ENVIRONMENTAL STRATEGY DESIGN FOR THE

ENVIRONMENTAL STRATEGY DESIGN FOR THE JAPANESE IRON AND STEEL INDUSTRY -A global perspectiveD.J. Gielen Y. Moriguchi

- DRAFT 21/6/2001 -

CONTENT GLOSSARY

5

1

7

INTRODUCTION

1.1

General industry structure

1.2

Environmental problems in the life cycle of iron and steel

10

1.3

Problem and research questions

22

2

JAPANESE GHG EMISSION REDUCTION STRATEGIES

8

23

2.1

Increase the energy efficiency

23

2.2

Substitute coal by other fuels

28

2.3

Increase the recycling rate

30

2.4

Increase the efficiency of materials use

33

2.5

Initiate JI and CDM projects

33

2.6

Overview

35

3

MODELLING ISSUES

36

3.1

System Boundaries

39

3.2

Emission Accounting

40

3.3

Energy and material flow modelling

42

3.4

Process Characterization

42

3.5

Regional detail

43

3.6

Demand projections

45

3.7

Market distortions

47

3.8

International trade modelling

49

3.9

Scrap management modelling

50

3.10

Software and model operation

51

3.11

Scenario definition and policy simulation

51

4

MODEL RESULTS

53

4.1

Model validation

53

4.2

Global GHG policies

57

4.3

Japanese and European stand-alone policies

65

5

SENSITIVITY ANALYSIS

71

5.1

Interest rate

71

5.2

Higher price elasticity

72

5.3

Lower gas price

73

5.4

Technology mix: including smelting reduction

73

5.5

Technology mix: no CO2 removal

73

5.6

Technology mix: no CO2 free electricity

74

5.7

Market mechanism: monopolies

75

5.8

Introduction of import tariffs

76

5.9

Overview of sensitivities

77

6

CONCLUSIONS

78

6.1

GHG emission reduction potentials

78

6.2

The impact of GHG emission taxation on the iron and steel industry

80

6.3

Consequences for R&D

81

7

REFERENCES

82

ANNEXES ANNEX 1 Overview of energy carriers and materials in the STEAP model ANNEX 2 Overview of processes in the STEAP model ANNEX 3 Transportation costs and trade tariffs ANNEX 4 Investment costs, labour costs, energy and resource costs relative to Japan ANNEX 5 Assumptions for demand forecast ANNEX 6 Waste collection costs ANNEX 7 Global energy consumption and energy efficiency in the iron and steel industry ANNEX 8 Energy efficiency in the Japanese iron and steel industry

The Iron and Steel Industry

Gielen and Moriguchi, 2001


GLOSSARY AMEI BC BF BOF CaCO3 CDM CH4 CIS CO2 DRC DRI EAF GATT GHG GJ GWP HFC HHV IISI IPCC JI LCA LHV MFA MSW NAFTA NIES N2O OHF PCI PFC PVC RDF SF6 STEAP UNFCCC WTO

Autonomous Materials Efficiency Improvement Base Case Blast Furnace Basic Oxygen Furnace CalciumCarbonate Clean Development Mechanism Methane Community of Independent States Carbon dioxide Direct Reduced Iron Electric Arc Furnace General Agreement on Trade and Tariffs Greenhouse gas GigaJoule Global Warming Potential HydroFluoroCarbon Higher Heating Value International Iron and Steel Institute Intergovernmental Panel on Climate Change Joint Implementation Life Cycle Analysis Lower Heating Value Material Flow Analysis Municipal Solid Waste North American Free Trade Association National Institute of Environmental Studies Nitrous Oxide Open Hearth Furnace Powder Coal Injection PerFluoroCarbon PolyVinylChloride Refuse Derived Fuel SulphurHexaFluoride Steel Environmental Assessment Program United Nations Framework Convention on Climate Change World Trade Organization



1 Introduction This study is part if a two-year research programme at the National Institute of Environmental Studies in Tsukuba during the period 2000-2002, focusing on environmental energy and materials policies1. In an earlier study, Japanese petrochemicals have been analysed (Gielen and Moriguchi 2001). This study focuses on the Japanese iron and steel industry. Global steel exports increased from 22.6 to 38.1% of the total steel production during the period 1975-1997 (IISI 2001). It is likely that trade will increase further in future years. As a consequence a study concerning the future of the Japanese steel industry must have a global perspective. The study is based on a techno-economic life cycle perspective. The environmental impact depends on production volume and the environmental impact per unit of production (the environmental efficiency). The production volume depends on demand trends and the competitiveness of the Japanese industry and government policies that determine the market structure. The environmental efficiency depends on the sector structure and the technology that is applied. Only a limited number of key factors are considered explicitly in this study: • Demand trends; • The emergence of new technology; • Emerging environmental policies; • Future natural resource availability and resource prices; • Trade liberalisation and changing market structure. The reason for the selection of these factors will be elaborated briefly. Other factors such as legislation, labour policies etc. may also be of significance, but are not considered in this study in order to keep a clear focus. A ‘ceteris paribus’ condition applies to all factors that are not considered in more detail. Technology is a key driving force in steel making. Research in the iron and steel industry focuses on more simple and small-scale primary steel production2 routes. A number of technological breakthroughs in steel production have been forecast during the last two decades (Zervas 1996) but so far none of them has 1

The stay of Dolf Gielen at NIES is enabled by a fellowship of the Science and Technology Agency. This fellowship is gratefully acknowledged. 2 Primary steel production refers to the production of steel from primary (natural) resources, c.q. iron ore. Steel production from scrap is called secondary production. Gielen and Moriguchi, 2001

The Iron and Steel Industry captured a significant market share. However this may change in the future. Changing technology can have a significant impact on the environment, to the good or to the worse. The iron and steel industry used to be an important source of air pollution and waste. However the steel industry has improved its environmental performance significantly during the last 50 years, see e.g. (Philipp and Theobald 1993). The emission of carbon dioxide (CO2) is probably the most important remaining environmental problem (see below). Regarding the sustainability of natural resource consumption, coal and iron ore are abundant resources. However the availability of steel scrap and comparatively clean energy resources such as natural gas and renewables can pose an important competitive advantage in the future. The availability of these resources is region specific. Traditionally the iron and steel industry is considered a key sector from a national security point of view. Moreover the sector employs many people. This has resulted in large-scale government intervention favouring the steel industry, such as subsidies and import barriers. This intervention is apparent in many countries, see e.g. (International Trade Administration, 2000). Because the strategic relevance of the steel industry has decreased in time, government intervention is decreasing and the market structure is changing towards a globally free market. This trend will affect the future industry location choice. The impact of changing market conditions will be considered in more detail in this study. In this chapter the general industry characteristics will be discussed in more detail. Next in chapter 2, CO2 emission reduction strategies for the Japanese iron and steel industry are detailed. In chapter 3, the STEAP model is discussed in detail. This model serves as a comprehensive framework for the analysis of the interact ion of factors mentioned above. In chapter 4, the results from the model are discussed. The sensitivity of the results for key variables is elaborated in chapter 5. This is followed by the conclusions in chapter 6. 1.1

General industry structure

The steel industry produces steel products. Currently two main process routes exist for crude steel production: the blast furnace (BF) – Basic Oxygen Furnace (BOF) route and the Electric Arc Furnace (EAF). The first route is based on the use of coal and iron ore. The second route is based on the use of scrap and electricity. Global steel production amounted to 786 Mt3 crude steel (705 Mt 3

In order to make this quantity more tangible the annual global steel production amounts to a single block of 1 km2 approximately 100 metres high. In comparison to steel, the global plastics production amounts to 150 Mt per year and the global production of wood products amounts to 750 Mt oven dry matter. Gielen and Moriguchi, 2001

The Iron and Steel Industry finished steel products) in 1999 (IISI 2001). 59.8% of the production is based on BF-BOF technology, 33.4% is based on EAF technology. The remainder (6.8%) is based on other, outdated, technologies. The use of EAF is limited by scrap availability. The fraction EAF is increasing in time because the scrap quantity is increasing more quickly than the total steel production volume. In this study, cast iron production is included with the iron and steel industry because the production of cast iron requires iron or scrap feedstock material. Cast iron production data are scare, estimates suggest it amounts approximately to 70Mt per year (Farla and Blok 2001). Global steel production is concentrated in industrialised countries. North East Asia (Japan, South Korea, and Taiwan), The European Union (EU-15) and North America (NAFTA) together account for 53% of the global steel production. While steel production has been stagnating or even declining in Japan, Western Europe and the United States during the last decades, it has been increasing rapidly in developing countries. Especially the Chinese steel production is growing by more than 10% per year. China is nowadays the largest steel producing country in the world (124 Mt/yr in 1999), followed by the USA and Japan (IISI 2001). Rapid changes are taking place on a company level. Especially the scale of primary steel producing companies is increasing rapidly. Nippon Steel was in the 70’s the first company with a production capacity in excess of 20 Mt per year. Other companies such as Posco, Arbed, Krupp-Thyssen, Corus and Bao steel are following suit, based on mergers or capacity expansions. Especially in the last 10 years, a new class of large-scale steel producers is emerging. Amongst others this trend is driven by economies of scale. One modern blast furnace produces approximately 3.5 Mt iron per year, one modern production site contains between 2 and 7 blast furnaces. Each company has several production sites, resulting in a production capacity in excess of 20 Mt. In Japan there are five primary steel producers (Nippon steel, NKK, Kawasaki steel, Kobe steel and Sumitomo, see annex 8). The announced merger of NKK and Sumitomo will result in another steel company with a production capacity in excess of 20 Mt per year. In contrast to primary steel production, EAF based secondary steel production has a much smaller scale, usually less than 0.5 Mt annual capacity. This smaller size of operations is caused by less pronounced economies of scale. However this industry is subject to economies of scale too, resulting in a tendency toward larger scale operations. For example the Nucor EAF steel plant in Berkeley (USA, SC) has recently been expanded to 3 Mt per year (Fonner 2001). However the scale of an average EAF steel production site is less than one tenth of the scale of a blast furnace production site. Regional scrap availability is the major bottleneck for further capacity increase.


The Iron and Steel Industry 1.2

Environmental problems in the life cycle of iron and steel

Only the Japanese situation will be discussed in more detail. The situation abroad, especially in developing countries, may be different. Despite the huge quantities of resources that are consumed, resource scarcity is less of a problem. Both coal and iron ore resources will last for several hundred years. Regarding coal one must add that the scarcity of high quality coking coal resources may increase more rapidly. However new steel production processes have been developed that can use low quality steam coal (see chapter 2 and 3). The SO2 and NOx emissions from the iron and steel industry are of secondary importance. Because of the reducing environment in the blast furnace, this process is no source of NOx. The bulk of the sulphur from the coal ends up in the slag. Coke ovens used to be a source of important organic pollution. However modern coke ovens with closed water systems are no major emission source anymore. Japanese industrial dioxin emissions are dealt with by METI, the Ministry of Economy, Trade and Industry. An agreement has been reached that emissions in the steel industry will be reduced. The emissions in steel sintering will be reduced from 101.3 g-TEQ (2,3,7,8- tetrachlorodibenzo-p-dioxin equivalents) in 1999 to 93.2 g-TEQ in 2002. The emissions from EAFs will be reduced from 141.5 g-TEQ in 1999 to 130.3 g-TEQ in 2002 (METI 2000). These figures can be compared to a total Japanese dioxin emission of 3981 g-TEQ in 1998. The emissions from the steel industry represent approximately 6% of the total Japanese dioxin emissions. This fraction may increase because emissions from waste incinerators, the main dioxin source, will be reduced significantly. Steel scrap is an important resource and is recycled completely. Primary steelmaking results in blast furnace slag and steel slag. Both residues can be used in the building and construction industry, and pose no major environmental problem. Dust discharged from EAFs contains 20-30% zinc. EAF steel producers generate about 15 kg of dust for every ton of steel they produce. In Japan the annual volume of EAF dust amount to 0.5 Mt per year. Zinc is recovered from only 65% of the dust because of the high cost of zinc removal (1997 figures) (Furukawa 1997). The remaining 35% ends up in landfills (about 45 kt of zinc is landfilled). However Kawasaki steel has developed a new smelting reduction process that can reduce processing cost by 40%, and recover 99% of the zinc metal. Greenhouse gas (GHG) emission reduction is a key issue for environmental policies in the first half of the 21st century. CO2 is the most important greenhouse gas, representing 70-75% of the total annual GHG emission. The countries of the European Union, the United States and Japan agreed at the UNFCCC (United


The Iron and Steel Industry Nations Framework Convention on Climate Change) conference in Kyoto in December 1997 to reduce their emissions by 8, 7, and 6% in the period 20082012, respectively, compared to their emissions for a reference year4. The US government has announced it is reconsidering its participation in the Kyoto agreements. In case the US does not participate the protocol becomes in practice irrelevant. However the GHG issue does not disappear and even further emission reductions are likely on the long run. The bulk of the CO2 emissions (80%) is related to the use of fossil energy carriers. As a consequence energy intensive materials such as steel can be affected significantly by GHG emission reduction policies. The Kyoto agreement covers six categories of greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs) and sulphurhexafluoride (SF6). These emissions are aggregated on the basis of their global warming potential (GWP) for a time horizon of 100 years. Approximately 4% of the global CO2 emissions can be attributed to the production of iron and steel (Yoshiki-Gravelsins et al. 1993). The iron and steel industry is the single most important industrial source of CO2 emissions. The emissions of non-CO2 greenhouse gases by this industry (e.g. methane or nitrous oxide) are negligible and will not be discussed in more detail5. The bulk of the CO2 emissions in the iron and steel industry is related to the use of fossil fuels (especially coal). The fossil fuel consumption depends to a large extent on the crude steel production technology. Similar to the situation in most other major steel producing countries, the Japanese iron and steel industry is based on two production routes. The Blast Furnace (BF) is used to reduce iron ore into liquid iron, which is subsequently converted into steel in a Basic Oxygen Furnace (BOF). Electric Arc Furnaces (EAFs) are used to produce steel from scrap. In 1999 69.5% of the Japanese was produced in BOFs, 30.5% in EAFs. Scrap based EAF steel production requires considerably less energy than the BF/BOF route, mainly because the chemical energy for the ore reduction can be

4

The reference year is 1990 for CO2, CH4 and N2O, 1995 for HFCs, PFCs and SF6 5 CH4 emissions due to deep coal mining can contribute 5-15 kg CO2 equivalents per GJ coal, but will not be discussed in more detail. In case these emissions are allocated to the final coal consumers, the emissions for steel increase by 5-15%. However technologies are available to capture the methane and use it for energy purposes. Moreover these emissions do not occur in case of surface mining. Gielen and Moriguchi, 2001

The Iron and Steel Industry saved6. The preparation of coke, ore pellets or sinter for the BF requires also considerable amounts of energy. Table 1.1 provides an overview of energy consumption for different processes in the iron and steel industry. The second column (IISI 1982) relates to the reference plant from a study by the International Iron and Steel Institute IISI that incorporates many energy efficient technical facilities and operating practices, which have been proven commercially viable and which have been widely implemented in the early 1980s. The second and third weighting scheme related to reference plants in the mid-1990s. The ‘EcoTech’ reference process that was defined by IISI includes all financially viable and proven energy-saving technologies and therefore represents state of the art steel making. The ‘All Tech’ reference process includes all proven energy saving technologies regardless of profitability and therefore represents a more severe standard for energy efficiency. All units are expressed per ton of cured steel. The figures indicate a decline from 15.58 to 13.96 GJ per ton average finished steel product in case all cost-effective technologies are applied (a 10% decline in 13 years), and a potential reduction to 12.84 GJ in case all technologies are applied that are not yet cost-effective. The blast furnace is the main energy consuming operation, but energy consumption in finishing is important, too. A tendency exists toward more advanced steel products (from hot rolled to cold rolled and galvanized) that increases the energy consumption per ton of product. However the functionality of this steel is higher (less steel required per unit of product), which results in considerable energy savings. A life cycle assessment if required for proper assessment of such energy saving options (Gielen 1999).

