Issue
Sci. Tech. Energ. Transition
Volume 77, 2022
Selected Papers from 7th International Symposium on Hydrogen Energy, Renewable Energy and Materials (HEREM), 2021
Article Number 9
Number of page(s) 13
DOI https://doi.org/10.2516/stet/2022007
Published online 24 May 2022

© The Author(s), published by EDP Sciences, 2022

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

1.1 Energy transition in China

Energy is an important material for the survival and development of human beings, and is also regarded as the economic lifeline of all countries in the world, playing a pivotal role in national development strategies. With the rapid consumption of energy in the world and the destruction of ecological resources in pursuit of economic benefits, a series of energy shortage and environmental pollution problems continue to appear. Energy security will threaten the security of the whole country and affect the development of society, so it has become a common concern of all countries in the world (Winzer, 2012). With the continuous development of China’s industrialization and urbanization, in order to meet the demand of energy resources for economic development, the exploration of energy resources has been increasingly intensified (Ahmed et al., 2020). At present, China has developed into the world’s largest energy producer and consumer. However, the imbalance between energy production and consumption also brings a series of related problems (Ahmad and Zhang, 2020). In order to alleviate the contradiction between energy production and consumption, various countries and governments, including China, have formulated a series of energy structure optimization plans to varying degrees to promote the transformation of the energy industry.

1.2 Energy transition in spatial scale

In this context, the optimization of energy supply and demand structure has become a research hotspot in the field of energy. Many scholars have studied energy structure, spatial layout, evolutionary trend and other aspects, and a large number of academic achievements with scientific guiding significance in this field have emerged. Some scholars, based on the focus of energy development, have made an in-depth investigation of the characteristics of energy spatial differentiation, and made empirical thinking and verification of the practical results of energy policy. In the study of energy spatial and temporal differentiation, different scholars have expounded the evolution process of energy spatial pattern from various angles with different methods. Among them, some scholars study the regional variation of energy from different angles. Tao et al. (2018) and Ning et al. (2018) empirically analyzed the change of spatial pattern of rural energy consumption in China through factor analysis, cluster analysis and regression analysis, combined with SPSS and ArcGis 10.2. In the evolution process of energy spatial and temporal pattern, most scholars’ research can be summarized as the results of time and space. In the study of time evolution of energy, scholars build models to study the time difference of energy in a certain region by processing time series data. Rahman et al. (2020) derived the time series of monthly data of coal, oil and natural gas consumption in the China from 1981 to 2016 from energy consumption data. The study found that the consumption of coal and natural gas showed a significant seasonal pattern, with the peak of coal consumption in winter and the peak of natural gas consumption in summer and winter.

Schulz (2010) used exponential decomposition analysis method to study energy consumption changes and proposed a new mixed model to decompose energy consumption changes. The traditional model and the mixed model were respectively applied to the statistical data of Singapore from 2000 to 2010 to analyze the differences in energy consumption over time. Khan (2015) investigated the short-term and long-term natural gas consumption dynamics of Pakistan through econometric model, specifically estimated the sectoral income, price and cross price elasticity of natural gas demand during 1978–2011, and determined the future prospect of natural gas demand based on the econometric equation. More attractive, some scholars focus on the spatial differences in the field of energy and start to investigate the regional differences in energy production and consumption. Gregg et al. (2009) estimated the monthly variation of carbon emissions in each region of North America, and found that the total carbon emissions in North America mainly came from the United States, and its spatial pattern showed a gradient change pattern of progressively decreasing from north to south and from east to west. Dagher (2012) discussed consumers’ consumption behavior and dynamic elasticity of natural gas in unit time based on spatial autoregressive distribution model from a spatial perspective. Zhiguo et al. (2017) based on the statistical data from 2005 to 2014 in Bohai Rim region, analyzed energy consumption behavior from time and space perspectives respectively, and the results showed that there were obvious spatio-temporal differences in energy consumption behavior in this region. The study of spatial differentiation is common in the evolution of the spatial pattern of coal resource flow, and the application methods of spatial measurement are different. Jiang et al. (2019) comprehensively consider various factors of coal production and consumption, divide different types of production and consumption regions, and put forward targeted suggestions for coal production and consumption pattern. Ye et al. (2011) study the spatial evolution process of coal resources by constructing a comprehensive evaluation system of coal resources and establishing a regression model for the results of comprehensive evaluation.

