Analytical Input-Output and Supply-Chain Study of China's
Coke and Steel Sectors
by Yu Li
Bachelor of Economics 1998 Tsinghua University, China Bachelor of Architecture, 1998
Tsinghua University, China Master of City Planning, 2001
University of Cincinnati, OH Submitted to the Center for Transportation and Logistics
the requirements for the degrees of
in partial fulfillment of
Master of Science in Transportation at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2004
@ 2004 Yu Li. All Rights Reserved
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis docypment in whole or in part.
Author
Center for Transportation and Logistics June 2004 Certified by
Professor of Regional Political Economy and PlanningProfessor Karen R. Polenske Thesis Supervisor Accepted by
, Professor Nigel Wilson Professor of Civil and Environmental Engineering Director, Center for Transportation and Logistics
MASSACHUSETTS INSTitUE OF TECHNOLOGY
OCTL2RAR
Analytical Input-Output and Supply-Chain Study of China's
Coke and Steel Sectors
by Yu Li
Submitted to the Center for Transportation and Logistics in partial fulfillment of the requirements for the degrees of Master of Science in Transportation
ABSTRACT
I design an input-output model to investigate the energy supply chain of coal-coke-steel in China. To study the demand, supply, and energy-intensity issues for coal and coke from a macroeconomic perspective, I apply the model to test two hypotheses: (1) coal and coke intensities in individual economic sectors decline as China's overall energy efficiency improves, and (2) the supply of coal and coke will satisfy the demand in China in the future three years given a business-as-usual assumption. The results support the first hypothesis but do not support the second. I summarize the policy implications in four areas: (1) energy, (2) environment, (3) trade, and (4) investment.
ACKNOWLEDGMENTS
I would like to thank my research and thesis supervisor, Professor Karen R. Polenske, for her continual help, support, and encouragement throughout the entire time I have worked with her. This thesis would not have gone anywhere without her strong support and insightful comments.
This research was sponsored by two Alliance for Global Sustainability (AGS) grants (No. 005151-042 and 008282-008), a National Science Foundation (NSF) grant (No. 006487-001), an external United National Industrial Development Organization (UNIDO) research grant, and an MIT Martin Fellowship. I thank AGS, NSF, UNIDO, and Martin Fellowship Foundation for making this research
possible.
TABLE OF CONTENTS TITLE ... 1 ABSTRACT... 2 ACKOW LEDGMENT... 3 CONTENTS... 4 FIGURES ... 6 TABLES... 7 1 INTRODUCTION... 8
1.1. Significance of China's Coal-Coke-Steel Supply Chain... 9
1.2. Research Objective and Hypothesis... 11
1.3. Data Methodology... 12
1.4. Input-Output Analysis as a Powerful Tool in Policy Studies... 14
2 LITERATURE REVIEW ... 17
2.1. Energy Supply, Demand, and Intensity... 17
2.2. Supply Chains and Supply-Chain Management... 19
2.2.1. Firm-scale Supply-Chain Studies... 20
2.2.2. Industry-level Supply-Chain Studies... 21
2.3. Input-Output Techniques... 22
2.3.1. Enterprise Input-Output Models... 23
2.3.2. Macroeconomic Analyses and Policy Implications... 23
2.4. Summary... 24
3 MODEL BUILDING... 25
3.1. Identify Key Supply-Chain Components... 25
3.2. Redesign Input-Output Tables... 31
3.3. Calculate and Estimate the Share of Final Demand in Each Sector... 33
3.4. Calculate Total Outputs... 34
3.5. Calculate Energy Intensities and Forecasts with Time-Series Models. 35 3.6. Forecast GDP and Final Demand for Each Sector... 37
3.7. Forecast Demand and Supply of Energy Products... 38
4 ENERGY SUPPLY- CHAIN ANALYSIS... 39
4.1. Overview of the Supply Chain of Coal-Coke-Steel... 39
4.2. Sector-Based Analyses of the Coal-Coke-Steel Supply Chain... 47
4.2.2. Summary of the Intensity Studies... 52
4.2.3. Summary of the Consumption Studies... 56
4.3. Steel Demand and Supply... 60
4.4. Forecast Demand for and Supply of Coal and Coke... 61
4.4.1. Forecast Coal and Coke Supply... 61
4.4.2. Forecast Coal Demand... 62
4.4.3. Forecast Coke Demand... 64
5 POLICY IMPLICATIONS AND CONCLUSIONS ... 66
5 .1. E nergy P olicy... . 66 5.2. Environmental Policy... 67 5 .3 . T rad e P o licy ... 6 8 5.4. Investment Policy... 69 5 .5 . C o nclusions... . 69 APPENDICES... 72
Appendix 1. Intermediate Sector Classification... 72
Appendix 2. China National Input-Output A Matrix... 73
Appendix 3. China National Input-Output (l-A)1 Matrix... 78
Appendix 4. Demand for and Supply of Coal and Coke in China... 83
Appendix 5. Sector-Based Analysis of the Coal-Coke-Steel Supply Chain.. 84
Appendix 5.1. Coal Consumption and Intensities... 84
Appendix 5.2. Coke Consumption and Intensities... 98
Appendix 5.3. Time-Series Models for Coal and Coke Intensities... 113
Appendix 6. Shares of Final Demand of Each Sector (S;)... 114
Appendix 7. Coal and Coke Consumption in 14 Sectors in China... 115
Appendix 8. Coal and Coke Intensities in 14 Sectors in China... 117
Appendix 9. Forecasted Coal and Coke Intensities in 14 Sectors in China. 119 Appendix 10. Economic Sectors Ranked by Coal or Coke Intensities... 120
FIGURES
Figure 2.1: Energy Consumption and Energy Intensity in China 1955-1997. 18
Figure 2.2: Energy Intensity in Selected Countries 1970-2020... 19
Figure 3.1: Key Components in a Supply Chain... 25
Figure 3.2: The Intersectoral Coal-Coke-Steel Supply Chain... 26
Figure 3.3: Primary Energy Source in China, 1997... 28
Figure 4.1: Coal Consumption and Production in China, 1985-2001... 41
Figure 4.2: Coal Intensity in China, 1985-2001... 41
Figure 4.3: Coke Consumption and Production in China, 1985-2001... 42
Figure 4.4: Coke Intensity in China, 1985-2001... 42
Figure 4.5: Steel Consumption in China, 1985-2001... 44
Figure 4.6: Steel Intensity in China, 1985-2001... 44
Figure 4.7: Automobile Output in China, 1990-2001... 46
Figure 4.8: Air-Conditioner Output in China, 1990-2001... 46
Figure 4.9: Household-Refrigerator Output in China, 1990-2001... 47
TABLES
Table 1.1: Coal, Coke and Crude Steel Production, 2000... 9
Table 1.2: GDP, CPI, and Real GDP of China, 1985-2001... 13
Table 1.3: China Partial 1997 Input-Output Flow Table... 15
Table 1.4: China Partial 1997 Direct-Input Coefficient Table ... 15
Table 1.5: China Partial 1997 Direct-and-Indirect-Coefficient Table... 16
Table 3.1: Crude Steel Production by Process, 2000... 29
Table 3.2: Steel Consumption in China by Market, 1997... 30
Table 3.3: Intermediate Sector Classification... 32
Table 4.1: Summary of Coal- and Coke-Intensity Analyses... 55
Table 4.2: Rank of Economic Sectors by Coal Consumption, 2000... 58
Table 4.3: Change Patterns of Coal Consumption in 14 Sectors... 58
Table 4.4: Rank of Economic Sectors by Coke Consumption, 2000... 59
Table 4.5: Change Patterns of Coke Consumption in 14 Sectors... 59
Table 4.6: Forecasted Coal Demand in 14 Sectors in China, 2003-2005... 63
CHAPTER 1 INTRODUCTION
Traditionally, researchers (Flaherty, 1996, Simchi-Levi et al., 2000; Copacino and Byrnes, 2001) studied supply chains and supply-chain
management (SCM) at a firm level and focused on a corporation's demand forecasting, inventory control, and distribution-network optimization. Recently, some researchers have expanded the scope to emphasize SCM's industrial impacts. They focus on industrial structures and restructuring within different SCM attributes: physical, technological, strategic, and organizational (Carbonara et al., 2000). However, most of them have not studied supply chains from a macroeconomic industrial-sector perspective, nor have they applied the input-output techniques to assist policy decision-making for energy supply chains.
