Impact of Carbon Prices on Corporate Value: The Case of China’s Thermal Listed Enterprises

Abstract

The emission trading scheme (ETS) has become a significant tool to solve the climate change problem. China has built domestic carbon trading pilots to control energy consumption and reduce emissions. This paper explores the linkage between the carbon market and covered corporate value in China. To address the relationship, this paper estimates the impact that the carbon prices of different pilots in China have on the value of thermal listed enterprises and the extent of this impact. By using weekly data from July 2014 to June 2017, we analyze the overall effect and perform a comparative study of influences of the three trading years. Moreover, we test if the effect of carbon trading pilots on electricity corporate value is market-specific. The results demonstrate that carbon prices have a significantly negative impact on stock value when looking at the full sample and the effects vary between markets.

1. Introduction

As one of the fastest-growing economies, China has become the largest energy consuming and Greenhouse Gas (GHG) emitting country in the world. In 2013, China’s fossil energy consumption accounted for 23.6% of the consumption [1]. In 2016, China emitted 9.15 billion tons of CO₂, accounting for 27.3% of the world [2]. GHG emissions are the main cause of global warming and climate change. In response to global climate change, in 2010 China signed the Copenhagen accord and promised that by 2020 the unit of gross domestic product (GDP) of carbon dioxide emissions would have reduced by 40% to 45% lower than 2005 [3]. Under dual pressure from domestic environmental deterioration and international climate negotiation, the Chinese government has established seven carbon-trading pilots located in Beijing, Shenzhen, Hubei, Guangdong, Shanghai, Tianjin, and Chongqing, respectively, since 2013. Carbon pilots in China covered approximately 1.2 billion tons of CO₂, following the EU emission trading scheme (ETS), becoming the second largest carbon emission trading system.

The emission trading scheme is an effective method for energy conservation and emission reduction [4]. For instance, in 2016, the European Union GHG emissions, including emissions from international aviation and indirect CO_2, were down by 22.4% compared with 1990 levels by operating ETS (Data from the eurostat website. http://ec.europa.eu/eurostat/statistics-explained/index.php/Europe_2020_indicators_-_climate_change_and_energy). According to the European Commission, in 2010 greenhouse gas emissions from big emitters covered by the EU ETS had decreased by an average of more than 17,000 tons per installation from 2005, a decrease of more than 8% since 2005. In 2013, the emissions of 635 ETS covered companies in Shenzhen decreased by 11.5% compared with the 2010 level after performing ETS. ETS sets a cap on the quantity of GHG emissions for covered polluters. Regulated companies hold a certain amount of emission allowances at the beginning of the trading period. Emission allowances can be exchanged during the trading period. Companies with redundant allowances can sell them or hold them to cover for future needs, meanwhile companies who are short of allowances are able to buy from the carbon markets. If the covered entities’ emission permits cannot cover their emissions amount until the compliance, they are fined. ETS gives carbon allowances financial value and affects the covered companies’ operation. The covered entities may benefit by reducing carbon emissions. Carbon prices should be taken into consideration as entities’ production costs.

The literature on the carbon market mainly focuses on the EU ETS. The understanding of the carbon market in China is rather limited. ETS in China has gone through more than three performance years. Learning about the effect of the carbon market is important for both government and regulated entities. This paper aims to investigate the effectiveness of the carbon market, the pricing behavior of carbon emissions, and the links between carbon markets and corporations’ value. From an economic point of view, ETS sets caps, manages carbon allowance to control GHG emissions, and gives carbon emissions financial attributes [5]. The carbon market makes carbon allowances valuable and motivates companies to innovate, change production strategy, and find new production processes, such as using cleaning product substituting fossil fuels. ETS affects the regulated firms and further influences the economy, ensuring a pollution intensive economy will transfer to a more sustainable and cleaner economy in the long run.

The existing empirical studies on the impact of the carbon market on corporate value mainly focus on the EU ETS, as the EU ETS is the largest and the most successful carbon market in the world. The influences depend mostly on different phases, specific sectors, or even company-specific characteristic. Comparing the effects in different phases, the EU ETS gives an advantage to electricity firms in phase I, as the majority of emission allocations are free and leads to a negative impact on corporate values in phase II due to the allowance allocation becoming stricter [6]. Carbon prices decline resulting in the highest negative impact on stock returns of carbon-intensive industries by using daily returns of more than five hundred stocks from EURSTOXX [7]. The returns of different polluting sectors respond to the carbon prices diversely [8,9]. EU ETS has no impact on the revenue performance of cement and iron or steel industries; however, there is a positive impact on revenue in the electricity sector [8]. EU Emission Allowance (EUA) effects are power firm-specific. Conventional electricity firms and renewable electricity companies are principally affected by the different impacts of the carbon market [10].

