1. Introduction
At present, China’s economy is in a critical period of high-quality economic development. Maintaining high-quality economic development necessitates improving the efficiency of resource allocation, deepening economic restructuring and increasing the proportion of economic growth accounted for by green growth. Traditionally, a mode of economic growth that involves high energy consumption, high pollution, and high input is gradually restricted by environmental protection, carbon dioxide emissions, and a shortage of energy resources. At the same time, problems such as resource mismatch in the factor market also plague high-quality economic development [
1,
2]. The existing research results show that there are widespread distortions in the factor market in China, and the marketization process of the factor market lags behind the marketization process of the product market. Additionally, the marketization process of factor markets is inconsistent between different regions [
3,
4]. Some scholars, such as Lin and Du (2013) [
2], believe that China’s factor market distortion inhibits the improvement of energy efficiency to a certain extent, but is this in line with China’s current factor market goals? Compared with the single-index energy efficiency of the ratio of energy consumption to total economic output, is the impact of factor distortion on multi-index energy technology efficiency consistent?
According to World Bank estimates, China’s energy intensity (energy industry chain consumption per unit of GDP) was reduced by 19.1% (the goal was 20% in “The Eleventh Five-Year Plan”) (“The Twelfth Five-Year Plan” was mainly the twelfth five-year plan outline formulated by the Chinese government for economic and social development. It was a grand blueprint for China’s economic and social development from 2011 to 2015. “The Eleventh Five-Year Plan” period was from 2006 to 2010, “The Tenth Five-Year Plan” period was from 2001 to 2005, and “The Thirteenth Five-Year Plan” period was from 2016 to 2020.) and 18.2% (the goal was 16% in “The Twelfth Five-Year Plan”), respectively, during “The Eleventh Five-Year Plan” and “The Twelfth Five-Year Plan”, while the government has set an energy intensity target of 15% in “The Thirteenth Five-Year Plan” (2016–2020). At the same time, China has pledged to achieve peak carbon emissions by 2030, which requires China to pay attention to environmental pollution issues such as carbon dioxide emissions, while deepening the reformation of the market economic system and promoting economic development.
Therefore, this study focuses on the impact of factor market distortions on energy technology efficiency, and the impact of carbon emissions on heterogeneity. The clarification of the above problems will aid our understanding of factor market distortions and the relationship between carbon dioxide emissions and energy technology efficiency in the context of China’s commitment to achieving peak carbon emissions by 2030. Additionally, reducing factor distortions and carbon dioxide emissions to achieve high-quality economic development has important theoretical and practical significance. In view of this, this paper will discuss the far-reaching impact of resource allocation as represented by factor distortions, and environmental protection as represented by carbon dioxide emissions from the perspective of energy industry chain technical efficiency.
2. Literature Review
The energy industry chain technical efficiency (TE) is a measure of the relationship between the actual and potential economic output in the energy industry chain, and involves energy exploitation, energy processing, energy supply, etc. [
5,
6,
7,
8,
9,
10]. Its economic definition is: how much energy input can be saved if the output remains unchanged; or under the condition of constant input, how much economic output can be increased? Energy technical efficiency puts the whole industrial chain in an “input–output” framework to evaluate the technical efficiency of the whole industrial chain [
11,
12]. Beatrice and Simone (2017) [
3] called for the expansion of traditional energy efficiency research to industrial and supply chains in future research, and also pointed out that the original efficiency evaluation methods are still applicable in industrial chain efficiency analysis, but the analysis needs to be deeper. However, for now, this kind of research is scarce. One example is the work of Dong et al. (2021) [
13], which applied a three-stage DEA method to evaluate the efficiency of the upstream, middle, and downstream areas of China’s wind power industry chain.