In this study, I design an input-output technique-based model to study supply chains of China's coke and steel sectors. The study is from a
macroeconomic perspective, especially from an inter-sector perspective using China's national input-output accounts. I apply the model to analyze the demand, supply, and energy-intensity issues of the two major energy products in the chain: coal and coke. The structure of the study is as follows. In this chapter, I present the significance of the research and hypotheses. In Chapter 2, I review pertinent literature. I present analytical model is Chapter 3, including the data collection methodology, and perform a sector-based analysis in Chapter 4. In Chapter 5, I summarize policy implications in four areas and draw conclusions.
1.1. Significance of China's Coal-Coke-Steel Supply Chain
China is the world largest producer of coal, coke, and crude steel (IEA, 1999; IEA Coal Research Center, 2001; Table 1.1, llSI, 2002).
TABLE 1.1
COAL, COKE AND CRUDE STEEL PRODUCTION, 2000 (MILLION TONNES)
World China USA Japan India
Coal 4531 998 974 3 311
Coke 333 122 19 39 12
Steel 847 127 102 106 27
Source: International Iron and Steel Institute, Steel Statistical Yearbook, 2002 Coal accounted for more than 70% of primary energy consumption of China (IEA, 1999). Out of nearly 1,400 million tonnes of China's coal production in1997, 14% is used for coking, a process to produce metallurgical coke. Coke is a crucial material to make steel, particularly, high-quality steel. Since the late
1990s, China has dominated the world coke market and exported coke to many countries, including India, Japan, and the United States. Domestic demand for coal, coke, and steel is surging because of the dramatic economic growth in China in the last two decades. Construction and manufacturing industries, such as automobile and electric-appliance industries, all require high-quality steel, and,
in turn, need a vast quantity of coke.
Rapid urbanization and heavy investment in infrastructure have been intensifying the demand for steel products. Although more than 100 million rural population have migrated to the urban area in the past two decades, China still has 70 percent rural population, about 900 million, among which more than 20 percent are expected to be under-employed and probably will migrate to urban
areas in the near future. Urbanization has been accelerating since the economic opening, particularly since the early 1990s. To boost the domestic demand and improve the employment situation, as well as to attract foreign investment and to prepare for the future economic development, China has been investing
tremendously in infrastructure projects, including Transferring Western Natural Gas to the East, Transferring Southern Water to the North, and the Tibetan Railroad System. All these gigantic projects require a vast quantity of steel.
Moreover, since the mid 1990s, China has been eager to develop its automobile industry. The government has encouraged local manufacturers to form joint ventures with foreign automakers or even permitted foreign makers to set up their own plants in China. Central and local governments offer
automakers tax subsidies to attract them and have been pouring tremendous amounts of money into railway and highway systems to accommodate the surging transportation demand. The household electric-appliances industry is another surging steel-consuming industry in China. Giant appliance makers, such as Haier and Changhong, have aggressively expanded their production capacities as well as market shares, both domestically and globally. Hence, the supply chain of coal-coke-steel is critical to China's economy.
Additionally, China is facing significant environmental challenges with the prospect of a further deterioration of its environment unless governments
introduce new technologies and remedial policies rapidly. The environmental problem can be partly attributed to the pollution from the coal-coke-steel supply chain, including both production and transportation (Chen, 2002), because the
major industries in the supply chain are heavy polluters. Therefore, the coal-coke-steel chain is significant not only to the future of China, but also to the entire world. The study I present here should provide valuable insights for both
domestic and foreign decision-makers.
1.2. Research Objective and Hypothesis
As just discussed, with the rapidly growing economy, China is becoming a "world factory" and is facing dramatically increasing energy demands. On the one hand, fast-growing manufacturing sectors, like automobiles and electric-appliances, as well as traditional steel-consumer sectors, like construction, all require vast quantities of steel and, in turn, coke and coal. On the other hand, China is the world's largest producer and a major exporter of coal, coke, and crude-steel. In particular, it now dominates the global coke market (Polenske, 2003). The central and local governments have been investing heavily in energy sectors and infrastructure projects to accommodate the surging demand for production and transportation of these energy-intensive products.
I examine coke and steel industries from a macro-level supply-chain perspective. I focus on two major energy products in China: coal and coke. By building an input-output, econometric model and applying it to analyze the supply chain of the coke and steel industries, I test two hypotheses: (1) both coal and coke intensities in each economic sector have declined as China's overall energy efficiency improves, and (2) the supply of coal and coke will satisfy the demand in the future three years in China given the business-as-usual (BAU) assumption.
My research is partially funded by the Alliances for Global Sustainability (AGS), the National Science Foundation (NSF), the United Nations Industrial
Development Organization (UNIDO), and the Martin Fellowship Foundation. As a part of our multiregional planning (MRP) research on China's energy efficiency, this study is primarily empirical concerning the demand, supply, and
energy-intensity issues for coal and coke. I use data from the past two decades for the analysis.
1.3. Data Methodology
I collect data from a variety of sources. The major sources are China National Input-Output Tables 1981, 1987, 1992, 1995, 1997, China Statistical Yearbooks from 1985 to 2002, and International Energy Agency Reports on coal, coke, and steel. Other data sources include: China's Energy Statistic Reports, China's 10th Five-Year National Plan, and pertinent research papers and reports.