In summary, there are three views on discussing the impact of the ETS on companies. Firstly, the carbon market has a negative impact on companies. Companies spend more capital, human and material sources, on improving production technology aimed at reducing carbon emissions. This action reduces the companies’ profits [11]. ETS increases the cost regardless of the free distribution of allowances [12]. Carbon quota shortages lead to a decrease in profitability for the listed companies [13]. Secondly, some scholars find that the EU ETS has a positive impact on firms. Economics principles show that carbon price can also affect the cost structure of an enterprise, because carbon price changes the investment preference and the production cost of enterprises, and then changes the profits. A sharp fall in carbon prices has a negative impact on carbon-intensive company’s stock returns [7,14]. The EUA price changes are shown to be positively related to stock returns of the most important electricity corporations, although the effect does not work asymmetrically [15,16]. Power generation, the largest affected industry, is correlated with rising prices for emission rights positively in EU ETS [17]. The EU ETS is found to have a statistically significant positive long-term impact on the aggregated power sector stock market return in Spain regarding Phase II and works asymmetrically [10]. Thirdly, the final point of view is that the carbon market has little influence on corporations. The impact of relative allowance allocation on both economic performance and employment of German companies are not found [18]. EU ETS has had an important impact on small-scale investments, while as regards the large-scale investments, the impact is limited [19]. In brief, the overview of the current literature on the effect of the carbon market on corporate value is shown in

Table 1. Overview of current research on effect of carbon market on corporate value.

Impact	Research	Method or Model	Results
Negative impact	[6]	Modified multifactor market model	EU Emission Allowance (EUA) prices affect corporate value negatively in phase II.
	[11]	Simulation model	Emission trading scheme (ETS) would increase cost no matter that the allowances are free distribution
	[12]	Simulation model	ETS would increase cost no matter that the allowances are free distribution.
	[13]	Event study	Carbon quota shortages lead to a decrease of profitability for the listed companies.
Positive impact	[7]	Event study	EU CO2 allowance price dropped 50 percent, equating to a €28 billion reduction in the value of aggregate annual allowances. We examine daily returns for 552 stocks from the EUROSTOXX index.
	[8]	Difference in difference estimation	As to the electricity sector, the relationship is positive.
	[9]	Multifactor market model	Positive effect and negative effect on stock market is found in Phase II and III, respectively, and the EUA price effects are sector-specific.
	[10]	Multifactor market model	Company-specific effect of carbon price is found in the Spanish power sector.
	[14]	Event study	Firms are more positively valued with a lower carbon-intensive production
	[15]	Model of rent creation	The impact of allowance trading will continue to create surplus rents in the electricity sector as a whole, although their distribution will differ widely between different producers.
	[16]	CAPM model	EUA price changes are shown to be positively related to stock returns of the most important electricity corporations.
	[17]	APT	Power generation, the largest affected industry, is correlated with rising prices for emission rights positively in EU ETS
No impact	[8]	Difference in difference estimation	EU ETS has no impact on revenue performance of cement and iron and steel industries.
	[18]	Ordinary least squares	The impact of relative allowance allocation on both economic performance and employment of German companies are not found

.

As to the research on ETS in China, scholars conducted studies focusing on scheme mechanism analysis using a Computable General Equilibrium (CGE) model to analyze the carbon markets’ impact on society and environment, and the carbon market’s prospects [5,20,21,22]. Empirical evidence on the economic consequence of ETS in China is rather scant because of the amount of trading data. Some researchers investigated the link between carbon prices and macro risks in China’s cap and trade pilot scheme [23]. The results demonstrate that industrial sector indices are positively related to the allowance prices in Shenzhen and Guangdong. However, no relationship shows statistical significance between the industrial sector indices and carbon prices of the Beijing trading pilot. Researchers have also estimated the volatility in the Shenzhen market and its relationship with expected return premium [24]. The results indicate that influence of carbon price on regulated entities’ value is ambiguous. The existing papers have not yet explored the carbon market effects among different pilots.

This paper mainly concerns whether and how carbon markets in China influence a corporation’s value using the conventional thermal listed companies. On one hand, the electricity industry is one of the main CO₂ emitters in China, and it has become one of the most important regulated sectors in the carbon market. The electricity industry is the leading GHG emitter in both production and consumption. Carbon emissions from the electricity industry accounted for about 50% of the total carbon emissions across the country in 2010 [25]. Power generating facilities, especially the conventional thermal companies, are affected by the carbon market in China. On the other hand, as a developing country, the government in China controls the emerging carbon markets to a large extent. Most electricity companies in China are state-owned companies, who would perform actively in the carbon market to perform government commands and tasks. Thus, we select the conventional thermal listed companies as samples. This paper contributes to the existing literature in three dimensions. Firstly, our paper explores the relationship between carbon markets and corporations in China from the economic aspect. Secondly, this paper provides a thorough analysis of the effects with respect to the full samples (from the first trading year to the third trading year) and sub-samples (the individual carbon trading pilots). Thirdly, we conduct an analysis to further carbon market management for the policy makers and governments.

