Response of scale and leverage of thermal power enterprises to renewable power enterprises in China

Applied Energy 251 (2019) 113288

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Response of scale and leverage of thermal power enterprises to renewable power enterprises in China Dequn Zhou, Changsong Wu, Qunwei Wang, Donglan Zha

T

⁎

College of Economics and Management, Nanjing University of Aeronautics and Astronautics, P.O.150 Jiangning District, 211106 Nanjing, China Research Center for Energy Soft Science, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China

HIGHLIGHTS

power enterprise may adopt resistive strategy to positive shock to renewables. • Thermal tested the scale and leverage responses of thermal power enterprises. • We to scale of renewable power enterprise is easier to cause resistive strategy. • Shock • The scale strategy is preferential for thermal power enterprise. ARTICLE INFO

ABSTRACT

Keywords: Strategy response Power generation enterprise Generalized impulse response analysis China

Power generation structures in China remain dominated by coal, despite the overwhelmingly favorable response to the poll: To resist or not to resist? Since the renewable power enterprises have to compete with thermal power enterprises in a limited incremental market, the thermal power enterprises have the motivation to develop strategic responses to protect their own interests and meet policy requirements. Previous studies on renewable energy policies may have underrated or ignored the possible responses of rivalry from thermal power enterprises to the positive shock to renewable power enterprises. Considering the adverse effects of this response on the development of renewable power enterprises, it is necessary to reveal the short-term competitive response of thermal power enterprises. This paper conducts generalized impulse response analysis to estimate the scale and leverage of the response of thermal power enterprises to the unexpected positive impact on renewable power enterprises’ scale and leverage using a VAR-based model. The results indicate that thermal power enterprises are more likely to adopt resistant strategies to the positive shock to the scale of renewable power enterprises in the first quarter, whether or not these responses are profitable. Particularly, scale strategy is preferential for thermal power enterprises, which may have negative effect on the development of renewable power enterprises and slowed renewable energy transition. We suggest that the policies should consider the possible scale expansion of thermal power enterprises and mitigate the competition between thermal and renewable energy enterprises.

1. Introduction The renewable power grew by 17% in 2017, which accounted for almost half of the growth in global power generation (49%), even though coal remains the largest source with its share of 38.1% in 2017 [1]. Especially, China accounts for 50.7% the world’s coal use in 2017, which is the main result of a more than a 350% increase of Chinese electric generation from 1368.5 TWh in 2000 [1,2]. The thermal power generation accounted for nearly 67.14% of total power generation in 2017 [1]. Meanwhile, China is a representative country introducing the

competition into the electricity market which is undergoing in most jurisdictions worldwide [3]. Transition to a low carbon electricity market in China is critical to global efforts to reduce the risks of climate change [4]. The depletion of fossil energy resources and the polluted environment have resulted in the importance of renewable electricity generation as an alternative source of energy, to promote environmental sustainability and sustainable development [5]. As Marchetti highlighted energy sources are comparable to commercial products competing for a market niche [6], the competition existed between different types of energy in a wide range of countries.

⁎ Corresponding author at: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, P.O.150 Jiangning District, 211106 Nanjing, China. E-mail address: [email protected] (D. Zha).

https://doi.org/10.1016/j.apenergy.2019.05.091 Received 27 November 2018; Received in revised form 5 May 2019; Accepted 6 May 2019 0306-2619/ © 2019 Published by Elsevier Ltd.

Applied Energy 251 (2019) 113288

D. Zhou, et al.

Furlan and Mortarino investigated whether the diffusion level of traditional sources (coal, oil, gas, and nuclear systems) sustained or prevented the spread of renewables in the US, Europe, China, and India [7]. Through an extended Lotka–Volterra model, Guidolin and Guseo investigated the social effects on competition and substitution dynamics between nuclear power and renewable energy on the case of Germany, which underlined the main substitution effect in electricity production depending on the evolution of these two competitors [8]. Nevertheless, there are challenges on how to support the renewable energy industry in ways that does not interfere with efficient workings of competitive markets [9]. Due to the increasing competitiveness of renewable energy technologies and less government intervention, and the degree of substitution between renewables and fossil energy is increasing [10,11]. However, from the 1970s, coal has shown a certain resistance to decline and this is too a matter that is open to further investigation [12]. The extraordinary growth in renewables in recent years, and the huge policy efforts to encourage a shift away from coal into cleaner, lower carbon fuels, however, there has been almost no improvement in the power sector fuel mix over the past 20 years worldwide [1]. Existing policy instruments appear either inadequate or inefficient at achieving the substitution of fossil energy sources by renewables in a competitive power market [13,14]. The China power market, for example, has initiated the transformation from a centrally-planned form to a marketoriented one. Competition between renewable power enterprises (RPEs) and thermal power enterprises (TPEs) will not be just about costs, such as the limited power transmission resources [15]. By means of direct power supply trading, large-scale TPEs with qualified productive capacity could obtain the priority rights of access to the power grid that should be owned by RPEs. RPEs and TPEs may even compete for investment and land resources [16]. Since alternative abatement activities are less costly, some of the abatement cost could further offset by energy input cost savings [17]. Current policy may not prevent TPEs from adapting strategic behaviours in order to maintain their competitiveness. However, similar competitive strategies may have adverse effects on the development of RPEs. Unfortunately, the possible resistance from TPEs seems to have not been taken into consideration in the current renewable energy policy. In order to design effective policies to support the deployment of renewable electricity, it is necessary to identify the major strategic responses of TPEs in the market. A series of actions (moves) and reactions (countermoves) among firms in an industry could create competitive dynamics [18]. Competitive dynamics can serve as a synthesizing framework for linking strategy content and process, strategy development and implementation, and macro-competitive and micro-actor viewpoints [19,20]. Previous studies concerned about competitive dynamics that the relative scale could influence competitive tension [21] and high levels of debt and bankruptcy threats always deter enterprises from entering into monopolistic markets [22]. Machado and Sousa studied 21 enterprises on the case of Brazilian utilities and pointed out to the existence of substantial economies of scale only for the group of the largest power enterprises [23]. The existence of scale economies for the enterprises always means that they can experience increasing returns and hence declining average costs in the long-run [24,25]. Besides that, Steffen found the low investment risks and project finance have much larger importance for renewables than for fossil fuel-based power plants in Germany [26]. Bobinaite got that bankruptcy probabilities of wind electricity producing enterprises are related to financial leverage in the Baltic States [27]. Considering the importance of enterprise scale and financial leverage for power generation sectors, TPEs could apply these business strategies to cope with the market penetration of RPEs and maintain their market competitiveness. In contrast to previous studies, which usually conducted under the pure market competition context, RPEs are received policy facilitation [28]. A belief is held that market-based renewable energy policy could incorporate the cost of environmental

