Comprehensive Evaluation of the New Energy Power Generation Development at the Regional Level: An Empirical Analysis from China

Abstract

In order to build an environment-friendly society and realize the coordinated allocation and effective utilization of resources and finally achieve China’s energy supply security, it is imperative to vigorously develop new energy sources. This study establishes a four-level new energy power generation (NEPG) development index system from multiple dimensions. Taking the installed capacity and generating capacity of China’s NEPG in 2016 and 2017 as samples, we used the improved entropy method, to analyze the development of different types of NEPG among 31 provinces from three aspects: absolute value, relative value, and incremental value. Finally, we comprehensively evaluated the NEPG development in each province. The empirical analysis shows that the spatial distribution of NEPG development in China is uneven, the growth rate is different, the development gap is obvious, and the development efficiency is quite different.

1. Introduction

Climate deterioration, the energy crisis, and air pollution have become the three major problems facing humanity. It is necessary to implement energy conservation and emission reduction, develop new energy sources, and promote green sustainable development [1]. Global energy is transforming into an efficient, clean, and diversified sector, and the energy supply and demand pattern is shifting from traditional energy to new energy [2]. New energy generally refers to renewable energy that is developed and utilized on the basis of new technologies, including nuclear energy, hydro power, wind energy, solar energy, bioenergy, and tidal energy [3]. Countries around the world have paid more and more attention to environmental protection, advocated energy conservation and emission reduction, and introduced measures to encourage the development of NEPG, thus promoting the rapid development of NEPG.

In recent years, China’s economy has developed very rapidly, the consumption of resources such as oil and coal has leapt to the top of the world. The excessive exploitation of traditional energy has also brought about various environmental problems [4]. It is an irresistible trend to develop NEPG instead of traditional energy generation [5]. In recent years, China’s NEPG development is obvious to all. In 2018, the global installed capacity of renewable energy power generation reached 2157.76 GW, of which China’s was 695.87 GW, accounting for 32.35% [6]. In his report on the 19th National Congress of the Communist Party of China, General Secretary Xi pointed out that it is necessary to vigorously develop new energy sources and promote the construction of ecological civilization, which fully reflects China’s high regard for the development of new energy sources [7]. For all levels of government in China, it is necessary to improve the regional development of new energy, prioritize the development of new energy sources, and promote the sustainable development of resources and the environment as the overall tone of current resource and economic development [8].

2. Literature Review

Since the United Nations Conference on New Energy and Renewable Energy was held in 1981, countries around the world have paid more and more attention to new energy [9]. In September 2015, new energy was listed as one of the UN’s sustainable development goals [10]. Domestic and foreign researches on the development of NEPG mainly focuses on three aspects: The first is the influencing factors and policy researches on the development of NEPG; as human beings ignore environmental protection while developing the economy [11], improving relevant policies can promote the development of NEPG [12], which can effectively reduce carbon dioxide emissions [13]. The second is the innovation and development of NEPG technology. Technological innovation plays an effective role in the development of NEPG [14], especially in underdeveloped countries and regions [15]. The third is the trend and significance of the development of NEPG. Traditional energy sources are facing problems such as resource depletion and environmental pollution, and new energy sources can be the best alternative [16]. Since 2000, China’s power demand has soared, but power generation efficiency is low [17]. The development of NEPG has become the main focus of China’s energy policy [18].

The assessment of the development of traditional energy power generation and its impact on the environment economy has been the focus of experts at home and abroad. For example, the spatial correlation model of the economy-energy-pollution phase analyzes the effect of river water as a link between production area and urban living space [19]. The socioeconomic status and energy environment of countries are studied through the energy and environment coupling coordination model [20]. In the energy-economy model, advanced technology has played a decisive role, and with the support of favorable policies, it can improve the development of new energy, which is an endogenous factor of economic incentives [21].

