Toward Renewable Energy in China: Revisiting Driving Factors of Chinese Wind Power Generation Development and Spatial Distribution

Abstract

As the biggest renewable energy installation and generation country globally, it is important to deeply understand China’s wind power production determinants and draw implications for energy policy. This paper analyzes local electricity deployment, electricity consumption, investment in wind power, and price of wind power electricity on-grid apart from traditional GDP and CO₂ factors in the panel data regression model, and some interesting results are found. The investment of installation and the price of wind power electricity on-grid have negative impacts on wind power generation, while local electricity consumption and inter-provincial power transmission capacity significantly impact wind power generation positively. GDP and CO₂ emission per capita have negative and positive impacts on wind power production, respectively. As for different wind power zones, the most influencing factors are local electricity consumption. Hence, this paper concludes that local absorbing capacity is still an important limiting factor to Chinese renewable energy development. At last, some policies are suggested to enhance the local absorbing capacity of renewable energy.

1. Introduction

With rapid economic development, China has become the largest carbon dioxide (CO₂) emitter since 2006 and has contributed about 30% of the world’s total CO₂ emissions [1]. Despite significant challenges to reducing emissions as an emerging economy with a huge population, China has adopted more vigorous policies and measures to have CO₂ emissions peak before 2030 and achieve carbon neutrality before 2060 [2]. However, it is not easy to achieve such targets due to China’s higher proportion of fossil fuels in energy consumption. Consequently, China set up national development priorities to encourage the use of clean energy.

In 2005, China approved a ‘Renewable Energy Law’ aimed at promoting renewable energy in the country. The law requires the local government to support the research and development (R&D) of renewable energy technics and to provide financial subsidies through the Renewable Energy Development Fund to industrialize renewable power generation [3]. Apart from this law, many renewable energy policies have been proposed. All these policies have promoted the development of renewable energy in China. According to data from the Chinese Energy Bureau, the installed capacity of renewable energy sources has been growing by an average annual rate of 12 percent since 2016, with new energy sources such as wind and solar power taking up over half of the total installed capacity of renewable energy sources [2]. In 2011, China became the greatest country with wind power installation, and in 2020, the capacity of wind power installation on-grid in China reached 289.53 million kW and accounted for 12.79% of the total installed capacity.

The increased attention on renewable energy sources can be attributed to several factors. First, it is important to deeply understand the determinants of renewable energy production and draw resulting implications for energy policy. A growing number of researchers have devoted their efforts to examining the renewable energy-growth nexus studies [4], while comparatively fewer studies investigate the components that affect renewable energy [5]. Second, renewable energy offers solutions to energy security and global warming initiated environmental factors such as CO₂ emission. Many scholars have examined the influence of real GDP per capita, CO₂ emissions per capita, real oil prices, and real income per capita on renewable energy consumption for G7, African, Balkan, and emerging countries [6,7,8,9,10,11,12,13]. Additionally, Omri et al. studied the impacts of these factors on renewable energy consumption of different countries and found significant differences among different income groups. Increases in CO₂ emissions and trade openness are the major drivers of renewable energy consumption, while oil price increases have a smaller but negative impact on renewable energy consumption in the middle-income and global panels [14].

A few papers investigate the technical, institutional, and political factors, given that many studies focus on the causal relationship between renewable energies development with economic growth and CO₂ emission [15]. Apergis and Eleftheriou investigated the effects of institutional and political factors on renewable energy usage and found that both factors exert a strong and statistically significant effect on renewable energy consumption after controlling the economic environment. Further, Biresselioglu and Karaibrahimoglu examined the effect of government orientation on renewable energy consumption levels [16]. In addition, Rasoulinezhad and Saboori studied the impacts of trade and financial openness apart from the above factors in the Independent States of the Russian Commonwealth. They found a long-run bidirectional relationship between all the variables except for economic growth-renewable energy use linkage [17]. Finally, Alam and Murad addressed the effects of trade openness and technological progress on renewable energy use for 25 Organization for Economic Co-operation and Development (OECD) countries. They found that all factors influence renewable energy use positively and significantly [18].

