Open Access

Quantify the energy and environmental benefits of implementing energy-efficiency measures in China’s iron and steel production

Future Cities and Environment20151:7

https://doi.org/10.1186/s40984-015-0005-8

Received: 2 March 2015

Accepted: 1 May 2015

Published: 21 August 2015

Abstract

As one of the most energy-, emission- and pollution-intensive industries, iron and steel production is responsible for significant emissions of greenhouse gas (GHG) and air pollutants. Although many energy-efficiency measures have been proposed by the Chinese government to mitigate GHG emissions and to improve air quality, lacking full understanding of the costs and benefits has created barriers against implementing these measures widely. This paper sets out to advance the understanding by addressing the knowledge gap in costs, benefits, and cost-effectiveness of energy-efficiency measures in iron and steel production. Specifically, we build a new evaluation framework to quantify energy benefits and environmental benefits (i.e., CO2 emission reduction, air-pollutants emission reduction and water savings) associated with 36 energy-efficiency measures. Results show that inclusion of benefits from CO2 and air-pollutants emission reduction affects the cost-effectiveness of energy-efficiency measures significantly, while impacts from water-savings benefits are moderate but notable when compared to the effects by considering energy benefits alone. The new information resulted from this study should be used to augment future programs and efforts in reducing energy use and environmental impacts associated with steel production.

Keywords

Iron and steel Energy-efficiency measure Energy benefits Environmental benefits Cost effectiveness

Introduction

China is currently facing significant challenges in energy use, and emissions of associated air pollutants and carbon emissions. Controlling emissions of air pollutants and CO2 not only is important for protecting the environment, but also is essential for achieving sustainability in the country’s economic and societal development. According to International Energy Agency (IEA), more than one-third of global energy consumption and 36 % of CO2 emissions are attributable to manufacturing industries (IEA, 2007). According to the 5th Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), greenhouse gas (GHG) emissions from industry (30 % of total global GHG emissions) arise mainly from material processing. For example, production of iron and steel and nonmetallic minerals results in 44 % of all industrial CO2 emissions (IPCC, 2014). The crude steel production in China was 731 Million tonnes (Mt) in 2012, accounting for half of the world’s total annual production (WSA, 2013). With such a high level of production and related energy consumption and CO2 emissions, China’s iron and steel industry must play an important role in the country’s energy savings and emission reduction programs (Wang 2014b).

To improve energy efficiency and mitigate CO2 emissions, Chinese governments have implemented many efficiency measures since the last decade. During the 11th and the 12th Five Year Plan (FYP), the National Development and Reform Commission (NDRC) released a series of National Extension Directories of Important Energy Conservation Technology (NDRC, 2008, 2009, 2011a, 2011b, 2012, 2013); the Ministry of Industry and Information Technology (MIIT) established the Energy Savings and Emission Reduction Information Platform and released the Guidebook of Advanced and Applicable Energy Savings and Emission Reduction Technologies in Iron and Steel Industry in 2012 (MIIT, 2012a, 2012b). In addition, NDRC, MIIT and the Ministry of Environmental Protection (MEP) jointly issued the Cleaner Production Evaluation System for the Iron and Steel Industry in 2012 (MEP, 2013). These government agencies proposed about 60 energy-saving and emission-reduction measures for the iron and steel industry collectively. Although many of the measures have been proposed, lacking full understanding of costs and benefits has created barriers against implementing these measures widely. It is important to evaluate cost effectiveness of energy-efficiency measures and select the most suitable and cost effective measures for implementation.

Literature review

Developing bottom-up energy system models and energy conservation supply curves (CSC) are two common methods for quantitative analyses of specific energy-efficiency measures. With the bottom-up modeling method, Wen et al. (2014) applied the AIM model to estimate the potentials of energy conservation and CO2 mitigation in China’s iron and steel industry during 2010–2020. Xu et al. (2014) and Karali et al. (2014) used the ISEEM model to analyze the roles of energy-efficiency measures in achieving specific carbon reduction targets in the same industry of U.S. Chen et al. (2014) also applied the China-TIMES model to study the carbon mitigation strategies and corresponding impacts. Using the CSC method, Morrow et al. (2014) analyzed 25 energy-efficiency measures applicable to India’s iron and steel industry. Earlier, Hasanbeigi et al. (2013) assessed the costs of energy savings and emission reductions from applying energy-efficiency measures in the China’s iron and steel sector. Recently, Li and Zhu (2014) also estimated the costs of energy savings and CO2 emission reduction in China’s iron and steel production. These papers report important works for specific energy-efficiency measures of iron and steel industry; however, knowledge gaps still exist. On one hand, detailed analyses of energy-efficiency measures promoted by the Chinese government during the 11th and the 12th FYP are limited in those studies; on the other hand, the majority of these studies only quantify energy-savings benefits, whereas non-energy benefits are neglected, such as carbon-emission mitigation, air-pollutants reduction, and water savings that are very important for China’s sustainable development.

From a macro-perspective, inclusion of non-energy benefits would result in reducing costs and improving cost-effectiveness of energy-efficiency measures, thus influence the assessment of cost-effective potentials (Worrell et al., 2003). There are many types of non-energy benefits, such as: (1) saved water and minimized wastes, (2) reduced GHG emissions, (3) reduced air pollutant emissions, (4) saved labor and time, (5) improved working environment (Worrell et al., 2003; Lung et al., 2005; IEA, 2012). Given the limitations, uncertainties, and challenges of quantifying non-energy benefits, this study focused on three types of environmental benefits, namely the carbon emission reduction, the air pollutants reduction, and the water-savings benefits. This paper aims to advance the understanding of costs, benefits, and cost-effectiveness of energy-efficiency measures in China’s iron and steel industry, by including both energy- and environmental benefits.