6

The minimum energy requirement for reducing Hematite (Fe2O3, the main iron ore type) at room temperature is 7400 MJ/t iron. The minimum amount to carbon that is needed for the chemical reduction is 320 kg if CO is formed and would be 160 kg if CO2 were formed (Birat et al. 1999)



Table 1.1: Representative process energy consumption, expressed in primary energy equivalents (Farla and Blok 2001)(efficiency of electricity production = 37% (1982) and 39% (1995), respectively, for purchased electricity). Excludes coke oven. IISI IISI IISI All-Tech Eco-Tech 1995 1995 1982 Sinter 1.54 1.12 Pellets 1.26 1.26 Pig Iron 14.43 12.33 12.23 BOF steel7 0.17 -0.16 -0.16 EAF steel, scrap 5.66 5.40 4.41 based7 Hot rolled 2.97 2.16 1.82 products Cold rolled 6.33 4.08 3.35 8 products Tinmill products9 6.46 5.61 Galvanized 6.10 4.93 10 products Total weighted 15.58 13.96 12.84 11 average Table 1.2 shows an analysis of the energy consumption in the Japanese iron and steel industry. Note that the original statistical data are in higher heating values (HHV). All energy data and emission coefficients in this paper have been expressed in lower heating values in order to allow comparison with international energy data that are expressed in lower heating values. The CO2 emission from fossil fuel consumption can be calculated from the energy consumption data and the CO2 emission coefficients per unit of energy, see table 1.3. In Japan, national electricity production ranges from nuclear or hydropower plants with zero CO2 emissions to coal fired power plants with 0.25 t CO2 per GJ electricity. On average, electricity is produced with a specific CO2 emission of approximately 0.096 t CO2 per GJ electricity (based on IEA 1999).

7

Includes continuous casting Cold rolling + hot rolling 9 Cold rolling + tinmill 10 Cold rolling + galvanizing. Assumption 50% hot-dip galvanizing and 50% electro-galvanizing 11 See IISI (1998) for the production shares in the basket 8





Table 1.2: Energy balance of the Japanese iron and steel industry, 1999 (MITI 2000)

[PJLHV/yr] Kerosene Gas oils Fuel oils Hydrocarbon oil LPG Petroleum coke Coking coal Other coal Coal coke Tar Coke oven gas Blast furnace gas Converter gas Electric furnaces gas LNG Town gas Oxygen Electric power Steam Total

Sintering Pelletising 0.00 0.00 0.00 0.00 0.14 0.08 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 28.90 1.81 110.98 1.16 0.09 0.00 2.40 0.86 0.17 0.00 0.23 0.00 0.00 0.03 0.03 0.00 11.70 0.00 154.70

0.00 0.00 0.00 0.00 1.04 0.00 4.94


BF Ferroalloys 0.00 0.06 0.00 0.02 0.12 2.66 0.00 0.00 0.83 0.04 17.40 0.17 75.32 0.00 203.91 12.92 858.87 9.88 2.36 0.00 24.81 0.07 -279.28 0.00 9.60 0.05 0.00 0.11 0.15 0.00 6.77 0.00 920.98

0.05 0.00 0.00 0.00 9.39 0.00 35.32

BOF 0.01 0.05 0.35 0.00 2.74 0.00 0.00 13.81 3.46 0.00 5.75 0.03 -69.90

EAF 1.98 0.00 2.07 0.00 0.15 0.20 0.00 0.03 3.88 0.00 0.49 0.00 0.02

Forging 0.93 0.01 2.87 0.00 0.40 0.00 0.00 0.00 0.00 0.00 0.55 0.01 0.07

Casting 0.50 0.00 0.73 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00

0.00 0.04 0.30 0.00 13.28 0.00 -30.08

-0.81 0.50 0.67 0.00 49.22 0.00 58.41

0.00 0.51 0.84 0.00 1.89 0.00 8.09

0.00 0.00 0.24 0.00 1.38 0.00 3.10

Power generation Other boiler & iron& steel Rolling & Coke pipe cogeneration sectors manufacturing 3.17 0.53 1.53 0.00 0.52 0.00 0.45 0.00 29.48 20.64 3.69 0.00 0.00 0.00 0.00 4.23 8.05 3.51 3.62 0.00 0.00 0.00 0.00 15.35 0.00 0.00 0.00 1092.85 0.00 69.88 0.03 0.00 0.00 0.00 6.90 -750.58 0.00 0.69 0.10 -23.78 65.75 29.55 5.88 -162.57 2.97 97.42 1.33 47.01 11.58 19.02 0.31 1.95 0.00 14.44 15.64 0.00 64.91 0.00 216.51

0.00 2.05 5.77 0.00 -66.17 0.00 182.88

0.00 2.41 6.06 0.00 45.71 0.00 78.01

0.00 0.03 0.03 0.00 3.94 0.00 228.46

Other sectors 0.67 0.22 1.37 0.00 1.77 0.01 0.00 0.00 0.34 0.01 1.23 0.06 0.72

Total 9.39 1.28 64.20 4.23 21.34 33.14 1168.18 331.29 244.89 -20.53 -25.19 -130.30 -26.35

0.34 0.01 2.20 0.00 9.42 0.00 18.37

-0.42 20.13 31.93 0.00 152.49 0.00 1879.69


The use of fossil fuels is not the only source of CO2. CO2 is also emitted during iron production because of the decarbonisation of limestone (CaCO3). Birat et al. (1999) indicate a total of 0.135 t CO2 per ton primary crude steel for limestone dissociation. The Japanese iron and steel industry consumed 12.34 Mt limestone, 4.30 Mt quicklime, 0.34 Mt baked dolomite and 1.17 Mt dolomite in 1999 (MITI 2000). Assuming that these carbonaceous components are completely dissociated. The emission amount to 0.44 t CO2 per ton limestone and dolomite, and 0.75 t CO2 per ton quicklime and baked dolomite (Gielen 1997). This is a source of 9.4 Mt CO2 (0.144 t CO2 per ton primary crude steel, close to the literature figure). The iron and steel industry produces significant amounts of energy by-products: coke oven gas, blast furnace gas and BOF-gas. If these gaseous energy carriers are sold, their carbon content can either be allocated to the user of the gas (generally a power producer) or to the steel industry. In the current practice the carbon content of residual gas deliveries to electricity producers is allocated to the electricity producers, not to the steel industry. However the CO2 emission coefficient for blast furnace gas is relatively low (see table 1.3). If all the carbon monoxide in this gas would be counted as energy carrier, its CO2 emission coefficient would increase to 0.2 t CO2/GJ (twice as high). This approach would decrease statistical emissions in the iron and steel industry. However the iron and steel industry as a whole is a net buyer of electricity and the only party that can influence blast furnace gas production, blast furnace gas use allocation to the iron and steel industry will pose such an incentive. The difference (0 allocation to iron & steel vs. 0.2 t CO2/GJ) amounts to 25 Mt CO2. Coke production and cast iron production can be considered part of the iron and steel industry, but two independent coke producers and iron casting companies are not accounted for in the Japanese the iron and steel industry CO2 emissions. The coke ovens of Mitsubishi and Mitsui represent 12% of the Japanese coking capacity (Japan Institute of Energy, 2000). Also blast furnace slag is produced as a by-product. This slag can be used as a cement substitute. As a consequence the emissions in cement production are reduced. Blast furnace slag production amounts approximately to 250 kg per ton of iron. It consists of dissociated limestone and coal ash. Given a blast furnace pig iron production of 73.9 Mt, blast furnace slag production amounts to 18.5 Mt. Production of Portland cement results in 0.8 ton CO2 per ton cement. Assuming substitution on a mass par basis, the savings amount to 14.8 Mt CO2. Finally Japan is a net exporter of steel scrap (about 4 Mt/yr) and steel products (net export about 20 Mt/yr). These emissions could be allocated to the importers of these commodities. In total these credits may amount to 40-50 Mt CO2 (assuming high quality primary steel is exported).


The Iron and Steel Industry Given these system boundary problems, the CO2 emission in the Japanese iron and steel industry ranges from 115 Mt (no inorganic emissions, no emissions from electricity production, trade correction) to 210 Mt (inorganic emissions, coal based emissions in electricity production, carbon content of gas by-products allocated to iron and steel), see table 1.4. Because the steel industry uses coal as its main energy source, its relevance is higher from a CO2 point of view than from an energy point of view. Table 1.3: Energy content and specific CO2 emissions for energy carriers (Environment Agency 1992, NTK 1993, IPCC 1997, IEE 2000, IEA 2000) Energy carrier Coal products

Coking coal Steam coal, briquet Cokes Blast furnace gas Coke oven gas

Oil products

Gas

Converter gas/EAF gas Tar Kerosene Gas oil Fuel oil Hydrocarbon oil LPG Petroleum coke LNG Town gas

Electricity (external)

12

Unit [t] [t] [t] [1000 m3] [1000 m3] [1000 m3] [t] [t] [t] [t] [t] [t] [t] [t] [1000 m3]

HHV Japan [GJ/unit] 31.7 27.1 30.0 3.0

LHV12 Japan [GJ/unit] 30.2 25.8 29.8 3.0

[kg CO2/GJ LHV] 0.094 0.098 0.111 0.098

19.7

17.8

0.059

9.4

9.4

45.9 45.5 43.4-45.0 45.0 50.0 36.1 54.7 41.7

44.0 42.8 42.6 41.3-42.3 42.5 45.0 35.2 49.4 37.7

0.076 0.073 0.074 0.076 0.076 0.067 0.094 0.057 0.057 0.096

LHV Lower Heating Value can be calculated from HHV higher heating value (for As Received figures) based on the formula: LHV = HHV – 0.212H-0.0245M-0.0008O, where M is % moisture, H is % Hydrogen and O is % Oxygen. Typically M = 10%, H=4%, O =5% (25% Volatile Matter CH2). For typical bituminous coal the difference between LHV and HHV amounts to 1.09 GJ/t (World Coal Institute, 2000) Gielen and Moriguchi, 2001




Table 1.4: CO2 emissions in the Japanese iron and steel industry, 1999

[Mt CO2/yr] Sintering Pelletising Kerosene 0.0 0.0 Gas oils 0.0 0.0 Fuel oils 0.0 0.0 Hydrocarbon oil 0.0 0.0 LPG 0.0 0.0 Petroleum coke 0.0 0.0 Coking coal 0.0 0.0 Other coal 2.8 0.2 Coal coke 12.3 0.1 Tar 0.0 0.0 Coke oven gas 0.1 0.1 Blast furnace gas 0.0 0.0 Converter gas 0.0 0.0 Electric furnaces gas 0.0 0.0 LNG 0.0 0.0 Town gas 0.0 0.0 Oxygen 0.0 0.0 Electric power 1.1 0.1 6.5 0.0 Inorganic CO2 Total 23.0 0.5

BF 0.0 0.0 0.0 0.0 0.1 1.6 7.1 20.0 95.3 0.2 1.5 -27.4 0.9 0.0 0.0 0.0 0.0 0.7 0.1 100.0


Ferro alloys 0.0 0.0 0.2 0.0 0.0 0.0 0.0 1.3 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 0.0 3.5

BOF 0.0 0.0 0.0 0.0 0.2 0.0 0.0 1.4 0.4 0.0 0.3 0.0 -6.6 0.0 0.0 0.0 0.0 1.3 2.7 -0.3

EAF Forging Casting 0.1 0.1 0.0 0.0 0.0 0.0 0.2 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.7 0.2 0.1 0.1 0.0 0.0 5.6 0.6 0.3

Rolling & pipe 0.2 0.0 2.2 0.0 0.5 0.0 0.0 0.0 0.0 0.0 3.9 0.3 1.1 0.0 0.8 0.9 0.0 6.2 0.0 16.3

Power generation Coke boiler & Other iron& manufacturi cogeneration steel sectors ng 0.0 0.1 0.0 0.0 0.0 0.0 1.6 0.3 0.0 0.0 0.0 0.3 0.2 0.2 0.0 0.0 0.0 1.4 0.0 0.0 102.7 6.8 0.0 0.0 0.0 0.8 -83.3 0.1 0.0 -1.8 1.7 0.3 -9.6 9.5 0.1 4.6 1.8 0.0 0.2 0.0 0.0 0.0 0.1 0.1 0.0 0.3 0.3 0.0 0.0 0.0 0.0 -6.4 4.4 0.4 0.0 0.0 0.0 15.9 6.8 15.0

Other sectors 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.1 0.0 0.9 0.0 1.5

Total 0.7 0.1 4.9 0.3 1.4 3.1 109.8 32.5 27.2 -1.6 -1.5 -12.8 -2.5 0.0 1.1 1.8 0.0 14.6 9.4 188.7

The steel industry represents between 9% and 16% of the total Japanese emission, depending on the allocation scheme. This contribution is well above the global average of 4%. The difference is related to the strong Japanese export position for steel and steel products. This affects the emissions two-fold: production is much higher than consumption, and scrap availability is limited, resulting in a low penetration of EAF steel production. For example in the USA, EAF represents 46.2% of the total crude steel production, in Western Europe (EU-15) 38.1%, in Japan only 30.5% (1999 figures). 1.3

Problem and research questions

Many studies have analysed new steel production technologies and emission reduction options within the basic metals industry (e.g. (Eketrop 1989, Elliott 1991, Gruebler 1993, Daniels and Moll 1997, Birat et al. 1999). In this study, the emission reduction is analysed within the framework of the changing (inter-) national energy and materials system configuration. This includes the whole environmental life cycle ‘from cradle to grave’. Not only metals production, but also metals consumption is considered in the optimisation. The effects of materials substitution, changing consumer preferences, changing energy prices, and changing environmental policies are integrated into the analysis. The approach that is applied in this study combines a number of topics into an integrated analysis: • System dynamics are considered, such as the standing capital equipment stock from previous years, the changing scrap availability, and changing energy prices; • Changing technology characteristics is considered; • The interaction of emission abatement options in the materials life cycle is considered; • Changing international market conditions are considered. Similar studies based on the MARKAL model have been done for the Netherlands (Gielen and Dril 1997a), for Western Europe (Gielen and Dril 1997b, Gielen and Dril 1999) and for South Korea (Hong, unpublished data). In this study the global perspective including changes in international trade represents the main new element. The following questions will be studied: - What is the emission reduction potential in the World and in Japan? - What are the consequences of GHG policies for the location choice of the iron and steel industry? - Which policy strategies can reduce emissions in Japan significantly while maintaining international competitiveness? - What actions are required by industry, government and the scientific community in order to achieve a meaningful emission reduction?