At present, there have been many studies on the evaluation of the development of a single new energy source. The wind energy development index is already a relatively mature indicator, mainly used to analyze the climatic characteristics of wind energy, short-term, medium-term, and long-term prediction and development of other wind energy resources [22]. Effective assessment of wind energy potential and design of wind farms by analyzing the statistical characteristics of wind energy and selecting appropriate wind turbines [23]. The water resources development index can reflect which indicators affect hydropower development and the actual changes in the level of sustainable water resources [24]. The “new policy scenario” of the International Energy Agency provides a support for the development of nuclear power, and described the economic and social impact of developing nuclear power [25]. The latest research has found several power quality problems of new energy power generation and proposed treatment methods [26]. Energy security is the basic guarantee for meeting the demand for future energy generation. In order to minimize the risk, the best energy combination decision involving multiple new energy sources is superior to traditional energy planning [27]. Overall, there are relatively few comprehensive evaluation of the development of various NEPG.

More and more scholars pay attention to NEPG and make a comprehensive evaluation of China. Xiaomin Xu et al. used the idea of matter element extension to study the coordination between renewable energy generation and traditional power grids [28]. Shiwei Yu et al. used the analytic network process to comprehensively evaluate the development and utilization of renewable energy from the aspects of energy, economy, environment, technology, and society [29]. In the research, which focuses more on technical evaluation, Yuan Zeng et al. proposed a comprehensive evaluation method of a renewable energy technology plan based on data envelopment analysis [30]. As different from previous studies, this study mainly analyzes the development of NEPG in different regions, and analyzes the development of various NEPG.

The evaluation methods for the development of new energy sources include the grey prediction method, multi-objective decision making method, and scenario analysis method; for example, using grey prediction models for forecasting China’s growth trends in renewable energy consumption [31]. A new error correction grey prediction model is used to predict the degradation of renewable energy storage, this method can ensure the accuracy of long-term prediction [32]. Exponential decomposition analysis studies the energy consumption or energy efficiency of a country or region over time [33]. A multi-objective decision making method is often used to construct a comprehensive indicator system to evaluate sustainable energy indices in different economies [34]. In view of the fact that the selection of new energy supply systems has many conflicting standards, the fuzzy ideal point method is used to rank renewable energy supply systems in a certain region [35]. The entropy method determines the weight of each indicator in the model by judging the discrete degree of an index, so that the development is evaluated objectively [36].

In summary, domestic and foreign researches on the development of NEPG mainly focuses on influencing factors and technological innovation. The evaluation of the development of traditional energy and single new energy is relatively mature, but the evaluation for the new energies from a comprehensive view is relatively underdeveloped. Compared with the previous studies, this paper mainly explores the comprehensive evaluation. First, we built a comprehensive evaluation model for the NEPG development. Then, we analyzed the NEPG development among 31 mainland China provinces and from multiple dimensions. The entropy method is improved when the indices are weighted, to ensure the objectivity and accuracy of empirical analysis, and using the k-mean clustering method to cluster the evaluation results [37]. The research results of this study can help the provinces in China to clearly understand the development of their NEPG, and also contribute to the policy formulation.

3. Model Construction and Variables Description

This study built a comprehensive evaluation model using the entropy method from different dimensions, so as to directly reflect the NEPG development. Then we conducted an empirical analysis of the NEPG development in China. We first built the indicator system, then weighted the indices, and finally conducted a comprehensive evaluation [38].

3.1. Regional NEPG Development Index System in China

Based on principles such as representativeness, rationality, operability, and sustainability, the four-tier comprehensive evaluation index system, as shown in

Table 1. New energy power generation (NEPG) development index system.

