Seasonal anomalies in electricity intensity across Chinese regions

Applied Energy 112 (2013) 1548–1557

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Seasonal anomalies in electricity intensity across Chinese regions M.J. Herrerias ⇑ The University of Nottingham, School of Contemporary Chinese Studies, Wollaton Road, NG8 1BB Nottingham, United Kingdom

h i g h l i g h t s " We analyze seasonal anomalies in electricity intensity in China. " Regional and time dimensions are investigated from 2003 to 2009. " Results suggest that seasonality is stochastic. " We find four main effects: Summer, Winter, Spring and Lunar New Year effects. " Differences are observed between northern regions and east-south of China.

a r t i c l e

i n f o

Article history: Received 17 November 2012 Received in revised form 4 January 2013 Accepted 16 January 2013 Available online 20 February 2013 Keywords: Seasonality Unobserved components China Energy intensity

a b s t r a c t This paper provides evidence on the relevance of modeling the seasonal nature of electricity intensity across Chinese regions in a suitable manner with monthly data from 2003 to 2009. In contrast to previous works, this study relaxes the assumption of deterministic seasonality, allowing for time and regional variation in the Chinese economy. In doing so, unobserved-components models are used to analyze the type of seasonality – stochastic or deterministic – that prevails. Regional differences in the seasonal patterns and their evolution over time are also examined. Results provide new empirical evidence on the stochastic nature of electricity intensity in the majority of the provinces. In addition, we find four main effects as regards seasonal patterns: (i) Lunar New Year, (ii) Summer, (iii) Spring, and (iv) Winter effects. In the first two effects seasonality becomes positive, thus indicating that electricity intensity increases, and the last two are negative, showing improvements in the use of electricity per unit of output. However, differences are observed between northern regions and the east-south of China. In addition, once we control our estimates for temperature and prices, no significant differences are seen in the results. Conclusions from this analysis are useful for empirical modeling in the energy-economics literature, and also for designing energy policies to improve the efficiency of the use of energy resources across Chinese regions. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction One of the most important concerns of developed countries today is the important demand for energy resources from emerging economies, and how this phenomenon influences their domestic economies and international markets. Among the fast-growing economies, China – the most populous country in the world – has without a doubt attracted the most attention from international organizations and developed countries owing to its responsibility in the observed increase in energy prices and the more than likely shortage of energy resources in the near future. As a result of such concern, one of the most active lines of research focuses on energy intensity and the causes of its fluctuations in China [1–3]. Even though analyzing seasonal patterns of energy intensity has major policy implications, such analyses involving the characteristics of the energy sector and the essential geograph⇑ Tel.: +44 (0)1158466448; fax: +44 (0)1158466324. E-mail address: [email protected] 0306-2619/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2013.01.050

ical and time dimensions have received little attention in the literature. Indeed, seasonal anomalies around the year have been ignored or, at best, used as a proxy, that is, as a simple deterministic seasonal dummy and time trend with the risk of producing significant bias in the analysis [4,11]. In addition, seasonality may evolve over time and can be heterogeneous across countries or regions. Different economic policies or technologies implemented in a region may affect seasonal patterns, and as a result they will be characterized by a deterministic or stochastic behavior over the year. Knowing of the existence of such limitations in previous works, it is essential to consider a flexible framework that allows for the stochastic character of these components in order to provide robust and consistent evidence on the changes in the seasonal patterns and their evolution over time [4,5]. This paper offers new empirical evidence with which to address this issue across Chinese regions. The analysis of the seasonal patterns is not only important for energy modeling purposes, but also for the current energy