In view of this, we need to find theoretical and methodological references from a large number of energy technology efficiency studies. The pioneering contribution to the study of technical efficiency should be attributed to Farrell (1957) [
14], while the research and analysis of energy technical efficiency comes from Hoffmann (1982) [
15] and Bengtson (1983) [
16]. The existing studies on energy technology efficiency are mainly divided into the following categories:
The first kind studies the spatiotemporal differentiation characteristics and evolution law of technical energy efficiency. This kind of study mainly combines different individuals in different industries and regions. For example, from the existing literature on energy efficiency, the overall energy efficiency estimated by some scholars is about 60% to 70% [
17]. Additionally, an “inverted U-shaped” trend has been observed [
18]. Most scholars believe that there are obvious regional differences in energy efficiency in China, which are characterized by regional imbalances [
8,
17,
18,
19,
20], and there are also large inter-provincial energy efficiency differences in China [
20]. Although the DEA model has the advantage of not considering the production function, it does not consider the error. When estimating the operating efficiency of an energy industry chain with a heterogeneous production mode, not considering the error may cause obvious measurement errors [
21]. Therefore, the SFA model is favored in the research on energy efficiency estimation. Hu and Honma (2014) [
22] estimated total-factor energy efficiency (TFEE) scores for 10 industries in 14 developed countries for the period 1995–2005 using the stochastic frontier analysis (SFA) technique. They found that most of the OECD industries have much room for improvement in terms of their total-factor energy efficiency. Haider and Mishra (2021) [
23] estimated the energy efficiency, and quantified the energy-saving potential of Indian iron and steel firms. The results show that energy efficiency slightly declined over time. The SFA method has been widely used in energy efficiency evaluation and has the advantage of paying attention to statistical errors. From the perspective of the industrial chain, the error problem exists objectively, and must be paid attention to, which is why the original intention of this study was to measure energy efficiency via the SFA method.
The second category sorts out the factors that affect the energy technology efficiency. The first is institutional heterogeneity, which involves opening to the outside world and environmental regulation. Representative studies include those by Wei and Shen (2007) [
24], Mandal (2010) [
25], and Bi et al. (2014) [
26]. Opening to the outside world can introduce relevant technology from developed countries, especially through innovation that improves the level of energy technology and management methods, to improve energy technical efficiency [
27]. Its mechanism involves the introduction of foreign capital, and the positive spillover effect in the management system and technical level can improve energy efficiency. However, due to the existence of a “pollution paradise”, the level of opening may aggravate the consumption of energy resources [
26], subsequently reducing energy efficiency; that is, opening to the outside world has a negative impact on China’s energy efficiency [
24,
27]. Environmental regulation mainly means that the government gives full play to the “promising government” and the “visible hand”, which aim to target the shortcomings of a market economy, such as negative externalities and information asymmetry, and limit unreasonable energy consumption by controlling environmental pollution so as to improve and enhance energy technology efficiency [
25,
26]. The second is the market structure. The existing literature believes that the market structure is an important factor affecting energy technology efficiency [
27,
28,
29]. Market structure factors mainly affect energy technology efficiency in two ways: on the one hand, by expanding investment in the energy industry, and then increasing resources and energy consumption, which may lead to more serious pollution problems [
8,
9]; on the other hand, by increasing energy consumption through changes in the industrial economic structure and the reallocation of resources and factor demand, which makes use of technological innovation to have a positive effect. Wei and Zheng (2017) [
30] found that market segmentation suppresses energy efficiency by affecting technical efficiency, scale efficiency, and allocation efficiency. The third is factor distortion. Lin and Du (2013) [
2] used Wang and Ho’s (2010) [
31] stochastic frontier fixed effect model to analyze the impact of factor market distortions on energy efficiency in China from 1997 to 2009. The results show that factor market distortions had a significant negative impact on China’s energy efficiency; eliminating factor market distortions can improve energy efficiency by an average of 10% per year. The fourth is environmental pollution. Li and Zhang (2019) [
32] proposed that air pollution increases enterprise environmental costs and enterprise production costs, distorts factor redistribution efficiency [
1,
27], and reduces productivity. In addition, environmental pollution crowds out the R&D expenditure of enterprises and reduces the level of R&D [
33], which is not conducive to improving technical efficiency. The fifth is other factors. For example, Fu et al. (2021) [
34] and Chen (2018) [
35] analyzed the impact of government size, added value of secondary industry, urbanization rate, and openness on China’s energy consumption and output efficiency.
By combing the relevant literature, we found that the existing research on the impact of factor distortion on energy technology efficiency has made some progress, and that systematic research has led to a certain consensus. However, the model assumptions used in the current energy technical efficiency analysis literature are based on the optimal production scale. Due to the problems of market information asymmetry, imperfect competition and resource and environmental constraints, the assumption of an optimal production scale cannot be established completely, so there is room for improvement in the research model. In addition, there is still no consensus on whether reducing the degree of factor market distortion can improve energy efficiency or energy technology efficiency, especially in the context of carbon dioxide emission restrictions. It is worth noting that some authors face deficiencies when discussing how to reduce the degree of factor distortion and improve efficiency. Lin and Du (2013) [
2] took Shanghai as the benchmark, and set the degree of factor market development in all regions and at all times to the level of factor market development in Shanghai—that is, there is no relative distortion in the factor market—and then set the value of the factor market distortion variable to 0. Although this method can help us to analyze the counterfactual impact of factor distortion to a certain extent, it is also subject to its factor market hypothesis. In addition, there are few studies on the interpretation of energy technology efficiency from the perspective of industrial chains.