As discussed later in Chapter 3 on model building, different input-output tables have different classifications of economic sectors. Based on the
classifications of available input-output accounts and key components, I
aggregate the data to 14 sectors in my output table. The reclassified input-output tables for 1981, 1987, 1992, 1995, and 1997 are listed in Appendix 2. The corresponding (I - A) 1 (Leontief's Inverse) matrices are listed in Appendix 3.
To compare energy intensity, I use real GDP to account for inflation. I calculate real GDP (1985) by the formula:
where CPI, a measurement of inflation, represents the Consumer Price Index. The base year is 1985, i.e., the CPI for 1985 is 100. The GDP, CPI, and real GDP are listed in Table 1.2. Given the CPI has not changed much since 1997, 1 assume the CPI would remain the same as it was in 2001 through the forecasting period.
TABLE 1.2
GDP, CPI, AND REAL GDP OF CHINA, 1985-2001
CPI 100.0 106.0 113.7 134.8 158.8 165.2 170.8 181.7 208.4 258.6 302.8 327.9 337.1 334.4 329.7 331.0 333.3 Real GDP (billion yuan) 896.4 962.7 1051.9 1107.9 1065.1 1122.8 1265.7 1466.1 1661.9 1808.2 1931.3 2070.3 2208.9 2342.9 2489.2 2702.2 2878.3 GDP Growth (percent) 7.4 9.3 5.3 -3.9 5.4 12.7 15.8 13.4 8.8 6.8 7.2 6.7 6.1 6.2 8.6 6.5 Source: China Statistical Yearbook 1986-2002 and
CPI = Consumer Price Index GDP = Gross Domestic Product
calculated by the author.
I perform statistical tests on the proposed models and make forecasts using SAS, a statistical-analysis package developed by SAS Inc., a software company in the United States.
Year 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 GDP (billion yuan) 896.4 1020.2 1196.3 1492.8 1690.9 1854.8 2161.8 2663.8 3463.4 4675.9 5847.8 6788.5 7446.3 7834.5 8206.8 8944.2 9593.3
1.4. Input-Output Analysis as a Powerful Tool in Policy Studies
Input-output analysis is a powerful tool to assist policy decision-making. It provides information on the flow of goods and services among an economy's different sectors. Input-output tables consist of intermediate transactions between producing and purchasing sectors, as well as each sector's final demand and value added. They show the state and process of an economic system and are particularly useful when analyzing the impacts of changes in final demands of certain sectors on the overall economic system.
Table 1.3 shows an input-output table of physical goods flows in the coal-coke-steel supply chain. Cokemaking is the second largest sector consuming coal (10,731 million tonnes) in China after the power-generation sector
(electricity), and the two largest coke-consuming sectors are iron- and steel-making. Table 1.4 shows the direct-input coefficients of each sector in the chain. Excluding other inputs and labor, coal is the largest input into the cokemaking sector (35.5%), coke is the largest input into the iron-making sector (15.3%), and iron is the largest input into the steel-making (9.8%). As shown in Table 1.5, the direct-and-indirect coefficient table shows that the largest backward linkage, defined as the sum of a column of direct-and-indirect coefficients in an input-output table, is from the Motor Vehicles sector (1.618), which means that
investment in this sector would generate the largest output in the economy. This partially explains China's current investment policy in the automobile industry.
TABLE 1.3
CHINA PARTIAL 1997 INPUT-OUTPUT (MILLION YUAN) Sector Coal Coke Iron Steel Motor Vehicles Electricity Water Labor Other Inputs Total Coal 6,229 0 59 989 1,252 10,526 299 78,052 125,341 222,748 Coke 10,731 220 0 0 74 2,047 37 3,374 13,759 30,243 Iron 1,816 8,916 720 1,123 350 2,301 75 7,134 35,676 58,111 Source: 1997 Input-Output Table of China, Bureau of Statistics, People's Republic of C Note: 1 yuan ~ US$ 1.0.
FLOW TABLE Steel 1,385 1,693 6,891 3,109 525 6,247 245 9,997 40,208 70,300 Motor Vehicles 872 156 775 8,159 104,558 3,007 254 28,650 155,694 302,125 Electri-city 65,825 10 0 0 1,841 12,644 1,101 40,843 255,100 377,363 Water 83 0 0 0 256 7,653 2,242 7,212 20,833 38,279 Department of National Accounts, National
TABLE 1.4
CHINA PARTIAL 1997 DIRECT-INPUT COEFFICIENT TABLE (DIRECT INPUT PER UNIT OF OUTPUT)
Motor Electri-Sector Coal Coke Iron Steel Motor Vehicles Electricity Water Labor Other Total Inputs Coal 0.028 0.000 0.000 0.004 0.006 0.047 0.001 0.350 0.563 1.000
each entry in a column by the Coke 0.355 0.007 0.000 0.000 0.002 0.068 0.001 0.112 0.455 1.000 Iron 0.031 0.153 0.012 0.019 0.006 0.040 0.001 0.123 0.614 1.000 Steel 0.020 0.024 0.098 0.044 0.007 0.089 0.003 0.142 0.572 1.000 Vehicles 0.003 0.001 0.003 0.027 0.346 0.010 0.001 0.095 0.515 1.000 city 0.174 0.000 0.000 0.000 0.005 0.034 0.003 0.108 0.676 1.000 Water 0.002 0.000 0.000 0.000 0.007 0.200 0.059 0.188 0.544 1.000 Source: Calculated by author from Table 1.3 by dividing
TABLE 1.5
CHINA PARTIAL 1997 DIRECT-AND-INDIRECT-COEFFICIENT TABLE (DIRECT-AND-INDIRECT INPUT PER UNIT OF FINAL DEMAND)
Motor
Electri-Sector Coal Coke Iron Steel Vehicles city Water
Coal 1.038 0.384 0.101 0.059 0.011 0.188 0.042 Coke 0.000 1.007 0.157 0.042 0.003 0.000 0.000 Iron 0.001 0.000 1.015 0.104 0.008 0.000 0.000 Steel 0.005 0.002 0.021 1.049 0.043 0.001 0.001 Motor Vehicles 0.009 0.008 0.012 0.014 1.530 0.009 0.013 Electricity 0.052 0.090 0.060 0.108 0.021 1.045 0.222 Water 0.002 0.002 0.002 0.005 0.002 0.004 1.063 Total 1.107 1.494 1.368 1.381 1.618 1.247 1.341 Source: Calculated by author by taking the I-A inverse, where I is a 7x7 identity matrix and A is the matrix of direct-input coefficients given in Table 1.4.
Traditional input-output analyses, however, require too much data and computation. Although the results are relatively comprehensive, the process is
usually too complicated for researchers to conduct an efficient analysis. I
present models that simplify the process by consolidating key components in the supply chain and focusing on the key sectors.
CHAPTER 2 LITERATURE REVIEW
The literature relevant to my analysis covers three major topics: (1) energy demand (consumption), supply (production), and intensity (energy consumed per unit of output), (2) supply-chain concepts and supply-chain management, and (3) input-output accounting and techniques.