First-Tier Indicators	Second-Tier Indicators	Third-Tier Indicators	Four-Tier Indicators
Comprehensive evaluation of NEPG development (S)	Absolute development of NEPG (A)	Absolute development of hydropower (A1)	Absolute installed capacity of hydropower (A11) Absolute power generation of hydropower (A12)
		Absolute development of wind power (A2)	Absolute installed capacity of wind power (A21) Absolute power generation of wind power (A22)
		Absolute development of nuclear power (A3)	Absolute installed capacity of nuclear power (A31) Absolute power generation of nuclear power (A32)
		Absolute development of photovoltaic power (A4)	Absolute installed capacity of photovoltaic power (A41) Absolute power generation of photovoltaic power (A42)
		Absolute development of bioenergy power (A5)	Absolute installed capacity of bioenergy power (A51) Absolute power generation of bioenergy power (A52)
	Relative NEPG development (B)	Relative development of hydropower (B1)	Relative installed capacity of hydropower (B11) Relative power generation of hydropower (B12)
		Relative development of wind power (B2)	Relative installed capacity of wind power (B21) Relative power generation of wind power (B22)
		Relative development of nuclear power (B3)	Relative installed capacity of hydropower (B31) Relative power generation of hydropower (B32)
		Relative development of photovoltaic power (B4)	Relative installed capacity of photovoltaic power (B41) Relative power generation of photovoltaic power (B42)
		Relative development of bioenergy power (B5)	Relative installed capacity of bioenergy power (B51) Relative power generation of bioenergy power (B52)
	Incremental NEPG development (C)	Incremental development of hydropower (C1)	Incremental installed capacity of hydropower (C11) Increased generation of hydropower (C12)
		Incremental development of wind power (C2)	Incremental installed capacity of wind power (C21) Increased generation of wind power (C22)
		Incremental development of nuclear power (C3)	Incremental installed capacity of nuclear power (C31) Increased generation of nuclear power (C32)
		Incremental development of photovoltaic power (C4)	Incremental installed capacity of photovoltaic power (C41) Increased generation of photovoltaic power (C42)
		Incremental development of bioenergy power (C5)	Incremental installed capacity of bioenergy generation (C51) Increased generation of bioenergy generation (C52)

Note: Installation capacity and generation capacity of NEPG are annual data. The unit of the four-tier indicator is 100 million kWh.

, is constructed to reflect China’s regional NEPG development [39].

The first-tier indicators are the overall level of development of NEPG. The second-tier indicators analyze the absolute, relative, and incremental development of NEPG in different provinces. The third-tier indicators assesses the development of five different types of new energy sources through comparing the absolute/relative/incremental values. The four-tier indicators take installation capacity and generation capacity as two sub-indicators to measure the NEPG development.

3.2. Regional NEPG Development Model in China

3.2.1. Indicators Weight of Regional NEPG Development in China

The entropy method is a relatively objective method for determining weights. We made some improvements to this method with reference to other scholars’ researches [40]. Assume that there is n provinces, $m$ evaluation indicators, and raw indicator data matrix in the comprehensive evaluation model of China’s regional NEPG development is:

$$A={(x_{ij})}_{n\times m}$$

(1)

The raw data x_ij is the jth indicator of the $i$ province, and the $\max (x_{ij})$, $\min (x_{ij})$ respectively represent the maximum and minimum values of the indicator. $x_{ij}{}^{\prime }$ is the standardized data, $x_{ij}{}^{\prime }\in \left[0,1\right]$.

$$x_{ij}{}^{\prime }=\frac{x_{ij}-\min (x_{ij})}{\max (x_{ij})-\min (x_{ij})}$$

(2)

$p_{ij}$ is the proportion of $x_{ij}{}^{\prime }$:

$$p_{ij}=\frac{x_{ij}{}^{\prime }}{{\displaystyle {\sum }_{i=1}^m{x}_{ij}{}^{\prime }}}$$

(3)

Entropy value $e_j$ of the index $m$:

$$e_j=-(\frac{1}{\ln m}){\displaystyle \sum _{i=1}^m{p}_{ij}}\ln p_{ij}$$

(4)

$w_j$ is the weight of $m$:

$$w_j=\frac{1-e_j}{{\displaystyle {\sum }_{j=1}^n(1-e_j)}}$$

(5)

3.2.2. NEPG Development Comprehensive Evaluation Model in China

Under the calculation order, NEPG development comprehensive evaluation model in China is composed of the three-layer model, which are underlying, middle, and top layer. Additionally, the comprehensive evaluation model adopts the linear weighted synthesis method:

$$S_j={\displaystyle \sum _{j=1}^m{w}_j}\times q_{ij}$$

(6)

$S_j$, $w_j$, $q_{ij}$ respectively represent the comprehensive evaluation result, indicator weight, and the standardized indicator value.

