Integrating cost information in energy efficiency measurement: An empirical study on thermal power companies

Abstract

Energy saving and emission reduction sometimes mean high cost, so companies do not have enough motivation to always support the related policy. By neglecting the economic cost and the imperfect substitution among input factors, widely used energy efficiency indicators such as “energy intensity” will sometimes lead to uneconomic results. Based on the theory of economic efficiency, “energy economic efficiency” is proposed as a new energy efficiency measurement to integrate cost information. In this paper, we further discuss energy economic efficiency, propose supplementary properties, and measure the efficiency of twelve public thermal power companies during the period of China’s 12th five-year plan. Our results show that (2) the economic efficiency of the twelve public companies decreased slowly. The average economic efficiency was 0.82, and there was approximately 40 billion RMB in potential cost savings in 2015, accounting for 18% of the total cost. (2) The energy economic efficiency of these twelve companies increased by approximately 10% during 2011–2015. (3) The primary mission of most thermal power company is to improve the coal combustion technology. (Christensen 4) When expanding production, the input factors will sometimes be uncoordinated, which will lead to increased costs and decreased energy economic efficiency.

Appendices

Appendix 1. The proof

Assuming that:

E_e:

is economic efficiency;

k_e:

is capital economic efficiency;

l_e:

is labor economic efficiency;

e_e:

is energy economic efficiency.

ck:

is the capital cost under real situation,

ck^∗:

is the capital cost under optimal situation;

cl:

is the labor cost under real situation,

cl^∗:

is the labor cost under optimal situation;

ce:

is the energy cost under real situation,

ce^∗:

is the energy cost under optimal situation.

According to the definitions, we have:

$$ {k}_e=\frac{c{k}^{\ast }}{ck} $$

(9)

$$ {l}_e=\frac{c{l}^{\ast }}{cl} $$

(10)

$$ {e}_e=\frac{c{e}^{\ast }}{ck} $$

(11)

$$ {E}_e=\frac{c{k}^{\ast }+c{l}^{\ast }+c{e}^{\ast }}{ck+ cl+ ce} $$

(12)

So,

$$ {E}_e=\frac{c{k}^{\ast }+c{l}^{\ast }+c{e}^{\ast }}{ck+ cl+ ce}=\frac{k_e\times ck+{l}_e\times cl+{e}_e\times ce}{ck+ cl+ ce}=\frac{k_e\times ck}{ck+ cl+ ce}+\frac{l_e\times cl}{ck+ cl+ ce}+\frac{e_e\times ce}{ck+ cl+ ce}=\frac{ck}{ck+ cl+ ce}\times {k}_e+\frac{cl}{ck+ cl+ ce}\times c{l}_e+\frac{ce}{ck+ cl+ ce}\times {e}_e $$

(13)

Let,

$$ {\displaystyle \begin{array}{c}\alpha =\frac{ck}{ck+ cl+ ce}\\ {}\beta =\frac{cl}{ck+ cl+ ce}\\ {}\gamma =\frac{ce}{ck+ cl+ ce}\end{array}} $$

(14)

We have,

$$ \alpha +\beta +\gamma =\frac{ck}{ck+ cl+ ce}+\frac{cl}{ck+ cl+ ce}+\frac{ce}{ck+ cl+ ce}=1 $$

(15)

That is,

$$ {\displaystyle \begin{array}{c}{E}_e=\alpha {k}_e+\beta {l}_e+\gamma {e}_e\\ {}\ \alpha +\beta +\gamma =1\end{array}} $$

(16)

Appendix 2. The full names, abbreviations, and stock codes of the 12 companies

No.	Company name	Company code	Assets (billion RMB)	Location
1	Huadian Energy Company	HDNY	23.4	Harbin
2	Huadian Power International	HDGJ	176.1	Jinan
3	Jilin Electric Power	JDGF	18.9	Changchun
4	Guodian Changyuan Electric Power	CYDL	11.6	Wuhan
5	Zhejiang Zheneng Electric Power	ZNDL	100	Hangzhou
6	Gd Power Development	GDDL	225.4	Beijing
7	Inner Mongolia Mengdian Huaneng	NMHD	36.9	Huhhot
8	Datang International Power Generation	DTFD	282.9	Beijing
9	Hebei Jiantou Energy Investment	JTNY	20.3	Shijiazhuang
10	Henan Yuneng Holdings	YNKG	8.8	Zhengzhou
11	Huaneng Power International	HNGJ	268.7	Beijing
12	Datang Huayin Electric Power	HYDL	16.6	Changsha