2.1. Energy Supply, Demand, and Intensity
Generally, researchers forecast energy supply and demand based on annual total consumption. Such methods only show a big picture of energy consumption but miss the valuable information in each sector and overlook complex relationships among different sectors of a supply chain. It is thus difficult for policy-decision makers to find the underlying reasons, i.e., problems within a certain economic sector or links between two sectors, which have
incurred surplus or shortage of certain raw materials or energy products. Therefore, it is difficult for governments and businesses to make or adjust
policies and investment decisions accordingly.
Energy intensity is defined as the energy consumption per unit of economic output (Sinton, Levine, and Wang, 1998). When we study demand and supply of energy products and consider possible shortages or surpluses, energy intensity is a useful tool to help build forecasting models. It links energy consumption and total output of an economy or of an economic sector. An analyst can use changes in energy intensity to study possible ways technological innovation and/or structural reform can lower energy consumption. Researchers,
governmental officials, business managers have widely used energy intensities as a key indicator in energy-policy analyses and management decision-making.
3500 - 1.2 Consumption at 1977 Intensity 3000 2500 . 2000 Energy Intensity 0.6 1500 -- Actual Consumption E 1000 -500 0 0 1965 1970 1975 1980 1985 1990 1995
Average annual energy intensity decline since 1977: 4.1 percent
Source: http://www.pnl.qov/china/aboutcen.htm Mtce = million tonnes coal equivalent
FIGURE 2.1
ENERGY CONSUMPTION AND ENERGY INTENSITY IN CHINA 1955-1997 Since 1977, energy intensity in China has declined more than 50 percent. As shown in Figure 2.1, if China had maintained the energy-intensity level of 1977, the total energy consumption would be twice the actual consumption in 1997. Researchers (Sinton, Levine, and Wang, 2001; Sinton, 1996; Polenske and Lin, 1994; Xie, 1994; Huang, 1993) have attributed China's great
achievements in energy intensity to the great improvement in technology and internal structural changes within industries. This is a remarkable reduction, but compared to other major countries, China's energy intensity is still higher (Figure 2.2). Policy makers not only in China but in many other developed countries
wonder how they can further lower energy intensity and thereby lower total energy consumption, because China has been the second largest energy-consumption country in the world and is expected to be the largest one by the middle of this century (IEA, 2001).
140- History Projections 120-0 o00- China 80, S 60- 40-S 20-United States
0
.... ... I 7" -- l-T- rTI" 1 .r~ '1[ TT-1 7r.FITW 11 1 " 1970 1975 1980 1965 1990 1995 2000 2005 2010 2015 2020Source: IEA, "International Energy Outlook 2000"
Btu = British thermal units FIGURE 2.2
ENERGY INTENSITY IN SELECTED COUNTRIES 1970-2020
2.2. Supply Chains and Supply-Chain Management
In the logistics and management literature, a supply chain is often defined as an integrated process wherein manufacturers acquire raw materials from suppliers, convert these raw materials into final products, and deliver these final products to distributors and retailers (Ellram, 1991). Supply-chain management (SCM) is a set of approaches utilized to integrate suppliers, manufacturers, warehouses, and stores efficiently, so that merchandise is produced and
distributed in an optimized manner, thereby minimizing system-wide costs while satisfying service-level requirements (Simchi-Levi et al., 2000). Traditionally, supply-chain analysts have focused on the firm-scale SCM and industrial-level supply-chain design.
2.2.1. Firm-scale Supply-Chain Studies
The initial objectives of SCM are to reduce firms' transportation and inventory costs and to improve their service levels. Analysts usually consider SCM a way to minimize costs by restructuring physical logistics networks. For instance, to expand the market share in China in the late 1990s, Shell found that selling its lubricants through Chinese agents, then relying on state-controlled distribution, was not a satisfactory mix. In a major corrective step, the oil giant established three manufacturing plants in China and turned to a local logistics company, Hong Kong-based EAC Logistics, to manage its supply-chain network
in China, and thereby setting up a supply chain for fast, direct delivery to customers nationwide (Bowman, 1999).
Another good example happened in the steel industry. In the current business environment, margins of steel products are shrinking as service and quality demands continue to escalate. To remain competitive, many steel firms try to reduce production lead-time, slash planning-cycle time, and eliminate unnecessary work-in-process inventory in their plants. Bethlehem Steel in the United States hired a consulting firm, Experio Solution, to identify areas where the company could retain product and service quality while trimming costs. After the implementation of Experio Solutions' strategy, Bethlehem Steel was able to
eliminate overloads, increase throughput, and identify and refuse orders that it could not fulfill. Consequently, the steel firm reduced its inventory by 15%, the production lead-time by one week, and the weekly cash flow by $1.75 million. (The Internet: http://www.experio.com, October 5, 2001)
2.2.2. Industry-level Supply-Chain Studies
Recently, many researchers and analysts have broadened the scope of SCM and considered it not just as a cost-reduction mechanism, but also as a way to integrate the key business processes from suppliers to the end users. A supply chain provides products, services, and information that add customer values (Carbonara et al., 2000). When analysts study supply chains in such a context, they are more concerned about the roles SCM plays in firms' or
industrial restructuring than about those of reducing costs and improving service levels. Also, as globalization expands, managers must make their business strategies with reference to international markets, customers, suppliers, and competitors as a whole.
From an industrial perspective, SCM often plays a crucial role in industrial restructuring. Ellram (1990, p. 21) cites the Japanese automobile industry in the
1980s as a good example of a successful SCM system. Car manufacturers acquired automobile parts from a number of trader companies, who shared trade
information with their subcontractors. These subcontractors needed information from transportation firms who could provide timely delivery of raw materials and
intermediate products. In the supply chain, dealers sold cars and sent the demand-forecasting information to distributors and manufacturers. In this way,
they were able to maintain an excellent coordination within the supply chain. As Ellram (1990, p. 19) points out, the SCM mechanism is more suitable for those firms with differentiated (customized) products than for those with standardized ones.
So far, however, few scholars have studied supply chains and SCM from a macroeconomic perspective, especially, from an interindustry view. In this study, I use input-output techniques to study the energy supply chain of China's coal-coke-steel-manufacturing. I develop an input-output framework to model the energy intensities of coal and coke consumption and make forecasts. I apply the model to test the two hypotheses discussed in Chapter 1.
2.3. Input-Output Techniques
Polenske and Fournier indicate (1993) that an input-output table provides a detailed statistical account of the flow of goods and services between the
producing and purchasing sectors of an economy. It shows all intermediate transactions among producers and purchasers within a consistent accounting framework.
Since Leontief (1972 Nobel Laureate in Economics) completed the first input-output table in the 1930s, researchers from all over the world have
extensively used input-output techniques to study economic issues and analyze government policies. Input-output models provide direct, indirect, and induced effects among different sectors within an economy and among economies.
