/semi-humid, and humid regions based on ambient humidity. Meanwhile, in terms of topographic and geomorphic conditions, counties are also grouped into four types, including plains, plateaus, hills, and mountainous regions. Referring to research by Jiang and Yang (2009), the counties are classified into three categories of terrain elevation based on average local altitude [76]: counties in the first category have an average altitude of above 2,600 m, counties in the second category have an average altitude of between 600 and 2600 m, and counties in the

third category have an average altitude of below 600 m. Additionally, concerning differences in natural resources endowment, counties are divided into four levels: resource-rich, relatively resource-rich, general, and resource-poor. We also classify the counties into five groups according to per capita GDP, the proportion of non-agricultural industry, and policy index respectively. Natural Breaks method was applied to the classifications mentioned above using ArcGIS 10.2 in consideration of the natural groupings inherent in the data. The results are shown in Fig. 10. As shown in Table 4, taking the REIs as a whole, the impacts of environmental, natural and policy's variables on the spatial patterns of all REIs are statistically significant, with p-values less than 0.05. This indicates that the environmental conditions, natural resource enrichment, and policy instruments significantly influence the spatial patterns of the REIs in China. Furthermore, there are significant differences in the magnitude of different factors' impacts on spatial patterns, including topography, altitude, hydropower source, supportive policies and instruments, solar power source, wind power source, and climate condition in decreasing order of relative contribution. As expected, except for environmental and natural factors, the presence of energy policies is

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Fig. 9. Clustering patterns of the biomass power industry on the county-level, 2015.

positively and significantly associated with the scale of renewable energy enterprises at the county-level. In contrast, economic development level and industrialization level seem to have insignificant effects on the development of REIs. These results are reasonable and consistent with the current conditions of China's REIs which are capital- and technology-intensive, and mainly promoted through a top-down approach by the national government. However, these factors play different roles in the development of three subsectors of REIs in China. For the solar power and wind power industries, there is no clear evidence showing that policy instruments have promoted the development of these industries, although environmental conditions and natural resources did so (Tables 5 and 6). In contrast, the hydropower industry shows a clear dependence on supportive policies, as the q value for policy instruments was 0.031 (Table 7), higher than the corresponding values for the other two subsectors of REIs. Investigating the reason for this finding, it can be found that compared with the local county's government in western China, those in central and eastern China have been more active in establishing policies and measures to promote REIs development. By 2015, 983 policies and instruments related to renewable energy development have been

established and implemented (Fig. 11). Of these policies, 652 were developed by local governments in eastern and central China, such as in Zhejiang, Anhui, Guangdong, Shandong, Hebei, Shanxi, Hunan, Henan, Jiangsu, and Jiangxi. Yet, for Inner Mongolia, Gansu, Qinghai, and Xinjiang, where the solar and wind power industries are concentrated, only 92 policies were put forward, accounting for less than 10% of the nation's total. 4.3. Policy implications As shown by our analysis, although REIs have made great strides nationwide in recent years, room for improvement remains for support policies that consider the regional features and future prospects. On the one hand, the REIs in China have a common tendency to cluster spatially, which deeply affects the balance between renewable power generation and final consumption. This conclusion can be attributed to the long distance between the power generation locations in western China and the dominant energy consumption markets in eastern China with well-developed economy and high population density. This distribution problem has presented some challenges to the capacity of existing power

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Fig. 10. Assessment of factors influencing the distribution pattern of renewable energy industries on the county-level,2015.

grids, including pooling and delivering capacity, peak load and frequency regulation. In this case, national governments should further optimize the grid layout, strengthen peak-load regulations, and construct special high-voltage power grids to enhance the grids' capacity to absorb large-scale renewable energy inflow. On the other hand, with the rapid development of large-scale renewable power in the western counties of China, efficient use of local renewable energy sources has become a new issue in the

systems operation. At present, China's subsidy mechanisms consist of single types with feed-in tariffs and quota obligations as the core measures to encourage and promote western counties to develop REIs in order to enforce national energy security and eradicate local poverty; however, a series of effective rules and policies are still lacking. Therefore, driven by win-win cooperation between local government and enterprise, the REIs in western counties have enjoyed a boom. Due to the difficulty of improving the electricity

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Table 4 q values for each influencing factor of REIs development calculated by the GeoDetector method. Determinants

Variables

q statistic

p-value

Rank

Environmental conditions

Topography Altitude Climate Wind resource Hydro resource Solar resource Supportive policies Proportion of non-agricultural industries Per capita GDP

