Modeling the Seasonal Patterns of Coal and Electricity Production across Chinese Provinces

Transcription

1 Modeling the Seasonal Patterns of Coal and Electricity Production across Chinese Provinces Eric Girardin * M.J. Herrerias Université de la Méditerranée, Aix Marseille II, GREQAM 20 July 2011 Abstract This paper provides evidence on the relevance of modeling adequately the seasonal character of coal and electricity production across Chinese regions. Unlike other work, this paper relaxes the assumption of deterministic seasonality, allowing for time and regional variation in this economy. More specifically, we analyze and distinguish the type of seasonality around the year that prevails in the case of coal and electricity production of each individual Chinese province. We use unobserved-components models with monthly data for a fifteen-year period up to Our results indicate that for the majority of the provinces seasonality is stochastic in both types of energy considered. Besides, our findings provide new evidence of a Lunar New-Year effect in February and Summer as well as Winter effects in coal and electricity production. However, in terms of seasonal patterns and their evolution over time, there are significant differences between the Northern regions that produce coal and the Southern ones that generate electricity. In addition, we find that such results are not substantially altered once we control our estimates for temperature, income and energy prices. Besides for each type of energy, regional clusters matter for the appropriate design of energy-development policy. Key words: Seasonality, Unobserved Components, China, Energy JEL Classification: Q43, Q47, Q50, O11, R11 Corresponding author: M.J. Herrerias, Université de la Méditerranée, Aix Marseille II, GREQAM. Centre de la Vieille Charité 2, rue de la Charité Marseille cedex 02 (France). address: maria-jesus.herrerias@univmed.fr. * E. Girardin, Université de la Méditerranée, Aix Marseille II, GREQAM. Centre de la Vieille Charité 2, rue de la Charité Marseille cedex 02 (France). eric.girardin@univmed.fr. The authors gratefully acknowledge the financial support of European Union, project No The usual disclaimer applies.

2 1. Introduction Today, the Chinese economy is one of the new major players in international markets and one of the countries that contributes most to world GDP. The Asian locomotive, as many authors call China, however, plays also an important and increasing role in the production and consumption of energy resources to fuel economic activity, which of course has implications not only for its domestic economy but also for international markets. This paper argues that in order to disentangle the complexity of the energy situation in a sub-continent like China, one needs to account for three major dimensions. First the predominance of raw coal and its processing to generate electricity; second, the major importance of the regional dimension through transmission grids, due to the notable distances between the producing and consuming regions; third, the wide seasonal changes around the year in coal and electricity production, and the time variation of such seasonal patterns. This latter aspect is important in the energy-economics literature because a well-known stylized fact on energy modeling is the underlying trend and seasonal behavior around the year (Hunt et al., 2003). However, previous work have either ignored it or, at best, used as a proxy a simple deterministic seasonal dummy and a time trend with the risk of producing significant bias in the analysis. For this reason, considering a flexible framework that allows for the stochastic [2]

3 character of both components provides robust evidence on the changes in the seasonal patterns and their evolution over time (Hunt and Judge, 1996; Rao, 2010). In this paper, we show the need to relax the assumption of deterministic seasonality, allowing for time and regional variation, in order to fully understand the seasonal character of coal and electricity production of each Chinese region. One of the characteristics of domestic energy resources in China is the predominance of coal as the main source of energy in a direct or indirect way, like when it is used to produce electricity. This domination has also been reflected in international markets since the Chinese economy has become the largest consumer and producer of coal in the world with and million tons oil equivalent respectively in These figures are higher than in developed countries such as the United States and Japan, or even some developing countries like India. 2 Moreover, in terms of electricity generation, China has reached the second position in the world with terawatt-hours in 2009, behind only the United States. However, coal and electricity production in the Chinese economy are unevenly distributed across regions, the spatial dimension being the key 1 However, in terms of reserves USA continues to be the first country and China the second. 2 All the data in this section and the next comes from Statistical Review of World Energy in 2010 and China energy Databook in [3]

4 aspect to understand China s energy development. 3 Northern regions, like Inner Mongolia and Shanxi, hold the majority of coal reserves, while the generation of electricity is mainly located in the South and East of China, in provinces like Guangdong, Jiangsu and Shandong. Nonetheless, there is a large distance between inland producers and the most developed provinces on the coast that require notable amounts of energy. The transportation of energy among these regions is crucial to fuel development, but still presents problems inherited from the planned system. The issue of seasonality has been intensively investigated in other fields, particularly for stock markets such as Kramer, (1994), Bouman and Jacobson (2002) or more recently in Worthington (2010) and, in the case of China, Girardin and Liu, (2005) for stochastic versus deterministic seasonality. Moreover, evidence on seasonality is also found in industrial production data (Frances and Kunst, 2007) or, outside economics, in fields like biological conception (Rizzi and Dalla-Zuanna, 2007). However, in energy-economics, evidence on seasonality is relatively scarce. Some work, such as Clements and Madlener (1999) and Mitchell et al., (2000), examine the nature of seasonality of energy demand in the case of the United Kingdom and energy prices in the case of Australia respectively, assuming deterministic seasonality. By contrast Hunt and Judge (1996) and Hunt et 3 See Wang (2007) also for the imbalance development of coal and electricity industries. [4]