conservation into operating costs, equalize the incremental amount that was used to reduce pollution (marginal cost), and ultimately, achieve the renewable energy transformation [29]. However, the market-based approaches in environmental policy and conservation have usually, paradoxically, coincided with market liberalization [30]. Due to the natural monopoly of the power market, the result of market competition in the short term may not be as efficient as expected [31]. In addition, because of the existence of externalities, which are not factored into traditional economic accounting systems [32], the competition between TPEs and RPEs is difficult to achieve theoretical consequence. In fact, TPEs are motivated to develop strategic responses to protect their own interests and meet policy requirements rather than enact any radical transformation. These strategic responses could cause the deterioration of the competitive environment for RPEs, which may lead to a slowed renewable energy transition and higher costs than anticipated. Several prior studies show how multi-barriers, such as cost [33,34], technology [35,36] and environmental barriers [37–39] influence renewable energy utilization. Given the remarkable political pressure and significant costs of pollution reduction incurred by enterprises, the existence of resistance strategies requires further examination. In order to design effective renewable policies and appropriate competitive strategy for RPEs, it is necessary to identify the major strategic responses of TPEs in the market. Our study is motivated by the insights of the neglected strategic response of TPEs from China, which are representative [4]. We based on the case in China and set up a measurable function to examine the feasibility of the strategic response. We used the VAR-based model to estimate the parameters and the response of TPEs to the favorable shock to renewables. Compared to alternative specifications, vector autoregressive (VAR) model could combine the long run and short run information in data by exploiting the cointegration property [40]. The VAR model could well estimate the longterm competitive dynamics between the thermal and renewable enterprises through the endogenous treatment of own and rivals’ strategy. In addition, the generalized impulse response analysis could show how the TPEs react to the favorable shocks to renewables in short-term based on the estimation of VAR model. Compared with the Cholesky decomposition, generalized impulse response method proposed by Koop et al. is more suitable because it does not rely on the order of variables in the model [41]. Although a growing number of countries are introducing the competition into power market, the competitive dynamics between different types of power enterprises are lack of study, especially the strategic response of thermal power enterprises under the context of global renewable energy transition. Our new contributions are as follows. (i) Based on the competitive dynamics theory, we constructed a theoretical model for characterizing the inter-enterprise rivalry, which make the strategic response easy to be estimated by the VAR-based model. (ii) By analyzing the generalized impulse response, we found that the thermal power enterprises could adopt the resistant strategies to cope with the positive shock to the renewables. It means that the competitive dynamics theory is tenable and the policies aimed at delivering the global renewable energy transition are offered. (iii) Through the discussion on the possible occurrence of thermal power enterprises’ resistive strategies to the market penetration of renewables, we provided a new viewpoint to understand why the transition of renewable energy is slower or more costly than anticipated. The next section presents measuring formulation and data. The empirical study and generalized impulse response analysis are addressed in Section 3. The discussion and practical implications of the results are presented in Section 4. The final section we summarize our findings.

2

Applied Energy 251 (2019) 113288

D. Zhou, et al.

2. Model specification and data sets

i vt

h t 1

2.1. Model specification i t

This paper focuses on the strategic response of TPEs in terms of scale and leverage, which have key roles in the power sector. Following Godfrey et al., we defined a measurable function that could describe the interaction between the TPEs and RPEs [42]. Since we focus on two specific strategic responses (scale and leverage decisions) there are six disjointed classes of sets and each of them reflects the strategy of different enterprises. We express them as: TPE e

TPE v

TPE d

m = 1, 2,

, n},

m {eRPE ,

m = 1, 2,

, n} (scale);

m = {vTPE ,

m = 1, 2,

, n},

m = {vRPE ,

m = 1, 2,

, n} (leverage);

m = {dTPE ,

m = 1, 2,

, n},

m {dRPE ,

m = 1, 2,

, n} (other factors).

