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
MASSACHUSETTS INSTitUE
OF TECHNOLOGY
OCTL2RAR
LIBRARIES
Master of City Planning, 2001
University of Cincinnati, OH
Submitted to the Center for Transportation and Logistics in partial fulfillment of
the requirements for the degrees 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 Karen R. Polenske
Professor of Regional Political Economy and Planning
Thesis Supervisor
Accepted by
,
Professor Nigel Wilson
Professor of Civil and Environmental Engineering
Director, Center for Transportation and Logistics
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 coalcoke-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.
I also want to thank my parents and my wife for their love and support all the time.
TABLE OF CONTENTS
TITLE ...............................................................................................
1
ABSTRACT.......................................................................................
2
ACKOW LEDGMENT..........................................................................
3
CONTENTS......................................................................................
4
FIGURES .........................................................................................
6
TABLES..........................................................................................
7
1 INTRODUCTION...........................................................................
1.1. Significance of China's Coal-Coke-Steel Supply Chain....................
1.2. Research Objective and Hypothesis...........................................
1.3. Data Methodology...................................................................
1.4. Input-Output Analysis as a Powerful Tool in Policy Studies..............
8
9
11
12
14
2 LITERATURE REVIEW ..................................................................
2.1. Energy Supply, Demand, and Intensity.........................................
2.2. Supply Chains and Supply-Chain Management..............................
2.2.1. Firm-scale Supply-Chain Studies.........................................
2.2.2. Industry-level Supply-Chain Studies......................................
2.3. Input-Output Techniques..........................................................
2.3.1. Enterprise Input-Output Models...........................................
2.3.2. Macroeconomic Analyses and Policy Implications....................
2.4. Summary..............................................................................
17
17
19
20
21
22
23
23
24
3 MODEL BUILDING..........................................................................
3.1. Identify Key Supply-Chain Components.......................................
3.2. Redesign Input-Output Tables...................................................
3.3. Calculate and Estimate the Share of Final Demand in Each Sector...
3.4. Calculate Total Outputs..........................................................
3.5. Calculate Energy Intensities and Forecasts with Time-Series Models.
3.6. Forecast GDP and Final Demand for Each Sector..........................
3.7. Forecast Demand and Supply of Energy Products.........................
25
25
31
33
34
35
37
38
4 ENERGY SUPPLY- CHAIN ANALYSIS.............................................
4.1. Overview of the Supply Chain of Coal-Coke-Steel..........................
4.2. Sector-Based Analyses of the Coal-Coke-Steel Supply Chain..........
4.2.1. An Example of the Sector-Based Statistical Analysis................
39
39
47
48
4.2.2. Summary of the Intensity Studies..........................................
4.2.3. Summary of the Consumption Studies...................................
4.3. Steel Demand and Supply..........................................................
4.4. Forecast Demand for and Supply of Coal and Coke.........................
4.4.1. Forecast Coal and Coke Supply............................................
4.4.2. Forecast Coal Demand........................................................
4.4.3. Forecast Coke Demand.......................................................
52
56
60
61
61
62
64
5 POLICY IMPLICATIONS AND CONCLUSIONS ...................................
5.1. Energy Policy.........................................................................
5.2. Environmental Policy................................................................
5.3 . T rad e Po licy ............................................................................
5.4. Investment Policy.....................................................................
5.5. Co nclusions...........................................................................
66
. 66
67
68
69
. 69
APPENDICES.....................................................................................
Appendix 1. Intermediate Sector Classification.....................................
Appendix 2. China National Input-Output A Matrix.....................
Appendix 3. China National Input-Output (l-A) 1 Matrix...............
Appendix 4. Demand for and Supply of Coal and Coke in China..............
Appendix 5. Sector-Based Analysis of the Coal-Coke-Steel Supply Chain..
Appendix 5.1. Coal Consumption and Intensities..............................
Appendix 5.2. Coke Consumption and Intensities.............................
Appendix 5.3. Time-Series Models for Coal and Coke Intensities.........