1. The underlying layer model:

The underlying layer model is based on the four-level indicators, which are the installation capacity and generation capacity of five types of new energy generation (absolute value, relative value, incremental value). On the basis of obtaining the weights of various indicators, the development of different types of NEPG are further calculated.

• Absolute development evaluation model

$$A_x=A_{x1}\times W_{1x1}+A_{x2}\times W_{1x2}$$

$x$ is one of five new energy source, $A_x$ is the absolute development of a new energy source, $A_x{}_1$, $A_x{}_2$ are the absolute installation capacity and absolute generation capacity of a new energy source. $W_{1x}{}_1$, $W_{1x}{}_2$ are the weights of $A_x{}_1$ and $A_x{}_2$ respectively.

• Relative development evaluation model

$$B_x=B_{x1}\times W_{2x1}+B_{x2}\times W_{2x2}$$

$B_x$ is the relative development of a new energy source, $B_x{}_1$, $B_x{}_2$ are the relative installation capacity and relative generation capacity of a new energy source. $W_{2x}{}_1$, $W_{2x}{}_2$ are the weights of $B_x{}_1$ and $B_x{}_2$ respectively.

• Incremental development evaluation model

$$C_x=C_{x1}\times W_{3x1}+C_{x2}\times W_{3x2}$$

$C_x$ is the incremental development of a new energy source, $C_x{}_1$, $C_x{}_2$ are the incremental installation capacity and incremental generation capacity of a new energy source. $W_{3x}{}_1$, $W_{3x}{}_2$ are the weights of $C_x{}_1$ and $C_x{}_2$ respectively.

2. The middle layer model:

The middle layer model mainly compares the NEPG development in different provinces. It reflects the development of the same evaluation index among different provinces from absolute value, relative value, incremental value.

• Absolute development evaluation model

$$A={\displaystyle \sum _{x=1}^5{A}_x\times W_{1x}}$$

$A$ is the absolute NEPG development, and $W_{1x}$ is the weight of $A_x$.

• The relative development evaluation model:

$$B={\displaystyle \sum _{x=1}^5{B}_x\times W_{2x}}$$

$B$ is the absolute NEPG development, and $W_{2x}$ is the weight of $B_x$.

• The incremental development evaluation model:

$$C={\displaystyle \sum _{x=1}^5{C}_x\times W_{3x}}$$

$C$ is the absolute NEPG development, and $W_{3x}$ is the weight of $C_x$.

3. The top layer model:

According to the calculations mentioned above, we can further comprehensively evaluate the NEPG development in each province.

$$S=A\times W_1+B\times W_2+C\times W_3$$

(13)

$S$ is the comprehensive evaluation result of the NEPG development in a certain province, and $W_1$, $W_2$ and $W_3$ respectively represent the weight of three aspects (absolute value, relative value, incremental value) development of NEPG in a certain province.

In view of the fact that the results obtained by the improved entropy method are not convenient for further calculation, we use the deviation standardization method to convert the index value into a value of interval [0, 100] [41].

4. Empirical Analysis

4.1. Basic Data

We selected the installation capacity and generation capacity data of NEPG in the year of 2016 and 2017 in 31 mainland China provinces as the main evaluation indicators. All the data used in this research is sourced from the website of the National Bureau of Statistics of the People’s Republic of China [42].

4.2. Analysis and Discussion

4.2.1. Spatial Distribution of NEPG Development

According to the installation capacity and generation capacity of NEPG in each province in 2016 and 2017, the NEPG development in each province was analyzed from different dimensions. The comprehensive evaluation results are shown in Appendix A

Table A1. Comprehensive evaluation results of China’s regional NEPG development (Based on deviation standardization).