1.3 Research gap: Micro-level evolution

Although previous studies focused on energy consumption and energy production in space, most relevant studies can be divided into macro and micro levels. From a macro point of view, scholars pay more attention to the production and consumption pattern of energy resources (Alvarado et al., 2018; Khan et al., 2021; Liu et al., 2019; Shahbaz et al., 2018). For example, they explore the spatial characteristics and evolution rules of energy (such as coal transportation), summarizes patterns and provides guidance for the layout optimization of energy system (Khan et al., 2019, 2020). In terms of consumption pattern, most studies explore the evolution of terminal consumption structure at spatial scale and its influencing factors. For example, based on the spatial evolution of the consumption structure of four categories of energy (coal, oil, natural gas and electricity), the future spatio-temporal scenarios were discussed (Chishti et al., 2021; Kockel et al., 2022; Mesfun et al., 2018; Muhammad et al., 2021; Teng et al., 2021; Zhang et al., 2021; Zhang et al., 2022a). These two types of research occupy an overwhelming position in space-relevant energy research, which has led to the literature neglecting the endogenous dynamics of micro-category industries. Haseeb et al. (2019) point out that the lag of spatial literature based on annual data currently occupies the main position at the data level makes the research on energy transition unable to provide more practical decision support. Bai et al. (2021) found that the spatial POI (Point of Interest) data of enterprises can not only define the industry types of energy enterprises but also provide their spatial location information. By matching enterprise-related innovation, economic and development data, more evolution dynamics can be explored at the micro level. However, relevant micro-exploration is still lacking at present, the main reason is that few scholars associate big data with spatial dynamics in the energy field. In order to make up for the inadequacy of the research present situation, this article follows Bai et al. (2021) selects energy industry’s POI data in Hebei province between 2005 and 2021 as the research object, and these POI data can be divided into two kinds of energy industry, respectively traditional and renewable. ArcGIS is used to visually compare the temporal and spatial distribution changes of the energy industry in Hebei province. This paper aims to provide a reference for the future development of regional energy industry layout by analyzing the influencing factors of the evolution.

2 Methodology

2.1 Moran’s I and LISA

In the past studies, one of the more effective ways to evaluate the effect of spatial agglomeration is the combination of Moran’s I and Local Indicators of Spatial Association (LISA). We have selected it here, based on its advantages in point data processing which have been proven by Yan et al. (2017), Ziakopoulos (2021) and Li et al. (2021). In this paper, Global Moran’s I is used to observe the spatial distribution relationship and correlation degree among the energy industry in Hebei province, and detect the existence of spatial agglomeration in the energy industry. Spatial autocorrelation can judge whether spatial patterns are clustered, discrete or random according to its measurement value (Fortin and Dale, 2009). The formula of Moran’s I is as follows:

The global Moran’s I cannot observe the local spatial rules and specific spatial differences in the study area, therefore, LISA will also be used to detect the distribution and change of hot and cold spots in the energy industry of Hebei province. The local space autocorrelation formula is as follows:

Among which, n is the total number of elements, which refers to the total number of subdistricts and units at the same level; x i and x j are the value of attribute x in element i and j.

2.2 Kernel density

Kernel density is an analytical method that uses kernel function to calculate the magnitude of points per unit area and form the points into a smooth surface. This approach optimizes visual presentation and helps explore spatial dynamics (Ding et al., 2021). This paper mainly uses kernel density to observe the specific form and concentration intensity of the spatial pattern of the energy industry in Hebei province (Elgammal et al., 2002). The formula of kernel density function is:

Among which, n is the number of samples; r is the search radius calculated by kernel density; d lm is the distance between subdistrict l and subdistrict m.

2.3 Standard Deviational Ellipse

Standard Deviational Ellipse (SDE) is an important method to observe the distribution direction of elements (Zhang et al., 2022b). The analysis result is an important method to observe the distribution direction of elements. The long half axis of the ellipse generated by the analysis result represents the distribution direction of elements, and the short half axis represents the distribution range of elements. The size of ellipse oblateness indicates the directivity level of element distribution (Kauth and Thomas, 1976). This paper uses it to analyze the spatial distribution of the energy industry. The standard deviation of the X-axis and Y-axis of the ellipse is:

Among which, n is the number of elements; and are the differences between the average center of elements and the coordinates of x and y.