Appendix 3. The results

Company code	Year	Economic efficiency	Capital economic efficiency	Labor economic efficiency	Energy economic efficiency	Energy utilize efficiency
HDNY	2011	0.75	1.57	0.38	0.68	0.88
HDNY	2012	0.75	1.53	0.34	0.68	0.88
HDNY	2013	0.72	1.13	0.31	0.72	0.83
HDNY	2014	0.73	1.06	0.35	0.74	0.82
HDNY	2015	0.73	1.01	0.32	0.75	0.81
HDGJ	2011	0.81	1.55	0.99	0.70	0.90
HDGJ	2012	0.81	1.38	0.99	0.71	0.87
HDGJ	2013	0.84	1.16	1.04	0.76	0.88
HDGJ	2014	0.85	1.09	1.06	0.77	0.88
HDGJ	2015	0.83	0.92	0.97	0.78	0.84
JDGF	2011	0.76	1.29	0.75	0.68	0.81
JDGF	2012	0.76	0.86	0.75	0.74	0.77
JDGF	2013	0.77	0.80	0.75	0.76	0.77
JDGF	2014	0.74	0.66	0.66	0.78	0.75
JDGF	2015	0.70	0.54	0.58	0.84	0.76
CYDL	2011	0.75	1.49	0.63	0.69	0.87
CYDL	2012	0.75	1.31	0.55	0.70	0.83
CYDL	2013	0.78	1.46	0.68	0.70	0.87
CYDL	2014	0.80	1.31	0.55	0.75	0.86
CYDL	2015	0.79	1.00	0.55	0.80	0.83
ZNDL	2013	0.92	1.56	1.79	0.77	1.00
ZNDL	2014	0.88	1.17	1.38	0.78	0.94
ZNDL	2015	0.84	0.91	1.14	0.79	0.89
GDDL	2011	0.99	1.21	0.84	0.96	1.00
GDDL	2012	0.99	1.14	0.81	0.97	1.00
GDDL	2013	0.99	1.13	0.95	0.96	1.00
GDDL	2014	1.00	1.00	1.00	1.00	1.00
GDDL	2015	0.98	0.84	0.95	1.06	1.00
NMHD	2011	0.92	1.21	1.15	0.80	0.96
NMHD	2012	0.88	1.03	1.12	0.79	0.90
NMHD	2013	0.89	0.87	0.59	0.96	0.92
NMHD	2014	0.87	0.77	0.50	0.98	0.88
NMHD	2015	0.78	0.61	0.43	0.98	0.80
DTFD	2011	0.88	1.43	1.79	0.74	0.98
DTFD	2012	0.85	1.14	1.40	0.74	0.91
DTFD	2013	0.86	1.04	1.35	0.75	0.91
DTFD	2014	0.82	0.85	1.13	0.76	0.87
DTFD	2015	0.77	0.68	1.01	0.77	0.84
JTNY	2011	0.79	1.64	0.62	0.70	0.91
JTNY	2012	0.77	1.58	0.55	0.71	0.90
JTNY	2013	0.83	1.37	0.80	0.75	0.92
JTNY	2014	0.84	1.19	0.87	0.76	0.88
JTNY	2015	0.82	0.95	0.83	0.77	0.83
YNKG	2012	0.73	1.40	2.27	0.60	1.00
YNKG	2013	0.85	1.15	1.87	0.74	1.00
YNKG	2014	0.83	1.08	1.00	0.76	0.86
YNKG	2015	0.96	0.82	0.86	1.03	0.97
HNGJ	2011	0.83	1.55	1.29	0.71	0.92
HNGJ	2012	0.80	1.27	1.17	0.72	0.87
HNGJ	2013	0.85	1.27	1.17	0.76	0.91
HNGJ	2014	0.83	1.11	1.08	0.76	0.88
HNGJ	2015	0.84	1.03	1.06	0.76	0.87
HYDL	2011	0.82	1.85	0.63	0.73	1.00
HYDL	2012	0.83	1.73	0.60	0.73	0.98
HYDL	2013	0.81	1.40	0.54	0.74	0.92
HYDL	2014	0.77	0.91	0.54	0.78	0.84
HYDL	2015	0.65	0.57	0.40	0.77	0.72