Province	Comprehensive Evaluation Level	Absolute Development	Relative Development	Incremental Development
Shandong	0.372739	0.2914	0.100251	0.51194
Jiangsu	0.310845	0.273211	0.106734	0.430755
Inner Mongolia	0.170555	0.22079	0.114061	0.175047
Guangdong	0.374026	0.399573	0.21434	0.285213
Henan	0.151462	0.122917	0.060641	0.193957
Shanxi	0.146435	0.134216	0.082839	0.174601
Zhejiang	0.467766	0.383206	0.23135	0.50183
Xinjiang	0.16126	0.198411	0.127109	0.163395
Anhui	0.291262	0.179382	0.109416	0.414993
Hebei	0.249446	0.198267	0.137085	0.327145
Liaoning	0.185255	0.156084	0.200129	0.130668
Guizhou	0.119804	0.09313	0.114529	0.130941
Shaanxi	0.123387	0.080949	0.080402	0.199427
Fujian	0.26084	0.221569	0.286275	0.162503
Hubei	0.212726	0.194563	0.178105	0.18744
Ningxia	0.135314	0.116551	0.151096	0.126155
Shanghai	0.116186	0.026104	0.056815	0.195631
Hunan	0.171264	0.10731	0.161569	0.193418
Guangxi	0.436239	0.517119	0.575757	0.134346
Heilongjiang	0.282787	0.095394	0.193297	0.430787
Gansu	0.183019	0.167117	0.21172	0.165086
Jiangxi	0.153381	0.086091	0.113692	0.192766
Jilin	0.130254	0.080936	0.176961	0.116364
Yunnan	0.227869	0.23606	0.224692	0.230424
Sichuan	0.321204	0.254522	0.209359	0.602018
Chongqing	0.092655	0.033641	0.105331	0.13484
Tianjin	0.040473	0.009381	0.03009	0.060811
Beijing	0.102121	0.027417	0.106781	0.130142
Hainan	0.168618	0.049273	0.367527	0.077558
Qinghai	0.17805	0.131182	0.312359	0.116354
Tibet	0.110431	0.010877	0.266754	0.079713

. Based on the comprehensive evaluation results, we have drawn a spatial distribution map of the NEPG development. The deep color area indicates that the province has a higher level of development of NEPG, as shown in Figure 1. It can be seen from the figure that the provinces with higher development are mainly distributed in the southwest and eastern coastal areas.

4.2.2. Analysis of the NEPG Development

In order to further understand the comprehensive development level of new energy sources as well as the absolute, relative, and incremental development levels in China, the provinces are ranked based on the indicators of the NEPG development, thus obtaining the development status and development priorities of NEPG in China at the provincial level, as shown in

Table 2. Ranking of regional NEPG development in China.

Rank	Comprehensive Evaluation	Absolute Development	Relative Development	Incremental Development
1	Guangxi	Guangxi	Guangxi	Sichuan
2	Zhejiang	Guangdong	Hainan	Shandong
3	Sichuan	Zhejiang	Qinghai	Zhejiang
4	Shandong	Shandong	Fujian	Heilongjiang
5	Guangdong	Jiangsu	Tibet	Jiangsu
6	Jiangsu	Sichuan	Zhejiang	Anhui
7	Heilongjiang	Yunnan	Yunnan	Hebei
8	Anhui	Fujian	Guangdong	Guangdong
9	Yunnan	Inner Mongolia	Gansu	Yunnan
10	Hebei	Xinjiang	Sichuan	Shaanxi
11	Fujian	Hebei	Liaoning	Shanghai
12	Hubei	Hubei	Heilongjiang	Henan
13	Gansu	Anhui	Hubei	Hunan
14	Qinghai	Gansu	Jilin	Jiangxi
15	Inner Mongolia	Liaoning	Hunan	Hubei
16	Xinjiang	Shanxi	Ningxia	Inner Mongolia
17	Liaoning	Qinghai	Hebei	Shanxi
18	Hunan	Henan	Xinjiang	Gansu
19	Hainan	Ningxia	Guizhou	Xinjiang
20	Shanxi	Hunan	Inner Mongolia	Fujian
21	Jiangxi	Heilongjiang	Jiangxi	Chongqing
22	Henan	Guizhou	Anhui	Guangxi
23	Ningxia	Jiangxi	Beijing	Guizhou
24	Shaanxi	Shaanxi	Jiangsu	Liaoning
25	Jilin	Jilin	Chongqing	Beijing
26	Guizhou	Hainan	Shandong	Ningxia
27	Tibet	Chongqing	Shanxi	Jilin
28	Shanghai	Beijing	Shaanxi	Qinghai
29	Chongqing	Shanghai	Henan	Tibet
30	Beijing	Tibet	Shanghai	Hainan
31	Tianjin	Tianjin	Tianjin	Tianjin

.

