How Energy Price Distortions Affect China’s Economic Growth and Carbon Emissions

Abstract

China’s energy market reform is characterized by “asymmetry” as a result of the transition from a planned economy to a market economy, leading to typical distortions in energy prices. Using panel data from 30 Chinese provinces during 2006–2018, this paper examines the impact of the price distortions of fossil energy sources (coal, oil, and natural gas) as well as renewable energy on China’s economic growth and carbon emissions. The results show that coal price distortion is the highest among the fossil energy sources, followed by oil and natural gas. Renewable energy price is positively distorted. Not all price distortions of energy sources significantly impede economic growth—only those of coal and renewable energy. In contrast, oil and natural gas price distortions promote economic growth. All four energy price distortions contribute significantly to the increase in carbon emissions. Further analysis reveals that regional heterogeneity exists in the impact of energy price distortions on economic growth and carbon emissions. Additionally, this study finds that technological innovation, industrial structure upgrading, the investment effect, the optimization of energy consumption structure, and environmental regulations are important transmission mechanisms of energy price distortions affecting China’s economic growth and carbon emissions. The findings of this study can help us to understand the relationship between energy price distortions and China’s economic growth and carbon emissions, and provide a reference for formulating energy price reform policies that benefit the win-win development of China’s economy and environment.

1. Introduction

Energy plays a key role in a country’s economy and well-being, as well as its competitiveness. China’s economic achievements cannot be separated from its massive use of low-cost energy. However, the over-exploitation of energy has led to issues such as energy scarcity and environmental degradation while promoting economic development. Meanwhile, during the transformation from a planned economy to a market economy, China’s energy market reform is characterized by “asymmetry”; that is, factor market reform lags behind product market reform, resulting in severe price distortions [1]. Distorted prices do not accurately reflect supply and demand, leading to inefficient energy allocation, which in turn has a negative impact on China’s economic growth and carbon emissions reduction. The Chinese government has also recognized the harm caused by distorted energy prices, and since the Third Plenary Session of the 18th CPC Central Committee in 2013, it has proposed accelerating energy market reform and letting the market play a decisive role in resource allocation. In 2020, the Chinese State Council issued Opinions on Building a Better Institutional Mechanism for Market-Based Allocation of Factors, which emphasized the urgent need to build a unified, open, competitive, and orderly market system, as well as to improve the market allocation of factors. A series of guidelines imply that finding and resolving energy price distortions has become a significant issue to be addressed in the process of achieving high-quality economic development in China.

Despite the considerable progress made in energy market-oriented reforms since the reform and opening up, energy pricing is still subject to government control [2,3], and energy allocation under government domination is inefficient, implying that energy prices are distorted. Distortions in energy prices occur when the real price deviates from its equilibrium level under a perfectly competitive market, meaning that energy is not allocated optimally. The majority of existing studies use energy as an input factor to measure price distortions [4,5,6]. Since market-oriented reforms of various energy prices in China have not been synchronized, the distortions vary across different energy products. Thus, it is not possible to get a comprehensive picture of the current energy price distortions in China. Moreover, most studies analyze the impact of energy price distortions on only one aspect of the economy or the environment [7,8,9]. As China strives to coordinate economic growth and carbon emissions reduction, the inclusion of energy price distortions, economic growth, and carbon emissions in one research framework is crucial for achieving a coordinated win-win development of high-quality economic growth and carbon emissions reduction from the perspective of market-based energy price reform. Distorted energy prices may indirectly influence economic growth and carbon emissions through other channels such as technological innovation, industrial structure, and so on. Therefore, this paper proposes hypotheses and tests the transmission mechanisms of energy price distortions through an empirical approach. Given differences in energy endowment, economic development, and carbon dioxide emissions in China’s regions [10], this paper incorporates the possible regional heterogeneity of energy price distortions affecting economic growth and carbon emissions into the study, which provides a theoretical reference and practical basis for promoting energy market reforms and formulating energy price policies that meet individual objectives.

Therefore, the contribution of this paper is reflected in the following aspects. First, based on provincial data, this study measures the price distortions of three fossil energy sources (coal, oil, and natural gas) as well as renewable energy prices and analyzes them at the national and regional levels, which provides a more comprehensive and detailed description of the energy price distortions in China. Second, the different impacts of price distortions in various energy sources on economic growth and carbon emissions are examined. Third, besides examining the effects of different types of energy price distortions on economic growth and carbon emissions, this paper also explores the transmission mechanisms through which price distortions influence economic growth and carbon emissions via various pathways such as technological innovation, industrial structure upgrading, environmental regulation, etc., providing a theoretical reference for subsequent studies on energy price distortions. This has not been explored in previous research. Furthermore, regional-level differences in the impact of energy price distortions on economic growth and carbon emissions are examined, which contributes to developing regionally tailored energy price reform policies that facilitate regional economic growth and carbon emissions reduction.

The rest of the paper is arranged as follows. Section 2 provides a review of the existing literature, and the measurement and analysis of energy price distortions are presented in Section 3. Section 4 shows the empirical models and data sources. The estimation and results analysis are given in Section 5. Finally, in Section 6, the conclusions and policy implications are presented.

2. Literature Review

The difference between the actual price of energy and its price under a perfectly competitive market is referred to as the energy price distortion [11,12,13]. Bhagwati [14] proposed the general theory of distortion and welfare, explaining for the first time the causes of distortion formation and its nature. The causes of distortions are twofold: one is endogenous to the market, including market monopolies or failures; the other is exogenous to the market, including government policy interventions. The energy price distortions studied in this paper are mainly policy-imposed distortions, which are caused by government intervention.

2.1. Research on Measuring Energy Price Distortions

According to scholars’ research on measuring price distortions, the following categories of approaches have been developed: first, the factor marketization index method. This method was first used to calculate price distortions of factors such as energy. As energy prices are difficult to obtain, earlier studies used market indices as a proxy for the marketization of factors such as energy. For example, Lin and Du [15] constructed a factor market distortion index based on a provincial product marketization index, a factor marketization index, and an overall marketability index. Since the above indexes are transformed by manual calculations, the results are subject to large biases. Second, is the production possibility frontier approach. Based on this method, Skoorka [16] calculated the distortions in product and factor markets. Although the factor market distortion is measured, this study fails to distinguish between distortions caused by different input factors. Third, is the shadow price model. It measures the ratio of factor prices under optimal conditions (i.e., cost minimization or output maximization) and compares it to 1. Compared to the first two methods, the shadow price model has the advantage of measuring both the absolute and relative price distortions of input factors. Therefore, it has been adopted by many studies. For instance, Tao et al. [17] employed a shadow price model to assess the factor price distortion of energy in the industrial sector and concluded that energy is second only to labor in terms of distortions. Ouyang and Sun [2] calculated the distorted prices of energy, capital, and labor in the industrial sector in China using a shadow price model and arrived at similar conclusions to Tao et al. [17]. Fourth is the C–D production function, which is widely used to measure price distortions. Similar to the shadow price model, it is a measurement of the deviation between energy factor prices and marginal output. The study by Han and Hu [5] adopted the production function to calculate energy price distortions, suggesting that distorted energy prices inhibited capacity utilization. On this basis, the more flexible trans-log form has gained wide acceptance [18,19] to avoid the unit substitution elasticity that causes estimation errors. In addition, some research focused on the price distortion of a particular energy product. For example, Shi and Sun [8] used China’s gasoline prices to estimate energy price distortions and argued that distortions negatively affected economic growth. The study by Ju et al. [20] calculated three different types of distortions for five energy products in China. Cui and Wei [21], Brown et al. [22], and Yin et al. [23] investigated coal, electricity, and natural gas price distortions.

2.2. Research on the Impact of Energy Price Distortions on Economic Growth

According to classical economic theory, the efficient allocation of resources is the key to economic development, and a perfect market mechanism promotes the full allocation of resources and the smooth flow of factors necessary to make the economy grow. However, distorted price signals hinder the efficient allocation of energy resources and lead to a loss of economic productivity. On one hand, the distortion makes it easier for energy resources to flow to inefficient sectors, resulting in a long-term reduction in the average productivity of the entire industry; on the other hand, distortions prevent scarce energy from flowing to technologically innovative sectors, leading to low energy utilization efficiency. Therefore, some scholars believe that energy price distortions hinder economic growth [8,24]. The theory of the relationship between factor resource misallocation and economic productivity proposed by Restuccia and Rogerson [25] provides a foundation for explaining the effects of energy price distortions on economic growth. A study by Lin and Wang [11] showed that energy prices in China have been at low levels under strict controls, and the measures taken in 2008 that prevented price adjustments for refined oil products and natural gas caused significant damage to the economy. Hsieh and Klenow [26] analyzed international differences in factor distortions and total factor productivity by selecting firm-level microdata for manufacturing in China, India, and the U.S. They found that in the presence of price distortions resources are misallocated, leading to lower productivity and reduced economic growth. Using data on Chinese energy firms, Dai and Cheng [4] found that energy market distortions generate resource misallocation among firms, which inhibits the increase in aggregate productivity in the energy sector. Therefore, removing energy price distortions induces resource reallocation and a significant increase in economic output [27]. Similarly, Tan et al. [19] argue that if price distortions were eliminated between energy, labor, and capital in the secondary industry, total factor energy efficiency would improve.

For most countries, especially large developing ones like China, it can be difficult to achieve economic growth during the take-off phase by simply relying on market forces. At the beginning of the new China, a “catch-up strategy” was implemented, which prioritized the development of heavy industries and incorporated energy as a strategic resource into strict planning. The distortions caused by interventions in energy prices created a comparative advantage for the energy sector in the short term, resulting in rapid economic growth. Ouyang and Sun [2] indicated that distortions transfer the wrong price signals and result in lower energy prices, thereby reducing production costs. Xu and Tan [28] explored energy allocation distortions in three industries in China and argued that in the short run these distortions reduced firms’ production costs and stimulated economic growth. Based on the general equilibrium model (CGE), Lin and Jiang [29] showed that energy subsidies were an important policy tool for regulating prices, and they produced a positive impact on the macroeconomy of China at the early stage of development. Therefore, there is a need for the implementation of subsidies. According to Sun and Lin [30], the government regulation of factor prices could curb excessive energy price hikes and boost short-term economic growth. Using a path analysis model, Ju et al. [20] evaluated how various types of energy price distortions affect economic growth in China, and relative and dynamic distortions were shown to play a facilitating role.