=

h t 1

RPE e

RPE v

t

i

=

+

i {TPE , RPE }

j, k {e, v, d}

(

i k, t l ) i j pj

(

= pji (x ijm ) = = 1, 2,

n

n

m=1

if j

e ijm ,

i

(1)

k

= c1i +

{TPE , RPE }, j

{e, v, d}, m (2)

,n

h t 1

i 11

+

i et 1 i et

,

+

i 12

i vt 1

h

+ i

i 13

h et 1

+

i 14

{TPE , RPE }

+

+

h

i

{TPE , RPE }

i 32

i vt 1

i t,

i 33

+

h

i 23

i

h et 1

h et 1

+

+

i 24

h vt 1

+

i 25

i t 1

+

i 26

(4) i 34

{TPE , RPE }

h vt 1

+

i 35

i t 1

+

i 36

(5)

h vt 1

+

=C+

1

t 1

+

+

k

t l

+

t

(6)

2.2.2. Sample selection In order to enable the samples to represent the two types of enterprises effectively, we carefully classified the sample enterprises. Our initial sample consists of 64 power generation enterprises, which covered all the listed power generation enterprises based on the industry classification of China Securities Regulatory Commission. Based on the indicators of main business and the revenue structure, the enterprises with serious mixed operation were excluded, which leaves 25 RPEs and 34 TPEs. To exclude the influence of the accounting system reform in 2007 and minimize the interference of mixed operation, this study considered the period 2008–2015, quarterly. 2.2.3. Variable measurement In the VAR model, variables in Eq. (6) are set as the scale factors, leverage factors and profit factors for both sectors. Following Aktas et al. and Eshima and Anderson, this study has selected the growth rate of fixed assets and the growth rate of total assets of both TPEs and RPEs [45,46] to measure the enterprise scale factor of TPEs and RPEs ( iet ). This is because the commonly used operating leverage and financial leverage are chosen to merge and get the leverage factor of TPEs and RPEs ( ivt ) [47,48]. In accordance with prior studies, the net asset yield, return on assets and net profit margin of fixed assets have been chosen to measure profit factors ( it ) as the application in [47,49]. To deal with the multiple collinearity between variables, this paper

where ij denotes j type strategy of i enterprises cluster. e ijm represents j type strategy for i cluster of m enterprise. Assume the enterprise makes the strategy according to the previous observation, which constitutes an autoregressive lag polynomial and the number of lags can be tested. This paper proposes a linear dynamic multi-equation model to describe the strategic response based on Eqs. (1) and (2): i et

i et 1

+

2.2.1. Data source We collected the data from the Chinese Stock Market Research (CSMAR) database. The CSMAR database offers data on the China stock markets and the financial statements of China’s listed enterprises. CSMAR is a high-quality Chinese database produced by a Hong Kong based company called GTA and it is one of the most commonly used database.

i k , t l),

where j represents the marginal effectiveness of different strategies to i . Eq. (1) means that the impact path of each strategy in specific sets RPE }, j = {e, v, d} ) has its particular mapping. ( ji , i = {TPE , t l From the point of economics, it means the incompatible strategy ( ik, t l ) has its specific influence function ( pji (·) ) to the profit of enterprises. For Eq. (1), it is easy to prove that i (·) is a measurable function on when j = k . Function pji (·) can be seen as a mapping rule from individual enterprise strategy to the specific cluster profit. Based on the same principle, we get a similar mapping relationship between (·) and (·) . In order to estimate the interactive strategy of two clusters of enterprises more pertinently, this paper gives the specific form of function pji (·) to measure the strategies of specific enterprises cluster: i j

i 31

i vt 1

2.2. Data and variable processing

if j = k

0,

i vt ,

i 22

represents a 6 × 1 disturbance vector with elements eit , vit and ti . The parameters in each equation are estimated by the unstructured econometric approach, which has always been used in the discussion of the dynamic market response [43,44]. In this paper, it can be used to explore the interaction between TPEs and RPEs. Based on the regression, generalized impulse response analysis is adopted to discuss the intensity, direction and time of appearance of the response of TPEs if there are unexpected positive impacts on RPEs’ scale or leverage.

j {e, v, d}

i j pj

+

+

where t l represents a 6 × 1 dimension vector composed of endogenous variables iet , ivt and it . l is lag period for endogenous variables. C represents a constant vector made up of c1i , c2i and c3i . k represents a 6 × 6 dimension coefficient matrix with elements fgi . t

RPE d

we are able to define measurable functions whose function domain and i = {TPE , RPE } range are represented as (here = R ) and i , RPE }, j = {e, v, d} is respectively. Given that pji (·), i = {TPE , one of such finite real-valued function and considering the possible time-lag effect on sectoral profit, the mapping relationship can be written as: j, k {e, v, d}

i et 1

where, Eqs. (3) and (4) is used to test the emergence of strategic response. Eq. (5) is used to test whether the possible strategic response could a cause positive response of profit. iet represents enterprise scale of sector i . ivt represents enterprise leverage of sector i . c1i , c2i and c3i represent constant terms. fgi are the parameters to be estimated. eit , vit

where m represents different enterprises. ring on set If we let set R represents a , and i R) . Then R = i {TPE, RPE } j , we get the measurable space ( ,

i {TPE , RPE }

= c3i +

i 21

and ti represent disturbance terms and are white noises time series. In order to test the response of TPEs to the unexpected shock to RPEs, this paper conducts generalized impulse response analysis based on the Vector Autoregressive Regression (VAR) model. The basic regression model is shown in Eq. (6):

m = {eTPE ,

=

= c2i +

i 15

i t 1

+

i 16

(3) 3

Applied Energy 251 (2019) 113288

D. Zhou, et al.

1.5

Table 1 The result of cointegration test. Data trend:

None

None

Linear

Linear

Quadratic

1.0

Test type

No intercept No trend 2 3

Intercept No trend 3 2

Intercept No trend 4 2

Intercept Trend 3 2

Intercept Trend 6 2

0.5

Trace Max-Eig

0.0

Selected (0.05 level*) number of cointegrating relations by model.

applies the principal component analysis to reduce multiple indexes and compute principal component scores to represent these indexes. The principal component eigenvalue and variance contribution rate are presented in the appendix. In Eq. (2), all of the data are required to be positive. This study used S curvilinear transformation to adjust the time series into a positive value. The transformation function is:

1 hij = 1 + e xij

-0.5 -1.0 -1.5 -1.5

(7)

-0.5

0.0

0.5

1.0

1.5

Fig. 1. Inverse roots of AR characteristic polynomial.