The structure of the paper is organized as follows: Section 2 presents the methodology of empirical study; Section 3 describes the used sample and data; Section 4 reports the empirical results and discussion; Finally, Section 5 concludes and explores the implications.

2. Methodology

2.1. Model

To estimate the effect of carbon prices, we apply a model based on the multifactor market model, which is broadly used in corporate value and stock market analysis [26]. As to the carbon market research, the multifactor market model is frequently employed to explain the influence of carbon price on the European electricity listed firms’ stock value [6,10,16,17]. In their studies, the EUA prices, financial market variables, and energy market variables, such as electricity prices, oil prices, gas prices, coal prices, and so on, are taken into consideration as explanatory variables [27].

The results of existing empirical studies focusing on the EU ETS show that stock value is closely related to the financial market [6,10]. Except from the financial market, energy market performance is also closely related to stock value. An energy companies’ stock value is associated with the oil, gas, electricity, and coal markets [16]. Therein, oil price is one of the main indicators for energy prices and so influence energy corporations [6,10]. Many empirical results show stock value is influenced by its main product’s price. Thus, the econometric analysis of the latest research inferred that the electricity price affects the stock market return of electricity companies.

Regarding this study, carbon price is explanatory variable. Moreover, financial market index, coal market index, oil market index, and gas market index are considered as other explanatory variables. Electricity price is not taken into consideration in the model. As the public utility, the demand of electricity is low elastic, and seldom changes in the short term. In addition, the Chinese government greatly controls electricity price and dispatch. The National Development and Reform Commission (NDRC) sets wholesale and retail electricity prices and adjusts them infrequently [28]. The volatility of China’s electricity market is low. Thus, the price of electricity has little impact on thermal listed corporate stock value in China.

The basic model could be employed as follows:

$$P_{it}={\alpha }_0+{\alpha }_1{P}_{it}^c+{\alpha }_2{P}_{it}^{coal}+{\alpha }_3{P}_{it}^{oil}+{\alpha }_4{P}_{it}^{gas}+{\alpha }_5{P}_{it}^m+{\epsilon }_{it}$$

(1)

$P_{it}$ is the price of stock i at time t, $P_{it}^c$ is the carbon prices (for the corresponding pilot carbon market), $P_{it}^{coal}$ is the coal prices, $P_{it}^{oil}$ the oil prices, $P_{it}^{gas}$ is gas prices, $P_{it}^m$ is the financial market index, ${\epsilon }_{it}$ is the disturbance term with $E({\epsilon }_{it})=0$, $\mathrm{var}({\epsilon }_{it})={\sigma }^2$, ${\alpha }_0,\dots ,{\alpha }_5$ mean the unknown parameters of the function which would be estimated in the equation. The statistical significance and the estimated sign of ${\alpha }_1$ reflects the relationship between carbon price return and corporation values.

2.2. Diagnostics Tests Process and Method

In this paper, we perform several diagnostics tests on the sample panel dataset to determinate the appropriate estimation. The testing procedures proceed as follows: (i) Im. Pesaran, and Shin panel data unit root test [29] has been applied in this paper. The null hypothesis is that all the series follow a unit root process, the alternative hypothesis is that some series have unit roots. The unit root test is conducted with individual intercepts and automatic selection of maximum lag length based on Schwarz Information Criterion (SIC), the Newey-West automatic bandwidth selection and Bartlett kernel; (ii) Hausman test and Breusch and Pagan Lagrange multiplier test are applied to see if the panel fixed effect model or random effect model is appropriate for the data; (iii) Wooldridge test for autocorrelation in panel data is performed. The null hypothesis is that there is no first order autocorrelation within each panel; (iv) Likelihood Ratio (LR) test for panel level heteroscedasticity is applied to suggest that the existence of heteroscedasticity; (v) Pesaran’s test is performed to find whether there is cross-sectional independence between the panels; (vi) Panel estimation is then performed using Cross-sectional Time-series Generalized Least Square method.

We estimate the regression using Feasible Generalized Least Squares (FGLS) Model which allows estimation in the presence of Autoregressive model (AR (1)) autocorrelation within panels and cross-sectional correlation and heteroscedasticity across panels. We estimate the Equation (1) using Stata command xtgls.