2 Japanese GHG emission reduction strategies A number of technical options exist to reduce CO2 emissions in the Japanese iron and steel industry (Gielen 1999): • Increase the energy efficiency; • Substitute coal by other fuels; • Increase the recycling rate; • CO2 removal and disposal in the sea; • Increase the efficiency of materials use; • Initiate JI (Joint implementation) and CDM (Clean Development Mechanism) projects. These strategies will be discussed individually. 2.1 Increase the energy efficiency The energy efficiency of the Japanese steel producers is high, see e.g. (Ishikawa et al. 1994, Worrell et al. 1999, Farla and Blok 2001). This high efficiency can be explained by the comparatively high energy prices in Japan and the relative recent industry start-up (in comparison to European and US producers). A number of options exist to increase the energy efficiency: • Increase the coal injection rate in blast furnaces (as a substitute for coke); • Increase the efficiency of energy recovery from blast furnace gas; • Introduce thin slab casting and near net shape casting; • Introduce direct smelting technologies, avoiding coking and possibly also avoiding ore preparation. Increased coal injection Japan consumed 10.4 Mt pulverised coal for blast furnace injection in 1999 (World coal institute 2000). This figure can be compared to a total coking coal consumption of 61.9 Mt in the same year. The current Japanese coal injection rate ranges from 67 to 207 kg/t pig iron (see Annex 8). Assuming a maximum of 200 kg/t, coke substitution on a thermal par basis and energy use for coke production of 8 GJ/t (based on Daniels and Moll 1997), the potential for CO2 emission reduction amounts to 3.8 Mt per year in case all blast furnaces apply 200 kg coal injection. This is a conservative estimate. In case coke consumption can be reduced to 200 kg/t pig iron (coal injection rate 300 kg/t) (Edström and Scheele 1993), the potential increases to 8.9 Mt CO2 per year. In the Japanese case, the coke ovens have not yet reached the end of their technical life span. This situation is different from the European case. This

23

The Iron and Steel Industry difference explains the lower coal injection rates in Japan. Rebuilding coke ovens is costly, a major consideration in Europe to maximize coal injection. However the only driving force in Japan is the lower price of steam coal (for coal injection) in comparison to coking coal. However nowadays it is possible to use steam coal for cokes production. As a consequence the financial incentives for coal injection are reduced (personal communication N. Takamatsu, Nippon Steel). Full oxygen blast furnaces may allow even further reductions of the coke rate to 174 kg/t pig iron. Superheating of the hot blast using a plasma arc may reduce coke rates to approximately 105 kg/t pig iron (Daniels and Moll 1997). The coal injection increases accordingly. However the additional energy use for oxygen production and preheating will offset the CO2 benefits of coal injection. As a consequence, these options have not been analysed in more detail. Increase the efficiency of energy recovery The quantity of blast-furnace gas amounts to 4.9 GJ/t of pig iron (Daniels and Moll 1997). The quality of this gas is low. The energy content is about 4 MJ/m3, vs. 35 MJ/m3 for natural gas (Gielen and Dril 1997). This poses a problem for the efficient use of this gas. The quality was too low for use in electricity production using conventional gas turbines. As a consequence it used to be mixed with natural gas or it was used in special gas engines with comparatively low efficiencies of 25-30%. In recent years, new gas turbines have been developed that are able to use low quality gas such as blast furnace gas. Such gas turbines are for example used by Baoshan, the largest Chinese steel producer (personal communication energy manager Baoshan). These turbines have been developed in cooperation with ABB, and the total electric efficiency of the gas turbine is reportedly 40%. Assuming that such turbines are not yet applied by Japanese steel makers and assuming that the gain in energy efficiency is 10% and the reference electricity production has an CO2 intensity of 0.094 kg/GJel, the potential for emission reduction amounts to 3.2 Mt CO2. Introduce thin slab casting and near net shape casting Steel is traditionally cast into slabs and billets of 15-20 cm thickness that are cooled down to room temperature and reheated before they are rolled into hot rolled sheet of 0.5-3 cm thickness. More advanced is the direct connection between the steel caster and the hot rolling mill, saving part of the reheating energy. This technology is nowadays widely applied. However rolling and reheating require still considerable amounts of energy (see table 1.1). The more the size of the cast steel resembles the final product, the less energy is required for finishing. Nowadays Japanese producers apply direct rolling to 12-80 mm thickness (“thin slab casting”). Casting products with a reduced thickness (1-10 mm) will reduce the energy requirements for steel rolling even further (“Near Net Shape casting”). Such technologies are currently being developed, but important technical problems remain regarding the steel quality. Purity and surface quality are the main issues for thin sheet casting, especially for low alloy, low carbon steel qualities (C100) 2000-5000 Deep aquifers 2000-5000 Materials 5 (20) -10,000-10,000 efficiency JI/CDM China 55 (0) 0-2000 Tree 2 (10) 0-2000 plantations

15

Costs are expressed relative to a business-as-usual situation 35


3 Modelling issues In order to study the complex interactions between technological change, trade patterns and CO2 emission reduction a model has been developed, called STEAP (Steel Environmental strategy Assessment Program). A life cycle approach has been used in this study. The basic model code is similar to the one for other materials that have been discussed in detail in previous publications (MARKAL-MATTER, Cheap and Reap models, Gielen 1999, Gielen and Yagita 2001, Gielen and Moriguchi in press). This model has been developed from an industrial ecology perspective. The iron and steel economy is considered to be a system consisting of processes that are linked via flows of energy and materials and monetary flows. These flows can change due to government, industry and consumer decisions. The algorithm reflects the mechanism of an ideal market. A number of new elements have been added compared to previous models in order to account for the specific characteristics of the steel industry: • The model scope has been extended from a purely national scope to a regionalized global scope with materials trade between the regions, in line with the Freak model for global petrochemicals (Gielen and Yagita in press); • The life span of capital equipment can be specified individually per process type; • The model can be run with the assumption of perfect market or with regional monopolies, with or without international competitors. The model is written in the GAMS modelling language (General Algebraic Modelling System, Brooke et al. 1992). The model is based on a so-called 'perfect foresight' approach; all future developments are taken into account in current investment decisions. The optimisation is done from a national cost perspective, excluding subsidies and taxes. The STEAP model structure in comparison to life cycle analysis (LCA) and material flow analysis (MFA) is shown in figure 3.1. The figure has two dimensions: the life cycle stage (horizontal) and time (vertical). MFA focuses on the material flows in a certain area in one year. LCA focuses on the flows throughout the life cycle of one product. The STEAP model encompasses the material flows in the life cycle of all products in an area for a period of 75 years (1965-2040). STEAP is limited to iron and steel (but could in principle be extended to other materials as well). As a consequence the current model version is not suited to study to study materials competition, see (Gielen 1999). An overview of energy carriers and materials in this model is provided in annex 1 of this report

36


LIFE CYCLE STAGE CHEAP TIME

PROD PROD

USE

WASTE

T

USE

MFA WASTE

T+1

LCA PROD

USE

WASTE

T+2

Figure 3.1: CHEAP model structure vs. LCA and MFA The STEAP model covers the full life cycle of all steel products (see figure 2). This enables a proper comparison of options such as industrial energy efficiency improvements and fuel switches vs. increased recycling. The relation between current materials consumption and future scrap release is considered in the model. The life cycle of iron and steel is modelled from ‘cradle to grave’. Both heat and electricity production (and the related CO2 effects) are included in the model. It is an aggregated national model without regional detail. The main model features are listed in table 3.1.

37


IRON ORE ORE PREPARATION SINTER/PELLETS BLAST FURNACE IRON BOF

EAF

CRUDE STEEL

CRUDE STEEL

FINISHING

FINISHING FINISHED STEEL

FINISHED STEEL

CONSUMPTION SCRAP DISPOSAL Figure 3.2: Life cycle model structure in STEAP The model is dynamic. This includes the following features: • The waste quantities are calculated from materials consumption in previous years; • Capital equipment vintage is accounted for; • Future availability of new technology is accounted for; • Changing prices of scarce natural resources (fossil fuels, biomass, disposal sites etc.) are accounted for.

38

The Iron and Steel Industry Table 3.1: Summary of STEAP model characteristics Driving force Materials service demand Coverage Full life cycle Spatial boundary Global (11 regions) Time period 1965-2040, 5 year periods Coverage All iron & steel products Objective function Consumer/producer surplus Energy & heat production Endogenous Valuation waste treatment revenues Endogenous (electricity, recycled materials etc.) Strategies considered Fuel/feedstock switch, energy efficiency, demand reductions, recycling, energy recovery, CO2 storage Number of energy and material flows 44 Number of processes 75

Processes are characterized by a limited number of variables. The number of variables in the model exceeds the number of relations (equations) among the variables. One so-called objective function is defined which is minimized or maximized (in this case, the loss of consumer/producer surplus is minimized). The special feature of linear programming models (such as STEAP) is the proportional relations between inputs and outputs of processes (and also costs), an obvious abstraction from reality (e.g. the energy efficiency of an industrial plant and the cost per unit of production capacity depend to some extent on its size). This limitation does not apply to non-linear programming models. However, in practice, the use of non-linear programming is severely limited by the speed of current computers and data availability. 3.1 System Boundaries The model covers the time period 1965-2040, divided into five-year periods. There are three reasons why such a broad time span has been considered. The first reason is that computer models often produce results for the first and last time periods that are affected by this system boundary (e.g. because investments are not depreciated properly). The results for the initial periods and the last periods are not accurate. Especially in the steel life cycle with long life capital equipment and long life products, a long term perspective is essential. The second reason is to allow some model validation, based on the comparison of modelling results and the actual economic data for the last three decades. The third reason is proper accounting of waste release (from steel consumption in earlier periods). Regarding spatial boundaries, the model covers the life cycle of steel from the iron ore mining to the waste scrap-handling stage. The model is global. The materials that are considered in the model are listed in annex 1. For each material, at least one production process has been modelled. In some cases, a number of alternative production options have been considered, selected on the basis of the GHG emission reduction potential (see chapter 2). 39


3.2 Emission Accounting GHG emissions are calculated according to the IPCC (Intergovernmental Panel on Climate Change) emission accounting guidelines (IPCC 1997). The emission accounting takes place at the moment the fuels that enter the system (buying of fuels). Credits are given for net exports of fuels from the system. Energy outputs of the system have been credited on the basis of the CO2 equivalent carbon content of the energy carriers. The use of blast furnace gas is completely allocated to the steel life cycle. Credits are given in case natural gas is substituted by blast furnace gas of coke oven gas outside of the iron and steel sector. These credits are equal to the CO2 emissions of natural gas combustion. No CO2 emissions are accounted for in biomass use (the quantity of carbon stored in the growing biomass is equal to the quantity of carbon released when the biomass is combusted). Carbon storage in iron and steel is neglected. Emissions for limestone calcinations are also accounted for. Regarding CO2 emissions from electricity and steam production, it is assumed that the industry produces its own energy. Seven types of power plants are considered: • BF gas engines • Natural gas fired power plant (full electric mode) • Wood gasifier IGCC (integrated gasifier combined cycle) • Coal fired steam cycle • Coal fired IGCC (integrated gasifier combined cycle) • Nuclear power plant • Hydro power plant Availability of these power plant types depends on regional natural resources (hydro) and social acceptance (nuclear). Because the iron and steel industry represents only a small part of the total electricity demand, allocation of one specific type of electricity source to the iron and steel industry is questionable. However this approach was chosen in order to account for a decreasing CO2 intensity of electricity in case of CO2 taxes. Model calculations indicate that electricity production will become virtually CO2-free at comparatively low CO2 tax levels, see e.g. (Gielen 1999). In case these fuels are not sufficient to cover the demand for heat and electricity, natural gas can be used for heat generation (either via combined heat and power generation or stand-alone electricity production, depending on the energy demand structure). Because of insufficient regional detail the model is not suited to study district heating or heat cascading. As a consequence the model underestimates the energy efficiency potentials.

40

The Iron and Steel Industry Because of the sheer volumes and the transportation distances, transportation of resources, materials, and waste in the steel cycle is also a considerable source of GHG emissions. Total marine bunkers (for ocean-going vessels) amounted to 452 Mt CO2 in 1998 (World Coal Institute, 2001). An overview of world seaborne trade of main commodities is provided in table 3.2. Approximately 35% of the trade in coal is accounted for by coking coal. Assuming an energy consumption of 0.2 MJ/t.km, the total CO2 emission amounts to 327 Mt. This is slightly lower than the actual total emission. The gap can be explained by bunkering for passenger transportation, fishing etc.. The trade of iron ore and coking coal contributes approximately 45 Mt CO2. Trade of finished steel products must be added to this figure. The quantity is approximately 300 Mt (IISI 2001). Assuming an average transportation distance of 10,000 km, this adds another 45 Mt. It is estimated that 25 Mt of scrap is traded intercontinentally, based on (IISI 2001). Assuming an average transportation distance of 10,000 km this adds 4 Mt CO2. In total trade contributes 120 Mt CO2 emissions, compared to 2000 Mt emissions in production. Seaborne transportation adds 6% to the total emissions in the steel life cycle. This quantity is not negligible, especially if the 100% increase of trade in finished steel products during the last 10 years is considered. As a consequence the emissions during seaborne transportation have been included in the model, but they have not been taxed because they occur outside the national emission framework. Table 3.2: World seaborne trade of main bulk commodities, 1999 (Fearnleys, 1999) Tonnage Tonne-km Fraction CO2 9 [Mt] [10 t.km] [%] [Mt/yr] Iron ore 410 2220 10.3 33 Coal 480 2430 11.3 36 Grain 210 1170 5.4 18 Bauxite/alumina 53 295 1.4 4 Phosphate rock 31 135 0.6 2 Crude oil 1480 7500 34.9 112 Oil products 410 2010 9.4 30 Others 2140 6150 28.6 92 Total 5100 21480 100.0 327 Trade among the regions has been modelled for all types of solid products (ore, DRI, scrap, steel products. Liquid iron and liquid steel cannot be traded). Trade has been split into two categories: bulk commodities (ore, DRI, scrap) and steel products. Bulk commodities can be traded at lower cost because no packaging is required and larger bulk carriers can be used. The assumptions regarding transportation cost and transportation distances are summarized in Annex 3.

41

The Iron and Steel Industry Inland transportation (shipping, rail and road transportation) has been neglected in this study because of the scale of the model does not allow analysis of transportation on such a detailed level. It is estimated that transportation of iron and steel adds another 250 Mt CO2, based on an assumption of 2000 km throughout the life cycle (the total for steel, steel product and scrap transportation) and 2 MJ/t.km. This represents approximately 15% of the emissions in steel production. 3.3 Energy and material flow modelling The model flow variables have been divided into four categories (see Annex 1): • Energy carriers; • Resources; • Materials; • Waste. All energy carriers are expressed in energy units (GJ) and all resource, materials and waste flows are expressed in mass flows (tons). Within the category energy carriers, two types are discerned. One type is endogenous to the system (BF gas, coke oven gas and electricity), the other energy carriers can be exchanged with across the system boundary (at given price levels). For materials and for waste, a mass balance principle applies: the production is equal to the consumption. Nine types of finished iron and steel products are modelled: • Cast iron; • Concrete reinforcement bars; • Hot rolled sheet; • Steel wire; • Alloyed steel; • Heavy plate; • Cold rolled coil; • Cold rolled coil, annealed and tempered; • Galvanized sheet. 3.4 Process Characterization Each process is characterized by its physical inputs and outputs of energy (in GJ per unit of activity) and its physical inputs and outputs of materials (in metric tons per unit of activity). These process characteristics are the same for all time periods. The model input data for processes are characterised in Table 3.3. The model input data consist of physical data (energy and materials balance) and financial data. Moreover maximum capacity constraints can be added. A detailed listing of input parameters is provided in Annex 2-6.