The above studies were mostly carried from the consumption perspective. However, some scholars explored the factors from the production perspective, including political, social, economic, and country-specific factors [19]. Political factors include price regulation, quotas of renewable energy, and even the government’s orientation and political and government ideology [20,21]. Social and economic factors include the price of fossil fuel energy, CO₂ emission, the contribution of other energy to electricity generation, size of energy consumption, and income level. Country-specific factors include the wealth of production potential of renewable energy and continuous commitment to renewable energy. The International Energy Agency’s (IEA) ‘Medium-Term Renewable Energy Market Report 2015’ documents five factors that impact renewable energy development and deployment at the country level, including regulatory and institutional, financial and economic, technical and innovation, and social and environmental factors [22]. The income per capita, or GDP per capita, is an important determinant of renewable energy production as an economic factor. Institutional factors cover regulations and laws that make deployment of renewable energy generation either faster and easier or slower and harder. Technology is an essential factor to have less costly and efficient renewable energy generation. Social factors cover a range of factors, including social acceptance and openness to new ideas. Environmental factors include the effects of greenhouse gases. As for the development of renewable energy in China, some scholars investigate the relationship amongst renewable energy consumption, gross domestic product, carbon dioxide emissions, foreign direct investment, foreign trade, and urbanization in 31 Chinese provinces, and found that economic growth would undoubtedly bring about the growth of renewable energy consumption while the impact of other variables is different in eastern, central, and western China [23,24,25,26].

In summary, most of the studies only take economic, social, environmental, and political factors into the analysis model for renewable energy consumption; few studies focused on the production side. Additionally, deployment factors such as on-gridding of renewable energy were seldom taken into concern. However, on-grid is an important limitation to Chinese renewable development. In addition, most existing studies have mainly focused on impacts from a national perspective without much consideration for regional disparities for China, leading to a lack of understanding of the anthropogenic impacts on renewable energy production across China regions.

We aim to analyze the main driving factors behind renewable energy production in China using panel data techniques of 31 provinces from 2013 to 2019. We mainly take wind power as a research object as it is the main part of Chinese new renewable energy. Our contribution is fourfold. First, we add the factors of local electricity deployment, electricity consumption, investment in wind power, and price of wind power electricity on-grid into the model apart from traditional GDP and CO₂ factors. Second, we incorporate spatial differences to investigate the main regional factors influencing different wind resource zones. Third, we use the standard panel data techniques method to assess whether it affects renewable energy commitment. Fourth, we focus on renewable energy production mainly with a background of the Chinese carbon summit and neutrality goals.

2. Materials and Methods

2.1. Data Source and Data Description

The sample period is from 2013 to 2019. We use data collected from several sources, namely, the Chinese Energy Statistic Yearbooks (multiple issues), annual statistics compilations of the Chinese power industry (multiple issues), and Chinese Statistic Yearbooks (multiple issues). The data of Hong Kong, Macau, and Taiwan are not available, while the data of 31 provinces in mainland China are utilized.

In 2005, the Chinese central government issued the ‘Renewable Energy Law’ to encourage renewable energy development. However, wind power in China was still developed on a small scale, limited by higher generation costs and lower on-gridding proportion. In 2009, the Chinese National Development and Reform Commission issued the ‘Notice on Improving the On-grid Tariff Policy for Wind Power’ and put forward that the Government will provide subsidies to wind power at higher prices than the local thermal generation [27]. At the same time, there was huge progress in the Chinese wind power manufacturing industry which greatly decreased the cost of wind power infrastructure. Figure 1 presents the growth of wind power generation and wind power installation from 2005 to 2019. It can be found that there is rapid growth after 2009.