RPEs’ scale and leverage, what kind of strategies developed by TPEs should be conducted?

3. Model estimation and generalized impulse response analysis 3.1. Model estimation and stationarity test

3.2.1. Scale strategic response of TPEs Based on the VAR model, this paper gives a generalized one standard deviation shock to the scale and leverage factors of RPEs. The results are shown in Figs. 2a, b and 3a, b. The horizontal axis represents the lag period of impact action and the vertical axis represents the scale response of TPEs. From Fig. 2a, after the initial shock to RPEs’ scale, the TPEs’ scale shows a positive response and reaches its maximum (87.85%) in the first quarter. However, the response is temporary and then becomes negative from the second to the fourth quarter, until the next year, when there is a slight positive response. This indicates that the scale strategic expansion occurs very fast and intense, though it is impermanent. The response of scale of TPEs cannot be neglected. Fig. 2b shows the accumulated response of TPEs’ scale to a shock to RPEs’ scale, which converges to a positive value at nearly 25%. This is mainly due to the substantially positive response of TPEs scale in the first quarter, and cannot be mitigated by the negative responses scale strategic expansion. These results indicate that the TPEs would expand their scale to quickly respond to the unexpected positive impact on the scale factor of RPEs. Although temporary, the accumulated response remains positive. Fig. 3a displays the response of TPEs’ scale when RPEs’ leverage received a positive shock. The scale of TPEs have a positive response in the first three quarters and also reaches a peak (49.62%) in the first quarter. There is only a small negative response at −5.4% in the fourth quarter. Therefore, there is a rapidly rising, accumulated response of

(1) Cointegration testing To avert the spurious regression, this study conducted the cointegration test to verify the existence of a cointegration relationship. We performed the cointegration test based on five different hypotheses, because different cointegration test hypotheses may lead to varied test results. As shown in Table 1, we can acknowledge that there is at least one cointegration relationship between variables. (2) The lag order selection Based on the selection criteria and results in Table 2, the lag order is one. (3) Inverse roots of the AR characteristic polynomial test This paper uses least square method to estimate the model. The roots of the characteristic polynomial were computed to ensure the stability of the VAR model and the inverse roots of AR characteristic polynomial figure are shown in Fig. 1. As is shown in Fig. 1, all the inverse roots of AR characteristic polynomial are in the unit circle. It proves that the VAR model is stable. 3.2. Generalized impulse response analysis

1.6

The dynamic analysis of VAR model is usually realized by orthogonal impulse response function. Since the result of the Cholesky decomposition serious relies on the order of variables in the model, we applied the generalized impulse response functions proposed by Koop et al. [41]. To investigate the generalized impulse response of the scale and leverage factors of TPEs, a positive shock to the scale and leverage factors of RPEs were tested. If there is an unexpected positive impact on

1.2 0.8 0.4 0.0

Table 2 VAR lag order selection criteria.

-0.4

Lag

LogL

LR

FPE

AIC

SC

HQ

0 1 2

−59.1054 −10.8500 18.0126

NA 73.9916a 32.7110

3.09e−06 1.44e−06a 3.18e−06

4.3404 3.5233a 3.9992

4.6206a 5.4850 7.6423

4.4300 4.1509a 5.1646

a

-1.0

-0.8

1

2

3

4

5

Scale of TPEs

6

7

8

9

±2 Standard Errors

Fig. 2a. Response of scale of TPEs to a shock to RPEs’ scale.

Denotes the lag order selected according to the corresponding criteria 4

10

Applied Energy 251 (2019) 113288

D. Zhou, et al.

70

1.6

60

1.2

50 0.8

40 30

0.4

20

0.0

10 -0.4 -0.8

0 1

2

3

4

5

Scale of TPEs

6

7

8

9

1

2

3

4

5

6

7

8

9

10

Fig. 4a. The contribution degree of RPEs’ scale to TPEs’ scale.

10

±2 Standard Errors

70

Fig. 2b. Accumulated response of scale of TPEs to a shock in RPEs’ scale.

60 50

1.0

40

0.8

30

0.6

20 0.4

10

0.2

0

0.0

1

2

3

4

5

6

7

8

9

10

Fig. 4b. The contribution degree of RPEs’ leverage to TPEs’ scale.

-0.2 -0.4

1

2

3

4

5

Scale of TPEs

6

7

8

9

represents the contribution degree of the specific variable to TPEs’ response. According to Figs. 4a and b, for TPEs’ scale, RPEs’ scale contribution increases and reaches 39.41%, especially in the first quarter (more than 60%). The leveraging impact of RPEs contributes only 8.48%. The scale strategy response of TPEs is primarily contributed to by the scale shock of RPEs. Another focus of our study is whether the scale response is due to a profit motive. To answer this question, we gave a positive shock to the scale factor of TPEs to compute a generalized impulse response of TPEs’ profit. The results are shown in Figs. 5a and b. The horizontal axis represents the lag period of impact action and the vertical axis represents the profit response of TPEs. In Fig. 5a, When TPEs’ scale received a positive shock, the TPEs’ profit factor reaches the lowest point (−0.08%) in its first quarter and remains negative until the third quarter. It returns to a positive level in the fourth and the following quarter. Due to the high amount of negative response for the TPEs’ profit, the accumulated response remains

10

±2 Standard Errors

Fig. 3a. Response of scale of TPEs to a shock to RPEs’ leverage. 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

1

2

3

4

5

Scale of TPEs

6

7

8

9

.008

10

.004

±2 Standard Errors

Fig. 3b. Accumulated response of scale of TPEs to a shock in RPEs’ leverage.