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patterns and their stochastic or deterministic nature in the structural time series approach, which may provide new insights on how seasonal effects evolve over time. Thirdly, the economic significance of the seasonal patterns in terms of energy intensity may provide authorities with new directions for the design of energy-saving policies across Chinese regions, and may prevent the reappearance of significant problems such as electricity power cuts that some regions suffer every summer. Finally, our results show evidence of four calendar effects across regions. First, we find a strong Lunar New Year Effect in January and February in the majority of the provinces, where electricity intensity rises. Second, we detect a Summer Effect, where seasonality becomes positive, but unlike the previous one, there are differences between east-south grids and the northern ones due to climate conditions. In contrast, improvements in efficiency are found from March to June (Spring Effect), and from September to December (Winter Effect). In the last one, there are some exceptions in October and December, with a seasonality which becomes positive. On the other hand, the interest of some local governments in preventing power cuts as a result of electricity shortages in summer seems to be reflected in the fact that for these months the trend of seasonal patterns decreases for 13 regions. However, those of the winter months are mixed, depending on the province considered. This paper is organized as follows. In Section 2, we describe the data and technical aspects. In Section 3 the results are explained. A robustness analysis is presented in Section 4. Conclusions are drawn in Section 5.

shortages that are appearing every summer in the Chinese regions. Last summer (2011), China underwent the severest electricity shortage in its recent history, achieving 30 gW and an estimated peak at 40 gW. Nonetheless, the China Electricity Council denied the existence of a countrywide electricity crisis, claiming it was merely a seasonal shortage. Regions located in the eastern and central parts of China suffer the consequences of this scarcity the most, and regions located in the north-east and north-west show an electricity surplus. During January and April 2011 these shortages reached more serious levels.1 Since the limitations of the capacity for power generation throughout the year, its differences across regions, and the increasing trend in electricity consumption in this country are known, an essential aspect to be investigated is the efficiency of electricity around the year, by analyzing the seasonal patterns of electricity intensity across provinces. This study will allow us to know the months that each individual region is more efficient in the use of energy resources, and may be useful when it comes to designing more appropriate energy policies at both the local and the national levels. Calendar effects have been popular in other fields such as stock markets [8,9], industrial production data [10] or biological conception [11]. However, in the energy-economics literature, empirical evidence on seasonal anomalies is relatively scarce. Traditionally, researchers have made use of deterministic seasonal dummies to account for calendar effects, as for example in [12,13], which examine seasonal effects of energy demand in the case of the United Kingdom and energy prices in the case of Australia, respectively. This approach assumes that seasonality is constant over time. In contrast, within the structural time series approach, other researchers such as [4,5] relax the previous assumption and allow for variations in time in the case of energy demand in the United Kingdom. A similar approach can be found in [14–16]. In the case of China, some works, like [17,18], have applied panel data techniques to investigate the factors that affect energy consumption across Chinese regions, as well as the case of Hong Kong [19], through the application of the principal components approach. Thus, to the best of our knowledge there is no work on electricity intensity that analyzes the seasonal patterns of each individual province and their evolution over time, which would provide fresh developments in the relevant literature.2 In this context, the specific aim of this paper is to investigate the calendar effects in electricity intensity across Chinese regions with monthly data from 2003 to 2009. We use the unobservedcomponents model to analyze the type of seasonality – stochastic or deterministic – that prevails in this economy. Regional differences in the seasonal patterns and their evolution over time are examined. The attractiveness of analyzing seasonal patterns from the economic point of view in this work lies in: firstly, the investigation of the geographical dimension across Chinese regions, since this is an aspect that has been overlooked in the energy-economics literature. However, it has received considerable interest in regional energy economics due to the uneven distribution of energy resources as well as its unbalanced growth across regions. These two facts, therefore, may show notable differences in the efficient use of energy per unit of output in the Chinese economy. Secondly, the traditional search for seasonal anomalies relies on the assumption that they are deterministic. This means that these anomalies do not seem to disappear or reverse after some period of time. A recent approach to address the issue of seasonality, like the one applied in this work, focused on the time variation of seasonal

where lt is the trend, ct the seasonal, vt captures the autoregressive component of order one – AR(1) – in errors, and et is the irregular. All these components are stochastic, but they can be deterministic in limited cases. et is white noise and stationary, lt is normally only stationary in first or second differences, while ct is stationary when multiplied by the seasonal-summation operator, such as:

1 See [6] for an overview of the reforms in the energy sector and the relevance of the power cuts. 2 The search and identification of high/low levels of electricity intensity around the year, and the type of seasonality that is present in the models is an important issue in later stages of the analysis, e.g., forecasting. However, this paper focuses only on the examination of seasonal anomalies.