Guangxi ranks first in terms of comprehensive development mainly due to its endowment of natural sources and efficient utilization. There is no need for further development at this stage. Zhejiang, Sichuan, Shandong, Guangdong, Jiangsu and other provinces have developed new energy power in recent years. However, traditional energy sources still dominate.

4.2.3. NEPG Development Cluster Analysis

Based on the result of deviation standardization [43], we use MATLAB R2016a to perform k-means cluster analysis on the comprehensive evaluation results of China’s regional NEPG development. The results are shown in Figure 2. The results of cluster analysis divide the NEPG development in China into five categories:

From the results of the cluster analysis, it can be seen that the NEPG development in China is quite different among provinces. The average scores of the five types of NEPG development are 98.23, 76.07, 56.65, 37.65, and 21.14, respectively, and the gap is very obvious.

4.2.4. Analysis of Development Efficiency of NEPG

This study further analyzes the efficiency of China’s regional NEPG development by calculating the corresponding relationship between the comprehensive evaluation results of NEPG development and the per capita GDP of each province, as shown in

Table 3. Analysis of development efficiency of NEPG in China.

Province	Comprehensive Evaluation Level	Per Capita GDP	Development Efficiency
Guangxi	100.00	38,102	0.002625
Sichuan	95.60	44,651	0.002141
Yunnan	55.37	34,221	0.001618
Heilongjiang	60.67	41,916	0.001447
Gansu	40.71	28,497	0.001429
Anhui	60.49	43,401	0.001394
Hebei	55.13	45,387	0.001215
Shandong	80.95	72,807	0.001112
Zhejiang	99.09	92,057	0.001076
Guangdong	76.22	80,932	0.000942
Qinghai	39.97	44,047	0.000907
Xinjiang	36.88	44,941	0.000821
Hubei	43.10	60,199	0.000716
Hunan	33.99	49,558	0.000686
Shanxi	28.38	42,060	0.000675
Hainan	32.26	48,430	0.000666
Jiangsu	71.04	107,150	0.000663
Liaoning	35.06	53,527	0.000655
Jiangxi	28.09	43,424	0.000647
Fujian	51.58	82,677	0.000624
Inner Mongolia	39.26	63,764	0.000616
Henan	27.60	46,674	0.000591
Guizhou	22.24	37,956	0.000586
Tibet	20.83	39,267	0.000530
Ningxia	26.88	50,765	0.000530
Shaanxi	25.68	57,266	0.000448
Jilin	24.43	54,838	0.000446
Chongqing	16.18	63,442	0.000255
Shanghai	18.11	126,634	0.000143
Beijing	15.19	128,994	0.000118
Tianjin	5.95	118,944	0.000050

. The “Development efficiency” is obtained by dividing “Comprehensive evaluation level” by “Per capita GDP”. Take Guangxi as an example: $0.002625=100÷38102$.

It can be seen from Figure 3 that the development efficiency of NEPG in Tianjin, Beijing, Shanghai, Chongqing is relatively low, while that of Guangxi, Sichuan Yunnan, and Heilongjiang is relatively high. Obviously, the development efficiency among provinces changes greatly from each other. Provinces with higher per capita GDP have lower development efficiency.

4.3. Robustness Test

In order to test the accuracy and credibility of the above calculations, we tested the robustness of the analysis by changing the calculation method for standardizing the sample data. In the previous, we used the deviation standardization method to process the sample data. Now we use the Z-Score standardization method to process the sample data [44].