3. Sample and Data Selection

3.1. Sample Selection

Our samples are selected in the following two steps. Firstly, we select the spot price of active carbon market pilots. Seven carbon market pilots are currently operating in China. We choose five of them, including the markets in Shanghai, Guangdong, Beijing, Shenzhen, and Hubei, ignoring the other two markets in Tianjin and in Chongqing, respectively, whose trading behavior is too inactive to obtain sufficient trading data (only 28 dealings with 277,099 ton carbon emission have taken place in Chongqing carbon pilot over 18 months since it began trading (19 June 2014)). Secondly, we chose the thermal listed companies regulated by the carbon markets. The power industry is one of the most important regulated industries in all carbon pilots [30]. As a significant energy-intensive industry, the power industry emits CO₂ at both the manufacturing stage and consumption stage. Hydropower, nuclear power, and other renewable energy source power companies are not included in our samples, because they produce less CO₂ emissions and are less affected by the carbon emission trading mechanism compared to the conventional thermal companies [10].

Following the above considerations, we select 10 thermal listed companies regulated by ETS in different carbon trading pilots in China as samples. In

Table 2. The description of selected enterprises.

City/Province	Name	Abbreviation	Stock Code	Generation Style
Beijing	Huaneng Power International.INC	HNP	600011.SH	Thermal Power
	Jingneng Power Co.Ltd.	JNP	600578.SH	Thermal Power
Guangdong	Guangdong Electric Power Development Co. Ltd.	YEPD	000539.SH	Thermal Power
	Shaoneng Group Co. Ltd.	SNG	000601.SZ	Thermal Power
	Baolihua New Energy Co. Ltd.	BLH	000690.SZ	Thermal Power
	Guangzhou Development Group Incorporated	GZD	600098.SH	Thermal Power
Hubei	Guodian Changyuan Electric Power Co. Ltd.	CYEP	000695.SZ	Thermal Power
Shanghai	Shanghai Electric Power Co.Ltd.	SHEP	600021.SH	Thermal Power
	Shenergy Co.Ltd.	SN	600642.SH	Thermal Power
Shenzhen	Shenzhen Energy Co.Ltd.	SZE	000027.SZ	Thermal Power

, we report the sample distribution. The samples include Huaneng Power International.Inc. (HNP), Jingneng Power Co., Ltd. (JNP), Guangdong Electric Power Development Co., Ltd. (YEPD), Guangdong Shaoneng Group Co., Ltd. (SNG), Guangdong Baolihua New Energy Stock Co., Ltd. (BLH), Guangzhou Development Group Inc. (GZD), Guodian Changyuan Electric Power Co., Ltd. (CYEP), Shanghai Electric Power Co., Ltd. (SHEP), Shenergy Co., Ltd. (SN), and Shenzhen Energy Co., Ltd. (SZE). We obtain the stock prices of the selected companies weekly to represent the companies’ value.

3.2. Data Sources

The weekly samples used in our analysis range from July 2014 to June 2017, including 108 observations and covering three carbon allowance trading years. We compare the results not only among the trading periods but also among the carbon trading pilots. Carbon prices (P^c, yuan/ton) of the corresponding carbon pilots are used as explanatory variables. As mentioned above, electricity stock prices are influenced by the financial market, energy markets, and other factors. Thus, we chose the following four variables. The CSI 300 (P^m, a capitalization-weighted stock market index) is used to proxy market performance, reflecting the overall trend for both the Shanghai and Shenzhen stock market. The index includes China’s major listed enterprises from the two financial markets. It is therefore the most representative market index for financial market performance. As to the energy market variables, we select China’s coal price index (P^coal) in different regions to present the coal market price (coal price data are difficult to obtain because there is no accurate price for each carbon pilots area). Specifically, The South China coal price index proxies the coal price in Guangdong and Shenzhen; the East China coal price index proxies the coal price in Shanghai; the North China coal price index proxies the coal price in Beijing; and the Middle China coal price index proxies the coal price in Hubei. We take Brent spot price to proxy the oil price (P^oil, yuan/barrel). We transformed the prices using the current exchange rate at the time. We chose the industrial piping gas price in different regions to proxy gas price (P^gas, yuan/cubic metre). All data are extracted from Wind Info Database (WIND) (WIND is a leading finical data service. See: http://www.wind.com.cn/en/). Summary statistics of independent variable and explanatory variables are reported in

Table 3. Summary descriptive statistics.

	P	Pc	Pm	Pcoal	Poil	Pgas
Mean	8.73	27.25	3518	142.6	346	4215
Median	7.76	24.24	3445	140.7	346.9	3925
Min.	3.68	4.3	2405	122.2	182.9	2875
Max.	31.97	59.78	5324	168.3	618.3	5700
Std. Dev.	3.69	13.76	583.3	10.51	73.01	744.4
Skewness	2.10	0.502	0.861	0.406	0.822	0.462
Kurtosis	10.56	2.06	4.186	2.655	4.811	2.122

. When compared to other variables, the standard deviation of carbon prices and the coal price index are relatively small, which indicates the low price volatility of these variables.