5 al., (2003), in the case of the United Kingdom, relax this hypothesis by investigating the stochastic character of energy demand in the structural time series approach. Other work analyzing the trend and seasonal components within the latter approach are Hunt and Ninomiya (2003) or more recently Amarawickma and Hunt (2008), Dilaver and Hunt (2011) and Sa ad (2011). In the case of China, some research, such as Wang and Feng (2005) and Asadooorian et al., (2008), through the application of panel data techniques, examines the factors that cause energy consumption across Chinese regions. However, none of this work investigates the seasonal patterns and their evolution over time for energy. 4 Lam et al., (2008) also do not consider the issue of seasonality. Instead, these authors analyze the causes of energy consumption in the case of Hong Kong with the principal components approach. 56 Thus, in this paper we complement existing literature on the seasonality of China s energy by investigating the seasonal patterns of coal and 4 There is one exception in Yu et al (2011) that investigate the seasonal effects of wind energy across the different transmission grids. However, these authors do not consider the seasonal behavior of each region for the most important sources of energy in China. 5 However, energy aspects have received considerable interest for other industrial countries like in Yu and Choi (1985), and Erol and Yu (1987), and for other developing countries some analysis is provided in Huang et al., (2008) and Beenstock et al., (1999). 6 Other works in the case of China on energy issues analyze the causes of the decrease in energy intensity like in Garbaccio et al., (1999), Zhang (2003), Fisher-Vanden et al., (2004), Liao et al., (2007) and Ma and Stern (2008) or in forecasting the demand for energy like in Crompton and Wu (2005), Adam and Shachmurove (2008) and Dong et al., 2010). In addition, evidence supporting the energy-growth relationship is provided in Yuan et al., (2007) and Yuan et al., (2008). [5]

6 electricity production of each individual Chinese province. In particular, we analyze whether these seasonal patterns differ depending on the type of energy examined, the nature of the province, and the time period. The monthly data used covers the period January 1996 through August 2010, which includes the most important economic events in the Chinese economy like the Asian and energy crises, electricity shortages and the current global financial and economic crisis. The analysis is performed by using the unobserved-components model developed by Harvey (1989) and applied by Hunt and Judge (2003), Hunt and Ninomiya (2003), Dilaver and Hunt (2011) and Sa ad (2011) to the empirical modeling of energy demand and seasonal anomalies. By analyzing the seasonal patterns of coal and electricity production across Chinese regions, this paper contributes to the existing literature in four aspects. First, we analyze in detail coal and electricity production individually instead of looking at aggregate and broad measures of energy, which enables us to link our results with the grid transmission across provinces as one of the explanations of the observed stochastic seasonality. Second, we consider explicitly the spatial dimension by examining individually the Chinese provinces, since their high degree of heterogeneity is a well-known stylized fact. Thus, knowing their seasonal behavior in coal [6]

7 and electricity may help policy makers to design an appropriate regional energy policy or investors to better operate in the electricity market. Third, by considering the structural time series framework we can introduce into the model the stochastic or deterministic trend and seasonal components. However, in the case of Chinese regions, allowing for the stochastic character of these two components, especially the trend, is a more realistic modeling strategy, due to its higher flexibility to account for structural changes given the significant transformation of this economy. Fourth, we use a rich dataset, which contains monthly data of coal and electricity production. In the best of our knowledge, there is no previous work in this field that uses monthly data at the provincial level in the case of the Chinese economy, which makes this study singular, providing new evidence of seasonal patterns of coal and electricity production by province. Specifically in our fourth contribution, we find that seasonality is stochastic for the majority of the provinces both in coal and electricity production (19 and 17 regions respectively). These seasonal patterns share some similarities across Chinese provinces, i.e. we find a Lunar New-Year effect in February and Summer and Winter effects in coal and electricity production. However, some differences are observed across provinces. In the Northern transmission grid, we detect a negative seasonality for the Summer in coal production, but a positive one for electricity production. [7]

8 This pattern is also observed in the case of Sichuan, Hubei and Hunan in the central part of China. However, the most striking differences compared with the Northern regions appear in Southern and Eastern provinces that produce electricity, which are characterized by a positive seasonality in the second half of the year and a negative one from January to April. On the other hand, the time variation of the monthly seasonality over the considered period also shows interesting regularities. This variation takes the form of a decreasing December effect for the majority of those provinces that display stochastic seasonality in coal. By contrast, in the case of electricity the opposite movement is present for Summer months, like July and August. Finally, once we control the estimates for temperature, income and energy prices, we cannot observe substantial differences in the results. The paper is organized as follows. Section 2 covers data and methodological issues. In section 3 we report the empirical results. We discuss our conclusions in Section Data and Methodology The data used in this work consist of two macroeconomic time series for each of the 30 provinces in China. We use monthly data from 1996:1 to [8]

