An Empirical Analysis of Carbon Emission Price in China

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Applied Energy Symposium andSymposium Forum 2018: Low carbon cities andcarbon urbancities energy systems, CUE2018-Applied Energy and Forum 2018: Low and Applied Energy Symposium and Forum 2018: LowShanghai, carbon cities and urban energy systems, CUE2018, 5–7 June 2018, China urban CUE2018, energy systems, 5–7 2018, June 2018, Shanghai, 5–7 June Shanghai, ChinaChina

An Empirical Analysis Symposium of Carbon Emission Price in China The 15th International on District Heating and Cooling An Empirical Analysis of Carbon Emission Price in China a,b, a,b Kaile Zhou *, Yiwen a,b Assessing the feasibility ofa,b,using theLi demand-outdoor Kaile Zhou *, Yiwen Liheat School of Management, Hefei University of Technology, Hefei 230009, China temperature function for aHefei long-term district heatChina demand School of Management, University of Technology, Hefei 230009, Key Lab of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, Chinaforecast a a

b b

Key LabDepartment of Process Optimization and City Intelligent Decision-making, of Education, of Public Policy, University of Hong Kong,Ministry Kowloon, Hong KongHefei SAR,230009, China China c

a,b,c a a of Hong Kong, Kowloon, c China University Hong Kong SAR, I. AndrićDepartment *, A.of Public PinaPolicy, , P. City Ferrão , J. Fournierb., B. Lacarrière , O. Le Correc c

a

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

b Abstract Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Abstract Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Carbon emission trading, as an effective economic tool to deal with climate change issues, has received widespread attention in Carbon emission as an effective economic to deal with change issues, has received widespread attention in recent years. As atrading, major carbon emitter, China playstool an important roleclimate in global climate change. This paper uses the Vector Error recent years.(VEC) As a major emitter, plays an important role inenergy globalprice, climate change. This paper uses the Correction modelcarbon to explore the China dynamic relationship between macroeconomic indicators, airVector quality,Error and Abstract Correction (VEC)trading model to explore dynamic energy price, macroeconomic indicators, quality, and carbon emission price. The the results showrelationship that there between is a long-term equilibrium relationship between air these selected carbon emission trading price. The results that there a long-term equilibrium relationship between these selected indicators and carbon emission trading price.show In addition, thisis paper also uses the Generalized Auto-Regressive Conditional District heating networks aremodel commonly addressed in theprice literature as of the Generalized most effective solutions for decreasing the indicators and carbon emission trading price. Intheaddition, this paper alsoone uses Auto-Regressive Heteroskedasticity (GARCH) to analyze carbon fluctuation characteristics. It is found that there isConditional a positive greenhouse from the building sector. These require investments are returned the heat Heteroskedasticity (GARCH) model toofanalyze the carbon price fluctuation characteristics. Itwhich isnews found that there is a positive leverage effectgas onemissions the price fluctuation the selected carbonsystems emission return high series. External bad will have a through greater impact Due toon the climate conditions andcarbon building renovation heat demand in will the have futurea greater could decrease, leverage effect thechanged price than fluctuation of the selected emission returnpolicies, series. External bad news impact onsales. carbon price fluctuation good news. thefluctuation investmentthan return period. onprolonging carbon price good news. The main©scope this paper to rights assessreserved. the feasibility of using the heat demand – outdoor temperature function for heat demand Copyright 2018of Elsevier Ltd.isAll Copyright © 2018 Elsevier Ltd. All All rights rights reserved. forecast.and The district of under Alvalade, located inof Lisbon (Portugal), was used as a case study. The district consisted 665 Copyright © 2018 Elsevier Ltd. reserved. Selection peer-review responsibility the scientific committee of Applied Energy Symposium and is Forum 2018:of Low Selection and peer-review under responsibility ofand thetypology. scientificThree committee of the CUE2018-Applied Energy Symposium and buildings that vary inenergy both construction period weather scenarios (low, medium, high) and three Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and Forum 2018: district Low carbon cities and urban systems, CUE2018. Forum 2018: Low carbon cities and urban energy systems. renovation scenarios (shallow, intermediate, deep). To estimate the error, obtained heat demand values were carbon cities and urban were energydeveloped systems, CUE2018. compared with results a dynamic heat demand model, developed and validated by the authors. Keywords: Carbon market; from Carbon emission trading; GARCH model;previously Price fluctuation The results showed thatCarbon whenemission only weather considered, the margin of error could be acceptable for some applications Keywords: Carbon market; trading;change GARCHismodel; Price fluctuation (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.scenarios, Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.The Introduction decrease in decades, the number of heating hours of 22-139hof during theeconomy heating season (depending ondemand the combination weather and In recent with the rapid development global and the increasing for fossiloffuels, large renovation scenarios considered). On development the other hand,offunction intercept increased for 7.8-12.7% per decade (depending on the In recent decades, with the rapid global economy and the increasing demand for fossil fuels, large amounts of carbon dioxide and other greenhouse gas (GHG) emission have exacerbated the greenhouse effect. How coupled of scenarios). The values suggested could be used to modify the function parameters forthe the scenarios considered, and amounts carbon dioxide and other greenhouse gas (GHG) emission exacerbated How toimprove deal with the greenhouse effect and climate issues has become onehave of the most complexgreenhouse challenges effect. that human the accuracy of heat demand estimations.