.000

enterprise scale of TPEs to the shock to RPEs’ leverage factor. Although the scale response to a shock to RPEs’ leverage is not as strong as the response to a shock to RPEs’ scale, the accumulated response maintains a persistent increase and converges to a positive value at about 80%. This indicates that the RPEs' receipt of more financial resources may stimulate the TPEs to expand their assets to prepare the possible threat. In order to understand the relative importance of different shocks to the scale response of TPEs, this paper applied a variance decomposition to analyze the contribution degree of RPEs’ shock to TPEs’ response variation. The results are shown in Figs. 4a and b. The horizontal axis represents the lag period of impact action and the vertical axis

-.004 -.008 -.012 -.016 -.020

1

2

3

4

5

Profit of TPEs

6

7

8

9

±2 Standard Errors

Fig. 5a. Response of the profit of TPEs to a shock to its scale. 5

10

Applied Energy 251 (2019) 113288

D. Zhou, et al. .7

.01

.6 .00

.5 .4

-.01

.3 .2

-.02

.1 -.03

.0 -.1

-.04

1

2

3

4

5

Profit of TPEs

6

7

8

9

-.2

10

1

2

3

±2 Standard Errors

4

5

6

Leverage of TPEs

Fig. 5b. Accumulated response of the profit of TPEs to a shock in its scale.

7

8

9

10

±2 Standard Errors

Fig. 6b. Accumulated response of leverage of TPEs to a shock in RPEs’ scale.

negative and converges to −1.34% (shown in Fig. 5b). This indicates the expansion of TPEs’ scale could hardly be profitable in the short term. This evidence provides support for the notion that the scale of TPEs has a positive response to the shock to RPEs (especially the scale factor), however, this reaction is unprofitable in the short term. On the one hand, existing literature shows investigations of a firm's inclination to match or to imitate, a rival's strategic move [50]. Hsieh and Hyun stress that a firm is more likely to match modestly weaker competitors to sustain its current lead and match modestly stronger competitors to eschew lagging further behind [51]. On the other hand, the demand could likely be satisfied by existing plants and the creation of idle productive capacity appears to be a potent deterrent to new entrants [52]. Sufficient scale assures the existence of a vector of sustainable prices for the products of a natural monopolist [53]. TPEs have an incentive to scale up to protect their market position. Sometimes, the rent-seeking behavior by various interest groups, pursuing private agendas, may not always be consistent with profit goals [54]. These imply that the scale expansion of TPEs could still happen, though the resistive action may not be profitable in short term.

in the first quarter. This response shows a fluctuant decreasing tendency but remains positive in the following quarters. In addition, according to Fig. 6b, because of the continuous positive response of TPEs’ leverage, the accumulated response converges on a positive value at nearly 23.63%. These results indicate that TPEs will increase the leverage ratio progressively to respond the scale expansion of RPEs. Fig. 7a displays the response of TPEs’ leverage when RPEs’ leverage received a positive shock. The leverage of TPEs has a relatively high negative response (−7.2%) at first quarter and remains negative so that the accumulated response of TPE’s leverage holds negative and converges on nearly 23% (Fig. 7b). These results indicate that TPEs tend to take a conservative financial approach to cope with the financial expansion of RPEs. In order to understand the relative importance of different shocks to the leverage response of TPEs, this paper applied a variance decomposition to analyze the contribution degree of RPEs’ shock to TPEs’ response variation. The results are shown in Figs. 8a and b. According to Figs. 8a and b, the scale impact of RPEs contributes 16.04% to the variation of TPEs’ leverage, while the leveraging impact of RPEs contributes 17.35%. The results show that the two shocks are of similar importance to the variation of the leveraging strategic response of TPEs. In summary, in contrast to the response of TPEs’ scale, the direction of TPEs’ leverage response depends on the shock target, namely, TPEs’ leverage shows a positive response to a shock to RPEs’ scale but a negative response to a shock to RPEs’ leverage. Another focus of our study is whether the leverage response will result in profit for the TPEs. To answer this question, we exerted a positive shock on the leverage factor of TPEs to compute a generalized impulse response of TPEs’ profit. The

3.2.2. Leverage strategic response of TPEs To test the strategic response of TPEs’ leverage, this paper gave a generalized one standard deviation shock to the scale and leverage factor of RPEs. The results are shown in Figs. 6a and b. The horizontal axis represents the lag period of impact action and the vertical axis represents the leverage response of TPEs. From Fig. 6a, after the initial shock to RPEs’ scale, the TPEs’ leverage shows a positive response and reaches the highest point (7.2%) .16

.04 .02

.12

.00

.08

-.02

.04

-.04 -.06

.00

-.08

-.04 -.08

-.10

1

2

3

4

5

Leverage of TPEs

6

7

8

9

-.12

10

±2 Standard Errors

1

2

3

4

5

Leverage of TPEs

Fig. 6a. Response of leverage of TPEs to a shock to RPEs’ scale.