Overview of China’s iron and steel production

China’s iron and steel industry has grown rapidly in recent decades. The rapid growth has been attributed largely to increasing domestic demand; this growth is expected to continue in the coming years. During 1996–2012, China’s crude steel production increased from 107 to 731 Mt; its corresponding share of world steel production increased from 13.5 to 50.0 %. Additionally, China’s iron and steel industry made great progress in improving energy efficiency during the past decade. Average intensity of total energy use in key steel enterprises decreased from 761 kgce (kilogram of coal equivalent)/tonne steel in 2004 to 592 kgce/tonne in 2013. From 2004 to 2013, energy intensity of iron-making process decreased from 466 to 398 kgce/tonne iron; BOF (Basic oxygen furnace) process decreased from 26.6 to negative 7.7 kgce/tonne; and EAF (Electric arc furnace) process decreased from 209.9 to 60.8 kgce/tonne. Large “efficiency gap” still exists between the lowest and the highest energy intensity enterprises, as shown in Table 1 (Wang 2005, 2009, 2011, 2014a, 2014b).
Table 1

Process primary energy intensity for Chinese key steel enterprises (kgce/tonne)

 

Sintering

Pelleting

Coking

Iron-making

BOF

EAF

Rolling

Integrated energy consumption

The average primary energy intensity of key steel enterprises by process (kgce per tonne of product)

2004

66.4

42.0

142.2

466.2

26.6

209.9

92.9

761.0

2007

55.2

30.1

121.7

426.8

6.0

81.3

63.1

628.0

2010

52.7

29.4

105.9

407.8

−0.2

74.0

61.7

604.6

2013

49.1

28.3

100.5

398.1

−7.7

60.8

59.5

592.0

Lowest primary energy intensity of key steel enterprises by process (kgce per tonne of product)

2004

52.1

19.2

88.1

395.4

−3.8

146.3

53.7

-

2007

38.0

18.2

82.8

377.9

−16.1

46.7

28.2

-

2010

43.1

17.6

63.6

343.2

−13.3

27.5

25.9

-

2013

35.4

14.5

59.2

320.0

−23.6

21.9

32.3

-

Highest primary energy intensity of key steel enterprises by process (kgce per tonne of product)

2004

108.6

83.3

229.2

591.8

75.2

325.5

286.9

-

2007

85.3

51.3

434.6

569.3

38.0

171.6

220.7

-

2010

66.8

45.5

188.3

502.8

29.1

221.33

255.7

-

2013

56.8

44.9

154.6

474.0

14.9

176.6

209.5

-

Note: Negative value means this process can produce additional energy, such as converter gas

Methodology

In order to quantify energy and environmental benefits of implementing energy-efficiency measures in China’s iron and steel production, and to evaluate their impacts on cost effectiveness of the measures, we use the compiled data and information from literature reviews and developed a new evaluation framework in this paper, as shown in Fig. 1.
Fig. 1

Evaluation framework for quantifying energy and environmental benefits

Data collection and basic assumption

The analysis for China’s iron and steel industry is based on both international and Chinese technologies. Many energy-efficiency measures promoted by NDRC and MIIT are used in this analysis because other studies do not provide consistent and comprehensive data on energy-savings, emission-reduction, or associated costs of different energy-efficiency measures (NDRC, 2008, 2009, 2011a, 2011b, 2012, 2013; MIIT, 2012a, 2012b).

We use 2012 as the base year because that was the latest year for which energy and environmental data have been published by China’s national statistical agencies at the time of this study. Data on total production of different products are obtained from China Iron and Steel Association (CISA, 2013) and the World Steel Association (WSA, 2013). For estimating the adoption rates and technology availability of different measures, we developed a questionnaire and sent it to several experts in the Chinese iron and steel industry. Additionally, we obtained data from two recent reports: Key Industrial Energy-efficient and Emission Reduction Technologies and Measures (MIIT, 2012b) and Roadmap Study on Achieving Technical Energy Conservation Potential in China’s Industrial Sector by 2020 (ERI, 2013).

The carbon emission factors for fuels used for calculating CO2 emissions from energy consumption are taken from the IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2007). The emissions factor for electricity in 2012 is assumed to be 0.77 kg CO2/kWh (NBS, 2013a). Given most of the fossil fuels used in the China’s iron and steel industry are coal and coke, we use the weighted average CO2 emission factor for coal and coke consumed in the iron and steel industry in 2012 as the CO2 emission factor for fuel in this research, which is approximately 83.8 kg CO2/GJ (NBS, 2013a). In the processes of steel production and power generation, there are usually air pollution removal facilities, such as desulfurization equipment and de-nitrification equipment. For simplicity, we estimate the air pollutant emission factors based on the emissions and energy consumption in steel and power sectors, which are approximately 5 kg/tce and 1,654 kg/GWh for SO2, 2 kg/tce and 2,114 kg/GWh for NO x , and 4 kg/tce and 462 kg/GWh for PM10 (NBS, 2013a, 2013b). Additionally, we assume that impacts from interactions among energy efficiency measures are minimal, i.e., measures are analyzed as if they were implemented separately. For this reason and to avoid overestimation of total cumulative energy-saving potential, we have used the lower end of energy-saving range that was available for each energy-efficiency measure. We also estimate the average water coefficients (i.e., water volumes per energy production unit) based on published water factors in fuel production, and power generation processes, as shown in Table 2.
Table 2