2.3. Research on the Influence of Energy Price Distortions on Carbon Emissions

Distorted price signals cause the inefficient allocation of energy resources, leading to excessive use of high-carbon energy sources, which increases carbon dioxide emissions and creates environmental pollution. Through an analysis of the impediments and drivers of energy efficiency, Reddy [31] observed that distortions in energy prices create a serious impediment to the optimal allocation of energy, which inhibits energy efficiency and leads to environmental degradation. The IEA [32] report suggests that the signals of distortions undermine the rational allocation of energy resources by encouraging excessive energy consumption, thereby exacerbating CO₂ emissions. Ouyang and Sun [33] claimed that distortions caused energy prices to drop and led to the overuse of fossil energy, which decreased energy efficiency and harmed China’s efforts to reduce emissions. Wang et al. [9] found that oil price distortions hindered CO₂ emissions reduction in China’s transportation sector, but removing the distortions significantly reduced carbon emissions by 599 million tons. Liu and Li’s [34] research found that excessive energy consumption caused by fossil fuel subsidies is the main cause of large CO₂ emissions. Moreover, Ju et al. [20] argued that China’s current energy pricing policy actually lowered the energy cost and facilitated the over-exploitation of energy-intensive and low-value-added industries, which was detrimental to carbon emissions reduction. According to He et al. [35], reducing CO₂ emissions can be achieved by removing energy price distortions and promoting pricing mechanism reform and optimization. The above research has mainly focused on the environmental impacts of price distortions in fossil energy and there is little literature on how renewable energy price distortions affect carbon emissions. Most scholars focus on the relationship between renewable energy consumption and carbon emissions. In an analysis of the ten countries with the highest CO₂ emissions, Azam et al. [36] demonstrated that renewable energy consumption contributed to reducing carbon emissions. This conclusion is supported by the studies of Chen et al. [37] and Ike et al. [38]. Nevertheless, Salem et al. [39] concluded that renewable energy consumption had an inverted U-shaped relationship with CO₂ emissions, where it increases CO₂ emissions initially but then reduces them at a later time. Moreover, Khan et al. [40] indicated that the effects of renewable energy on carbon emissions differed between high- and low-income countries.

2.4. The Transmission Mechanisms and Hypotheses

(1)

Analysis and hypothesis of mechanisms for energy price distortions affecting economic growth

Technological innovation is an important tool for enhancing economic growth [41]. Technological innovation can facilitate green technology research and practical application, as well as transform traditional industries and create new markets, promoting long-term economic growth [42]. However, distorted energy prices have a significant inhibiting effect on technological innovation, which in turn may impede economic growth. First of all, distorted energy prices tend to make firms choose cheap energy inputs for greater profits, which discourages technological innovation [21] and therefore inevitably hinders productivity. Second, distorted price signals weaken the allocation of innovation resources in the energy market, resulting in less efficient R&D output, and therefore firms are unable to reap the original benefits of innovation [28]. To balance costs and benefits, firms are more likely to choose rent-seeking behavior rather than high-risk innovation inputs. Furthermore, the government benefits by lowering factor prices and reducing workers’ earnings [43], which stifles people’s innovation and investment in education for future generations, thereby inhibiting the nurturing of innovators and hindering technological innovation.

As an important way to achieve economic growth [44], industrial structure upgrading generally promotes economic growth through various channels such as industrial spillover, division of labor specialization, and factor substitution [45]. Distorted energy prices bring low-cost energy advantages and increase corporate profits but simultaneously lead to a reversal of the industrial structure from high-end to low-end, seriously impeding industrial structure upgrading [46]. Moreover, the distortion increases the sunk cost of enterprise exit, and some non-competitive enterprises that are being eliminated from the industry may survive due to distortion gains [47,48], which hampers industrial restructuring.

As one of the “troika”, investment is the most important driving force of economic growth. The most direct and effective way for the government to achieve rapid economic growth is to increase investment [49]. According to Lin and Chen [50], foreign direct investment can bring advanced production and technology to improve productivity, thereby promoting economic development. However, distortions may also limit productivity gains by inhibiting investment spillovers. By relying on cheap factors, exporters can maintain a price advantage in international trade but lack the incentive to acquire advanced technology and upgrade production to meet the needs of international markets. Simultaneously, firms rely on low-cost factors to expand and lack the incentive to absorb international advanced production experience, high technology talent, and knowledge spillovers. Thus, distortions in energy prices can impede economic growth by dampening the investment effect. Moreover, distortions allow local governments to access cheaper energy resources, expanding the benefits to their users and stimulating investment, but resulting in inefficient investment. In the case of diminishing returns to scale, overinvestment can hinder productivity gains [51]. Another scenario is that distortions result in higher energy prices, which in turn make production more expensive, discouraging investment [52], and ultimately inhibiting economic growth. Hence, the following hypothesis is put forward:

Hypothesis 1 (H1).

Energy price distortions can affect economic growth through technology innovation, industrial structure upgrading, and the investment effect.

(2)

Analysis and hypothesis of mechanisms for energy price distortions affecting carbon emissions

Technological innovation can encourage the development of clean energy technologies, thereby increasing energy efficiency, mitigating CO₂ emissions, and improving environmental quality [42]. However, distortions make energy prices cheaper and have a significant crowding-out effect on firms’ innovative R&D [4,23]. Moreover, distortions greatly reduce the inducement of green technology innovation on energy prices [53], which is detrimental to the improvement of innovation in energy technologies. In addition, distortion depresses the price of energy factor, which is not conducive to cultivating innovative talent in the energy industry, restricts green energy technology innovation, and inhibits carbon emissions reduction.

The optimization of the energy consumption structure is the key to reducing carbon emissions and promoting green economic growth [54]. Nevertheless, distortions cause the inputs of cheap energy to be of low quality [55], resulting in a continuous increase in pollution emissions and is thus a further stumbling block for the upgrading of the energy mix. Second, local governments offer concessions to firms that generate more output and taxes, which allows them to purchase energy with high carbon at excessively low prices [56], thus inhibiting energy mix optimization and discouraging carbon emissions reduction. Furthermore, the theory of the ‘energy ladder’ suggests that as economic status and income levels rise, residents consume more abundant energy sources in their daily lives [57], switching from burning firewood and crop fertilizer to consuming clean energy as if crossing the energy ladder. Nevertheless, distorted energy prices hinder residents’ choice of clean energy consumption, which in turn inhibits the optimization of the energy consumption mix.

According to the Porter hypothesis, environmental regulation has a positive effect on CO₂ reduction and environmental quality improvement in the long run [58]. A series of environmental regulatory policies has been developed by the Chinese government, including increased support for clean energy, the introduction of pollution emission standards, and the establishment of a pollution levy system to address air and water pollution issues. Due to the stringent environmental regulation policies, enterprises must respond to the additional costs of environmental regulation externalities by increasing investment in green technology and innovation, saving resources, and reducing pollution emissions, thus contributing to the reduction in carbon emissions [59]. This is also in line with the trend of sustainable economic development in China. Some studies suggest that price distortions lead to a weakening of the positive impact of environmental regulations on carbon emissions reduction, thus exacerbating CO₂ emissions [60]. As a result of distortions, low-cost energy promotes economic development, while the implementation and effect of environmental regulations will be limited. Therefore, energy price distortions hinder carbon emissions reduction by hindering the impact of environmental regulations. On the other hand, the positive effect of energy price distortions on the increase in carbon emissions diminishes as environmental regulations are strengthened [61], e.g., increased fiscal spending on energy conservation and environmental protection, thus contributing to carbon emissions reduction. Hence, we form the following hypothesis:

Hypothesis 2 (H2).

Energy price distortions can affect carbon emissions through technology innovation, the optimization of the energy consumption structure, and environmental regulation.

In summary, the previous literature has the following characteristics. First, most of the existing literature measures the degree of energy price distortions by embedding it in factors of production, without a careful classification and analysis of different energy products. Second, most studies have shown a single effect of energy price distortions on the economy and environment, without systematically considering the combined effects. Third, given the significant differences in economic growth, CO₂ emissions, and energy endowments across regions in China, it is important to incorporate regional heterogeneity into estimates of the impacts of energy price distortions on economic growth and carbon emissions to formulate policy recommendations suitable to local conditions. Additionally, few studies have examined the transmission mechanisms of energy price distortions. Apart from their direct effects on economic growth and carbon emissions, energy price distortions may have indirect effects via other pathways.

3. Measurement and Analysis of Energy Price Distortions

3.1. Measurement of Energy Price Distortions

The measurement of energy price distortions can assist policymakers in gaining a better idea of the actual prices of energy and laying the foundation for a rational energy pricing mechanism. The current pricing approach ignores environmental externalities, actual values, and scarcity features, whereas the theoretical price is a comprehensive index that incorporates all of these factors. Although the theoretical price cannot be equated with the actual price, it is a useful measure of economic returns and a desirable price reform target. Moreover, the theoretical price is critical for macroeconomic management.

(1)

Price distortions of fossil energy

This paper adopts the marginal opportunity cost pricing method to determine the theoretical prices of fossil energy. As shown in Equation (1).

$$\mathrm{P}=\mathrm{MPC}+\mathrm{MUC}+\mathrm{MEC}$$

(1)

MPC stands for the marginal production cost associated with energy extraction. Lei’s research [62] indicates that access to natural resources necessitates the payment of production expenditures including raw materials, labor, equipment, and electricity. According to this definition, MPC includes all direct input costs incurred during production, as well as some reasonable profits and taxes. Moreover, there are additional costs associated with untapped natural resources, such as exploration costs. A study by Ju et al. [63] stated that the value of MPC is included in the actual price.

MUC represents the scarcity of resources and is closely related to the degree of depletion of exhaustible resources. The “user cost” refers to the cost incurred for current rather than future use. As the user cost of fossil energy reflects intergenerational equity, the MUC is measured based on the fossil energy user cost method proposed by El Serafy [64]. However, MUC is an indivisible entity that is difficult to quantify. Therefore, MUC is measured by an overall indicator. In recent years, China has adopted a series of compensation measures such as different resource taxes and resource compensation fees for exhaustible energy, so the MUC in this paper has two categories: compensated ($\mathrm{MU}{\mathrm{C}}_{\mathrm{com}}$) and uncompensated ($\mathrm{M}\mathrm{U}{\mathrm{C}}_{\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}}$) user costs.

