Undesirable and desirable energy congestion measurements for regional coal-fired power generation industry in China

Energy Policy 125 (2019) 122–134

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Undesirable and desirable energy congestion measurements for regional coal-fired power generation industry in China

T

Zhenling Chena,b, Jinkai Lia, , Weigang Zhaoc, , Xiao-Chen Yuanc,d, Guo-liang Yange,f ⁎

⁎⁎

a

Center for Energy, Environment & Economy Research, Zhengzhou University, Zhengzhou 450001, China School of Economics, Henan University of Economics and Law, Zhengzhou 450046, China c Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China d Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China e Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China f University of Chinese Academy of Sciences, Beijing 100049, China b

ARTICLE INFO

ABSTRACT

Keywords: Energy congestion Desirable energy congestion Undesirable energy congestion Natural disposability Managerial disposability

The blind expansion and increasing carbon emissions of the coal-fired power generation industry in China have attracted wide attention at home and abroad. To detect the technical ineffectiveness in the coal-fired power generation industry and the effects of carbon emission reductions, this study developed energy congestion models using data envelopment analysis (DEA). Energy congestion is classified into undesirable energy congestion (UEC) and desirable energy congestion (DEC) under natural disposability and managerial disposability. The UEC and DEC models were used to identify energy congestion, measure the amounts of UEC and DEC, and analyze the sources of inefficiency for regional coal-fired generation industry in China from 2004 to 2013. Our empirical analysis revealed: i) The UEC of coal-fired generation industry has occurred in many regions, most of which are less-developed areas. This indicates that energy is wasted in coal-fired generation industry due to congestion inefficiency. ii) DEC has occurred in a few regions and did not occur in 2013. These provinces where DEC occurred may have a high potential for eco-technology innovation. The method of energy congestion measurement proposed in this study and the research conclusion have reference effect on regional energy conservation and eco-technology innovation.

1. Introduction Due to industrialization and urbanization, China's energy demand has been increasing rapidly, and the share of electricity demand has been growing steadily in the past few decades (Yuan et al., 2017; Zhao et al., 2018). China has the largest total installed capacity of coal-fired power generation in the world. Many provinces in China (e.g. Xinjiang, Shandong, Jiangsu, and Inner Mongolia) have a higher installed capacity of power generation than in the United States and European Union. From 2000–2015, the new electricity capacity increased to a historic peak of 724 GW (Green Peace, 2016). Notably, the blind asset and equipment expansion of regional coal-fired power plants has caused an oversupply of electricity. As a result, the capacity utilization rate (the ratio of the actual capacity to the maximum expected capacity) of coalfired generation plant decreased to 49.4% in 2015, which was the lowest point. The increasing of electricity capacity and the decreasing

capacity utilization rate of power generation raise the question of the presence of energy congestion in the regional coal-fired power generation industry in China. On the other hand, the coal-fired power generation industry is criticised for high emissions and pollution. Coal, as the main fuel of thermal power plants in China, has produced substantial carbon emissions and pollutants. China's coal-fired power plants accounted for over 50% of the CO2 from the power generation sector in 2013 (IEA, 2014). As a result, the electricity sector was the only controlled and scheduled industry in the national unified emissions trading scheme (ETS) of China in 2017. This new scheme is expected to bring great pressure on the cost of thermal power plants. Operators may have to manage losses or cost increases if they fail to take actions towards eco-technology innovation, such as low-carbon fuel or technologic innovation. Therefore, understanding the sources of inefficiency and identifying energy congestion for the regional coal-fired power generation industry in

Corresponding author at: Center for Energy, Environment & Economy Research, Zhengzhou University, Zhengzhou 450001, China. Corresponding author at: Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China. E-mail addresses: [email protected] (Z. Chen), [email protected] (J. Li), [email protected] (W. Zhao), [email protected] (X.-C. Yuan), [email protected] (G.-l. Yang). ⁎

⁎⁎

https://doi.org/10.1016/j.enpol.2018.10.027 Received 24 January 2018; Received in revised form 12 September 2018; Accepted 15 October 2018 0301-4215/ © 2018 Elsevier Ltd. All rights reserved.

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Nomenclature APEC CO2 DDF DEA DEC DMU ETS GDP GW

IEA PPSs RAM SO2 UE UEC UEM UEN UHV VRS

The Asia-Pacific Economic Cooperation Carbon Dioxide Directional Distance Function Data Envelopment Analysis Desirable Energy Congestion Decision Making Unit Emissions Trading Scheme Gross Domestic Product Gigawatts

China is crucial. Energy congestion implies energy inefficiency and refers to the economic phenomenon that “reducing energy input results in an increase in one or more outputs without worsening other inputs or outputs” (Zhou et al., 2017; Cooper et al., 2001). Wu et al. (2016) argued energy congestion is related to the law of diminishing returns. The elimination of energy congestion could reduce energy waste and improve energy efficiency (Sueyoshi and Goto, 2016; Wu et al., 2016; Zhou et al., 2017). Unlike previous studies (Section 2), the energy congestion in this study is decomposed into undesirable energy congestion (UEC) and desirable energy congestion (DEC) according to the heterogeneity of output. UEC means that the increasing inputs can result in decreasing desirable outputs (e.g. GDP or revenue), and DEC indicates that the increasing inputs can result in decreasing undesirable outputs (e.g. CO2). UEC is an inefficient production situation, and DEC benefits carbon reductions. The main difference between the two concepts is the strategies to cope with the pollutants. When operators adopt natural disposability for pollutants, efficiency improvements are mainly focusing on desirable outputs. Poor operational management may lead to UEC. When operators adopt managerial disposability for pollutants, efficiency improvements are mainly focusing on undesirable outputs. Improved operational management may lead to DEC. The coal-fired generation sector has required the improvement of operational management to eliminate UEC and the encouragement of DEC by ecotechnology innovation (e.g. using low-calorific value fuel or technologic innovation) for energy conservation and carbon reduction. In our study, energy congestion is decomposed into UEC and DEC under natural disposability and managerial disposability, respectively. The congestion models measure the UEC and DEC of regional coal-fired generation industry in China and detect the sources of technical inefficiency and identify regions of eco-technology innovation for carbon reduction. The structure of this paper is as follows. Section 2 reviews the related literature. Section 3 contains the methodology, including an analysis of the concepts of UEC and DEC under natural disposability and managerial disposability and the proposed DEC and UEC models. Section 4 introduces the data source and indicators. Section 5 examines UEC and DEC for regional coal-fired power industry in 30 provinces from 2004 to 2013. Section 6 concludes and policy implications.

