Japanese Steel Industry - Engineering approach, rather than economic - - PowerPoint PPT Presentation

Carbon Intensity and its Determinants in Japanese Steel Industry

The 5th IAEE Asian Conference, Perth, Australia Feb 14-17, 2016 Junichiro Oda*, Keigo Akimoto, Takashi Homma

*Contact us: jun-oda@rite.or.jp

Systems analysis group Research Institute of Innovative Technology for the Earth (RITE) Kyoto, Japan

• Engineering approach, rather than economic approach -

Contents

2

• 1. Introduction
• 2. Analysis results: quick view
• 3. Methodology, and results
• 4. Summary

1.1 Overview of steelmaking process in Japan

3 Iron ore, etc Coal Coke oven Blast furnace (BF) Sintering furnace Hot meal (Pig iron) Steel scrap Oxygen Basic oxygen furnace (BOF) Electric arc furnace (EAF) Continuous casting Crude steel Heating furnace Rolling mill Steel products Liquid steel Ref) RITE  Carbon intensity, e.g., 196 MtCO2 / 113 Mt crude steel = 1.743 (tCO2/t crude steel)  Hot metal ratio, e.g., 83 Mt pig iron / 113 Mt crude steel = 0.74 Upstream Downstream

Appendix: Energy and climate policies of Japanese industry

4

 Regulations  The Energy conservation Act  Voluntary Actions (VAs)  [From 1997 to 2012 ]

Voluntary Action Plan on the Environment

 [From 2012]

Commitment to a Low Carbon Society

 RD&D financial and technical supports  Taxes  Energy taxes  Duties on imported energy  Carbon taxes  Tax credits  Investment in RD&D  Investment in energy saving

facilities

 Green energy, e.g., PV, wind

1.2 Carbon intensity of Japanese steel industry (1/2)

5

 The Japanese steel industry has been reporting their carbon intensity under  [From 1997 to 2012 ] “Voluntary Action (VA) Plan on the Environment”, and  [From 2012] “Commitment to a Low Carbon Society 0.0 0.5 1.0 1.5 2.0 FY00 FY02 FY04 FY06 FY08 FY10 FY12 FY14

Carbon intensity (tCO2/t crude steel)

Ref) RITE estimates based

• n the Japan Iron and

Steel Federation report

1.743

Reported carbon intensity in Japan

 These VAs are “negotiated agreements” rather than

“VAs”

 The coverage of VAs is around 98%  FY2016 time schedule is fixed:

Next slide

Higher intensity (Lower efficiency) Lower intensity (Higher efficiency) FY2015 2015 Apr 2016 Mar Jun Dec Data gathering Third party commissions about FY2015 carbon intensity

1.2 Carbon intensity of Japanese steel industry (2/2)

6

1.65 1.70 1.75 1.80 1.85 FY00 FY02 FY04 FY06 FY08 FY10 FY12 FY14

Carbon intensity (tCO2/t crude steel)

 Including EAFs  Fixed emission

factor for grid electricity, i.e., 0.42 kgCO2/kWh

1.743

The 2008 financial crisis The 2011 East Japan Earthquake  Carbon intensity (tCO2/t crude steel) was improving until the 2008 financial crisis  After that, apparently, there was no clear time trend of carbon intensity

Ref) RITE estimates based

• n the Japan Iron and

Steel Federation report

Higher intensity (Lower efficiency) Lower intensity (Higher efficiency)

Reported carbon intensity in Japan

1.3 Objectives

7

Question 1 What is the determinant of carbon intensity (tCO2/t crude steel)?

 Short-term (yearly) fluctuation of carbon intensity  Long-term (FY2000-FY2014) trend of carbon intensity

Question 2 Can we observe a long-term trend of carbon intensity improvement? Did VAs (negotiated agreements) have an effect on carbon intensity improvement?

 To answer these two questions:

2.1 Analysis results: FY2007 vs FY2008

8

FY2007 FY2008 1.691 1.763 +0.072 +4.3% point Reported carbon intensity

 The effect of the 2008 financial crisis (effect of CF decrease) was significant

⊿x2 (downstream): -0.004 ⊿x2 (upstream): +0.030 [Hot metal ratio 0.723  0.744] ⊿x1 (capacity factor): +0.063 [CF 104.8  91.2 (FY2005=100)] ⊿x3 (time trend variable): -0.005 ⊿Residual error: -0.012