0.058 0.051 0.011 0.008 0.038 0.016 0.032 0.002 0.003

0.000 0.000 0.000 0.013 0.000 0.000 0.000 0.199 0.100

1 2 6 7 3 5 4 e e

Abundance of natural resources

Economic development environment

Table 5 q values for each influencing factor of solar power industry development calculated by the GeoDetector method. Determinants

Variables

q statistic

p-value

Rank

Environmental conditions

Topography Terrain Climate Solar source Supportive policies Proportion of non-agricultural industries Per capita GDP

0.015 0.005 0.077 0.069 0.002 0.003 0.004

0.000 0.044 0.000 0.000 0.515 0.134 0.046

3 4 1 2 e e 5

Abundance of natural resources Economic development environment

Table 6 q values for each influencing factor of wind power industry development calculated by the GeoDetector method. Determinants

Variables

q statistic

p-value

Rank

Environmental conditions

Topography Terrain Climate Wind source Policy instruments Proportion of non-agricultural industries GDP per capita

0.013 0.005 0.051 0.026 0.002 0.001 0.004

0.000 0.030 0.000 0.000 0.361 0.710 0.036

4 5 1 3 e e 6

Abundance of natural resources Economic development environment

Table 7 q values for each influencing factor of hydropower industry development calculated by the GeoDetector method. Determinants

Variables

q statistic

p-value

Rank

Environmental conditions

Topography Terrain Climate Hydro source Policy instruments Proportion of non-agricultural industries GDP per capita

0.054 0.079 0.002 0.045 0.031 0.004 0.007

0.000 0.000 0.195 0.000 0.000 0.051 0.003

2 1 e 3 4 e 5

Abundance of natural resources Economic development environment

grid's load in the short-term, as the rapid construction of renewable energy plants, it has been impossible to absorb all the generated electricity from renewable sources by local consumers. Therefore, to avoid excess capacity, national and local governments should focus on power absorption plans for the REIs and coordinate the harmonious development between the renewable energy demand and production. Local governments should have compulsory targets for the scale of REIs development and for the share of renewable power in total power consumption. In addition, given the existing local policy support, there is a considerable potential for the development of distributed energy systems in counties of central and eastern China, where there are powerful economy, human resources, advanced technology, and mature markets. In general, compared with the current centrally planned transmission and distribution system, locally-based distributed energy systems have the advantages of higher working efficiency and more flexible management systems for

distributing power from renewable energy. In this regard, government should encourage households to set up distributed generation systems, and promote locally-based power purchasing and production entities to purchase surplus power with a series of supportive policies.

5. Conclusion Using enterprise data at the county-level, this study reveals the spatial patterns and disparities of the REIs in China that have been booming and expanding rapidly in recent years, especially the wind- and solar power industries. Our findings show that the REIs are mainly concentrated in 81 counties in southwestern and northwestern China, exhibiting a “double-core” feature. Moreover, except economic development level and industrialization level, environmental conditions, the abundance of natural resources and supportive policies have significant influences on the spatial

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Fig. 11. Number of policies supporting REIs development on the different levels.

distribution of the REIs, thus the distribution patterns of the REIs present two characteristics in common, namely resourcedependent and policy-led. Finally, considering the crucial importance of supportive policies to the REIs development, the relationship between the scale of REIs and supportive policy's index is investigated and discussed, then the results suggest that policy likely has a significant effect on promoting the development of hydropower industry, but not yet on the development of solar- and wind power industries. One of the possibilities may be attributed to the mismatch between abundance of renewable resources and the effect of supportive policies. Specifically, although most counties in western China have a sound foundation for solar- and wind power industries development, local governments paid less attention to the influence of supportive policies, when compared with governments in eastern- and central China. As the population and economy grow, the demand for energy has risen sharply in China. Meanwhile, high dependence on imported energy will risk the possibility of an energy crisis. In this case, China has made a great effort to invent new ways to extract renewable energy from renewable sources. While there are still many challenges and opportunities for the research work in the future. An important issue is to improve the electricity grid's load in order to easily deliver the power from renewable energy plants in western China to the large consumers in eastern China. At present, in western China, the power grid's planning and construction lagged behind the rise of renewable power plants, leading to the load level of grids did not match the capacity of power generation. For example, the capacity of power generated by renewable sources had reached 85.59 GW in 2015, while the cross-regional transmission capacity was only 16.10 GW. Another big challenge is how to widely promote the distributed renewable energy system development. Although various policies were designed to promote this segment, the progress made by the distributed solar/wind system had been far below the development trajectory forecast for the past few years because of the low economic benefits. Acknowledgments The authors would like to thank the anonymous reviewers for their valuable comments on the manuscript. This study was funded by the National Natural Science Foundation of China (Grant

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