International Energy Agency Production Probability Sets Rang-Adjusted Measure Surlfur Dioxide Unified Efficiency Undesirable Energy Congestion Unified Efficiency under Managerial Disposability Unified Efficiency under Natural Disposability Ultra High Vacuum Variable Returns to Scale

analysis (DEA). From then on, congestion effects have attracted many scholars’ attention, and DEA has become an important tool to analyze congestion effects. The congestion effects occur when “reductions in one or more inputs can be associated with increases in one or more outputs—or proceeding in reverse, when increases in one or more inputs can be associated with decreases in one or more output—without worsening any other input or output” (Brockett et al., 1998; Cooper et al., 2000, 2001). The main models of congestion can be classified into three families: the Färe family models (Färe et al., 1985, 1994), the Cooper family models (Cooper et al., 1996, 2000, 2002; Noura et al., 2010) and the WY-TS models (Tone and Sahoo, 2004; Wei and Yan, 2004). The Färe family models confirm the congestion effect as a ratio of the observed amounts to the expected amounts. The Cooper family models assert that the congestion effect can be calculated by the differences between observed and expected amounts (Cooper et al., 1996, 2000, 2002). Congestion can be understood as a type of ‘technical inefficiency’, and inefficiency can be decomposed into technology inefficiency and congestion inefficiency (Cooper et al., 2000). These two types of family models assess congestion effects using excessive inputs. From the perspective of outputs, the WY-TS models detect congestion effects based on the tangent of the hyperplane (Tone and Sahoo, 2004; Wei and Yan, 2004). Based on these three models, empirical studies on congestion have been conducted (Khodabakhshi, 2009; Noura et al., 2010; Marques and Simões, 2010; Kao, 2010; Simões and Marques, 2011; Khoveyni et al., 2013; Hu et al., 2017). Undoubtedly, the traditional DEA congestion models provide primary approaches to study this topic; however, a common feature of those traditional models has seldom considered the undesirable outputs (e.g. CO2, SO2) (Sueyoshi and Goto, 2012a). Due to the impact of climate change on the economy, scholars have begun to incorporate undesirable outputs into traditional congestion models. For example, Sueyoshi and Goto (2012a) classified congestion into undesirable congestion and desirable congestion. Wu et al. (2013) applied the congestion model with undesirable outputs to the analysis of congestion on Chinese regional industry. Wu et al. (2015) built a DEA congestion model that incorporated desirable and undesirable outputs by using the directional distance function (DDF). Wu et al. (2016) calculated and analyzed the energy congestion of Chinese industrial sectors using Färe family models. Zhou et al. (2017) used the Cooper family models to evaluate the energy congestion and mixed energy efficiency at the national level of the APEC. Notably, the above literature has not distinguished the congestion effects between the energy factors and other factors of production. Energy has been regarded as a primary and special productive factor; unlike labor and capital, it can simultaneously produce desirable and undesirable outputs. China's regional coal-fired generation industry is highly dependent on fossil energy and produces many undesirable outputs such as CO2. The increasing desirable outputs and decreasing undesirable outputs indicate better operational performance. Thus, energy congestion should be classified into UEC and DEC according to the heterogeneity of the outputs. In 2012, Sueyoshi and Goto (2012b) proposed the undesirable

2. Literature review Many studies have evaluated total-factor energy efficiency and discovered influencing factors (Morfeldt and Silveira, 2014; Wei et al., 2007; Emrouznejad and Yang, 2016; Trotta, 2018; Wang et al., 2016). The literature on total-factor energy efficiency has often assumed that input factors have positive effects on outputs (e.g. increasing energy input increases output) (Zhou et al., 2017). In some cases, however, if energy inputs increase, the outputs decrease, that is, the marginal returns of energy to outputs are negative. Economists call such an inefficiency production situation a ‘non-economic area’. In 1985, Färe et al. (1985) first built congestion models by using data envelopment 123

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congestion and desirable congestion models based on the concepts of natural disposability and managerial disposability (Appendix A), and an empirical analysis of Japan's thermal power industry was conducted (Sueyoshi and Goto, 2016). Their models proposed the method to identify undesirable congestion and desirable congestion; however, they fail to calculate the amount of energy congestion and analyze the sources of technical inefficiency, which facilitate the operational improvement. Few examples in the literature have investigated the UEC and DEC of the regional coal-fired power generation industry in China. Following the Cooper family models, this paper extends Sueyoshi and Goto (2012a, 2016) and Zhou et al. (2017)’s researches and proposes the UEC and DEC models. The congestion models assess the UEC and DEC of the regional coal-fired generation industry in China, calculate the amount of UEC and DEC, and analyze the sources of technical inefficiency.

K

PPSM (X ) =

K k=1

K

Yk

k,

k=1 K

Bk

k ,X

k=1

K

Xk k=1

B

k,

k

= 1,

k=1

k

0,k = 1, …, K

B

Bk

k ,X

k=1

k,

k

= 1,

k

k=1

0,k = 1, …, K

(2)

Sueyoshi and Goto (2016) further extended the models that include undesirable congestion and desirable congestion under natural disposability and managerial disposability. Based on their research, in view of energy has been confirmed as a primary and special productive factor, this paper aims to build UEC and DEC models. Fig. 1 illustrates how the UEC and DEC of coal-fired generation plants occurred under natural disposability and managerial disposability. As shown in Fig. 1a, the PPSs to the left of point B is the traditional productive regions in which increasing energy inputs would lead to power generation or revenue increased. The inefficient coal-fired plants such as DMUA can improve their unified efficiency (operational and environmental efficiency) and move to point A1, in the production frontier. This change is under the condition that no environmental regulations have occurred or been neglected, namely, the natural disposability to CO2. If the operator is optimistic about the electricity market or for other reasons, they may impulsively expand their capacity. Clearly, coal-fired plants would have easy access to low-cost financing or obtain government subsidies because the majority of coalfired plants in China are stated owned. Therefore, some coal-fired plants expand scale at point C where UEC has occurred, that is, inputs increase but power generation or revenues decrease. It is observed that the PPSs of natural disposability determined by formula (1) fails to identify UEC at point C. If the inequality constraints on undesirable output are converted into equality constraints in formula (1) with other constraints unchanged, the PPSs will bend downward to detect point C where UEC have occurred. If the central government implemented strict environmental regulations for coal-fired plants, some coal-fired plants would implement eco-technology, such as low-carbon coal utilization or technology equipment replacements. The PPSs would shift from PPS0 to PPS1, and the production state of DMUA would also transfer A1 to A2. For convenience, we can shift our attention to Fig. 1b. Then, DEC may occur at point A2 if the generation amounts or the revenue increases, and CO2 decreases with the increasing energy inputs; clearly, such a change would not occur naturally. The operator needs to make managerial efforts to reduce carbon emissions; meanwhile, outputs wouldn’t decline when strict environmental regulations are implemented by the government. Therefore, these efforts are called managerial disposability. Notably, the PPSs under managerial disposability in formula (2) cannot reflect a situation such as a point A2 because its restraints on desirable outputs are