2.2 Analysis results: FY2000 vs FY2014

9

FY2000 1.761 1.846

• 0.085
• 4.6% point

Reported carbon intensity

 The time trend variable implies diffusion of energy saving from FY2000 to FY2014

⊿x2 (downstream): -0.019, ⊿x2 (upstream): +0.013 [Hot metal ratio 0.755  0.764] ⊿x1 (capacity factor): +0.010 [CF 93.9  91.7 (FY2005=100)] [Effect of steel product mix] ⊿x3 (time trend variable): -0.071 ⊿Residual error: -0.018 FY2014

3.1 Methodology

10

• x1. Capacity factor index
• x2. Production process index

 Upstream: Hot metal ratio  Downstream: Steel product mix

• x3. Time trend variable

 Improvement factor: Diffusion of energy saving tech. (?)  Worsening factor: Aging facilities (?)  Parametric regression

Effects on carbon intensity = β1×(x1,t – x1,2005) Unknown parameters solved by ordinary least squares (OLS) regression Effects on carbon intensity are exogenously given Effects on carbon intensity = β2×(t –2005)

3.2 Capacity factor

11

94 91 96 99 101 100 103 105 91 82 93 89 89 91 92

70 80 90 100 110 00 02 04 06 08 10 12 14 Capacity factor index (FY2005 = 100) FY

Blast furnaces (BFs) Electric arc furnaces (EAFs) Industrial Production Index Weighted average

Capacity factor index (x1) Production process index (x2): Upstream, Downstream

Note) We referred to monthly capacity factors (reported by METI) and convert them into annual data

Capacity factor index

Weight 3%-5% 62%-65% 32%-33%

3.3 Upstream production process

12

y = 1.42x + 0.70 1.72 1.74 1.76 1.78 1.80 0.72 0.74 0.76 0.78 Upstream index (FY2005=1.743) Hot metal ratio (t pig iron/t crude steel) FY05 FY01 FY12 FY14 FY06 FY07 FY02 FY08 FY00 FY11 FY09 FY13 FY10 FY08 FY03

FY05

Relationship between hot metal ratio and upstream index

0.755 0.774 0.743 0.746 0.734 0.736 0.721 0.723 0.744 0.752 0.748 0.754 0.764 0.751 0.764 0.72 0.74 0.76 0.78 2000 02 04 06 08 10 12 14 Hot metal ratio (t pig iron/t crude steel) FY

Iron ore dominant Scrap dominant Note) We referred to METI statistics Iron ore dominant

0.70 2.12 0.0 2.5 1 Upstream index Hot metal ratio

Capacity factor index (x1) Production process index (x2): Upstream, Downstream Hot metal ratio

177 237 367 323 273 26 44 82 69 59 100 200 300 400 00 02 04 06 08 10 12 14

Export price (nominal US$/t steel)

FY

Steel bar Stainless steel

except seamless pipe

3.4 Downstream production process (1/2)

13

Examples of steel products Steel bar Wide flange & H beams

Ref) http://sumitomothailand.co.th/

Seamless pipe Stainless steel sheet Export price from Japan

Note) RITE estimates based on Trade Statistics of Japan

“Steel product share” דintensity” Capacity factor index (x1) Production process index (x2): Upstream, Downstream

29.6% 29.2% 26.7% 25.4% 24.2% 24.5% 25.0% 23.9% 23.3% 21.6% 19.2% 20.5% 21.0% 21.9% 21.2% 8.7% 9.6% 9.0% 10.2% 11.0% 12.0% 11.4% 12.2% 14.5% 13.1% 12.9% 13.0% 11.8% 11.0% 11.2% 13.2% 13.8% 14.7% 13.3% 13.5% 11.8% 12.6% 13.6% 12.7% 16.2% 15.7% 15.5% 17.7% 17.9% 18.8% 9.4% 8.9% 9.2% 9.6% 9.5% 9.0% 8.8% 8.5% 8.5% 10.0% 10.1% 9.3% 8.7% 8.6% 8.3% 16.8% 16.1% 16.8% 17.3% 17.0% 17.1% 17.0% 17.0% 16.4% 16.7% 16.7% 16.1% 16.1% 15.6% 15.1% 6.2% 6.1% 5.5% 5.5% 5.7% 5.7% 5.6% 5.5% 5.6% 4.5% 4.6% 4.7% 4.9% 4.9% 4.8% 2.6% 2.9% 2.9% 3.0% 2.7% 2.5% 2.7% 2.6% 2.4% 2.7% 2.7% 2.6% 2.5% 2.5% 2.6% 5.8% 5.6% 6.4% 6.9% 7.2% 7.9% 7.6% 7.9% 7.4% 6.7% 8.2% 8.5% 7.9% 8.2% 8.3% 3.0% 3.5% 4.2% 4.2% 4.3% 4.4% 4.6% 4.4% 4.6% 4.4% 5.2% 5.1% 5.4% 5.2% 5.5% 3.2% 3.0% 3.3% 3.4% 3.5% 3.5% 3.2% 3.0% 2.9% 2.8% 3.2% 3.2% 2.7% 2.8% 2.8% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FY00 FY02 FY04 FY06 FY08 FY10 FY12 FY14 Share of final steel products (%) 26-29,31. Other special steel