to deal are withcurrently the greenhouse effect andaround climatethe issues hashave become of therealized most complex challenges human beings facing. Countries world also one gradually the seriousness of that the climate beings are currently facing. Countries around the world have also gradually realized the seriousness of the climate issue. The Convention on Climate Change was adopted at the United Nations © 2017 The United Authors.Nations PublishedFramework by Elsevier Ltd. issue. The United Nations Framework Convention Climate Change was adoptedonatDistrict the United Nations Peer-review under responsibility of the Scientific Committeeon of The 15th International Symposium Heating and *Cooling. Corresponding author. Tel.: +86-551-62901501 * E-mail Corresponding Tel.: +86-551-62901501 address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities Selection peer-review under responsibility the scientific Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. and urban energy systems, CUE2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.196

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Environment Development Conference in 1992. Subsequently, the Kyoto Protocol was promulgated in 1997. It is these two conventions that gave birth to the carbon finance market [1, 2]. In 2015, the Paris Climate Change Conference adopted the "Paris Agreement" [3]. It focuses on how to deal with climate change after 2020, which is the second legally binding climate treaty after the Kyoto Protocol [4]. In order to effectively respond to global climate change, Chinese government has taken many measures to control GHG emission. In October 2011, the National Development and Reform Commission approved the implementation of the “Twelfth Five-Year Plan” for the gradual establishment of a domestic carbon emission trading market and agreed to establish carbon emission trading pilots sites in Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong and Shenzhen [5, 6]. In 2017, the unified carbon market covering the whole country was also officially launched, and it is expected that actual transactions will begin before 2020. China's carbon market is still in its infancy. The price of carbon emission trading in seven pilot markets varies greatly, and abnormal fluctuations often occur. This is not conducive to the establishment of a unified national carbon trading market. Therefore, based on the carbon emission trading price and related data from China Hubei Emission Exchange, this study quantitatively analyzes the dynamic relationship between carbon emission price and energy price, macroeconomic indicators and air quality. It also investigate the fluctuation characteristics of carbon emission trading price. The results allow us to further understand the fluctuation characteristics of the carbon market rules and price and provide some policy implications for reducing carbon market risks, establishing a unified national carbon market pricing mechanism, and improving the carbon emission trading system. 2. Methods

(1) VEC model and cointegration analysis

Although some economic variables are themselves non-stationary sequences, their linear combinations may be stationary. This smooth linear combination is called a cointegration equation [7, 8]. The idea embodied in the VEC model is that there may be a long-term equilibrium relationship between the relevant variables [9]. For the VAR model with co-integrated rank h , there is the following cointegration equation:

yt ν  δt  Φ1 yt 1 

 Φp yt  p  εt，t 1, 2,

The VEC model based on the VAR model is as follows.