The marginal external cost (MEC) is the cost of damage caused to the external environment and ecology in the process of developing energy resources. The cost of environmental remediation is often borne by society as a whole rather than by the companies themselves, so it is not reflected in energy prices. According to environmental economic policies, companies that cause pollution should compensate for the environmental damage they wreak. In practice, however, existing energy pricing policies fail to achieve this goal, and therefore theoretical prices of fossil energy include all types of MECs. The MEC in this article is also divided into compensated ($\mathrm{M}\mathrm{E}{\mathrm{C}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$) and uncompensated ($\mathrm{M}\mathrm{E}{\mathrm{C}}_{\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}}$) external costs.

Various types of compensation for energy resources are reflected in the real price of energy, as shown in Equation (2):

$${\mathrm{P}}_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{l}}=\mathrm{M}\mathrm{P}\mathrm{C}+\mathrm{M}\mathrm{U}{\mathrm{C}}_{\mathrm{c}\mathrm{o}\mathrm{m}}+\mathrm{M}\mathrm{E}{\mathrm{C}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$$

(2)

Next, substituting Equation (2) into Equation (1) yields the theoretical price of fossil energy in Equation (3).

$${\mathrm{P}}_{\mathrm{t}}={\mathrm{P}}_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{l}}+\mathrm{M}\mathrm{U}{\mathrm{C}}_{\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}}+\mathrm{M}\mathrm{E}{C}_{\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}}$$

(3)

where ${\mathrm{P}}_{\mathrm{t}}$ stands for the theoretical price, ${\mathrm{P}}_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{l}}$ is the real price, $\mathrm{M}\mathrm{U}{\mathrm{C}}_{\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}}$ and $\mathrm{M}\mathrm{E}{\mathrm{C}}_{\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}}$ represent the uncompensated MUC and MEC, respectively, and $\mathrm{M}\mathrm{U}{\mathrm{C}}_{\mathrm{u}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}}$ is obtained from $\mathrm{M}\mathrm{U}\mathrm{C}-\mathrm{M}\mathrm{U}{\mathrm{C}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$. According to El Serafy’s user cost method, the MUC must choose the price in a perfectly competitive market. Nevertheless, energy prices in China are still regulated by the government and state-owned enterprises and thus they do not reflect the true values, so international energy prices are chosen as benchmarks. For example, Japanese boiler coal prices are regarded as international coal prices; Brent crude oil prices represent international oil prices; Japanese LNG prices represent international natural gas prices. All data are from the annual BP Statistical Yearbook and converted to Yuan. The compensated user cost ($\mathrm{M}\mathrm{U}{\mathrm{C}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$) is expressed in terms of resource tax and mineral compensation fee. The MEC data are taken from Ju et al. [63]. The detailed data sources are listed in

Table 1. Data sources for theoretical prices.

Variables	Coal	Oil	Natural Gas	Renewable Energy
$\mathrm{MPC} ({\mathrm{P}}_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{l}}$)	CEIC database; Price Statistical Yearbook	CEIC database; Energy Statistical Yearbook	CEIC database; National Bureau of Statistics	Wind database; National Energy Administration; Energy Statistical Yearbook, Almanac of China Guodian Corporation;
MUC	Annual BP Statistical Yearbook			—
MUCcom	Annual Reports of China Shenhua Energy Company Limited and China National Petroleum Corporation			—
MEC	Ju et al. [63]			Chen [66]

.

After the theoretical price (${\mathrm{P}}_{\mathrm{t}}$) is obtained, the degrees of fossil energy price distortions can be expressed as:

$${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}=({\mathrm{P}}_{\mathrm{c}}-{\mathrm{P}}_{\mathrm{t}1})/{\mathrm{P}}_{\mathrm{t}1}$$

(4)

$${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}=({\mathrm{P}}_{{}_{\mathrm{o}}}-{\mathrm{P}}_{\mathrm{t}2})/{\mathrm{P}}_{\mathrm{t}2}$$

(5)

$${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}=({\mathrm{P}}_{\mathrm{g}}-{\mathrm{P}}_{\mathrm{t}3})/{\mathrm{P}}_{\mathrm{t}3}$$

(6)

where ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$, ${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}$, and ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ indicate price distortions in coal, oil, and natural gas, respectively. ${\mathrm{P}}_{\mathrm{c}}$, ${\mathrm{P}}_{\mathrm{o}}$, and ${\mathrm{P}}_{\mathrm{g}}$ are the real prices. ${\mathrm{P}}_{\mathrm{t}1}$, ${\mathrm{P}}_{\mathrm{t}2}$, and ${\mathrm{P}}_{\mathrm{t}3}$ are the theoretical prices.

(2)

Renewable energy price distortions

Based on the exhaustible resource theory, and the study of Liu et al. [65], the value of renewable energy should be equal to its marginal opportunity cost (MOC). This means that the theoretical price of renewable energy is composed of the marginal production cost (MPC), the marginal user cost (MUC), and the marginal external cost (MEC), as shown in Equation (1).

The MPC is a measure of the direct input costs involved in the development of renewable energy, including exploration costs, machinery and equipment, manpower, and taxes. The marginal production cost is determined primarily by infrastructure investment in renewable energy but is limited by the availability of data. Considering that the value of renewable energy depends on its substitution relation with exhaustible energy, it can replace exhaustible energy when its marginal cost is equal to the marginal cost of exhaustible energy. Therefore, this paper measures the MPC of renewable energy use when the marginal cost of both reaches a critical point. MUC refers to the loss of future benefits from renewable energy due to its unsustainable use today, i.e., the maximum amount of benefit gained from another resource that is forgone in the use of a resource. As renewable energy sources such as wind and solar cannot be exhausted, it is considered that MUC = 0 for these resources. MEC is the cost incurred as a result of damaging the external environment during the development of energy resources. Since renewable energy is clean and non-polluting, it has positive environmental externalities, so its marginal external cost is negative, i.e., MEC < 0. MEC can also be interpreted as environmental external benefits; that is, the measurement of the environment value based on reduced emissions or reduced pollutants under certain environmental value criteria. For renewable energy, MEC can also be interpreted as an environmental external benefit; that is, a measure of environmental value. Under certain environmental value criteria, the environmental value is usually calculated based on the reduction or emission of pollutants. Chen [66] evaluated the environmental value of wind power generation and found that wind power has a value of 0.28 yuan/kWh of pollution reduction. Therefore, this environmental value data is used as a proxy for MEC in this paper. The relevant data sources are listed in Table 1.

The most common method of utilizing renewable energy is the conversion of renewable energy into electricity [67,68]; that is, renewable energy generation. Therefore, this paper uses real electricity prices as a proxy variable for renewable energy prices (${\mathrm{P}}_{\mathrm{r}}$). The price distortion of renewable energy is also based on the degree of deviation between the actual and theoretical prices of renewable energy, as shown in Equation (7):

$${\mathrm{D}}_{\mathrm{r}\mathrm{e}}=({\mathrm{P}}_{\mathrm{r}}-{\mathrm{P}}_{\mathrm{t}4})/{\mathrm{P}}_{\mathrm{t}4}$$

(7)

where ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$ represents the renewable energy distortion, ${\mathrm{P}}_{\mathrm{r}}$ denotes the actual price of renewable energy, and ${\mathrm{P}}_{\mathrm{t}4}$ is the theoretical price.

Data sources: Prices of cleaned coal in provincial capitals represent provincial coal prices. Prices of gasoline and diesel in the province’s capitals are weighted by consumption to calculate provincial oil prices. The price of natural gas in each province is indicated by the price of gas for industrial purposes in the provincial capitals. The average feed-in tariff for wind power is selected to represent the actual price level of renewable energy [69].

3.2. Analysis of Energy Price Distortions

The four energy price distortions are calculated by Equations (4)–(7) and their overall trends are shown in Figure 1. Figure 1 shows that coal, oil, natural gas, and renewable energy prices are all distorted to different degrees. This is similar to the results of Ju et al. [20], but the difference lies in the choice of the benchmark value in the measurement of distortions. First, the former adopts U.S. energy prices as a benchmark to measure the energy price distortions in China, including gasoline, coal, etc. Although the U.S. has a high degree of energy marketization, its energy prices cannot be equated with perfectly competitive market prices. Second, China’s energy price reform has undergone a special process from planning to market, and U.S. energy prices may not reflect the actual situation in China, possibly resulting in a large error in the distortions. This study calculates the distortions through the gap between the actual and the theoretical prices. The marginal opportunity cost pricing method is used to establish the theoretical values of energy, and China’s energy prices are selected, which accurately reflect the degree of the distortions.

In particular, the overall fossil energy price distortion is negative, with a trend of “increasing then decreasing”, whereas the overall renewable energy price distortion is positive, showing a continuous downward trend. In terms of the average distortion, those of coal, oil, and natural gas are −17.73%, −10.50%, and −8.35%, respectively, indicating that coal price distortion is the most serious among fossil energy sources, followed by oil and natural gas price distortions; renewable energy price distortion is 58.45%. It is noteworthy that energy price distortions tend to decline over time, indicating that China’s energy price distortions are gradually improving as the market-based reform of energy pricing deepens.

Negative price distortions for fossil energy indicate that their actual prices are lower than the theoretical values. The lower prices ensure fossil energy’s cost advantage, stimulate investment, contribute to more production, and keep Chinese products competitive internationally. Additionally, a low level of marketization of fossil energy prices and a lag in reform also contribute to the existence of negative distortions in fossil energy prices. Further, fossil energy sources are scarce, and the development of such sources is associated with high environmental costs, especially for coal and oil. Natural gas has a lower marginal external cost (MEC) in its theoretical price because it is a comparatively clean fossil fuel. With the implementation of the natural gas tax law reform in 2010, its price has been more compensated, resulting in a less distorted price than coal and oil.

Renewable energy has a positive price distortion in general, indicating that its real price is higher than its theoretical price. Renewable energy is mainly utilized for power generation in China. Due to the importance of energy security, economic growth, and social stability, electric power has been considered a “quasi-public good”, which leads to distortions in electricity prices. Furthermore, R&D investments for renewable energy are high because of the technology, scale, and market issues [70,71], leading to high utilization costs, thus increasing the gap between actual and theoretical prices.