6

7

8

9

10

±2 Standard Errors

Fig. 7a. Response of leverage of TPEs to a shock to RPEs’ leverage. 6

Applied Energy 251 (2019) 113288

D. Zhou, et al. .2

.04

.1

.03

.0

.02

-.1

.01

-.2

.00

-.3

-.01 -.02

-.4

-.03

-.5

1

2

3

4

5

6

Leverage of TPEs

7

8

9

10

40 30 20 10

3

4

5

6

7

8

9

10

Fig. 8a. The contribution degree of RPEs’ scale to TPEs’ leverage. 60 50 40 30 20 10 0

1

2

3

4

5

6

7

8

9

10

Fig. 8b. The contribution degree of RPEs’ leverage to TPEs’ leverage. .010

5

6

7

8

9

10

±2 Standard Errors

4. Discussion and practical implications

.005

This paper critically discussed the possible occurrence of TPEs’ resistive strategies to the market penetration of RPEs. The previous literatures demonstrate competitive dynamics existed widely in the market condition and have generated useful insights in the field of business strategy. However, these responses of TPEs have failed to be covered in the current renewable energy policy. Our study highlighted TPEs may adopt resistive strategy to positive shock to renewables under market condition. In a general sense, the detailed strategical responses have practical implications for understanding the competitive dynamics between TPEs and RPEs in China and provide reference for other countries organizing the power sector under market orientation. Our results clearly demonstrated the scale and leverage of TPEs will have nearly 88% and 24% positive response respectively in the first

.000

-.005

-.010

-.015

4

results are shown in Figs. 9a and b. The horizontal axis represents the lag period of impact action and the vertical axis represents profit response of TPEs. According to Fig. 9a, when TPEs’ leverage receives a positive shock, its profit factor receives the greatest negative response (−0.5%) in the first quarter, which lasts until the third quarter. Then, the profit factor becomes positive in the following quarters and reaches its highest point (0.33%) in the first quarter of the following year. This means that the positive impact on TPEs’ leverage does not lead to an increase in its profits immediately. However, one year later there is a positive response. The accumulated response of the profit of TPEs to its leverage remains negative until the second quarter of the following year, as shown in Fig. 9b. This means that TPEs’ leverage expansion takes a relatively long period of time to result in profit. This evidences that the leverage of TPEs have a positive response to a scale shock to RPEs but shows a negative response to RPEs’ leverage shock. In contrast to matching the possible RPEs’ scale expansion, the TPEs’ leverage shows an asymmetric response. Previous studies have stressed that financial strategy usually determines the capital structure and aims to minimize the weighted average cost of capital and improve the competitiveness of the enterprise [55]. On the one hand, the highlevel leverage could pose a threat to RPEs’ potential growth. However, enterprise competitiveness is enhanced when a firm’s dependence on an illiquid or partially segmented capital market is reduced [56]. Therefore, there is a tradeoff in using the leverage strategy. As far as the results of this paper, if TPEs increase the leverage ratio to respond to the scale expansion of RPEs, they could be encouraged by the motivation of setting an entry deterrent and long-term profit. In contrast, TPEs take a conservative financial approach to cope with the financial expansion of RPEs due to the need of optimizing the capital structure and the shortterm revenue in conjunction with the result of Figs. 9a and b.

50

2

3

Fig. 9b. Accumulated response of the profit of TPEs to a shock in its leverage.

60

1

2

Profit of TPEs

±2 Standard Errors

Fig. 7b. Accumulated response of leverage of TPEs to a shock in RPEs’ leverage.

0

1

1

2

3

4

5

Profit of TPEs

6

7

8

9

10

±2 Standard Errors

Fig. 9a. Response of the profit of TPEs to a shock to its leverage.

7

Applied Energy 251 (2019) 113288

D. Zhou, et al.

quarter after a positive shock to the scale of renewables, no matter whether the response is profitable or not. However, the scale response of TPEs to the impact on RPEs’ leverage is less intense than the response to a shock to RPEs’ scale, and even the leverage response of TPEs shows a tighten state. Therefore, we suggest the Chinese government should limit the possible scale expansion of TPEs, especially at the beginning of the favorable policies promulgation for RPEs. Meanwhile, financial support policy, e.g. diversiform of low-interest loans to renewable projects, may be more appropriate than the direct subsidy in facilitating RPEs because of less resistive action of TPEs. Several crucial inspirations for RPEs’ competitive strategies can be drawn from the results. From the major resistive strategies and their schedules of TPEs we derived, we suggest RPEs should focus on longterm competitive advantage rather than short-term competitive tension, because no matter what resistive strategies of TPEs take, it is difficult for them to make more profit. Meanwhile, RPEs are still suggested to delay the implement of competitive strategies to avoid the fierce resistive strategy of TPEs, especially the scale strategy. In addition to applications in the Chinese context, the results also indicate the competitive dynamics theory is tenable, which can be applied for understanding some counterintuitive phenomenons. For example, as the discussion of Furlan and Mortarino [7], the diffusion of fossil sources is sustained by the spread of renewables, and the adoption of clean technologies might have supported the exploitation of nonrenewable sources in US. Nevertheless, the spread of renewables have a negative impact both on themselves and on non-renewable sources in India, while fossil sources have a positive impact both on themselves and on renewables. To explain the phenomenon, our proposed method in this paper can be applied to these countries. They also face the problem of strategic responses of fossil fuel power enterprises when introducing renewable energy to a market-oriented electricity market. In context of the electricity marketization, the government is also suggested to divide the electricity market into several independent trading markets, allowing the trading and pricing of thermal and renewable power separately to ensure the competition occurs only between the same types of enterprises based on the policy of renewable portfolio standard.

