Productivity Assessment of Coal-Fired Thermal Power Plants: An Application of Hicks-Moorsteen Total Factor Productivity Index

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ScienceDirect Materials Today: Proceedings 5 (2018) 23824–23833

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IConAMMA_2017

Productivity Assessment of Coal-Fired Thermal Power Plants: An Application of Hicks-Moorsteen Total Factor Productivity Index Amritpal Singh Dhillona,*, Hardik Vachharajanib a b

Finance –officer, GIPCL, Gujarat, India AIH Higher Education, Sydney, Australia

Abstract In this paper, both technical and technological efficiency of Indian coal-fired power plants is studied using Hicks-Moorsteen Total Factor Productivity Index. Out of seventy-four, six power plants, i.e., Chandrapura, Dahanu, Korba-west, Ramagundem–B, Rayalaseema and Talcher, have achieved positive growth in all the indexes of productivity. 43.24% of power plants saw the average TFP growth of 20.2% while remaining observed a negative average change of -14.9%. Further, the outcome of this study shows that central-owned power generation plants have outperformed in all the efficiency indexes as compared to private and state-owned power plants. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017]. Keywords: Productivity change; Thermal power plant; Hicks-Moorsteen productivity index; Benchmarking; Data envelopment analysis.

1. Introduction In today’s world, energy is one of the most crucial factors contributing directly to the growth of an economy and plays a vital role in developing the nation as well as human race. This statement also stands true in the case of developing countries like India, which is highly focused on achieving higher economic growth by way of mitigating

* Corresponding author. Tel.: +91-951-008-6557; E-mail address: [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017].

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the shortage of electricity. Knowing the importance of electricity, the Indian government has started investing heavily in power sector, especially in a renewable source of energy. However, coal-fired thermal power plants account for 53% of installed capacity and contribute to more than 60% of electricity generation [1]. Also, energy planning authorities of India is also well acquainted with limitations of renewable sources of energy. Therefore, many initiatives are taken to improve the performance of these coal-fired thermal power plants. A small amount of improvement in this segment can have a tremendous effect on the overall economy as well on human development index. In other words, it is essential to improve the productivity of coal-fired thermal power plants to achieve the goal of mitigating the shortage of electricity from India. A large number of studies have been carried out to measure the efficiency and productivity of power plants using non-parametric approaches. For example, Behera, S. K.et al., 2010, Dhillon A. S. et al., 2014, Lam and Shiu, 2001, Jha, D. K. et al.,2007, Vachhrajani and Dhillon, 2014, Song, C. et al., 2015 and so on [2,3,4,5,6,7]. The majority of these studies have utilized the Malmquist productivity index as a tool to evaluate the performance of power plants using various parameters. However, O’Donnell [8] and Grifell-Tatje and Lovell [9] argued against the usage of MPI as a performance assessment tool and concluded that MPI lacks the precision in evaluating the productivity change. Furthermore, they have also highlighted the pitfalls of Malmquist productivity index. To overcome the problems of previous approaches, O’Donnell [8] proposed a new method to decompose Total factor productivity (TFP) indices into different measures of efficiency change and technical change. Looking into the overall benefits of HicksMoorsten TFP index as explained under the heading of Methods, i.e. in Section 2, this study is also in favor of using HMPI as an evaluating instrument. This study, therefore, uses the Hicks-Moorsten TFP index to evaluate productivity changes of Indian coal-fired thermal power plants for the year 2002-12. This paper is organized as follows: Section 2 explains the methods employed in this study with proper justifications and highlights the advantages; Section 3 describes the incorporated samples and variables classification; Section 4 presents our primary results and interpretations, and the study is concluded with final remarks in Section 5. 2. Methods HMPI is defined as a ratio of aggregate output-quantity over aggregate input-quantity index [10]. The major advantage of HMPI is that it can calculate the productivity with the help of input and output quantity data (i.e. without emphasizing on price data). Secondly, HMPI does not pre-assume any market environment (i.e., perfect competition, regulated market or industries) or behavioral objectives (i.e., maximize output, minimize cost, and maximize profit). Further, in HMPI, one does not have to make a choice between input or output orientation, such as in MPI. HMPI exhibits a simultaneous input and output orientation; because it combines output and input quantity indices using the Shephard output and input distance functions, respectively [11,12]. Further, O’Donnell [8] refers to such index numbers as multiplicatively –complete TFP indexes. TFPhs,it =