An extensive body literature has investigated how to address the separation of desirable and undesirable outputs in DEA under environmental regulation. One of the most influential approaches is the weak disposability and strong disposability proposed by Färe et al. (1989). Sueyoshi and Goto (2012b) stated that weak disposability can fail to cooperate with eco-technology when the decision-making units (DMUs) must manage strict environmental regulations. To extend the concepts of weak and strong disposability, Sueyoshi and Goto (2012a) proposed new concepts of disposability: natural disposability and managerial disposability. We assume there are DMUs with the number of K and denote X R+M , Y R+K and B R+J as the input factors, desirable outputs, and undesirable outputs’ vectors, respectively. The production possibility sets (PPSs) are assumed to be a convex hull. The PPSs for the two conceptions are as follows: Natural disposability: K

k,

K

Xk

3.1. UEC and DEC under natural disposability and managerial disposability

(Y , B ): Y

Yk k=1

3. Methodology

PPSN (X ) =

K

(Y , B ): Y

(1)

Managerial disposability:

Fig. 1. UEC and DEC under natural disposability and managerial disposability. 124

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unequal. If the inequality constraints are converted into equality restraints in formula (1) with other constraints unchanged, the PPSs would bend downward to detect point A2 where the DEC may occur. According to this analysis, the PPSs of UEC and DEC under natural disposability and managerial disposability are formulated as follows: K N PPSUEC (X )

=

Yk

k,

B=

k=1

Bk

k,

k=1

k ,X

k=1

k

= 1,

0,k = 1, …, K

k

(3)

k=1

K

K

(Y , B ): Y =

Yk

k,

B

Bk

k=1 K

k ,X

k=1

K

Xk

k,

k=1

k

= 1,

k

0,k = 1, …, K

(4)

k=1

The difference between formulas (3) and (1) is the equality constraints on the undesirable output (B) in formulas (3) and the inequality constraints on the undesirable output (B) in formulas (1). While the difference between formulas (4) and (2) is the equality constraints on desirable output (Y) in formulas (4) and the inequality constraints on the desirable output (Y) in formulas (2). Through the equality constraints, the PPSs bend down, which is a necessary condition for the occurrence of congestion.

s. t .

N n=1

I i=1

Rne sne +

Ri y si y +

J j =1

k

= 1,

k

0,k = 1, 2,

smx, sne, siy,s jb

0

J

*

i=1

Rmx smx* +

Riy si y +

j=1

N n=1

Rne sne*

Rjb sjb* (6)

3.2.1. UEC model under natural disposability Model (5) is considered the benchmark model in this study. The following analysis will build the UEC and DEC models of coal-fired plants based on model (5). According to the PPSs of UEC under natural disposability, as in formula (3), model (5) must exclude the slack variables related to emissions. Additionally, the inputs and desirable outputs of coal-fired plants have slack variables due to inequality constraints. Therefore, model (5) can be modified as the following model (7):

max + (

s. t .

M m =1

Rbj s bj

N n= 1

Rmx smx +

I i=1

Rne sne +

K x + smx = xmo , m = 1, 2, k = 1 k mk K e + sne = eno, n = 1, 2, k = 1 k nk K y si y yi0 = yio , i = 1, k = 1 k ik K k=1

Ri y siy ) M N

2,

k bjk + bj0 = bjo , j = 1, 2,

K k=1

k

= 1,

k

0,k = 1, 2,

I J

K

smx, sne, siy 0

(7)

Notably, variables from the model (7) are the total energy slacks (i.e. the maximum inefficiency of energy input). According to Sueyoshi and Goto (2012a, 2016), the unified efficiency under natural disposability (UEN) can be expressed by

sne

K x + smx = x mo , m = 1, 2, M k = 1 k mk K e + sne = eno, n = 1, 2, N k = 1 k nk K y siy yi0 = yio , i = 1, 2, I k = 1 k ik K b b + s + b J j0 = bjo , j = 1, 2, j k = 1 k jk K k=1

m=1

In formula (6), all slacks are determined by optimizing model (5). The scores of UE incorporate operational and environmental efficiency. If a region of the coal-fired generation industry satisfies UE = 1 and the slacks of inputs and desirable outputs are zero (e.g.smx, sne, si y, ands jb = 0 ), the coal-fired generation industry in this region is fully efficient.

= (y1k , y2k , …, yI k ) T , andBK = (b1k , b2k, …, bJ k ) T The unified efficiency (environmental and operational efficiency) of coal-fired generation industry in different regions based on RAM-DEA model (Zhou et al., 2017) can be formulated as follows:

Rmx smx +

I

+

The Rang-Adjusted Measure (RAM) was first proposed by Cooper et al. (1999) and has been widely used to assess total-factor environmental or energy efficiency (e.g. Emrouznejad and Yang, 2016; Wang et al., 2013; Ramli and Munisamy, 2015). This study builds UEC and DEC models based on the RAM model. The reasons for employing this model are as follows:1) This model simultaneously accounts for desirable outputs and undesirable outputs; 2) can address clear slacks that occur during input surpluses and output shortages; and 3) accurately express the natural and managerial disposability for coal-fired generation industry. The followings are on how to construct the UEC and DEC models. There is K coal-fired generation industry in different regions in China. The regional coal-fired generation industry requires M non-energy inputs (e.g. Labor and capital) and N energy inputs (e.g. Coal, oil or gas). We assume that a regional coal-fired generation industry produces I desirable outputs and J undesirable outputs. Then, XK = (x1k, x2k, …, xMk) T , EK = (e1k , e2k, …, eN k ) T ,YK .

M m=1

M

*+

UE = 1

3.2. UEC and DEC model

max +

min (bjk ))]

In model (5), variables smx denote the slacks in non-energy inputs to coal-fired plants. Variables sne denote the slacks of energy inputs. Variables siy and s bj are the slacks of the undesirable outputs and desirable outputs of coal-fired plants, respectively. Variables Rmx , Rne , Ri y ,andRjb denote the range of non-energy inputs, energy inputs, and desirable and undesirable outputs of regional coal-fired generation industry, respectively. Variable represents the flex factor of desirable and undesirable outputs that measures the distance between the observed coal-fired plants and an fully efficient coal-fired plant. Scalar value , a non-Archimedean element, is set as 0.0001. The vector k denotes the intensity levels at which the coal-fired plants can produce. K The equation constraint k = 1 k = 1 in model (5) represents variable returns to scale (VRS). Then, the unified efficiency (UE) can be expressed by

K

Xk

M (X ) = PPSDEC

Rjb = 1/[(M + N + I + J )(max(bjk )