Summary of water coefficients in fuel production and power generation processes

Type

Unit

Water coefficient

Data source

Coal production

m3/TJ

4.0

Hejazi et al. (2014)

Coke production

m3/TJ

0.02

Pan et al. (2012)

Nature gas production

m3/TJ

0.01

Hejazi et al. (2014)

Crude oil production

m3/TJ

44.0

Hejazi et al. (2014)

Unconventional Oil production

m3/TJ

6.0

Hejazi et al. (2014)

Uranium

m3/TJ

2.0

Hejazi et al. (2014)

Thermal power

m3/MWh

2.85

Pan et al. (2012)

Nuclear power

m3/MWh

2.6

McMahon and Price (2011); Li et al. (2012)

Wind Power

m3/MWh

0.004

Li et al. (2012); Davis et al. (2013)

PV power

m3/MWh

0.1

Davis et al. (2013)

The average unit price of electricity is assumed to be 120 $/MWh (SERC, 2011), while the average unit price of thermal coal for industrial use is approximately 111 $/tonne in 2012, which is used as the fuel price in this report (CCTD, 2013). To convert costs reported in RMB to US dollars, we use an average exchange rate of 6.31 RMB/US$ (CFETS, 2013).

Estimate energy savings, emission reductions, and water savings

The technical potential of fuel savings and electricity savings from energy-efficiency measure j can be calculated using Eq. (1) and Eq. (2), respectively
$$ S{F}_j={P}_i\cdot \left(100\%-{k}_j\right)\cdot T{A}_j\cdot R{F}_j,\ \mathrm{in}\ \mathrm{G}\mathrm{J} $$
(1)
$$ S{E}_j={P}_i\cdot \left(100\%-{k}_j\right)\cdot T{A}_j\cdot R{E}_j,\ \mathrm{in}\ \mathrm{k}\mathrm{W}\mathrm{h} $$
(2)

Where

SF j  = technical potential of fuel savings from measure j (GJ);

SE j  = technical potential of electricity savings from measure j (kWh);

P i = production in step i (Mt);

k j = current adoption rate of measure j (%);

TA j = technology availability of measure j, the extent to which the remaining adoption potential of the technology in Chinese iron and steel industry;

RF j = specific fuel savings for measure j (GJ/Mt-production i);

RE j = specific electricity savings for measure j (kWh/Mt-production i).

The air pollutants considered in this study are SO2, NO x , and PM10, without considering PM2.5 because of a lack of reliable emission and cost data for PM2.5. Reduction of pollutant emissions (CO2, SO2, NO x , and PM10) corresponding to measure j can be calculated using Eq. (3).
$$ R{C}_j=S{F}_j\cdot E{F}_1+S{E}_j\cdot E{F}_2,\ \mathrm{in}\ \mathrm{tonne} $$
(3)

Where

RC j = emissions reduction corresponding to measure j, tonne;

EF 1 = direct emission coefficient of fuels (tonne/GJ);

EF 2 = indirect emission coefficient of electricity (tonne/kWh).

Water withdrawal is an important indicator for the iron and steel industry and power industry (NDRC 2013). In this study, we only consider energy related water savings, based on the energy savings and water coefficients, as shown in Eq. (4).
$$ W{S}_j=S{F}_j\cdot W{F}_1+S{E}_j\cdot W{F}_2 $$
(4)

Where

WS j = total water savings due to measure j (m3);

WF 1 = average water withdrawal coefficient for fuel production (m3/GJ);

WF 2 = average water withdrawal coefficient for power generation (m3/kWh).

Quantify the benefits and evaluate the cost-effectiveness

To quantify the emission reduction benefits, we use the concept of an air pollutant (AP) price index, as shown in Eq. (5) (Mao et al., 2012; 2014).
$$ AP=A\cdot R{C}_{CO2}+B\cdot R{C}_{SO2}+C\cdot R{C}_{NOx}+D\cdot R{C}_{PM10},\ \mathrm{in}\ \$ $$
(5)

Where

RCC02, RCSO2, RCNOx, and RCPM10 represents the emission reduction of CO2, SO2, NO x , and PM10, respectively, in tonne.

A, B, C, and D is the price weighting factor of CO2, SO2, NO x , and PM10, respectively, in $/tonne.

A is the average carbon price, based on historical trading volumes and trading turnovers in China’s seven regional carbon markets, i.e., Beijing, Shanghai, Tianjin, Shenzhen, Guangdong, Hubei, and Chongqing (WHECA, 2014). As there are no trading markets for air pollutants in China, we use external environmental damage cost as the weighting factors of SO2, NO x , and PM10 (Zhang et al., 2007; Mao et al., 2012). The price weighting factors are listed in Table 3.
Table 3

Price weighting factors for CO2 and air pollutant emissions in the iron and steel industry

Price weighting factors

Zhang et al. (2007)

Yang et al. (2013)

Wei and Zhou (2003)

Liu et al. (2014)

Low

Average

High

CO2 ($/tonne) (WHECA, 2014)

-

-

-

-

3.5

6.0

11.0

SO2 ($/tonne)

1006.1

3680.0

983.0

1056.6

983.0

1682.0

3680.0

NO x ($/tonne)

902.0

2438.0

1311.5

750.0

750.0

1350.0

2438.0

PM10 ($/tonne)

7720.0

2623.9

360.0

1169.5

360.0

2968.0

7720.0

In this study, we define the carbon abatement cost for a specific measure as the change of total costs divided by the CO2 reduction potentials, as shown in Eq. (6).
$$ {c}_j=\frac{P_i\cdot \left(1-{k}_j\right)\cdot T{A}_j\cdot \left[\frac{I_j\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta O{M}_j\right]-{B}_j}{R{C}_{CO2,j}} $$
(6)