(

,

, )

(

,

, )

(

,

, )

(

,

, )

(

,

, )

(

,

, )

(

,

, )

(

,

, )

TFPit=dTechit · dTFPEit

/

(Hicks-Moorsteen)

(1) (2)

O'Donnell [11] demonstrated TFP is the product of technological change (dTech) and technical efficiency change (dTFPE) [see Equation (2)]. Furthermore, the proportionate increase (for output orientation) in dTFPE can be enhanced into the three component equations: dTFPE it = dOTEit · dOMEit · dROSEit

(3)

dTFPE it = dOTEit · dOSEit · dRMEit

(4)

*Output-oriented technical efficiency (dOTE), Output-oriented scale efficiency (dOSE), Output-oriented mix efficiency (dOME), Residual output-oriented scale efficiency (dROSE), Residual mix efficiency (dRME).

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TFPE measures unit movement towards or away from the efficient production frontier and also known as a catching-up index [13,14]. Alternatively, TFPE measures overall productivity, while measuring the residual scale and mix efficiency of productivity performance associated with economies of scale and scope. O’Donnell [14,15,16] further decomposes the TFPE into sub-indices using two production frontiers as reference i.e. unrestricted production frontier and mix-restricted production frontier. 3. Sample description In this study, assessment is carried out of balanced panel productivity growth data of 74 coal-fired thermal power plants for the period 2002-03 to 2011-12. The basic requirement of DEA techniques to have homogenous units is duly taken care. This study includes three outputs and five inputs as shown in Table 1. Since quantity variables are having different orders of magnitude, we have rescaled the data (so that all output and input quantity variables have unit’s means) to avoid numerical problems. On another hand, Hicks-Moorsteen TFP index may be sensitive to such rescaling. Table 1. Input and Output Variables

Variables

Inputs

Outputs

Capacity (MW)

Plant load factor (%)

Planned Maintenance (%)

Operating Availability Factor (%) Generation (Mu)

Forced Outage (%) Reserve Shutdown+ Low Sys Demand (%) Partial Unavailability (%)

Selection of right variable as output and input is significant for assessing the performance of power plants. Previous studies had evaluated electricity generation plants choosing capital, auxiliary energy consumption, planned maintenance cost and unplanned maintenance cost as inputs and power generation as output. For example Behera, S. K. et al., 2011, Vachhrajani, H. and Dhillon, A. S., 2014, Jain, S. and Thakur, T., 2010, Shanmugam, K. R. and Kulshreshtha, P., 2005 [2,3,17,18]. Table 2. Descriptive statistics of variables Variables Inputs

Outputs

2002-03

2011-12

Avg.

Standard deviation

Avg.

Standard deviation

Capacity (MW)

783.51

539.26

998.64

718.87

Planned Maintenance (%)

8.36

7.71

7.38

9.85

Forced Outage (%)

12.22

12.36

13.24

14.75

Reserve Shutdown+ Low Sys Demand (%)

1.54

2.57

2.73

6.69

Partial Unavailability (%)

11.57

7.60

10.47

7.70

Plant load factor (%)

67.90

19.95

69.27

22.11

Operating Availability Factor (%)

79.42

15.55

79.38

18.95

Generation (Mu)

4980.43

4224.85

6476

5532.03

Table 2 provides descriptive statistics for the variables used in this study. It indicated that on an average reserve shutdown + low sys demand, forced outage, capacity, plant load factor, and generation increased from 2002-03 to 2011-12. Further, partial unavailability, planned maintenance, and operational availability factor observed decreasing trend during the same period. Highest increase about 77% is exhibited by input variable, i.e. reserve shutdown + low sys demand, over the ten years.