3 Electricity consumption is measured by kW h bn, industrial output is expressed in 100 million yuan (RMB), constant prices, temperature is measure in degrees Celsius, and electricity price is measured in RMB/kW h. 4 A complementary way to investigate these issues is provided by the analysis of seasonal integration and cointegration. 5 We have omitted the subscripts of each province for the sake of simplicity.

2. Data and methodology We investigate the seasonal patterns of electricity intensity (ratio of electricity consumption over industrial output) across Chinese regions over the period January 2003 through December 2009. In order to test for the robustness of our results, in the following stages of the analysis we use monthly temperature and electricity prices. The source of this data is CEIC and the National Bureau of Statistics of China.3 To carry out this research, we focus on the unobservedcomponents model developed by [20]. This approach allows us to distinguish whether seasonality evolves over time, i.e., if it is stochastic, or remains constant (deterministic), as other previous work has often considered. Moreover, another advantage of this method is that it enables us to provide stylized facts about electricity intensity. In particular, 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 in structural time series modeling [21].4 The formal statistical formulation of the unobservedcomponents models of the variable Yt considered here is as follows5:

Y t ¼ lt þ ct þ v t þ t

ð1Þ

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M.J. Herrerias / Applied Energy 112 (2013) 1548–1557

SðLÞ ¼ I þ L þ L2 þ    þ Ls1

ð2Þ

where s is the number of seasons, L is the lag operator, and S(L) contains both real and complex unit roots. ct is said to be seasonally integrated. The use of S(L) in defining seasonality is a fundamental feature of univariate time series models because it ensures that the seasonal component, which is non-stationary, is not confounded with the trend and that the seasonal pattern projected into the future sums to zero over s consecutive time periods. Following the structural time series approach, the trend is formulated as:

lt ¼ lt1 þ bt1 þ gt gt  NIDð0; r2g Þ

ð3Þ 3. Results

where

bt ¼ bt1 þ nt

nt  NIDð0; r2n Þ

ð4Þ

and the autoregressive component AR(1) as:

v t ¼ qv v t1 þ et et  NIDð0; r2e Þ

ð5Þ

If r2n = 0, then Eq. (3) collapses to a random walk plus drift, and to a deterministic linear trend if in addition r2g = 0. Setting r2g to zero when r2n is positive tends to give a trend which changes relatively smoothly [22]. The seasonal component, ct, can be modeled in different ways. The simplest method is as follows:

SðLÞct ¼

s1 X

ctj ¼ xt

ð6Þ

j¼0

where xt  NID(0, r2w ). In the literature on the subject, this way of representing seasonality is known as the dummy variable form of stochastic seasonality. If r2w is zero, the seasonal component becomes deterministic. Another way to model the seasonal component is by formulating the trigonometric seasonality, which in turn can be written as follows:

ct ¼

take place in the Chinese economy. Normality is tested with the Jarque–Bera statistics, which is distributed as v2 under the null hypothesis of normally-distributed errors. H(h) is the heteroskedasticity test statistics distributed as a f(h, h) with (h, h) degrees of freedom under the null of homoskedasticity. Q(p, d) is the Ljung-Box statistic based on the sum of the first p autocorrelations, and is tested against a v2 distribution with d degrees of freedom. 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.

s=2 X

cjt

ð7Þ

j¼1

where

cj;t ¼ cj;t1 cos kj þ cj;t1 sin kj þ xjt

ð8Þ s 2

cj;t ¼ cj;t1 sin kj þ cj;t1 cos kj þ xj;t j ¼ I; . . . ;  I; kj ¼ 2pj=s

ð9Þ

and

cj;t ¼ cj;t1 þ xj;t j ¼ s=2

ð10Þ

where xj,t and x are distributed normally with zero mean and independently distributed. The seasonal pattern becomes deterministic if r2w = 0. Trigonometric seasonality is more desirable than the dummy variable form of stochastic seasonality because it allows for smoother changes in the seasonal component. For this reason, in this work we focus our analysis on the first of the two ways to model seasonality. All the disturbances are assumed to be mutually uncorrelated, and the extent to which the trend and seasonal components evolve over time depends on the parameters r2g , r2n , r2x , and r2 , which can be estimated by maximum likelihood [20]. After this step, the trend and seasonal components may be extracted by a smoothing algorithm [23]. All the estimations are performed with STAMP [24]. The reliability of the estimations is guaranteed by performing a set of diagnostic tests following [25]. However, in order to satisfy the traditional Markov assumptions, intervention dummies must be introduced into the model due to the significant changes that  j;t