Appendix 6. Shares of Final Demand of Each Sector (S;)........................
Appendix 7. Coal and Coke Consumption in 14 Sectors in China.............
Appendix 8. Coal and Coke Intensities in 14 Sectors in China.................
Appendix 9. Forecasted Coal and Coke Intensities in 14 Sectors in China.
Appendix 10. Economic Sectors Ranked by Coal or Coke Intensities........
72
72
73
78
83
84
84
98
113
114
115
117
119
120
BIBLIOGRAPHY.................................................................................
122
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
Figure 4.10: Share of Final Demand in Sector 7.....................................
48
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
Table 4.7: Forecasted Coke demand in 14 Sectors in China, 2003-2005.....
64
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 inputoutput 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. Inthis 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 infour 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)
India
Japan
USA
World China
311
3
974
998
4531
Coal
12
39
19
122
333
Coke
27
106
102
127
847
Steel
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 coalcoke-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 electricappliances, 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 energyintensity 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 input-output table. The reclassified inputoutput 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:
Real GDP = GDP*100/CPI,
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
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
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 calculated by the author.
CPI = Consumer Price Index
GDP = Gross Domestic Product
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.
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 coalcoke-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 steelmaking. 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 inputoutput 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 FLOW TABLE
(MILLION YUAN)
Motor Electricity Water
Vehicles
Steel
Iron
Coal Coke
Sector
83
872 65,825
6,229 10,731 1,816 1,385
Coal
10
0
156
220 8,916 1,693
0
Coke
0
775
0
720 6,891
0
59
Iron
0
0
8,159
0 1,123 3,109
989
Steel
Motor
256
1,841
525 104,558
350
74
1,252
Vehicles
3,007 12,644 7,653
10,526 2,047 2,301 6,247
Electricity
254
1,101 2,242
245
75
37
299
Water
28,650 40,843 7,212
78,052 3,374 7,134 9,997
Labor
Other Inputs 125,341 13,759 35,676 40,208 155,694 255,100 20,833
222,748 30,243 58,111 70,300 302,125 377,363 38,279
Total
Source: 1997 Input-Output Table of China, Department of National Accounts, National
Bureau of Statistics, People's Republic of C
Note: 1 yuan ~US$ 1.0.
TABLE 1.4
CHINA PARTIAL 1997 DIRECT-INPUT COEFFICIENT TABLE
(DIRECT INPUT PER UNIT OF OUTPUT)
Motor Electricity Water
Vehicles
Steel
Iron
Coke
Coal
Sector
0.003 0.174 0.002
0.028 0.355 0.031 0.020
Coal
0.001 0.000 0.000
0.000 0.007 0.153 0.024
Coke
0.003 0.000 0.000
0.000 0.000 0.012 0.098
Iron
0.027 0.000 0.000
0.004 0.000 0.019 0.044
Steel
0.346 0.005 0.007
0.006 0.002 0.006 0.007
Motor Vehicles
0.010 0.034 0.200
0.047 0.068 0.040 0.089
Electricity
0.001 0.003 0.059
0.001 0.001 0.001 0.003
Water
0.095 0.108 0.188
0.350 0.112 0.123 0.142
Labor
0.515 0.676 0.544
0.563 0.455 0.614 0.572
Other Inputs
1.000 1.000 1.000
1.000 1.000 1.000 1.000
Total
Source: Calculated by author from Table 1.3 by dividing each entry in a column by the
output of the respective sector.
TABLE 1.5
CHINA PARTIAL 1997 DIRECT-AND-INDIRECT-COEFFICIENT TABLE
(DIRECT-AND-INDIRECT INPUT PER UNIT OF FINAL DEMAND)
Motor Electricity Water
Iron Steel Vehicles
Coal Coke
Sector
0.011 0.188 0.042
1.038 0.384 0.101 0.059
Coal
0.003 0.000 0.000
0.000 1.007 0.157 0.042
Coke
0.008 0.000 0.000
0.001 0.000 1.015 0.104
Iron
0.043 0.001 0.001
0.005 0.002 0.021 1.049
Steel
1.530 0.009 0.013
0.009 0.008 0.012 0.014
Motor Vehicles
0.021 1.045 0.222
0.052 0.090 0.060 0.108
Electricity
0.002 0.004 1.063
0.002 0.002 0.002 0.005
Water
1.618 1.247 1.341
1.107 1.494 1.368 1.381
Total
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
.