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4. Results and discussion Results provided estimates of TFP change in the 74 power plants evaluated throughout 2002-03 to 2011-12 using HMPI. TFP change is further decomposed into technology change (dTech) and various types of efficiency i.e. TFP efficiency (TFPE), output-oriented technical efficiency (dOTE), output-oriented scale efficiency (dOSE), outputoriented mix efficiency (dOME), residual output-oriented scale efficiency (dROSE) and residual mix efficiency (dRME). We used the DPIN software Version 3.0 written by O’Donnell [19] to estimate different measures of efficiency and TFP components. Table 3. TFP changes and its components for India’s coal-fired power plants between 2002-12. SR No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

UNIT/PLANT A.E.CO. AMAR KANTAK EXT ANPARA BADARPUR BAKRESWAR BANDEL BARAUNI BHUSAWAL BOKARO B BUDGE BUDGE CHANDRAPUR CHANDRAPURA D.P.L. DADRI DAHANU DURGAPUR ENNORE FARAKKA GANDHINAGAR GH (LEH.MOH.) GND (BHATINDA) HARDUAGANJ IB VALLEY KAHALGAON K'KHEDA II KOLAGHAT KORADI KORBA KORBA WEST KORBA-III KOTA KOTHAGUDEM KUTCH LIG MEJIA METTUR NASIK NEW COSSIPORE NEYVELI -I NEYVELI -II NORTH CHENNAI OBRA PANIPAT PANKI PARAS PARICHHAA PARLI PATRATU RAICHUR RAJGHAT

dTFP 1.100 0.587 0.770 0.804 0.700 0.801 1.189 0.893 1.305 0.981 0.903 1.419 0.679 1.162 1.444 0.835 0.784 0.866 0.993 1.305 0.980 0.800 0.965 1.032 0.976 1.008 0.769 0.959 1.471 1.073 0.756 0.850 0.907 1.340 1.250 1.018 0.685 0.798 1.318 0.861 0.997 1.110 1.366 0.989 1.330 0.995 1.188 0.924 1.145

dTech 0.841 0.608 0.857 1.006 1.005 1.011 0.851 0.983 0.695 0.953 1.111 1.075 1.098 0.880 1.078 1.298 0.818 0.987 0.958 1.135 0.941 0.717 0.575 0.994 0.986 0.991 0.897 1.161 1.005 0.929 1.033 0.982 0.980 0.987 0.999 0.968 0.994 1.208 0.990 0.926 0.999 0.999 0.801 0.559 0.837 0.978 0.842 0.990 0.832

dTFPE 1.308 0.965 0.898 0.799 0.697 0.792 1.397 0.909 1.879 1.029 0.814 1.320 0.618 1.321 1.339 0.643 0.958 0.877 1.037 1.149 1.041 1.116 1.677 1.038 0.990 1.018 0.857 0.826 1.464 1.155 0.732 0.866 0.925 1.358 1.251 1.052 0.689 0.661 1.331 0.931 0.998 1.110 1.705 1.768 1.588 1.018 1.412 0.933 1.375

dOTE 1.000 0.947 1.004 0.993 0.995 0.998 0.950 1.009 1.022 0.998 1.011 1.096 0.971 1.007 1.003 1.054 0.953 1.006 0.992 0.997 0.962 0.937 0.993 1.000 1.000 1.006 0.966 0.986 1.012 1.024 1.000 1.003 0.993 1.001 1.000 1.009 0.979 0.990 1.007 0.995 0.973 1.020 1.015 0.981 0.981 0.978 0.874 0.980 1.003