• 30. High-tensile steel

24,25. Structural steel 21-23. Tool steel

• 20. Stainless

18,19. Special pipe 16,17. Ordinary pipe 12-14',15. Coated sheet, coil 10,11. Cold rolled sheet , coil 9,9'. Hot rolled coil 7,7',8. Hot rolled plate, sheet 1-6. Long products

Ordinary steel (20) Special steel (14)

3.4 Downstream production process (2/2)

14

 We aggregated steel products (about 45 steel products by METI stat. and about 620 steel products by Trade Stat.) into 34 categories

Capacity factor index (x1) Production process index (x2): Upstream, Downstream

94.7 97.5 100.4 103.3 106.1 1.65 1.70 1.75 1.80 1.85

FY00 FY02 FY04 FY06 FY08 FY10 FY12 FY14 Relative carbon intensity (FY05=100) Carbon intensity (tCO2/t crude steel) Reported carbon intensity Estimates: 1.743+⊿x1+⊿x2+⊿x3 Capacity factor index: x1 Production process index: x2 Time trend variable: x3

⊿x2 ⊿x1 ⊿x3

Coefficient of capacity factor index (x1):

• 0.26% per 1% point [t-stat: -13.6]

Time trend variable (x3): -0.005 tCO2/t/y [t-stat: -8.7] This is equivalent to a 0.3%/y of improvement rate. 100

3.5 Results from FY2000 to FY2014

15

 The estimates well explain the reported carbon intensity trajectory  Determinant of annual fluctuation: capacity factor  Determinant of long-term trend: time trend variable

3.6 Discussion about long-term trend of carbon intensity improvement

16

 We can still observe the long-term trend of carbon intensity improvement  This means that “improvement factors” have been overcoming “worsening factors”

(a) Improvement factor (?) (b) Worsening factor (?) Diffusion (retrofitting) of technologies such as (a1) regenerative burner, and (a2) use of waste plastics in coke oven and BF Replacement/aggregation of facilities such as (a3) BF, (a4) EAF, and (a5) combined cycle power plant firing by- product gases Fuel substitution such as (a6) from heavy oil to natural gas Aging effects of facilities such as (b1) aging of silica bricks in coke oven, and (b2) accident partly being caused by the aging Implementation of environmental measures such as (b3) air pollution abatement measures, and (b4) dust recycling system

>

• 4. Summary (1/2)

17

 We empirically examined determinants affecting the carbon intensity (tCO2/t crude

steel) trajectory in the Japanese steel industry. Question 1 What is the determinant of carbon intensity (tCO2/t of crude steel)?

 Short-term (yearly) fluctuation of carbon intensity

Answer: Capacity factor

 Long-term (FY2000-FY2014) trend of carbon intensity

Answer: It is combination of Improvement factor

• Diffusion of energy saving tech.

Worsening factor

• Aging effect of facilities

• 4. Summary (2/2)

18

Question 2 Can we observe a long-term trend of carbon intensity improvement? Answer: Yes We observe a 0.3%/y of carbon intensity improvement during the period from FY2000 to FY2014

Did VAs (negotiated agreements) have an effect on carbon intensity improvement?

Answer: The results (indirectly) suggest “Yes”

Appendix: Nonparametric regression

19

Key message  The result show that the residue has been decreasing with time  We reconfirm the long-term trend of carbon intensity improvement  The effects of x1, and x2 are exogenously given here.

• Capacity factor (x1)
• Production process (x2)
• Time trend variable (x3)

 We conduct one-variable regression, i.e., smoothing spline.

Appendix: What is the “Industrial Production Index”?

20

 Industrial Production Index represents activity levels of industry  Hybrid indicator (economic + physical)

The structure of Japanese Industrial Production Index

Manufacturing (9989) Industry (10000) Mining (21) Iron and steel (391) Machinery and automotive (5125) … Hot-rolled steel products (110) Machinery (1273) Electronics (1940) Transport machinery (1912) Passenger cars (764) … … Production tons Production number The weight is based on the value-added