K

(Y , B ): Y K

Rmx = 1/[(M + N + I + J )(max(xmk ) min (xmk ))] Rne = 1/[(M + N + I + J )(max(enk ) min (enk ))] Riy = 1/[(M + N + I + J )(max(yik ) min (yik ))]

UEN = 1

K

*+

M m=1

Rmx smx* +

N n=1

Rne sne* +

I i=1

Ri y si y

*

(8)

In formula (8), all slacks are determined by optimizing model (7). If a region of the coal-fired generation industry satisfies UEN = 1 and the slacks of inputs and desirable outputs are zero (e.g. smx, sne, si y = 0 ), the

(5)

where 125

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region of the coal-fired generation industry is fully efficient. Different from UE in formula (6), the slacks of undesirable outputs are excluded in formula (8). Therefore, the scores of UEN assess unified efficiency under natural disposability, which incorporates all the inputs and desirable outputs but excludes undesirable outputs. If <1, then smx, sne, si y 0 , and there may occur UEC. In some cases, although a coalfired plants satisfies UEN = 1, the values of optimal slacks are not all zero (e.g. * = 0 , smx, sne, si y 0 ). In this case, it indicates DMU may suffer from UEC. The subsequent analysis is on how to identify UEC. According to Brockett et al. (1998) and Cooper et al. (2001), the UEC model of the coal-fired plant is defined as follows:

K x smx = x mo smx*, m = 1, 2, k = 1 k mk K e sne*, n = 1, 2, n = eno k = 1 k nk K * y = (1+ *) yio + siy , i = 1, 2, k = 1 k ik K b = (1 *) bio , j = 1, 2, J k = 1 k jk K k=1

k

= 1,

k

smx 0,

0,k = 1, 2, n

N I

(9)

s. t .

(10)

s. t .

N n=1

Rmx smx +

Rne sne +

J j=1

gjk = k

h

= 1,

bjk ,k = 1, 2, k

0,k = 1, 2,

smx, sne,sjg 0

I i=1

Rjg sjg

*

k gjk

*

= (1+ *) gio + s jg , j = 1, 2, k

= 1, 0,

k

0,k = 1, 2, n

sne*

M N

J

K (13)

Then, the amounts of DEC for the regional coal-fired generation industry can be calculated by

DEC = sne*

*

n

(14)

Here, variables sne* are total energy slacks from the model (11). Variables n* from the model (13) represent the energy technical efficiency. If the DEC values are greater than zero according to formula (14), they indicate that the carbon emissions decrease as the energy inputs increase, and we believe the region have conducted eco-technology innovations in the coal-fired generation industry. If the DEC values are equal to zero according to formula (14), they indicate no DEC occurred, and we believe the region have a low potential to make eco-technology innovations in coal-fired generation industry. 4. Data and indicators This study focuses on the energy congestion of regional coal-fired industry. We selected 30 provincial regions in China from 2004 to 2013. Regions such as Tibet, Hong Kong, and Macau are excluded because their data are unavailable. Based on the indicators used in previous studies, this paper chose two output variables (one desirable output and one undesirable output) and three input variables (employees, installed capacity, and coal consumption). We confirmed total revenue not total electricity generation as the desirable output variable in our study due to total revenue can both incorporate operational performance and market demand. For the greater concern of carbon emissions at home and abroad, this study chooses it as undesirable output. The index selection and data sources are as follows:

Rjg sjg

K x + smx = xmo , m = 1, 2, M k = 1 k mk K e + sne = eno, n = 1, 2, N k = 1 k nk K y yi 0 = yio , i = 1, 2, I k = 1 k ik K g g sj gj0 = gio , j = 1, 2, J k = 1 k jk

K k=1

K k=1

smx

3.2.2. DEC model under managerial disposability This study extends Zhou et al. (2017)’s research to build DEC model for detecting the probability of eco-technology innovation. As the method to build UEC model, we first measure unified efficiency under managerial disposability and then build DEC model. According to the PPSs of DEC under managerial disposability in formula (4), model (5) must eliminate the slack variables related to desirable outputs in order to be considered as equality constraints. Due to inequality constraints, inputs and undesirable outputs keep slack variables. Different from the UEC model, undesirable output needs to be converted into a new desirable output according to the approaches proposed by Seiford and Zhu (2002). Therefore, model (5) can be modified as the model (11): M m=1

Rne sne* +

K x smx = x mo smx*, m = 1, 2, k = 1 k mk K e sne*, n = 1, 2, n = eno k = 1 k nk K y = (1+ *) yio , i = 1, 2, I k = 1 k ik

K k=1

Here, the total energy inefficiency can be divided into energy technical inefficiency and energy congestion inefficiency. The value of UEC can be calculated by the difference between the total energy inefficiency sne* and energy technical inefficiency n*. The value of UEC/sne* can measure the degree of UEC under natural disposability.

max +

N n=1

N n=1 n

max

K

* n

Rmx smx* +

(12)

Variables sne* are the total energy slacks from the model (7), which represents the total energy inefficiency. Variables n* from the model (9) * represent the energy technical inefficiency. If sne* n = 0 , there is no e* * UEC for DMU. If sn n > 0 , the DMU suffers from UEC. The amounts of UEC can be calculated by

UEC = sne*

m =1

In formula (12), all slacks are determined by optimizing model (11). If a region of the coal-fired generation industry satisfies UEM = 1, and the slacks of inputs and desirable outputs are zero (e.g. smx, sne, si y = 0 ), the region of coal-fired generation industry is fully efficient. Different from the UE in formula (6), the slacks of desirable outputs in the model (11) are eliminated. Therefore, the scores of UEM assess unified efficiency under managerial disposability, which incorporates all the inputs and undesirable outputs but excludes desirable outputs. The DEC model of the regional coal-fired generation industry is defined as follows:

M

sne*

M

*+

UEM = 1

N n=1 n

max

s. t .

Sueyoshi and Goto (2012a, 2016), the unified efficiency under managerial disposability (UEM) can be expressed by

(1) Employees: Employees refers to the amount of labor force in a region's coal-fired power generation industry. The China Statistical Yearbook does not include the exact amount in the labor force for the regional coal-fired power generation industry; thus, the amount of labor force is represented by the number of employees in the sector of the supply for electric and thermal (Such as Bai and Song, 2009; Wang et al., 2010, 2016). The data are from the China Industry Economy Statistical Yearbook, 2005–2014.