dOSE 1.000 0.987 0.985 0.987 0.989 1.006 1.011 0.958 1.000 0.994 0.984 1.022 0.987 1.006 1.000 1.008 0.980 1.000 0.993 0.995 1.001 1.051 1.000 0.988 0.986 1.001 0.998 0.989 1.008 1.031 1.000 0.969 0.972 0.996 1.000 0.963 0.978 0.979 0.993 1.004 0.999 0.996 1.032 0.971 1.051 0.973 1.080 0.979 1.000

dOME 1.000 0.994 1.001 1.000 0.983 1.002 1.000 1.000 1.015 0.971 0.999 1.011 1.003 0.990 1.000 1.012 0.994 1.000 0.997 0.994 1.000 1.014 1.000 0.986 1.000 1.000 1.014 1.000 1.000 1.012 0.992 0.997 1.010 0.990 1.001 1.000 0.999 1.000 0.995 1.002 1.000 1.001 1.011 0.935 1.003 1.000 1.000 1.002 0.989

dROSE 1.308 1.026 0.894 0.805 0.712 0.792 1.470 0.901 1.811 1.062 0.806 1.192 0.635 1.325 1.335 0.603 1.011 0.872 1.049 1.159 1.083 1.176 1.688 1.052 0.990 1.011 0.875 0.838 1.447 1.114 0.738 0.866 0.923 1.371 1.250 1.043 0.705 0.667 1.328 0.933 1.026 1.087 1.661 1.927 1.613 1.041 1.617 0.950 1.387

dRME 1.308 1.033 0.908 0.815 0.708 0.789 1.454 0.941 1.838 1.038 0.818 1.178 0.645 1.304 1.335 0.606 1.025 0.872 1.053 1.158 1.082 1.133 1.688 1.050 1.004 1.010 0.889 0.847 1.436 1.094 0.732 0.891 0.959 1.362 1.251 1.083 0.719 0.681 1.331 0.931 1.027 1.093 1.627 1.856 1.539 1.070 1.497 0.973 1.371

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Amritpal Singh Dhillon / Materials Today: Proceedings 5 (2018) 23824–23833 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74

RAMAGUNDEM RAMAGUNDEM -B RAYALASEEMA RIHAND ROPAR SABARMATI SANJAY GANDHI SANTALDIH SATPURA SIKKA REP SIMHADRI SINGRAULI SOUTHERN REPL SURATGARH TALCHER TALCHER (OLD) TANDA TENUGHAT TITAGARH TROMBAY TUTICORIN UKAI UNCHAHAR VINDHYACHAL WANAKBORI AVG SD

0.904 1.227 1.292 0.942 0.975 1.205 0.975 1.138 0.985 1.226 0.475 0.808 0.987 0.904 1.463 1.193 1.244 0.952 0.766 1.001 0.701 1.080 1.082 1.003 0.996 0.988 0.218

0.964 1.192 1.060 1.575 0.999 1.281 0.999 1.020 0.999 1.048 0.836 0.924 0.797 1.057 1.089 0.966 0.744 0.573 0.795 0.986 0.965 0.994 0.948 1.036 0.999 0.948 0.162

0.938 1.030 1.219 0.598 0.977 0.941 0.976 1.117 0.986 1.171 0.568 0.875 1.239 0.855 1.343 1.235 1.673 1.661 0.964 1.016 0.727 1.087 1.141 0.968 0.998 1.043 0.293

1.000 1.000 1.000 1.000 1.010 1.000 0.993 1.060 0.982 0.999 1.000 0.994 1.006 0.989 1.007 1.044 1.041 1.029 0.998 0.985 0.997 1.004 1.009 1.000 0.996 0.997 0.028

1.000 1.000 1.000 1.000 0.999 1.000 0.998 1.000 0.967 0.982 1.000 0.997 1.000 1.004 1.007 1.013 1.010 1.000 0.986 0.979 0.990 0.994 1.004 1.008 0.989 0.997 0.019

0.995 1.000 1.004 0.999 1.001 0.998 1.000 1.063 1.000 0.993 1.000 1.001 0.993 0.999 1.003 1.002 1.001 1.003 0.997 0.996 0.999 1.000 1.002 1.005 0.997 1.000 0.013