42

The Iron and Steel Industry Table 3.3: Example of model input data for a process, i.c. EAF steel production INPUT ELECTRICITY [GJ/t] 1.4 STEELSCRAP [t] 1.05 OUTPUT LIQUID STEEL [t] 1.0 INVESTMENT VARIABLE COST

[Y/t.yr] [Y/t]

50,000 2,000

UPPER BOUND CAPACITY LIFE

[Mt/yr] [years]

Year and region specific 20

Costs have been divided into investment costs, fixed costs and variable costs. The investment costs and the fixed costs are proportional to the installed capacity. Investment costs occur in the time period that the process comes on line. Fixed costs arise for all years the process is operational (the full life span). Annual fixed costs have been set at 4% of the investment costs. Variable costs are proportional to the inputs of energy and materials. All costs have been expressed in costs of the base year, based on a 8% discount rate that reflects industrial profitability criteria. The life of all processes can be defined exogenously (in periods of 5 years). The data for the processes have been derived from a Western European database (Daniels and Moll 1997, MATTER2000). Data have been adjusted to reflect regional differences and new processes have been added in order to reflect production technologies in other regions (e.g. iron ingot casting, open hearth furnaces, and beehive coke ovens). 3.5 Regional detail Because this study focuses on Japan, the region around Japan has been modelled in detail, while other world regions have been modelled on the continent level. This has resulted in a model with 11 regions: • Japan; • China; • South Korea; • Oceania (Australia and New Zealand); • The community of independent states CIS (the former USSR); • North America (Canada, USA and Mexico); • Middle East (Iran, Irak, Saudi Arabia, Kuwait, Oman, UAE, Yemen, Egypt, Lybia, Algeria); • Europe (EU15 + Norway, Iceland, Switzerland, Czech republic, Slovakia, Poland, Romania, former Yugoslavia, Turkey, Bulgaria); • Other Latin America; • Other Asia; • Other Africa. 43


The Middle East has been modelled separately because of the gas reserves and the CO2 storage potential in depleted oil and gas fields. Japan, China and South Korea are the main players in the North East Asian steel market. Their production amounted to 94.2, 123.7 and 41 Mt, respectively, in 1999. Their joint production (259 Mt) represents 33% of the global steel production. The investment costs differ per region, the labour costs differ per region, resource availability differs, regional market size differs and the technology availability differs. The regional diversity of investment costs, labour costs, energy costs and resource costs has been expressed relative to the costs in Japan. The assumptions are summarized in Annex 4. Figure 3.3 shows the model structure for steel production. Three coal-based technologies are considered for production of liquid iron: the blast furnace, Corex and a combined Corex/DRI production process. Four types of coke ovens have been considered (Moll and Daniels 1997, Jiang et al. 1998, Buss et al. 1999): - Recovery type, wet quenching; - Recovery type, dry quenching; - Non-recovery type; - Small-scale beehive oven. Increased coal injection is considered as autonomous development for blast furnaces. Charcoal and waste plastic are considered as substitutes for (limited amounts of) coal in iron making. Three steel making routes are considered: OHF (Open Hearth Furnace, which is disappearing), BOF (the Basic Oxygen Furnace) and EAF (the Electric Arc Furnace). DRI (Direct Reduced Iron, a solid iron product based on iron and natural gas) can substitute scrap in EAF steel making. Two steel qualities are considered: high quality (e.g. sheet < 3 mm, coated sheet, wire and tubes) and conventional (e.g. heavy plate, bars, castings). This reflects the fact that impurities can limit the steel application. Scrap based EAF steel can only be used for the conventional quality. It is assumed that the high quality can only be produced from iron ore.

44

The Iron and Steel Industry COKING COAL

IRON ORE

COKING

SINTERING

COKE PCI PLASTICS PLASMA FUEL OIL CHARCOAL

NATURAL GAS

SCRAP

PELLETISING

SINTER

PELLETS

BLAST FURNACE OFF-GAS

STEAM COAL

COREX LIQUID IRON

OFF-GAS

CASTING

DRI PROD.

PIG IRON

DRI

REHEAT OHF

BOF

EAF

HQ LIQ. STEEL INGOT CASTING

EAF LIQ. STEEL

CONT. CASTING

NNS CASTING

CONT. CASTING

STEEL INGOTS REHEATING

ROLLING

ROLLING

FINISHED STEEL PRODUCTS

Figure 3.3: STEAP model structure for steel supply. See glossary for acronyms. 3.6 Demand projections Demand has been forecast as a function of GDP, income elasticities, and autonomous efficiency gains. The assumptions are summarised in Annex 3. The demand vector in STEAP has been split into national demand and (net) exports. National demand has been divided into 8 demand categories (packaging, building materials etc., based on (Crompton 2000)). Each product category is characterized by a fixed mix of plastics and by a fixed product life. The steel mix in each sector has been calibrated with the actual consumption data. Future demand is estimated as a function of GDP, an income elasticity of 0.5 (1% GDP growth results in 0.5% physical demand growth, based on (Mannearts 2000)), and an autonomous materials efficiency improvement (AMEI) of 0.5% per year. The latter variable reflects technological progress based on product re-design, improved materials quality, etc.. Also price elasticities of demand have been considered. This elasticity reflects the fact that the demand decreases in case prices rise. This decline is caused by a number of mechanisms (Gielen 1999): a substitution of product services, redesign of products with less materials, materials substitution, re-use of products and increased product life. Figure 3.4 illustrates the optimisation procedure for one material (Loulou and Lavigne 1996).

45

The Iron and Steel Industry P

D S’ S

SP’ SP

EQ’

EQ

Q

Figure 3.4: Supply and demand equilibrium Figure 3.4 shows a supply curve (S) and a demand curve (D) for the base case (without GHG tax). Both curves are simulated with step-wise functions in order to be able to use a linear programming algorithm, which has major advantages from a computing point of view. The horizontal axis Q represents the quantity, the vertical axis P represents the price. The demand decreases if the price increases. Equilibrium between supply and demand is reached in point EQ (in model terms, the area to the left between the supply curve and the demand curve is maximised). The price that is set in this market is the shadow price SP. Supply curves are derived from the database of supply options in the model. Each supply option is characterised by costs, physical inputs and outputs and emissions. The potential contribution of each option is limited by the availability of the physical inputs and by the bounds on each supply option (e.g. a limited biomass availability because of the limited availability of land). Supply options are selected on the basis of cost minimisation, thus simulating the supply curve. In case a CO2 tax is introduced, the supply curve moves in an upward direction because all emissions in the supply chain are penalised and transferred in the production chain through increasing energy and materials prices (S changes to S’). Demand decreases because of increasing prices and a new equilibrium price and equilibrium quantity are achieved (EQ’). Three variables are used to model the elastic demand function: the demand elasticity, the maximum decrease of the demand, and the number of demand steps. The demand function is defined as (Loulou and Lavigne 1996):

46

The Iron and Steel Industry Qip/Qib = (Pip/Pib)ei where: Qip = demand for product (demand category) i after introduction of GHG tax Qib = demand for product i in the base case Pip = price of product i after introduction of the GHG tax Pib = price of product i in the base case e = price elasticity of demand This function is split into a fixed number of steps, characterised by a set fraction of demand and a cost level. These “demand reduction options” compete with options for emission reduction in materials production and waste management. The costs of these demand reductions are equivalent to the loss of consumer surplus (the decrease of the area below the demand curve D, see figure 3.4). The minimum and maximum demand is 50% lower respectively 25% higher than the default demand in the base case. This section of the curve is split into 30 steps (each step represents 2.5% of the BC demand). The demand elasticity has been set at –0.2 (a 1% price increase results in a 0.2% demand reduction) (Mannaerts 2000). 3.7 Market distortions During the last two years the US government has been claiming that in other countries, especially Japan use non-competitive practices (Tilton 1998, International Trade Administration 2000). Amongst others, they claim with regard to Japan that: • Japanese steel prices are 60% above world market prices; • Despite the high prices, imports into Japan are insignificant; • The market shares of the five primary steel producers have been constant over a period of more than two decades; • A secret agreement of European and Japanese producers exists not to trade in each others markets, resulting in increasing sales in the US market; • The Japanese steel producers control the trading companies, thus preventing any imports; • The Japanese steel producers get METI involved in production planning, so the Japanese Free Trade Commission cannot intervene (one ministry cannot act in the competence field of another ministry). It is not clear whether all these allegations are true. The bilateral trade restrictions that were raised by the US have been rejected by WTO (WTO 2001). However the amounted evidence suggests that the ideal market mechanism may not reflect the real world adequately. This is especially valid for historical years. For this reason an alternative market mechanism has been analysed, the regional monopoly. Probably the real world is in between a monopoly and the ideal market.

47

The Iron and Steel Industry Market distortions have been modelled via: • Upper limits for trade; • Upper limits for regional production activity; • Import tariffs; • Additional trade costs reflecting market barriers such as regulations and transaction costs; • Monopoly algorithm (in a separate model run). Trade modelling is discussed in more detail in the next section. The monopoly is discussed in this section. In the ideal market simulation, the loss of consumer/producer surplus (the area between the supply curve and the demand curve in figure 3.4) is minimized. In the monopoly simulation, the profit of the monopolist is maximized. This situation is illustrated in figure 3.5. In the ideal market, the equilibrium is given by (Q1,P1). In the monopoly, the equilibrium is (Q2,P2). This new equilibrium is characterised by the condition of maximised profits (Francois 1998): (P2 – MC)/P2 = 1/e e = - dQ/dP X P/Q e is the market elasticity of demand (the first order derivative of the demand curve). MC is the marginal cost level (the cross-section of the vertical line from P2Q2 and the supply curve). P2 can be calculated as a function of the base case price and quantity: P2 = P1 x (Q2/Q1)1/e Q2 = Q1 x (1 – N x 0.025)

48


P

DEMAND

SUPPLY

P2 P1

MC Q2 (MON)

Q1 (IM)

Q

Figure 3.5: Supply-demand equilibrium in an ideal market (IM) and in a monopoly (MON). P = price, Q = quantity, MC = marginal costs 3.8

International trade modelling

This model is based on the assumption that imports are prefect substitutes for national iron and steel products. However such international trade is constrained by trade barriers. Trade barriers can be split into natural trade barriers (transportation cost) and government policies. Governments regulate imports through a combination of tariff and non-tariff measures (Worldbank 2000). The most common form of tariff is an ad valorem duty (a percentage of the trade value in monetary units), but tariffs may also be levied on a specific, or per unit basis. Non-tariff barriers may take many forms. Some common ones are licensing schemes, quotas, prohibitions, export restraint agreements. Moreover a wide range of domestic policies and regulations may act as non-tariff barriers. It is difficult to prove the existence of non-tariff barriers, but studies suggest that they

49

The Iron and Steel Industry are common practice for steel (International Trade Administration, 2000). It is even more difficult to quantify non-tariff barriers and to include them in the model. A ban on imports has been considered. Other non-tariff barriers have been expressed in monetary terms (annex 3). The data basis for these values is nonexistent. The estimates are based on information regarding interregional trade (ECE 1987, International Trade Administration 2000, IISI 2001). In case trade does not occur, despite model calculations indicate trade, significant non-tariff barriers have been added. It is assumed that these barriers will disappear in the next decade because of the increasing influence of GATT. Two types of trade barriers have been considered in the model: • Transportation costs; • Import tariffs. Import tariffs have been estimated on the basis of the mean tariff for primary products16 (Worldbank 2000). In case the model region covers a number of countries, the tariffs for the largest country (in terms of steel markets) have been used (e.g. Brazil for Latin America, India for Other Asia). The product prices have been taken from a STEAP BC model run (excluding tariffs). The resulting tax has been set as a fixed value. Also a correction has been applied for the decreasing import tariffs in time. The assumptions are listed in Annex 3. 3.9 Scrap management modelling Not all steel ends up as scrap. Part of it is oxidized during use. Another part is ‘lost’, for example for foundations, during mining, drilling, sea pipelines etc.. However data regarding these losses are not available. In this study it has been assumed that these losses range from 10% (transportation equipment, machinery, buildings) to 30% (packaging). Scrap has been split into four types: • Bulk scrap (transportation equipment, machinery, scrap in building and construction waste); • Diluted steel scrap (either distance >500 km, very low population (scrap) density or more than 25% other materials included); • Steel scrap in municipal solid waste (beverage cans, tins, part of electric appliances); • Collected and prepared scrap, ready for re-use. The first three scrap types can be upgraded for recycling. The upgrading costs depend on the scrap quality. For example galvanised and painted sheet “diluted steel scrap ”requires significant pre-treatment before it can be used in EAFs. The collection costs and the upgrading costs depend also on the distance, the

16

Covering SITC revision 2 sections 0-4 plus division 68 (non-ferrous metals) 50

The Iron and Steel Industry population density and the scrap quality. These have been varied across regions (see annex 5). Different collection costs have been assumed, ranging from 2000 to 4000 Y/t (based on global scrap prices). The costs for pre-treatment range from approximately 2000 Y/t for bulk scrap collection and preparation in industrialised countries to 8000 Y/t for scrap recovery from MSW in CIS. The opportunity cost of scrap depend also on the alternative waste treatment (i.c. disposal). Assumed disposal costs in 2015 range from 1.800 Y/t (Oceania, Middle East, CIS) to 18,000 Y/t (Japan, Korea, Europe). Note that all these costs are assumptions, based on fragmentary knowledge from earlier modelling studies for Europe and other regions. 3.10 Software and model operation The STEAP model consists of the following elements • An Excel spreadsheet with model input parameters • A model code with equations • A program which builds a matrix from the input data and the model code (GAMS) • A matrix solver (e.g. CPLEX, OSL or CONOPT) • A model code for the report writing The model consists of 175,000 rows and columns. The model solves in 30 minutes, using a PC with a Pentium III processor. This model run time is short enough to allow extensive sensitivity analysis and scenario analysis. 3.11 Scenario definition and policy simulation The assumptions regarding economic growth, income elasticity of steel demand and autonomous efficiency gains are shown in annex 5. Given the high uncertainty which is inherent to the forecasting method and given the relative insensitivity of steel demand to growth figures in comparison to other materials), only one socio-economic scenario is considered. This approach reduces the volume of output data significantly and it allows a policy analysis and discussion of results in greater detail. At a later stage more scenarios can be developed for strategy development. A base case (BC) has been analysed that represents a ‘business-as-usual’ scenario without any additional policies. Five CO2 tax levels have been analysed, ranging from 1,250 Y/t CO2 (CO2tax12) to 20,000 Y/t CO2 (CO2tax200), see figure 3.6. A global tax has been analysed (the same tax in all countries) and a tax in Japan and Europe, and no tax in other regions (indicated by the code JE).

51


[Y/t CO2]

20000 17500 15000

CO2tax200 CO2tax100 CO2tax50 CO2tax25 CO2tax12

12500 10000 7500 5000 2500 0 2000

2005

2010

2015 [YEAR]

Figure 3.6: CO2 tax levels analysed

52

2020

2025

2030


4 Model results The discussion of model results is split into four sections • Model validation – how close are the results for the past and the actual data; • Results in case of a global tax; • Results in case of a Japanese/European tax; • Comparison of the results with other studies. 4.1

Model validation

Each model should be tested for its ability to simulate the reality. However it is per definition impossible to predict the future. At the very best, a model may provide some insights regarding mechanisms that would have been overlooked if simple extrapolation was applied. Many scientists demand that a model should at least be validated for the past. Unfortunately even this validation has only limited value. The reason is that we know now what we did not know in the past. For example the oil prices have fluctuated greatly, and very few people have forecast these fluctuations accurately at that time. The same holds true for technological development. A model simulation of the past with this added knowledge provides little information regarding the forecasting quality for the future. Comparison with other forecasting studies proves little relief: the studies are not independent. The forecasting community is small, often the same data sources are used etc.. As a consequence the results of any long-term technology forecast should be discounted heavily. “The illusion of knowing what’s going to happen is worse than not knowing” (Utterback, quoted by Sherden, 1998). Data regarding coal inputs into the iron and steel industry are available from statistics (table 4.1). The model coal consumption for 1980-1995 is within 10% of the actual consumption, which is sufficiently close given the uncertainties in statistical data. Some data sources suggest that part of the coal use in countries such as China is accounted for by heating of company employee residences etc., a demand category that is not considered in this model. As a consequence the data are even closer. However for the year 2000 the gap widens to 14%. This gap can be explained by an overestimation of steel recycling (see below). This is an indication that product life may be underestimated or recovery rates are overestimated.