K K (11)

Variables denote the slack variables in response to the new desirable output in the model (11). Variable h is a large enough positive value to ensure that each variable gjk is greater than zero. According to

s jg

126

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(2) Installed capacity: The installed capacity refers to the amount of total fixed assets in China's regional coal-fired power generation industry. The data are from the China Electric Power Yearbook, 2004–2014. (3) Coal consumption: The main energy input is coal. In the China Energy Statistical Yearbook, we can obtain the value of standard coal consumption per kilowatt hour in the 30 regions. The total amount of electricity is equal to the standard coal consumption per kilowatt hour multiplied by the total electricity generation. (4) Total revenue of the coal-fired industry:This indicator is not reported in the China Statistical Yearbook. Total revenue of the coalfired industry is the product of total electricity generation and ongrid electricity price in each province in this study. Total electricity generation was obtained from the China Electricity Yearbook, 2004–2015. The on-grid electricity prices are from the website of the National Development and Reform Commission, and the electricity price in Inner Mongolia is represented by that in the western region of Inner Mongolia (Appendix B.1). To ensure comparability, we also transform the value of this indicator to constant 2010 prices by using the consumer price index. (5) CO2 emissions: CO2 emissions are the only undesirable output in our study. The data for this indicator cannot be directly obtained from the China Statistical Yearbooks; we must estimate them based on the different types of fossil fuels according to the IPCC (IPCC, 2006). The consumption levels of 14 fossil fuels are included, for example, raw coal, gasoline, and natural gas, and first converted into calorific values according to the average net calorific values (unit:Joule/Gram) provided by the China National Bureau of Statistics; next, the calorific values are converted into CO2 emissions according to the default emission factors (unit: Gram CO2/Joule) suggested in the IPCC Guidelines.

regions indicates energy waste and environmental pollution that could lead to social issues. For example, large-scale coal-fired electricity facilities in Inner Mongolia are located in the Ordos Basin and Xilin Gol Basin, which may negatively affect herdsmen. Therefore, the newly increased asset and equipment of coal-fired plants in those regions should be banned and clean energy projects should be encouraged. The sources of inefficiency are decomposed into technical inefficiency and congestion inefficiency, therefore, the inefficient regions can be divided into three categories according to the sources of inefficiency: technical inefficiency, congestion inefficiency, and mixture inefficiency. Category A comprises regions where the main source of inefficiency is from technical inefficiency rather than energy congestion inefficiency. As shown in Table 2, Tianjin, Anhui, Fujian, Henan, Hubei, and Chongqing should be included to Category A. Category B comprises regions where the main source of inefficiency is from energy congestion inefficiency rather than technical inefficiency, e.g. with a full degree of UEC. Heilongjiang, Shandong, Guizhou, and Yunnan should be included to Category B. Category C includes regions where the sources of inefficiency are from the mixture of energy congestion and technical inefficiency. Shaanxi, Inner Mongolia, Gansu, Ningxia, and Xinjiang should be included to Category C. The UEN scores across 30 regions in 2013 are shown in Fig. 3. Approximately 50% of the region received full scores under natural disposability, whereas Yunnan, Xinjiang, and Shaanxi received the lowest scores. The gaps between the worst and the best are substantial, indicating there is a long way to go for the worst region to catch up the optimal technical level. 5.1.2. Temporal pattern of UEC In this section, we measure UEC in China by analyzing panel data from 2004 to 2013. First, we focused on the shift of UEC in three major areas1 (the eastern, central and western areas) from 2004 to 2013 (Fig. 4). Firstly, the amounts of UEC in the central area declined annually, although the total average was stable in the sample period. In 2011, no undesirable congestion is observed in the central areas, that is, the over-investment in coal-fired firms in the central area has been controlled in recent years. Additionally, except for in 2011, the eastern area has the least UEC compared with other regions in the sample period, whereas the western area retains the highest relative amounts of UEC. This result indicated that energy in the eastern areas is fully utilized, and there is serious UEC in the western area. The sharp reduction in the profits of the coal-fired electricity sector represents another piece of evidence the coal-fired industry has suffered from UEC. The profits of the five largest Power Generation Groups in China decreased by 68.6% in 2016, compared to the same period in 2015. The coal-fired electricity industry experienced further heavy losses in 2017. As shown in Fig. 5, the three areas’ performance in terms of UEN scores from highest to lowest is eastern, central, and western areas. From 2004–2011, the differences of in UEN scores between the central and eastern regions gradually narrowed; after 2011, the gap between the two regions increased. The UEN scores of the central area have almost caught up with the leading eastern area. These results indicate that the coal-fired plants located in central China have improved their operational performance in recent years, whereas the coal-fired plants located in western China have poor operational performance (possibly due to the wider use of clean energy).

The descriptive statistics of the collected data are presented in Table 1. We observe that all the inputs and outputs significantly increased. From 2004–2013, coal consumption increased from 2120.63 to 4244.17 in 1000-tonne units, and CO2 emissions increased from 5860.297 to 11,983.24 in 1000-tonne units. The installed capacity increased from 1302.44 to 2899.07 in 1000-tonne units, and the total revenue increased from 231.39 to 575.01 in units of 100 million yuan. We also observe that the average of the total revenue slowly increased from 2012 to 2013. Fig. 2 also presents the number of coal-fired power stations of the installed capacity of greater than 1 million kW across regions in 2006, 2010, and 2014, respectively. Zhejiang in 2014 has the largest number of power stations followed by Jiangsu and Shanxi. There are more than 10 coal-fired power stations in Inner Mongolia, Guangdong, Anhui, Hebei, Henan, and Shandong provinces. Areas such as Qinghai and Chongqing have no coal-fired stations with this kind of installed capacity. Generally, the number of coal-fired power stations from 2006 to 2014 increases from 164 to 264. Notably, in Zhejiang and Anhui, there is a significant increment. 5. Results and discussion 5.1. Undesirable energy congestion (UEC) 5.1.1. The spatial pattern of UEC In this section, we measure the UEC of coal-fired industry in 30 provincial regions by analyzing a 2013 dataset (as analyzed in Section 3). Table 2 shows the results of UEC under natural disposability according to models (7)–(10). Inner Mongolia, Heilongjiang, Shandong, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, and Xinjiang have UEC. Among them, Heilongjiang, Shandong, Guizhou, and Yunnan have full scores in the degree of UEC (the degree of UEC are equal to the ratio of total energy slacks to the value of UEC). Most of these provinces are in less-developed or border areas, except for Shandong. UEC in those

1 The eastern area includes eight well-developed coastal provinces and three municipalities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central area includes inland nine provinces: Shanxi, Jilin, Heilongjiang, Anhui, Henan, Hubei, Hunan, Inner Mongolia, and Jiangxi. The western area includes one municipality and nine less-developed provinces: Guangxi, Chongqing, Sichuang, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.