Energy Intensity
2000
0.6
1500 -E
Actual Consumption
1000 500
0
1965
0
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 energyconsumption country in the world and is expected to be the largest one by the
middle of this century (IEA, 2001).
140-
Projections
History
1200
o00-
China
80,
S 6040-
S 20-
0
United States
I 7" -- l-T- rTI"1
....
......
7r.FITW
.r~'1[ T T-1
11
1
"
1970 1975 1980 1965 1990 1995 2000 2005 2010 2015 2020
Source: 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
Inthe 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 coalcoke-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
22
multipliers, and employment multipliers, to assist policy design and decisionmaking. 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). Inaddition 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 inputoutput framework to project U.S. commodity 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
activities and makes policy recommendations.
2.4. Summary
Inthis 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.
24
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 cokeconsumer 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 CoalConsumers
Other CokeConsumers
Other SteelCustomers
mprt/Export
Source: the author
FIGURE 3.2
THE INTERSECTORAL COAL-COKE-STEEL SUPPLY CHAIN
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.
Gas
2%
Hydro
2%
Nuclear
0.4%
EJCoal
HOil
EJGas
. Hydro
SNuclear
Coal
77%
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
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
Open
Crude Steel
EAF Hearth
BOF
Production
Country
China
Japan
USA
Worldwide
(million tonnes)
123.7
94.2
97.3
786.4
(%)
66
70
54
60
Other
(%)
(%)
(%)
16
30
46
33
2
0
0
4
16
0
0
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
infrastructure investments, demand for steel in China has more than quadrupled
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
(1000 tonnes) (Percent)
Consuming Industries
41.5
45,110
Construction
34.9
37,950
Manufacturing
9
9,821
Machinery
6.6
7,151
Transportation (Railroads and other)
3.2
3,451
Electrical machinery
2.5
2,660
Mining, quarrying, lumbering
2.3
2,445
Oil and gas
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
30
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 inputoutput 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 inputoutput table by consolidating sectors. Generally, an 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
Food
3
Textile, Sewing, Leather, and Fur Products
4
Other Manufacturing
5
Production and Supply of Electric Power, Steam, and Hot Water
6
Coking, Gas, and Petroleum Refining
7
Chemicals
8
Building Materials and Non-metal Mineral Products
9
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 indirectcoefficient 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 first-order autoregressive model, AR(1), or the firstorder 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 $1represent the similar variables and parameters in AR(1).
To test whether Ei follows a random walk, I apply the Augmented DickeyFuller (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 = Eit 1 + 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.
Inaddition, 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. Inthe 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. Inthis 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 the
independent time variable t, and et is the disturbance error term with the attribute
2
). In addition to the standard t-test and F-test, I apply the Durbinei~ N(0, cy
Watson test to test the serial correlation in the data.
37
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 Xj
and 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.
38
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
intensity. As discussed in Chapter 2, many researchers have attributed the
improvement in energy efficiencies primarily to the introduction of new energyefficient 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
o o 0o 0o 00 00
-+-
Coal Consumption
C\1 CO 1; LO C0 P- 00 o o
o o) oM M M M M M
o o
Year
-u- Coal Production
Source: China Statistical Yearbook 1986-2002
FIGURE 4.1
COAL CONSUMPTION AND PRODUCTION IN CHINA, 1985-2001
1.2
1.0
Cz
0
0.8
0.6
>
0
.:aoa- 0.4 0.
E
'0
0.2
0
0
0.0
Year
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data
FIGURE 4.2
COAL INTENSITY IN CHINA, 1985-2001
160
c120
100
E80
0
40
20
~......