4. Empirical Results

4.1. Estimation for Full Sample

In

Table 4. Im-Pesaran-Shin panel unit root test for full sample.

Variables	Test Statistics	p-Value
Pc	−4.4280	0.0000
d.Pcoal	−10.8699	0.0000
Poil	3.3718	0.0000
d.Pgas	9.4485	0.0000
Pm	−4.9197	0.0000

, the unit root test results of the full sample show that coal prices and gas prices are not stationary. To address the issue, we conduct the first differences of gas prices and coal prices. For instance, the first difference of P^gas is calculated as the difference in the natural log of current gas market price and the natural log of previous period gas market price. The first differences are tested for stationary and are labeled as d.P^gas and d.P^coal. Other variables are calculated in the natural log of the original prices. The Hausman test result is 0.85, with a p-value of 0.9740, indicating that the random effect model is more appropriate.

Considering the variables are stationary in the different order, we apply the Pooled Mean Group (PMG) estimator proposed by Pesaran et al. (1999), which allows the short-term parameters to differ between groups while imposing equality of the long-term coefficients between groups. Thus, we apply this method to explore the long-term and short-term relationship between the carbon market and stock value.

The model is as follow:

$$\begin{array}{l}\Delta \ln P_{it}=a_i(\ln P_{it-1}^{}+b_1\ln P_{it}^c+b_2{P}_{i,t-1}^{coal}+b_3{P}_{i,t-1}^{oil}+b_4{P}_{i,t-1}^{gas})\\ +c_{1i}\Delta \ln P_{it}^c+c_{2i}\Delta \ln P_{it}^{coal}+c_{3i}\Delta \ln P_{it}^{oil}+c_{4i}\Delta \ln P_{it}^{gas}+{\epsilon }_{it}\end{array}$$

(2)

In

Table 5. Results for full sample.

Convergence Coefficient	Z Statistics
Lag.logP	−0.0605 ***	(−5.37)
Long-run coefficients
logPc	0.0697 *	(1.8)
logPcoal	−1.090 ***	(−3.63)
logPoil	0.401 ***	(3.19)
logPgas	0.409 ***	(2.17)
logPm	1.695 ***	(13.2)
Short-run coefficient
∆ logPc	−0.0173	(−0.62)
∆ logPcoal	−0.223	(−1.21)
∆ logPoil	0.0572	(1.62)
∆ logPgas	0.0158	(1.22)
∆ logPm	1.163 ***	(9.64)
Constant	−0.746 ***	(−5.35)
Log likelihood	1756.762
Observations	1070

z-statistics in parentheses; *** p < 0.01, * p < 0.1.

, we summarize the results from the regression of carbon prices on the thermal listed corporation value for full samples (2014–2017). Regarding the long-term parameters, the results show that the long-term relationship between the carbon market and stock value is positive, but the impact is small. The coefficient is 0.0697 at the 10% significance level, which implies that a 1% increase in the carbon prices are associated with 0.0697 increase in power enterprises’ value. Coal price has a negative influence on stock value, with −1.090 coefficient at the 1% significance level. As to the oil price and natural gas price, they have positive significant influence on enterprises’ value. Financial market performance also has a significant positive impact on corporate value with a 1.65 coefficient. The Wooldridge test result is significant at the 1% level. LR test for panel level heteroscedasticity is applied to suggest that the existence of heteroscedasticity at a 1% level significance.

Moving to the short-term effect, the effect of carbon market on stock value is not significant. Only the financial market has a positive impact on stock value in the short term. The influence of the energy market on stock value is also not significant. The result of Pesaran’s test for the cross-sectional independence is significant at a 1% level showing that the cross-sectional dependence exists between the panels. The diagnostics testing results imply that the data suffer from panel-level autocorrelation, cross-sectional dependence, and heteroscedasticity.

In summary, the emission trading scheme leads to a positive influence on corporate value in China in the long term. The former research demonstrated that profits for the marginal production unit for electricity will rise in respect to the CO₂ cost for this unit if Emission Allowances are fully grandfathered [31]. Profit changes depend on carbon intensity of marginal units. Under full grandfathering, electricity generation could profit from EU ETS. Coal prices have a negative influence on electricity generation in the long term. The reason for this is that coal is the main production material of electricity generation. The price volatility influences the cash flow of the thermal listed companies. As to the short-term effects, most explanatory variables have no significant impact on stock value, because both the ETS mechanism and energy prices are under the control of the NDRC in China. Different from other countries, electricity prices in China are set and adjusted by governments, not following the demand and supply rules. Thus, power companies have no right to increase electricity price and could not transfer the additional cost to the consumers [17].