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Table 1 Descriptive statistics of the input and output variables. Inputs and outputs

Statistics

2004

2006

2008

2010

2012

2013

Employees (1000 people)

Mean Std. Min Max Mean Std. Min Max Mean Std. Min Max Mean Std. Min Max Mean Std. Min Max

8.49 5.10 1.57 19.77 2120.63 1629.65 189.95 5691.47 1302.44 1113.64 189.95 5691.47 231.39 204.58 17.69 827.48 5860.30 4310.00 456.74 15,962.76

8.62 5.05 1.43 20.62 2690.65 2222.66 252.46 8396.86 1612.71 1385.90 151.70 5177.60 303.05 275.64 20.08 1072.92 7357.48 5649.21 632.40 20,625.11

8.63 4.81 1.18 20.11 3018.06 2420.39 335.98 8793.03 2009.30 1564.49 200.00 5593.00 366.66 322.43 30.43 1189.12 8392.27 6585.24 778.22 23,363.17

9.18 5.54 1.21 22.01 3553.23 2755.85 365.15 10,080.25 2364.50 1744.29 193.00 6002.00 448.81 378.20 33.53 1446.45 9988.82 7541.45 936.26 27,088.65

9.10 4.88 1.19 18.95 3989.08 3088.04 393.60 11,671.28 2731.07 1937.19 230.00 6982.00 546.40 449.83 42.02 1763.40 11,443.83 9096.46 1195.35 37,407.41

9.79 5.55 1.23 21.13 4244.17 3215.94 447.05 12,241.09 2899.07 2079.14 235.00 7555.00 575.01 463.51 46.84 1822.05 11,983.24 9012.17 1298.40 34,094.55

Coal consumption (1000 t) Installed capacity (1000 kW)

Revenue (100 M yuan) CO2 (1000 t)

Note: Due to space limitation, this table shows only the descriptive statistics of input and output variables in even numbered years and in 2013; the other results are available upon request.

Fig. 2. A number of coal-fired power stations across 30 regions. Note: coal-fired power stations only include the installed capacity greater than 1 million kW. Data are unavailable for 2013, the number of coal-fired power stations in 2014 is shown.

We employ a Tobit model and correlation analysis to detect regional factors contributing to the degree of UEC and UEN. Total energy consumption and energy intensity (total energy consumption per GDP) are taken as the variables of interest (Wu et al., 2016); as shown in Tables 3 and 4, energy intensity has a significant negative effect on the scores of UEN and positive effect on UEC. That is, the increase in energy intensity would result in the decrease in regional unified efficiency and amounts of UEC. Energy consumption has a significant positive effect on UEN and UEC, which indicates that regions with high energy consumption tend to produce more coal-fired electricity and more likely suffer from UEC.

emissions compared with the efficiency frontier. Notably, most of the regions (e.g. Shanxi, Guizhou, Shaanxi, Gansu, Xinjiang, Ningxia, Liaoning, Chongqing, and Hebei) are in the central and western areas (except for Shandong). In those DEC regions, measures (e.g. the support of the technical innovation fund and strict environmental regulations) have been taken to encourage coal-fired plants conducting technological upgrades and energy conservations. By contrast, the remaining regions have no ability to conduct eco-technological innovations because no DEC have occurred in those regions. During the sample period, the number of regions where DEC occurred in 2012 was one, and the number decreased to zero in 2013 (Fig. 6). In 2007, the number of provinces where DEC occurred was the greatest. In fact, the central government has implemented policies and measures to promote energy savings and emission reductions since 2006. For example, in the 11th Five-Year Plan for national economic and social development (2006–2010), the Chinese government stated that energy consumption per unit of GDP should be reduced by 20%. To achieve this constrained goal, the State Council issued its ‘decision to strengthen energy conservation’ in 2006 and promulgated the ‘notification on a comprehensive programme of energy conservation and emission reduction by State Council’ in 2007. According to these policies, during the ‘11th Five-Year Plan’ period, the State was supposed

5.2. Desirable energy congestion (DEC) Regarding DEC under managerial disposability, we must keep in mind that the amounts of DEC represent carbon emission performance of the input-output productivity technology. Therefore, the higher amounts indicate better eco-technology improvement. According to models (11)–(14), the amounts of DEC under managerial disposability from 2004 to 2013 are calculated and illustrated in Fig. 6. It is observed that some regions have occurred DEC throughout the study period; in those areas, a one-unit increase in energy input produces fewer carbon 128

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Table 2 UEC under natural disposability. Province

sne (1000 t)

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.00 80.75 0.00 0.00 1693.30 0.00 0.00 176.82 0.00 0.00 0.00 86.91 41.89 0.00 544.33 91.62 61.97 0.00 0.00 0.00 0.00 47.72 0.00 618.43 31.82 365.75 393.95 0.00 974.34 1635.10

*

* (1000 t)

n

0.00 80.75 0.00 0.00 6.26 0.00 0.00 0.00 0.00 0.00 0.00 86.91 41.89 0.00 0.00 91.62 61.97 0.00 0.00 0.00 0.00 47.72 0.00 0.00 0.00 198.58 89.10 0.00 235.69 131.25

UEC (1000 t)

Degree of UEC (%)

Hydropower proportion (%)

Wind power proportion (%)

Thermal power proportion(%)

0.00 0.00 0.00 0.00 1687.00 0.00 0.00 176.82 0.00 0.00 0.00 0.00 0.00 0.00 544.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 618.43 31.82 167.16 304.85 0.00 738.65 1503.80

0.00 0.00 0.00 0.00 99.60 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 100.00 45.70 77.40 0.00 75.80 92.00

1.00 0.00 0.00 2.00 1.00 5.00 16.00 3.00 0.00 0.00 6.00 2.00 22.00 14.00 0.00 4.00 53.00 34.00 8.00 38.00 10.00 30.00 77.00 25.00 76.00 6.00 30.00 72.00 2.00 10.00

1.00 1.00 6.00 2.00 10.00 6.00 7.00 8.00 1.00 1.00 0.00 0.00 2.00 1.00 2.00 0.00 0.00 0.00 1.00 0.00 3.00 0.00 0.00 1.00 2.00 1.00 10.00 0.00 5.00 4.00

98.00 99.00 93.00 96.00 89.00 84.00 76.00 88.00 99.00 95.00 81.00 98.00 72.00 85.00 97.00 96.00 47.00 66.00 78.00 62.00 87.00 70.00 23.00 74.00 22.00 94.00 59.00 23.00 92.00 85.00

Fig. 3. Scores of UEN in 2013.

to arrange special funds for the energy-saving technology upgrades. By 2013, most of the coal-fired power plants completed the technological upgrades and no further potential for energy saving and emissions reduction due to the cumulative effect of eco-technology. From a perspective of regional UEM scores, the average UEM decreases from 2004 to 2011 (Fig. 7). Since 2012, the UEM scores have begun to increase. In the sample period, the average UEM of the eastern area was always higher than those of the central and western regions. It indicates that the carbon reduction efforts in the eastern regions are more effective. Compared with Figs. 7 and 5, we observe a notable phenomenon. Before 2011, the western UEM scores were higher than the central region under management disposability, and vice versa, under the natural disposability (the western UEN were lower than those of the central regions). This seemingly contradictory conclusion indicates that the coal-fired plants located in western China focused more on carbon

reductions than those of the central regions, and the coal-fired plants located in central China focuses more on productive performance than those of the western regions. 6. Conclusions and policy implications This study provides a framework for the measurement of energy congestion. Energy congestion is classified into UEC and DEC by output heterogeneity under natural disposability and managerial disposability. The UEC and DEC models were created to detect and assess the UEC and DEC of the regional coal-fired generation industry in China from 2004 to 2013. We draw the following conclusions. (1) Some regions, such as Inner Mongolia, Heilongjiang, Shandong, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, and Xinjiang, have suffered from UEC. Most of those regions are in the less-developed 129

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Fig. 4. UEC in the three major regions from 2004 to 2013 in China.