Where

c j = carbon abatement cost for an energy-efficiency measure j ($/tonne);

P i = production in step i (Mt);

k j = current adoption rate of measure j (%);

TA j = technology availability of measure j, the extent to which the remaining adoption potential of the technology in Chinese iron and steel industry;

I j = change in total capital investment for an energy-efficiency measure j ($/tonne);

ΔOM j = change in non-energy annual operation and maintenance cost for measure j ($/tonne);

r = discount rate (15 %), it should be noted that the choice of the discount rate depends on the purpose and approach of the analysis (prescriptive versus descriptive) used (Hasanbeigi et al., 2013).

n = lifetime (years);

B j = benefits of measure j ($), include energy benefits (i.e., reduced energy costs) and environmental benefits (i.e., emission reduction benefits and reduced water costs);

RC CO2, j  = annual reduction of CO2 emissions for measure j.

The cost-effectiveness for specific measure is determined by the carbon abatement cost: A negative cost c j means the measure j is cost-effective.

If we consider the change in measure cost and energy-savings benefits only, Eq. (6) becomes the following
$$ {c}_1=\frac{P\cdot \left(1-k\right)\cdot TA\cdot \left[\frac{I\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta OM\right]-{p}_1\cdot SF-{p}_2\cdot SE}{R{C}_{CO2}} $$
(7)
If we consider the change in measure cost, energy-savings benefits, and carbon-reduction benefits, Eq. (6) becomes the following:
$$ {c}_2=\frac{P\cdot \left(1-k\right)\cdot TA\cdot \left[\frac{I\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta OM\right]-{p}_1\cdot SF-{p}_2\cdot SE-AP}{R{C}_{CO2}} $$
(8)
If we consider the change in measure cost, energy-savings benefits, and environmental benefits (CO2 and air pollutant emission reduction benefits and water savings), Eq. (6) becomes the following:
$$ {c}_3=\frac{P\cdot \left(1-k\right)\cdot TA\cdot \left[\frac{I\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta OM\right]-{p}_1\cdot SF-{p}_2\cdot SE-AP-{p}_3\cdot WS}{R{C}_{CO2}} $$
(9)

Where

P 1 = average fuel price ($/GJ);

P 2 = average electricity price ($/kWh);

AP denotes the total emission reduction benefits; see Eq. (5);

P 3 = average water price ($/m3);

WS = total water savings (m3).

Typical energy-savings and emission-reduction measures

Considering data uncertainties and information availability for some measures (e.g., emerging measures), we selected 36 energy-efficiency measures for the analyses and presentations in this study. Table 4 presents the compiled results from these measures, including energy savings, capital and change in O&M costs, adoption rates in 2012, and product amount for each process in China.
Table 4

Compiled data of 36 energy-conservation and emission-reduction measures

 

Technology

Product (Mt)

Fuel savings (kgce/t)

Electricity savings (kWh/t)

Capital cost ($/t)

O&M cost ($/t)

Lifetime (Year)

Current adoption rate k (%)

Data source

 

Coke making

1

Coal moisture control (CMC)

145.1

15.0

0

13.53

5.01

20

50 %

(Zhu and Chen 2004; MIIT, 2012b; ERI, 2013)

2

Coke dry quenching (CDQ)

145.1

0

75.0

36.69

11.00

18

50 %

(Xue, 2009; ERI, 2013; NDRC, 2013)

 

Sinter

3

Generation of sinter waste heat

808.9

0

65.0

3.96

0.12

10

21 %

(Lu, 2008; MIIT, 2012b)

4

Cooler fluid sealing ring

808.9

0

3.0

2.01

n/a

20

3 %

(Chen et al. 2012; MIIT, 2012b; ERI, 2013)

5

Improved process control in sintering

808.9

0.34

0

0.95

n/a

20

90 %

(MIIT, 2012b; Hasanbeigi et al. 2013)

6

Small pellet sintering process

808.9

9.0

0

0.21

0.19

20

50 %

(NDRC, 2011a; Hong et al. 2012)

7

Reduction of leakage rate in sintering process

808.9

0

2.0

0.12

0.40

20

80 %

(Lai et al. 1995; MIIT, 2012b)

8

Low temperature sintering technology

808.9

9.0

0

0.19

0.40

20

75 %

(Song, 2001; MIIT, 2012b; Morrow et al. 2014)

9

Low carbon and thick sinter-bed sintering

808.9

2.0

0

0.38

0.08

20

80 %

(Liu et al. 2006; MIIT, 2012b; Morrow et al. 2014)

 

Pellet

10

Grate-Kiln

232.7

9.9

0

39.62

n/a

15

48 %

(Feng et al. 2007; MIIT, 2012b; ERI, 2013)

11

Recovery of waste heat in pelletizing process

232.7

3.0

0

1.43

0.18

10

60 %

(Hasanbeigi et al. 2013; MIIT, 2012b; Wang 2014a, 2014b)

 

Iron making

12

Top-pressure recovery turbines (TRT)

657.9

0

50.0

2.38

0.63

15

62 %

(Zhang et al. 2011; NDRC, 2013; Morrow et al. 2014)

13

Recovery of BFG gas

657.9

1.37

0

0.44

n/a

10

94 %

(ERI, 2013; Hasanbeigi et al. 2013; Zhang, 2013)

14

Dehumidification blast

657.9

8.0

0

2.69

0.71

10

10 %

(NDRC, 2009,2011b; MIIT, 2012b)