The unobserved-components model is used because we are interested in uncovering three different features of electricity intensity across the Chinese provinces. First, whether seasonality evolves over time, that is, is it stochastic or deterministic? The answer to this question is provided in Table 1, which reports the q-ratio. Seasonality is said to be stochastic when the ratio of the corresponding estimated standard deviation over the largest standard deviation of the seasonal component is non-zero. Second, we analyze the differences of the observed seasonal patterns across regions. Finally, if seasonality is stochastic, we examine its time variation. Table 1 presents the two types of results from the structural time series approach, that is, seasonality evolves over time (e.g., it is stochastic) or remains constant over the analyzed period (e.g., deterministic). The q-ratio associated to the seasonal component gives us the relevant information to discriminate between the two types of seasonality. According to our results, all Chinese regions except Guangdong province display stochastic seasonality. In addition, by applying the q-ratio to the remaining components (slope, AR(1), level and irregular) we can investigate whether they are deterministic or stochastic, and if they are present in the models for each individual province. Among the remaining components, level receives considerable interest due to the continuous transformation of the Chinese economy over time, and for modeling purposes. We find that in some cases the level component becomes deterministic towing to the introduction of level-dummies in the specification, especially the ones that account for the important reforms introduced into the energy sector around 2005 and onwards. However, such intervention dummies are relevant for the reliability of the estimations and the robustness of our conclusions.6 Once we know the type of seasonality that prevails in each region in terms of electricity intensity, another essential feature to investigate is the seasonal patterns over the year and their time variation. This information is captured in Figs. 1–6, which is presented as an example of such seasonal patterns by transmission grids. Moreover, when seasonality is deterministic, we complement that analysis with the seasonal effects in the final stage (Table 2).7 In the north of China, there are three transmission grids, the north-east, which comprises regions such as Inner Mongolia, Heilongjiang, Jilin and Liaoning. In the north-west there is another one covering provinces such as Gansu, Qinghai, Shaanxi and Ningxia, and the north grid includes Beijing, Hebei, Shanxi, Shandong and Tianjin. For all such grids, we find a Lunar New Year Effect, where seasonality becomes positive in January and February in the regions belonging to the north-eastern and northern grids, and in Qinghai in the north-western grid. In the latter grid, this 6 We do not detect any problems in terms of the specification of the residuals. These misspecification tests are available upon request from the authors. 7 The coal transmission grid includes six grids: North-East (Heilongjiang, Jilin, Liaoning, and Inner Mongolia; North (Beijing, Tianjin, Hebei, Shanxi, and Shandong); East (Shanghai, Zhejiang, Anhui, Jiangsu, and Fujian); Central (Jiangxi, Henan, Hubei, Hunan, and Sichuan); South (Guangdong, Guangxi, Yunnan, and Guizhou); NorthWestern (Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang).

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M.J. Herrerias / Applied Energy 112 (2013) 1548–1557 Table 1 Standard deviation of electricity intensity with dummies, q-ratio. Seasonal

Slope

East Shanghai Beijing Tianjin Liaoning Jiangsu Zhejiang Guangdong Hainan Shandong Fujian Guangxi Hebei

0.0002[0.07] 0.0003[0.08] 0.0001[0.03] 0.000[0.01] 0.000[0.01]

Central Heilongjiang Jilin Hubei Shanxi Hunan Anhui Jiangxi Henan Inner Mongolia

0.0006[7.74] 0.0004[7.74] 0.0006[7.20] 0.001[0.06] 0.0009[13.68] 0.0002[0.24] 0.001[15.92] 0.0005[0.71] 0.002[0.12]