Fig. 5. Average unified efficiency under natural disposability (UEN)scores in the three major regions.

or border areas, except for Shandong province. Those regions should receive a red alert for electricity over-capacity and be banned from constructing new coal-fired power generation facilities. However, it is difficult for the inefficient coal-fired plants to withdraw from the market because they have already provided job opportunities and tax revenues for the local government. Therefore, the exit mechanism of power plants with UEC should be established by government. In addition, coal-fired electricity is used to do the peak adjustment and clean electricity in western China can be transported to the central and eastern regions through Ultra High Vacuum (UHV)transmission lines. (2) The central government should identify the sources of the total inefficiency of the regional coal-fired industry. They can make effective policies to improve efficiency or adjust the invest scale. In this study, the inefficient regions are divided into three categories according to the sources of inefficiency. In specifically, those regions in category A should improve technical efficiency, those in category B should reduce investments in coal-fired facilities, and those in category C should focus more on technical efficiency and the investment scale of coal-fired plants. (3) Some provincial regions have DEC throughout the study period. In those areas, a one-unit increase in energy input produces fewer carbon emissions compared with the efficiency frontier, and this result indicated that those provinces have conducted eco-technology innovations. Notably, most of the regions are located in the central and western areas (except for Shandong). By contrast, the remaining provinces do not have DEC under managerial disposability. In order to encourage DEC for the regional coal-fired industry, the government should prioritize support for R&D for ecotechnology innovation in the areas where DEC have occurred, such

Table 3 Estimated results of Tobit model for UEC and UEM. Variables

UEN

UEC

Energy consumption

0.0525** (0.0257) − 0.0647** (0.0292) 0.939*** (0.0689) 0.179*** (0.0283) 0.116*** (0.00642)

138.2*** (52.41) 116.5* (65.57) − 152.7 (140.0) 283.4*** (44.97) 298.9*** (14.14)

Energy intensity _cons sigma_u sigma_e

Note: Standard errors in parentheses. * p < 0.1. ** p < 0.05. *** p < 0.01. Table 4 Correlation matrix for energy consumption, energy intensity, UEC, and UEM. Correlation

UEN

UEC

Energy consumption

Energy intensity

UEN UEC Energy consumption Energy intensity

1 − 0.0169 0.2991*** − 0.2399***

– 1 0.1180** 0.3644***

– – 1 − 0.2182***

– – – 1

Note: * p < 0.1. ** p < 0.05. *** p < 0.01.

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Fig. 6. DEC in different regions from 2004 to 2013. Note: Results in the same color belong to the same year.

Fig. 7. Average UEM scores in the three major regions under managerial disposability.

as Hebei, Inner Mongolia, Jilin, Anhui, Fujian, Henan, Shaanxi, and Gansu. In those areas without DEC, they should learn from the areas with advanced eco-technology to conduct facilities upgrades or use cleaner energy.

step to improve energy technical efficiency. There will be three cases. One case is the DMU have UEC, some measures should be taken to eliminate UEC. Though UEC is eliminated, energy technical inefficiency still exists. Therefore, further measures should be taken to improve energy technical efficiency. Another case is the DMU has no UEC but has energy technical inefficiency. Then measures should be taken to improve energy technical inefficiency. The third case is the DMU has DEC, which has been standing on the technological frontier. The main reasons for the occurrence of UEC in the coal-fired power generation industry are as follows:(a)Blind and unordered expansion. Lots of coal-fired generation plants have been built in the past for its lower-cost and electricity generation stability. China has the largest electricity capacity in the world. Blind and unordered expansion of regional coal-fired power plants has led to an oversupply of electricity. (b)The effects of environmental regulation. Measures have been implemented to prevent the regional coal-fired power generation industry from further expansion for its pollutants. For example, China's 13th Five-Year Plan ordered that greater than 20 million kilowatts of coalfired power generation facilities should be eliminated, and this goal was decomposed into the regional level. Additionally, the coal-fired power generation industry is also the first and only emission control industry in the coming national unified ETS in 2017. (c)The crowding-out effects from regional clean energy development. The renewable energy sector is supported by the government because this industry is clean, pollution-free, and renewable. In recent years, China's government has been attempting to build a clean, low carbon, safe, and highly efficient

In our study, energy congestion is classified into UEC and DEC. While traditional energy congestion often is considered as undesirable energy congestion (UEC). Energy congestion and energy technical inefficiency are different and related concepts. Firstly, UEC and energy technical inefficiency are both a part of total technical inefficiency which is undesirable states. However, the marginal output of energy is negative when UEC is occurred and positive when energy technical inefficiency occurs. While DEC is technical efficiency for carbon reduction which is a desirable state. It is opposite to UEC and energy inefficiency. Secondly, energy technical inefficiency is mainly caused by poor operational management. UEC is mainly caused by excessive energy waste due to blind expansion or poor management. While DEC is mainly driven by eco-technology innovation for carbon reduction. Measures to eliminate UEC include the prevention of over-capacity, increasing energy cost (Rødseth, 2013), replacing fossil energy with clean energy, energy-saving strategies (Wu et al., 2016) and so on. Measures to encourage DEC are mainly supported technical innovation for carbon reduction. Therefore, we can infer that most of the policies aiming to reduce UEC are almost the same as that to improve energy inefficiency, whereas the policies aiming to encourage DEC will help to improve energy technical efficiency. Thirdly, identifying UEC is the first