15

Cyclone type top combustion hot stoves

657.9

7.96

0

34.80

n/a

20

50 %

(Gong and Chen 2012; ERI, 2013)

16

Injection of pulverized coal

657.9

4.3

0

9.33

−0.01

20

60 %

(ERI, 2013; Hasanbeigi et al. 2013; Morrow et al. 2014)

17

CCPP

657.9

16.0

0

15.06

n/a

15

20 %

(Zhang et al. 2006; MIIT, 2012b; ERI, 2013)

18

Process control of blast furnace

657.9

12.3

0

9.98

n/a

20

30 %

(ERI, 2013; Hasanbeigi et al. 2013; Zhou, 2013)

19

Waste plastic injected into blast furnace

657.9

3.76

0

9.51

n/a

20

3 %

(Xu, 2003; ERI, 2013; Morrow et al. 2014)

 

Steel making-BOF

20

Recovery of BOF gas

643.5

0

9.0

3.96

0.92

20

48 %

(Zhou, 2008; ERI, 2013)

21

Converter steelmaking with negative energy consumption

643.5

25.0

0

23.77

0.55

20

48 %

(MIIT, 2012b; ERI, 2013; Morrow et al. 2014)

 

Steel making-EAF

22

Scrap preheating

72.5

0

61.0

7.13

−3.49

20

10 %

(Zhou, 2008; ERI, 2013)

23

Generation of EAF waste heat

72.5

12.0

0

5.94

n/a

20

3 %

(Cheng and Shi 2009; ERI, 2013)

24

Improved process control

72.5

0

13.9

60.22

n/a

20

50 %

(Zheng, 2003; ERI, 2013)

25

UHP transformer

72.5

0

58.3

29.16

n/a

20

40 %

(Zheng, 2003; ERI, 2013)

 

Casting

26

Integrated casting and rolling

704.7

8.53

42.0

2.38

0.24

20

50 %

(ERI, 2013; Hasanbeigi et al. 2013; NDRC, 2013)

27

Thin slab casting (TSC)

704.7

0

25.0

6.34

7.13

20

15 %

(Song et al. 2009; ERI, 2013)

 

Hot Rolling

28

Regenerative burners

716.7

0

85.0

1.58

0.36

10

22 %

(Pan, 2002; ERI, 2013; Hasanbeigi et al. 2013)

29

Process control in hot rolling

716.7

10.24

0

1.39

n/a

10

0 %

(ERI, 2013; Hasanbeigi et al. 2013; Morrow et al. 2014)

30

Enhanced radiation technology

716.7

6.5

0

0.48

n/a

10

10 %

 

31

Recovery of hot-rolling waste heat

716.7

1.02

0

3.71

0.32

20

80 %

(Pan; 2002; ERI, 2013; Hasanbeigi et al. 2013)

 

Cold Rolling

32

Recovery of cold-rolling waste heat

123.9

10.24

3.0

4.29

0.19

15

45 %

(ERI, 2013; Ma and Sun 2013)

33

Multi rolling technique on the bar rolling

123.9

5.12

0

1.98

n/a

20

10 %

(Yang, 2011; MIIT, 2012b; ERI, 2013)

34

Continuous annealing technology

123.9

12.97

0

17.61

n/a

20

5 %

(MIIT, 2012b; Hasanbeigi et al. 2013; Morrow et al. 2014)

 

General measures

35

Energy management and system optimization

657.9

10.9

2.78

4.75

n/a

20

90 %

(MIIT, 2012a, 2012b; Hasanbeigi, 2013; Morrow, 2014)

36

Preventative maintenance

657.9

14.7

5.56

3.96

1.30

20

90 %

(MIIT, 2012a, 2012b; Hasanbeigi et al. 2013; Morrow et al. 2014)

Results and discussion

Technical energy savings and environmental impacts

Table 5 summarizes the technical potential of energy savings, emission reduction, and water savings for each energy-efficiency measure in China’s iron and steel production.
Table 5

Technical potentials of energy savings, emission reduction, and water savings in China’s iron and steel industry (2012)

No.

Energy savings

Emission reduction (1,000 tonne)

Water savings (million m3)

Fuel savings (1,000 tce)

Electricity savings (GWh)