As for spatial distribution, China′s wind energy resource-rich areas are mainly concentrated in two belt-shaped areas. One is the ‘Three Norths’ regions, covering nearly 200 km wide areas of the Chinese Northeast, North, and Northwest, mainly in Heilongjiang, Jilin, Inner Mongolia, Gansu, Xinjiang, and Tibet. The other is the ‘rich belt of the southeast coast and its islands’ in Southeast China, mainly in Liaoning, Hebei, Jiangsu, Zhejiang, and Guangdong provinces. These geographical features of wind energy resources’ distribution dominated the spatial distribution of wind power installation and generation. Figure 2 and Figure 3 are the geographical distribution of wind power generation and installation across provinces in 2019 and annual increments from 2013 to 2019. It could be easily found that most western provinces and coastal provinces with abundant wind power resources have relatively high levels of wind power electricity. In contrast, most provinces in central China with poor wind power resources have relatively low levels of wind power electricity production and growth from 2013 to 2019.

2.2. Model

This study applies the baseline model in Equation (1) to examine the impact of the price, investment, power transmission, and local electricity consumption on the electricity generated by wind power plants along with GDP per capita and CO₂ emissions per capita following the relative studies.

$$windpowe{r}_{it}=\mathrm{f}\left(perin{v}_{it},pric{e}_{it},transmi{t}_{it},consum{e}_{it},contro{l}_{it}\right)$$

(1)

The natural logarithmic form of Equation (1) can be expressed as:

$$windpowe{r}_{it}={\beta }_{1}lnperin{v}_{it}+{\beta }_{2}lnpric{e}_{it}+{\beta }_{3}lntransmi{t}_{it}+{\beta }_{4}lnconsum{e}_{it}+{\beta }_{5}lnC{O}_2{}_{it}+{\beta }_{6}lnperGD{P}_{it}+u_{it}$$

(2)

The later model for the individual fixed effects regression model just added fixed effects on the basis of Equation (2), which has been expressed in Equation (3):

$$windpowe{r}_{it}={\lambda }_i+{\beta }_{1}lnperin{v}_{it}+{\beta }_{2}lnpric{e}_{it}+{\beta }_{3}lntransmi{t}_{it}+{\beta }_{4}lnconsum{e}_{it}+{\beta }_{5}lnC{O}_2{}_{it}+{\beta }_{6}lnperGD{P}_{it}+u_{it}$$

(3)

where $windpowe{r}_{it}$ refers to the electricity generated by wind power of provinces i at time t. ${\lambda }_i,{\beta }_1,{\beta }_2,{\beta }_3,{\beta }_4,{\beta }_5,\mathrm{and}{\beta }_6$ are the unknown parameters to be estimated. $lnperinv$ is the Ln value of investment per installation capacity of wind power; $lnprice$ is a variable representing the average price of wind power electricity on-grid; $lntransmit$ means the inter-provincial power transmission capacity; $lnconsume$ denotes local electricity consumption. We assume these four variables are independent variables of this model. The control variables include carbon dioxide emissions $lnC{O}_2$ and GDP per capita $lnperGDP$. u_it is the error term.

We also take the Chinese wind power resource regions as virtual variables to investigate the impact of wind resource differences on wind power development. According to the document of ‘Notice on Improving the On-grid Tariff Policy for Wind Power’ issued in 2009, Chinese land territory was divided into four zones following the status of wind energy resources and project construction conditions, and corresponding benchmark on-grid tariffs were assigned to different zones (

Table 1. Zoning of wind power resource of Chinese territory.

Zone	Prefectural Units	Provinces	Group
1	Inner Mongolian except for Chifeng, Tongliao, Xing’an league, and Hulunbuir; Wulumuqi, Yili, Karamay, and Shihezi of Xinjiang	Inner Mongolian, Xinjiang	1
2	Zhangjiakou and Chengde of Hebei province, Chifeng, Tongliao, Xin’an League and Hulunbuir of Inner Mongolian, Jiayuguan and Jiuquan of Gansu, Yunnan province	Yunnan, Gansu
3	Baicheng and Songyuan of Jilin, Jixi, Shuangyashan, Qitaihe, Suihua, Yichun, Daxinganling of Heilongjiang province, Gansu province except for Jiayuguan and Jiuquan, Xinjiang except for areas in zone 1.	Jilin, Heilongjiang, Ningxia
4	Others areas out of the territory of zones 1, 2, and 3.	Others	2

). Normally, the zone with an abundant wind power resource and a good location near the consumption center will have a relatively lower on-grid tariff. However, the basic unit of resource zoning is a prefectural unit. This paper re-divided 31 researching provinces into four zones according to the principle of maximum areas and highest tariff. We merged zones 1, 2, and 3 with abundant wind power energy into Group 1 and took zone 4 as Group 2.