9 2010:8. 7 We focus on coal and electricity production. They are measured in million Tons and billion KWH respectively. The source of this data is CEIC and the National Bureau of Statistics of China (NBS). 89 In addition, in the subsequent stages of the analysis, we use temperature, industrial output, cooking coal and electricity prices, also extracted from CEIC and NBS. This allows us to check the robustness of our results in a similar way than Jalles (2009). We use Harvey s (1989) approach based on the unobserved-components model to investigate the seasonal patterns of the considered variables in the structural time series framework (Engle, 1978).The attractiveness of this approach is that it allows us identifying the salient features of the series. This method has been characterized by its ability to decompose the series into unobserved components such as trend, seasonal and irregular, which have a direct interpretation (Harvey and Shephard, 1993) In this paper the covered period depends on the selected macroeconomic variable. For instance for coal production the sample is from 1996:1 to 2010:3 and for electricity production the period is from 1996:1 to 2010:8. 8 See Sinton (2001) and Fisher-Vanden et al., (2004) for the debate of the accuracy of the energy statistics. 9 Notice that electricity could be generated from Thermal plants, Nuclear, Hydropower and Wind, apart from the transformation of coal. Our variable here is total electricity production. 10 A complementary way to investigate these issues is provided by analysis on seasonal integration and cointegration and the associated tests and the methodology proposed by Hyllebert et al (1990). However, as argued by Dilaver and Hunt (2011) Harvey (1997) criticizes the co-integration approach because of its poor statistical properties and argues that the co-integration technique is misleading. In structural time series modeling, stationarity of time series does not have a fundamental role, therefore the structural time [9]

10 Thus, the formal statistical formulation of the unobserved-components models for the logarithm of the considered variable is as follows 11 : (1) where is the trend, the seasonal, captures the AR(1) component in errors, and the irregular. All these components are stochastic, but they can be deterministic in limiting cases. is white noise, and stationary, is normally only stationary in first or second differences, while is stationary when multiplied by the seasonal summation operator, such as: Ι (2) where s is the number of seasons, L is the lag operator and S(L) contains both real and complex unit roots. Following Hylleberg et al (1990), is said to be seasonally integrated. In order to allow for the stochastic trend, the autoregressive component and the trend are formulated as: 0, (3) 0, (4) 0, 5 Equation (3) collapses to a random walk plus drift if = 0, and to a deterministic linear trend if = 0 as well. Setting to zero when is series modeling approach combines the flexibility of time series with the interpretation of regression analysis (Harvey, 1997, and Harvey and Shephard, 1993). 11 We have omitted the subscripts of each province for simplicity. [10]

11 positive tends to give a trend which changes relatively smoothly (Harvey and Jaeger, 1993). As regards seasonality, we prefer the trigonometric seasonality over seasonal dummies for its higher flexibility, which also can be expressed as: / (6) All the disturbances are assumed mutually uncorrelated, and the extent to which the trend and seasonal components evolve over time depends on the parameters σ, σ, σ and,σ that can be estimated by maximum likelihood (Harvey, 1989). After this step, the trend and seasonal components may be extracted by a smoothing algorithm (Koopman, 1993). All the estimations are performed with STAMP version 8.10 (Koopman et al., 1995). Diagnostic tests are performed on the residuals following Harvey and Koopman (1992). However, in order to restore the normality assumption, intervention dummies must be introduced into the model. 12 This is particularly relevant in the case of the Chinese economy, since the period that we investigate covers the Asian crisis ( ), the energy crisis ( ), the energy shortages (2004) and the current global financial and economic crisis ( ). Normality is tested with the Jarque-Bera 12 We have omitted this information to save space in the paper, however it is available upon request from the authors. [11]

12 statistics which is distributed as χ 2 under the null hypothesis of normallydistributed errors. H(h) is the heteroskedasticity test statistics distributed as a F(h,h) with (h,h) degrees of freedom under the null of homocedasticity. Q(P,d) is the Ljung Box statistics based on the sum of the first P autocorrelations and is tested against a χ 2 distribution with d degrees of freedom. There, the null hypothesis of no autocorrelation is tested against the alternative of autocorrelation. Finally, for each residual component, we report the Browman Shenton test to detect skewness and kurtosis. The rejection of the null hypothesis implies that these features are present in the model. 3. Results The discrimination between deterministic and stochastic seasonality concludes in favor of the latter when the standard deviation of the disturbances of the stochastic components is different from zero. This information is presented in Table 1 for coal production and in Table 2 for electricity production. The reliability of the inference made is assessed by specification tests reported in the Appendix. Moreover, in Tables 3 and 4 are presented the seasonal effects in the last year of the sample (the final state) in the case of coal and electricity production respectively for those provinces displaying either deterministic or stochastic seasonality. [12]