It is worth noting that the energy price distortions in China have distinct period characteristics. In 2008, there was the most severe distortion of fossil energy prices, which was the result of government policies to suppress inflation by not adjusting energy prices (e.g., the price of refined oil) in the short-term [11], but also directly led to a shortage of fossil fuels. Although the shortage was not widespread, it caused considerable damage to the Chinese economy. After 2008, the degree of distortion in fossil energy prices significantly improved, indicating that the government’s economic policies gradually influenced the marketization of fossil energy prices [72]. In particular, energy prices stepped into a stable and increasing phase after 2009, and the fossil energy price distortions gradually decreased.

Compared to fossil energy price distortions, renewable energy price distortion declined significantly between 2006 and 2008. This may have been caused by the fluctuations in international energy prices in 2008. With the uncertainty of fossil fuel prices, companies have actively pursued the use of renewable energy. After 2009, the renewable energy price exhibited slight up-and-down fluctuations but ultimately showed a downward trend, indicating that the marketization of renewable energy prices gradually strengthened under the vigorous promotion of market-oriented reform. Furthermore, the continuous decline in the cost of wind and solar generation has also contributed to a reduction in price distortion. During the 12th Five-Year Plan period, the cost of developing wind power and solar power fell by 30% and 60%, respectively [73], which greatly contributed to the increase in renewable energy consumption.

As is shown in Figure 2, there were significant regional differences in price distortions for coal, oil, natural gas, and renewable energy during 2006–2018. The high distortions in three fossil energy prices are concentrated in the central and western regions, and low distortions are found in the eastern region. In contrast, the renewable energy price distortion is high in the eastern region and low in the central and western regions. These findings confirm the existence of a regional energy market in China, which is in accordance with Ma and Oxley [74].

There are some reasons for regional heterogeneity in energy price distortions: first, since the reform and opening up, China has supported reforms and economic development in the eastern region with preferential policies. Therefore, the eastern region has obvious location advantages and thus economic development has been much more rapid than in other regions, allowing it to participate more deeply in the international division of labor. The central and western regions possess stronger comparative advantages in terms of energy resources, which results in a cross-regional flow of energy factor, with energy from the central and western regions moving to the east, thus indirectly contributing to the international division of labor. In general, factor inputs participating in the international division of labor should generate corresponding returns. However, there is a high level of market openness and a more convenient environment for energy price reforms in the eastern region [75], which explains its less distorted prices for fossil energy. In addition, the central and western regions lack a diversified industrial structure and are highly dependent on fossil energy. To protect regional energy advantages, local governments intervene more in energy prices, which leads to highly distorted fossil energy prices in the central and western areas. The case of renewable energy price distortion is the opposite. Although the central and western regions are rich in renewable energy, it is difficult to develop resources intensively and the market demand is small. The eastern region has a high demand for renewable energy due to industrial transformation. However, the market-oriented reform of renewable energy pricing has been slow, resulting in higher distortions.

4. Empirical Models

4.1. Baseline Regression Model

This study uses panel data regression analysis to estimate the effects of China’s energy price distortions on its economic growth and carbon emissions. Compared with cross-sectional or time-series data, panel data are more useful for eliminating unobservable variables and improving parameter estimation precision. In this paper, using GDP per capita and CO₂ emissions per capita as dependent variables and price distortions of coal, oil, natural gas, and renewable energy as explanatory variables, the static regression model is set as follows:

$$\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\alpha }_0+{\alpha }_{1,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}{\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}+{\alpha }_2{\mathrm{X}}_{\mathrm{i},\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\omega }_{\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}$$

(8)

$$\mathrm{l}\mathrm{n}\mathrm{C}{\mathrm{O}}_2{}_{{}_{\mathrm{i},\mathrm{t}}}={\varphi }_0+{\varphi }_{1,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}{\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}+{\varphi }_2{\mathrm{X}}_{\mathrm{i},\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\omega }_{\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}$$

(9)

In consideration of the endogeneity of models, the System-GMM model is adopted [76,77]. The Equations (8) and (9) can be redefined as:

$$\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\beta }_0+{\beta }_1\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i},\mathrm{t}-1}+{\beta }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}{\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}+\theta {\mathrm{X}}_{\mathrm{i},\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\omega }_{\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}$$

(10)

$$\mathrm{l}\mathrm{n}\mathrm{C}{\mathrm{O}}_2{}_{{}_{\mathrm{i},\mathrm{t}}}={\lambda }_0+{\lambda }_1\mathrm{l}\mathrm{n}\mathrm{C}{\mathrm{O}}_2{}_{{}_{\mathrm{i},\mathrm{t}-1}}+{\lambda }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}{\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}+\eta {\mathrm{X}}_{\mathrm{i},\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\omega }_{\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}$$

(11)

Here, $\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i},\mathrm{t}}$ and $\mathrm{l}\mathrm{n}\mathrm{C}{\mathrm{O}}_{2\mathrm{i},\mathrm{t}}$ are the dependent variables. To eliminate the effect of heteroskedasticity, we take the natural logarithm of the variables. $\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i},\mathrm{t}-1}$ and $\mathrm{l}\mathrm{n}\mathrm{C}{\mathrm{O}}_{2\mathrm{i},\mathrm{t}-1}$ are the first-order lagged terms of the dependent variable, which can reflect the cumulative effect of the independent variable in the previous period on the current period. ${\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}$ represents the explanatory variable and stands for coal, oil, natural gas, and renewable energy price distortions in the sub-regressions, respectively. ${\beta }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}$ and ${\lambda }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}$ are the coefficients to be estimated for the four energy price distortions. ${\mathrm{X}}_{\mathrm{i},\mathrm{t}}$ represents the control variables. $\mathrm{i}$ and $\mathrm{t}$ indicate year $\mathrm{t}$ of the province $\mathrm{i}$; ${\beta }_0$ and ${\lambda }_0$ denote coefficients to be evaluated. ${\mathrm{v}}_{\mathrm{i}}$ and ${\omega }_{\mathrm{t}}$ represent the individual and time fixed effects, respectively, and ${\epsilon }_{\mathrm{i},\mathrm{t}}$ is a random disturbance term.

4.2. Transmission Mechanism Model

Based on the previous analysis of transmission mechanisms, it is more important to analyze the influencing pathways through which energy price distortions affect economic growth and carbon emissions rather than just focusing on the direct effects. Therefore, the analytical steps introduced by Edwards and Lambert [78] are applied to test the transmission mechanisms. The models are constructed as follows:

$${\mathrm{M}}_{\mathrm{i},\mathrm{t}}={\gamma }_0+{\gamma }_1{\mathrm{M}}_{\mathrm{i},\mathrm{t}-1}+{\gamma }_2{\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}+{\gamma }_3{\mathrm{X}}_{\mathrm{i},\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\omega }_{\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}$$

(12)

$$\mathrm{l}\mathrm{n}{\mathrm{Y}}_{\mathrm{i},\mathrm{t}}={\sigma }_0+{\sigma }_1\mathrm{l}\mathrm{n}{\mathrm{Y}}_{\mathrm{i},\mathrm{t}-1}+{\sigma }_2{\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}+{\sigma }_3{\mathrm{M}}_{\mathrm{i},\mathrm{t}}+{\sigma }_4{\mathrm{X}}_{\mathrm{i},\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\omega }_{\mathrm{t}}+{\epsilon }_{\mathrm{i},\mathrm{t}}$$

(13)

where ${\mathrm{M}}_{\mathrm{i},\mathrm{t}}$ denotes the mediating variables and ${\mathrm{D}}_{\mathrm{T}\mathrm{y}\mathrm{p}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}$ represents the core variables ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}{\mathrm{l}}_{\mathrm{i},\mathrm{t}}}$, ${\mathrm{D}}_{\mathrm{o}\mathrm{i}{\mathrm{l}}_{\mathrm{i},\mathrm{t}}}$, ${\mathrm{D}}_{\mathrm{g}\mathrm{a}{\mathrm{s}}_{\mathrm{i},\mathrm{t}}}$, and ${\mathrm{D}}_{\mathrm{r}{\mathrm{e}}_{\mathrm{i},\mathrm{t}}}$, respectively. $\mathrm{l}\mathrm{n}{\mathrm{Y}}_{\mathrm{i},\mathrm{t}}$ indicates $\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i},\mathrm{t}}$ and $\mathrm{l}\mathrm{n}\mathrm{C}{\mathrm{O}}_{2\mathrm{i},\mathrm{t}}$, respectively. If ${\beta }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}$ and ${\lambda }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}$ in Equations (10) and (11) are statistically significant, the mediating effect is effective and the subsequent tests are continued. The fact that both ${\gamma }_2$ and ${\sigma }_3$ are statistically significant implies that energy price distortions indirectly affect economic growth and carbon emissions. Hence, the mediating effect is ${\gamma }_2\times {\sigma }_3$. If either ${\gamma }_2$ or ${\sigma }_3$ is not significant, the test is conducted using the Bootstrap method, and if the result is significant, it indicates that there is an indirect effect and the test continues to the next step; conversely, the test is stopped. In the case that both ${\gamma }_2$ and ${\sigma }_3$ are significant, a partial mediating effect is indicated in the case of the simultaneous significance of ${\sigma }_2$, and a complete mediating effect is indicated otherwise. The next step is to compare the signs of ${\gamma }_2\times {\sigma }_3$ and ${\sigma }_2$. In the case of the same signs, the partial mediating effect’s weight to the total effect is ${\gamma }_2\times {\sigma }_3/{\beta }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}$ or ${\gamma }_2\times {\sigma }_3/{\lambda }_{2,\mathrm{T}\mathrm{y}\mathrm{p}\mathrm{e}}$; if different, there is a masking effect. Its indirect effect weighs $\left|{\gamma }_2\times {\sigma }_3/{\sigma }_2\right|$ of the direct effect.

