- Email: [email protected]
Dynamic relationship between carbon price and coal price: perspective based on Detrended Cross-Correlation Analysis
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Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
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www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)3470–3475 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Dynamic relationship between carbon price and coal price: The relationship 15th Internationalbetween Symposiumcarbon on Districtprice Heating and coal Cooling Dynamic and price: perspective based on Detrended Cross-Correlation perspective based on Detrended Cross-Correlation Assessing the feasibilityAnalysis of using the heat demand-outdoor Analysis temperature function for a long-term district heat demand forecast Xinghua Fan, Xuxia Li, Jiuli Yin* Xinghua Fan,a Xuxia Li, Jiuli Yin* a b c c of Science, University, Zhenjiang, Jiangsu China , O. Le Corre I. Andrića,b,c*, Faculty A. Pina , P. Jiangsu Ferrão , J. Fournier ., B. 212013, Lacarrière
Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu 212013, China IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract a
Unlike the European Union market, the main driver of CO2 price in China seems to be coal price rather than oil and gas prices, Unlike the Union energy market,structure the mainindriver of CO2 price in China seemsthe to be coal price rather than oilcoal andand gascarbon prices, considering theEuropean coal-dominant this country. This study analyzes interrelationship between considering thetime coal-dominant energyinterval structure in this country. This the study analyzes the interrelationshipAnalysis betweenmethod. coal andChina’s carbon prices in both and time-scale perspectives by using Detrended Cross-Correlation Abstract prices in both timemarkets and time-scale perspectives using the Detrended Analysis method. China’s seven pilot carbon are taken interval as the sample. Resultsbyshow a weak correlationCross-Correlation between coal and carbon prices in short time seven pilot carbon markets are taken as the sample. Results show a weak correlation between coal and carbon prices in short time scales (shorter than a quarter) but medium in long time scales (longer than a quarter). For short time scales, the correlation is District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the scales (shorter a quarter) butthemedium insector. long time scales (longer thanfor a other quarter). ForFor short time scales, thethrough correlation is negative in Chongqing and from Shanghai, undetermined in Hubei, and positive pilots. long time scales, Shenzhen greenhouse gasthan emissions building These systems require high investments which are returned thepilot heat negative Chongqing and Shanghai, undetermined Hubei,others and positive for other pilots. For long scales, Shenzhen pilot remains ainnegative Chongqing a positive while a varying correlation. Results from method sales. Due to the correlation, changed climate conditions andinbuilding renovation policies, heat demand intime therolling futurewindow could decrease, remains a negative correlation, Chongqing a positive others aperiod varyingofcorrelation. from rolling window method prolonging the investment return period. during show a dominant weak cross-correlation the while development these pilot Results markets. However, the direction of The main scope this paper istheir to assess the feasibility of using demand temperature function fordirection heatmarket. demand show a dominant cross-correlation during the development period of these pilottime markets. of correlation variesofaweak lot during developing periods both for the theheat short and the– outdoor long scales However, except for the Beijing forecast. The district located in periods Lisbon was used astheaexists case atime study. Thecross-correlation district for is consisted of 665 correlation varies a lot of during their developing both forInthe short and long scales except Beijing market. Correlation is enhanced inAlvalade, Shenzhen but weakened in (Portugal), Hubei. general, there positive in Chinese buildings that vary in market. both construction and intypology. Three scenarios medium, and three district Correlation is and enhanced in Shenzhen but period weakened Hubei. coal In general, there exists a (low, positive cross-correlation in Chinese carbon market coal Such cross-correlation between andweather carbon prices is not usually seen inhigh) the European market. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were carbon and coal energy market.structure Such cross-correlation between coal carbon prices is not usually seen in the European market. China’smarket coal-dominant may be the reason behind thisand phenomenon. compared with results from a dynamic heat demand model, previously developed and validated by the authors. China’s coal-dominant energy structure may be the reason behind this phenomenon. The results that when weather change is considered, the margin of error could be acceptable for some applications Copyright © showed 2018 Elsevier Ltd. only All rights reserved. ©(the 2019 The Published by Elsevier Ltd.20% for all weather scenarios considered). error annual demand was lower However, after introducing Copyright ©inAuthors. 2018 Elsevier Ltd. Allresponsibility rights than reserved. Selection and peer-review under of the scientific committee of the 10th International Conference onrenovation Applied This is an open accessvalue article under theupCC BY-NC-ND license on (http://creativecommons.org/licenses/by-nc-nd/4.0/) scenarios, the error increased to 59.5% (depending the weather and renovation scenarios combination considered). th Selection and peer-review under responsibility of the scientific committee of the 10 International Conference on Applied Energy (ICAE2018). Peer-review under responsibility the scientific committee of ICAE2018 10thupInternational Applied Energy. The value of slope coefficient of increased on average within the range –ofThe 3.8% to 8% per Conference decade, thatoncorresponds to the Energy (ICAE2018). decrease cross-correlation; in the number of heating hours of 22-139h during the coal heating Keywords: carbon market; energy market; carbon price; price season (depending on the combination of weather and renovation scenarios considered). On the other hand, function Keywords: cross-correlation; carbon market; energy market; carbon price;intercept coal price increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86-511-88780164; fax: +86-511-88791467. Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +86-511-88780164; fax: +86-511-88791467. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. th Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10 International Conference on Applied (ICAE2018). SelectionEnergy and peer-review under responsibility of the scientific committee of the 10 th International Conference on Applied Energy (ICAE2018). 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 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.925