13 However, for those regions with time-varying seasonality this information is complemented with figures (a)-(d) that show some typical examples of the evolution over time of seasonal patterns for both types of energy examined across Chinese provinces. The robustness of this analysis is shown in Table 5 and in Table 6 is presented the income elasticity of coal and electricity. 3.1 Coal Production In Table 1 are reported the two types of results that are generated when using this approach to analyze seasonality. On the one hand, we can find whether seasonality evolves over time (i.e. stochastic), or remains constant over the considered period (i.e. is deterministic). This information, from a statistical point of view, is captured by the q-ratio associated with the seasonal component in Table 1. It reports the estimated standard deviation on the largest standard deviation of the seasonal component. In the case of stochastic seasonality, the q-ratio is larger than zero and in the case of deterministic seasonality such ratio is zero, and therefore is not reported in the tables. It is possible to test in a similar way if the remaining components (slope, AR(1), level, and irregular) are stochastic or deterministic and if they are present in the model. [13]

14 East Shanghai Table 1: Standard Deviation of coal production with Dummies, q-ratio Seasonal Slope AR(1) Level Irregular ρ Beijing 0.004[0.03] [0.68] [0.02] 0.137[1.00] 0.95 Tianjin Liaoning 0.004[0.15] 0.064[2.24] [0.25] 0.028[1.00] 0.92 Jiangsu 0.001[0.17] [4.00] 0.009[1.00] 0.65 Zhejiang 0.002[0.05] 0.049[1.00] 0.044[0.90] 0.17 Guangdong 0.004[0.04] 0.086[0.94] 0.041[0.44] 0.091[1.00] 0.64 Hainan Shandong 0.025[1.00] 0.022[0.88] 0.025[0.99] 0.00 Fujian 0.002[0.02] 0.002[0.03] 0.086[1.00] 0.38 Guangxi 0.109[1.04] 0.019[0.18] [1.00] 0.80 Hebei 0.039[1.00] 0.57 Central Heilongjiang 0.060[1.00] 0.008[0.13] 0.46 Jilin 0.003[0.04] 0.048[0.55] 0.021[0.24] 0.088[1.00] 0.49 Hubei 0.005[0.04] 0.058[0.46] 0.125[1.00] 0.88 Shanxi 0.001[0.01] 0.076[1.00] [0.15] 0.000[0.01] 0.80 Hunan 0.002[0.06] 0.002[0.08] 0.116[3.79] [1.00] 0.83 Anhui 0.001[0.04] 0.038[1.55] 0.007[0.31] 0.024[1.00] 0.94 Jiangxi 0.002[0.01] 0.002[0.01] 0.116[1.00] 0.048[0.41] 0.83 Henan 0.002[0.03] 0.081[1.00] 0.80 Inner Mongolia 0.008[0.33] 0.001[0.07] 0.000[0.01] 0.024[1.00] 0.99 West Sichuan 0.002[0.09] 0.001[0.07] 0.075[3.32] 0.022[1.00] 0.52 Chongqing Xinjiang 0.039[0.64] 0.061[1.00] 0.041[0.67] 0.59 Qinghai 0.020[0.17] 0.176[1.42] 0.123[1.00] 0.98 Ningxia 0.001[0.01] 0.001[0.01] 0.085[1.00] 0.30 Gansu 0.004[0.06] 0.030[0.45] 0.015[0.22] 0.066[1.00] 0.98 Shaanxi 0.005[0.06] 0.074[1.00] 0.81 Yunnan 0.003[0.02] 0.003[0.02] 0.114[1.00] 0.70 Guizhou 0.106[2.34] 0.035[0.77] [1.00] 0.80 Note: Each column represents the value of q-ratio for each of the unobserved-components considered in this work seasonal, slope, AR(1), the level and the irregular. The last column shows the correlation coefficient. The same notation is used in the Table 2. In the Northern grid, which is divided into North Eastern, North- Western and Northern grids, Jilin and Liaoning show stochastic seasonality [14]