0.942 1.030 1.215 0.599 0.966 0.942 0.983 0.991 1.004 1.180 0.569 0.879 1.241 0.865 1.329 1.181 1.607 1.610 0.969 1.035 0.729 1.082 1.129 0.963 1.005 1.046 0.295

0.938 1.030 1.220 0.598 0.968 0.941 0.985 1.053 1.038 1.194 0.568 0.883 1.232 0.861 1.324 1.169 1.592 1.614 0.980 1.053 0.736 1.089 1.126 0.960 1.012 1.046 0.292

Average HMI values showed that during the sample period, TFP decreased by -1.2%, i.e. -0.12% per year. It shows the overall regress sign which is further decomposed indicates -0.52% and 0.43% per year due to technological regress and efficiency progress respectively (Table 3). KORBA WEST power plant records highest positive average TFP change, i.e. 4.71% per year, followed by TALCHER i.e. 4.63% per year and DAHANU i.e. 4.44% per year. .On another hand, highest negative average TFP is recorded by SIMHADRI power plant i.e. -5.25% per year, followed by AMAR KANTAK EXT, i.e. -4.13% per year and D.P.L., i.e. -3.21% per year (as shown Fig. 1). 73 74 71 72 1.6 6970 1.4 68 67 66 1.2 65 1.0 64 63 0.8 62 61 0.6 60 0.4 59 58 0.2 57 0.0 56 55 54 53 52 51 50 49 48 47 46 45 4443 42 41 40 39

1

38

2 3 4 5 6 7

37 36 35

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 dTFP 3433

Fig. 1. Average TFP change of power plants

Amritpal Singh Dhillon / Materials Today: Proceedings 5 (2018) 23824–23833

73 74 71 72 1.6 6970 1.4 68 67 66 1.2 65 1.0 64 63 0.8 62 61 0.6 60 0.4 59 58 0.2 57 0.0 56 55 54 53 52 51 50 49 48 47 46 45 4443 42 41 40 39

1

38

2 3 4 5 6 7

37 36 35

34

8

23829

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 dTech 3332

Fig. 2. Average Tech change of power plants

Average HMI values showed that during the sample period, Tech decreased by -5.2%, i.e. -0.52% per year. Thus, a negative shift in the efficient production frontier is observed. This finding shows that an insignificant capital investment is made in technical updates. Rihand power plant records highest positive average Tech change, i.e. 5.75% per year, followed by DURGAPUR i.e. 2.98% per year and SABARMATI i.e. 2.81% per year. On another hand, highest negative average Tech is recorded by PARAS power plant i.e. -4.41% per year, followed by TENUGHAT, i.e. -4.27% per year and IB VALLEY, i.e. -4.25% per year (as shown Fig. 2). 73 74 7172 2.0 6970 68 67 66 1.5 65 64 63 1.0 62 61 60 0.5 59 58 57 0.0 56 55 54 53 52 51 50 49 48 47 46 45 4443 4241 40 39

1

38

2 3 4 5 6 7

37 36

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 dTFPE 3332 3534

Fig. 3. Average TFPE change of power plants during 2002-12

BOKARO B plant achieved the highest average TFPE, i.e. 8.79% per year, followed by PARAS and PANKI i.e. 7.68% and 7.05% per year respectively. On another hand, SIMHADRI achieved the highest negative average TFPE, i.e. -4.32% per year, followed by RIHAND and D.P.L., i.e. -4.02% and -3.82% respectively (as shown in Fig. 3). Overall positive average TFPE change of 4.3%, i.e. 0.43% per year, is observed during the sample period. This result