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Xinghua Fan et al. / Energy Procedia 158 (2019) 3470–3475 Author name / Energy Procedia 00 (2018) 000–000
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1. Introduction Fluctuations in energy and carbon markets seem to be closely related considering that fossil fuel consumption has been recognized as the main source of carbon emissions. Several researches have shown the interrelationship between the European Union Allowance (EUA) trading prices and energy prices. Previous literature has identified energy prices as the main driver of EUA spot prices [1, 2]. Fezzi and Bunn [3] investigate the interrelationships among electricity, gas, and CO2 prices in the UK. Mansanet-Bataller et al. [4] examine correlations in CO2 prices, energy, and weather. They find that the major factors in the determination of CO2 prices are the oil and gas which are the most emission intensive energy sources. However, they also reported that extreme temperatures affect CO2 prices. Wang et al. [5] study the relationship of return volatilities among oil, gas, and CO2. Their cross-correlations are found to be multi-fractal and are diverse at different time scales. However, Nazifi and Milunovich use the VAR model to analyze EUA prices and energy prices and find that there is no significant link between the two prices [6]. There also exist cross-correlations between carbon and energy future markets either on different time scales or on the whole [7]. China launched emission trading system (ETS) pilots in five cities and two provinces between 2013 and 2014, namely, Beijing, Chongqing, Guangdong, Hubei, Shanghai, Shenzhen, and Tianjin [8]. The seven pilot ETS in China combined to form the second largest carbon market in the world, after the European Union (EU) ETS. Carbon market in China is still in the early stage with the purpose to provide for experiences to its national scheme started in the end of 2017. There has been an increasing interest in China’s carbon pilots, mainly focus on the market design [9], market efficiency [10], operational performance and maturity [11]. This study contributes to the literature by analyzing the time-varying interrelationship between coal and carbon prices. The existing literature mainly confirms that natural gas prices, crude oil prices, and electricity prices are positively correlated with EUA prices while coal prices negatively correlated. This is consistent with the energy structure in EU. However, China presents an obvious high-carbon energy structure with approximately 70% of its total primary energy to be coal [9]. And this percentage is not likely to change over the next few years. Correlation between energy and CO2 prices may differ a lot as to the Chinese carbon pilot markets. Researchers have shown that Chinese carbon prices are closely related to coal, rather than oil or gas. Zhao et al. point out that coal price is the dominant factor in pilot carbon markets [12]. Zeng et al. find that an increase of one standard deviation in the coal price leads to an initial increase of approximately 0.1% in the Beijing carbon price [13]. This study presents a detailed relationship between coal and carbon prices in both short and long time scales. 2. Method The detrended cross-correlation coefficient [14] is a quantity to measure the level of cross-correlation between two synchronized time series. The main steps of the algorithm are indicated as follows. Step1. Given two time series xi and yi with the equal length N , where i 1, 2, , N , we first integrate the series to get the profiles
X (k )
k
k
, Y (k ) y, k x i
i
i
i
1, 2,
, N.
(1)
Step2. The profiles are divided into Ns N / s segments of equal size s . Considering that the length of the profiles is generally not an integer multiple of the time scale s , we repeat the entire procedure from the opposite end of the profiles, obtaining 2 N s segments. Step3. In the v th segment, we fit the profile by using a discretized linear polynomial X v , fit , so that we obtain the detrended value
X v X (i) X v, fit (i) , (v 1)s i vs , , s (i )
which leads to the local detrended covariance 1 s 2 f X v,s ((v 1)s i)Yv,s ((v 1)s i). xy (v, s ) s i 1
(2) (3)
Xinghua Fan et al. / Energy Procedia 158 (2019) 3470–3475 Author name / Energy Procedia 00 (2018) 000–000
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Step4. We average over all segments to obtain the detrended covariance 1 2 NS 2 Fxy2 ( s) [ f xy (v, s)] . 2 N S v 1 Step5. The DCCA coefficient is defined as Fxy2 ( s) . DCCA ( s) Fxx ( s) Fyy ( s)
3
(4)
(5)
The cross-correlation coefficient has the advantages in that it measures the correlations between two nonstationary time series at different time scales 错误！未找到引用源。. In a given scale, the coefficient indicates the presence of cross-correlation ( 0) or anti cross-correlation ( 0) between the two series, as well as the strength of this effect. DCCA was well applied in the economy [5,15,16]. Detrended cross-correlation coefficient is more robust than Pearson coefficient, mainly in the case of non-stationary time series [17]. 3. Data and experimental results 3.1. Data We choose the daily closing data of carbon price in seven pilot carbon markets in China, namely, Beijing, Chongqing, Guangdong, Hubei, Shanghai, Shenzhen, and Tianjin market. These data are obtained from the Carbon Trading Network (http://k.tanjiaoyi.com/), covering the period from the first trading date of each market to the end of the year 2017. Since multiple vintage years of carbon allowances are traded in the Shenzhen market, 2013 Shenzhen carbon emission allowances, known as SZA-2013, is selected as the representative carbon price for Shenzhen pilot. Considering that the coal consumption accounts for the largest proportion of energy consumption in China, coal is selected to represent the energy. Coal price is represented by Qinhuangdao 5500 size obtained from http://www.shcce.com/. All prices are labeled as Renminbi Yuan per ton. Let t be the price of the index on day t . The daily price return rt is calculated as its logarithmic difference, rt log( Pt ) log( Pt 1). 3.2. Overall cross-correlation
Fig.1. The DCCA cross-correlation coefficient versus time scale for pilot markets.