53

The Iron and Steel Industry Table 4.1: Coal consumption for steel production, comparison of model output and actual data (World Coal Institute 2000b, IEA2000). Figures in brackets indicate the additional coal use for electricity production. Assumption 27 GJ/t coal (Steap model figures are in energy units). STEAP STEAP Actual Coal for electricity Coal for I&S Coal for I&S [Mt/yr] [Mt/yr] [Mt/yr] 1980 622 650 84 1990 628 676 81 1995 573 635 94 2000 608 527 115 Actual interregional trade in the period 1970-1985 is considerably lower than the model results (ECE1987). This gap may be explained by non-price trade barriers. The implicit assumption in the model is that these barriers are gone after 2000. If this is not the case, the model is not a good representation of the real world situation. However in such a case any CO2 policy becomes feasible and the analysis of trade effects becomes irrelevant. Table 4.2: Global trade in iron and steel (ECE 1987). Excludes intra-regional trade. Actual trade STEAP model [Mt/yr] [Mt/yr] 1970 62.5 235 1975 83.2 258 1980 99.8 266 1982 99.1 273

54


Table 4.3 shows the actual EAF steel production in comparison to the model estimates. The model results for 1985-1995 are within 10% of the actual values. However EAF steel recycling in 2000 is overestimated by 15%. In model terms this can be explained by large quantities of steel scrap from the building sector that are released after a product life of 35 years. Buildings from 1965 (the first year within the model time horizon) are scrapped in 2000. Because the building sector is a major steel market, this results in a significant increase of steel scrap quantities. A more sophisticated model regarding product life may generate more realistic results. Table 4.3: EAF steel production (Fritz 1999). Actual [Mt/yr] 1980 1985 1990 1995 2000

185 215 245 300

STEAP model [Mt/yr] 165 195 218 219 349

Table 4.4 shows the actual crude steel production and the production according to the model. One must add that the model steel production data do not account for losses in steel casting. These losses are probably accounted for in the statistics and account for 2-5% of production. The results show that the model overestimates production. In 2000, the overestimation amounts to 7%. Table 4.4: Global crude steel production (IISI 2001). Actual production STEAP model 1970 1975 1980 1985 1990 1995 2000

[Mt/yr] 595 644 714 719 770 756 828

[Mt/yr] 672 712 748 774 828 857 890

STEAP Cast iron [Mt/yr] 31 37 43 49 57 62 68

Table 4.5 compares the actual finished steel production and the model results. The model overestimates final steel production by 10%.

55

The Iron and Steel Industry Table 4.5: Global finished steel production (IISI 2001). Actual production STEAP model 1970 1975 1980 1985 1990 1995 2000

[Mt/yr] 445 520 555 585 630 648

[Mt/yr] 479 529 573 624 688 719 757

STEAP Cast iron [Mt/yr] 31 37 43 49 57 62 68

Prices of finished products and model shadow prices are compared in table 4.6. The results indicate that the shadow prices are very close to the real world prices. However one must add that the shadow prices change significantly in time. Table 4.6: Actual prices of finished steel products (World Bank 2001) vs. model shadow prices (100 Y = 1 US$) Product Japanese export price STEAP model BC 17 fob 1999-2000 2000 [Y/t] [Y/t] Cold rolled coil 32,000-38,500 31,000 Hot rolled coil/sheet 23,000-29,500 24,700 Rebar 23,400-25,000 23,400 Wire rod 29,000-31,000 25,900 In conclusion, the model reflects the results for the past with 10% accuracy. As a consequence the model is not suited for the analysis if small scale system distortions, but may provide new insights regarding the impact of significant distortions (e.g. the impact of a 10% coal price increase can not be analysed properly, but a doubling of coal prices can be analysed).

17

fob free on board 56


4.2 Global GHG policies Global steel production for increasing GHG taxes is shown in figure 4.1. Steel production continues growing during the next three decades, but the growth levels off. In 2030, production amounts to 818 Mt. This represents a growth of 14% from 2000 levels. The impact of the CO2 tax on the consumption is comparatively small. The maximum decline in the (hypothetical) 20,000 Y/t tax case amounts to 63 Mt (-6.5% of the total production). 1200 1000 BC

[Mt/yr]

800

CO2tax12 CO2tax25

600

CO2tax50 CO2tax100

400

CO2tax200

200

19 65 19 75 19 85 19 95 20 05 20 15 20 25

0

Figure 4.1: Global steel production 1965-2030 (final products) Figure 4.2 shows the CO2 emissions. The emissions for the period 1965-1965 are almost stable at a level of 2000 Mt, despite a significant increase of production (that is compensated by energy efficiency gains and increasing EAF steel production). BC emissions decline during the period 2000-2025 to a level of 1525 Mt. The gap between the increasing production and the declining emissions can be explained by increasing energy efficiency, increasing scrap availability and fuel switches (including switches in electricity production). The introduction of a global CO2 tax has a significant impact on the emissions. They decline by up to 560 Mt in 2015 (-34%) and they decline by up to 1170 Mt (-75%) in 2030.

57


[Mt CO2/yr]

2500 BC

2000

CO2tax12

1500

CO2tax25 CO2tax50

1000

CO2tax100

500

CO2tax200

20 25

20 15

20 05

19 95

19 85

19 75

19 65

0

Figure 4.2: Global CO2 emissions in the steel life cycle, 1965-2030 The changes in foreign trade are shown in figure 4.3. In BC the figures indicate a gradual increase. The model results suggest strong growth of interregional trade in the period 2000-2030 (+250%). A global CO2 tax has no significant effect on trade. However this does not imply that the trade patterns remain unchanged (see the results for Japan below). 1000 900 800

BC

[Mt/yr]

700

CO2tax12

600

CO2tax25

500

CO2tax50

400

CO2tax100

300

CO2tax200

200 100

25 20

10 20

95 19

80 19

19

65

0

Figure 4.3: Interregional trade in iron and steel products, 1965-2030 The distribution of production in BC is shown in figure 4.4. The results suggest a gradual decline of steel production in the US, and a gradual increase in Europe and especially China.

58


1000

OAFRICA

900

OASIA

[Mt/yr]

800

LAMERICA

700

MEAST

600

CIS

500

NAMERICA

400

EUROPE

300

OCEANIA

200

KOREA CHINA

100

JAPAN 25 20

15 20

05 20

95 19

85 19

75 19

19

65

0

Figure 4.4: Distribution of production, 1965-2030, BC Comparison of figure 4.4 and figure 4.5 shows the impact of a CO2 tax on global steel production. The impact is very limited. Production increases in Europe and the US from 2025 onward. Production declines in China. The impact on other regions (including Japan) is rather limited. This result suggests that a global CO2 reduction policy would not affect the competitive position of countries, an important conclusion that opens the way to meaningful emission reductions. Off course the key assumption in this analysis is the establishment of a global tax. In case the tax is only levied in a limited number of countries, the results may look quite different (see below).

[Mt/yr]

1000 900

OAFRICA

800

OASIA

700

LAMERICA

600

MEAST

500

CIS

400

NAMERICA

300

EUROPE

200

OCEANIA

100

KOREA CHINA 25

JAPAN

20

15 20

05 20

95 19

85 19

75 19

19

65

0

Figure 4.5: Distribution of production 1965-2030, CO2tax50 59

The Iron and Steel Industry Figure 4.6 shows the global primary energy use, which is almost constant during the period 1965-1995. The 20EJ energy demand can be compared to a total global energy consumption of approximately 400 EJ (5%). However beyond 1995 energy consumption declines by 25%, in line with CO2 emission reduction. The demand for steam coal keeps growing (for electricity production for EAF steel production), while the demand for coking coal in 2030 is reduced to a third of the demand in 1995. 25000

20000 NUCLEAR HYDRO

15000 [PJ/yr]

TAR FUEL OIL

10000

STEAM GAS

5000

COKING COAL STEAM COAL

25 20

15 20

05 20

95 19

85 19

75 19

19

65

0

-5000

Figure 4.6: Primary global energy use, BC The changing primary energy use due to CO2 taxes is shown in figure 4.7. Total primary energy use declines only marginally. However a significant switch occurs in the fuel mix. Hydro energy, nuclear energy and wood are introduced at the expense of coal. This fuel switch occurs in electricity production.

60

18000 16000 14000 12000 10000 8000 6000 4000 2000 0 -2000

HYDRO NUCLEAR TAR WOD FUO STE GAS COKCOA

B 2t C C ax1 O 2t 2 C ax2 O 5 C 2ta O x5 2 0 C tax1 O 2t 0 0 ax 20 0

[PJ/yr]


C

O

STECOA

Figure 4.7: Primary energy use, 2020 Figure 4.8 shows the strong decline in iron production from 600 Mt in 1995 to 400 Mt in 2030. Gas based DRI production disappears after 1995. The combined Corex/DRI steel production is introduced from 2025 onward. This pattern is barely affected by a CO2 tax. 700 600 500 [Mt/yr]

COREX/DRI MIDREX/DRI

400

COREX 300

BF BF old

200 100

2030

2025

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

1965

0

Figure 4.8: Iron production, BC Figure 4.9 shows the steel production mix. The Open Hearth Furnace disappears gradually. Scrap based EAF steel production shows the strongest growth, but

61

The Iron and Steel Industry BOF steel production continues growing, too. Note the overestimation of EAF steel production in 1965: the model is not valid in this initial period. The steep increase of EAF steel production in 2000 is caused by the assumed 35-year life span for steel products in the building and construction sector. 1000 900 800 700 OHF

[Mt/yr]

600

BOF

500

EAF/DRI

400

EAF/SCRAP

300 200 100

25 20

15 20

05 20

95 19

85 19

75 19

19

65

0

Figure 4.9: Steel production mix 1965-2030, BC Table 4.7 shows the model shadow prices for iron and steel products in 2020. Note that the price for iron is significantly lower than the current market price. The price for a number of EAF products becomes negative, indicating that increased production would not cost money but gain money. The reason is the steel scrap quality that prevents any additional recycling. Instead steel scrap is disposed. The costs for disposal can be saved in case of recycling, causing the negative shadow price. Note the significant price increases caused by CO2 taxes. However because a prices rise in all regions, the effects on competition are rather limited.

62

The Iron and Steel Industry Table 4.7: Changing shadow prices, Japan, 2020 BC MMFI MMFIING MMFIBF MMFDRI MMFLSH MMFLSL MMFCSING MMFCSTH MMFCSTL MMFCI MMFREB MMFHRS MMFWIR MMFALL MMFHEP MMFHRC MMFCRC MMFCRCAT MMFGSH MMFSIN

2,993 2,122 2,628 9,870 4,698 -804 4,266 4,503 -79 73,241 1,908 14,716 10,687 30,322 17,518 12,867 17,924 22,341 27,602 2,044

CO2tax12 CO2tax25 CO2tax50 CO2tax100 CO2tax200 5,741 9,282 12,216 16,336 17,492 3,777 7,201 9,060 12,170 12,555 5,357 8,877 11,771 15,912 17,045 15,815 18,275 21,937 30,386 32,012 7,549 10,844 13,894 18,415 19,374 -359 -506 -581 -1,121 -1,152 7,121 10,542 13,432 17,502 18,588 7,629 11,111 13,935 18,692 19,245 420 291 209 -462 -516 75,617 76,818 77,822 79,502 80,148 2,358 2,245 2,269 1,627 1,547 15,462 15,129 17,326 21,418 21,424 11,684 11,042 11,859 14,850 14,800 31,307 30,941 34,320 42,092 41,353 21,122 24,930 30,375 37,195 37,942 16,081 19,290 23,918 30,968 32,640 21,369 24,635 29,511 36,924 38,665 26,020 29,428 34,834 43,386 45,305 31,341 34,584 40,848 51,194 53,310 2,398 2,739 3,050 3,509 3,794

1000 900 800 700 [Mt/yr]

600

Disposal

500

BF EAF

400 300 200 100

Figure 4.10: Scrap treatment, BC

63

2035

2030

2025

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

1965

0

Figure 4.11: Scrap shipments, BC

64

25 20

15 20

05 20

95 19

85 19

75 19

65

250 200 150 100 50 0 -50 -100 -150 -200 -250

19

[Mt/yr]


OAFRICA OASIA LAMERICA MEAST CIS NAMERICA EUROPE OCEANIA KOREA CHINA JAPAN


4.3

Japanese and European stand-alone policies

In case only Japan and Europe implement the tax of 1250-5000 Y/t, the emission reduction is less significant than in case of a global tax: a reduction of 93 Mt in 2015 (-5.6%) and a reduction of 114 Mt (-7.5%) in 2030. Global emissions actually increase in case higher taxes are implemented. This effect can be attributed to relocation of production to countries with lower energy efficiency, and increased emissions for transportation.

[Mt CO2/yr]

2500 BC

2000

CO2tax12JE

1500

CO2tax25JE CO2tax50JE

1000

CO2tax100JE

500

CO2tax200JE

25

20

15 20

05 20

95

19

19

85

75

19

19

65

0

Figure 4.12: Global CO2 emissions in the steel life cycle, 1965-2030, in case Japan and Europe introduce a tax A tax limited to Japan and Europe results in an increase of trade volumes by 50150 Mt. 1000 900 800

BC

[Mt/yr]

700

CO2tax12JE

600

CO2tax25JE

500

CO2tax50JE

400

CO2tax100JE

300

CO2tax200JE

200 100

20 25

20 10

19 95

19 80

19 65

0

Figure 4.13: Trade in steel in case of a Japanese/European tax

65

The Iron and Steel Industry Figure 4.14 shows the production per region in case only Japan and Europe introduce a tax of 5000 EUR/t. Production in Japan declines by 35-50%, production in Europe declines by 35%. Production increases in all other regions by 10-20%.

[Mt/yr]

1000 900

OAFRICA

800

OASIA

700

LAMERICA

600

MEAST

500

CIS

400

NAMERICA

300

EUROPE

200

OCEANIA

100

KOREA CHINA 25 20

15 20

05 20

95 19

85 19

75

JAPAN 19

19

65

0

Figure 4.14: Distribution of production 1965-2030, CO2tax50JE Table 4.8 shows the carbon leakage effects. Carbon leakage is defined as: CL = Emission increase outside the tax region/ Emission reduction inside the tax region The carbon leakage in the period 2010 to 2030 amounts to 54-75%. This is a clear indication that such a policy would make little sense: all emission reductions in Japan and Europe are balanced by increasing emissions in other regions. Such a tax policy makes only sense in case the industry is protected (e.g. by taxing imports).

66

The Iron and Steel Industry Table 4.8: Carbon leakage effects in case Japan and Europe introduce a 5000 Y/t tax Japan/Europe Global tax [Mt CO2/yr] 23.3 23.3 101.4 119.4 147.3 102.5 112.9

2000 2005 2010 2015 2020 2025 2030

Japan/Europe JE tax [Mt CO2/yr] 38.0 34.8 180.7 253.0 340.8 324.3 339.6

Global JE tax [Mt CO2/yr] 16.6 22.2 82.5 93.2 113.2 80.8 114.0

Leakage JE tax [%] 38 36 54 63 67 75 66

80 70 60 50 40 30 20 10 0

CO2tax12 CO2tax25 CO2tax50 CO2tax100

20 35

20 30

20 25

20 20

20 15

CO2tax200

20 10

20 05

Leakage [%]

Figure 4.15 shows the carbon leakage as a function of the tax level. The leakage increases in time, and it increases for increasing tax levels. Even for a comparatively low tax level of 1250 Y/t CO2, the leakage in 2020 amounts to 35%. The most significant increase of carbon leakage occurs in the range from BC to 5,000 Y/t. For higher tax levels, the increase is small. The results show in no case a leakage in excess of 100%. This result is in contrast with industry claims that leakage would exceed 100% because of relocation of industry to countries with lower energy efficiencies. The model results suggest that developing countries improve their energy efficiency during the next two decades significantly, thus limiting leakage.

Figure 4.15: Carbon leakage as a function of the tax level

67


Japanese results Figure 4.15 shows the historical steel production and the model results for different policy cases. Historical demand is slightly underestimated in the model. However figures for 1995 match with the actual production data. The decline for future production is in line with other studies, e.g. (Crompton 2000). In case of a global tax, production is not affected. In case of a Japanese/European tax, production is halved in 2020.