West Sichuan Qinghai Ningxia Gansu Shaanxi Yunnan Guizhou

0.0009[5.89] 0.002[6.03] 0.003[5.74] 0.001[0.07] 0.0009[0.06] 0.005[0.23] 0.001[1.90]

0.0003[0.26] 0.0009[0.31] 0.0003[0.32] 0.0002[3.96] 0.0004[0.08] 0.0003[0.01]

AR(1)

Level

0.000[0.01] 0.003[1.00] 0.0009[1.00] 0.003[51.96] 0.005[1.00] 0.020[1.00] 0.009[93.37] 0.013[3.56] 0.003[1.00]

0.000[0.02] 0.0001[0.15] 0.0005[8.10]

0.0004[0.50]

Irregular

q

0.001[1.00]

0.99 0.75 0.95 0.87 0.93 0.96 0.78 0.65 0.82 0.99 0.67 0.85

0.00[0.03] 0.00[1.00] 0.00[0.01]

0.0002[0.02]

0.00[1.00] 0.003[1.00]

0.00[0.00]

0.004[1.00] 0.000[1.00] 0.009[1.64]

0.005[1.00]

0.001[16.21] 0.005[90.88] 0.004[41.20] 0.028[1.00] 0.006[95.53] 0.001[1.30] 0.006[69.30] 0.004[6.60] 0.019[1.00]

0.00[0.06] 0.00[0.64]

0.001[12.61] 0.0004[7.68] 0.001[13.06] 0.001[0.06]

0.007[49.0] 0.041[98.57] 0.031[58.47] 0.017[1.00] 0.014[1.00] 0.025[1.00] 0.028[38.25]

0.00[1.00] 0.00[1.00] 0.00[1.00]

0.003[4.47]

0.00[1.00] 0.0009[1.00] 0.00[1.00] 0.00[1.00]

0.014[33.75] 0.035[66.16]

0.00[1.00] 0.00[1.00] 0.00[1.00]

0.0004[0.53]

0.0007[1.00]

0.1

Jan March May

0.98 0.86 0.96 0.90 0.83 0.53 0.78 0.94 0.73 0.96 0.94 0.88 0.71 0.77 0.83 0.72

Feb April June

0.0

2002

2003

2004

2005

2006

2007

2008

0.025

2009

July Sep. Nov.

Aug. Oct. Dec.

0.000

-0.025 2002

2003

2004

2005

2006

2007

2008

2009

Fig. 1. Seasonal component, Northern Grid, Beijing.

effect is only observed in January for Shaanxi and Ningxia, and it is extended until April in the case of Gansu. On the other hand, in the central grid (Henan, Hubei, Hunan, Jiangxi and Sichuan), the eastern one (Anhui, Fujian, Jiangsu, Shanghai, and Zhejiang), and the southern grid (Hainan, Guangxi, Guizhou, and Yunnan) a similar

Lunar New Year Effect is detected, except in Hunan in the central grid, Zhejiang in the eastern one, and Hainan in the southern one, where that pattern is only found for a single month. It is important to keep in mind that a positive seasonality implies an increase in electricity intensity, and therefore the efficiency level is

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M.J. Herrerias / Applied Energy 112 (2013) 1548–1557

0.1

Jan March May

Feb April June

July Sep. Nov.

Aug. Oct. Dec.

0.0

-0.1

2002

2003

2004

2005

2006

2007

2008

2009

0.05

0.00

-0.05

-0.10

2002

2003

2004

2005

2006

2007

2008

2009

Fig. 2. Seasonal component, North-Eastern Grid, Inner Mongolia.

0.20 0.15 Jan March May

0.10

Feb April June

0.05 0.00 -0.05 2002

2003

2004

2005

2006

2007

2008

2009

0.050

July Sep. Nov.

0.025

Aug. Oct. Dec.