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Author name / Energy Procedia 00 (2018) 000–000
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(a)
(b)
(c)
(e)
(f)
(g) (h) Fig.2. Time dependency of return series of carbon prices (upper panel) and coal prices (middle panel). The lower panel shows the dependency of the DCCA cross-correlation coefficient as a function of the time and time scale. Three diagrams share the same time axis.
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A preliminary analysis indicates that all the return series are non-stationary. Thus it is of importance to apply the detrended cross-correlation coefficient rather than the Pearson coefficient as the former is more robust [15]. For the seven ETS points, the calculated cross-correlation coefficients of carbon price and coal price are shown in Fig.1. There are at a most medium correlation between the coal and carbon prices because none of the coefficients excess the range (-0.66, 0.66). The correlation is weak for small time scales as the coefficient ranges in the interval [0.2,0.2] . But the correlation becomes stronger as the cross-correlation coefficients show an increasing fluctuation for large time scales. For the ETS pilots of Beijing, Guangzhou, and Tianjin, the fluctuations of correlation coefficient are similar, wandering between -0.2 and 0.2, indicating that there is a weak correlation between carbon price and coal price. For Chongqing ETS pilot, the correlation coefficient is negative in the scales 050. It increases, ranging between 0.2-0.4 as the scale passes 50. When the scale is greater than 200, the value rises to 0.6, which indicates that there is a medium correlation between the carbon price and the coal price. For Hubei ETS pilot, the coefficient changes its signs in the scales of 0-150. The correlation coefficient becomes positive when the scale is larger than 150. The correlation coefficient rises to 0.4 and above, which indicates that there is a medium correlation between carbon price and coal price. For Shanghai ETS pilot, the coefficient value is negative for scales between 0 and 200. The coefficient becomes positive as the scale increases. The absolute value is wandering between 0-0.4, which indicates that there is at most a moderate degree correlation between the carbon price and the coal price. For Shenzhen ETS pilot, it fluctuates between positive and negative and finally tends to negative. The absolute value reaches the peak at about 160 scales, which indicates that there is the highest correlation between carbon price and coal price. 3.3. Evolution of the cross-correlation We analyze the evolution of the cross-correlation between carbon and coal prices by rolling window technique. The window width is fixed to be N w 250 (about one year) and the sliding is set to be one (a single trading day). At first, we calculate the cross-correlation coefficient of the first window, covering the series from the first data point to the 250th point. Then, the window moves forward by deleting the first point and adding the 251st point. For a given scale s , we assign the cross-correlation of the window to its middle point. The upper and the middle panels in Fig.2 show the return series of carbon prices and coal series. We note that those return series seem to evolve independently. Coal return series presents periodic-like positive or negative blocks. Five out of the seven carbon return series show dense variation (Chongqing and Tianjin have many sparse return segments). Thus the carbon prices do not have a strong relationship with the coal. The lower panels in Fig.2 show the cross-correlation coefficient as a function of the time and time scale. The color bar shows the range of the coefficient. Obviously, there are weak or medium correlations. Several common features can be observed from Fig.2. First of all, green or yellow parts dominant the diagrams except for Fig.2 (d), meaning that the correlations are mainly positive either in small or large scales. Second, the diagrams are primarily filled by light green and light blue regions of weak correlation (-0.33, 0.33). This indicates that there are mainly weak correlations between coal and carbon prices, either positive or negative. The light green region surpasses the light blue region, meaning that the weak correlation is positive. Third, medium correlation (positive correlation shown in yellow and negative in dark blue) locates in the upper part of the diagrams while weak correlation (green and light blue) the lower part. This confirms the finding in the previous subsection that these markets have a stronger correlation in large time scale than the short scale. Fourth, there are the fewest bright or dark islands (corresponding to medium correlation) in Fig.2 (a), meaning that the correlation is the most stable for Beijing market among all the markets. Fifth, the correlation is enhanced for Shenzhen (Fig. 2(h)), weakened for Hubei and Chongqing (Figs. 2(b and d)) and not significantly changed for other pilots as the pilots develop. Finally, the magnitude of correlation depends on the density of return series. Large flat segments for the returns correspond to weak correlation while dense to medium correlation (Figs. 2(b and h). 4. Conclusion We presented a detailed analysis of the cross-correlation between coal price and carbon price in China’s pilot carbon markets based on the DCCA coefficient. Our results indicate obvious differences in both the level and the