0
o o00 00 00 0
---
Coke Consumption
-m-
Coke Production
> M> M> > o>
MM
> > o>
MM
> M>
oc
Year
Source: China Statistical Yearbook 1986-2002
FIGURE 4.3
COKE CONSUMPTION AND PRODUCTION IN CHINA, 1985-2001
0.07
o
0.06
-
S0.05
co
-
0.04
-,~
.
0 0.03
O 0.02
- -----
0.01
o
0
0.00
LO
C '- C
M0
00 w)
~ ~w0)
~
\1M
t
C
0)~ MMMM
-)CO
)
r-
0
0
Year
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data
FIGURE 4.4
COKE INTENSITY IN CHINA, 1985-2001
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.
160
140
c
.o
120
0- oD
E c: 100
0. ~' 40
80
20
0
LO C0
C)
o
0
\
CO It LO C0
YearM
M0M00
0 oo
Year
Source: China Statistical Yearbook 1986-2002
FIGURE 4.5
STEEL CONSUMPTION IN CHINA, 1985-2001
0.07
'O.06
c
. 0.05
&.=======.4..42.m====
0
00.0
E
=3
0.03
0 c--' 0.02
CD0
0.00
L0 Co P
C
o-
MIt LO CD 1'*Y
e
CN
Year
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data.
FIGURE 4.6
STEEL INTENSITY IN CHINA, 1985-2001
44
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.
45
80
70
60
50
2E
o 40
10
0
20
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
1-
CM
It
LO
CN
1- -
1 1-
1-
-
a)
0
D1
1-
1-
Year
Source: China Statistical Yearbook 1990-2002
FIGURE 4.8
AIR-CONDITIONER OUTPUT IN CHINA, 1990-2001
C~j
C\1
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.
47
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-andquarrying 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%
+-~
--
1987
1981
19
1992
-0.5%
Year
Source:
calculated
by
the
author
from
the
China's
national
1987,1992,1995,1997.
FIGURE
SHARE
4.10
OF
FINAL
DEMAND
IN
7
SECTOR
48
input-output
accounts
1981,
Using the estimated shares of this sector in the final demand and the GDP
data for each year, I calculate the final demand in this sector:
Yit = GDPt * Sjt.
Then, the total output (Xjt) of this sector is the simple product of the final demand
and the Leontief inverse:
Xit = Yt * (I - At)-'.
To study the coal and coke intensities in this sector, I need to know the
coal and coke consumption. The amount of the coal consumed in this sector
increased gradually from 32.6 million tonnes in 1985 to about 80 million in the
mid1990s and then remained at that level (Figure 12.7.1). The consumption
curve is very similar to that of the total coke production (Figure 4.3). This may
due to the fact that the coking industry is the major consumer in this sector.
According to some recent research (literature review in Chapter 2), about 14%
(on average) of the total coal output in China is consumed in coking. Thus, as
the total coke production stabilized from the mid1990s, the coal consumption in
this sector has stabilized as well.
With the total output (Xit) and the coal consumption (Cit) in this sector, I
compute the coal intensity Ejt = Cjt / X jt. The coal intensity in this sector dropped
from 1200 tonnes per million yuan of the output in 1989 to about 620 tonnes per
million yuan in 2000, decreasing by almost 50 percent (Appendix 5). I will
present the possible reasons that coal intensities in most sectors decline after the
late 1980s in Section 4.3 (next section). Inthis section, I focus on the model
implications and statistical analyses.
49
Given a time series of E1t, I build a time-series model for the demand
forecasting. I first test the simple time-series model AR(1), as defined in Chapter
3, using the statistical-analysis software, SAS, but the resulting autocorrelation
functions are not stationary. I then test the first-order differential model,
ARIMA(1,1,0). All its autocorrelation functions and partial-correlation functions
satisfy the stationary requirement and the resulting coefficients are statistically
significant. The equation is: (1 -B) * E7t= -0.14495 + 1/(1 + 0.1717 * B) * Et,
where E7r represents the coal intensity in Sector 7 in year t, Et is the disturbance
error term, and B is the backward-shift operator with a one-period time lag.