4.3. Variable Descriptions and Data Sources

Dependent variables: Economic growth is expressed in GDP per capita, which is calculated by dividing the real GDP by the total population at the end of the year. This variable can be a true reflection of a country’s macroeconomic development [79]. Accordingly, the GDP per capita is commonly used to estimate a country’s economic growth [80]. This study adopts GDP per capita data from 30 provinces in China to calculate economic growth using 2006 as the base year. We use CO₂ emissions per capita to express the carbon emissions, which are measured based on the IPCC [81] method as follows:

$$\mathrm{C}{\mathrm{O}}_2={\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{E}}_{\mathrm{k}}\times \mathrm{N}\mathrm{C}{\mathrm{V}}_{\mathrm{k}}\times \mathrm{C}\mathrm{E}{\mathrm{F}}_{\mathrm{k}}}$$

(14)

$$\mathrm{C}\mathrm{E}{\mathrm{F}}_{\mathrm{k}}=\mathrm{C}{\mathrm{C}}_{\mathrm{k}}\times {\mathrm{O}}_{\mathrm{k}}\times \raisebox{1ex}{$44$}\!\left/ \!\raisebox{-1ex}{$12$}\right.\times {10}^3$$

(15)

According to Equations (14) and (15), $\mathrm{k}$ represents the type of energy, such as raw coal, coke, crude oil, gasoline, and so on, a total of 11 types of energy; ${\mathrm{E}}_{\mathrm{k}}$ denotes the end consumption of the ${\mathrm{k}}_{\mathrm{t}\mathrm{h}}$ energy source; $\mathrm{N}\mathrm{C}{\mathrm{V}}_{\mathrm{k}}$ is the average caloric value; $\mathrm{C}\mathrm{E}{\mathrm{F}}_{\mathrm{k}}$ stands for the carbon emission factor of 11 energy sources. $\mathrm{C}{\mathrm{C}}_{\mathrm{k}}$ indicates the carbon content; ${\mathrm{O}}_{\mathrm{k}}$ is the carbon oxidation factor. The molecular weights of carbon dioxide and carbon are 44 and 12, respectively. Lastly, we divided the measured CO₂ emissions by the end-of-year population of each province to determine the per capita CO₂ emissions.

Independent variables: Four types of energy price distortions are the independent variables, namely ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$, ${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}$, ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$, and ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$.

Control variables: Some variables are controlled to avoid estimation errors due to unobservable factors. Population size (Pop) is measured by the population number at the year-end. Population growth and human activity are considered to be the basis of economic development, so population size contributes to economic growth. Additionally, it may impact carbon emissions through agglomeration and scale. On one hand, population size increases CO₂ emissions due to increased concentration of economic activity [82]; on the other hand, increased economies of scale, cost savings, and technology spillovers can reduce CO₂ emissions [83]. The industry is a major contributor to economic growth and the industrial sector produces more CO₂ because it consumes more energy [84,85], so this paper adds industrial structure (Indus) as one of the control variables affecting economic growth and carbon emissions. This variable is measured by the value-added of the secondary sector as a share of GDP. Urbanization (Urban) generates an industrial agglomeration effect that facilitates the intensive use and integration of resources, which contributes greatly to economic development. Furthermore, a higher urban population results in more carbon emissions [86]. This indicator is measured by the ratio of the urban population to the total population. The openness (Open) is expressed using the share of total exports and imports to GDP. A higher degree of openness promotes the economic development of a country (region) [87]. There are two ways in which openness may affect carbon emissions: first, the technological spillover effect from trade opening contributes to improvements in local production technology and cuts energy consumption, which in turn reduces carbon emissions [88]; second, increased openness may introduce foreign polluting firms, which exacerbates regional carbon emissions [89].

Transmission mechanism variables: The ratio of R&D input to GDP is taken to represent technological innovation (TECH). We use the proportion of the consumption of natural gas to aggregate consumption to measure the optimization of the energy consumption structure (OECS). The product of the share of value-added by primary, secondary, and tertiary industries to GDP and their respective labor productivity is employed as a measure of industrial structural upgrading (ISU) [5]. The investment effect (FDI) is expressed as the ratio of the amount of actual utilized FDI to GDP [90]. In this paper, we select industrial wastewater, waste gas, and solid waste data and use the entropy weight method to synthesize an indicator as a proxy for environmental regulation (ER).

These data are obtained from the National Bureau of Statistics, CEIC database, China Price Statistical Yearbook, Energy Statistical Yearbook, Environmental Statistical Yearbook, Wind database, China Statistical Yearbook, and Provincial Statistical Yearbooks. Missing data were supplemented by interpolation. This study involves provincial panel data for 30 provinces in mainland China (except Tibet) over the period 2006 to 2018 and the descriptive statistics of variables are displayed in

Table 2. Description of variables.

Variables	Units	Mean	Std. Dev.	Min	Max
lnGDP	Yuan/person	10.065	0.563	8.541	11.550
lnCO2	Tons/person	1.911	0.514	0.746	3.433
Dcoal	%	−0.177	0.420	−0.831	1.507
Doil	%	−0.105	0.108	−0.362	0.299
Dgas	%	−0.083	0.246	−0.629	0.667
Dre	%	1.468	1.819	−0.434	9.186
Open	%	0.316	0.363	0.017	1.712
Pop	Person	4480.233	2687.224	548.000	11,346.000
Indus	%	0.434	0.082	0.165	0.620
Urban	%	0.541	0.136	0.275	0.896
TECH	%	1.543	1.061	0.204	5.651
OECS	%	0.140	0.111	0.013	0.562
ISU	%	0.896	0.530	0.154	3.080
FDI	%	452.098	481.928	0.119	2257.262
ER	\	9.393	5.171	2.267	54.793

.

5. Results and Discussion

5.1. Baseline Regression Analysis

5.1.1. Results of the Impact on Economic Growth

First, the impacts of four energy price distortions on China’s economic growth (lnGDP) are analyzed from the national perspective. Based on a comparison of static and dynamic estimation methods, a panel fixed-effect model and a two-step System-GMM model are used to estimate the Equations (8) and (10). The p-value of the Hausman test is less than 0.05, and thus the fixed-effect model (FE) is preferred.

As shown in

Table 3. Estimation results of the effect of energy price distortions on economic growth.

Variables	FE(1)	FE(2)	FE(3)	FE(4)	SYS-GMM(5)	SYS-GMM(6)	SYS-GMM(7)	SYS-GMM(8)
L.lnGDP	—	—	—	—	0.935 *** (0.036)	0.941 *** (0.009)	0.930 *** (0.009)	0.857 *** (0.017)
Dcoal	−0.130 *** (0.038)				−0.083 ** (0.034)
Doil		1.112 *** (0.109)				0.103 *** (0.005)
Dgas			0.379 *** (0.053)				0.101 *** (0.012)
Dre				−0.166 *** (0.027)				−0.025 *** (0.005)
lnOpen	−0.041 * (0.022)	0.024 (0.021)	−0.046 ** (0.021)	−0.026 (0.021)	0.003 (0.008)	0.011 *** (0.003)	0.006 * (0.003)	−0.021 *** (0.003)
lnPop	1.158 *** (0.151)	0.708 *** (0.142)	1.098 *** (0.144)	0.945 *** (0.150)	0.015 (0.012)	−0.006 ** (0.003)	−0.007 * (0.004)	0.017 ** (0.008)
lnIndus	−0.293 *** (0.070)	−0.061 (0.064)	−0.120 * (0.067)	−0.264 *** (0.066)	0.009 (0.025)	0.108 *** (0.010)	0.005 (0.014)	0.172 *** (0.014)
lnUrban	2.742 *** (0.057)	2.113 *** (0.079)	2.491 *** (0.064)	2.589 *** (0.060)	0.093 (0.112)	0.039 (0.032)	−0.063 * (0.033)	0.434 *** (0.054)
Constant	2.018 * (1.216)	5.736 *** (1.151)	2.542 ** (1.158)	3.832 *** (1.215)	0.665 * (0.368)	0.867 *** (0.114)	0.832 *** (0.107)	1.777 *** (0.178)
Hausman	44.85 ***	29.73 ***	40.29 ***	21.94 ***	—	—	—	—
Obs.	390	390	390	390	360	360	360	360
AR(1)	—	—	—	—	−2.57 [0.010]	−1.51 [0.131]	−1.74 [0.082]	−0.54 [0.592]
AR(2)	—	—	—	—	−0.99 [0.324]	−1.69 [0.091]	−1.36 [0.174]	−1.54 [0.124]
Hansen test	—	—	—	—	25.31 [0.117]	28.15 [0.106]	26.28 [0.123]	24.76 [0.074]

Note: 1%, 5%, and 10% significance levels are expressed by ***, **, and *, respectively. p-values are in brackets. Robust standard errors are enclosed in parentheses.

, both p-values of AR (2) and Hansen tests are greater than 0.05, suggesting that the System-GMM evaluation is effective. The estimated coefficients of ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$ and ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$ in the fixed-effect models (1) and (4) are negative, while the coefficients of ${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}$ and ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ are positive and both are significant at the 1% level. ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$ and ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$ in models (5) and (8) have significant negative coefficients at the 5% significance level, and ${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}$ and ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ in columns (6) and (7) are positive and significant at the 1% level. As a result, the static FE model and the dynamic System-GMM estimation are consistent, confirming that the model adopted is reliable and robust.

To avoid possible endogeneity issues between variables, this study uses the System-GMM model estimation as the main results. It can be noted that there is a significant negative correlation between ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$, ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$, and lnGDP, which is consistent with the findings of Shi and Sun [8]. The difference between the two is that Shi and Sun [8] measure energy price distortions negatively affecting China’s economic output based on time series data, while this paper uses panel data and innovatively measures the price distortion of renewable energy. In contrast, ${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}$ and ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ are positively correlated with economic growth, in line with Ju et al. [20]. The measurement and classification of distortions differ between the two studies. In particular, each unit increase in coal and renewable energy prices is associated with 8.3% and 2.5% decreases in national economic growth, respectively. However, each unit increase in oil and gas price distortions leads to an increase in economic growth of 10.3% and 10.1%, respectively.

The coal price distortion inhibits economic growth enhancement because distortions result in misallocation and inefficient use of coal resources. The reason why renewable energy price distortion inhibits economic growth is mainly as follows. First of all, China’s renewable energy market is still developing and not yet mature, so the cost of renewable energy use is high. Currently, China is more concerned with the economic benefits of energy use, so the development of renewable energy might conflict with short-term economic gains. Second, the price of renewable energy in China is still higher than at the international level [91]. Renewable energy has high technical input costs, and the government has been providing subsidies for renewable energy, but these subsidies have not kept up with its scale expansion, and the subsidy funding gap continues to grow or even faces cancellation. Third, the development of renewable energy requires large capital investments, such as the construction of photovoltaic power plants and large hydropower plants. Many producers lack adequate capital investment, which means they require more financial help from local governments [92]. To encourage the rapid development of renewable energy, local governments implement preferential policies, such as lowering income taxes and tax breaks for renewable energy producers, but this would increase the local fiscal burden and thus impede economic growth. In addition, under the cost-plus pricing method, the renewable energy price only depends on the scale of application, the desired profitability level, etc., which makes it simple to operate. However, the renewable energy price cannot be linked to other fossil fuel prices and their economic effects, so distorted renewable prices do not reflect their social preferences and linkages with fossil energy.