Additionally, logarithms are taken for all continuous variables to reduce the influence of extreme values and heteroscedasticity.

3. Results

3.1. Stationarity Test

A unit root test is needed to test the stationarity of the data to avoid spurious regression. Since the data used is short panel data, the Harris and Tzavalis Panel Unit Root Test, which employs a null hypothesis of a unit root, is used. Before the HT test, missing values were filled by calculating the mean value of the data before and after the province’s year. Then, individual fixed effects were added In the HT test, and the mean of each unit was subtracted first to mitigate cross-section correlation.

Table 2. Panel unit root tests.

Variables	Statistic	z	p-Value
windpower	0.990	5.451	1.000
lnperinv	0.149	−7.123	0.000
lnprice	0.106	−7.766	0.000
lntransmit	0.205	−6.283	0.000
lnconsume	0.651	0.382	0.649
lnCO2	0.115	−7.622	0.000
lnperGDP	−0.021	−9.663	0.000

presents the results of the panel unit root tests on variables used in this study. The results confirm that the variables windpower and lnconsume are non-stationary at the level but stationary at the first difference, while other variables are stationary.

3.2. Cointegration Test

Since some variables are non-stationary, a cointegration test is needed to examine the long-run relationships among the variables. Therefore, three-panel cointegration tests developed by Pedroni, Kao, and Westerlund are applied.

Table 3. Panel cointegration tests.

Cointegration Test Methods		Statistic	p-Value
Pedroni	Modified Phillips–Perron t	13.587	0.000
	Phillips–Perron t	4.026	0.000
	Augmented Dickey–Fuller t	9.230	0.000
Kao	Modified Dickey–Fuller t	4.048	0.000
	Dickey–Fuller t	4.551	0.000
	Augmented Dickey–Fuller t	3.123	0.001
	Unadjusted modified Dickey–Fuller t	2.015	0.022
	Unadjusted Dickey–Fuller t	1.643	0.050
Westerlund	Variance ratio	319.545	0.000

present the results of cointegration tests. All results confirm a long-run relationship between variables windpower, lnperinv, lnprice, lntransmit, lnconsume, lnCO₂, and lnperGDP; spurious regression is avoided.

3.3. Main Factors Influencing Wind Power Electricity

The summary statistics of the variables are shown in

Table 4. Descriptive statistics of variables.

Variables	n	Mean	Sd	Min	Max	VIF
windpower	213	84.009	108.137	0.090	666.000
lnperinv	194	6.704	1.430	1.327	13.326	1.409
lnprice	211	6.357	0.144	5.900	6.779	1.317
lntransmit	217	5.960	1.443	−0.446	8.455	1.462
lnconsume	212	7.384	0.756	3.526	8.809	4.807
lnCO2	210	5.575	0.710	3.676	6.759	4.330
lnperGDP	217	10.871	0.411	10.050	12.009	1.275

Note: n means the number of cases with a value.

. Variance Inflation Factors (VIFs) were used to evaluate the severity of multi-collinearity in regression analysis. The VIFs for the independent variables are below 5, suggesting that the estimations are free from severe multi-collinearity. Since the White test for heteroscedasticity rejects the null hypothesis of no heteroscedasticity, clustered standard error was used in estimation to correct the effect of heteroscedasticity.