$x_{\mathrm{ij}}{}^{″}$ indicates standardized data:

$$x_{ij}{}^{″}=\frac{x_{ij}-\overline{x}}{\sigma }$$

(14)

$\overline{x}$ is the mean of the sample data, and $\sigma$ is the standard deviation of the sample data. To eliminate the effects of negative numbers, we add a positive $a$ pairs of data to translate:

$$b=x_{ij}{}^{″}+a$$

(15)

Then, the improved entropy method is used to recalculate the comprehensive evaluation results of China’s regional NEPG development level, as shown in Appendix A

Table A2. Comprehensive evaluation results of China’s regional NEPG development (Based on Z-score standardization).

Province	Comprehensive Evaluation Level	Absolute Development	Relative Development	Incremental Development
Shandong	0.372739	0.2914	0.100251	0.51194
Jiangsu	0.310845	0.273211	0.106734	0.430755
Inner Mongolia	0.170555	0.22079	0.114061	0.175047
Guangdong	0.374026	0.399573	0.21434	0.285213
Henan	0.151462	0.122917	0.060641	0.193957
Shanxi	0.146435	0.134216	0.082839	0.174601
Zhejiang	0.467766	0.383206	0.23135	0.50183
Xinjiang	0.16126	0.198411	0.127109	0.163395
Anhui	0.291262	0.179382	0.109416	0.414993
Hebei	0.249446	0.198267	0.137085	0.327145
Liaoning	0.185255	0.156084	0.200129	0.130668
Guizhou	0.119804	0.09313	0.114529	0.130941
Shaanxi	0.123387	0.080949	0.080402	0.199427
Fujian	0.26084	0.221569	0.286275	0.162503
Hubei	0.212726	0.194563	0.178105	0.18744
Ningxia	0.135314	0.116551	0.151096	0.126155
Shanghai	0.116186	0.026104	0.056815	0.195631
Hunan	0.171264	0.10731	0.161569	0.193418
Guangxi	0.436239	0.517119	0.575757	0.134346
Heilongjiang	0.282787	0.095394	0.193297	0.430787
Gansu	0.183019	0.167117	0.21172	0.165086
Jiangxi	0.153381	0.086091	0.113692	0.192766
Jilin	0.130254	0.080936	0.176961	0.116364
Yunnan	0.227869	0.23606	0.224692	0.230424
Sichuan	0.321204	0.254522	0.209359	0.602018
Chongqing	0.092655	0.033641	0.105331	0.13484
Tianjin	0.040473	0.009381	0.03009	0.060811
Beijing	0.102121	0.027417	0.106781	0.130142
Hainan	0.168618	0.049273	0.367527	0.077558
Qinghai	0.17805	0.131182	0.312359	0.116354
Tibet	0.110431	0.010877	0.266754	0.079713

. We compare and rank the two results of the comprehensive evaluation based on different methods, as shown in

Table 4. Comparison of comprehensive evaluation results of China’s regional NEPG development by two methods.

Rank	Deviation Standardization Method		Z-Score Standardization Method
	Province	Comprehensive Evaluation Level	Province	Comprehensive Evaluation Level
1	Guangxi	100.00	Zhejiang	100.00
2	Zhejiang	99.09	Guangxi	92.62
3	Sichuan	95.60	Guangdong	78.06
4	Shandong	80.95	Shandong	77.76
5	Guangdong	76.22	Sichuan	65.70
6	Jiangsu	71.04	Jiangsu	63.28
7	Heilongjiang	60.67	Anhui	58.69
8	Anhui	60.49	Heilongjiang	56.71
9	Yunnan	55.37	Fujian	51.57
10	Hebei	55.13	Hebei	48.91
11	Fujian	51.58	Yunnan	43.86
12	Hubei	43.10	Hubei	40.31
13	Gansu	40.71	Liaoning	33.88
14	Qinghai	39.97	Gansu	33.36
15	Inner Mongolia	39.26	Qinghai	32.20
16	Xinjiang	36.88	Hunan	30.61
17	Liaoning	35.06	Inner Mongolia	30.44
18	Hunan	33.99	Hainan	29.99
19	Hainan	32.26	Xinjiang	28.27
20	Shanxi	28.38	Jiangxi	26.42
21	Jiangxi	28.09	Henan	25.97
22	Henan	27.60	Shanxi	24.80
23	Ningxia	26.88	Ningxia	22.20
24	Shaanxi	25.68	Jilin	21.01
25	Jilin	24.43	Shaanxi	19.40
26	Guizhou	22.24	Guizhou	18.57
27	Tibet	20.83	Shanghai	17.72
28	Shanghai	18.11	Tibet	16.37
29	Chongqing	16.18	Beijing	14.43
30	Beijing	15.19	Chongqing	12.21
31	Tianjin	0.00	Tianjin	0.00