15 and constitute a small cluster. In addition, in the North, for the two most important producers of coal (Shanxi and Inner Mongolia), along with Beijing, a similar behavior is observed, while in the North-West seasonality is stochastic in the case of Shaanxi, Gansu, Qinghai and Ningxia. In the Eastern and Central grids, all the provinces that belong to these grids display stochastic seasonality, except Shanghai and Chongqing, while in the South of China, stochastic seasonality is only observed in Yunnan. Besides in some cases the level component is deterministic (Table 1), which is likely caused by the existence of structural changes that are captured by level-break dummies. These breaks appear mainly during the Asian and energy crises, which influence the seasonal pattern of coal production. 13 Furthermore, in the same table it is possible to observe that the slope component in the majority of the cases is deterministic, and the AR(1) component is present in most of the cases. Table A1 in the Appendix reports the specification tests for each province, including the likelihood and the standard error along with a battery of mis-specification tests. These models do not display any autocorrelation, non-normality and heteroscedasticity at the 1%, 5% and 10% levels of significance depending on the specification. However, a small kurtosis is detected for the case of Hubei in the irregular and level 13 We estimated in a subsample these cases, and the level component became stochastic. [15]

16 residuals. Nonetheless, apart from the latter case, these models behave adequately for statistical inference. Once the type of seasonality across Chinese regions is known, it is essential to investigate its evolution over time and the differences among provinces. This information is presented in Table 3, which reports the seasonal patterns of the final stage for each province and for each type of seasonality. In the case of those regions that display stochastic seasonality, this analysis is complemented by figures (a) and (b), which provide an illustrative example of how the seasonal patterns evolve over time. From our results, it is possible to conclude on the existence of important differences between regions that belong to the Northern and Central transmission grids and provinces that appear in the Southern and Eastern grids. This result is expected since the majority of coal is produced in the former provinces, while electricity is generated in the latter, the seasonal patterns of both types of energies being different. The Northern and Central transmission grids share common stochastic seasonal anomalies. First, we find, as in the case of stock markets in many countries as we have shown earlier, a negative seasonality in January and February, the so-called Lunar-New-Year effect, for all the provinces that belong to these two grids and also in the case of Fujian and Zhejiang in the Eastern grid and Yunnan in the Southern one. Second, we find a Summer [16]

17 effect in the North-Eastern and North-Western grids, as well as in the cases of Yunnan in the South and in Sichuan, Hubei and Jiangxi in the Central grid. Third, in many cases seasonality becomes positive from March to June (Spring effect) such as in the case of Jilin, Liaoning, Beijing, Shanxi, Gansu, Shaanxi, Fujian, Anhui, Yunnan and all Central provinces. Here, there are some singularities for some provinces like Inner Mongolia where seasonality is positive from May to June, and Jiangsu from March to May. Such a Spring effect is expected since after holidays firms start to operate again and require extra energy. Fourth, November and December (so-called December effect) show a positive seasonality in the North-Eastern grid, in Shaanxi and Gansu in the North-West, in all Central and Eastern provinces, as well as in Yunnan in the South. However, some differences arise for example in Beijing and Shanxi, where seasonality becomes positive in July, while for some other regions we find that it is negative. In the case of Eastern regions seasonality in the second half of the year is positive. In the case of provinces that display determinist seasonality, we report the seasonal effect in the final stage in Table 3. Common features are present, such as the Lunar New-Year effect in Hebei, Heilongjiang, Shandong, Guangdong, Guangxi, Hainan, Guizhou, and Xinjiang. The latter province displays a singularity since its negative seasonality lasts from January until August, probably due to its location. [17]

18 Table 3: Seasonal Effect. Final Stage. Coal Production (coefficient in bold and p- value in italics) Provinces with Stochastic Seasonality Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. Beijing Fujian Jiangsu Liaoning Zhejiang Anhui Henan Hubei Hunan Inner Mongolia Jiangxi Jilin Shanxi Gansu Ningxia Shaanxi Sichuan Qinghai Yunnan [18]

19 Provinces with Deterministic seasonality Guangdong Guangxi Guizhou Hebei Heilongjiang Hainan Shandong Xinjiang Besides, the Summer effect is also noticed, especially in July and August, in a majority of provinces; however its duration varies depending on the region, i.e. in Southern provinces, like Hainan and Guangxi, the Summer effect comes earlier, and in an Eastern provinces like Shandong that effect is prolonged until November. Finally, the study of the evolution of the monthly seasonality from 1996 to 2010 provides three main findings. First, we observe a relative decrease in the production of coal in December in the case of provinces that belong to the Northern grids compared to other months. However, the opposite behavior is present in July for some regions located in the Northern and Central grids and in both January and February for some Eastern and Central [19]

20 provinces. In addition, we can also detect some singularities like in October, where regions such as Gansu, Ningxia, Zhejiang and Hunan increase more the production of coal over the considered period than other months, while the opposite is found in Anhui, Inner Mongolia and Liaoning. These differences are also present in November, when for Jiangxi, and Hunan are observed a relative increase of coal over time, while in the case of Fujian we find the opposite. (a) Jilin [20]

21 (b) Fujian Note: Monthly Seasonal in the case of Jilin and Fujian. 3.2 Electricity Production The uneven distribution of energy resources across China s regions makes necessary to go beyond the analysis of coal production, and consider the second most important source of energy, that is, electricity. This analysis allows us to assess the differences across regions between the two types of energy in terms of seasonality and their time variation. Table 2 presents the results of the q-ratio that, as we stated earlier, allows us to discriminate between stochastic and deterministic seasonality across regions. Then, in Table 4 and figures (c) and (d) their evolution over time is shown. [21]