The least-squares dummy variable model (LSDV) was used to determine if pooled regression is acceptable in the panel regression model. The result shows that most province dummy variables are significant where the p-value of the t-test of 26 in 31 province dummies is below 0.1, indicating that it is necessary to estimate a separate constant for each province. That is to say, the individual fixed effect model is more suitable than the pooled regression model. Then, an over-identification test was used to determine whether the fixed effects model or the random-effects model is appropriate. The result with a p-value of 0.00 rejects the null hypothesis that individual heterogeneity is unrelated to independent variables, indicating that random effects are inappropriate. Thus, an individual fixed effects regression model (Equation (3)) was adopted to analyze the effect of investment, price, transmit, and consumption on wind power production.

Logarithms are taken for all continuous variables to reduce the influence of extreme values and heteroscedasticity.

Table 5. Panel regression estimation result.

Model	(1)	(2)	(3)
	Full samples	Group 1	Group 2
Variables	windpower	windpower	windpower
lnperinv	−7.014 *	−5.519	0.030
	(−1.898)	(−1.213)	(0.011)
lnprice	−86.878 ***	−57.449	−67.455 *
	(−3.240)	(−0.656)	(−2.035)
lntransmit	15.108 *	−30.306	9.841
	(1.746)	(−0.993)	(1.485)
lnconsume	196.999 ***	504.923 ***	143.522 ***
	(3.215)	(3.953)	(3.610)
lnCO2	137.943 ***	27.828	52.838
	(2.770)	(0.314)	(1.146)
lnperGDP	−40.839	73.172	−0.657
	(−0.955)	(1.076)	(−0.018)
Constant	−1218.600 ***	−3799.555 **	−954.920 **
	(−2.789)	(−3.345)	(−2.399)
Observations	193	45	148
Number of regions	30	7	23
R-squared adjusted	0.689	0.884	0.581
F	17.251	4285.610	8.632
F test	0.000	0.000	0.000

Notes: Clustered standard error is used in estimation to correct the effect of heteroscedasticity. Statistical significance at the 1%, 5%, and 10% levels are indicated by ***, **, and *, respectively.

summarizes the regression results for the factors influencing the production of wind power electricity. When we take 31 provinces in China mainland as cases, it can be found that almost all variables and control variables except for lnperGDP all have a significant impact on the wind power electricity (Model 1).

The estimated coefficient value of variables lnperinv, lnprice, and lnperGDP are all negative, which means they have the opposite impact on wind power electricity production. The variable lnperinv is the value of investment per installation of wind power generation, which indicates the cost of the wind power plant. The higher the value, the higher the costs for wind power plant hardware, which is not conducive to wind power development. Thus, its coefficient value is negative. The variable lnprice is the price of wind power generation plants on-grid showing the market competitiveness of wind power electricity. As new renewable energy, the cost of researching and developing wind power energy is higher than that of traditional energy, leading to the higher price of wind power generated electricity compared to traditional thermal or hydropower generation. The higher price means the lower competitiveness of wind power in the local or national electricity market and negatively impacts wind power production. On the other hand, the coefficient of variable GDP per capita is negative and not significant, unlike that reported in related literature. We think the main reason is that the location of wind energy resources mainly drives the development of wind power in China. According to the above context, Chinese wind energy resources are mainly concentrated on the Three Norths belt and coastal belt, influencing Chinese wind power plants to be mainly located not only in the provinces in northeast, north, and northwest with relative lower GDP per capita but also in East China coastal provinces with relative higher GDP. This spatial pattern weakened the impact of GDP on the spatial distribution of wind power electricity.

The estimated coefficient value of variables lntransmit, lnconsume, and lnCO₂ are all positive, meaning they have a co-promotion effect on the growth of wind power generation. The variable lnconsume is the size of regional electricity consumption, and it is the most significant factor impacting wind power development in this model. Its estimated coefficient value is the highest of all the variables and significant. Regional electricity consumption reflects the size of the local electricity market. The higher the value, the higher the absorbing capacity to regional wind power generation. The CO₂ emission per capita is a policy variable that indicates the region with higher CO₂ emission will have stronger motivation to develop cleaner energy aimed at its carbon summit or neutral objects. Thus, the coefficient value is positive and significant. However, the provinces with abundant wind power energy in China are those with abundant coal resources, making them important regions of CO₂ emission reduction. Thus, it possibly led to a higher coefficient value. The variable lntransmit is the sum of power transmission in and out of a province, indicating the size of the provincial electricity grid-connected with that of other provinces. The higher the value means the province could send more electricity out. Since most wind power plants are located in the northwest and north of China, where there are abundant coal resources and low local electricity consumption, the local grid could not accept so much wind power generation and needs to transmit it to other provinces with insufficient local power supply. Then, it makes the power transmission capacity one of the significant factors influencing wind power development.