0.000

-0.025

2002

2003

2004

2005

2006

2007

2008

2009

Fig. 3. Seasonal component, North-Western Grid, Shaanxi.

deteriorated in that period. This seasonal anomaly can be explained by the fact that the Chinese New Year is either in January or February, and this gives rise to a significant increase in electricity consumption. In addition, we also find a Summer Effect, where seasonality becomes positive in July and August. In the eastern and southern transmission grids, this effect is more obvious due to the climatic conditions, such as the increase in temperature, which requires the use of air conditioning and other devices, thereby increasing

electricity consumption and deteriorating the efficiency of the use of electricity. This is also true for Jiangxi and Hubei in the central grid, Shaanxi in the north-western, Inner Mongolia in the north-eastern, and Hebei and Shanxi in the northern transmission grids. We observe differences in this seasonal effect between the east-south regions and the north of China, given that in the latter we find this positive seasonality only in July. Here it should be pointed out that the important shortages in electricity in some regions in the summer period have led to the implementation of ac-

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M.J. Herrerias / Applied Energy 112 (2013) 1548–1557

0.04

0.02

Jan March May

Feb April June

July Sep. Nov.

Aug. Oct. Dec.

0.00

-0.02 2002

2003

2004

2005

2006

2007

2008

2009

0.03 0.02 0.01 0.00 -0.01

2002

2003

2004

2005

2006

2007

2008

2009

Fig. 4. Seasonal component, eastern grid, Jiangsu.

0.05 Jan March May

Feb April June

0.00

-0.05

2002

2003

2004

2005

2006

2007

2008

2009

0.05

July Sep. Nov.

Aug. Oct. Dec.

0.00

-0.05

2002

2003

2004

2005

2006

2007

2008

2009

Fig. 5. Seasonal component, southern grid, Guangxi.

tive energy policies by local governments. One of these consists in rationalizing electricity early than the summer period, and this fact may be another reason accounting for the differences observed in the seasonal patterns. In contrast, improvements in the use of energy are found from March to June – the so-called Spring Effect. This effect is common to all the regions that belong to the eastern, north-eastern, and central grids. It is also met in the southern and in the northern ones, except in Guizhou in the former and Tianjin in the latter,

which present some singularities. However, in the north-western grid this pattern is irregular. One of the possible reasons that accounts for this effect lies in the fact that in the central and northern regions the central heating is shut down in March, so they use less electricity and therefore improve its efficiency. It may also be due to the fact that during these months the temperature is quite reasonable in the south of China and so they do not use heating or air conditioning, thus saving on electricity consumption.

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M.J. Herrerias / Applied Energy 112 (2013) 1548–1557

0.6

0.4

Jan March May

Feb April June

July Sep. Nov.

Aug. Oct. Dec.

0.2

0.0 2002

2003

2004

2005

2006

2007

2008

2009

0.000

-0.025

-0.050

-0.075

2002

2003

2004

2005

2006

2007

2008

2009

Fig. 6. Seasonal component, central grid, Hunan.

Finally, we find a Winter Effect in all Chinese transmission grids, where seasonality becomes negative from September to December, thus improving the electricity intensity. One of the reasons that may account for these improvements could be that in many cases firms close in December, so they do not consume electricity. However, there are some exceptions, such as in the case of Beijing, Hainan, Liaoning, Shandong, and Tianjin, where we observe a positive seasonality in December. This exception is explained for the low temperature in these regions, especially in Beijing and Tianjin achieving sometimes more than 15 °C in winter, which in turn encourages the Chinese population to use the heating more. The same happens in October in the case of Fujian, Guangxi, Hainan, Inner Mongolia, Jilin, and Yunnan. Table 2 reports the final stage of the seasonal patterns (the last 12 months) for all regions, including Guangdong, where we detect that its seasonal pattern is deterministic. These results confirm our previous findings in general. The dark cells in the table indicate those months where there is an increase in electricity intensity, while the green ones indicate improvements in efficiency. On the other hand, results from Guangdong confirm previous findings regarding the Lunar New Year and Summer Effects. In addition, seasonality, which appears early (in November and December), is negative. However, we find a difference in terms of the Spring Effect, since the seasonality now becomes positive. For other provinces, the time-variation of the seasonal component also provides important information. First, we find a decreasing trend in July and in August for 13 regions, while in the case of June such a trend is also observed in Anhui, Guangxi, and Shanghai. Second, two opposite trends are detected in January and February, depending on the region under consideration. In the case of Beijing, Heilongjiang, Hubei, Inner Mongolia, Jiangsu, Shanxi, and Yunnan the tendency in January is for the seasonal effect to increase over time, while the same happens in February for Gansu and Guangxi. However, a decreasing trend is found for Heilongjiang, Hebei, Inner Mongolia, Jiangsu, Qinghai, Shandong, Shanxi, and Sichuan, where the Lunar New Year Effect has been detected. Finally, December also displays these two distinctive trends. In the case of Inner Mongolia, Shanghai, and Shanxi the tendency is for the seasonal ef-