22 As expected, in the Southern grid where the majority of electricity is generated, we find that all provinces display stochastic seasonality. A similar behavior is found in Central and North-Western grids for all provinces, except Henan and Chongqing in the former and Ningxia in the latter. However, in the North and East there are fewer jurisdictions that display stochastic seasonality. Jilin and Liaoning constitute a small cluster in terms of electricity, similar to the one we detected earlier for coal, and the same conclusion can be drawn in the case of Beijing and Shanxi in the North and Fujian in the East. As we argued earlier in the case of coal production, level breaks are present in the case of electricity production, which explains why the level component becomes deterministic in four provinces. In this case we detect not only the Asian and energy crises, but also some positive shocks, which coincide with a period of fast development, and then a growing demand for energy. 14 In addition, as happens before, the slope plays a minor role in these models, and the AR(1) component is present in the majority of the cases. 14 As before, we estimated the model again in a subsample, and the level component became stochastic. [22]

23 East Table 2: Standard Deviation of electricity production with Dummies: q-ratio Seasonal Slope AR(1) Level Irregular ρ Shanghai 0.048[2.42] 0.009[0.49] 0.020[1.00] 0.31 Beijing 0.001[0.03] 0.065[2.45] 0.015[0.39] 0.046[1.00] 0.70 Tianjin 0.074[1.00] 0.57 Liaoning 0.001[0.05] 0.046[2.06] 0.016[0.75] 0.022[1.00] 0.78 Jiangsu 0.000[0.02] 0.015[0.45] 0.005[0.15] 0.034[1.00] 0.93 Zhejiang 0.001[1.16] 0.000[0.10] 0.001[1.00] 0.88 Guangdong 0.001[0.05] 0.030[1.10] 0.027[1.00] 0.19 Hainan 0.001[0.07] 0.024[0.99] 0.017[0.70] 0.024[1.00] 0.00 Shandong 0.000[0.04] 0.028[1.56] 0.018[1.00] 0.68 Fujian 0.001[0.04] 0.035[0.98] 0.011[0.32] 0.036[1.00] 0.92 Guangxi 0.001[0.02] 0.000[0.01] 0.066[1.00] 0.016[0.24] 0.027[0.41] 0.81 Hebei 0.043[2.40] 0.005[0.28] 0.018[1.00] 0.65 Central Heilongjiang 0.057[3.86] 0.012[0.82] 0.014[1.00] 0.56 Jilin 0.005[0.31] 0.004[0.22] 0.010[0.54] 0.005[0.31] 0.018[1.00] 0.84 Hubei 0.000[0.02] 0.001[0.02] 0.028[0.51] 0.042[1.00] 0.51 Shanxi 0.000[0.01] 0.001[0.03] 0.030[1.00] 0.007[0.25] 0.026[0.86] 0.63 Hunan 0.001[0.03] 0.070[1.45] 0.001[0.03] 0.048[1.00] 0.92 Anhui 0.000[0.01] 0.015[0.31] 0.008[0.16] 0.048[1.00] 0.98 Jiangxi 0.001[0.02] 0.060[1.30] 0.016[0.35] 0.046[1.00] 0.86 Henan 0.000[0.10] 0.042[5.68] 0.007[1.00] 0.57 Inner Mongolia 0.001[0.02] 0.058[1.00] 0.32 West Sichuan 0.003[0.21] 0.001[0.07] 0.048[2.91] 0.016[1.00] 0.72 Chongqing 0.000[0.01] 0.075[1.58] 0.047[1.00] 0.48 Xinjiang 0.002[0.18] 0.004[0.36] 0.010[0.79] 0.013[1.00] 0.07 Qinghai 0.006[0.30] 0.106[5.36] 0.037[1.89] 0.019[1.00] 0.56 Ningxia 0.084[6.54] 0.039[3.08] 0.012[1.00] 0.78 Gansu 0.003[0.06] 0.029[0.60] 0.020[0.41] 0.049[1.00] 0.73 Shaanxi 0.001[0.04] 0.000[0.02] 0.032[1.00] 0.017[0.53] 0.031[0.97] 0.13 Yunnan 0.002[2.80] 0.087[84.1] 0.001[1.00] 0.69 Guizhou 0.002[0.05] 0.049[1.00] [23]

24 Table 4: Seasonal Effects. Final Stage. Electricity Production (coefficient in bold and p-value in italics) Jan Provinces with Stochastic Seasonality Feb March April May June July Aug. Sept. Oct. Nov. Dec. Beijing Fujian Guangdong Guangxi Hainan Liaoning Hubei Hunan Jilin Jiangxi Shanxi Gansu Guizhou Qinghai Shaanxi Sichuan Yunnan Xinjiang Provinces with Deterministic seasonality [24]