CO2

SO2

NO x

PM10

1

761.5

-

1,868

4.0

1.6

3.0

1.3

2

-

3,249

2,512

5.4

6.9

1.5

73.5

3

-

21,716

16,787

35.9

45.9

10.0

491.4

4

-

665

514

1.1

1.4

0.3

15.1

5

115.5

-

283

0.6

0.2

0.5

0.2

6

2,548.2

-

6,251

13.4

5.4

10.0

4.3

7

-

227

175

0.4

0.5

0.1

5.1

8

1,274.1

-

3,125

6.7

2.7

5.0

2.2

9

226.5

-

556

1.2

0.5

0.9

0.4

10

838.5

-

2,057

4.4

1.8

3.3

1.4

11

182.5

-

448

1.0

0.4

0.7

0.3

12

-

8,750

6,764

14.5

18.5

4.0

198.0

13

37.7

-

93

0.2

0.1

0.1

0.1

14

1,473.7

-

3,615

7.7

3.1

5.8

2.5

15

1,832.9

-

4,496

9.6

3.9

7.2

3.1

16

792.1

-

1,943

4.2

1.7

3.1

1.3

17

2,210.5

-

5,422

11.6

4.7

8.7

3.8

18

3,965.2

-

9,727

20.8

8.4

15.7

6.7

19

1,679.6

-

4,120

8.8

3.6

6.6

2.9

20

-

486

376

0.8

1.0

0.2

11.0

21

5,855.9

-

14,364

30.7

12.4

23.1

9.9

22

-

2,786

2,153

4.6

5.9

1.3

63.0

23

529.8

-

1,230

2.8

1.1

2.1

0.9

24

-

775

599

1.3

1.6

0.4

17.5

25

-

1,766

1,365

2.9

3.7

0.8

40.0

26

1,262.6

6,215

7,901

16.9

15.8

7.9

142.8

27

-

6,783

5,243

11.2

14.3

3.1

153.5

28

-

24,734

19,119

40.9

52.3

11.4

559.7

29

5,137.0

-

12,601

26.9

10.9

20.3

8.7

30

2,935.0

-

7,199

15.4

6.2

11.6

4.9

31

102.8

-

252

0.5

0.2

0.4

0.2

32

310.8

91

832

1.8

0.9

1.3

2.6

33

222.0

-

545

1.2

0.5

0.9

0.4

34

1,068.7

-

2,621

5.6

2.3

4.2

1.8

35

502.0

138

1,338

2.9

1.4

2.0

4.0

36

677.0

276

1,874

4.0

2.0

2.8

7.4

Total of 36 measures

36,542.1

78,659

150,440

321.8

243.7

180.0

1,842.2

Total in steel sector in 2012

468,020

522,052

1,510,000

2,410

970

1,810

3,580

Savings (or emission reduction) percentage of the total (%)

7.8 %

15.1 %

10.0 %

13.4 %

25.0 %

10.0 %

51.5 %

For individual measure, the regenerative burner measure (measure #28) exhibits the largest technical potential in energy savings (24,734 GWh electricity), emission reductions (19.1 Mt CO2, 40.9 kiloton (kt) SO2, 52.3 kt NO x, and 11.4 kt PM10), and water savings (559.7 million m3); the recovery of BFG gas (measure #13) has the lowest technical potential in energy savings (37.7 ktce fuel), emission reductions (0.1 Mt CO2, 0.2 kt SO2, 0.1 kt NO x , and 0.1 kt PM10), and water savings (0.1 million m3).

27 energy-saving measures are process technologies and the other nine measures are technologies for waste energy recovery. The 27 measures account for 82.8 % of fuel savings and 64.4 % of electricity savings, while the other nine measures are responsible for 17.2 % of fuel savings and 35.6 % of electricity savings. The largest potentials for energy savings and emission reduction come from the iron-making processes (27.0 %) and hot-rolling processes (25.0 %); the pellet process has the lowest potential for emission reduction (about 1.0 %).

For the 36 energy-efficiency measures, technical potential of total energy savings are about 36.5 Mtce fuels and 78,659 GWh electricity, corresponding to 7.8 % fuel savings and 15.1 % electricity savings, respectively. Annual emission reduction is 150.4 Mt CO2, 321.8 kt SO2, 243.7 kt NO x, and 180.0 kt PM10, corresponding to 10.0 %, 13.4 %, 25.0 %, and 10.0 % of the total emissions (by type), respectively; Water savings are 1,842.2 million m3 (51.5 % of annual consumption in the sector).

CO2 abatement cost analyses

Based on the main technical cost data (change in capital and O&M costs), and associated benefits (energy savings, emission reduction, and water savings), CO2 abatement costs are calculated for each energy-efficiency measure listed in Table 6.
Table 6

Costs, benefits, and carbon abatement costs for individual energy-efficiency measures

No.

Technical cost data (million $)

Benefits (million $)

CO2 mitigation (kt)

CO2 abatement cost ($/tonne CO2)