It can be found that the order of these variables from highest to lowest is consumption, CO₂ per capita, price, GDP per capita, transmit, and investment per installation, comparing the absolute value of their coefficients. Obviously, the local market and political factors are the main influencing factors to renewable energy development, while the economic factors are the relatively minor influencing factors showing that, to a large extent, China′s renewable energy development, especially the spatial difference in wind power development, is macroscopically affected by resource distribution and regional absorption capacity. The impact of price, unit investment cost of wind turbines, and regional power grids′ transmission capacity is relatively limited.

3.4. Difference of Influencing Factors in Different Regions

To understand the regional difference of influencing factors on the development of wind power generation in China, we take the group regression taking wind resource zoning into the model to study the estimation coefficient in different zones. The models (2) and (3) in Table 5 show the regression results of the two groups, respectively.

As for wind power abundant Group 1, local electricity consumption is only one significant factor because of the small number of cases. The estimated coefficient value is positive, meaning the larger local electricity consumption will lead to a better market environment for wind power development. As for Group 2 provinces without sufficient land wind power resources, the price of unit generation installation is another significant variable to wind power development. As for this group, much wind power was consumed locally, so the local electricity consumption market size and price have been the deciding factors for local wind power production.

4. Discussion

From the beginning of the 1990s to the large-scale development after 2010, wind power installed in China has gradually expanded from small-scale point demonstrations to large-scale giant wind farms. Many million-kilowatt wind farms have been planned and constructed in Inner Mongolia, Gansu, Xinjiang, and other provinces in Western China. The establishment of these wind farms has effectively promoted the carbon emission reduction of China′s electricity production. Based on the above model analysis, we think technical–economic factors, local accepting ability, and general social and environmental factors are the main driving forces influencing Chinese wind power development and spatial distribution.

4.1. Technical–Economic Factors

In the perspective of technical and economic fields, the cost and price are two main influencing factors for the wind power industry development.

Renewable energy projects are extremely capital-intensive since the infrastructure, start-up, and operation costs are higher than fossil fuels [28,29]. Especially, the amount of initial cost accounts for a high proportion of the whole cost of the wind power project. Before 1990, China′s wind power equipment mainly relied on imports, which were expensive and small-scaled. Pushed by the trend of global energy green and low-carbon transition, China has strengthened renewable energy development in the twenty-first century. A key base of such rapidly increasing wind power installed capacity is the growth and synchronization of the wind equipment manufacturing industry. China’s central government issued policies to promote the independent innovation of key energy equipment technology research, test demonstrations, and popularization and application. As a result, China′s wind turbine manufacturing has formed a relatively complete ‘production, marketing, transportation, installation, operation and management, maintenance and overhaul’ industrial chain. The output of wind power equipment ranks first globally, driving the decline of wind power generation costs. In 2014, the price of Chinese mainstream models (1.5 MW turbines) was reduced by 35% compared with the price in 2009 [30]. In 2019, the average price of unit turbines had decreased to 2600 CNY/kWh from the level of 5000 CNY/kWh in 2009. Thus, the decreasing investment in unit installation facilitated the rapid development of the wind power industry. Furthermore, there is a small spatial difference in the price of wind turbines in the different provinces because of domestic market competition. Therefore, the variable of investment of unit installation is not significant in the group models.