fect to increase, while the opposite occurs in Ningxia, Shaanxi, Sichuan, and Yunnan. Knowing these differences across regions and around the year in the use of electricity consumption per unit of output (results from Tables 1–3), the next concern in the analysis consists on what type of measures local and national authorities can adopt to alleviate the current situation. Some of them can be the following: (1) Integration of the transmission grid: Currently the three corridors and the six transmission grids are operating independently in China, therefore creating some inefficiency. The Chinese government expects that by 2020 all corridors and transmission grids will work in a unified system, improving the efficiency of the supply of energy. (2) Use of clean energy: Coal is the most important source of energy in China; however is also the most polluted energy. Promoting the use of green energies like the one from wind or solar will reduce the coal dependence and will protect the environment. (3) Social responsibility: The Chinese government can promote a set of useful suggestions through the media to the Chinese population to save energy and inform them about the environmental consequences of the current trend in their consumption and in their future health. (4) Innovation: Chinese authorities can promote new innovations and an improvement in technology through subsides on energy sector. (5) Foreign Capital: Since investment on energy sector requires large amount of capital, allowing the entrance of foreign enterprises will bring new technology into the energy sector and will alleviate the budget constraint by domestic enterprises. (6) Infrastructure: Since the distance between regions is significant, improving the productive infrastructure can led to a better use of energy resources in the future. (7) Coordination: National and local governments should work coordinated to reduce the inefficiency and to be able to implement energy policies across regions.

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M.J. Herrerias / Applied Energy 112 (2013) 1548–1557 Table 2 Seasonal effect. Final stage. Electricity intensity.

Jan.

Feb.

March April May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Provinces with Stochastic Seasonality Beijing Fujian Jiangsu Liaoning Zhejiang Anhui Henan Hubei Hunan Inner Mongolia Jiangxi Jilin Shanxi Gansu Ningxia Shaanxi Sichuan Qinghai Yunnan Guangxi Guizhou Hebei Heilongjiang Hainan Shandong Tianjin Shanghai Provinces with deterministic seasonality: Guangdong Note: Dark cells indicate that in these months there is a deterioration of the use of electricity per unit of output, while the green ones indicate improvement in efficiency. (For interpretation of the references to color in this table legend, the reader is referred to the web version of this article.) Table 3 Summary of the results conditioned by price and temperature, and price elasticity. Stochastic Seasonality

Deterministic Seasonality

Shanghai, Beijing (0.06), Tianjin, Liaoning, Jiangsu, Zhejiang (0.09), Shandong, Fujian (0.05), Guangxi, Heilongjiang, Jilin (0.09), Hubei, Shanxi, Hunan, Anhui, Jiangxi, Henan, Inner Mongolia, Sichuan, Gansu, Yunnan, and Guizhou

Guangdong, Hainan, Hebei (0.37), and Shaanxi (0.09)

Note: In brackets it appears the price elasticity in those cases that it was found significant.

(8) Knowing the nature of the calendar effects around the year in terms of electricity intensity, Chinese authorities could try to balance those months where it is observed improvements of efficiency with those months where it is observed a deterioration of electricity intensity. 4. Robustness of the results One can argue that the previous results are spurious owing to the omission of relevant variables such as electricity price and the temperature of each region. In order to avoid that shortcoming,

we perform the analysis again but now including these two explanatory variables in the model. We then estimate the model as before by using the unobserved-components model with intervention dummies. Once we have a well-specified model, we can observe that on conditioning the analysis for electricity price and temperature, our results do not show any substantial differences. Indeed, Table 3 shows a summary of the conclusions on the type of seasonality that prevails in these new models and the price elasticity. Recall that in the previous analysis, all the regions except Guangdong presented stochastic seasonality. So an important issue to be settled is