Analysts can also derive valuable multipliers, including output multipliers, income
multipliers, and employment multipliers, to assist policy design and decision-making. I provide two examples of input-output applications.
2.3.1. Enterprise Input-Output Models
Researchers in the Anshan Iron and Steel Corporation (AISC) designed an enterprise input-output model to optimize production plans (Zhang et al., 1991). In addition to using fundamental input-output techniques and a consistent accounting framework, they also applied mathematical programming to construct optimization models and thereby maximize profit. The general constraints in their optimization models include: (1) equipment capacity constraints; (2) technology and safety constraints; (3) constraints of the quantities of the purchased raw materials, fuels, and materials from the market; (4) constraints of the national plan and quantities for sale in the market (given China was still in a semi-planned economy in 1991).
2.3.2. Macroeconomic Analyses and Policy Implications
Input-output models have been widely used in macroeconomic analyses, investment planning, and policy decision-making. Voigtlaender (2002) uses a dynamic input-output model to study U.S. freight transportation. He first projects final demands based on the historical U.S. GDP data and, then, uses an
input-output framework to project U.S. commodity input-output values for the next two decades with the results from the first step. After completing the economic part, he transforms commodity values into quantities of freight transportation demand. Finally, he derives environmental implications of growing freight shipment
2.4. Summary
In this study, I apply input-output techniques. Actually, input-output accounts can be considered interrelated supply chains. The direct coefficient
matrix shows direct relationships among all the supply-chain components. For instance, a simple supply chain of agriculture products includes the sectors of agriculture, transportation, food industry, trade, and final demand of end
customers. Leontief's inverse matrix (direct-and-indirect input-coefficient matrix) presents detailed direct and indirect transactions among different supply-chain components. I focus on the supply chains of the coke and steel sectors.
CHAPTER 3 MODEL BUILDING
I build an input-output model, combined with time-series analysis, in the following seven steps.
3.1. Identify Key Supply-Chain Components
To study a supply chain, an analyst first identifies key components, or major players, in the chain. Conceptually, a supply chain consists of four key components: supplier, distributor, manufacturer, and customer (Figure 3.1).
-Physi6al G6-ds-F1w ~ ~-~-~-~
Supplier Distributor Manufacturer Distributor Customer Information Flow ---
---Source: the author FIGURE 3.1
KEY COMPONENTS IN A SUPPLY CHAIN
Generally, there are many intermediate suppliers, distributors, and
customers. I study the supply chain from a macroeconomic perspective, in which individual economic sectors are the components of the supply chain. Because each of the original five input-output tables I used had a different number of sectors, I had to consolidate them to 14 sectors.
The major components in the coal-coke-steel supply chain include: (1) supplier sectors, such as Coal Mining, (2) distributor sectors, such as
Transportation, (3) intermediate customer sectors, such as Coking and Metal Products, and (4) final customer sectors, such as Construction, Manufacturing,
Machinery and Equipment. Figure 3.2 shows a simplified diagram of the
interactions of different sectors and the flows of physical goods and services in the coal-coke-steel supply chain. The products of the coal-mining sector are transported to the cokemaking sector, traded in the international market
(imported/exported), and/or transported to other coal-consumer sectors. Similarly, coke is transported to the metal-products sector, to other
coke-consumer sectors, and/or exported to the international market. On the right-hand side, construction, manufacturing, machinery and other steel-customer sectors receive inputs from the metal-product sector.
---Transportation Construction Manufacturing Minig PProducts Machinery and Equipment
Other Coal- Other Coke- Other
Steel-Consumers Consumers Customers
mprt/Export
Source: the author FIGURE 3.2
I need economic sectors with different suppliers and consumers to be specified separately in the table in order to meet the input-output homogeneity and proportionality assumptions and to make applications as accurate as possible. Therefore, I reclassify and consolidate economic sectors to some key sectors.
One key sector in this supply chain is the Mining and Quarrying sector, which includes coal-mining. China is the second largest energy-consuming country in the world after the United States, with a very heavy dependence on coal. In 1996, coal accounted for about 77% of primary energy supply (excluding combustible renewable and waste, see Figure 3.3) and over 62% of final
commercial energy consumption. At present, about 39% of Chinese coal is burnt in power stations, 14% is used for coking, 10% is used for domestic and
residential, 1% used for rail, and the rest (36%) is for other uses, such as in the chemical, cement, ceramics, and glass-making industries. By contrast, the United States burns some 87% of its coal in power utilities, much higher than the percentage in China. China has made many plans for new power stations that use coal as the primary fuel. (IEA, 1999) Therefore, the coal-mining sector is not only the direct supplier of the cokemaking plants, but it also is the primary energy supplier in China's economy.
Source: International Energy Agency, 1999 FIGURE 3.3
PRIMARY ENERGY SOURCE IN CHINA, 1997
The second key sector is Coking. China is the largest coke-producing country, with approximately one-third of worldwide production, and she exports over half of the global traded coke (lEA, 2001). On the one hand, China's
cokemaking industry is a crucial supplier of energy products for the steel-making industry. On the other hand, it now dominates the global coke market and has an active role in international trade and energy businesses. The objective of the coking process is to produce a high-strength coke at minimum cost, which will perform well in a blast furnace. The cost of coke is said to represent a significant proportion, about 15 to 20 percent, of the cost of steel (lEA Coal Research, 2001).
There are two major processes for steel making: Basic Oxygen Furnace (BOF) and Electric Arc Furnace (EAF). About 60 percent of the iron/steel output comes from the BOF process, in which pig iron/hot metal is produced from iron
Hydro Gas 2% Nuclear 2% 0.4% EJCoal HOil EJGas . Hydro SNuclear Coal 77%
ores in a blast furnace and then treated in a BOF to produce crude steel. In the process, coke is an essential ingredient used in blast furnaces. Generally, the EAF production process, by contrast, does not involve the use of coal (except in that the power used may be generated in coal-fired power plants, which is particularly true in China). It uses recovered scrap and accounts for about 30% of the global steel production, mainly of lower grade steel than that produced by the BOF process. Other processes, such as open hearth, for the production of pig iron do not require coke, but these currently account for only about seven percent of production in the world and are economic only under limited
circumstances. (IEA Coal Research, 2001)
Table 3.1 lists the percentages of crude steel production by process for three countries: China, Japan, and the United States.