3 Results

3.1 Study area overview

Hebei province, as a vast traditional industrial province in China, is a big consumer of coal, oil, natural gas and other traditional energy resources. In recent years, the supply and demand situation of coal, oil and natural gas in Hebei province has become more and more tense due to the limited supply of its own resources, and the bottleneck to economic and social development has become more and more prominent. Hebei province is one of 13 coal bases officially designated in China. The province’s raw coal output has remained at around 70 million tons until 2020. However, due to the long history of development and the small investment in strengthening mining and resource exploration, many coal mining areas in Hebei province have entered the middle and late stage of development, and a large number of mining enterprises are faced with resource exhaustion, serious shortage of reserve resources and low production potential.

Although Hebei province is rich in coal, oil and natural gas resources, its per capita resource reserves are relatively low compared with its population of 74.6 million. In particular, its per capita raw coal reserves are only 60% of the national average, indicating obvious resource constraints. Hebei province is now speeding up the pace of renewable energy development and utilization, but overall its development time is still short. Due to the constraints of technology, cost and other factors, renewable energy still cannot replace traditional energy in a large amount in the short term. Therefore, coal, oil and natural gas are seen as the main energy consumption in Hebei province for a long time.

3.2 Division of energy enterprises in Hebei

Since there is no unified standard for the definition of energy enterprises, this paper classifies energy enterprises in Hebei province according to their actual business scope and specific business categories. The renewable energy enterprises in this paper include those engaged in the development and utilization of renewable energy and related businesses. In addition, based on the point data of energy enterprises involved in “electric power, heat, gas and water production and supply industry” in the monitoring platform of Hebei province Government, this paper provides a statistical summary and divides the business types involved in energy enterprises in Hebei province. It is worth noting that all data obtained in our paper are open accessed in Qichacha Enterprise Platform which based on National Enterprise Credit Information Publicity System of China (http://www.gsxt.gov.cn/). All classification methods are defined by the platform, and this article follows the existing classification methods. The results are shown in Table 1.

Table 1

Classification of traditional energy and renewable energy industries in Hebei province.

3.3 Spatial and temporal distribution characteristics

By calculating the global spatial autocorrelation coefficient of energy industry enterprise data of 11 cities in Hebei province in 2005, 2010, 2015 and 2021, Moran’s I index of four years is obtained (see Tab. 2). All the obtained results are positive and the values are high, which indicates that the spatial distribution of the energy industry in Hebei province has a high degree of agglomeration.

Table 2

Moran’s I of energy industry in Hebei province.

From the results of Global Moran’s I, the traditional energy industry in Hebei province has always presented a positive spatial correlation from 2005 to 2015, indicating that the traditional energy industry in Hebei province has always presented a spatial agglomeration state during this period. However, from 2015 to 2021, the spatial agglomeration state of the traditional energy industry in Hebei province has gradually weakened. The interaction of the traditional energy industry among cities is becoming smaller. From 2005 to 2021, the spatial interaction of the renewable energy industry in various cities gradually increased, indicating that the exchanges of the renewable energy industry in Hebei province increased, and the well-developed cities could drive the surrounding cities with poor development, forming a spatial agglomeration state. However, the agglomeration effect of the renewable energy industry also gradually weakened from 2015 to 2021. The differences in the development of the renewable energy industry between cities are gradually widening. On the whole, Moran’s I in the four periods of the energy industry in Hebei province shows the remarkable effect of the energy industry transformation in Hebei province.

Local spatial autocorrelation analysis was performed on the data using GeoDa to obtain LISA graph of single year. In order to improve the spatial difference of energy industry heat in the process of time development that cannot be directly observed by LISA map in a single year, we assign the values of “High–High (HH)”, “Low–High (LH)”, “ High–Low (HL)”, and “Low–Low (LL)” clusters to 4, 3, 2 and 1 respectively, and superimpose the single-year maps to obtain LISA map in a whole period (Fig. 1). In the figure, when the color of a certain region is closer to red, it indicates that the corresponding energy industry distribution in the region is more intensive. The closer the color of a region is to blue, the less is distributed in the region (Field and Barros, 2014).

thumbnail Fig. 1

LISA map of the energy industry in Hebei province in 2021. (a) Traditional energy industry. (b) Renewable energy industry.