. The rankings outputted by the two methods are basically the same, and only a few provinces have slight change in ranking.

This study uses Pearson correlation analysis to verify the correctness of the NEPG development calculated by two different standardized methods [45].

$$\rho =\frac{{\displaystyle \sum _{i=1}^N\left({\alpha }_i-\overline{\alpha }\right)}\left({\beta }_i-\overline{\beta }\right)}{{\left[{\displaystyle \sum _{i=1}^N{\left({\alpha }_i-\overline{\alpha }\right)}^2}{\displaystyle \sum _{i=1}^N{\left({\beta }_i-\overline{\beta }\right)}^2}\right]}^{\frac{1}{2}}}$$

(16)

$\overline{\alpha }$, $\overline{\beta }$ are the average of $\alpha$ and $\beta$, $N$ is the total number of observations, in this study $N=31$. Using software SPSS 22.0 to analyze the two groups of data, the Pearson correlation coefficient is 0.993, which indicates that the results calculated by the two methods are highly positively correlated. It is concluded that the results of the calculation using the improved entropy method are accurate and reasonable.

5. Conclusions and Implications

5.1. Research Conclusions

In the context of global focus on new energy and advocacy for green sustainable development, China has made great achievements in improving the utilization rate of new energy power generation and reducing air pollutants emissions. However, there are both challenges and opportunities facing China [46]. This study quantitatively analyzes the development of regional NEPG based on panel data of 31 provinces in China. The conclusions are as follows:

• The spatial distribution of NEPG development in China is uneven. The coastal, southwestern, and northeastern provinces with better nature endowment in developing new energy gain a higher level of comprehensive development and utilization of new energy. The empirical analysis demonstrates that the comprehensive development of most central provinces, which lack the natural advantages of developing new energy, is still underdeveloped.
• The growth rate of NEPG in China varies from province to province. Provinces with higher levels of development, such as Guangxi, have slowed down in recent years, paying more attention to the scientific and rational use of new energy sources and moderate development. More and more provinces pay more attention to the development of NEPG.
• The NEPG development at regional level in China is unbalanced. The results of cluster analysis showed that the first two categories with higher comprehensive evaluation had only six provinces, and the comprehensive evaluation results of most provinces were below 65 points, and the gap between the regions was large.
• The province may perform quite different in development efficiency and the comprehensive evaluation results of NEPG in China. The comprehensive evaluation results of coastal developed provinces are relatively high, but the development efficiency is relatively low; the development of NEPG in emerging provinces is developing rapidly and the market development potential is huge there.

5.2. Practical Implications

Based on the above conclusions, the revelations are as followed:

• At the national level, macro-control of regional NEPG development should be implemented. Regions with poor development of NEPG should encourage vigorous development of new energy by increasing policy and financial support.
• At the provincial level, coordinated energy policy should be introduced across provinces. On the basis of the uneven development of NEPG in all dimensions, the provinces can actively explore and formulate corresponding NEPG development strategies according to their own development conditions and local conditions.
• More exchanges between the provinces and mutual experience. More interactions between different regions should be carried out, to replicate the advanced development concepts and methods of provinces with higher energy development, to help other provinces to reduce environmental damage, reduce waste of resources, and work for sustainable human development.
• The methods and conclusions of this study could be used for reference by other countries, especially for countries vast in territory. When developing NEPG, those countries probably encounter the problems of unbalanced regional development and unequal natural advantages of new energy. This research may provide reference in formulating policies and exploring the emerging market.