The positive effects of price distortions in oil and gas on economic growth suggest that not all price distortions of energy products inhibit economic growth. In recent years, with the coal de-capacity policy and the upgrading of the energy consumption structure in China, coal consumption has declined year after year. Meanwhile, the surge in industrial demand for oil and gas, combined with distortions that keep their prices low, has driven the continuous expansion of the oil/gas processing industry while promoting the rapid expansion of investment in the petrochemical as well as natural gas industries. Increased capital investments have led to an explosion in the number of oil and gas processors, resulting in a rapid increase in related industries, which are contributing to economic growth. Furthermore, oil and natural gas consumption are on the rise each year and are becoming a larger part of the fossil energy mix. They accounted for 19.0% and 8.0% of total energy consumption in 2019, up 0.1 and 0.4 percentage points when compared with 2018, respectively. Therefore, the price effects of oil and gas consumption on economic development are gradually increasing.

The results of control variables are mostly in line with the study’s assumptions. lnOpen in columns (6) and (7) have a significant positive effect on lnGDP, suggesting that the higher the openness of a country, the greater the positive effect on economic growth. lnPop in column (8) is significantly positive, indicating that population growth is beneficial to economic development. Except for the insignificant coefficients in (5) and (7), the industrial structure (lnIndus) has a significant positive impact on lnGDP in columns (6) and (8), indicating that industry is the main driver of economic development. Only urbanization (lnUrban) in column (8) shows a positive contribution to lnGDP, implying that urbanization facilitates the intensive use and integration of resources, which greatly promotes economic development.

5.1.2. Results of the Influence on Carbon Emissions

In

Table 4. Estimation results on the impact of energy price distortions on carbon emissions.

Variables	FE(1)	RE(2)	FE(3)	FE(4)	SYS-GMM(5)	SYS-GMM(6)	SYS-GMM(7)	SYS-GMM(8)
L.lnCO2	—	—	—	—	0.780 *** (0.031)	0.861 *** 0.020)	0.760 *** (0.042)	0.866 *** (0.024)
Dcoal	−0.049 (0.050)				0.040 *** (0.014)
Doil		0.446 *** (0.171)				0.156 *** (0.036)
Dgas			0.661 *** (0.084)				0.109 *** (0.042)
Dre				−0.021 (0.059)				0.038 *** (0.009)
lnOpen	−0.082 *** (0.029)	−0.019 (0.082)	−0.083 ** (0.039)	−0.083 *** (0.029)	−0.078 *** (0.008)	0.010 (0.011)	−0.044 *** (0.015)	−0.015 (0.009)
lnIndus	0.028 (0.110)	0.292 *** (0.098)	0.093 (0.127)	0.044 (0.109)	0.719 *** (0.034)	0.235 *** (0.038)	0.347 *** (0.051)	0.187 *** (0.028)
lnPop	−1.088 *** (0.225)	−0.131 (0.099)	0.560 ** (0.268)	−1.104 *** (0.225)	−0.159 *** (0.023)	−0.065 *** (0.007)	−0.087 *** (0.013)	−0.044 *** (0.010)
lnUrban	0.599 *** (0.167)	1.176 *** (0.130)	1.413 *** (0.095)	0.585 *** (0.167)	0.445 *** (0.050)	−0.056 (0.055)	0.251 *** (0.093)	0.057 (0.052)
Constant	10.790 *** (1.769)	4.045 *** (0.834)	2.865 * (1.724)	10.949 *** (1.773)	2.542 *** (0.284)	1.029 *** (0.139)	1.598 *** (0.221)	0.797 *** (0.135)
Hausman	15.48 ***	20.09 ***	11.55 **	13.83 **	—	—	—	—
Obs.	390	390	390	390	360	360	360	360
AR(1)	—	—	—	—	−3.22 [0.001]	−3.40 [0.001]	−3.21 [0.001]	−3.25 [0.001]
AR(2)	—	—	—	—	−1.62 [0.104]	−1.68 [0.093]	−1.50 [0.133]	−1.70 [0.090]
Hansen test	—	—	—	—	27.99 [0.924]	29.33 [0.448]	25.25 [0.118]	22.06 [0.281]

Note: 1%, 5%, and 10% significance levels are expressed by ***, **, and *, respectively. p-values are in brackets. Robust standard errors are enclosed in parentheses.

, we display the results of the influence of energy price distortions on carbon emissions. From the results of the Hausman test, the fixed-effect models are selected to estimate the coefficients of Equation (9). Additionally, the assessment of System-GMM was applied to estimate the coefficients in Equation (11), and the test results proved that the model is valid. The coefficients of ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$, ${\mathrm{D}}_{\mathrm{oil}}$, ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$, and ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$ in columns (5)–(8) are all positive and pass the 1% significance test. Moreover, the estimates of the static and dynamic models are generally consistent, except for the insignificant results in columns (1) and (4).

The lagged dependent variables in columns (5)–(8) are highly statistically significant, indicating that the System-GMM model is suitable for use in the empirical evidence in this section. It can be seen that there is a significant positive link between the four energy price distortions and carbon emissions; that is, the greater the distortions, the greater the carbon emissions. The results illustrate that energy price distortions exacerbate carbon emissions, which is consistent with the findings of Wang et al. [9]. The latter merely measured the distortion of oil prices in the Chinese transportation sector and assessed its impact on carbon dioxide reduction. This study is more comprehensive and consists of both fossil and renewable energy sources. Specifically, for each unit increase in coal, oil, natural gas, and renewable energy prices, carbon emissions increase by 4.0%, 15.6%, 10.9%, and 3.8%, respectively.

The reasons why energy price distortions aggravate carbon emissions are summarized below. First, the distortion induces fossil fuel prices to remain low, which encourages enterprises to prefer low-cost but high-emitting energy to increase their profits, causing carbon emissions to increase. Moreover, the excessive use of fossil fuels curbs the development of clean energy. Second, the positive effects of oil and gas price distortions on carbon emissions are greater than those of coal. With the upgrading of China’s energy consumption mix, the industrial sector’s demand for oil and natural gas has surged, leading to the continuous expansion of the oil and gas processing industry. Additionally, China’s natural gas industry has long been monopolized [93], and price distortions caused by interventions and import restrictions have affected the transmission of carbon prices [94], thereby causing carbon emissions to increase. Third, renewable energy has positive environmental externalities, and the development of renewable energy is beneficial to reducing carbon emissions [71]. However, distortions cause the actual price of renewable energy to deviate from its equilibrium level, which makes it less competitive than fossil fuels.

The results of control variables show that the openness (lnOpen), industrial structure (lnIndus), population size (lnPop), and urbanization (lnUrban) are all significantly associated with lnCO₂, which is consistent with the results expected in this study. In contrast to what was expected in this paper, population size (lnPop) has a negative impact on lnCO₂. lnOpen favors a reduction in carbon emissions, while lnIndus and lnUrban exacerbate the increase in carbon emissions.

5.2. Estimations of Transmission Mechanisms

The coefficients of all four energy price distortions in Table 3 and Table 4 are significant, a result that meets the criteria for judging the mediation effect and allows for a follow-up test. Based on Equations (12) and (13), the transmission mechanisms are tested.

Table 5. The transmission mechanisms of the impact of energy price distortions on lnGDP.

Variables	(1) lnTECH	(2) lnTECH on lnGDP	(3) lnGDP	(4) lnISU	(5) lnISU on lnGDP	(6) lnGDP	(7) lnFDI	(8) lnFDI on lnGDP	(9) lnGDP
Dcoal	−0.016 ** (0.007)	0.059 ** (0.027)	−0.078 *** (0.019)	−0.091 *** (0.015)	0.063 *** (0.009)	−0.026 *** (0.010)	−0.039 ** (0.020)	0.024 *** (0.003)	−0.060 *** (0.008)
Doil	−0.270 *** (0.082)	0.052 *** (0.006)	0.070 *** (0.017)	−0.083 ** (0.033)	0.141 *** (0.020)	0.092 *** (0.011)	−0.847 *** (0.130)	0.005 *** (0.002)	0.086 *** (0.010)
Dgas	−0.402 *** (0.060)	0.108 *** (0.011)	0.075 *** (0.010)	−0.012 (0.010)	0.106 *** (0.026)	0.071 *** (0.017)	−0.478 *** (0.168)	0.031 *** (0.006)	0.098 *** (0.011)
Dre	−0.092 *** (0.017)	0.034 *** (0.008)	−0.012 * (0.006)	−0.053 *** (0.005)	0.106 *** (0.012)	0.010 *** (0.003)	−0.161 ** (0.069)	0.021 *** (0.003)	−0.024 *** (0.008)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	360	360	360	360	360	360	360	360	360

Note: 1%, 5%, and 10% significance levels are expressed by ***, **, and *, respectively. p-values are in brackets. Robust standard errors are enclosed in parentheses.

and

Table 6. The transmission mechanisms of the effect of energy price distortions on lnCO₂.

Variables	(1) lnTECH	(2) lnTECH on lnCO2	(3) lnCO2	(4) lnOECS	(5) lnOECS on lnCO2	(6) lnCO2	(7) lnER	(8) lnER on lnCO2	(9) lnCO2
Dcoal	−0.016 ** (0.007)	−0.065 *** (0.023)	0.015 ** (0.008)	−0.162 *** (0.048)	−0.020 * (0.011)	0.017 * (0.010)	−0.561 *** (0.098)	−0.040 *** (0.013)	0.016 ** (0.007)
Doil	−0.270 *** (0.082)	−0.113 *** (0.034)	0.147 ** (0.067)	−0.290 *** (0.099)	−0.029 *** (0.008)	0.089 * (0.048)	−0.506 * (0.262)	−0.054 ** (0.023)	0.149 *** (0.042)
Dgas	−0.402 *** (0.060)	−0.066 *** (0.019)	0.069 * (0.040)	−0.076 (0.070)	−0.042 *** (0.008)	0.104 *** (0.023)	−0.291 ** (0.124)	−0.067 *** (0.016)	0.087 *** (0.031)
Dre	−0.092 *** (0.017)	−0.067 ** (0.034)	0.030 * (0.016)	−0.264 *** (0.039)	−0.016 ** (0.008)	0.027 ** (0.010)	−0.405 *** (0.032)	−0.039 ** (0.020)	0.038 *** (0.014)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	360	360	360	360	360	360	360	360	360

Note: 1%, 5%, and 10% significance levels are expressed by ***, **, and *, respectively. p-values are in brackets. Robust standard errors are enclosed in parentheses.

display the mechanisms of distortions affecting lnGDP and lnCO₂, respectively.