The distribution of the traditional energy industry in various regions of Hebei province is shown in Figure 1a. “High–High (HH)”, “Low–High (LH)”, “High–Low (HL)”, and “Low–Low (LL)” cluster types all exist, indicating that the development of the traditional energy industry in most cities of Hebei province has not formed a unified and coordinated pace. Among them: The HH type cities are mainly Chengde, Tangshan, Qinhuangdao, Zunhua, Baoding and Hengshui. These cities rely on rich resources and favorable conditions to develop the traditional energy industry at a better level, but the cities in the surrounding areas have poor development of the traditional energy industry due to their own resources and other problems. The diffusion effect of cities with better development level is not obvious (Rodríguez-Pose and Griffiths, 2021).

As can be seen from Figure 1b, the spatial relationship of the development of the renewable energy industry in cities of Hebei province is not stable at present, and the pattern is dominated by “LH” type. Zhangbei county of Zhangjiakou has the highest level of renewable energy development. Secondly, Quyang county and Yi county in the west of Baoding, Pingshan county in Shijiazhuang, Wuan county, She county, Wei county in Handan, and Huanghua in Cangzhou also have a high degree of solar energy resources development. However, the development of solar energy resources is weak in the coastal areas in the northeast of Hebei province and the hinterland in the central of Hebei province. Therefore, these areas have great potential for solar energy resources development in the future. The development degree of wind energy resources is relatively more concentrated. The developed wind energy resources are mainly concentrated in Zhangbei county, Chongli county of Zhangjiakou and Weichang Manchu and Mongolian Autonomous county of Chengde city. The development of wind energy resources in Tangshan and Xingtai is relatively concentrated. However, most of the wind energy resources in other regions are relatively underdeveloped, so these regions have great development potential in the future development of wind energy resources.

It can be seen that the highest “heat” of the energy industry is mainly concentrated in the northern part of Hebei province, followed by the locations of the cities around the highest “heat” area and the southeastern region, while the “heat” of the energy industry in other regions is relatively low. ArcGIS kernel density analysis tool was used to search 1000 m radius and output a grid of 300 m pixels to obtain the kernel density map of the development and evolution process of the energy industry in Hebei province (Fig. 2). It can be seen intuitively that the energy industry in Hebei province has always been concentrated in urban areas, and there is no agglomeration core in areas outside the urban areas, and there is little change with the development of time. The spatial agglomeration characteristics of the energy industry have been developing from centralized agglomeration to multi-core dispersion year by year.

thumbnail Fig. 2

Kernel density and SDE of the energy industry in 2005/2010/2015/2020. (a) Traditional energy industry in 2005/2010/2015/2020. (b) Renewable energy industry in 2005/2010/2015/2020.

According to Moran’s I results of the energy industry in Hebei province, the spatial distribution of all periods presents agglomeration (Tab. 2). The results of kernel density analysis showed that the intensity value of kernel density increased year by year, indicating that the intensity of agglomeration per unit area increased, and the number of cores increased and spread outward year by year, presenting the space-time characteristics of both agglomeration and diffusion (Fig. 2). The result of SDE shows that the difference of degree of angle is small and the flatness increases year by year, indicating that the direction of its distribution is relatively stable and its directivity becomes more significant (Cao et al., 2019).

In 2005, the energy industry presented the spatial distribution characteristics of multi-core agglomeration. In 2010, the number of renewable energy industry cores increased and spread to the southwest and northeast, with the overall development from cluster to axial shape. In 2015, the core areas of the energy industry expanded and connected, and the axial development from southwest to northeast intensified. Moreover, since 2015, the newly registered number of renewable energy enterprises in Hebei province has been basically the same as that of traditional energy enterprises, and then the annual newly registered number of renewable energy enterprises has rapidly surpassed that of traditional energy enterprises (Tab. 1). In 2021, the energy industry shows the characteristics of gathering core off axis into points and multi-core diffusion distribution. The number of main cores increases and the intensity of agglomeration intensifies, resulting in the formation of independent core points, presenting an overall spatial pattern around the axis from Langfang city in the northeast to Shijiazhuang city in the southwest (Fig. 2).