In general, there are some areas where further research on the SFA model needs to be done, including: (1) The lack of heterogeneity factors. Although different variables are considered in the existing literature, they are only considered as general variables in the practical application of the model. In this study, a heterogeneous stochastic frontier model is introduced to construct an analytical framework. (2) The heterogeneous SFA model is more suitable for the problems of incomplete competition and resource and environmental constraints, and deeply decomposes the chain technical efficiency of the energy industry. The main innovations of this study include the following: (1) Based on the fact that there are widespread distortions in China’s regional factor markets and that the marketization process of factor markets in different regions is very inconsistent, the Greene (2005) [
36] panel heterogeneity stochastic frontier model is used to empirically study the relationship between factor market distortion, carbon dioxide emissions and energy technology efficiency, and to deeply decompose the energy technology efficiency, which broadens the research scope. It is a useful supplement to the existing research. (2) The counterfactual analysis method of Chernozhukov et al. (2016) [
37] is introduced to analyze the counterfactual impact of factor distortions and carbon dioxide emissions on China’s energy technology efficiency, without setting specific values of factor market distortions and carbon dioxide emissions.
5. Conclusions and Policy Implications
There is a basic consensus that the process of factor marketization lags behind the process of product marketization in China, and that the agreement to achieve peak carbon emissions by 2030 means that economic growth will face serious environmental constraints. This paper briefly analyzed the impact of factor market distortion and carbon dioxide emissions on economic growth using Greene’s (2005) [
36] heterogeneity stochastic frontier analysis model. Then, we evaluated China’s energy industry chain technical efficiency under the influence of factor distortion and carbon dioxide emissions. Finally, the counterfactual measurement method was used to calculate the factor market distortion and the energy industry chain technical efficiency loss of carbon dioxide emissions. The main conclusions include the following: (1) Factor market distortion and carbon dioxide emissions are indeed the main sources of energy industry chain technical efficiency loss. (2) The overall energy technical efficiency is 0.959 in China, and the average value of energy technical efficiency by region from highest to lowest is east (0.961), central (0.957), northeast (0.955), and west (0.950). Among them, the space for efficiency improvement is 3.6377%, 4.5151%, 4.7669% and 5.2521%, respectively. (3) Although energy technical efficiency is subject to market factors, the structural factors caused by sustainable efficiency are more obvious. (4) In the situation of factor market distortion and carbon dioxide emissions, China’s energy industry chain technical efficiency slowly increased from 0.952 in 2000 to 0.964 in 2016. By reducing the degree of factor market distortion, China’s average energy industry chain technical efficiency could increase to 0.9651 from 0.9649, showing an improvement of 3.6162%.
From the perspective of the evolution of energy technical efficiency, this paper confirms that the implementation of energy-saving measures through fiscal policy during “The Eleventh Five-Year Plan” period improved the energy technical efficiency in this period to a certain extent, but there is an obvious lag in fiscal policy. As a result, the energy technical efficiency began to decline during “The Twelfth Five-Year Plan” period. This shows that in addition to playing the role of the “promising government” and “visible hand” in improving energy technical efficiency during “The Thirteenth Five-Year Plan” period, it is also necessary to speed up the market-oriented construction of factors and improve the allocation of factor resources. Specifically, we should first carry out the network reform of the main body of the energy market, carry out the reform of the whole industry chain from the upper and lower reaches of the energy market, deepen the reform plan of the oil and gas system, and speed up the flow and reallocation of factors; secondly, in the case of limited carbon dioxide emissions, we should consider regional heterogeneity and then divide the carbon emission quotas of the different main bodies of the energy market to avoid the excessive gradient transfer of the energy market and the excessive convergence of the energy industry. Then, based on the regional heterogeneity, especially the characteristic facts of energy resource endowment and factor market development in different regions [
8], we should develop a set of timely and feasible dynamic mechanisms to mobilize the enthusiasm for factor flow and adjust the adverse impact of carbon dioxide emissions on the development of the energy industry. In this way, we can reduce the inequality between economies and energy entities caused by distorted factors, and carbon dioxide emissions in different regions.