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

Ca

rbo

n in

ten

sity (

tCO

2/t c

rud

e s

tee

l)

Ref) RITE estimates based

on 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

Ca

rbo

n in

ten

sity (

tCO

2/t c

rud

e s

tee

l)

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

on 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

Cap

acit

y fa

cto

r in

dex

(FY

20

05

= 1

00

)

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 0.78U

pstr

eam

index (

FY

2005

=1.7

43)

Hot metal ratio (t pig iron/t crude steel)

FY05

FY01

FY12 FY14

FY06

FY07

FY02

FY08

FY00 FY11

FY09 FY13

FY10FY08 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

Ho

t m

etal

rat

io (

t p

ig ir

on

/t c

rud

e st

eel)

FY

Iron ore dominant

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

0.70

2.12

0.0

2.5

0 1

Up

str

ea

m in

de

x

Hot metal ratio

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

Hot metal ratio

177

237

367

323

273

2644

82 69 59

0

100

200

300

400

00 02 04 06 08 10 12 14

Exp

ort

pri

ce (

no

min

al U

S$/t

ste

el)

FY

Steel bar

Stainless steelexcept 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

Shar

e o

f fi

nal

ste

el p

rod

uct

s (%

)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

Re

lative

ca

rbo

n in

ten

sity (

FY

05

=1

00

)

Ca

rbo

n in

ten

sity (

tCO

2/t c

rud

e s

tee

l)

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