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Xinghua Fan et al. / Energy Procedia 158 (2019) 3470–3475 Author name / Energy Procedia 00 (2018) 000–000
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direction of the cross-correlation. There are an overall weak cross-correlation for short time scales (shorter than a quarter), while medium correlation for long time scales. For the long scales, Beijing, Tianjin, and Guanzhou show negative correlations while others positive. We observed the weak yet inconsistent direction of cross-correlation during the whole development period of these pilot markets with Beijing being the most stable one. The development periods are dominated by positively correlated regions both for the short and the long time scales. The correlation is enhanced for Shenzhen, weakened for Hubei and not significantly changed for other pilots. Findings in this study show weak to medium cross-correlations between carbon and energy markets, not the strong cross-correlation as one might expect. The reason may lie in the low activity of China’s pilot markets, particularly Chongqing and Tianjin pilots. Theoretically, when confronted to a rise of the price of coal, firms should have an intention to adapt their energy mix towards less CO 2-intensive energy sources other than coal, which conducts to less need of carbon allowance and a decrease carbon price. However, this study shows a weak positive cross-correlation between carbon price and coal price both in time and time-scale intervals. This finding indicates that China’s pilot carbon markets are far from mature in emission mitigation. Acknowledgements Research is supported by the National Natural Science Foundation of China (Nos.7167311671690242, 71403105) and the Humanistic and Social Science Foundation from Ministry of Education of China (Grant 16YJAZH007). References [1] E. Alberola, J. Chevallier, B, Chze. Price drivers and structural breaks in European carbon prices, Energy Policy, 36 (2) (2008) 787-797. [2] J. Chevallier. Time-varying correlations in oil, gas and CO2 prices: An application using BEKK, CCC and DCC-MGARCH models, Applied Economics, 44 (32) (2012) 4257-4274. [3] C. Fezzi, D. W. Bunn. Structural interactions of European carbon trading and energy prices, The Journal of Energy Markets, 2 (4) (2009) 5369. [4] M. Mansanet-Bataller, A. Pardo, E. Valor. CO2 prices, energy and weather, Energy Journal, 28 (3) (2007)73-92. [5] G. J. Wang, C. Xie, S. Chen, F. Han. Cross-correlations between energy and emissions markets: New evidence from fractal and multifractal Analysis, Mathematical Problems in Engineering, 2014. [6] F. Nazi, G. Milunovich. Measuring the impact of carbon allowance trading on energy prices, Energy & environment, 21 (5) (2010) 367383. [7] G.Cao, W. Xu. Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform, Physica A: Statistical Mechanics and its Applications, 444 (2016) 505-523. [8] W.A.Pizer, X. Zhang. China’s new national carbon market, Working Paper of Nicholas Institute for Environmental Policy Solutions, (2018) 18-01. [9] Z. Deng, D. Li, T. Pang, M. Duan. Effectiveness of pilot carbon emissions trading systems in China, Climate Policy, 0 (2018) 1-20. [10] Y. Hu, X. Li, B. Tang. Assessing the operational performance and maturity of the carbon trading pilot program: the case study of Beijing’s carbon market, Journal of Cleaner Production, 161(2017):1263-1274. [11] T. Feng, L. Sun, Y. Zhang, The relationship between energy consumption structure, economic structure and energy intensity in China, Energy Policy, 37 (12) (2009) 5475- 5483. [12] X. Zhao, Y. Zou, J. Yin, X. Fan, Cointegration relationship between carbon price and its factors: evidence from structural breaks analysis, Energy Procedia, 142 (2017) 2503-2510. [13] S. Zeng, X. Nan, C. Liu, J. Chen, The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices, Energy Policy, 106 (2017) 111-121. [14] B. Podobnik, H. E. Stanley, Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series, Physical Review Letters, 100 (8) (2008)084102. [15] A. S. S. Paiva, M. A. Rivera-Castro, R. F. S. Andrade. Dcca analysis of renewable and conventional energy prices, Physica A:Statistical Mechanics and its Applications, 490 (2018) 1408-1414. [16] G. Cao, Y. Han, Y. Chen, C. Yang. Multifractal detrended cross-correlation between the Chinese domestic and international gold markets based on DCCA and DMCA methods, Modern Physics Letter, B28 (11) (2014) 1450090. [17] L. Kristoufek. Measuring cross-correlation between non-stationary series with DCCA coefficient, Physical A, 402 (2014) 291-298.