Columns (1)–(3) in Table 5 present the results for the mechanism variable of technological innovation. All four energy price distortions negatively affect lnTECH at the 5% level. Adding technological innovation into the model produces a significant positive effect on lnGDP, which indicates an indirect effect of energy price distortions on economic growth. Meanwhile, coal price distortion negatively affects lnGDP, while price distortions of oil and natural gas, as well as renewable energy, have a significant positive impact. Furthermore, the signs ${\gamma }_2\times {\sigma }_3$ and ${\sigma }_2$ of ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$ are the same, indicating that technological innovation acts as a partial mediation in coal price distortion affecting economic growth, and the share of the partial mediating effect in the total effect is 1.14%. The other three price distortions have different signs, suggesting a masking effect for technological innovation [95], and their indirect effects account for 20.06%, 57.89%, and 25.01% of the direct effect, respectively.

The industrial structure upgrading is used as a mechanism variable in columns (4)–(6). The negative effects of ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$, ${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}$, and ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$ on lnISU are significant, while the coefficient of ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ is not. Thus, the Bootstrap approach is adopted to test whether there is an indirect effect of ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$. The result is that the original hypothesis of (${\gamma }_2\times {\sigma }_3=0$) is rejected. Thus, price distortions of four energy types have indirect effects on economic growth. Considering the mechanism variable of industrial structure upgrading, the coefficient of coal price distortion is significantly negative, while price distortions of oil, natural gas, and renewable energy have a significant positive effect. According to the signs ${\gamma }_2\times {\sigma }_3$ and ${\sigma }_2$, coal price distortion has a partial mediating effect on economic growth, and it accounts for 6.91% of the total effect. The other three price distortions produce a masking effect on economic growth, and their ratios of indirect effects to direct effects are 12.72, 1.79%, and 56.18%, respectively.

Columns (7)–(9) use the investment effect as a mechanism variable to illustrate that these four distortions indirectly affect economic growth. Additionally, the symbols ${\gamma }_2\times {\sigma }_3$ and ${\sigma }_2$ of ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$ and ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$ are consistent, demonstrating a partial mediating effect of price distortions in coal and renewable energy. Their partial mediating effects as a proportion of the total effect are 1.13%, and 13.52%, respectively. In contrast, the signs of oil and natural gas price distortions are different, suggesting that they have a masking effect on economic growth. Their indirect effects as a share of direct effects are 4.92% and 15.12%, respectively.

In light of the above results, technological innovation, industrial structure upgrading, and the investment effect are three important mechanisms of energy price distortions that affect China’s economic growth, which validates Hypothesis 1. The price distortions of coal and renewable energy will further hinder economic growth by inhibiting technological innovation, industrial structure upgrading, and investment effect. However, these mechanisms have a masking effect on distorted oil and gas prices that affect the economy. Moreover, when the estimated coefficients of the four energy price distortions are compared with those of the transmission mechanism variables, it can be found that the coefficients of coal price distortion decline more when adding industrial structure upgrading than when adding technological innovation and investment effects. The coefficient of renewable energy price distortion decreases the most when technological innovation is added.

The results for the mechanism variable of technological innovation are presented in columns (1)–(3) of Table 6. Energy price distortions all have a significant negative effect on lnTECH. lnTECH negatively affects lnCO₂ at the 5% level, which indicates an indirect impact of energy price distortions on carbon emissions. Moreover, the signs ${\gamma }_2\times {\sigma }_3$ and ${\sigma }_2$ are the same, indicating that a partial mediating effect exists in these models, and they account for 2.60%, 19.56%, 24.34%, and 16.22% of the total effects, respectively.

Columns (4)–(6) adopt the optimization of energy consumption structure as a mediating variable. Only the negative effect of ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ on lnOECS is insignificant, so the Bootstrap method is used to test whether there is an indirect effect of ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$. The results show that the original hypothesis (${\gamma }_2\times {\sigma }_3=0$) is rejected. Hence, price distortions of four energy types have indirect effects on carbon emissions. Adding the optimization of energy consumption structure, distortions’ coefficients are significantly positive, and their signs are consistent with ${\gamma }_2\times {\sigma }_3$. Therefore, all four energy price distortions have a partial mediating effect on carbon emissions, and their partial mediating effects account for 8.10%, 5.39%, 2.93%, and 11.12% of the total effects, respectively.

Columns (7)–(9) take environmental regulation as the mechanism variable. The findings indicate that these four distortions indirectly affect carbon emissions. Additionally, ${\gamma }_2\times {\sigma }_3$ and ${\sigma }_2$ are the same, suggesting the existence of a partial mediation effect. Their partial mediation effects as a proportion of the total effects are 56.10%, 17.52%, 17.89%, and 41.57%, respectively.

To sum up, energy price distortions can affect carbon emissions via technological innovation, environmental regulation, and energy consumption structure optimization, which confirms Hypothesis 2. Energy price distortions can further exacerbate carbon emissions by inhibiting the technological innovation effect, energy consumption structure optimization, and environmental regulation effect. Additionally, different transmission mechanisms have different impacts. The coefficients of distortions in coal and gas decrease more with technological innovation than with the optimization of energy consumption structure and environmental regulation; the coefficients of oil and renewable energy price distortions decrease the most when energy consumption structure optimization is included.

5.3. Robustness Estimation

To test the robustness of the model, this study substitutes the dependent variables and revisits the connection between energy price distortions and economic growth and carbon emissions, respectively. This section uses provincial real GDP to replace the original dependent variable GDP per capita; carbon intensity (CI) replaces CO₂ emissions per capita. According to the results, coal and renewable energy price distortions negatively and significantly influence economic growth, while price distortions in oil and natural gas have a positive impact on it. This is in accordance with the results listed in Table 3. Additionally, price distortions of all four energy types contribute significantly to carbon dioxide emissions, which is also in line with Table 4. Therefore, this study’s results are robust.

5.4. Extended Discussion

China’s regions differ greatly in terms of economic development, CO₂ emissions, and energy endowment; therefore, the whole sample is divided into two subgroups: the eastern region and the central and western regions, to investigate whether there is heterogeneity in the impact of energy price distortions on economic growth and carbon emissions.

As shown in

Table 7. Estimation results on the impact of energy price distortions on economic growth based on a regional perspective.

	Eastern				Central and Western
L.lnGDP	0.956 *** (0.057)	0.952 *** (0.032)	0.959 *** (0.023)	0.948 *** (0.071)	0.951 *** (0.016)	0.965 *** (0.012)	0.901 *** (0.012)	0.959 *** (0.010)
Dcoal	−0.025 ** (0.013)				−0.117 *** (0.020)
Doil		0.039 * (0.020)				0.030 * (0.017)
Dgas			0.057 ** (0.022)				0.051 * (0.026)
Dre				−0.012 (0.016)				0.012 *** (0.004)
lnOpen	0.049 *** (0.012)	0.026 *** (0.007)	0.001 (0.010)	0.020 ** (0.009)	−0.029 *** (0.008)	−0.006 ** (0.002)	−0.018 *** (0.006)	−0.009 *** (0.002)
lnPop	0.058 (0.051)	0.054 (0.057)	−0.043 (0.029)	0.060 (0.097)	0.018 *** (0.006)	0.003 (0.005)	0.006 (0.010)	0.007 * (0.004)
lnIndus	−0.110 (0.075)	−0.019 (0.036)	0.093 ** (0.047)	−0.014 (0.037)	0.051 ** (0.023)	0.140 *** (0.008)	0.237 *** (0.019)	0.104 *** (0.010)
lnUrban	−0.077 (0.144)	0.005 (0.082)	0.074 (0.083)	0.021 (0.196)	0.035 (0.033)	−0.017 (0.027)	0.220 *** (0.066)	0.003 (0.022)
Constant	−0.055 (0.344)	0.134 (0.245)	0.989 ** (0.402)	0.139 (0.344)	0.408 ** (0.168)	0.500 *** (0.130)	1.339 *** (0.124)	0.495 *** (0.104)
Obs.	132	132	132	132	228	228	228	228
AR(1)	−2.13 [0.033]	−1.51 [0.132]	−1.52 [0.129]	−2.66 [0.008]	−1.67 [0.096]	−1.38 [0.167]	−0.80 [0.426]	−1.50 [0.134]
AR(2)	−0.55 [0.586]	−0.86 [0.388]	−0.53 [0.596]	−1.11 [0.267]	−0.43 [0.667]	−1.77 [0.077]	−0.39 [0.696]	−1.63 [0.104]
Hansen test	7.17 [0.928]	7.49 [0.985]	5.84 [0.997]	8.45 [0.934]	17.82 [0.467]	18.55 [0.420]	15.01 [0.524]	18.52 [0.488]

Note: 1%, 5%, and 10% significance levels are expressed by ***, **, and *, respectively. p-values are in brackets. Robust standard errors are enclosed in parentheses.

, ${\mathrm{D}}_{\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}$ of the eastern region is significant and negative, indicating that coal price distortion hinders economic growth; ${\mathrm{D}}_{\mathrm{r}\mathrm{e}}$ is not significant but negative, suggesting that renewable energy price distortion does not significantly impact the economic development of the eastern region. The coefficients of ${\mathrm{D}}_{\mathrm{o}\mathrm{i}\mathrm{l}}$ and ${\mathrm{D}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ are significantly positive, indicating that price distortions in oil and gas contribute to the economic growth of eastern China. For the central and western regions, the coefficient of coal price distortion is significantly negative, while the other three energy price distortions are significantly positive, indicating that coal price distortion inhibits economic growth whereas the price distortions of oil, natural gas, and renewable energy promote the economic growth of the central and western regions.

