1. Introduction
With the need to reduce global carbon emissions and mitigate climate change, the development of renewable energy (RE) has aroused widespread interest because it offers multiple benefits, such as improved energy security, enhanced technological competitiveness, and reduced greenhouse gas emissions [
1]. At the Paris climate conference in 2015, China proposed specific carbon emission reduction targets, committing to reducing its carbon emission intensity by 60–65% by 2030, and a series of emission reduction plans have been formulated to achieve this goal [
2]. To this end, China has begun to prioritize the use of RE, and its energy system is developing in a clean and low-carbon direction [
3].
Compared with other types of RE, the technologies employed in wind power (WP) generation are mature and broadly available at a lower cost, making WP a significant source of RE [
4,
5,
6]. WP plays an essential role in promoting China’s green energy transformation and reducing carbon emissions [
7,
8]. Nevertheless, with the large-scale development and widespread use of WP, problems related to WP consumption, large-scale grid connections and excessive investment are starting to appear in China, increasing concerns about the prospects for WP utilization and representing a bottleneck in the sustainable development of China’s power system [
1,
9,
10,
11]. Notably, during the period 2014–2015, the installed capacity of WP achieved a steady growth, but the amount of WP generation declined to a greater extent (
Figure 1). Specifically, the average utilization hours of WP in 2014 were only 1905 h, a decrease of 120 h compared to 2013. Moreover, it achieved a continuous decline in 2015. This phenomenon may be due to the fact that Jilin and Gansu provinces, with enormous wind energy resources, have not effectively solved the problem of WP curtailment, and there are still insufficient WP transmission channels [
12]. At the end of 2014, the relevant government departments raised the feed-in tariff of WP, and applicable policies were implemented in 2016, which led to excessive investment in wind farms (WFs). Although WP generation has gradually increased, it still lags behind the development of WP installed capacity [
11]. As of 2020, the WP installed capacity accounted for 12.79%, while WP generation accounted for 6.12%, which means that nearly half of the wind turbines were wasted (
Figure 1). In this study, WP efficiency is defined as the difference between the actual power generation and the maximum power generation output in a specific province under a certain level of production input (WP installed capacity). WP efficiency falls into the conceptual category of relative efficiency, focusing on the distance between a province and the region with the best WP development. The data envelopment analysis (DEA) model is used in this research to measure the relative efficiency, reflecting the development of WP at the provincial level in China.
Furthermore, China’s provinces have different wind energy resource endowments, and WP construction differs significantly among provinces. Specifically, there is a certain degree of regional heterogeneity in the construction of WFs, leading to different WP development characteristics in various provinces [
13,
14]. Under these circumstances, exploring the geographical differences and spatial distribution of WP efficiency among provinces and accurately identifying the degree of influence of key factors are of fundamental importance to the Chinese government’s ability to implement a low-carbon and clean-energy strategy, which is crucial for the country’s efforts to achieve its carbon emission reduction targets and fulfil its international responsibilities.
Because WP is highly valued and is being actively developed, the study of WP efficiency calculation and its influencing factors has begun to attract an increasing number of researchers, who believe that improving WP efficiency is key to achieving carbon emission reduction goals and the sustainable development of the energy system. At present, most of the literature has focused on WFs, the operation of the WP industry and WP generation. Wu et al. [
5] assessed the efficiency of large-scale WFs and found that development was at an acceptable level but that approximately 50% of WFs had an excessive investment. Sağlam [
15] evaluated the production efficiency of WFs by employing the DEA and Tobit models in Texas and proposed that the technical level of wind turbines could have a significant impact on improving the efficiency of WFs. Iglesias et al. [
16] measured the production efficiency of a group of WFs from 2001 to 2004 and made recommendations for the efficient operation of WFs. Niu et al. [
17] evaluated the location of wind turbines in Chinese WFs, explored the relationship between efficiency and environmental variables and proposed that a rational environment can improve the efficiency of wind turbine production. Pieralli et al. [
18] analysed WFs’ efficiency by the nonconvex method in Germany and found that most losses in efficiency arise from changing wind conditions. Ederer [
19] applied DEA to assess offshore wind energy’s capital and operational efficiency and determined the best cost frontier. Li and Wu [
20] analysed the impact of financial support on WP efficiency based on the DEA-Malmquist index and proposed that the financial investment in China’s WP properties has a low success rate. Papież et al. [
21] assessed WP efficiency in the EU, focusing on the impact of policy measures on the efficiency value, and proposed that the implemented RE economic policies can effectively improve the efficiency of WP production. Sağlam [
22] evaluated the WP efficiency in 39 states in the US and found that WP is effective in more than half of the states, and it was proposed that investment policies and the technical level of WP equipment can affect WP efficiency. Pan et al. [
13] measured the efficiency value by DEA and symbolic regression to analyse the degree of influence of selected factors, and they found that there is a large discrepancy in the WP efficiency among areas in China and that factors such as geographic location, technological progress and carbon regulation can affect WP efficiency. Using an approach derived from the improved Super-SBM and LSTM network models, Li et al. [
3] measured and predicated the employment potency of WP in 30 regions in China and found that the general utilization potency of WP is low, with square measure regional variations; however, low-efficiency areas have greater potential for improvement.
The above studies have provided valuable insights into WP efficiency, but some problems must be further discussed. Most previous studies have advocated that the regions are independent and that there is no significant spatial correlation. Therefore, when traditional regression models are used to explore the degree of influence of different factors on the efficiency value, the impact of spatial factors is rarely considered. In other words, there is insufficient research on the spatial distribution characteristics of provincial WP efficiency or the influence of spatial factors. This deficiency could lead to certain deviations in the research results and a lack of regional specificity in the formulation of policies. To compensate for the lack of research on the spatial distribution of WP efficiency and the spatial effects of influencing factors at the provincial level, we explore the factors that affect the WP efficiency from the perspective of spatial spillover effects and increase the influence of spatial factors considering traditional regression models. The results of this study can provide approaches for accurately identifying the influencing factors of WP efficiency and narrowing the gaps among provinces. Moreover, the results can provide practical suggestions and references for national policymakers and implementers.
The remainder of the paper is arranged as follows.
Section 2 presents the research methods and data. Specifically, the DEA model and the spatial econometric model are used to measure the WP efficiency and the spatial spillover effects of the influencing factors. The description of the data mainly details the variables considered and the data sources.
Section 3 shows the results and provides a discussion.
Section 4 presents the conclusions and proposes corresponding policy suggestions.