Change in capital cost

Change in non-energy O&M cost

Energy savings benefits

CO2 mitigation benefits

Air pollutant reduction benefits

Water savings benefits

c1

c2

c3

1

147

349

119

11.0

19.4

1.1

1,868

202

185

185

2

239

240

399

14.8

21.3

53.0

2,512

32

17

−7

3

273

41

2,670

99.1

142.5

354.5

16,787

−140

−155

−179

4

101

-

82

3.0

4.4

10.9

514

37

22

−2

5

53

-

18

1.7

2.9

0.1

283

125

109

108

6

10

56

397

36.9

64.9

3.1

6,251

−53

−69

−70

7

2

46

28

1.0

1.5

3.7

175

119

105

81

8

4

58

198

18.4

32.4

1.6

3,125

−44

−60

−60

9

7

9

35

3.3

5.8

0.3

556

−34

−50

−51

10

594

-

131

12.1

21.3

1.0

2,057

225

209

208

11

19

12

28

2.6

4.6

0.2

448

6

−10

−11

12

74

115

1,076

39.9

57.4

142.8

6,764

−131

−146

−170

13

3

-

6

0.6

1.0

0.0

93

−36

−53

−53

14

102

136

230

21.3

37.5

1.8

3,615

2

−14

−14

15

1,324

-

285

26.5

46.7

2.2

4,496

231

215

214

16

284

−2

123

11.5

20.2

1.0

1,943

82

65

65

17

368

-

344

32.0

56.3

2.7

5,422

4

−12

−12

18

532

-

618

57.4

100.9

4.9

9,727

−9

−25

−26

19

702

-

262

24.3

42.8

2.1

4,120

107

91

90

20

35

52

60

2.2

3.2

7.9

376

73

58

34

21

951

134

912

84.8

149.1

7.2

14,364

12

−4

−5

22

54

−168

343

12.7

18.3

45.5

2,153

−212

−227

−251

23

43

-

83

7.7

13.5

0.6

1,230

−30

−46

−47

24

176

−139

95

3.5

5.1

12.7

599

−97

−112

−136

25

147

-

217

8.1

11.6

28.8

1,365

−52

−66

−90

26

58

36

961

46.6

72.9

103.0

7,901

−110

−125

−140

27

213

1,334

834

30.9

44.5

110.7

5,243

136

122

98

28

95

110

3,041

112.8

162.3

403.7

19,119

−148

−163

−187

29

144

-

800

74.4

130.8

6.3

12,601

−52

−68

−69

30

45

-

457

42.5

74.7

3.6

7,199

−57

−74

−74

31

61

33

16

1.5

2.6

0.1

252

312

295

294

32

23

6

60

4.9

8.5

1.9

832

−37

−53

−55

33

14

-

35

3.2

5.7

0.3

545

−37

−54

−55

34

240

-

166

15.5

27.2

1.3

2,621

28

12

11

35

36

-

95

7.9

13.7

2.9

1,338

−44

−60

−63

36

30

62

139

11.1

19.0

5.3

1,874

−25

−41

−45

Table 6 indicates that both energy-savings benefits and environmental benefits have important impacts on the CO2 abatement cost. When only energy-savings benefits are considered, the abatement costs range from negative $212/tonne CO2 (measure #22) to $312/tonne CO2 (measure #31); when the emission reduction benefits are considered, the abatement cost is reduced further, ranging from negative $227/tonne CO2 (measure #22) to 295 $/tonne CO2 (measure #31); when energy and all environmental benefits (including emission reduction and water-savings) are included, the abatement costs are reduced more, ranging from negative $251/tonne CO2 (measure #22) to 294/tonne CO2 (measure #31) $/tonne CO2.

The three individual technologies having the lowest reduction cost are scrap preheating (measure #22), regenerative burners (measure #28), and generation of sinter waste heat (measure #3), all with negative abatement cost. The three technologies with the highest abatement costs are heat recovery from hot-rolling (measure #31), cyclone type top combustion hot stoves (measure #15), and Grate-Kiln (measure #10), all with positive costs.

Cost-effectiveness analyses

Based on the individual measure costs, energy benefits, and environmental benefits, we can evaluate and calculate the cost-effective potentials of energy savings, emission reduction, and water savings, as shown in Table 7.
Table 7

Total costs, benefits, energy savings, emission reduction, and water savings under three different assumptions

Items

All measures

Cost effective measures

Include energy benefits only

Include energy benefits and emission reduction benefits

Include energy benefits and all environmental benefits (emission reduction and water-savings)

Number of measures

36

19

23

26

Total extra capital costs

7,204

1,767

3,207

3,787

Total extra O&M costs

2,520

186

468

707

Total costs (million $)

9,724

1,953

3,675

4,494

Energy savings benefits (million $)

15,362

11,325

12,839

13,487

CO2 mitigation benefits (million $)

888

591

732

765

Air pollutant reduction benefits (million $)

1,446

941

1,188

1,241

Water savings benefits (million $)

1,510

1,275

1,288

1,362

Total benefits (million $)

19,206

14,132

16,047

16,855

Fuel savings (Mtce)

36.5

19.6

29.3

29.3

Electricity savings (GWh)

78,659

67,249

67,249

71,164

CO2 reductions (Mt)

150

100

124

127

SO2 reductions (kt)

322

214

265

272

NOx reductions (kt)

244

184

204

213

PM10 reductions (kt)

180

109

147

149

Water savings (million m3)

1,842

1,555

1,572

1,660

When we consider the energy benefits only, 19 measures are cost effective, the total costs and benefits are 1,953 and 14,132 million $, and the cost-effectiveness energy savings are 19.6 Mtce for fuel and 67,249 GWh for electricity, while the emission reduction are about 100 Mt CO2, 214 kt SO2, 184 kt NO x and 109 kt PM10, respectively. Additionally, the total water savings are about 1,555 million m3.

When we consider the energy benefits and emission reduction benefits, 23 measures were identified cost effective, the total costs and benefits are 3,675 and 16,047 million $, and the cost-effectiveness energy savings are 29.3 Mtce for fuel and 67,249 GWh for electricity, while the emission reduction potential are about 124 Mt CO2, 265 kt SO2, 204 kt NO x and 147 kt PM10, respectively. Additionally, the total water savings are about 1,572 million m3.

When we consider the energy benefits and emission reduction and water-savings benefits, 26 measures were identified cost effective, the total costs and benefits are 4,494 and 16,855 million $, and the cost-effectiveness energy savings are 29.3 Mtce for fuel and 71,164 GWh for electricity, while the emission reduction potential are about 127 Mt CO2, 272 kt SO2, 213 kt NO x and 149 kt PM10, respectively. Additionally, the total water savings are about 1,660 million m3.

More than two-thirds of the 36 alternative measures are cost effective when energy and environmental benefits are accounted for, while many of them are not yet to be adopted widely. The main barriers to wider implementation include the following: (1) potential hidden costs associated with collecting and analyzing information, production disruptions, and inconvenience; (2) limited access to capital needed for investing in energy-efficiency measures; (3) risk aversion due to uncertain payback of energy-efficiency measure investments; (4) imperfect information about market conditions, technology characteristics and impacts of business’ own behavior; and (5) inertia, e.g., opponents to change within an organization may result in neglecting of energy-efficiency measures (Rohdin et al. 2007).