Δyt ν  δt  αβ'yt 1  Γ1 Δyt 1  Γ2 Δyt 2 

,T

 Γ p 1 Δyt  p 1  εt

(1) (2)

(2) GARCH family models

The GARCH model was proposed by Bollerslev [10]. It further models the variance of error terms and is therefore well-suited for analyzing and predicting fluctuation. The basic setting of the GARCH(1,1) model is as fallows. (3) Mean equation: yt  xt y  μt，t  1, 2, , T 2

Variance equation: σt

 ω αμt21  βσt21

(4)

Compared with the GARCH model, the TGARCH model sets up a threshold d . When the value of d is 1, it

indicates the influence of good news. When the value of d is 0, it indicates the influence of bad news.  is a parameter used to reflect the asymmetric impact of good and bad information on financial market. In the standard GARCH model, the value of the parameters is limited. So we consider the following Exponential GARCH model (EGARCH) [11].

lnσt2 ω0  γ

μt 1 μ 2  α t 1   βlnσt21 σt 1 σt 1 π

(5)

The left side of the Eq. (5) has a logarithmic form of the conditional variance, which indicates that the leverage effect will be exponential, so the predicted value of the conditional variance is also non-negative. If the leverage effect does exist, we can pass the hypothesis that   0 , and the impact of the impact will be asymmetric. At this time, the impact of the impact is asymmetric.



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3. Results and discussions 3.1. Influence factors of carbon emission price This section selects the following indicators as variables for investigating the influencing factors of China's carbon emission price. (1) Carbon emission price: HBEA daily closing price [12] of the China Hubei Emission Exchange. This is because the total volume and total amount of carbon emission trading of China Hubei Emission Exchange have been the largest among seven carbon emission trading pilots. (2) Energy price: The daily closing price of continuous coking coal futures (JM0) [13]. This is because the CO2 emission is directly related to the use of energy. In China's energy consumption structure, the proportion of coal is relatively large, which has a relatively large impact on carbon dioxide emissions. (3) Macroeconomic indicators: The Shanghai and Shenzhen 300 Index (HS300) [14] and The Shanghai Industrial Index (000004.SH) [15]. Macroeconomic activities have a certain influence on the carbon emission rights market. So, we choose these two indicators to reflect the macroeconomic situation. (4) Air quality: The Air Quality Index (AQI) [16] where the exchange is located (Wuhan, China). The development of carbon emissions trading in China is closely related to the increase in air pollution in recent years. The degree of air pollution can be used as a direct indicator of carbon emissions, that is, air quality represents a certain degree of industrial emissions, and GHG or considerable levels of carbon dioxide emissions increase. The above indicators are selected from April 2, 2014 to November 30, 2017. The trend of variables can be made more linear. Therefore, we perform logarithmic processing on all data. After taking the logarithm, the variable name is as follows:

lnHBEA=log(HBEA), lnJM0=log(JM0), lnHS300=log(HS300), lnSH=log(000004.SH), lnAQI=log(AQI). Before fitting the model, we first test the stationarity of the sequence. Here we use the Augmented DickeyFuller (ADF) unit root test [17]. The test results show that the P value of the original sequence is greater than 0.05, so all sequences are non-stationary sequences. Then, after all the sequence first-order differences are performed, the ADF test is performed again. The results show that all the sequences are first-order single integer sequences, and a VEC model can be established. Before fitting the VEC model, we first test whether these sequences have a cointegration relationship. The Johanson Cointegration Test [18] results are shown in Table 1. Table 1. Results of the Johansen cointegration test. Maximum rank

parms

LL

trace statistic

5% Critical Value

0

5

9209.5012

1

14

9323.468

0.22439

264.9269

68.52

36.9933*

47.21

2

21

9334.0496

3

26

9340.0847

0.02332

15.8301

29.68

0.01337

3.7600

15.41

4

29

5

30

9341.8807

0.00400

0.1679

3.76

9341.9647

0.00019

-

-

eigenvalue .

From Table 1, we can see that the original hypothesis was rejected at the 5% level of significance. This shows that there is a long-term equilibrium relationship between these five series, and the standardized cointegration equation is: (6) lnHBEA= -0.093lnJM0-2.781lnHS300  3.085lnSH  0.996lnAQI -2.38 (0.0986)

(0.5833)

(0.6349)

(0.0589)

From the cointegration equation, it can be seen that the Industrial Index has the greatest impact on the price of carbon emission rights, with a coefficient of 3.085. The impact of the Shanghai and Shenzhen 300 Index on carbon

Kaile Zhou et al. / Energy Procedia 152 (2018) 823–828 Kaile Zhou, Yiwen Li / Energy Procedia 00 (2018) 000–000