An additional comparison shows that coal price distortion has a greater hindering effect on economic growth in the central and western regions than in the east at 11.7%; distorted oil and gas prices exhibit a greater boosting impact on the economic growth of eastern China at 3.9% and 5.7%, respectively. Possible explanations are enumerated below. In the central and western regions, industrial development is still heavily dependent on coal resources, and other factors conducive to economic growth, such as technology research and development, are squeezed out under the distortion, resulting in a lack of momentum for economic growth. The eastern region is more industrialized and more dependent on oil than other regions. In conjunction with the industrial structure upgrading, the east has seen a surge in natural gas demand. Distorted oil and gas prices have lowered the costs of use, which has had a positive impact on eastern China’s economic development. The central and western regions possess abundant renewable energy and a large concentration of renewable energy enterprises, leading to the development of downstream industries (e.g., photovoltaics, solar panels, crystalline silicon cells and modules, etc.), which has contributed to the region’s growth by reducing production costs and product prices.

Table 8. Estimation results on the impact of energy price distortions on carbon emissions based on a regional perspective.

	Eastern				Central and Western
L.lnCO2	0.796 *** (0.121)	0.842 *** (0.112)	0.885 *** (0.062)	1.262 *** (0.234)	0.464 *** (0.056)	0.844 *** (0.058)	0.843 *** (0.039)	0.921 *** (0.034)
Dcoal	0.105 ** (0.050)				0.143 ** (0.069)
Doil		0.409 *** (0.129)				0.471 ** (0.237)
Dgas			0.401 *** (0.132)				0.175 ** (0.087)
Dre				0.081 ** (0.040)				0.070 ** (0.036)
lnOpen	−0.122 ** (0.048)	−0.127 *** (0.044)	0.131 (0.093)	0.178 (0.187)	0.037 ** (0.017)	0.015 (0.037)	−0.006 (0.020)	0.012 (0.011)
lnIndus	1.691 ** (0.703)	1.707 *** (0.638)	−0.295 (0.426)	−0.442 (0.644)	0.732 *** (0.124)	0.299*** (0.060)	0.327 *** (0.077)	0.141 *** (0.031)
lnPop	3.366 ** (1.335)	3.556*** (1.248)	0.111 (0.183)	0.053 (0.218)	−0.578 *** (0.070)	−0.121 *** (0.015)	−0.111 *** (0.022)	−0.064 *** (0.012)
lnUrban	−1.704 ** (0.702)	−1.775 *** (0.634)	−0.739 * (0.439)	−0.981 (0.901)	0.938 *** (0.162)	−0.210 (0.200)	−0.032 (0.083)	−0.088 (0.097)
Constant	1.550 ** (0.742)	2.145 *** (0.664)	−1.186 (2.012)	−1.693 (1.591)	7.198 *** (0.721)	1.496 *** (0.241)	1.490 *** (0.304)	0.763 *** (0.240)
Obs.	132	132	132	132	228	228	228	228
AR(1)	−0.63 [0.530]	−0.39 [0.699]	−2.52 [0.012]	−2.39 [0.017]	−2.11 [0.035]	−2.33 [0.020]	−2.50 [0.013]	−2.36 [0.018]
AR(2)	0.02 [0.987]	−0.15 [0.881]	0.57 [0.571]	0.30 [0.764]	−0.48 [0.631]	−1.73 [0.084]	−1.34 [0.182]	−1.64 [0.101]
Hansen test	3.06 [1.000]	1.50 [1.000]	2.88 [0.998]	5.39 [1.000]	15.25 [0.506]	15.95 [0.527]	14.83 [0.608]	16.42 [0.629]

Note: 1%, 5%, and 10% significance levels are expressed by ***, **, and *, respectively. p-values are in brackets. Robust standard errors are enclosed in parentheses.

shows that energy price distortions significantly contribute to the increase in carbon emissions in both the eastern region and the central and western regions. Coal and oil price distortions contribute more to carbon emissions in the central and western areas, while natural gas and renewable energy price distortions contribute more to carbon emissions in eastern China. A possible explanation is that the central and western regions, constrained by resource endowments, are more inclined to compete by depressing factor prices and relaxing environmental regulations. Thus, they are more sensitive to changes in energy prices. The extensive use of coal and oil has increased productivity but has also raised carbon emissions. In terms of natural gas price distortion, many polluting enterprises have been forced to move to the central and western areas due to the increasing environmental protection and industrial restructuring in the east [96]. This also encourages high-energy-consuming industries to use natural gas instead of coal, such as ceramic manufacturing and non-ferrous metal processing, which increases the demand for natural gas in the eastern region [97]. Due to a lack of natural gas, the eastern region has been forced to rely on large imports and West–East gas transmission. However, distortions impede the free flow of natural gas resources between regions, which is detrimental to carbon emissions reduction in the eastern region. Due to the high cost of technologies, renewable energy consumption is smaller, and industrial development in the east is heavily reliant on fossil fuels. Thus, distorted renewable energy prices result in higher carbon emissions.

6. Conclusions and Policy Implications

Based on the panel data of 30 Chinese provinces during 2006–2018, this paper first measures and analyzes the degree of price distortions of fossil energy (coal, oil, and natural gas) as well as renewable energy. Next, the empirical analysis uses the fixed effect model, the System-GMM model, and the mediation effect model to estimate the impact of energy price distortions on China’s economic growth and carbon emissions at the national and regional levels. Additionally, this paper explores the transmission mechanisms of energy price distortions. The main findings are summarized as follows:

(1)

Energy prices are significantly distorted. In terms of fossil energy price distortions, coal (−17.73%) is the highest, followed by oil (−10.50%) and natural gas (−8.35%). In contrast, the renewable energy price distortion is positive, at 58.45%. Additionally, all four energy price distortions are regionally heterogeneous. Fossil energy prices exhibit high distortions in the central and western regions and low distortions in the eastern region, whereas the renewable energy price distortion is characterized as low in the central and western regions and high in the eastern region.

(2)

Coal and renewable energy price distortions significantly impede national economic growth, but distortions in oil and natural gas prices promote economic growth. All four energy price distortions contribute significantly to the increase in carbon emissions.

(3)

At the regional level, energy price distortions exacerbate carbon emissions in both the eastern region and the central and western regions. In particular, price distortions in coal and oil in the central and western regions have a high positive impact on carbon emissions, while natural gas, as well as renewable energy price distortions in eastern China, make a more pronounced contribution. In terms of economic effects, coal price distortions have a greater hindering effect on the central and western regions; oil and gas price distortions have a greater promotion effect in the eastern region. The positive effect of renewable energy price distortion is significant in the central and western regions, but insignificant in eastern China.

(4)

Technological innovation, industrial structure upgrading, and the investment effect are important transmission mechanisms of energy price distortions affecting China’s economic growth. Industrial structure upgrading has the most obvious weakening effect on coal price distortion impeding economic growth, and the hindering effect of technological innovation on reducing the price distortion of renewable energy is more significant. Furthermore, energy price distortions can influence carbon emissions via technological innovation, environmental regulation, and the optimization of the energy consumption structure. Technological innovation contributes the most to reducing the promotional effect of distortions in coal and gas prices on carbon emissions. The optimization of the energy structure has the most pronounced weakening effect on oil and renewable energy price distortions.

Below we list some policy implications that can be drawn from the above findings.

First, we should aim to refine the market-oriented mechanism of energy pricing and construct a transparent and competitive price structure. The government should reduce its inappropriate intervention in the energy market and play an active role in regulation. Let the market determine energy prices and help break the integrated monopoly of the energy industry. Efforts should be made to shorten the price adjustment cycle of refined oil products, to separate natural gas pipeline network transportation and marketing, to introduce eligible enterprises into the natural gas market competition, to break industry monopolies, and to accelerate the reform of import rights. In the field of renewable energy price reform, some experimental policies can be implemented on a trial basis, such as setting a floating tariff for renewable energy based on the electricity market; establishing a subsidy regression mechanism to gradually reduce the scale of renewable energy subsidies; raising the sales tariff to add a renewable energy surcharge fee that is correlated with the reduction in subsidies. Furthermore, the government should strengthen transmission between different energy prices and form a unified and linked energy price system to achieve the optimal allocation of resources.

Second, authorities should formulate differentiated energy price policies. With respect to developed regions with low energy price distortions, we should seek to fully exploit its market’s role in energy pricing and guide enterprises in accelerating the renewal of energy-efficient capital so that energy input can achieve its true benefits. The government should adopt market-based methods to regulate energy prices in undeveloped regions with a high degree of distortion, focusing on unblocking the flow of energy prices to energy demand. Moreover, the government should provide more investment policy support and expand financing opportunities and channels for energy companies.

Third, the active role of technological innovation, industrial structure upgrading, and energy structure optimization needs to be fully promoted. This paper finds that technological innovation, industrial structure upgrading, and the optimization of energy consumption structures can alleviate the adverse effects of energy price distortions on China’s economic growth and carbon emissions to a greater extent. Therefore, the government should increase investments in technology research, set up earmarked funds for research, and provide subsidies for green technology. Especially in the area of renewable energy projects, incentives should be adopted and funds should be used precisely for renewable energy technologies with cost reduction potential, such as ocean energy generation technologies. Moreover, we should accelerate the elimination of backward production capacity and promote the upgrading and transformation of low value-added to high value-added industries.

Despite the focus of this study on China and its regions, the problems associated with energy price distortions and the resulting economic and environmental impacts are not unique to China but are global in scope. The research we conducted can be applied to countries with underdeveloped energy markets. This study also has some limitations. First, this paper uses provincial data, which does not permit an in-depth examination of different types of energy price distortions at the city level since comprehensive and publicly available microdata on all types of energy prices are not yet available. When more microdata are available in the future, we can consider the differences between various city clusters in the heterogeneous analysis, such as the Yangtze River Delta, Beijing–Tianjin–Hebei, and the Guangdong–Hong Kong–Macao Greater Bay Area. Furthermore, although this paper examines the impact of energy price distortions on economic growth and carbon emissions from a provincial perspective, it is constrained by the lack of micro-level data with which to explore the possible effects of micro-level characteristics on the relationship between energy price distortions, economic growth, and carbon emissions. Analyzing the economic and environmental effects of energy price distortions from a microscopic perspective merits further research.