4.2. Market-Specific Estimation Results

There is no national carbon market in China. The existing carbon trading markets are pilots for executing the ETS. Local government plays a crucial role in carbon trading, including selecting the covered companies, distributing allowances, developing punishment measures, etc. Carbon trading markets in China are not only regulated by the NDRC but are also controlled by local governments. The specific carbon trading implementation is under supervision by the local government. In this section, we explore the influences and the extent of carbon prices on stock value in different carbon trading pilots.

4.2.1. Estimated Results for Each Carbon Trading Pilots

Table 6. Results for different carbon trading pilots.

VARIABLES	Beijing	Guangdong	Hubei	Shanghai	Shenzhen
Pc	−0.29	−0.140 **	0.295 **	0.01	0.298 ***
	(−1.60)	(−2.08)	(2.47)	(0.16)	(3.6)
Pcoal	−0.452 ***	−0.758 ***	−10.96 ***	−0.82	−0.503 **
	(−2.58)	(−3.33)	(−20.59)	(−1.25)	(−2.07)
Poil	0.04	0.05	0.235 ***	0.11	0.134 **
	(0.39)	(0.77)	(3.33)	(0.69)	(2.4)
Pgas	0.276 **	0.01	0.675 ***	−0.29	0.849 ***
	(2.51)	(0.07)	(5.98)	(−1.20)	(12.1)
Pm	0.984 ***	1.111 ***	0.995 ***	1.796 ***	1.612 ***
	(9.93)	(18.04)	(13.27)	(12.06)	(28.33)
Constant	−5.301 ***	−3.191 **	40.59 ***	−6.64	−17.41 ***
	(−3.74)	(−2.06)	(18.05)	(−1.40)	(−11.86)
Wald chi-squared	142.25	383.34	1265.58	156.12	1289.36
Observations	216	432	108	216	108

z-statistics in parentheses; *** p < 0.01, ** p < 0.05.

presents the regression results of the different carbon trading pilots for the full sample period. The estimated coefficients show that carbon prices in Guangdong, Hubei, and Shenzhen have a statistical significant impact on enterprises’ value. Carbon prices of Guangdong carbon pilot show significant negative influence on corporate value, while the carbon prices of Hubei and Shenzhen have significant positive impact on corporate value. In addition, carbon prices in Shenzhen exhibit the highest factor (0.298) in its empirical analysis at a 1% level of significance. Carbon prices in Hubei is demonstrated as the second highest factor (0.295) at a 5% level of significance. Carbon prices in Beijing and Shanghai carbon trading pilots exhibit no statistically significant relationship with corporate value.

In terms of fuel prices, coal prices have statistically significant negative influences on corporate value in most selected regions, except in the Shanghai carbon pilot. Oil prices have statistically significant positive influences on corporate value in Hubei and Shenzhen. The findings indicate that a 1% increase in oil price causes a 0.235% increase in stock value of thermal listed companies in Hubei, holding other independent variables constant. Oil prices of the Shenzhen carbon trading pilot have a significantly positive impact on corporate value. We can point out that a 1% increase in oil prices causes a 0.135% increase in stock value of thermal listed companies covered by the Shenzhen carbon trading pilot if other explanatory variables are constant. Gas prices lead a positive impact on stock value in Beijing, Hubei, and Shenzhen. Regarding the financial performance, the financial market provides empirical evidence for a positive impact on corporate value in all the five regions.

4.2.2. Change in the Effect for the Three Trading Years

Comparing the effects of the different trading pilots on corporate value for the three trading years from 2014 to 2017, in

Table 7. Results for different carbon trading pilots in three year.