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Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
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www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)3470–3475 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Dynamic relationship between carbon price and coal price: The relationship 15th Internationalbetween Symposiumcarbon on Districtprice Heating and coal Cooling Dynamic and price: perspective based on Detrended Cross-Correlation perspective based on Detrended Cross-Correlation Assessing the feasibilityAnalysis of using the heat demand-outdoor Analysis temperature function for a long-term district heat demand forecast Xinghua Fan, Xuxia Li, Jiuli Yin* Xinghua Fan,a Xuxia Li, Jiuli Yin* a b c c of Science, University, Zhenjiang, Jiangsu China , O. Le Corre I. Andrića,b,c*, Faculty A. Pina , P. Jiangsu Ferrão , J. Fournier ., B. 212013, Lacarrière
Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu 212013, China IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract a
Unlike the European Union market, the main driver of CO2 price in China seems to be coal price rather than oil and gas prices, Unlike the Union energy market,structure the mainindriver of CO2 price in China seemsthe to be coal price rather than oilcoal andand gascarbon prices, considering theEuropean coal-dominant this country. This study analyzes interrelationship between considering thetime coal-dominant energyinterval structure in this country. This the study analyzes the interrelationshipAnalysis betweenmethod. coal andChina’s carbon prices in both and time-scale perspectives by using Detrended Cross-Correlation Abstract prices in both timemarkets and time-scale perspectives using the Detrended Analysis method. China’s seven pilot carbon are taken interval as the sample. Resultsbyshow a weak correlationCross-Correlation between coal and carbon prices in short time seven pilot carbon markets are taken as the sample. Results show a weak correlation between coal and carbon prices in short time scales (shorter than a quarter) but medium in long time scales (longer than a quarter). For short time scales, the correlation is District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the scales (shorter a quarter) butthemedium insector. long time scales (longer thanfor a other quarter). ForFor short time scales, thethrough correlation is negative in Chongqing and from Shanghai, undetermined in Hubei, and positive pilots. long time scales, Shenzhen greenhouse gasthan emissions building These systems require high investments which are returned thepilot heat negative Chongqing and Shanghai, undetermined Hubei,others and positive for other pilots. For long scales, Shenzhen pilot remains ainnegative Chongqing a positive while a varying correlation. Results from method sales. Due to the correlation, changed climate conditions andinbuilding renovation policies, heat demand intime therolling futurewindow could decrease, remains a negative correlation, Chongqing a positive others aperiod varyingofcorrelation. from rolling window method prolonging the investment return period. during show a dominant weak cross-correlation the while development these pilot Results markets. However, the direction of The main scope this paper istheir to assess the feasibility of using demand temperature function fordirection heatmarket. demand show a dominant cross-correlation during the development period of these pilottime markets. of correlation variesofaweak lot during developing periods both for the theheat short and the– outdoor long scales However, except for the Beijing forecast. The district located in periods Lisbon was used astheaexists case atime study. Thecross-correlation district for is consisted of 665 correlation varies a lot of during their developing both forInthe short and long scales except Beijing market. Correlation is enhanced inAlvalade, Shenzhen but weakened in (Portugal), Hubei. general, there positive in Chinese buildings that vary in market. both construction and intypology. Three scenarios medium, and three district Correlation is and enhanced in Shenzhen but period weakened Hubei. coal In general, there exists a (low, positive cross-correlation in Chinese carbon market coal Such cross-correlation between andweather carbon prices is not usually seen inhigh) the European market. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were carbon and coal energy market.structure Such cross-correlation between coal carbon prices is not usually seen in the European market. China’smarket coal-dominant may be the reason behind thisand phenomenon. compared with results from a dynamic heat demand model, previously developed and validated by the authors. China’s coal-dominant energy structure may be the reason behind this phenomenon. The results that when weather change is considered, the margin of error could be acceptable for some applications Copyright © showed 2018 Elsevier Ltd. only All rights reserved. ©(the 2019 The Published by Elsevier Ltd.20% for all weather scenarios considered). error annual demand was lower However, after introducing Copyright ©inAuthors. 2018 Elsevier Ltd. Allresponsibility rights than reserved. Selection and peer-review under of the scientific committee of the 10th International Conference onrenovation Applied This is an open accessvalue article under theupCC BY-NC-ND license on (http://creativecommons.org/licenses/by-nc-nd/4.0/) scenarios, the error increased to 59.5% (depending the weather and renovation scenarios combination considered). th Selection and peer-review under responsibility of the scientific committee of the 10 International Conference on Applied Energy (ICAE2018). Peer-review under responsibility the scientific committee of ICAE2018 10thupInternational Applied Energy. The value of slope coefficient of increased on average within the range –ofThe 3.8% to 8% per Conference decade, thatoncorresponds to the Energy (ICAE2018). decrease cross-correlation; in the number of heating hours of 22-139h during the coal heating Keywords: carbon market; energy market; carbon price; price season (depending on the combination of weather and renovation scenarios considered). On the other hand, function Keywords: cross-correlation; carbon market; energy market; carbon price;intercept coal price increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86-511-88780164; fax: +86-511-88791467. Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +86-511-88780164; fax: +86-511-88791467. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. th Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10 International Conference on Applied (ICAE2018). SelectionEnergy and peer-review under responsibility of the scientific committee of the 10 th International Conference on Applied Energy (ICAE2018). 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 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.925