3.4 Influencing factors of spatial evolution of the energy industry

Many researchers have paid attention to the influencing factors of the energy industry and produced abundant results, but the influencing factors of the dynamic change of the spatial pattern of the energy industry are rarely discussed. Total Factor Productivity (TFP) generally means the efficiency of resources (including manpower, material resources and financial resources) development and utilization (Wu et al., 2020). In recent years, in order to study the resource and environment effects of industrial growth, some scholars have included resource and environment factors into productivity measurement in traditional TFP analysis, and defined the input–output efficiency taking energy consumption and pollutant emission into consideration as Green Total Factor Productivity (GTFP). Based on GTFP concept, the datasets published by Chen and Tang (2018) from 2003 to 2018 are applied for explaining the evolution of spatial pattern.

Considering the availability and operability of data, this paper selects eight indicators (Tab. 3). The eight indicators in Table 3 were allocated to the time series according to their time attribute information, and the hierarchical visualization was carried out according to the numerical value to obtain the spatial differentiation characteristics of the eight GTFP indicators at four time nodes. First, the visualized results of spatial differentiation of each index were compared with the results of kernel density analysis of the energy industry for visual translation, and the influencing factors were preliminarily determined (Fig. 3). Then, the indicator graphs of the same time nodes in the visual translation results were superimposed with hierarchical assignment, and the result was the spatial differentiation pattern under the comprehensive action of each indicator, which had spatial coupling with the kernel density analysis results. The influence factor matrix of the spatial pattern evolution of the two energy industries from 2003 to 2018 was obtained by statistical analysis (Tab. 4).

thumbnail Fig. 3

Spatial differentiation diagram of influencing factors in 2005/2010/2015/2020. (a) Traditional energy industry in 2005/2010/2015/2020. (b) Renewable energy industry in 2005/2010/2015/2020.

Table 3

Indicators of influencing factors of spatial pattern.

Table 4

Statistics of influencing factors of spatial pattern.

3.5 Structural analysis of spatio-temporal pattern

The statistical results clearly show the dynamic changes of the influencing factors of the industry as a whole and the spatial pattern of traditional energy and renewable energy, and also intuitively compare the differences of influencing factors between traditional energy and renewable energy industries. On the whole, the spatial pattern evolution of the energy industry is produced by the comprehensive action of many factors. In the early stage of industrial development, GTFP mainly influences the spatial distribution of the energy industry by the number of employment, capital stock and total electricity consumption. Hebei province, as a vast traditional industry province, provides abundant and high-quality construction resources for the development of the energy industry in terms of industrial technical personnel reserve and power supply, which increases the possibility for the birth and development of traditional energy enterprises. In the middle and later period of the development of the energy industry, the emission of industrial soot, wastewater, sulfur dioxide and PM2.5 play a role in many industries, which not only curb the development of the traditional energy industry, but also promote the technological innovation of the renewable energy industry and accelerate the market application of renewable energy technology. Moreover, the influencing factors are different among industries. The overall development of China’s energy industry has a strong top–down control, and the administrative power has many influences on the industry. After 2005, the coal mining and washing industry, gas production and supply industry, oil, coal and other fuel processing industry and oil and gas extraction industry began to be affected by the government policies, and the spatial pattern also tilted toward the renewable energy industry where the government policies support.

4 Conclusion and limitations

4.1 Conclusion

Based on the collection of network point data of energy enterprises in Hebei province from 2005 to 2021 and combined with the division of enterprise types on the data platform and the monitoring data from Hebei Government, this paper classifies energy enterprises in Hebei province into traditional energy and renewable energy enterprises. From the perspective of time and space, the paper evaluates the development status of the energy industry in Hebei province, analyzes and summarizes the evolution characteristics in Hebei province, and maps the distribution of the energy industry in Hebei province, and draws the following conclusions:

  1. Moran’s I of the traditional energy industry increased from 0.254515 in 2005 to 0.289301 in 2021. Moran’s I of the renewable energy industry increased from 0.31409 in 2005 to 0.426467 in 2021. This indicates the energy transition in Hebei is progressing. It is worth noting that the wind and solar energy industries are both higher than the national average. The Moran’s I of the energy industry in Hebei province in 2005, 2010, 2015 and 2021 shows that the transformation of the energy industry in Hebei province is significant over time. Some cities, such as Chengde, Tangshan, Qinhuangdao, Zunhua, Baoding and Hengshui, have made good development of traditional energy enterprises by virtue of natural resource endowment, but the diffusion effect of these cities with better development level has not been shown. Compared with the weak diffusion effect of the traditional energy industry, the renewable energy in Hebei province shows a strong diffusion effect in the spatial structure distribution. In particular, the two types of renewable energy, solar energy and wind energy, benefit from the better sunlight conditions and wind energy resources in Hebei region, which makes this region has a great potential for development. Currently, the proportion of wind power and solar power generations in Hebei province are significantly higher than that of the national average.