TABLE 3.1
CRUDE STEEL PRODUCTION BY PROCESS, 2000
Crude Steel Open
Production BOF EAF Hearth Other Country (million tonnes) (%) (%) (%) (%)
China 123.7 66 16 2 16
Japan 94.2 70 30 0 0
USA 97.3 54 46 0 0
Worldwide 786.4 60 33 4 3
Source: International Iron and Steel Institute, 2000 BOF = Basic Oxygen Furnace
EAF = Electric Arc Furnace
The third key sector is Metal Products, which includes steel-making. With the rapidly growing economy, particularly the surge in construction and
since 1980. China consumed more than 130 million tonnes of steel in 2000, becoming the largest consumer in the world. Chinese steel-makers generate three percent of the nation's gross domestic product (GDP), employ more than three million people, and supply 87 percent of the domestic steel market
(Woetzel, 2001). Thus, it is one of the backbone industries of China's economy. The development of the Chinese steel industry is paralleled by
developments in its major customer industries: construction and manufacturing, including automobile, electric appliances, and shipbuilding (Table 3.2).
TABLE 3.2
STEEL CONSUMPTION IN CHINA BY MARKET 1997 Steel Consumed
Consuming Industries (1000 tonnes) (Percent)
Construction 45,110 41.5
Manufacturing 37,950 34.9
Machinery 9,821 9
Transportation (Railroads and other) 7,151 6.6
Electrical machinery 3,451 3.2
Mining, quarrying, lumbering 2,660 2.5
Oil and gas 2,445 2.3
Source: Central Iron & Steel Research Institute, Beijing, China. Reference in "China: The Changing Shape of The Chinese Steel Industry." The Internet
(http://www.newsteel.com/features/NS991 0f3.htm)
The fourth sector is Manufacturing and the fifth is Construction. Both sectors are end customers in the coal-coke-supply chain. Since the economic
reform in 1979, China has been experiencing an unprecedented urbanization. The tremendous volume of urban construction needs a vast volume of steel products and supporting energy products (coal and coke). Besides, the automobile and electric-appliances industries are the two emerging
manufacturing industries that also need a vast volume of steel (Hogan, 1999). Since the mid 1980s the electric-appliance industry has viewed dramatic
expansion in terms of its total production and global market shares. A number of giant appliance manufacturers, like Haier, Changhong, and Kelong, have
emerged. Regarding the automobile industry, it is another backbone industry, like steel making, in China's overall economy. The value of the industry's total production was 298.7 billion yuan ($36 billion) in 1998, accounting for 3.8% of the GDP (Friedl Business Information and Partners, 2001). By 2002, China's
automobile industry had become the fourth largest in the world, following the United States, Japan, and Germany.
The sixth key sector is Transportation. Geographically, China's economic activities and population are spread out extensively. Without transportation and trade, there would be only limited flows of physical goods. In addition, China is intensively involved in the international trade of coal, coke, and steel. To study the coal-coke-steel supply chain, researchers must consider the transportation of these products.
In addition to the above six key sectors, I also consider other coal-, coke-, and steel-consuming sectors by consolidating them into the remaining eight
sectors, as discussed next.
3.2. Redesign Input-Output Tables
After specifying components in the supply chain, I redesign the input-output table by highlighting the key sectors. A problem I encountered in this step
is that China's input-output accounts in different years are different in terms of the number of sectors and sectors' definitions. The 1981 input-output account has 24 sectors, while the input-output accounts of 1987, 1992, and 1995 have 33 sectors as well as 100 or more sectors. As of 2003, when I conducted this
research, the latest available (1997) input-output account has 17 sectors (China National Statistical Bureau, 2002). As discussed above, I redesign the input-output table by consolidating sectors. Generally, an input-input-output table consists of both intermediate and final-demand sectors for all sectors in the economy. I derive direct-input and direct-and-indirect-input coefficient matrices based on intermediate sectors. Thus, based on the key sectors discussed above, I consolidate sectors into 14 ones for the model implementation (Table 3.3). TABLE 3.3
INTERMEDIATE SECTOR CLASSIFICATION ID Economic Sectors
1 Agriculture
2 Mining and Quarrying
3 Food
4 Textile, Sewing, Leather, and Fur Products 5 Other Manufacturing
6 Production and Supply of Electric Power, Steam, and Hot Water 7 Coking, Gas, and Petroleum Refining
8 Chemicals
9 Building Materials and Non-metal Mineral Products 10 Metal Products
11 Machinery and Equipment 12 Construction
13 Transportation, Post, and Telecommunications 14 Services
Source: compiled by the author from China's national input-output accounts for 1981,1987,1992,1995,1997
3.3. Calculate and Estimate the Share of Final Demand in Each Sector In an input-output table, the basic formula to show the relationships among different economic sectors is:
AX + Y = X,
where A is the direct-input-coefficient matrix, X is the output matrix, and Y is the final-demand matrix. The summation of the total final demand (with some adjustments) is equal to the nation's gross domestic product (GDP). Suppose we have n sectors in the economy and if we define Sj as the share of the final demand in the j-th sector in the total final demand (GDP), then we can calculate the share Si as:
Sj= Yj /GDP.
GDP in previous years is available in a country's statistical yearbooks. The final demand of each sector in a certain year, however, is often unknown
unless the nation's input-output accounts are available for that year. Therefore, it is necessary to estimate the share Sj with empirical data. If we have sufficient data, i.e., input-output accounts for many years, we can perform econometric analysis to estimate the shares for each sector in the future. Unfortunately, such accounts are only available every three or five years for China. My study focuses
on the period from China's economic reform (1979) to the present (2004), and the available data include only five input-output tables (1981, 1987, 1992, 1995, and 1997). Thus, in this study, I estimate Si by smoothing the time-series data and averaging them between successive available data points. For instance, if we have input-output tables for 1987 and 1992 and we denote S;.x as the
percentage of final demand of the j-th sector in the total final demand in the year x, then
Sj-1989 = Sj-1987+ 2 * [(Sj-1992 - Sj-1987)/5].
Given the data constraint, this is a simplified approximation method. In
forecasting, I apply simple linear-regression models if they explain the data well. If they do not, I estimate Si based on careful qualitative analyses.
3.4. Calculate Total Outputs
Given the annual GDP and the yearly shares of final demand in each sector, I calculate the total output of each sector, Xi, in each year.
Xj = ( - A) -4 Y; = (1-A) - (Sj* GDP)
where the I represents the identity matrix and (I - A) -1 is the direct and indirect-coefficient matrix, often referred to as the "Leontief Inverse" in input-output economics. Here I encounter another problem due to the constraint of limited data: I need the direct-input-coefficient matrix, A, for each year to calculate X;, but I only have such matrices for five discrete years, so that I have to estimate A matrices for the years for which I do not have input-output accounts. In his master's thesis, Voigtlaender (2002) estimates the A matrix using linear
regression models with time as the independent variable. It is a possible way to estimate the A matrix if sufficient historical data are available and the forecasting is for a long term. For this study, however, I only have five years' input-output accounts and I am only interested in a short-term forecast. Because input coefficients generally do not change over a short period of time, I assume they
remain constant over three years. Admittedly, such an assumption might not be held strongly now, when technologies are advancing so quickly. But given the limited data and the time constraint on the research, I make this assumption in the model.