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1. Introduction Fluctuations in energy and carbon markets seem to be closely related considering that fossil fuel consumption has been recognized as the main source of carbon emissions. Several researches have shown the interrelationship between the European Union Allowance (EUA) trading prices and energy prices. Previous literature has identified energy prices as the main driver of EUA spot prices [1, 2]. Fezzi and Bunn [3] investigate the interrelationships among electricity, gas, and CO2 prices in the UK. Mansanet-Bataller et al. [4] examine correlations in CO2 prices, energy, and weather. They find that the major factors in the determination of CO2 prices are the oil and gas which are the most emission intensive energy sources. However, they also reported that extreme temperatures affect CO2 prices. Wang et al. [5] study the relationship of return volatilities among oil, gas, and CO2. Their cross-correlations are found to be multi-fractal and are diverse at different time scales. However, Nazifi and Milunovich use the VAR model to analyze EUA prices and energy prices and find that there is no significant link between the two prices [6]. There also exist cross-correlations between carbon and energy future markets either on different time scales or on the whole [7]. China launched emission trading system (ETS) pilots in five cities and two provinces between 2013 and 2014, namely, Beijing, Chongqing, Guangdong, Hubei, Shanghai, Shenzhen, and Tianjin [8]. The seven pilot ETS in China combined to form the second largest carbon market in the world, after the European Union (EU) ETS. Carbon market in China is still in the early stage with the purpose to provide for experiences to its national scheme started in the end of 2017. There has been an increasing interest in China’s carbon pilots, mainly focus on the market design [9], market efficiency [10], operational performance and maturity [11]. This study contributes to the literature by analyzing the time-varying interrelationship between coal and carbon prices. The existing literature mainly confirms that natural gas prices, crude oil prices, and electricity prices are positively correlated with EUA prices while coal prices negatively correlated. This is consistent with the energy structure in EU. However, China presents an obvious high-carbon energy structure with approximately 70% of its total primary energy to be coal [9]. And this percentage is not likely to change over the next few years. Correlation between energy and CO2 prices may differ a lot as to the Chinese carbon pilot markets. Researchers have shown that Chinese carbon prices are closely related to coal, rather than oil or gas. Zhao et al. point out that coal price is the dominant factor in pilot carbon markets [12]. Zeng et al. find that an increase of one standard deviation in the coal price leads to an initial increase of approximately 0.1% in the Beijing carbon price [13]. This study presents a detailed relationship between coal and carbon prices in both short and long time scales. 2. Method The detrended cross-correlation coefficient [14] is a quantity to measure the level of cross-correlation between two synchronized time series. The main steps of the algorithm are indicated as follows. Step1. Given two time series xi and yi with the equal length N , where i 1, 2, , N , we first integrate the series to get the profiles
X (k )
k
k
, Y (k ) y, k x i
i
i
i
1, 2,
, N.
(1)
Step2. The profiles are divided into Ns N / s segments of equal size s . Considering that the length of the profiles is generally not an integer multiple of the time scale s , we repeat the entire procedure from the opposite end of the profiles, obtaining 2 N s segments. Step3. In the v th segment, we fit the profile by using a discretized linear polynomial X v , fit , so that we obtain the detrended value
X v X (i) X v, fit (i) , (v 1)s i vs , , s (i )
which leads to the local detrended covariance 1 s 2 f X v,s ((v 1)s i)Yv,s ((v 1)s i). xy (v, s ) s i 1
(2) (3)
Xinghua Fan et al. / Energy Procedia 158 (2019) 3470–3475 Author name / Energy Procedia 00 (2018) 000–000
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Step4. We average over all segments to obtain the detrended covariance 1 2 NS 2 Fxy2 ( s) [ f xy (v, s)] . 2 N S v 1 Step5. The DCCA coefficient is defined as Fxy2 ( s) . DCCA ( s) Fxx ( s) Fyy ( s)
3
(4)
(5)
The cross-correlation coefficient has the advantages in that it measures the correlations between two nonstationary time series at different time scales 错误！未找到引用源。. In a given scale, the coefficient indicates the presence of cross-correlation ( 0) or anti cross-correlation ( 0) between the two series, as well as the strength of this effect. DCCA was well applied in the economy [5,15,16]. Detrended cross-correlation coefficient is more robust than Pearson coefficient, mainly in the case of non-stationary time series [17]. 3. Data and experimental results 3.1. Data We choose the daily closing data of carbon price in seven pilot carbon markets in China, namely, Beijing, Chongqing, Guangdong, Hubei, Shanghai, Shenzhen, and Tianjin market. These data are obtained from the Carbon Trading Network (http://k.tanjiaoyi.com/), covering the period from the first trading date of each market to the end of the year 2017. Since multiple vintage years of carbon allowances are traded in the Shenzhen market, 2013 Shenzhen carbon emission allowances, known as SZA-2013, is selected as the representative carbon price for Shenzhen pilot. Considering that the coal consumption accounts for the largest proportion of energy consumption in China, coal is selected to represent the energy. Coal price is represented by Qinhuangdao 5500 size obtained from http://www.shcce.com/. All prices are labeled as Renminbi Yuan per ton. Let t be the price of the index on day t . The daily price return rt is calculated as its logarithmic difference, rt log( Pt ) log( Pt 1). 3.2. Overall cross-correlation
Fig.1. The DCCA cross-correlation coefficient versus time scale for pilot markets.