Compared with wind power equipment, the price of wind power electricity on-grid is regulated strictly by the government and differentiated spatially. Since the 1990s, China’s wind power industry has been accompanied by policy support and power system reforms. As a result, on-grid electricity prices have undergone complete on-grid competition, approval of electricity prices, coexistence of bidding and approval of electricity prices, bidding and approval methods, fixed benchmark electricity prices, and competitive electricity prices [27,31]. Especially after the implementation of benchmark electricity prices after 2009, the wind power industry has entered a stage of rapid growth thanks to state financial subsidies. The mainland is divided into four types of wind resource zones and approved the corresponding benchmark on-grid tariff. All new onshore wind power projects shall be uniformly implemented in the wind power benchmark on-grid tariffs in the wind energy resource areas where they are located (Table 1). It is stipulated that the on-grid power price for wind power projects includes two parts, the desulfurization benchmark price and the green power subsidy. The portion of the on-grid power price within the benchmark on-grid tariff of the local desulphurization coal-fired units will be borne by the local provincial power grid and adjusted with the benchmark on-grid power price of the desulfurized coal-fired units. The higher part tariff out of the local desulfurization coal-fired units is subsidized by the surcharge of renewable energy levied nationwide.

This policy led to the uniform price of wind power generation in the same zones and facilitated the healthy competition of the wind power industry. Additionally, the central government adjusted the benchmark on-grid tariff of wind power of every zone annually with decreasing trends. The decline in wind power prices has also forced some companies to focus on reducing project costs and improving the efficiency of stock units. Further, the difference in price between different zones impacted the distribution of wind power projects. The geographical distribution of wind power investment showed the trend of concentration at two ends. On the one hand, many enterprises concentrated on large-scale wind farms in the Three Norths regions with abundant wind resources and lower land costs. On the other hand, some enterprises focused on distributed small-scale wind plants in the central and eastern provinces where wind resources are relatively good and the regional power demand is high. Thus, it makes the variable of the price a significant factor in the Group 2 model.

4.2. Local Demanding Ability

The development of renewable energy could be promoted from the production side; it also should be promoted from the demand side. Local electricity consumption and inter-provincial transmission are two important factors for the growth of renewable energy.

Local power consumption contributes greatly to the absorption of wind power. The dispatch of power resources and load and the larger-scale concentrated development mode makes China have the large-scale inter-provincial power transmission, unlike the scattered distribution and local accommodation mode of European countries. China’s wind power is normally on-grid with higher input voltage and transmitted to the load center through a long-distance and high-voltage channel [32]. In provinces rich in wind power resources, the construction of UHV power grids can provide a stimulus for the interprovincial consumption of wind power electricity [33]. However, regarding the grid integration due to the variability and uncertainty in the output of renewable energy generation, a large amount of curtailed electricity exists, which means some of the renewable energy generations must be wasted to keep the real-time balance between load and generation in local power system [34]. During 2009–2015, nine 10GW-level wind power bases were planned and constructed mainly in Gansu, Xinjiang, Inner Mongolia, and Jilin. However, because of safety, technology, grid access management, etc., the problem of wind curtailment and power rationing has gradually increased. In 2015, the national average wind curtailment rate exceeded 15%, and the wind curtailment rate in Gansu, Xinjiang, Jilin, and other places exceeded 30% [35]. Curtailment of electricity negatively impacts renewable energy development because it is causing the waste of energy and economic loss of wind power generation plants.

Therefore, if the regional grid has a higher power transmission capacity with other grids, wind generation could be consumed in other provinces. The Chinese Government constructed several Ultra High Voltage (UHV) transmission lines to address the serious wind curtailment problem. In 2017, 190 TWh of electricity generated from renewables was transmitted by the UHV lines, accounting for 63% of the total electricity transmission [36]. Additionally, it helps to decrease the price of wind power integration cost and strengthen its market competitiveness.

4.3. General Factors

The link between GDP and renewable energy is based on the environmental Kuznets curve (EKC) theory, which shows an inverted U-shaped relationship between income and environmental indicators. In other words, with the growth of income per capita, the pollutant per capita is increased at first, then it reaches the summit point at a special income level. Further, the pollutant is decreased with the continued growth of income, implying that only a certain income level has been reached to underpin cleaner or greener economic development and pollutant treatment. Given that renewable energy is an environmental and technical product, the higher regional income could underpin the construction and development of wind power generation plants and afford the relatively high wind electricity price. Many related studies have also proved that economic growth or GDP level positively impacts renewable energy consumption in the long term in China [29].