25 Anhui Chongqing Hebei Heilongjiang Henan Inner Mongolia Jiangsu Ningxia Shanghai Shandong Tianjin Zhejiang In Table A2 in the Appendix, we report for each province the test of the hypotheses of normality, homoscedasticity and absence of autocorrelation, which are accepted at the 1%, 5% and 10% levels of significance depending on the specification. However, for the case of Yunnan a small kurtosis is detected both in the irregular and level residuals, while for Heilongjiang it is only observed in the irregular component. On the other hand, in the case of Guangdong, a small skewness is detected in the level residuals. Nonetheless, in spite of these facts, all the models are well-behaved for statistical inference. [25]

26 On the other hand, it is useful to know whether seasonal patterns in electricity generation differ from those in coal extraction and the differences that exist across provinces. This information is captured by the monthly seasonality represented in Table 4 and Figures (c) and (d), that as before we use as illustrative examples to describe our results. We observe significant differences in the seasonal patterns of electricity and coal production, especially between the North and South of China. Although, we found this behavior before for coal, it is corroborated once seasonality in electricity is investigated. In the latter case, there are more regions displaying stochastic seasonality in the Southern, North-Western and Central grids and there are fewer provinces with this feature in those grids where the production of coal predominates. Other differences are observed with regards to the Summer period, since seasonality in electricity production is positive and is prolonged until September in all provinces. However, we found earlier that for coal production it was negative. In addition, the Lunar New-Year effect changes to a positive seasonality in Beijing and Shanxi in the Northern grid, Liaoning and Jilin in the North-Eastern grid, Jiangxi in the Central one and Shaanxi in the North-Western grid. For other jurisdictions, however, the negative seasonality not only covers January and February, but is even prolonged until April, especially in provinces located in the South of China. This [26]

27 decrease of electricity production until April is also observed in some Central and North-Western grids. However, in spite of these differences, in the majority of the regions, we find the same seasonal pattern in November and December in both types of energy. ( c ) Shanxi ( d ) Guangdong Note: Monthly Seasonal in the case of Shanxi and Guangdong. [27]

28 In Table 4, we report the seasonal effects in the final stage for provinces that display stochastic and deterministic seasonality. While our previous results on stochastic seasonality are confirmed in that table, an interesting finding emerges with regards to some provinces that show deterministic seasonality. For example, in the case of Inner Mongolia, we find that seasonality becomes positive in the Summer for coal production, but it is negative in electricity production. A similar effect is observed in Heilongjiang. Since both regions are respectively located in the North and the most Northern part of the North East of China, they probably alternate the use of energy depending on the season, i.e. in the Summer they use coal, but before and after these months (except for Lunar New-Year and Summer Effects), they switch to electricity to satisfy the energy demand. In the temporal dimension differences are also observed. For example, in February is observed a relative decrease in electricity production in the case of Guangxi, Guizhou, Hainan, Jilin, Liaoning, Yunnan, and Xinjiang compared to other months. In contrast, relative increases of electricity production are present in the Summer months like in June-August for jurisdictions like Beijing, Fujian, Guangxi, Hubei, Jilin, Liaoning, Shanxi, Sichuan, Yunnan, and Xingjian. Finally, the conclusions are mixed in December. On the one hand, for regions like Guangdong, Guangxi and Jilin, there is a relative increase of electricity production, while on the other hand [28]

29 for others like Gansu, Guizhou, Hubei, Qinghai, and Xinjiang the reverse is observed. Overall, we find substantial differences in terms of seasonality across regions and also between the two types of energy. Besides, the time variation displays some differences. Knowing such seasonal patterns should be useful for regulators to design the regional and national energy development policy in China. 3.3 Robustness of the Results and Policy Implications One can argue that previous calendar anomalies found can be the result of ignoring additional factors that account for these seasonal patterns. In order to study the robustness of our previous results, we first include industrial output and energy prices (cooking coal price and electricity price) as exogenous variables in these new models, and subsequently temperature is added as an additional regressor. Then, we proceed as before in estimating the unobserved components model in the case of coal and electricity production with intervention dummies We report only a summary of the conclusions from our results to save space. However, they are available upon request from the authors. Besides, we have deleted some provinces due the lack of the data either for prices or industrial output. [29]