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emission price is second only to the Industrial Index, with a coefficient of -2.778. The Air Quality Index and coal price index have a weaker impact on carbon emission rights price, with coefficients of 0.096 and -0.093. The VEC model's estimation equation for HBEA is as follows:

D(lnHBEA)  0.02(lnHBEA(  1)  0.093lnJM0(  1)  2.781lnHS300(  1) 3.085lnSH (  1)  0.996lnAQI (  1)  2.38)  0.0004

(7)

The adjustment coefficient of the VEC model is -0.02, which indicates that when the lnHBEA is too high and deviates from the long-term equilibrium value, it will slowly decrease towards the long-term equilibrium value. However, the absolute value of the adjustment coefficient is smaller, the long-term adjustment is weaker, and the cycle of ironing is longer. After fitting the VEC model, the cointegration equation is required to be stable and the number of cointegration equations is set correctly, so that all the inferences made are meaningful. Therefore, the stability of the cointegration equation needs to be tested. The stationarity test of cointegration equation is shown in Fig. 1.

Fig. 1. Stationarity test of cointegration equation.

As shown in Fig.1, in addition to the unit roots assumed by the model itself, the eigenvalues of the adjoint matrix all fall within the unit circle, indicating that the cointegration equation is stable, and the VEC model fitting results have reference significance. 3.2. Fluctuation characteristics of carbon emission price This section selects the closing price per day of carbon emission trading price (HBEA) [12] of China Hubei Emission Exchange from April 2, 2014 to December 31, 2017. We took its logarithmic return to study and named the HBEA logarithmic return series as rt . Next, we descriptive statistics on the rate of return series. The results are shown in Table 2. Table 2. Descriptive statistics of the return series. Statistical indicators

Statistics

Sample capacity

918

Mean value

-0.0003689

Standard deviation

0.0350529

Skewness

-0.3879878

Kurtosis

38.2017

JB test value

17043.96

P value

0.00000

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According to the results in Table 2, it can be concluded that the HBEA return series basically conforms to the characteristics of fluctuating aggregation, non-normal distribution and spikes and thick tails. Then, we test the ARCH effect of the return series. According to the ARCH test results, we can reject the null hypothesis without the ARCH effect at a significant level of 5%, which means that there is an ARCH effect in the return series. Next, GARCH family models are fitted to the return series. Estimated results are shown in Table 3. Table 3. GARCH model estimation results. GARCH models

GARCH

TGARCH

EGARCH

Constant term (ω)

-0.0005803

0.0010026

0.0006687

ARCH term (α)

0.7941102

0.3827581

-37.79859

Asymmetric term (γ)

-

0.9553126

0.7653341

GARCH term (β)

0.1604046

0.138553

197.4103

Finally, compare the fitting effects of these three models. We use the AIC value and the Bayesian Information Criterion (BIC) value to compare the model's fitting effect. The smaller the value, the better the fitting effect. Table 4. Comparison of the fitness of GARCH family models. GARCH models

GARCH

TGARCH

EGARCH

AIC

-393.850

-395.939

-381.907

BIC

-391.921

-393.528

-379.979

From the comparison shown in Table 4, it can be seen that among three GARCH models, the AIC and BIC values of the TGARCH model are the smallest, which means that the fitting effect of TGARCH is slightly better. Based on TGARCH results, the sustainability coefficient     of the carbon emission rate series in Hubei is 0.521, which satisfies the constraint condition less than 1, but not close to 1. This shows that the impact of external positive and negative information will not have a lasting impact on the profitability sequence, and the impact of external good news or bad news on the carbon emission price is within a short period of time. In addition, there is a positive leverage effect on the carbon price fluctuations in Hubei. Each time there is one good news, it will have 0.383   times impact on the yield series. Each time one bad news occurs, it will have a 1.338     impact on the yield series. This shows that the same bad news will have a greater impact on carbon price fluctuation than good news. 4. Conclusions Based on data of the China Hubei Emission Exchange, this study investigated the influencing factors and fluctuation characteristics of China's carbon emission price. First, we use the VEC model to explore the dynamic relationship between the carbon emission price and three indicators include energy price, macroeconomic indicators and air quality. The results show that there is a long-term equilibrium relationship between the carbon emission price and these indicators. When the carbon price is too high and deviates from the long-run equilibrium value, it will slowly decline toward the long-term equilibrium value. However, the absolute value of the adjustment coefficient is smaller, the long-term adjustment is weaker, and the cycle of ironing is longer. Among these selected indicators, the Industrial Index and the Shanghai and Shenzhen 300 Index in the macro economy have a great impact on the carbon emission price. This shows that the current price of carbon emission in China is greatly affected by the macroeconomic conditions. Rapid industrial development means that the amount of carbon emission will increase accordingly, resulting in an increase in the price of carbon emission rights. With the rapid development of the macro economy, the carbon emission trading quota will increase accordingly, and the transaction costs of the carbon market will also decrease, which will cause the carbon price to drop. In addition, the Air Quality Index and coal price have a relatively weak impact on carbon price.