120

[Mt/yr]

100 80

Actual BC

60

CO2tax50

40

CO2tax50JE

20

25

20

15

20

05

20

95

19

85

19

75

19

19

65

0

Figure 4.15: Japanese crude steel production 1965-2030 Figure 4.16 shows the CO2 emissions. The emissions in 1995 according to the model amount to 232 Mt CO2. This includes 14 Mt CO2 in electricity production. According to statistics, emissions in 1998 amounted to 160 Mt CO2 (including electricity production)(IEE 2000). It is not clear how the emissions from blast furnace gas are accounted for in this statistic. Coal consumption for iron and steel industry and for coke production amounted to 66.1 Mt in 1998. The model indicates a coal consumption of 77 Mt for 1995 and 60 Mt for 2000, in line with the statistics. BC emissions decline from 230 Mt in 1995 to 150 Mt in 2030. in case of a global tax, an emission reduction of 50 Mt is achieved. In case of a tax in Japan and Europe, emissions decline by 120 Mt in 2030. However the bulk of this additional emission reduction can be attributed to a reduction of primary steel output that is compensated by increased imports.

68


[Mt CO2/yr]

250 200 BC

150

CO2tax50

100

CO2tax50JE

50

25 20

20

15

05 20

95 19

85 19

75 19

19

65

0

Figure 4.16: CO2 emissions in the steel life cycle, Japan, 1965-2030 European results European steel production is shown in Figure 4.17. The model production data for 1990 and 1995 are within 5% of the actual production. The model indicates a 5-10% decline of production in case a global tax is implemented, and a 40% decline of production in case of a Japanese/European tax.

300

[Mt/yr]

250 200

Historical BC

150

CO2tax50

100

CO2tax50JE

50

25

20

15

20

05

20

95

19

85

19

75

19

19

65

0

Figure 4.17: European steel production, 1965-2030 Figure 4.18 shows the CO2 emissions in different policy cases. BC emissions decline from 500 Mt to less than 300 Mt in 2030. In case of a global tax, emissions decline by 10-100 Mt. In case of a Japanese/European tax emissions decline by 300-400 Mt (up to 90% emission reduction).

69


600 [Mt CO2/yr]

500 400

BC

300

CO2tax50

200

CO2tax50JE

100

25 20

15 20

05 20

95 19

85 19

75 19

19

65

0

Figure 4.18: CO2 emissions 1965-2030

70


5 Sensitivity analysis The model contains approximately half a million non-zeros. Fortunately a limited number of variables determine to a large extent the model output. The most important variables have been selected for sensitivity analysis. Table 5.1 provides an overview of the sensitivity model runs. Variables for sensitivity analysis have been selected based on experiences from previous modelling exercises (see e.g. Gielen et al. 1996, Gielen and Moriguchi 2001), and they have been selected in order to assess the special features of the models (in case of the market mechanism and trade barriers). Table 5.1: Overview of sensitivity model runs Standard runs Sensitivity analysis 1 Interest rate 8% 3% 2 Price elasticity -0.2 -0.5 3 Gas price Gas 550 Y/GJ Gas 400 Y/GJ (2020) (2020) 4 Technology No CCF CCF mix 5 Technology CO2 removal No CO2 removal mix No CO2-free 6 Technology CO2-free electricity mix electricity 7 Market Ideal market Regional monopoly mechanism 8 Trade barriers No regulatory +2,500 Y/t barriers/tariffs The discussion of results is limited to the key environmental issues in this study. Other variables may also be affected, but are not discussed in more detail. 5.1 Interest rate Figure 5.1 shows the impact of the interest rate on GHG emissions. The real interest rate (excluding inflation) has been decreased for 8% (an industry perspective) to 3% (a government perspective). In case of a tax of 2,500 Y/t Co2, emissions are initially lower in case of a lower interest rate, but emissions from 2020 onward are identical for the 8% interest rate and the 3% interest case. The initial gap can be explained by differences in electricity production (it is more attractive to build capital intensive hydropower plants or nuclear plants instead of coal fired power plants in case of low interest rates. However these plants are introduced in the 8% interest case at the 2,500 Y/t tax level, too.

71


[Mt CO2/yr]

2500 2000 1500

REF 8% 3%

1000 500

20 25

20 15

20 05

19 95

19 85

19 75

19 65

0

Figure 5.1: CO2 emissions in case of 3% interest rate, tax 2,500 Y/t CO2 5.2

Higher price elasticity

In the reference calculations the demand elasticity was set at –0.2, based on (Mannaerts, 2000). In the authors engineering experience, such a low elasticity is surprising. Ample engineering data exist that materials savings of 25-50% are technically feasible for limited additional cost. As a consequence a sensitivity analysis has been done with an elasticity of –0.5. The results are shown in figure 5.1. For a tax of 10,000 Y/t, the reference calculation (E=-0.2) shows a demand reduction of 60 Mt in 2020 (-6%), while the higher elasticity (E = -0.5) results in a demand reduction of 150 Mt (-16%). The impact on CO2 emissions is of a similar order of magnitude: the emission reduction declines by 65 Mt in 2020 (-6%). These results indicate that the elasticity is a key parameter, and more research regarding long-term future elasticities is strongly recommended. 1200 1000

[Mt/yr]

800 BC

600

100 EUR/t, E = -0.2 100 EUR/t, E = -0.5

400 200

5 20 2

0 20 1

5 19 9

0 19 8

19 6

5

0

Figure 5.1: Iron and steel demand for different elasticities 72


5.3

Lower gas price

The impact of the gas price has been analyzed in a sensitivity analysis at the 2,500 Y/t tax level where the gas price has been lowered to a level of 400 Y/t in Japan (and lower elsewhere, see annex 4), compared to 550 Y/t in the reference calculation. The results showed no difference compared to the reference calculations. This suggests that the results are fairly robust with regard to the gas price. 5.4

Technology mix: including smelting reduction

CCF technology represents an example of smelting reduction technologies that use coal and ore input (instead for pellets and coke for the blast furnace) in order to produce liquid iron. CCF has been simulated by changing the input for Corex from pellets to iron ore. In the BC, CCF iron production amounts to 25-30 Mt in 2025. The results for the 10,000 Y/t case show an increased CCF iron production (approx. 40 Mt from 2020 onward). CO2 emissions amount to 1030 Mt in 2020 (compared to 500 Mt in the reference calculations) emissions from 2025 onward are approximately 50 Mt higher than in the reference calculations. Still, the blast furnace remains the dominant iron production technology. 5.5

Technology mix: no CO2 removal

CO2 removal contributes significantly to the total emission reduction. However the future of this strategy is by no means certain. For example local residents may object to disposal. Another problem is the long-term effect of CO2 storage. Some authors claim the CO2 may escape in case of cap rock leakages, resulting in a zero storage effect over a period of decades. As a consequence the case has been considered without CO2 storage (for a tax of 10,000 Y/t). Only a high tax level has been analyzed because this option is comparatively costly and the option is not applied in the reference calculations at lower tax levels. The resulting CO2 emissions are shown in figure 5.2. CO2 emissions after 2015 are approximately 600 Mt higher (emission reduction declines from 1100 Mt to 500 Mt). Figure 5.3 shows the iron production. Comparison with figure 4.8 (BC) shows no significant differences (more CCF in 2020).

73


[Mt CO2/yr]

2500 2000 1500

REF No CO2 removal

1000 500

19 65 19 75 19 85 19 95 20 05 20 15 20 25

0

Figure 5.2: CO2 removal with and without CO2 removal

700 600 500 [Mt/yr]

CCF/DRI MIDREX/DRI

400

CCF 300

BF BF old

200 100

2030

2025

2020

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

1965

0

Figure 5.3: Iron production, tax 10,000 Y/t, no CO2 removal 5.6

Technology mix: no CO2 free electricity

Figure 5.4 shows the CO2 emissions in case of a 10,000 Y/t tax and no CO2-free electricity (and no biomass for charcoal injection). The CO2 emissions after 2015 are approximately 600 Mt higher. This shows the importance of electricity for the emissions throughout the iron and steel life cycle.

74


[Mt CO2/yr]

2500 2000 1500

REF No CO2 free electricity

1000 500

95 20 05 20 15 20 25

19

75 85 19

19

19

65

0

Figure 5.4: CO2 emissions without CO2-free electricity, tax 10,000 Y/t 5.7

Market mechanism: monopolies

Increasing scale of steel companies is an ongoing trend. This will affect the market structure, which will change from a market with many suppliers to a market of a few suppliers. One extreme has been analyzed in this study: regional monopolies (one supplier per region). This supplier has to compete with steel producers from other regions. It is assumed that these trends towards increased scale occur in the OECD countries, in China and in the CIS region. Producers in other developing regions (Latin America, Africa, Other Asia and the Middle East) remain of a smaller scale, resulting in competition. Because the regions compete amongst each other, the resulting market type could also be characterized as a global oligopoly. A natural limit is set to the profit maximization of the regional monopolist, given by the market entrance price levels for foreign producers. Two cases have been analyzed: a base case and a case of a 5,000 Y/t tax. 1200

[Mt/yr]

1000 800

BC

600

BC mon

400

CO2tax50 JE mon

200

19 65 19 75 19 85 19 95 20 05 20 15 20 25

0

Figure 5.5: Steel production in case of a regional monopoly 75


[Mt CO2/yr]

2500 2000 1500

BC

1000

CO2tax50 JE mon

BC mon

500

20 40

20 25

20 10

19 95

19 80

19 65

0

Figure 5.6: CO2 emissions in case of a regional monopoly 5.8

Introduction of import tariffs

One way to overcome the problem of carbon leakage is to erect trade barriers. Import tariffs are an example of effective barriers. Such barriers may even comply with the GATT agreement in case the environmental incentives are clear (e.g. on the basis of studies such as this one). The impact of trade barriers on carbon leakage is shown in table 5.2. A negative leakage indicates that emissions decrease globally while the decrease within the region, too. One must keep in mind that the reference for these calculations is the base case without import tariffs. In case trade tariffs would be introduced in the base case, emissions may decrease, too, for example because production is relocated from developing countries (cheap labor, low energy efficiency) to industrialized countries (expensive labor, high energy efficiency). The break-even point (0 carbon leakage) is reached at an import tariff between 2,500 and 5,000 Y/t. Table 5.2: Carbon leakage for increasing import tariffs in Japan and Europe, tax 2,500 Y/t CO2 Tariff 2010 2015 2020 2025

No tariff 83 59 84 128

2,500 Y/t 69 10 7 -48

76

5,000 Y/t 58 -13 -11 -113

10,000 Y/t 65 -17 0 -110

20,000 Y/t 68 -20 -12 -107

The Iron and Steel Industry 5.9

Overview of sensitivities

Table 5.3: Overview of sensitivity model runs, in comparison to reference calculations Policy regime Change of iron and Change of CO2 steel production emissions 2020 2020 [Mt/yr] [Mt/yr] 1 Interest rate 2,500 Y/t 0 0 2 Price elasticity 10,000 Y/t -90 -65 3 Gas price 2,500 Y/t 0 0 4 Technology 10,000 Y/t +20 Mt CCF +500 mix CCF 10,000 Y/t -10 +600 5 Technology mix CO2 removal 10,000 Y/t -30 +500 6 Technology mix CO2 free electricity BC/5,000 Y/t -195/-165 -310/-300 7 Market mechanism regional monopoly 2,500 Y/t 0 -50 8 Import tariff 2,500-5,000 Y/t

77


6 Conclusions During the last decades the environmental performance of the iron and steel industry has been improved significantly. The emission of CO2 is one of the remaining environmental challenges for this industry sector. Globally 6-7% of the GHG emissions are caused by iron and steel production (approximately 2000 Mt CO2 per year). The emissions are decreasing autonomously because of a steel demand that grows at a rate below the demand for other energy services, a gradual change in the sector structure from primary to secondary steel, and an increasing energy efficiency in primary steel production that is driven by technological progress. Emissions decline autonomously to a level of 1500 Mt in 2025 (-25% compared to 1995 levels), despite finished iron and steel output increases by 25% in the same period. As a consequence the autonomous decoupling amounts to a factor 1.67. 6.1 GHG emission reduction potentials The Japanese iron and steel industry may be affected significantly by CO2 policies because its relevance from a national GHG perspective. The Japanese steel industry emits 13% of the total national GHG emissions (approximately 160 Mt per year). This fraction is 2-3 times higher that the global contribution of the iron and steel industry. However in recent years emissions have been declining because of declining primary steel production.

The energy efficiency of Japanese steel producers is among the highest in the world. However this does not mean that the technical potential for CO2 emission reduction is exhausted. A number of options exist to reduce the emissions even further. An overview is provided in table 6.1.

78


Table 6.1: Overview of emission reduction options for the iron and steel industry. Figures in brackets indicate the potential for 2020 Category Option Potential 2010 Cost18 (potential 2020) [Mt CO2/yr] [Y/t CO2] Energy Coal injection 8.9 (8.9) 0 efficiency Near net 1.0 (4.5) -10,000-0 shape casting Energy 3.2 (3.2) ? recovery Smelting 10 (50) 0 reduction Fuel switch DRI/Natural 20 (70) 1000-2000 gas Charcoal 10 (35) 2000-5000 Waste plastics 15 (15) 0 CO2 storage Oceanic 0 (>100) 2000-5000 Deep aquifers 2000-5000 Materials 5 (20) -10,000-10,000 efficiency JI/CDM China 55 (0) 0-2000 Tree 2 (10) 0-2000 plantations Some of these options are characterised by zero or even negative cost. As a consequence they could be implemented without major negative impacts on the industry competitiveness, in fact they may even enhance the industrial competitiveness. However in a number of cases the technology is not yet fully developed, which may pose a barrier for rapid introduction. Options in this category include increase powder coal injection rates, enhanced energy recovery from blast furnace gas, smelting reduction technology, near net shape casting and waste plastic injection in blast furnaces. R&D should focus on the development of such technologies. In a number of other cases, significant costs arise. This is especially true in the case of CO2 storage. Introduction of such technology will affect the competitiveness of the national industry negatively, unless similar policies are introduced in other countries. This is also the case for the import of DRI and the use of charcoal or electricity for iron production. The potentials in table 6.1 cannot be added straightforward because options compete and interact. For example if EAF steel recycling replaces BF-BOF steel 18

Costs are expressed relative to a business-as-usual situation 79


making, emission reductions in BF-BOF steel making become infeasible. As a consequence integrated assessment is required for proper analysis. On a global scale, the energy efficiency differs significantly. Outdated steel production practices in developing countries are characterised by coal consumption rates that are 50%-100% higher than Japanese coal consumption rates. In case all global steel producers would be brought to the highest possible energy efficiency standard, CO2 emissions would be reduced by approximately 500 Mt per year. Because many of these emission reductions are cost-effective and the emission reduction potentials are significant on a global scale, the iron and steel industry should consider options within the sector instead of buying emission reduction permits of unclear origin. 6.2 The impact of GHG emission taxation on the iron and steel industry The STEAP model has been developed in order to study the economic impact of a CO2 tax on the global steel industry.

Model calculations suggest an ongoing shift from primary steel to scrap based steel production. This shift is driven by increasing scrap qualities and declining demand growth. New iron and steel production technologies do not gain a dominant position. This can be explained by the technology lock-in, caused by the long life span and high sunk cost of primary steel making plants. In case of global emission taxation, the impact on the selection of production locations is limited. Because the steel demand is comparatively inelastic to price changes, the production remains almost at the same level. In such a situation a 50% emission reduction is possible. In case Japan and Europe would implement a tax and other regions would not follow suit, the impact on Japan would be limited. However the impact on Europe would be a very substantial reduction of production. The emissions within both regions decline substantially but the emissions increase in other regions. The carbon leakage (the increase of foreign emissions divided by the emission reduction in Japan and Europe) amounts to 50-80%. Given the economic impacts, such a policy does not seem attractive unless measures are introduced in order to prevent carbon leakage. However GATT may prevent the introduction of such measures. A significant potential exists for improved materials efficiency. This potential is generally neglected in energy efficiency and CO2 reduction studies. Improved steel qualities can be of similar importance for CO2 emission reduction as increased energy efficiency. Assuming a moderate global efficiency potential of 10%, the emission reduction potential amounts to 150 Mt CO2 per year. It remains to see if this potential will be used to its full extent in the next decades.