The significant role of renewable energy associated with environmental concerns has inspired much attention in the literature to empirically explore the linkage of renewable energy consumption with pollutant emissions, particularly carbon emissions. Researchers have found a high relationship between two variables at the country level based on the hypothesis that the country with higher CO₂ emission will bear higher international environmental pressures to develop renewable energies. However, the relationship is different for different country groups at different development stages or income levels [37]. As for the provinces of China, the spatial differences of CO₂ emission per capita are mainly related to the energy structure, technological level, industrial structure, and economic development. Especially, the difference in energy structure was mostly caused by energy endowment that could not be changed. Thus, renewable energy development was more driven by the national government’s energy transition plan than by the local government’s environmental pressures. Why is there a significant positive relationship between CO₂ emission per capita and wind power generation? We think it is because the province with higher CO₂ emissions is mostly located in the west and north of China, with abundant coal and wind resources.

5. Conclusions and Policy Implications

Compared to relative literature putting more emphasis on GDP level and CO₂ emission factors and mostly from the consumption perspective, in this paper, we build a regression model on the influencing factors of wind power generation of China from a production perspective. We innovatively take the cost, price, size of the local electricity market, power transmission capacity, regional economic levels, and environmental pressures into the model. Our study proves that Chinese wind power development is a combination of various factors. Especially, our analysis demonstrates that the local market and political factors are the main influencing factors to renewable energy development, while the economic factors are the relatively minor influencing factors. While in the same resource-endowment regions, the main influencing factors are the local electricity consumption market and price. These two factors are all related to the profitability of wind power enterprises, which means the Chinese wind power industry has entered into the market economy era. Then, to facilitate the healthy development of wind power in China to reach the national aims of Carbon Neutrality, we put forward the following policy suggestions.

First, the regions with abundant wind power industry should greatly improve the local consumption capacity of renewable energy. Measures that could be taken include promoting the popularization of household distributed power stations via the establishment of business models, encouraging the consumption of surplus wind power via a negative electricity price similar to the German electricity market trading mechanism [33], and establishing demonstration projects for wind power heating, especially in northwest regions where the winter is cold and long.

Second, greater measures to reduce regulatory barriers and improve the system and grid integration of variable power resources, especially to build more cross-regional power transmission channels to deliver wind power to the power load center in the eastern part of China [22]. Compared to thermal power and hydropower, wind farms are mostly located in remote areas, so additional transmission lines need to be built for their on-grid connections. Moreover, with the increasing wind generation, the integration cost and the grid connection cost become more significant and should not be neglected [36]. Additionally, it brings higher costs and less profit to power grid corporations. Therefore, reasonable incentives should be provided for power grid corporations to motivate them to support renewable power integration.

Third, formulating a green institutional environment and planning the promulgation of different types of policies to support the development of wind power, including financial investment, green credit, and environment tax, which will affect the level of renewable energy investment with guiding renewable energy investment and consumption to facilitate the development of wind power industry [38]. Internalizing the carbon emission cost of coal generation is a promising approach to achieve the grid parity of wind generation [36]. According to the national plan, Chinese wind power will step into the stage of grid-parity time soon. It depends on the generation cost of different technologies and is affected by government policies [36]. The wind power generation costs have decreased greatly in the past decade, while the transmission, integration, and storing costs still need to be considered. Additionally, improved financing conditions with greater stakeholder consultation during policy design and development institution participation in reducing off-taker risks and concessional financing will contribute.

Furthermore, developing appropriate regional power production structure and renewable energy production-storage and transportation modes according to local conditions. Given that the provinces in the Chinese Northwest are endowed with coal resources, it should promote the integrated development of wind, solar, hydropower, fire, and nuclear generation to enhance the peak adjusting capacity of the regional power grid on the one side. On the other side, it should promote renewable energy development with energy storage and large-scale and concentrated wind power development with distributed development.