	First Trading Year (2014–2015)					Second Trading Year (2015–2016)					Third Trading Year (2016–2017)
VARIABLES	Beijing	Guangdong	Hubei	Shanghai	Shenzhen	Beijing	Guangdong	Hubei	Shanghai	Shenzhen	Beijing	Guangdong	Hubei	Shanghai	Shenzhen
Pc	−0.67	−0.161 ***	−1.054 **	0.22	−0.389 **	−0.06	0.07	−0.729 ***	0.06	0.16	−0.03	−0.43	0.705 **	0.06	0.01
	(−1.40)	(−2.69)	(−2.08)	(0.56)	(−2.00)	(−0.21)	(0.33)	(−5.40)	(0.25)	(1.11)	(−0.03)	(−1.21)	(2.28)	(0.30)	(0.26)
Pcoal	−2.73	−5.274 *	−22.86 ***	5.34	−2.47	2.20	−8.790 ***	−2.61	3.71	−7.744 ***	−0.27	2.767 **	−2.07	0.15	0.444 **
	(−0.69)	(−1.95)	(−3.21)	(0.52)	(−0.77)	(0.71)	(−2.80)	(−1.27)	(0.75)	(−3.25)	(−0.25)	(2.20)	(−1.15)	(0.10)	(2.52)
Poil	−0.09	0.18	−0.19	−0.05	0.407 **	−0.14	0.07	−0.13	−0.17	0.04	0.09	0.908 *	0.19	0.16	0.185 ***
	(−0.29)	(1.17)	(−1.46)	(−0.12)	(2.14)	(−0.40)	(0.47)	(−1.00)	(−0.35)	(0.42)	(0.13)	(1.91)	(0.61)	(0.12)	(3.06)
Pgas	0.85	1.081 **	0.428 **	−3.85	1.206 **	0.36	0.554 *	0.506 ***	−0.03	1.036 ***	−0.07	0.82	0.14	−0.23	−0.01
	(1.58)	(2.34)	(1.99)	(−1.63)	(2.00)	(1.02)	(1.75)	(2.65)	(−0.02)	(5.83)	(−0.09)	(0.80)	(0.37)	(−0.11)	(−0.07)
Pm	0.825 **	1.068 ***	0.517 ***	1.503 ***	1.641 ***	0.982 **	1.593 ***	1.521 ***	1.315 *	1.641 ***	1.04	−1.66	0.43	0.28	0.448 *
	(2.03)	(5.38)	(2.59)	(3.00)	(7.01)	(2.31)	(5.95)	(9.52)	(1.90)	(10.10)	(0.53)	(−1.27)	(0.35)	(0.10)	(1.86)
Constant	4.62	9.67	112.4 ***	−4.14	−10.01	−18.62	27.14 **	1.83	−25.58	17.37	−5.11	−9.32	4.33	−0.06	−5.006 ***
	(0.22)	(0.74)	(2.94)	(−0.10)	(−0.66)	(−1.52)	(2.01)	(0.22)	(−1.61)	(1.58)	(−0.34)	(−0.84)	(0.61)	(−0.00)	(−4.01)
Wald chi-squared	130.4 ***	743.03 ***	694.25 ***	174.76 ***	716.32 ***	34.92 ***	121.22 ***	159.79 ***	26.48 ***	488.6 ***	27 ***	68.43 ***	32.933 ***	25.706 ***	55.47 ***
Observations	78	156	39	78	39	74	148	37	74	37	64	128	32	64	32

z-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

, we can find that the companies in Guangdong (−0.161), Hubei (−1.054), and Shenzhen (−0.389) suffer a significantly negative impact from the carbon market in the first trading year. It suggests that in the first year, the emission allowance prices of Guangdong, Hubei, and Shenzhen carbon trading pilots had an inverse impact on stock value, the increase of the carbon prices probably caused depreciation of the corporate value at some degree. However, in the second trading year, only the carbon prices of the Hubei carbon trading pilot have a significant negative impact (−0.729) on corporate value at a 1% significance level. In terms of the third trading year, the results indicate that carbon prices lead to a positive impact on corporate value in Hubei (0.705). The results indicate that the sensitivity of corporate value changes to the carbon prices is variable depending on trading year. For instance, the Hubei carbon market has a significantly negative influence on stock value in the first two trading periods and switches to have a significantly positive influence on stock value in the third trading period. Only the Guangdong, Hubei, and Shenzhen carbon pilots lead to an impact on the stock market.

Moving to other explanatory variables, the financial market index has a significantly positive impact on selected companies in the three trading years. As to the energy variables, the results reveal that coal prices and oil prices are related to power corporations in Guangdong, Hubei, and Shenzhen. Furthermore, the influences are time-varying. For instance, in the first year, coal prices lead to negative influences on corporate value in Guangdong and Hubei. However, the influences changed in the following trading years. Coal prices changed to have a negative relation to corporations in Guangdong and Shenzhen and lead to no statistically significant impact on corporate value in Hubei in the second trading year. In the third year, coal prices tended to be positively related to thermal listed companies in Guangdong and Shenzhen. Oil prices have no statistically significant influences on most thermal listed companies. Gas prices have a statistically significant impact on stock value of Guangdong, Shenzhen, and Hubei.

4.2.3. Results Discussion

From the above results, we can conclude that carbon prices have a positive influence on electricity stock value in the long term. Comparing the relationship between carbon prices and electricity stock value, the impact is carbon market-specific. The Hubei carbon trading pilot shows the best performance with significant impact in the full sample period. The reason for the carbon market-specific effect may be due to the specific carbon trading development principles, such as relative allowance allocation, carbon prices volatility, trading turnover, etc.