3.5. Calculate Energy Intensities and Forecasts with Time-Series Models As discussed in Chapter 2, energy intensity in the j-th sector, Ej, can be defined as the energy consumption per unit of economic output in the j-th sector (Sinton, Levine, and Wang, 1998). With the result from the previous step (3.4), I calculate the total economic output of each sector, X;, in each year. Then, I derive the total consumption of coal or coke in each sector from the China Statistical Yearbook (1986-2002). However, I encounter the same problem as the one in calculating each sector's final demand (Y) from input-output accounts: the data table, Consumption of Energy and Its Main Varieties By Sector
(available for 1986-2002), has a different classification of economic sectors from the input-output table I am using. Thus, before using these tables, I reclassify their economic sectors according to the input-output accounts I redesigned. Then, I calculate the energy intensity of the j-th sector, E;, with the formula:
Ej = CI / X;,
where C represents the total consumption of a given energy product by the j-th sector in a given year. Given a time series of Ej, I build time-series models for empirical analysis and forecasting. Different possible time-series models include autoregressive (AR), moving average (MA), mixed autoregressive and moving
average (ARMA), and integrated ARMA with differential independent variables (ARIMA).
In this study, I find the order autoregressive model, AR(1), or the first-order differenced model, ARIMA(1,1,0), is often the most appropriately model for
most sectors. The general functional form of the AR(1) is as follows: Eit = 6 + 1/(1 - $1* B) * ct
where 6 is a constant term related to the mean of the stochastic process, Et is the
disturbance error term, B is the backward-shift operator with one period time lag, and $1is the coefficient of Et.1. The general functional form of ARIMA(1 ,1,0) is:
(1-B) * Eit= 8 + 1/(1 - $1* B) * si
where 6, Et, B, and $1 represent the similar variables and parameters in AR(1). To test whether Ei follows a random walk, I apply the Augmented Dickey-Fuller (ADF) unit-root test on each Ei series. If the test result does not reject the unit-root hypothesis, the model would be a random walk:
Eit = Eit1 + d + Et
where d accounts for the trend (upward or downward) in the series Eit. However, because the available data are very limited and the focus of the model building is mainly on the methodology, also, because the forecast in this study is only for a short run, I set the significance level at 10%. If a model makes generally
reasonable forecasts, I use it as an approximate model in the follow-up analysis. In addition, to crosscheck the correctness of each model, I compare the result with the output from Holt's procedure, a deterministic smoothing model widely used in demand forecasting for trended data in supply-chain management.
Generally, I find selected time-series models perform better or at least as well as the Holt's procedure. In the future studies, we need more detailed data to build statistically sounder models to make more accurate forecasts.
3.6. Forecast GDP and Final Demand for Each Sector
GDP is often given for each past year and a widely used GDP forecasting model is in an exponential functional form (IMF, 2001). In this study, however, I do not use such a theoretical model because the forecast is only for a short term,
i.e., the future three years. Many economists and governmental agencies have done extensive research work to forecast the growth rates of China's GDP
(World Bank 2002, IMF 2002, Asian Development Bank 2002). These forecasts are based on very comprehensive analyses of China's economy as well as the global economic context. Hence, they are more convincing than the results derived from a pure theoretical model. In this study, I use these forecasted GDP directly.
To forecast final demand in the j-th sector, X;, it is necessary first to estimate the share of each sector in GDP, Sj. I build a simple time-trend linear model to forecast Si in the short run:
Sj-t = Do + P1* t + Et
where
Bo
is the intercept of the linear model, 1 is the coefficient of theindependent time variable t, and et is the disturbance error term with the attribute ei~ N(0, cy2). In addition to the standard t-test and F-test, I apply the
Durbin-Watson test to test the serial correlation in the data.
After forecasting GDP and Si, I calculate the final demand in each sector, Xj, using the following formula:
Xj = (I - A) -' * (Sj * GDP).
3.7. Forecast Demand and Supply of Energy Products
Given Xj and Ej, I forecast the consumption, Ci, of energy product i in an economic sector by the following formula:
Cj = X; * Ej
For either coal or coke, there is corresponding energy intensity. Given an economic sector,
j,
the total consumption of coal or coke, Cj, is determined by Xjand Ej. Actually, I could calculate the steel-consumption intensity if steel
consumption by each economic sector is known. Unfortunately, after searching many data sources, I only find a sector-based steel consumption table (Table 3.2). Therefore, my analysis of demand and supply of steel is basically from a qualitative perspective.
My forecast of domestic supply (or domestic production) of an energy product is also based on time-series analysis. Given the selected forecast model and considering the current economic context in China, I forecast the supply of coal and coke for three years: 2003, 2004, and 2005. After comparing energy intensity, supply, and demand in each economic sector, I present related policy implications.
CHAPTER 4
ENERGY SUPPLY-CHAIN ANALYSIS
In this chapter, I investigate each sector in the supply chain of the coke and steel sectors in detail. Using the proposed models, I examine demand, supply, and energy intensity of each sector and make forecasts. I present a summary of the analysis to conclude the chapter.
4.1. Overview of the Supply Chain of Coal-Coke-Steel
As shown in Figures 4.1 and 4.2, the total domestic consumption of coal in China increased steadily from 816.03 million tonnes in 1985 to 1,447.34 million in
1996, and then declined to 1,245.37 million in 2000. However, the domestic energy intensity of coal consumption, particularly after 1989, steadily decreased from 0.9103 tonne per 1,000 yuan of GDP in 1985 to 0.4609 tonne per 1,000 yuan in 2000, thus by almost 50 percent. Similarly, Figure 4.3 shows that the total domestic coke consumption in China increased steadily from 46.90 million tonnes in 1985 to 107.25 million in 1995, and then remained at a stable level
between 100 million and 110 million tonnes. The energy intensity of domestic coke consumption fluctuated from 0.05 to 0.06 tonnes per 1,000 yuan of GDP
until 1995, after which the intensity decreased continuously from 0.0555 to 0.0386 tonnes per 1,000 yuan of GDP in 2000, dropping about 30% (Figure 4.4). Thus, the total consumption of coal and coke first increased from the mid1980s,
and then declined slightly or maintained a constant level; the energy intensities of both coal and coke declined continuously during the early 1990s, in general, but the coal-consumption intensity decreased at a faster pace than the coke
improvement in energy efficiencies primarily to the introduction of new energy-efficient technologies and the implementations of energy-efficiency policies.
Regarding the supply side, Figures 4.1 and 4.3 show China's annual total production of coal and coke, respectively, from 1985 to 2001. Both outputs have a relatively similar pattern to their respective consumption. From 1991, the surplus of coal became a deficit and the deficit kept growing, which means an
annual net import of coal to China in the past decade (Appendix 4). In contrast, the coke consumption was less than coke production in each year from 1985 to 2000, and the surplus increased substantially in 1993 -1994 to above 20 percent of the total coke production for the next four years. Although the net export dropped to around 14 percent in 1998 and has remained at that level since then,
China still dominates the global coke market.