  2. The “heat” (or aggregation) of the energy industry in the northern region of Hebei province is the highest, followed by the location of the cities around the “heat” region and the southeastern region, while the “heat” in other regions is relatively low. The spatial distribution characteristics of the energy industry in Hebei province initially showed that the energy industry was multi-core and clustered in 2005. In 2010, the number of cores increased and spread to the southwest and northeast, with the overall development from clumping to axial, and intensified the development trend from southwest to northeast in 2015. Until 2021, the energy industry has shown the characteristics of agglomeration of core off axis into points and multi-core diffusion distribution. The number of main cores increased and the intensity of agglomeration intensified, resulting in the formation of independent core points, presenting a spatial pattern of distribution around the axis from Langfang in the northeast to Shijiazhuang in the southwest. It reflects that the renewable energy industry in Hebei province has a stronger development momentum compared with the traditional energy industry.

  3. In the early stage of the energy industry development in Hebei province, GTFP mainly affects the spatial distribution of the energy industry by employment, capital stock and total electricity consumption. In the middle and later period of energy industry development, industrial soot emissions, discharge amount of wastewater, sulfur dioxide emissions and PM2.5 emissions play a decisive role in the transformation of the energy industry. In addition, these four indicators are also the main factors prompting the government to issue corresponding policies, making the overall spatial optimization pattern of the energy industry in Hebei province tilt toward the renewable energy industry.

4.2 Limitations and future directions

The innovation of this research lies in the innovative use of data and makes up for the shortcomings of the timeliness of the traditional data released by the governments. However, the limitation of this paper is still due to the lag of traditional data, so we cannot discuss the influencing factors of the transformation from 2018 to 2021. Therefore, although the conclusion of this paper can provide support for energy decision-making to a certain extent, it still cannot avoid the inference of the conclusion when combined with traditional data. Therefore, in the future, we need to explore more data and further apply them to develop data integration methods of population, enterprises and energy consumption data. In addition, further classification methods for energy enterprises need to be added in future studies. With the continuous enrichment of new energy types and overlapping definitions, the current classification methods will no longer be applicable when it comes to energy fields such as hydrogen energy. Therefore, potential future research also includes the construction of a classification framework for spatial data of energy industries.

Data availability

All data obtained in our paper are open accessed in Qichacha Enterprise Platform (https://www.qcc.com) which based on National Enterprise Credit Information Publicity System (http://www.gsxt.gov.cn/) of China.

Acknowledgments

The research is supported by: The National Natural Science Foundation of China: The match mechanism of social capital between audit office and group subsidiary: auditing and corporate governance effect (Project No. 71672009); The National Natural Science Foundation of China: Audit pricing mechanism, audit procedure and spillover effect within bilateral stochastic boundary model: price regulation versus relaxation (Project No. 71972011); The National Natural Science Foundation of China: Trace capture of tacit knowledge for analyst, governance and investor perception (Project No. 72002005).

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All Tables

Table 1

Classification of traditional energy and renewable energy industries in Hebei province.

Table 2

Moran’s I of energy industry in Hebei province.

Table 3

Indicators of influencing factors of spatial pattern.

Table 4

Statistics of influencing factors of spatial pattern.

All Figures

thumbnail Fig. 1

LISA map of the energy industry in Hebei province in 2021. (a) Traditional energy industry. (b) Renewable energy industry.

In the text
thumbnail Fig. 2

Kernel density and SDE of the energy industry in 2005/2010/2015/2020. (a) Traditional energy industry in 2005/2010/2015/2020. (b) Renewable energy industry in 2005/2010/2015/2020.

In the text
thumbnail Fig. 3

Spatial differentiation diagram of influencing factors in 2005/2010/2015/2020. (a) Traditional energy industry in 2005/2010/2015/2020. (b) Renewable energy industry in 2005/2010/2015/2020.

In the text

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