Sensitivity analysis

The emission reduction costs and the potentials of cost effective measures are influenced by the discount rates and the price factors. We perform sensitivity analyses with three levels of discount rates (10 %, 15 %, and 20 %) and three types of environmental price factors (average, low, and high), as defined in Table 8.
Table 8

The scenarios definitions of discount rates and environmental price factors

 

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Price factor

average

average

average

low

high

Discount rate (%)

15 %

10 %

20 %

15 %

15 %

CO2 ($/tonne)

6.0

6.0

6.0

3.5

11.0

SO2 ($/tonne)

1,682

1,682

1,682

983

3,680

NOx ($/tonne)

1,350

1,350

1,350

750

2,438

PM10 ($/tonne)

2,968

2,968

2,968

360

7,720

Fuel price ($/tce)

160

160

160

148

180

Electricity price ($/MWh)

125

125

125

106

140

Water price ($/m3)

0.7

0.7

0.7

0.5

1.0

Table 9 shows that an increase in discount rate decreases cost-effectiveness of energy-efficiency measures and results in lower levels of cost effective emission reduction and water savings. A higher environmental price factor corresponds to higher energy benefits and environmental benefits, leading to larger potentials of cost effective emission reduction and water savings.
Table 9

Sensitivity analysis of the cost effective emission reduction and water savings

  

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Energy benefits

CO2 reductions (Mt)

100

124

90

100

110

SO2 reductions (kt)

214

266

193

214

235

NO x reductions (kt)

184

206

175

184

192

PM10 reductions (kt)

109

147

93

109

124

Water savings (million m3)

1555

1587

1548

1555

1562

Energy benefits + emission reduction benefits

CO2 reductions (Mt)

124

129

104

100

129

SO2 reductions (kt)

265

277

223

214

277

NO x reductions (kt)

204

215

187

184

215

PM10 reductions (kt)

147

153

115

109

153

Water savings (million m3)

1,572

1662

1558

1555

1662

Energy benefits + environmental benefits

CO2 reductions (Mt)

127

129

104

100

130

SO2 reductions (kt)

272

277

223

214

278

NO x reductions (kt)

213

215

187

184

216

PM10 reductions (kt)

149

153

115

109

153

Water savings (million m3)

1,660

1662

1558

1555

1673

When the discount rate is 10 % (Scenario 2), the environmental benefits have limited effect on the cost effectiveness of energy-efficiency measures; whereas the discount rate is higher (e.g., 15 % in Scenario 2 or 20 % in Scenario 3), the environmental benefits have more significant effect on the cost effectiveness of energy-efficiency measures. When the discount rate is 15 % and the environmental price level is relatively high, the cost-effectiveness of energy-efficiency measures is more sensitive to the price factors, whether or not to include environmental benefits. When the price factor is low, such as in Scenario 4, the effects of including environmental benefits or not on CO2 abatement cost are more evident; however, the effects on the scales of cost-effective energy savings and emission reduction are minimal.

With only energy savings benefits, cost effective emission reduction exhibits the following ranges: 90 to124 Mt CO2, 193 to 266 kt SO2, 175 to 206 kt NO x , and 93 to 147 kt PM10, while water savings range from 1,548 to 1,587 million m3.

With inclusion of energy savings and emission reduction benefits, cost effective emission reduction exhibits the following ranges: 100 to 129 Mt CO2, 214 to 277 kt SO2, 184 to 215 kt NO x , and 109 to 153 kt PM10, while cost effective water savings range from 1,555 to 1,662 million m3.

With inclusion of all environmental benefits (i.e., CO2 emission reduction, air pollutants emission reduction benefits, and water savings benefits), cost effective emission reduction exhibits the following ranges: 100 to 130 Mt CO2, 214 to 278 kt SO2, 184 to 216 kt NO x , and 109 to 153 kt PM10, while cost effective water savings range from 1,555 to 1,673 million m3.

Conclusions and recommendations

In this paper, we quantify the energy and environmental benefits, and evaluate the cost-effectiveness of 36 energy-efficiency measures under different scenarios for Chinese iron and steel industry. The results show that while energy-savings benefits are the main driver in reducing the carbon abatement costs, environmental benefits also affect the cost-effectiveness of the efficiency measures significantly. Among the environmental benefits, including emission reduction benefits in calculations reduces the carbon abatement cost substantially. While the effects from including water-savings benefits are moderate under the assumptions in this study, such effects may become more influential as water price goes up. It is both important and necessary to quantify and monetize environmental benefits when evaluating the costs of energy savings and carbon abatement associated with energy-efficiency measures. Future studies may benefit from including additional non-energy benefits.

To improve energy efficiency and narrow the “efficiency gap” in iron and steel production addressed in this paper, we recommend enhancing adoption of process energy-efficiency measures and waste energy recovery technologies, especially the cost effective measures (e.g., scrap preheating, regenerative burners, and generation of sinter waste heat). We have found that more than two-thirds of the 36 efficiency measures are cost effective when energy and environmental benefits are accounted for, while many of them are yet to be adopted more widely. The main barriers to wider implementation of cost effective measures are discussed, including potential hidden, limited access to capital, risk aversion, imperfect information, etc.

Advancing the understanding of cost effectiveness provides opportunities to diffuse cost barriers against adopting efficiency measures, and may help to promote effective programs and policies to overcome the barriers, such as development of energy-efficiency information resources, technical assistance in identifying energy-efficiency measures, and financing programs for efficiency measures. The new information resulted from this study should be used to augment future programs and efforts in reducing energy use and environmental impacts associated with steel production.

Declarations

Authors’ Affiliations

(1)
Research Center for Contemporary Management (RCCM), Tsinghua University
(2)
Institute of Energy Environment and Economy, Tsinghua University
(3)
Lawrence Berkeley National Laboratory
(4)
Chinese University of Hong Kong

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