5. Conclusions This study emerged from a concern on the resistance to decline of coal in power generation worldwide [1,12] due to the competitive strategies of traditional power enterprises. To address this point, this study examined the response of thermal power enterprises to an unexpected change of renewable power enterprises’ scale and leverage. We set up a measuring formulation of the response based on the competitive dynamics theory, which allowed us to identify the intensity, direction and time of the strategic responses. The VAR model was applied to conduct generalized impulse response analysis by the specific sampling of listed Chinese power companies. The results demonstrated that the positive shock to the scale of renewable power enterprises are more likely to incur the resistant strategies of thermal power enterprises in the first quarter, whether these responses are profitable or not. More than 60% variation of thermal power enterprises’ scale is contributed to by renewable power enterprises’ scale shock in the first quarter. In other words, scale strategy is preferential for thermal power enterprises compared with the leverage strategy. Besides that, the positive impact on the leverage of renewable power enterprises will result in a negative response in thermal power enterprises’ leverage but a positive response of thermal power enterprises’ scale. It means thermal power enterprises should prefer to adopt a scale strategy to cope with the possible threat from renewable power enterprises' leverage expansion. This paper also indicates that the competitive dynamics theory is tenable, which in turn has strong implications for the design of the policy aimed at delivering the global renewable energy transition. Future studies could introduce other competitive strategies suitable for specific countries to examine the competitive dynamics between different types of power enterprises. Acknowledgements We are grateful for the financial support provided by the China Natural Science Funding (No. 71834003, 71673134) and the Fundamental Research Funds for the Central Universities (No. NE2018105).

Appendix A The construction of VAR model requires stable or homogeneous time series. The ADF test is applied to test whether the time series is stationary. The results are shown in Table A1. Table A1 The ADF unit root test results. Variables Level

First difference

T values

Prob.

Stability

RPE et RPE vt RPE t TPE et TPE vt TPE t

−1.7656

0.6941

Non-stationary

−2.5675

0.1107

Non-stationary

−3.5612

0.0502

Non-stationary

−2.9607

0.1608

Non-stationary

−2.5653

0.2973

Non-stationary

0.8724

0.8918

Non-stationary

RPE et RPE vt RPE t TPE et TPE vt TPE t

−17.2054

0.0000

Stationary

−10.4093

0.0000

Stationary

−6.0551

0.0001

Stationary

−17.4094

0.0000

Stationary

−4.8466

0.0030

Stationary

−5.5809

0.0006

Stationary

As it is shown in Table A1, the acceptance probability of the null hypothesis is that if the unit root exists and is greater than 5%, all variables are non-stationary in level. By first-differencing for all series, the null is rejected at the 1% significance level. Consequently, all variables are integrated of order one (see Table B1). 8

Applied Energy 251 (2019) 113288

D. Zhou, et al.

Table B1 The index characteristic value and cumulative proportion. Principal Component

Characteristic Value

Cumulative Proportion

Computed Using

Scale factors of RPEs Scale factors of TPEs Financial leverage factors of RPEs Financial leverage factors of TPEs Profit variables of RPEs Profit variables of TPEs

1.7676 1.6969 0.0616 0.0516 2.7276 2.9947

0.8838 0.8485 0.8565 0.8072 0.9092 0.9982

Ordinary Ordinary Ordinary Ordinary Ordinary Ordinary

correlations correlations covariances covariances correlations (uncentered) correlations

[29] Stavins RN. Market-based environmental policies. Public policies for environmental protection. Routledge; 2010. p. 41–86. [30] Gómez-Baggethun E, Muradian R. In markets we trust? Setting the boundaries of market-based instruments in ecosystem services governance. Ecol Econ 2015;117:217–24. [31] Wang Q, Chen X. China's electricity market-oriented reform: from an absolute to a relative monopoly. Energy Policy 2012;51:143–8. [32] DeFries R, Nagendra H. Ecosystem management as a wicked problem. Science 2017;356:265–70. [33] Pohekar S, Kumar D, Ramachandran M. Dissemination of cooking energy alternatives in India—a review. Renew Sustain Energy Rev 2005;9:379–93. [34] Hast A, Alimohammadisagvand B, Syri S. Consumer attitudes towards renewable energy in China—the case of Shanghai. Sustain Cities Soc 2015;17:69–79. [35] Quadir SA, Mathur S, Kandpal TC. Barriers to dissemination of renewable energy technologies for cooking. Energy Convers Manage 1995;36:1129–32. [36] Allen S, Hammond G, McManus M. Prospects for and barriers to domestic microgeneration: a United Kingdom perspective. Appl Energy 2008;85:528–44. [37] Aslani A, Mohaghar A. Business structure in renewable energy industry: key areas. Renew Sustain Energy Rev 2013;27:569–75. [38] Ellabban O, Abu-Rub H, Blaabjerg F. Renewable energy resources: current status, future prospects and their enabling technology. Renew Sustain Energy Rev 2014;39:748–64. [39] Luthra S, Kumar S, Garg D, Haleem A. Barriers to renewable/sustainable energy technologies adoption: Indian perspective. Renew Sustain Energy Rev 2015;41:762–76. [40] Juselius K. The cointegrated VAR model: methodology and applications. Oxford University Press; 2006. [41] Koop G, Pesaran MH, Potter SM. Impulse response analysis in nonlinear multivariate models. J Economet 1996;74:119–47. [42] Godfrey PC, Hill CWL. The problem of unobservables in strategic management research. Strateg Manage J 1995;16:519–33. [43] Pauwels K. How dynamic consumer response, competitor response, company support, and company inertia shape long-term marketing effectiveness. Market Sci 2004;23:596–610. [44] Pauwels K, Srinivasan S. Who benefits from store brand entry? Market Sci 2004;23:364–90. [45] Aktas N, Croci E, Petmezas D. Is working capital management value-enhancing? Evidence from firm performance and investments. J Corporate Financ 2015;30:98–113. [46] Eshima Y, Anderson BS. Firm growth, adaptive capability, and entrepreneurial orientation. Strateg Manage J 2017;38:770–9. [47] Javed ZH, Rao HH, Akram B, Nazir MF. Effect of financial leverage on performance of the firms: empirical evidence from Pakistan. SPOUDAI-J Econ Bus 2015;65:87–95. [48] Kahl M, Lunn J, Nilsson M. Operating leverage and corporate financial policies. AFA 2012 Chicago Meetings Paper; 2014. [49] Bamiatzi V, Bozos K, Cavusgil ST, Hult GTM. Revisiting the firm, industry, and country effects on profitability under recessionary and expansion periods: a multilevel analysis. Strateg Manage J 2016;37:1448–71. [50] Lieberman MB, Asaba S. Why do firms imitate each other? Acad Manage Rev 2006;31:366–85. [51] Hsieh K-Y, Hyun EE. Matching response to competitors' moves under asymmetric market strength. J Bus Res 2018;82:202–12. [52] Harrigan KR. Barriers to entry and competitive strategies. Strateg Manage J 1981;2:395–412. [53] Baumol WJ, Willig RD. Fixed costs, sunk costs, entry barriers, and sustainability of monopoly. Q J Econ 1981;96:405–31. [54] Joskow PL. Restructuring, competition and regulatory reform in the US electricity sector. J Econ Perspect 1997;11:119–38. [55] Kaličanin D, Todorović M. Interactions between business and financial strategies in Serbian companies. Econ Ann 2014:59. [56] Randøy T, Oxelheim L, Stonehill A. Corporate financial strategies for global competitiveness. Eur Manage J 2001;19:659–69.