30 Table 5: Summary of the results based on standard deviation (q-ratio) of seasonal component. Coal A Coal B Electricity A Electricity B East Shangai NA NA Shanghai D D Beijing NA NA Beijing D D Liaoning S S Liaoning D D Tianjin NA NA Tianjin D S Jiangsu S S Jiangsu S S Zhejiang D D Zhejiang D S Guangdong D D Guangdong S S Hainan NA NA Hainan S S Shandong NA NA Shandong S S Fujian NA NA Fujian S S Guangxi D S Guangxi S S Hebei D S Hebei D S Central Heilongjiang S S Heilongjiang S D Jilin S S Jilin D D Hubei S S Hubei S S Shanxi S S Shanxi D D Hunan S S Hunan S D Anhui S D Anhui D D Jiangxi S S Jiangxi D D Henan S S Henan D D Inner Mongolia S S Inner Mongolia S S West Sichuan D D Sichuan S S Qinghai NA NA Qinghai NA NA Ningxia S S Ningxia NA NA Gansu NA NA Gansu S S Shaanxi NA NA Shaanxi S S Yunnan NA NA Yunnan S S Guizhou NA NA Guizhou S S Note: NA means that there is no data for this region in this analysis; D stands for deterministic seasonality and S for the stochastic seasonality. In italics and in bold the changes compared with tables 1 and 2. Coal A show the conclusions when income and price are included in the models, while Coal B refers when is added in addition to these variables temperature. The same notation is applied to electricity production. Due the lack of data in the analysis of electricity production the sample starts in 2003:1 until 2009:12. [30]

31 Table 6: Elasticity to income. Models conditioned by price and temperature. Electricity Coal Hebei 0.44*** Liaoning 0.47*** Shanxi 0.16*** Jiangsu 1.02*** Inner Mongolia 0.50*** Guangxi 0.67*** Shanghai 0.23*** Hebei 0.81*** Jiangsu 0.88*** Sichuan 0.61*** Zhejiang 0.98*** Heilongjiang 0.50*** Fujian 0.63*** Jilin 0.73*** Jiangxi 0.60*** Hubei 1.75*** Shandong 1.03*** Shanxi 0.88*** Henan 0.80*** Henan 1.65*** Hunan 0.48** Inner Mongolia 0.60*** Guangdong 1.32*** Guangxi 0.29*** Hainan 0.20*** Gansu 0.74** Note: *** denotes that this coefficient is significant at 1%, ** at 5%. In the remaining regions, income is not significant and in consequence it is not reported. Having a well-specified model in all cases in terms of misspecification tests, in Table 5 we present a summary of the conclusions on the type of seasonality for both coal and electricity production. There on this basis, we can conclude that in the case of coal production, the observed stochastic seasonality found previously vanishes in two provinces (Zhejiang and Heilongjiang) after the inclusion of income and prices as control variables, but for the remaining regions our previous conclusions remain valid. When temperature is taken into account, we find that Anhui switches to deterministic seasonality and Guangxi and Hebei to the stochastic one. However, apart from these three cases our initial conclusions remain unchanged, showing that the movements in the seasonal patterns are [31]

32 driven by specific characteristics of the coal producing regions different from temperature, income and prices. On the other hand, in the case of electricity production, we find that, when income and price are included in the models, for seven provinces (Anhui, Beijing, Guangdong, Hunan, Inner Mongolia, Tianjin, and Zhejiang,) seasonality becomes deterministic, but it remains stochastic for the rest of the provinces. Moreover, we observe only few changes in our conclusions when temperature is added as an additional explanatory variable. Specifically, we find that Heilongjiang and Hunan now display deterministic seasonality, while in the case of Hebei, Tianjin, and Zhejiang seasonal patterns evolve over time. This implies that our findings are quite robust and in the majority of the cases seasonality of energy in each region is driven by specific characteristics or factors different from temperature, income and energy prices. On the other hand, with respect to the seasonal patterns in the final stage, results do not display notable differences when these control variables are introduced into the model. 16 Finally, in Table 6, we report the income elasticity for electricity and coal production when it is significant. From there, we can observe that there is a large difference in the coefficient of income across regions showing the aforementioned heterogeneity. Specifically, the income elasticity ranges from 0.16 to 1.32 in 16 This information it is available upon request from the authors to save space in the paper. [32]

33 the case of electricity production, and from 0.47 to in the case of coal. However, the average for the whole nation in the case of electricity in urban areas controlling for climate conditions is (Asadoorian et al., 2008). This evidence therefore also supports the idea that each region should be analyzed individually and seasonality matters for the full understanding of China s energy situation. Besides, given that income is significant in few regions our results suggest that not all regions have the same weight in explaining the regional energy situation in China. This is important because in consequence only few of them drive the energy sector for the economy as a whole. From the above results, we can highlight the following main conclusions: a) Unlike previous work, we provide evidence that seasonality varies over time for the majority of Chinese regions, even controlling our estimates by income, price and temperature. This reveals that seasonality needs to be properly modeled in order to proceed to the following stage of any energy analysis, i.e. forecasting. b) We observe significant differences in the seasonal patterns between Northern and Southern regions in energy production. Thus, specific energy policies should be designed to satisfy the demand, switching from one type of energy to another according to the observed seasonal anomalies to avoid any energy shortage. [33]