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suggests that coal-fired power plants management is efficient and productively using the available technology. Results show that positive average TFPE behavior in power plants is due to ROSE change, which increased by 0.46% per year. Further, OTE change is a major component of the adverse change in TFPE i.e.-0.03% per year (Table 3). However, technical efficient power plants can further improve their overall TFP by investing in new technologies. Thus, it seems essential for these power plants to improve in the expansion of the efficient frontier i.e. in technological change. Further, TFPE change is decomposed into OTE, OSE, OME, ROSE, and RME. Table 3 is a tabular representation of OTE change witnessed by power plants during the sample period. Highest negative average OTE change is attained by PATRATU, i.e. -1.26% per year, followed by HARDUAGANJ and AMAR KANTAK EXT, i.e. -0.63% and -0.53% per year respectively. On another hand, CHANDRAPURA plant out-performed with positive average OTE change of 0.96% per year, followed by SANTALDIH and DURGAPUR, i.e. 0.60% and 0.54% per year. The overall decreasing trend is observed in average OTE change during the sample period of -0.3% i.e. -0.03% per year. During the sample period, -0.3% of decreasing trend is noticed in average OSE change. PATRATU achieved the highest increase in average OSE change, i.e. 0.80% per year, followed by HARDUAGANJ and PARICHHAA. Both, i.e. HARDUAGANJ and PARICHHAA, achieve average OSE change of 0.51% per year. On another hand, BHUSAWAL achieved the highest decrease in average OSE change, i.e. -0.42% per year, followed by NASIK and SATPURA i.e. -0.37% and -0.33% per year respectively. No change is reported in overall Average OME value during the sample period. Further, highest negative change in average OME value is noticed in PARAS plant, i.e. -0.65% per year, followed by BUDGE BUDGE and BAKRESWAR, i.e., -0.29% and -0.17% per year. On another hand, SANTALDIH reported the highest positive average OME change, i.e. 0.63% per year, followed by BOKARO B, i.e. 0.15% per year (as shown in Table 3). PARAS witnessed the highest positive average ROSE change, i.e. 9.27% per year, followed by BOKARO B and IB VALLEY, i.e. 8.11% and 6.88% respectively. SIMHADRI attained highest negative average ROSE change i.e. 4.31% per year, followed by RIHAND and DURGAPUR, i.e. -4.01 and -3.97% per year. Increasing trend of 4.6% is observed in overall average ROSE change i.e. 0.46% per year. Average HMI values showed that during the sample period, RME rose 4.9 %, i.e. 0.49% per year. PARAS power plant records highest positive average RME change, i.e. 8.56% per year, followed by BOKARO B and IB VALLEY, i.e. 8.38% and 6.88% per year respectively. On another hand, highest negative average RME is recorded by SIMHADRI power plant i.e. -4.32% per year, followed by RIHAND and DURGAPUR, i.e. -4.02% and -3.94% per year (as shown Table 3). 1.500 1.300 1.100

dTFP

0.900

dTech

0.700

dTFPE

0.500

Fig. 4. TFP change and its components in India

From the graphical representation of Fig. 4, it is clear that the year 2007-08 and 2004-05 experienced highest TFP growth of 47% and 16.9% respectively and Tech change contributed significantly in both the years. Fig. 9 shows that highest TFPE growth of 36.6% and 27.2% is noticed in the year 2003-04 and 2006-07 respectively. Further, decomposing the year 2003-04 and 2006-07, it is found that RME change significantly positively contributed towards TFPE growth, i.e. 38.7% and 23.2% respectively (as shown in Fig. 5). RME and ROSE contributed

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negatively to TFPE growth during the year 2005-06 and 2010-11, due to which negative growth of TFP is observed during these periods. 1.40 1.30

dOTE

1.20

dOSE

1.10 1.00

dOME

0.90

dROSE

0.80 0.70

dRME

Fig. 5. Output oriented technical efficiency and scale change in India

% Coal-fired power plants 48.65

50 40

27.03

30

16.22

20 10

6.76

1.35

0 <0.50

0.50 <0.75

0.75<1.00

1.00<1.25

1.25≤

Fig. 6. Distribution of Coal-fired power plants according to its total factor productivity change (dTFP)