References [1] British Petroleum. BP Statistical Review of World Energy 67th edition; 2018. [2] State Statistical Bureau. China Energy Statistical Yearbook 2018. China Statistical Press; 2018. [3] International Energy Agency. Re-powering Markets Market design and regulation during the transition to low-carbon power systems; 2016. [4] Kahrl F, Williams J, Jianhua D, Junfeng H. Challenges to China's transition to a low carbon electricity system. Energy Policy 2011;39:4032–41. [5] Khanna NZ, Zhou N, Fridley D, Ke J. Quantifying the potential impacts of China's power-sector policies on coal input and CO2 emissions through 2050: a bottom-up perspective. Utilities Policy 2016;41:128–38. [6] Marchetti C. Primary energy substitution models: on the interaction between energy and society. Technol Forecast Soc Chang 1977;10:345–56. [7] Furlan C, Mortarino C. Forecasting the impact of renewable energies in competition with non-renewable sources. Renew Sustain Energy Rev 2018;81:1879–86. [8] Guidolin M, Guseo R. The German energy transition: modeling competition and substitution between nuclear power and Renewable Energy Technologies. Renew Sustain Energy Rev 2016;60:1498–504. [9] Sioshansi FP. Electricity market reform: what have we learned? What have we gained? Electr J 2006;19:70–83. [10] Zou P, Chen Q, Yu Y, Xia Q, Kang C. Electricity markets evolution with the changing generation mix: an empirical analysis based on China 2050 High Renewable Energy Penetration Roadmap. Appl Energy 2017;185:56–67. [11] Shah IH, Hiles C, Morley B. How do oil prices, macroeconomic factors and policies affect the market for renewable energy? Appl Energy 2018;215:87–97. [12] Vestrucci P, Schiavi S, Orlandelli CM. Long term dynamics of energy systems: the Italian case. Technol Forecast Soc Chang 2015;96:266–76. [13] Si S, Lyu M, Lawell C-YCL, Chen S. The effects of energy-related policies on energy consumption in China. Energy Econ 2018;76:202–27. [14] Blazquez J, Fuentes-Bracamontes R, Bollino CA, Nezamuddin N. The renewable energy policy Paradox. Renew Sustain Energy Rev 2018;82:1–5. [15] Bird L, Lew D, Milligan M, Carlini EM, Estanqueiro A, Flynn D, et al. Wind and solar energy curtailment: a review of international experience. Renew Sustain Energy Rev 2016;65:577–86. [16] Painuly JP. Barriers to renewable energy penetration; a framework for analysis. Renew Energy 2001;24:73–89. [17] Wang K, Mi Z, Wei YM. Will pollution taxes improve joint ecological and economic efficiency of thermal power industry in China? a DEA-based materials balance approach. J Ind Ecol 2018. [18] Smith KG, Ferrier WJ, Ndofor H. Competitive dynamics research: critique and future directions. Handbook Strateg Manage 2001;315:361. [19] Baum JA, Korn HJ. Competitive dynamics of interfirm rivalry. Acad Manage J 1996;39:255–91. [20] Chen M-J, Miller D. Competitive dynamics: themes, trends, and a prospective research platform. Acad Manage Ann 2012;6:135–210. [21] Chen M-J, Su K-H, Tsai W. Competitive tension: the awareness-motivation-capability perspective. Acad Manage J 2007;50:101–18. [22] Fulghieri P, Nagarajan S. On the strategic role of high leverage in entry deterrence. J Bank Financ 1996;20:1–23. [23] Machado MM, de Sousa MCS, Hewings G. Economies of scale and technological progress in electric power production: the case of Brazilian utilities. Energy Econ 2016;59:290–9. [24] Akkemik KA. Cost function estimates, scale economies and technological progress in the Turkish electricity generation sector. Energy Policy 2009;37:204–13. [25] Neuhoff K. Large-scale deployment of renewables for electricity generation. Oxford Rev Econ Policy 2005;21:88–110. [26] Steffen B. The importance of project finance for renewable energy projects. Energy Econ 2018;69:280–94. [27] Bobinaite V. Financial sustainability of wind electricity sectors in the Baltic States. Renew Sustain Energy Rev 2015;47:794–815. [28] Jacobsson S, Lauber V. The politics and policy of energy system transformation—explaining the German diffusion of renewable energy technology. Energy policy 2006;34:256–76.

9