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Author name / Energy Procedia 00 (2018) 000–000
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(a)
(b)
(c)
(e)
(f)
(g) (h) Fig.2. Time dependency of return series of carbon prices (upper panel) and coal prices (middle panel). The lower panel shows the dependency of the DCCA cross-correlation coefficient as a function of the time and time scale. Three diagrams share the same time axis.
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Xinghua Fan et al. / Energy Procedia 158 (2019) 3470–3475 Author name / Energy Procedia 00 (2018) 000–000
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A preliminary analysis indicates that all the return series are non-stationary. Thus it is of importance to apply the detrended cross-correlation coefficient rather than the Pearson coefficient as the former is more robust [15]. For the seven ETS points, the calculated cross-correlation coefficients of carbon price and coal price are shown in Fig.1. There are at a most medium correlation between the coal and carbon prices because none of the coefficients excess the range (-0.66, 0.66). The correlation is weak for small time scales as the coefficient ranges in the interval [0.2,0.2] . But the correlation becomes stronger as the cross-correlation coefficients show an increasing fluctuation for large time scales. For the ETS pilots of Beijing, Guangzhou, and Tianjin, the fluctuations of correlation coefficient are similar, wandering between -0.2 and 0.2, indicating that there is a weak correlation between carbon price and coal price. For Chongqing ETS pilot, the correlation coefficient is negative in the scales 050. It increases, ranging between 0.2-0.4 as the scale passes 50. When the scale is greater than 200, the value rises to 0.6, which indicates that there is a medium correlation between the carbon price and the coal price. For Hubei ETS pilot, the coefficient changes its signs in the scales of 0-150. The correlation coefficient becomes positive when the scale is larger than 150. The correlation coefficient rises to 0.4 and above, which indicates that there is a medium correlation between carbon price and coal price. For Shanghai ETS pilot, the coefficient value is negative for scales between 0 and 200. The coefficient becomes positive as the scale increases. The absolute value is wandering between 0-0.4, which indicates that there is at most a moderate degree correlation between the carbon price and the coal price. For Shenzhen ETS pilot, it fluctuates between positive and negative and finally tends to negative. The absolute value reaches the peak at about 160 scales, which indicates that there is the highest correlation between carbon price and coal price. 3.3. Evolution of the cross-correlation We analyze the evolution of the cross-correlation between carbon and coal prices by rolling window technique. The window width is fixed to be N w 250 (about one year) and the sliding is set to be one (a single trading day). At first, we calculate the cross-correlation coefficient of the first window, covering the series from the first data point to the 250th point. Then, the window moves forward by deleting the first point and adding the 251st point. For a given scale s , we assign the cross-correlation of the window to its middle point. The upper and the middle panels in Fig.2 show the return series of carbon prices and coal series. We note that those return series seem to evolve independently. Coal return series presents periodic-like positive or negative blocks. Five out of the seven carbon return series show dense variation (Chongqing and Tianjin have many sparse return segments). Thus the carbon prices do not have a strong relationship with the coal. The lower panels in Fig.2 show the cross-correlation coefficient as a function of the time and time scale. The color bar shows the range of the coefficient. Obviously, there are weak or medium correlations. Several common features can be observed from Fig.2. First of all, green or yellow parts dominant the diagrams except for Fig.2 (d), meaning that the correlations are mainly positive either in small or large scales. Second, the diagrams are primarily filled by light green and light blue regions of weak correlation (-0.33, 0.33). This indicates that there are mainly weak correlations between coal and carbon prices, either positive or negative. The light green region surpasses the light blue region, meaning that the weak correlation is positive. Third, medium correlation (positive correlation shown in yellow and negative in dark blue) locates in the upper part of the diagrams while weak correlation (green and light blue) the lower part. This confirms the finding in the previous subsection that these markets have a stronger correlation in large time scale than the short scale. Fourth, there are the fewest bright or dark islands (corresponding to medium correlation) in Fig.2 (a), meaning that the correlation is the most stable for Beijing market among all the markets. Fifth, the correlation is enhanced for Shenzhen (Fig. 2(h)), weakened for Hubei and Chongqing (Figs. 2(b and d)) and not significantly changed for other pilots as the pilots develop. Finally, the magnitude of correlation depends on the density of return series. Large flat segments for the returns correspond to weak correlation while dense to medium correlation (Figs. 2(b and h). 4. Conclusion We presented a detailed analysis of the cross-correlation between coal price and carbon price in China’s pilot carbon markets based on the DCCA coefficient. Our results indicate obvious differences in both the level and the