80


6.3 Consequences for R&D The results indicate a number of areas where more research is warranted: - The combined Corex/DRI production process; - Increased materials efficiency; - CO2 removal and storage; - Near net shape casting; - CDM based CO2 emission reduction in steel industries in China, India, and the former USSR - Removal of impurities from steel scrap in order to enable high grade recycling.

81


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Annex 1: Overview of energy carriers and materials in the STEAP model EN energy carriers STECOA COKCOA COK COALINJ GAS BFGAS COGAS COREXGAS ELE STE FUO WOD TAR

Steam coal Coking coal Coke Energy injection in blast furnace Natural gas Blast furnace gas Coke oven gas Corex gas Electricity Steam Fuel oil Wood Tar

ENEND(EN) endogenous energy carriers (produced and consumed within the system) COK COALINJ BFGAS COGAS COREXGAS ELE

87


M materials MMFI MMFIING MMFIBF MMFDRI MMFLSH MMFLSL MMFCSING MMFCSTH MMFCSTL MMFCI MMFREB MMFHRS MMFWIR MMFALL MMFHEP MMFHRC MMFCRC MMFCRCAT MMFGSH MMFSIN MMFPEL OXY CO2REM W waste materials WMF WMFBULK WMFDIL scrap) WMFMSW

Liquid iron for use in steel plants Iron ingots Liquid iron from blast furnace Direct reduced iron Liquid steel high quality Liquid steel low quality Cast steel ingots Cast steel liquid high quality Cast steel liquid low quality Cast iron Rebars Hot rolled sheet Wire rod Alloy steel Heavy plate Hot rolled coil Cold rolled coil Cold rolled coil, annealed and tempered Galvanised sheet Sinter Pellets Oxygen CO2 removed Prepared steel scrap Steel scrap, pure Steel scrap, diluted with other materials (e.g. shredder Steel scrap in municipal solid waste

R natural resources MMFORE Iron ore MMFLIM Limestone MMFALLOY Alloying elements AIR Air DISP Disposal space BFS Blast furnace slag

88


Annex 2: Overview of processes in the STEAP model E01 E02 E03 E04 E05 E06 E07 E08 GA1 IIO IS2 IS3 IS4 IS5 IS6 IS61 IS7 IS8 IS9 ISA ISB ISC ISD ISE ISF ISG ISH ISI ISI1 ISJ ISK ISL ISM ISN ISO ISP IT1 IT2 IU1 IU2 IU3 IV1 IV2 IV3

Electricity production from BF gas Electricity production from natural gas Wood gasifier Electricity production from coal conventional Electricity production from coal advanced Nuclear electricity Hydro electricity Oil fired power plants Steam production Oxygen production Sinter plant Pelletising plant Blast furnace Basic oxygen furnace Continuous casting high quality steel Near net shape casting high quality steel Hot strip mill Plate mill Wire rod mill Heavy section mill Rebar mill Cold rolling mill Hot dip galvanising Annealing and tempering Electrogalvanising Electric arc furnace scrap fed Electric arc furnace DRI fed Continuous casting low quality steel Near net shape casting low quality steel Steel ingot casting Steel ingot reheating Pig iron casting Pig iron reheating Open hearth furnace Hot connection Blast furnace small scale 1 Mt Cupola cast iron production EAF alloy steel production including finishing Corex DRI production gas based DRI production corex gas based Coke oven recovery wet quenching Coke oven recovery dry quenching Coke oven non recovery

89


IV4 IV5 IV6 XA1 XA2 XA3 XA4 XA5 XA6 XA7 XA8 XA9 XA10 XA11 XA12 XA13 XA14 XA15 XA16 XA17 XA18 XA19 XB1 XB2 XB3 XB4 XB5 ZA1 ZA2 ZA3

Coke beehive oven Charcoal traditional Charcoal advanced Dummy coke oven gas to regular gas Dummy iron to steel scrap Dummy high quality steel to low quality steel Dummy scrap disposal Dummy BF gas to gas Dummy ele surplus use Dummy corex gas to BF gas Dummy coal injection Dummy coke use instead of coal injection Dummy oil injection Dummy plastic waste injection Dummy plasma injection BF gas CO2 removal Coke oven gas CO2 removal Corex gas CO2 removal Gas based DRI CO2 removal Dummy DRI to scrap Dummy steel scrap and DRI mixing for upgrading Dummy historical losses Bulk scrap collection and separation shredders etc Diluted bulk scrap large distance and mixed with other materials Scrap in municipal solid waste collection and magnetic separation Diluted scrap disposal Scrap in municipal solid waste disposal CO2 storage deep sea CO2 storage deep saline aquifers CO2 storage depleted oil and gas fields

90


Annex 3: Transportation cost and trade tariffs Transportation costs are split into transportation costs for finished steel products and bulk commodity transportation costs. The costs for packaging and the handling costs are considerably higher for finished steel products. Moreover the size of shipments is generally smaller. As a consequence total transportation costs are much higher. Table A3.1: Assumptions regarding sea borne transportation costs for finished steel products, CIF, in Yen of 2000 (Gielen and Dril 1997) JAPAN CHINA KOREA OCEANIA EUROPE NAMERICA CIS MEAST LAMERICA OASIA OAFRICA

JAPAN

CHINA

KOREA

OCEANIA

EUROPE

NAMERICA

CIS

MEAST

LAMERICA

OASIA

2000 1500 3000 10000 3000 12000 4000 10000 3000 6000

1500 3000 10000 3000 6000 4000 10000 3000 6000

3000 10000 3000 10000 4000 10000 3000 6000

8000 4000 12000 4000 10000 3000 6000

3000 3000 2000 4000 6000 5000

6000 6000 2000 10000 8000

2000 4000 6000 5000

4000 6000 5000

6000 5000

5000

Table A3.2 shows examples of bulk commodity transportation costs. Vessel size and route are important variables (the route determines if a return load exists, which halves costs). Table A3.2: Transportation costs 1999 (1 US$ = 110 Y) (IEA 2000) [Y/t] Iron ore 150 kt Tubarao/Rotterdam 350-725 (Capesize) Iron ore 140 kt W. Australia/Beilun 300-630 (Capesize) Grain 50 kt US Gulf/Japan 1375-2550 (Panamax) Grain 50 kt US Gulf/MEAST 900-1500 (Panamax) The waterfront charges (port authority, ancillary, terminal) and taxes and royalties must be added to the commodity transportation costs in table A3.2. In the case of coal they range from 5 U$/t to 10 US$/t (550-1100 Y/t) (IEA, 2000). They are probably similar for other commodities. Apart from the seaborne transportation costs, in land-transportation can be costly. For example US steel producers in Pittsburg (IEA, 2000) or German steel makers in the Ruhr area face significant additional costs (Gielen and Dril 1997). Rail transportation costs (including loading/unloading) amount to 1000 Y/1000 km. Given the global character of the model such detailed cost data cannot be used. Bulk transportation cost estimates are listed in table 4.3.

91


Table A3.3: Assumptions regarding bulk transportation costs JAPAN

CHINA

KOREA

OCEANIA EUROPE NAMERICA CIS

MEAST

LAMERICA OASIA

OAFRICA

JAPAN CHINA

500

KOREA

500

OCEANIA

750

750

750

EUROPE

2000

2000

2000

2000

NAMERICA

2000

2000

2000

2000

750

CIS

2000

1500

1500

3000

1500

3000

MEAST

1500

1500

1500

1500

1500

2000

2000

LAMERICA

1250

1250

1250

1250

1000

1000

1500

750

750

750

750

1000

1250

1500

500

1250

1000

1000

1000

1000

1000

1000

1250

750

750

OASIA OAFRICA

500

1250 5000

Table A3.4: Relative seaborne transportation costs in time (2000=1) 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

3 1.5 1.4 1.3 1.2 1.1 1 1 0.95 0.93 0.9 0.89 0.88 0.87 0.86 0.85

Table A3.5: Non-tariff barriers [Y/t]

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

2015

JAPAN

20000

20000

20000

15000

15000

15000

10000

5000

2500

2500

0

CHINA

5000

5000

5000

5000

5000

5000

5000

5000

2500

2500

0

KOREA

2000

2000

2000

2000

2000

2000

2000

2000

1000

1000

0

OCEANIA

0

0

0

0

0

0

0

0

0

0

0

EUROPE

2000

2000

2000

2000

2000

2000

2000

2000

1000

1000

0

NAMERICA

2000

2000

2000

2000

2000

2000

2000

2000

1000

1000

0

CIS

5000

5000

5000

5000

5000

5000

5000

5000

2500

2500

0

0

0

0

0

0

0

0

0

0

0

0

5000

5000

5000

5000

5000

5000

5000

5000

2500

2500

0

20000

20000

20000

20000

15000

10000

10000

10000

5000

5000

0

0

0

0

0

0

0

0

0

0

0

0

MEAST LAMERICA OASIA OAFRICA

Trade tariffs have been estimated on the basis of average trade tariffs for primary industrial commodities. In fact trade tariffs vary per steel product and per country within a region.

92


Table A3.6: Trade tariffs, 2000 (World Bank 2000) MMFI MMFIING MMFIBF MMFDRI MMFLSH MMFLSL MMFCSING MMFCSTH MMFCSTL MMFCI MMFREB MMFHRS MMFWIR MMFALL MMFHEP MMFHRC MMFCRC MMFCRCAT MMFGSH MMFSIN MMFPEL

JAPAN 1250 1250 1250 1500 1500 1500 1700 1700 1700 10000

CHINA 2500 2500 2500 3000 3000 3000 3400 3400 3400 20000

KOREA 1750 1750 1750 2100 2100 2100 2380 2380 2380 14000

OCEANIA 125 125 125 150 150 150 170 170 170 1000

EUROPE 1250 1250 1250 1500 1500 1500 1700 1700 1700 10000

NAMERICA 750 750 750 900 900 900 1020 1020 1020 6000

2000 2500 2300 20000

4000 5000 4600 40000

2800 3500 3220 28000

200 250 230 2000

2000 2500 2300 20000

1200 1500 1380 12000

2500 2200 3000 3000 3200 200 100

5000 4400 6000 6000 6400 400 200

3500 3080 4200 4200 4480 280 140

250 220 300 300 320 20 10

2500 2200 3000 3000 3200 200 100

1500 1320 1800 1800 1920 120 60

Table A3.4: Trade tariff trends 1965-2040 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

4 3 2.5 1.5 1.5 1.5 1.5 1 1 0.8 0.7 0.6 0.5 0.5 0.5 0.5

93

CIS 1250 1250 1250 1500 1500 1500 1700 1700 1700 1000 0 2000 2500 2300 2000 0 2500 2200 3000 3000 3200 200 100

MEAST 1500 1500 1500 1800 1800 1800 2040 2040 2040 12000

LAMERICA 1250 1250 1250 1500 1500 1500 1700 1700 1700 10000

OASIA 3125 3125 3125 3750 3750 3750 4250 4250 4250 25000

OAFRICA 1250 1250 1250 1500 1500 1500 1700 1700 1700 10000

2400 3000 2760 24000

2000 2500 2300 20000

5000 6250 5750 50000

2000 2500 2300 20000

3000 2640 3600 3600 3840 240 120

2500 2200 3000 3000 3200 200 100

6250 5500 7500 7500 8000 500 250

2500 2200 3000 3000 3200 200 100


ANNEX 4: Investment costs, labour costs, energy and resource costs relative to Japan Table A4.1: Relative energy costs (Japan = 1) COA GAS FUO CHAR TAR

JAPAN 1 1 1 1 1

CHINA 0.5 0.75 0.8 0.7 0.8

KOREA 1 1 1 1 1

OCEANIA 0.5 0.5 1 1 0.7

EUROPE 1 0.7 0.9 0.9 0.9

NAMERICA 0.75 0.8 0.9 1 0.9

CIS 0.5 0.5 0.7 0.7 0.7

MEAST 1 0.3 0.5 1 0.8

LAMERICA 0.75 0.75 0.75 0.5 0.75

OASIA 1 0.75 1 0.75 1

OAFRICA 0.5 1 1 0.5 1

Table A4.2: Coking coal prices for industry (IEA, 2000) (1 US$ = 110 Y; 28 GJ/t). In the case of Japan, prices for the iron and steel industry are approximately 5% below the industrial average price. 1980 1985 1990 1995 2000 Japan [US$/t] 65.90 59.52 61.12 54.91 48.86 OECD Europe [US$/t] 75.65 58.00 61.18 59.60 39.36 USA [US$/t] 61.20 59.10 52.01 51.52 50.67 Japan [Y/GJ] 258 234 240 216 192 OECD Europe [Y/GJ] 297 228 240 234 155 USA [Y/GJ] 240 232 204 202 199 Table A4.3: Relative resource costs (Japan = 1) MMFORE MMFLIM MMFALLOY DISP AIR BFS

JAPAN 1 1 1 1 1 1

CHINA 1 0.5 1 0.2 1 1

KOREA 1 1 1 1 1 1

OCEANIA 0.5 0.5 1 0.1 1 0.5

EUROPE 1 0.5 1 1 1 1

NAMERICA 1 0.5 1 0.3 1 1

Table A4.4: Relative investment costs (Japan = 1) Japan China Korea Oceania Europe Namerica CIS MEAST Lamerica Oasia Oafrica

1 1 1 1.25 1 0.8 1.25 1.5 1.5 1.25 1.25

94

CIS 1 1 0.75 0.1 1 1

MEAST 1 1 1 0.1 1 1

LAMERICA 0.5 0.5 1 0.1 1 1

OASIA 1 0.5 1 0.2 1 1

OAFRICA 0.75 0.5 1 0.2 1 1


Table A4.5: Relative labour costs (Japan = 1) Japan China Korea Oceania Europe Namerica CIS MEAST Lamerica Oasia Oafrica

1 1 1 1.25 0.9 1.75 1 1 1 0.75 0.75

95


Table A4.6: Labour productivity analysis (IISI 2001, Jennings 1997) Workforce (1000)

1998

Production BF 1999 [%] [%]

Labour productivity [t/cap.yr]

EAF %]

1974

1990

1995

1996

1997

Austria

44

21

13

13

12

12

12

5.2

90.7

9.3

420

Belgium

69

26

24

23

21

20

20

10.9

82.2

17.8

493

Finland

10

10

7

7

7

8

7

4.0

77.6

22.4

502

France

158

46

39

39

38

38

38

20.2

62.4

37.6

432

Germany (1)

232

125

93

86

82

80

78

42.1

70.8

29.2

462

Italy

96

56

42

39

37

39

39

24.9

42.2

57.8

456

Luxembourg

23

9

6

5

5

4

4

2.6

-

100.0

310

Netherlands

25

17

13

12

12

12

12

6.1

97.9

2.1

514

Spain

89

36

25

24

23

23

22

14.9

28.1

71.9

434

Sweden

51

26

15

14

14

14

13

5.1

64.0

36.0

324

United Kingdom

194

51

38

37

36

34

31

16.3

77.6

22.4

474

European Union (total)

998

434

321

306

293

290

280

155.2

61.9

38.1

449

Canada

77

53

54

53

53

55

57

16.2

58.5

41.5

227

United States

521

204

171

167

163

160

153

97.3

53.8

46.2

488

Japan

459

305

252

240

230

221

208

94.2

69.5

30.5

384

South Korea

n/a

67

67

66

65

64

64

41.0

58.4

41.6

508

Australia

42

30

22

21

20

20

24

8.2

84.5

15.5

314

118

115

78

79

74

63

378

Developing countries Brazil Russia South Africa

100

112

76

71

70

61

59

25.0

78.1

21.9

600

51.5

58.9

12.8

80

55

7.3

62.1

36.6

108

3 000

123.7

66.3

15.8

India

50-100

China

96

38


Table A4.7: Indexes of hourly compensation costs for production workers in manufacturing, 29 countries or areas and selected economic groups, 1991-1999 (Index, U.S. = 100)(US Department of Labor, 2000) Country or area North America United States Canada Mexico Asia and Oceania Australia Hong Kong Israel Japan Korea New Zealand Singapore Sri Lanka Taiwan Europe Austria Belgium Denmark Finland France Germany, Former West Germany, Unified Greece Ireland Italy Luxembourg Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom Trade-weighted measures 3 All 28 foreign econ. OECD 4 less Mexico, Korea 5 Europe European Union Asian NIEs