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Kaile Zhou et al. / Energy Procedia 152 (2018) 823–828 Kaile Zhou, Yiwen Li / Energy Procedia 00 (2018) 000–000

A GARCH family model is used to explore the characteristics of China's carbon emission price fluctuations and the results revealed that positive and negative impact of external information on carbon price does not produce a long-term sustained response. The impact of outside good news or bad news on Hubei carbon emission rights price is within a short period of time. In addition, there is a positive leverage effect on the carbon price fluctuations in Hubei, and the same bad news will have a greater impact on carbon price fluctuation than good news. According to the results, the reason behind these phenomena may be the fact that China's carbon emission trading market is still in its initial stage, and market liquidity is still insufficient. There is a relatively long time lag for the market information transmission. The pilot carbon market has large price differences and lacks an effective pricing mechanism. China’s carbon market is in its infancy, related laws and regulations need to be further improved. Acknowledgements This work is supported by the National Natural Science Foundation of China (No. 71501056), Anhui Science and Technology Major Project (No. 17030901024), Hong Kong Scholars Program (No. 2017-167), the grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 11271716 and CityU 21209715), and China Postdoctoral Science Foundation (No. 2017M612072). References [1] Lin L. Research on International Carbon Finance Market Development and Risk in Low Carbon Economy. Contemporary Finance & Economics. 2012:51-8. [2] Wang Y, Liu Q. Carbon finance market: global situation, development prospects and China strategy. Studies of International Finance. 2010:64-70. [3] Graaf, De TV. Is OPEC dead? Oil exporters, the Paris agreement and the transition to a post-carbon world. Energy Research & Social Science. 2016;23. [4] Liobikienė G, Butkus M. The European Union possibilities to achieve targets of Europe 2020 and Paris agreement climate policy. Renewable Energy. 2017;106:298-309. [5] Xinhua. The 12th Five-year Plan for National Economy and Social Development of the People's Republic of China. 2011. [6] NDRC. Notice on Carrying out Pilot Emissions Trading. 2011. [7] Johansen S. Statistical analysis of cointegration vectors. Journal of Economic Dynamics & Control. 1988;12:231-54. [8] Johansen S. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica. 1991;59:1551-80. [9] Engle RF, Granger CWJ. Co-Integration and Error Correction: Representation, Estimation, and Testing. Journal of Econometrics. 1987;55:251-76. [10] Bollerslevb T. Generalized autoregressive conditional heteroskedasticity. Eeri Research Paper. 1986;31:307-27. [11] Heynen RC, Kat HM. Volatility prediction: A comparison of stochastic volatility, GARCH(1,1) and EGARCH(1,1) models. Social Science Electronic Publishing. 1994;2. [12] China's Carbon Trading Network ( http://k.tanjiaoyi.com/). [13] Sina Finance (http://finance.sina.com.cn/futures/quotes/JM0.shtml). [14] Sina Finance (http://finance.sina.com.cn/realstock/company/sh000300/nc.shtm. [15] Sina Finance (http://finance.sina.com.cn/realstock/company/sh000004/nc.shtml). [16] Ministry of Environmental Protection of the People's Republic of China ( http://datacenter.mep.gov.cn/index). [17] Dickey DA, Fuller WA. Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica. 1981;49:1057-72. [18] Johansen S, Juselius K. Maximum Likehood Estmation and Inference on Cointegration - with Applications to the Demand for Money. Oxford Bulletin of Economics & Statistics. 1990;52:169-210.