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Xinghua Fan et al. / Energy Procedia 158 (2019) 3470–3475 Author name / Energy Procedia 00 (2018) 000–000
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direction of the cross-correlation. There are an overall weak cross-correlation for short time scales (shorter than a quarter), while medium correlation for long time scales. For the long scales, Beijing, Tianjin, and Guanzhou show negative correlations while others positive. We observed the weak yet inconsistent direction of cross-correlation during the whole development period of these pilot markets with Beijing being the most stable one. The development periods are dominated by positively correlated regions both for the short and the long time scales. The correlation is enhanced for Shenzhen, weakened for Hubei and not significantly changed for other pilots. Findings in this study show weak to medium cross-correlations between carbon and energy markets, not the strong cross-correlation as one might expect. The reason may lie in the low activity of China’s pilot markets, particularly Chongqing and Tianjin pilots. Theoretically, when confronted to a rise of the price of coal, firms should have an intention to adapt their energy mix towards less CO 2-intensive energy sources other than coal, which conducts to less need of carbon allowance and a decrease carbon price. However, this study shows a weak positive cross-correlation between carbon price and coal price both in time and time-scale intervals. This finding indicates that China’s pilot carbon markets are far from mature in emission mitigation. Acknowledgements Research is supported by the National Natural Science Foundation of China (Nos.7167311671690242, 71403105) and the Humanistic and Social Science Foundation from Ministry of Education of China (Grant 16YJAZH007). References [1] E. Alberola, J. Chevallier, B, Chze. Price drivers and structural breaks in European carbon prices, Energy Policy, 36 (2) (2008) 787-797. [2] J. Chevallier. Time-varying correlations in oil, gas and CO2 prices: An application using BEKK, CCC and DCC-MGARCH models, Applied Economics, 44 (32) (2012) 4257-4274. [3] C. Fezzi, D. W. Bunn. Structural interactions of European carbon trading and energy prices, The Journal of Energy Markets, 2 (4) (2009) 5369. [4] M. Mansanet-Bataller, A. Pardo, E. Valor. CO2 prices, energy and weather, Energy Journal, 28 (3) (2007)73-92. [5] G. J. Wang, C. Xie, S. Chen, F. Han. Cross-correlations between energy and emissions markets: New evidence from fractal and multifractal Analysis, Mathematical Problems in Engineering, 2014. [6] F. Nazi, G. Milunovich. Measuring the impact of carbon allowance trading on energy prices, Energy & environment, 21 (5) (2010) 367383. [7] G.Cao, W. Xu. Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform, Physica A: Statistical Mechanics and its Applications, 444 (2016) 505-523. [8] W.A.Pizer, X. Zhang. China’s new national carbon market, Working Paper of Nicholas Institute for Environmental Policy Solutions, (2018) 18-01. [9] Z. Deng, D. Li, T. Pang, M. Duan. Effectiveness of pilot carbon emissions trading systems in China, Climate Policy, 0 (2018) 1-20. [10] Y. Hu, X. Li, B. Tang. Assessing the operational performance and maturity of the carbon trading pilot program: the case study of Beijing’s carbon market, Journal of Cleaner Production, 161(2017):1263-1274. [11] T. Feng, L. Sun, Y. Zhang, The relationship between energy consumption structure, economic structure and energy intensity in China, Energy Policy, 37 (12) (2009) 5475- 5483. [12] X. Zhao, Y. Zou, J. Yin, X. Fan, Cointegration relationship between carbon price and its factors: evidence from structural breaks analysis, Energy Procedia, 142 (2017) 2503-2510. [13] S. Zeng, X. Nan, C. Liu, J. Chen, The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices, Energy Policy, 106 (2017) 111-121. [14] B. Podobnik, H. E. Stanley, Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series, Physical Review Letters, 100 (8) (2008)084102. [15] A. S. S. Paiva, M. A. Rivera-Castro, R. F. S. Andrade. Dcca analysis of renewable and conventional energy prices, Physica A:Statistical Mechanics and its Applications, 490 (2018) 1408-1414. [16] G. Cao, Y. Han, Y. Chen, C. Yang. Multifractal detrended cross-correlation between the Chinese domestic and international gold markets based on DCCA and DMCA methods, Modern Physics Letter, B28 (11) (2014) 1450090. [17] L. Kristoufek. Measuring cross-correlation between non-stationary series with DCCA coefficient, Physical A, 402 (2014) 291-298.