A coal-fired power plant congruent with productivity change is depicted in Fig. 6. It shows that 56.76% of power plants having a negative growth of TFP, while the remaining 43.24% showed improvement in productivity. Further, it is noticed that 16.22% of power plants improved their TFP more that 25%. However, 8.11% of power plants require urgent attention from energy planner. Also, further distribution is done by HMPI as shown in Fig. 7. 43.24% of power plants witnessed the average TFP growth of 20.2% while remaining observed a negative average change of -14.9%. 1.3 1.2 1.1

HMPI ≥ 1, (N=32)

1

HMPI < 1, (N=42)

0.9 0.8 dTFP

dTech

dTFPE

dOTE

dOSE

dOME

dROSE

dRME

Fig. 7. TFP changes and its components for India’s coal-fired power plants between 2002-03 and 2011-12: Performance-Wise Distribution

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6.5 4.9 0.0

0.0

-4.6

-0.6

0.0 0.0

0.0 -0.9 -0.4

-0.3 -2.4

-5.0

-0.3 -0.8

1.0 -1.0 -3.0

1.3

0.2

1.5

5.0

1.3

5.6 4.4

7.0 3.0

6.5 5.5

Surprisingly, it is notable from Fig. 7 that technology change between two groups i.e. HMPI ≥ 1 and HMPI <1, is minimum and reported negative average growth of -3.6% and -6.4% respectively. Furthermore, average technical efficiency change of 33.9% is observed between these two groups, which is a matter of concern to policy makers. Moreover, power plants operating under the flagship of central government have performed very well in achieving TFP growth of 1.5% during the sample period as compared to its counter ones (as shown in Fig. 8). It seems that central owned power plants invested in new technologies to remain ahead of its competitors. However, at same time state as well private-owned power plants failed miserably in recognizing the same, due to which negative Tech growth of -7.6% and -4.6% is observed respectively. Nevertheless, state as well private coal-fired power plants management looks efficient and productively using the available technology, due to which TFPE growth of 5.6% and 4.4% is achieved respectively. A major contribution to TFPE growth, in this case, is from RME change.

-9.0 dTFP

-7.6

-7.0

dTech

dTFPE Central

dOTE

dOSE State

dOME

dROSE

dRME

Private

Fig. 8. Productivity assessment of Coal-fired power plants: Sector-Wise Distribution

5. Conclusions This study investigates the efficiency and productivity changes of 74 Indian coal-fired power plants using MHPI over the period 2002-2012. The results of this study are considered as an eye-opener for the energy planners and authorities because it is identifying the major elements of TFP changes, i.e. TFPE and Tech. Moreover, the results are further decomposed into several factors, i.e., RME, ROSE, OME, OSE, and OTE. On an average, India’s coalfired power plants exhibited progression and regression regarding technical efficiency growth and technological growth respectively. Korba-west power plant reported the highest TFP growth rate due to greater positive technical efficiency change. On the contrary, Rihand power plants reported negative TFP growth despite achieving the highest technological growth rate. Hence, decision-makers can easily identify the area of improvement and can initiate the further course of action. The major outcomes of this research can be summarized as follows: (a) on an average, TFP decreased by 0.12% per year, primarily on account of increased in capacity, forced outages and reserve shutdown; (b) Tech was largely responsible for the decline in TFP, on another hand, TFPE exhibited a positive trend; (c) central government governed power plants outperformed in all the indexes as compared to state and private owned power plants; and (d) private owned power plants is miserably failed in implementing the technological innovations, which resulted in overall reduction in performance of these power plants. From the results of this study, energy planners can easily identify the relevant managerial and policy implication. Firstly, power plants can improve their overall performance by implementing the technological innovations. Secondly, management looks efficient in using the existing technology productively. Thirdly, the average TFP growth, i.e. 20.2%, is reported by 43.24% of power plants, which can be a role model for the other power plants. Lastly, 8.11% of power plants required immediate attention from policy makers as their negative TFP growth is observed more than -25%. Hence, this information is going to assist decision-makers in making a crucial decision,

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