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whether the stochastic nature of the series is crowded out by introducing these two factors into the model. We find that in the case of Hainan, Hebei, and Shaanxi seasonality becomes deterministic, therefore indicating that these factors matter in the seasonal patterns. Specifically, our results suggest that in the case of Hebei and Shaanxi the main driver is the price, since it is significant, while in the case of Hainan it is the temperature. However, for the remaining regions, there are specific characteristics or mechanisms (other than price and temperature) that account for the stochastic nature of the series. One can hypothesize that, given the fact that there is a higher presence of the state-sector in energy issues in China, factors related with market forces do not operate as in other economies, and this may be one of the explanations of the results. As regards price elasticity, we find that it becomes significant in only six cases, with an ambiguous sign. For three regions, price is a factor that decreases the level of efficiency, but the opposite conclusion can be drawn for the remaining three provinces. This ambiguous result can be explained by the fact that energy prices are managed by the government. An important policy recommendation in addition to the previous ones to be implemented in the near future could be to liberalize energy prices allowing that the market forces set the price, since electricity prices in China were highly subsidized and below the average total costs of generation and transmission [26], but an essential factor to decrease electricity intensity [27]. This measure is expected that increase the efficiency of energy sector in China. A complementary policy to this one could be to create an energy tax for those families and enterprises that consume more in a similar fashion than the tiered electricity price examined by [28].

5. Conclusions The decline in energy intensity in developed and developing countries has been one of the most active areas of research in energy economics in recent years. One of the reasons for that interest is fast economic growth in emerging economies and their economic integration in international markets, which has led these economies to demand large amounts of energy resources to fuel economic growth. Within these countries, China is probably one of the most interesting cases, since it has been characterized by fast unbalanced growth across regions. Moreover, the uneven distribution of coal and electricity – the most important energy resources in this country – across regions and the notable distance between producers and consumers of energy have introduced a concern for the efficient use of energy resources into this country. However, researchers in energy economics have extensively focused on the factors of the observed decline in energy intensity, but to the best of our knowledge they have not been concerned with the seasonal patterns that evolve over time or with regional differences, which are crucial to be able to design any energy policy. This paper provides the first evidence on these two dimensions. In particular, our results suggest that all the regions except Guangdong show stochastic seasonality. In addition, we have detected four main seasonal patterns. On the one hand, from our results it is possible to conclude that January and February display a positive seasonality, known as the so-called Lunar New Year Effect. This pattern is quite common in the majority of regions. Furthermore, as expected, we find a Summer Effect, where seasonality becomes positive. However, this effect is more apparent in the east and south of China than in the north, due to the climatic conditions. On the other hand, improvements in electricity intensity are found from March to June (Spring Effect) in the majority of the regions, except the ones located in the north-west of China. Finally, with some exceptions, such improvements are also seen

from September to December in the so-called Winter Effect. The evolution over time of such seasonal patterns may reflect policies adapted to prevent electricity power cuts in China, since there is a tendency to decrease electricity intensity in the summer months. The same happens in other months, like the celebration of the Chinese New Year, where the amount of electricity used is more than usual. However, such a decreasing trend is not equal for all the provinces, which display a number of differences. We test the robustness of the previous results by including temperature and price. Nevertheless, we cannot observe any significant differences compared with our initial results, except in the case of Hebei, Shaanxi, and Hainan, where the stochastic nature is crowded out by the introduction of price in the first two regions, and due to the introduction of temperature in the models in the case of Shaanxi. To sum up, a deeper understanding of the problems of electricity power cuts across the Chinese regions, gained by analyzing the seasonal patterns over the year, may help local and national governments to prevent electricity shortages and improve its efficiency by designing suitable energy policies that take into account the seasonal patterns found in the vast territory of China.

Acknowledgements The author gratefully acknowledges the help received from Eric Girardin, Qiao Yongyuan, and Guy Liu, and the comments received from the participants of ICAE 2012 in Suzhou on an earlier version of this paper and three anonymous referees. The usual disclaimer applies.

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