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modern energy industry system. As shown in Table 2, the proportion of wind power in Inner Mongolia is 10%, the proportion of hydropower in Guizhou is 25%, and in Xinjiang, hydropower accounts for 10% and wind power for 4%. The regulations that renewable energy generation has priority to access power grid will limit the development of coalfired generation plants. In conclusion, based on the three reasons, the coal-fired plants cannot avoid UEC due to the synthesis of a variety of factors that from the government and market. In conclusion, analyzing UEC and DEC is valuable for policymakers to detect which regions have an over-capacity of coal-fired generation industry and the potential to develop advanced eco-technology. However, this study has limitations. First, the DMUs are assumed to be independent. In reality, the DMUs in different regions are always affected by common factors, and the plants may be owned by the same group company; therefore, their interaction should be considered in the further study. Second, the desirable output is operating revenues rather than profits; the former always has a positive value, and the later may experience negatives that represent losses. Congestion assessed with

negative values has been explored by DEA literature (e.g. Khoveyni et al., 2016), but desirable congestion and undesirable congestion with negative outputs had not. Finally, we assume that the PPSs is a convex set in our research. If the PPSs are not a satisfied convex set (as in the free disposal hull model), how to calculate the congestion would be the subject of another future study. In summary, congestion estimation is a notable topic and challenging to study. Acknowledgements The authors are grateful for the financial support from the National Natural Science Foundation of China (71704047, 71601020, 71704009, 71473070), the Science Foundation of Ministry of Education of China (17YJC90015), the China Postdoctoral Science Foundation (2017M620821) the Young Talents Fund of Henan University of Economics and Law, the Fundamental Research Funds for the Central Universities (FRF-BD-17-008A), the Education Department of Henan Humanities and Social Sciences Research Foundation (17A790003).

Appendix A Basic concepts in this paper. Summaries of the related concepts are shown in Table A.1. Table A.1 Summaries of the related concepts. Strategies

Main Thoughts

Formulas

Strong disposability

As input is increased, the output can be indefinitely increased.

PPS S (X ) = {(Y , B ) :Y

Desirable outputs will be reduced when undesirable outputs are reduced.

PPSW (X ) = {(Y , B ) :Y

Given a reduced vector of inputs, the firm increases the directional vector of desirable outputs as much as possible and does not incorporate an occurrence of eco-technological innovation on undesirable outputs; rather, it focuses upon a managerial effort to improve the operational performance of the DMU. Given the increased input vector, the firm increases the directional vector of desirable outputs as much as possible and incorporates an occurrence of ecotechnological innovation on undesirable outputs. Even if the DMUs increase the amount of fossil fuels, the increase can reduce carbon emissions through managerial effort. As the energy inputs increased, desirable outputs (e.g. GDP and profits) decreased.

PPSN (X ) = {(Y , B ) :Y

Weak disposability Natural disposability

Managerial disposability

UEC DEC

As the energy inputs increased, undesirable outputs (e.g. CO2) decreased.

K k = 1 Yk k ,B

K k = 1 Bk k ,X

K k = 1 Xk k ,

K k= 1 k

= 1,

k

0, k = 1, …, K } K k = 1 Yk k ,B

K k = 1 Bk k ,X

K k = 1 Xk k ,

K k= 1 k

= 1,

k

K k = 1 Yk k ,B

K k = 1 Bk k ,X

K k = 1 Xk k ,

K k= 1 k

= 1,

k

K k = 1 Yk k,B

K k = 1 Bk k ,X

K k = 1 Xk k ,

K k =1 k

= 1,

k

=

0, k = 1, …, K } 0, k = 1, …, K }

PPSM (X ) = {(Y , B) :Y 0, k = 1, …, K }

N PPSUEC (X ) = {(Y , B ) :Y

K k = 1 Yk k ,B

=

K k = 1 Bk k ,X

K k = 1 Xk k ,

K k=1 k

= 1,

k

K k = 1 Bk k ,X

K k = 1 Xk k ,

K k=1 k

= 1,

k

0, k = 1, …, K } M PPSDEC (X ) = {(Y , B ) :Y =

0, k = 1, …, K }

132

K k = 1 Yk k ,B

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Appendix B (See Table B.1) Table B.1 Annual average desulphurization on-grid price (yuan/kWh) for coal-fired power generation in China. Region

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.372 0.372 0.372 0.291 0.293 0.378 0.366 0.378 0.454 0.430 0.465 0.401 0.425 0.407 0.378 0.355 0.390 0.419 0.489 0.390 0.425 0.361 0.361 0.291 0.297 0.320 0.281 0.285 0.285 0.273

0.380 0.380 0.378 0.291 0.289 0.384 0.360 0.377 0.457 0.431 0.465 0.408 0.424 0.413 0.382 0.368 0.402 0.425 0.490 0.394 0.422 0.364 0.368 0.296 0.301 0.319 0.277 0.277 0.280 0.262

0.385 0.384 0.380 0.296 0.287 0.390 0.362 0.382 0.455 0.427 0.460 0.406 0.417 0.417 0.383 0.377 0.412 0.433 0.491 0.403 0.421 0.367 0.375 0.305 0.307 0.323 0.276 0.276 0.275 0.267

0.385 0.385 0.378 0.301 0.288 0.393 0.368 0.387 0.451 0.423 0.456 0.402 0.412 0.418 0.382 0.379 0.415 0.437 0.492 0.410 0.421 0.368 0.378 0.312 0.311 0.326 0.277 0.279 0.273 0.267

0.390 0.391 0.387 0.313 0.291 0.399 0.368 0.390 0.463 0.435 0.468 0.406 0.423 0.425 0.393 0.392 0.426 0.446 0.506 0.429 0.435 0.380 0.387 0.322 0.327 0.333 0.283 0.284 0.275 0.261

0.402 0.403 0.401 0.331 0.297 0.410 0.374 0.397 0.480 0.452 0.484 0.414 0.439 0.437 0.412 0.410 0.444 0.460 0.524 0.455 0.455 0.396 0.401 0.336 0.346 0.345 0.293 0.294 0.280 0.255

0.402 0.388 0.387 0.329 0.288 0.392 0.378 0.395 0.457 0.438 0.466 0.399 0.417 0.426 0.396 0.393 0.428 0.444 0.498 0.441 0.443 0.388 0.399 0.341 0.343 0.344 0.295 0.308 0.271 0.250

0.401 0.389 0.394 0.349 0.289 0.394 0.380 0.396 0.458 0.438 0.467 0.407 0.210 0.438 0.410 0.404 0.438 0.455 0.499 0.443 0.456 0.400 0.405 0.347 0.345 0.359 0.307 0.319 0.272 0.246

0.398 0.409 0.419 0.382 0.306 0.408 0.395 0.411 0.470 0.447 0.475 0.428 0.438 0.478 0.437 0.432 0.471 0.494 0.513 0.470 0.483 0.438 0.442 0.385 0.363 0.391 0.334 0.350 0.284 0.246

0.391 0.402 0.411 0.376 0.300 0.401 0.389 0.406 0.459 0.437 0.464 0.420 0.430 0.470 0.430 0.425 0.461 0.485 0.503 0.460 0.475 0.430 0.437 0.378 0.360 0.385 0.327 0.344 0.279 0.237

Note: The on-grid p price in Inner Mongolia refers to that in western Inner Mongolia. The data come from the website of the National Development and Reform Commission.

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