Comparing production, consumption, and energy intensities of coal and coke, I find the following: (1) the total coal consumption is more than the total output so that China has been a net-importer of coal since 1991, (2) the total coke consumption is less than the total coke output, so that China has been a coke net-exporter since 1985, the starting year of my analysis, and (3) both coal and coke intensities have declined for the last decade, and the coal intensity decreased faster than the coke intensity. I will explain the possible underlying reasons in Section 4.3.
1600 1400 cn 1200 1000 0 800 0 E600 E 400----200 L) CO r- 00 0' O0 C\1 CO 1; LO C0 P- 00 o o o o 0o 0o 00 00 o o) o M M M M M M o o
-+- Coal Consumption Year -u- Coal Production
Source: China Statistical Yearbook 1986-2002
FIGURE 4.1
COAL CONSUMPTION AND PRODUCTION IN CHINA, 1985-2001
1.2 Cz 1.0 0 0.8 > 0.6 0 .: a- 0.4 -ao0. E '0 0.2 0 0 0.0 Year
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data
FIGURE 4.2
160 c120 100 0 E80 40 20 ~... 0 o o 00 00 00 0 > > M M > > o> M > M > o> M > M > M oc
--- Coke Consumption Year
-m- Coke Production
Source: China Statistical Yearbook 1986-2002
FIGURE 4.3
COKE CONSUMPTION AND PRODUCTION IN CHINA, 1985-2001
0.07 o 0.06 -S0.05 c - 0.04 o -,~ . 0 0.03 O 0.02 - ---0.01 o 0.00 0 LO C '- C M0 \1M t -)CO r- 0 00 w) w0) ~ ~ ~ 0) ~ MMMM C ) 0 Year
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data
FIGURE 4.4
To study the supply chain of coal-coke-steel, I also examine the total production of steel and some steel-consuming industries. As shown in Figures 4.5 and 4.6, China's total steel production increased continuously and almost in a
linear trend from 46.8 million tonnes in 1985 to 151.6 million in 2001. The sharp upward shift in 2001 is important. However, assuming that the total steel
consumption approximates the total steel production, the steel-consumption intensity did not change significantly over the past 17 years and fluctuated from 0.05 to 0.06 tonnes per 1,000 yuan of GDP. Therefore, in the short term, analysts can expect that the steel consumption in China will keep growing at approximately the same rate as China's GDP growth, which is around seven to eight percent annually. A caveat, of course, is that the sharp upward shift in 2001 could change such a prediction.
Source: China Statistical Yearbook 1986-2002
FIGURE 4.5
STEEL CONSUMPTION IN CHINA, 1985-2001
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data.
FIGURE 4.6
STEEL INTENSITY IN CHINA, 1985-2001
44 160 140 c .o 120 0- oD E c: 100 ~' 40 0. 80 20 0 LO C0 C) o 0 \ CO It LO C0 0 oo YearM M0M00 Year 0.07 'O.06 . 0.05 &.=======.4..42.m==== c 0 00.0 E =3 0.03 0 c--' 0.02 CD0 0.00 L0 Co P C o- CN eM It LO CD 1'*Y Year
Steel consumption in China increased sharply in recent years. This is primarily due to the dramatically increased domestic demand for steel products and the vast volume of investments in steel-related industries from central and local governments as well as from foreign companies and agencies (Woetzel, 2001). The International Iron and Steel Institute (IISI) recently published a report, forecasting that the average annual growth rate of steel demand in China would be 6.7 percent; but it only would be about 1.7 percent in the rest of the world
(Asia Pulse, 2003). As discussed in Chapter 1, China is building and planning to build many steel-intensive projects, including the West-East Gas Transmission project, South-North Water Diversion project, the Qinghai-Tibet Railway, urban subway systems in major metropolitan areas, and urban and rural infrastructure construction projects in almost every major city. China's market demand for such
large engineering machinery as excavators, loaders, and caterpillar tractors is expected to increase sharply. Figures 4.7, 4.8, and 4.9 show the outputs of several fast-growing steel-consuming industries: automobiles, air-conditioners,
and household-refrigerators. All three charts show a pattern of fast growth for the major steel-intensive industries during the past decade. From a supply-chain
perspective, all these industries require a vast volume of steel and, in turn, coke and coal, as discussed in Section 4.2.
80 70 60 2E 50 o 40 10 02 0 ---- --10 0 o0 - C It L( CD - O ) 0 Year Source: China Statistical Yearbook 1990-2002
FIGURE 4.7
AUTOMOBILE OUTPUT IN CHINA, 1990-2001
2500 2000 C 1500 0 01000 500 0 0- C CM It LO CN D- a) 0 1- 1- - 1 1- 1- - 1 1- 1- C~j C\1 Year Source: China Statistical Yearbook 1990-2002
FIGURE 4.8
1600 1400 1200 U, 2E 1000 o 800 0/ *-600 400 200 0 Year Source: China Statistical Yearbook 1990-2002 FIGURE 4.9
HOUSEHOLD-REFRIGERATOR OUTPUT IN CHINA, 1990-2001
4.2. Sector-Based Analyses of the Coal-Coke-Steel Supply Chain Based on the analysis of the key components of the coal-coke-steel supply chain in Chapter 3, 1 redesigned China's input-output table with 14 economic sectors. In the following two sections, I analyze individual sectors in detail. Given data constraints, I focus on the sector-based analysis of coal- and coke-intensity and consumption issues, using the framework developed in Chapter 3. Because of the similarity of the quantitative analyses of individual sectors, I only present a detailed analysis of one key sector in the supply chain: Sector 7-Coking, Gas, and Petroleum Refining. I include the analytical results for the other 13 sectors in Appendix 5 and summarize them in Section 4.3.
4.2.1. An Example of the Sector-Based Statistical Analysis
In this section, I apply the analytical framework developed in Chapter 3 to the sector of Coking, Gas, and Petroleum Refining (Sector 7). First, using the simple-regression model developed in Chapter 3, I estimate the share of final demand in this sector, S7 (Appendix 5 and Figure 4.10). It is notable that the share dropped sharply from 1981 to 1987. This is due to the different accounting methods used in the 1981 input-output table from those used in the tables of 1987, 1992, 1995, and 1997: the output of this sector includes the mining-and-quarrying output in the 1981 table, but not in the other four tables; therefore, the share of final demand is much larger in the 1981 table than in other ones. The share S7 decreased to less than zero (-0.02%) in 1997, which indicates that China had became a net exporter in this sector. This is the case given China has been the dominant supplier in the global coke market since the mid1990s.
2.5% c 2.0%-E E 1.5% 1.0% 0.5% CU $5) 0.0% -- +-~ 1981 1987 1992 19 -0.5% Year
Source: calculated by the author from the China's national input-output accounts 1981, 1987,1992,1995,1997.
FIGURE 4.10
SHARE OF FINAL DEMAND IN SECTOR 7