Firstly, emission permit allocation status varies in the different regions. The grandfathering allocation method is based on the former emissions and allows the covered emission intensive companies to get allowances freely. Although all carbon trading pilots in China use the grandfathering method to allocate emission permits, the free allocation ratio varies in different regions. For the first trading year, China’s carbon trading pilots distribute most allowances freely, ranging from 90% to 100%. Specifically, Beijing allocates 95% of permits freely, Guangdong allocates 97%, Hubei allocates 90%, Shanghai allocates 100%, and Shenzhen allocates 95% (Data are taken from individual website). The carbon emission amount of a regulated corporation is at a certain level, and could not be reduced in the short time. If the companies get less free emission permits, they would buy more allowances from the carbon market through active trading. The Hubei carbon pilot has put forward strict control on allocated allowances of existing pollutant sectors and dynamic adjustment for surplus quotas. In addition, the Hubei carbon trading pilot has changed the carbon quota allocation method since 2017. Taking the amount of benchmark emissions multiplied by the actual production time ratio of a year substitutes the simple grandfathering method to promote the effect of energy savings and emission reduction.

Secondly, carbon price volatility and trading turnover vary in the different regions. The abnormal carbon prices in China are caused by the immature carbon market mechanism. The second market is not completely developed. Only a spot market exists, with a lack of future and option trading, which affects the carbon prices and the trading volume of the carbon assets. Different from other carbon pilots, Hubei implements a series of measures to activate carbon trading. Compared with other carbon pilots, the Hubei carbon trading pilot remained more liquid with an average daily volume of 48,012 tons in the sample period. For instance, Hubei NDRC stipulates redundant emission permits will be invalid in the next trading year. Thus, the covered enterprises will trade their surplus allowances positively.

Thirdly, the effect of carbon prices in China on stock value is not stable. This is due to the attitudes of covered enterprises. The carbon emitters’ awareness of the ETS is mainly influenced by government regulations and policy. The carbon market has not been in existence for a long time, and at the beginning requires the covered firms to know and be familiar with this kind of emission trading scheme.

Energy prices do not lead to influences on thermal listed companies in the long term, the main reason is that energy prices in China are not market-driven. Energy price fluctuation in China does not function according to supply and demand but controlled by the government. For instance, the oil price in China is subject to the NDRC and lasts for a certain time until the next adjustment.

5. Conclusions

In this paper, we use a panel data econometric model to investigate the effect of the carbon market on listed thermal companies’ stock value. We demonstrate that the carbon trading pilots have a significantly positive effect as regards all phases in the long term, but have no significant impact in the short term.

By estimating the effect for different carbon trading pilots, we can conclude that the effect of carbon prices on corporate value is market-specific. For the full sample period, the Hubei and Shenzhen carbon trading pilots have a significantly positive impact on thermal listed corporate value. The Guangdong carbon trading pilots have a significantly negative influence on stock value. Carbon pilots are not only regulated by the NDRC but also take orders from local regional government. Moving to the effects of the carbon markets in different trading years, obtained results indicate that the relationship between carbon price changes and electricity generation stock value is unstable, which is consistent with previous analysis [32]. The primary reason is that the carbon markets in China are not mature and are improving gradually. China’s carbon market pilots only have about three years experience. The mechanism and allocation methods are imperfect.

These findings have important policy implications as follows: Improving the carbon market mechanism. Firstly, the government should allocate more rigorously and tighten the punishment policy for the companies who do not perform. The effects of ETS will be more pronounced. Quota allocation policy is crucial for the construction of a successful national market [6]. When the allocation method becomes stricter, the participants will become more active in the market. It will break the current oversupply situation. Then, the carbon price will become higher, which would lead to a significant impact on regulated companies. Secondly, establishing a perfect measurable, reportable, and verifiable (MRV) administrative system. In the construction of this carbon market, quality greenhouse gas emission data is the basis, and thus precise calculation and reporting of greenhouse gas emissions will be a key job. It is important to set a MRV greenhouse gas emission data management mechanism for the carbon market. An effective management system for the MRV system should be built, specifying the related party’s obligation, such as government, enterprises, and verification institutions. Increasing companies’ awareness of energy saving and emission reduction. At present, the regulated companies in China are mostly state-owned companies. Some companies trade in the carbon markets to comply with the government’s order, but not from self-willingness. Improving a companies’ willingness to reduce energy consumption and emissions is not only a matter of achieving the energy-reduction goal but also helps to develop sustainably. Companies applying cleaning production and achieving energy conservation and emission reduction would benefit from the ETS. Investor strategy changes. Except for energy related factors, the investors should also pay close attention to the carbon markets when they make investments. Investors could make use of carbon prices as an additional and comparable capital market indicator for the electricity industry. Carbon market specific effects have different influences on stock value. Investors could hold carbon allowances of Guangdong and Shenzhen carbon trading pilots and the thermal listed stock in these two regions as an investment portfolio to hedge. Since the two series have adverse relationship in the long term. The development of carbon trading will be a significant element for investors to benefit from both the carbon market and stock market.

This study is almost the first empirical contribution to the economic impacts of carbon trading pilots on the financial market in China. Although we have focused on the power corporations in China, our study could be extended to other industries including energy intensive industries and non-energy intensive industries. In summary, this study provides useful information for carbon market traders, policy makers, and investors.