1991

1992

1993

1994

1995

1996

1997

1998

1999

100 111 12

100 107 13

100 100 15

100 94 15

100 94 9

100 94 9

100 90 10

100 84 10

100 81 11

87 23 56 94 30 53 28 3

81 24 56 102 32 48 31 2

76 26 53 116 34 48 32 3

84 27 54 127 38 52 37 3

89 28 61 139 42 58 43 3

95 29 64 119 46 61 47 3

91 30 66 107 43 59 45 3

80 29 64 98 29 48 42 3

83 28 62 109 35 48 37 -

28

32

32

33

35

34

32

28

29

116 127 118 136 100 145

126 137 126 124 109 158

122 130 116 101 102 153

128 137 120 113 105 158

147 155 140 140 116 184

140 147 136 132 113 176

120 125 121 117 98 152

119 124 122 116 98 147

114 119 120 110 94 140

45 76 118 110 116 139 27 79 142 139 88

47 82 120 119 125 143 32 84 153 144 89

148 44 72 96 114 121 122 27 70 107 137 75

153 46 74 94 121 123 124 27 68 110 148 76

178 53 79 94 136 140 142 31 75 125 170 80

171 54 79 100 127 131 142 32 76 138 160 80

147 50 74 96 104 115 130 29 67 122 132 85

143 48 72 92 100 113 126 29 65 118 131 88

136 71 86 109 125 63 112 123 86

86

89

87

89

95

91

84

79

79

93 107

96 111

94 108

96 110

103 118

98 112

90 103

85 98

86 98

115 114 28

122 121 30

111 110 31

114 112 34

128 126 37

125 123 39

112 111 37

110 109 31

106 105 32

97


Annex 5: Assumptions for demand forecast Table A5.1: GDP growth rate [%/yr] JAPAN CHINA KOREA OCEANIA EUROPE NAMERICA CIS MEAST LAMERICA OASIA OAFRICA

20002005 2 6 4 4 2 2 5 2 3 2 2

20052010 2 5 3 4 2 2 5 2 3 2 2

20102015 1.5 4 2 3 2 2 5 2 3 2 2

20152020 1.5 3 1 3 2 2 5 1 3 2 2

20202025 1 2 1 2 2 1 4 1 3 2 2

20252030 1 2 1 2 1 1 4 1 3 2 2

20302035 1 2 1 1 1 1 3 1 2 2 2

20352040 1 2 1 1 1 1 3 1 2 2 2

20102015 1.5 1.5 1 0.5 1 1 1 0.5

20152020 1.5 1 1 0.5 1 1 1 0.5

20202025 1.5 1 1 0.5 1 1 1 0.5

20252030 1.5 1 1 0.5 1 1 1 0.5

20302035 1.5 1 1 0.5 1 1 1 0.5

20352040 1.5 1 1 0.5 1 1 1 0.5

Table A5.2: Income elasticities PACK TRANS MACH CONSTR FABMETPR ELMACH OTHMAN LOSS

20002005 1.5 1.5 1 0.5 1 1 1 0.5

20052010 1.5 1.5 1 0.5 1 1 1 0.5

Table A5.3: Autonomous efficiency gains [%/yr] 0.5 1 0.2 1 0.2 0.5 0.5 0

PACK TRANS MACH CONSTR FABMETPR ELMACH OTHMAN LOSS

98


Annex 6: Waste collection cost Table A6.1: Waste generation per product category PACK TRANS MACH CONSTR FABMETPR ELMACH OTHMAN LOSS

WMFBULK 0 0.9 0.45 0.7 0.3 0.3 0.2 1

WMFDIL 0 0 0.45 0.1 0.3 0.3 0.3 0

WMFMSW 0.7 0 0 0 0.3 0.3 0.3 0

Table A6.2: Waste collection and upgrading costs XB1 Japan China Korea Oceania Europe Namerica CIS MEAST Lamerica Oasia Oafrica

XB2 500 500 500 1000 500 500 500 500 1000 1000 1000

XB3 1000 2000 2000 2000 1000 2000 2000 2000 2000 2000 2000

1000 1000 1000 2000 1000 1000 2000 2000 2000 2000 2000

99


Annex 7: Global energy consumption and energy efficiency in the iron and steel industry Table A7.1: Coal consumption in the iron and steel industry, based on (IISI 2001, IEA 2000)

Austria Belgium-Luxembourg Finland France Germany Italy Netherlands Portugal Spain Sweden United Kingdom European Union (15) Bulgaria Czech Republic Hungary Poland Romania Slovakia Turkey Others Other Europe Kazakhstan Russia Ukraine Other ex- USSR Former USSR Canada Mexico United States NAFTA Argentina Brazil Chile Venezuela Other LA Central and South America Egypt South Africa Other Africa Africa Iran China India Japan South Korea Taiwan Other Asia Asia Australia New Zealand World

Iron production [Mt/yr] 1999 (1) 3.9 8.4 3.0 13.9 27.9 10.6 5.3 0.4 4.1 3.2 12.1 92.9 1.1 4.0 1.3 5.2 3.0 3.0 5.2 0.3 23.1 3.5 40.0 21.9 65.4 8.9 4.8 46.4 60.0 2.0 24.5 1.0 0.5 28.1 1.3 6.1 0.6 8.0 2.1 125.4 20.1 74.5 23.3 8.9 1.3 253.6 7.0 0.6 541.0

Coking coal Coke consumption consumption PCI [Mt/yr] [Mt/yr] [Mt/yr] 1999 1999 1999 (2) (3) (4) 2.0 2.2 0.30 3.9 3.8 1.53 1.3 1.4 0.40 7.6 6.2 2.39 11.0 12.6 2.28 6.8 5.2 1.00 4.4 2.1 0.92 0.3 3.4 2.1 0.67 1.7 1.5 0.29 9.0 6.3 0.49 0.9 4.6 1.2 16.1 3.4 1.7 4.6

0.30 -

2.6 0.8 5.6 3.3

SCC iron Coke production production [t C/t iron] [Mt/yr] 1999 1999 (5) (6) 0.64 1.6 0.63 3.1 0.61 0.9 0.62 5.4 0.53 8.6 0.58 5.0 0.57 2.3 0.77 0.4 0.67 2.3 0.56 1.1 0.56 5.9

Coal for GER iron coking Coking eff prod [Mt/yr] [t coke/t coal] [t coal/t iron] 1999 1999 1999 (7) (8) (9) 2.2 0.73 0.85 3.9 0.79 0.75 1.3 0.69 0.81 7.1 0.76 0.76 13.0 0.66 0.77 6.8 0.74 0.76 2.7 0.85 0.64 0.0 3.8 0.61 1.00 1.7 0.65 0.81 8.2 0.72 0.76

0.65 0.61 1.07

3.3 0.9 8.5

5.3 1.3 12.7

0.64

2.8

4.4

3.9 51.8 40.4 4.7 1.6 26.5

3.3 3.0 20.2

0.26 4.50

0.40 0.62 0.53

3.3 2.2 18.2

4.4 2.7 27.1

0.60 0.62 0.69 0.67 0.60 0.60 0.64

0.80 1.05 0.89 1.60 1.60 0.67 1.04

0.60 0.60

1.12 1.29 1.84

0.75 0.81 0.67

0.52 0.77 0.74

0.5 12.7

-

0.25 0.52

1.9 3.3

-

1.46 0.54

1.5 132.6 53.9 62.2 18.4 8.0 10.0

8.76 2.55 -

9.0 526.5

36.2 13.7

4.1

26.65

100

0.56 0.60 0.70

38.0 13.7

58.1 18.3

0.65 0.75

0.58

4.4

6.8

0.65

0.71 1.06 2.68 0.92 0.90 0.90 7.69 1.21 1.02


Iron production data are available from IISI statistics (IISI 2001) and global coal consumption data for the iron and steel industry are available from IEA statistics (IEA 2000). They are shown in table A7.1. PCI refers to Powder Coal Injection into blast furnaces. SCC refers to the Specific Coal Consumption per ton of iron, the total of coke consumption and PCI divided by the iron production (a measure for the energy efficiency of the blast furnace) (in table 2 columns [(3+4)/1]). GER refers to the Gross Energy Requirement, the coal consumption per ton of iron, including coking, ore preparation and PCI (a measure for the energy efficiency of the whole iron production process, in table 2 columns [(3/8+4)/1]). A correction has been applied for international coke trade. No correction has been applied for the sales of energy by-products such as blast furnace gas, coke oven gas and steam. Also no correction has been applied for the use of non-coal derived energy carriers (which results in an underestimation of the GER value). In case coke oven efficiency data were not available, the ratio of coal consumption and iron production has been applied as a proxy. This approach results in an overestimation of the GER value in case significant quantities of coal are used for rolling, and it neglects coke trade (which may be a source of either underestimation or overestimation, depending on the net trade flows). The SCC data for all countries are rather close. Remarkable anomalities are Poland (too high) and Canada (too low). The high value for Portugal can be attributed to rounding and is not significant. If these three countries are omitted, the range for all others is 0.53-0.70 t C/t iron. This small range suggests this is a meaningful figure, and efficiency potentials are limited. The GER values show a much wider range from 0.25 (Argentina) to 2.68 (India). This range is too wide to be meaningful, given similar production technology throughout the world. However a closer look at national production practices reveals the sources of this wide range. The low values for Latin American countries (Argentina, Brazil) can be explained by the use of charcoal from wood instead of coke. The low value for South Africa can be explained by the use of Corex technology for iron production, a new process that uses steam coal instead of coking coal (see below). The low value for Canada can be explained by the injection of other fuels into the blast furnace. The low value for the Netherlands can be explained by high pellet use rates (50%) and errors in the energy statistics (Farla and Blok 2001). The high values for Japan and Korea (both countries with an energy efficient iron and steel industry) can be explained by high electricity prices (making maximised electricity generation attractive, using the blast furnace and coke oven as a kind of coal gasifier).

101


Annex 8: Energy efficiency in the Japanese iron and steel industry Annex 8.1: Japanese blast furnaces, 1999

Company

Site

Pig iron Coke Coke Coke Other production production purchase consumption fuels [t/yr]

Nippon steel

NKK Kawasaki steel Sumitomo

Kobe steel

Coke Total fuel Iron prod cons

PCI

Savings by PCI increase to 200 kg/t

PCI

[t/yr]

[t/yr]

[kg/t]

[kg/t]

[kg/t]

[kg/t]

[Mt/yr] [PJ HHV/yr] [PJ HHV/yr]

Yahata

3,531,591 1,371,031

0

348

0

152

500

3.53

36.9

17.0

[PJ/yr] 4.18

Nagoya

5,938,613 2,341,528

0

363

0

133

496

5.94

64.7

25.0

7.93

Kimitsu

8,451,112 3,876,517

0

371

0

140

511

8.45

94.1

37.5

10.82

Ooita

7,160,608 2,924,330

120,759

352

0

130

482

7.16

75.7

29.5

9.74

Muroran

1,710,683

945,186

0

367

0

132

499

1.71

18.8

7.2

2.30

Keihin

3,151,987 1,811,418

0

454

0

101

555

3.15

43.0

10.1

5.02

Fukuyama

9,720,726 4,116,664

256,510

387

0

157

544

9.72

112.9

48.4

11.12

Chiba

4,071,054 2,184,941

0

476

0

67

543

4.07

58.2

8.6

7.59

Mizushima

8,223,143 4,182,340

0

416

0

104

520

8.22

102.7

27.1

12.89

Kokura

1,161,692

0

464,849

371

0

128

499

1.16

12.9

4.7

1.60

Wakayama

3,136,214 1,550,756

0

413

0

130

543

3.14

38.9

12.9

4.27

Kashima

6,182,937 3,031,519

7,428

409

5

83

497

6.18

75.9

16.3

10.73

Kobe

1,317,250

0

521,960

333

0

176

509

1.32

13.2

7.3

1.31

Kakogawa

6,014,670 2,440,880

69,280

329

0

207

536

6.01

59.4

39.5

69.77

807.4

291.1

89.50 8.86

102


Tale A8.2: Japanese coking oven characteristics, 1999 Site Nippon steel

Yahata Nagoya

Ooita

Hokkai seitetsu

Muroran

Shinnintetsu Kagaku Kimitsu

NKK

Keihin Fukuyama

Kawasaki steel

Chiba Mizushima

Sumitomo

Wakayama Kashima

Kansainetsukagaku

Kagokawa

Nakayamaseikou

Funamachi

Mitsubishi kagaku

Sakaide

Mitsuikouzan

Kitakyushu

Oven 4 5 1 2 3 4 1 2 3 4 5 6 1 2 3 4 5 1 2 3 4 5 5 6 7 1 2 3 4 5 6 4 5 6 1AB 1CD 2AB 2CD 1,2 3,4 2A 2B 1 2 3 1 2

Work rate Cycle time Load Chambers [hrs] [t/chamber] [-] 117.5 16.5 13.7 90 114.8 17.1 29.1 110 100.4 19 19.32 75 106.9 18.8 18.97 110 100.3 26 25.97 90 101.2 19.3 25.96 100 124.3 16 26.8 78 123.4 16 26.8 78 121.3 16.3 26.8 82 121.5 16.3 26.8 82 109.8 17 19.48 91 123 13 28.13 42 116.2 17.6 26.13 90 116.6 17.5 26.13 95 116.3 17.5 26.11 100 111 17.5 30.55 92 102.5 17.5 28.94 92 111.5 18.3 36.9 124 102.1 19.8 36.7 74 123 16.8 27.7 104 126.7 16.5 27.8 175 128.1 16.3 28.3 165 95.9 20.8 26.2 92 107.9 18.5 30.56 102 127.8 16.6 30.33 66 103.5 18.1 26.95 78 104.6 18.3 26.99 86 123.3 16.8 29.74 86 123.1 16.6 29.77 86 123.9 16.3 29.74 86 125 16.3 29.48 43 92.1 23.2 21 76 92.1 23 21 92 94.5 22.5 26.4 106 96.8 19.3 35.64 72 96.8 18.5 35.64 82 100 18.8 35.64 92 100 19 35.64 87 121 17.5 30.3 120 121.1 17.3 30.4 128 155.6 13.3 15.135 34 150.2 13.1 15.135 32 128 15.3 25.9 100 124 16.4 32.3 223 124 16.4 32.3 223 89.8 21.5 29.932 46 115.2 17.3 31.195 108

103

Energy use Energy use HHV LHV Production [kcal/kg] [GJ/t] [Mt/yr] 499 2.01 0.77 504 2.03 1.88 538 2.17 0.67 547 2.20 1.04 499 2.01 0.79 528 2.13 1.19 599 2.41 1.42 599 2.41 1.41 592 2.38 1.43 575 2.32 1.43 630 2.54 1.00 592 2.38 0.98 568 2.29 1.36 569 2.29 1.45 550 2.21 1.52 509 2.05 1.56 564 2.27 1.37 594 2.39 2.44 594 2.39 1.23 597 2.40 1.85 620 2.50 3.27 571 2.30 3.21 530 2.13 0.97 504 2.03 1.59 529 2.13 1.35 508 2.05 1.05 508 2.05 1.16 498 2.01 1.64 496 2.00 1.66 500 2.01 1.70 501 2.02 0.85 604 2.43 0.56 569 2.29 0.68 559 2.25 1.03 582 2.34 1.13 576 2.32 1.34 554 2.23 1.53 578 2.33 1.43 591 2.38 2.20 581 2.34 2.39 591 2.38 0.53 581 2.34 0.49 653 2.63 1.90 647 2.60 4.77 647 2.60 4.77 566 2.28 0.50 594 2.39 1.97 72.5


104