Then, I test the unit root (random walk) of the dependant variable using the
Augmented Dickey-Fuller (ADF) test. The null hypothesis is rejected, so the
model is statistically sound, and I will use it to forecast coal intensity and demand
in Section 4.5.
Similarly, I apply the analytical framework to the coke consumption and
coke intensity. The annual coke consumption in this sector kept growing until
1992, when there was a sharp decrease in demand (Appendix 5). Given that
coke is primarily used in petroleum refining in this sector, the underlying reason
for the sharp decrease may be: (1) new technologies had enabled the petroleumrefining industry to reduce the consumption intensity of coke, or (2) lessexpensive substitutes had enabled the refining industry to decrease the
consumption of coke. It may also be due to (3) the reduction of coke
consumption in petroleum refining and coking industries in both intermediate use
and final demand as governments enacted strict environment regulations.
50
From 1994, however, the coke consumption rebounded and increased by
more than 10 times, from 56 million tonnes in 1994 to 630 million in 2000. This
may be attributed to the increased surplus in China's domestic coke market since
1993 and, as a result, the falling price from 1993 until 1998. Another possible
reason is the rapidly growing demand in petroleum products since the midl990s
as China started developing its automobile industry and Chinese people
purchased more and more private automobiles.
The coke intensity in this sector follows a similar pattern to the coke
consumption's (Appendix 5) except that the increase rate after 1994 is less than
that of the coke consumption. This indicates that the growth rate of the total
economic output in this sector is much greater than the growth rate of the coke
consumption in the sector. Given that the coke consumption in this sector has
been less than one percent of China's total coke consumption since 1994, I
assume there would be no significant changes in coke demand in this sector in
the years of the study period.
I apply similar analyses to the other 13 sectors and present the results of
coal and coke consumption, intensities, and the corresponding time-series
models in Appendix 5.
4.2.2. Summary of the Intensity Studies
I summarize the intensity studies in Table 4.1. First, coal intensities in all
the 14 sectors observed a downward trend, and some of them, such as
Chemicals, Transportation, and Services, decreased substantially. As many
researchers have pointed out, the decreases in energy intensity are primarily due
to the technological innovation (Lin, 1995). In addition, environmental regulations
may have played significant roles, particularly in heavy-polluting sectors, such as
chemicals. However, two major coal-consuming sectors, Electric Power (Sector
10) and Coking (Sector 7), which in 2000 consumed about 45% and 6%,
respectively, of the total coal consumed in China have observed fluctuations in
coal intensities.
With the rapidly growing economy, China is demanding an enormous
volume of coal for its electric-power sector. The coal sector has not shown
significant improvement in the efficiency of coal consumption given its stabilized
coal intensities in recent years. Similarly, coal intensity in the coking sector has
not decreased significantly for reasons that are not immediately obvious. As
China's demand for steel and, in turn, coke is increasing rapidly, I expect the
demand for coal grow very fast. For the remaining 12 sectors, coal intensity has
decreased almost linearly in the past decade, so that increased economic output
in these sectors may not incur increased demand for coal.
Second, most decreases in coal intensities occurred from the late 1980s,
particularly 1989. Referring to Figure 4.1, coal consumption and production from
1985 to 1996 follow a very smooth linear upward curve, so that disruption in
supply or demand could not be the underlying reason. A possible major factor
could be the rapidly and steadily growing GDP after 1989 (Table 1.2). China's
annual GDP growth rate was -3.9% in 1989, but jumped to two-digit levels in the
early 1990s and then remained at more than 6%. Fast-growing GDP has
fundamentally increased the total output in each sector. At the same time, the
consumption of coal is stabilizing, thereby implying decreased coal intensities in
most sectors.
Third, for coke intensity, only the agriculture (Sector 1) and construction
(Sector 12) sectors have an upward trend. All the other 12 sectors have had a
general downward trend in coke intensities, with fluctuations in most sectors in
recent years, and some intensities have stabilized, which indicates that the
demand for coke will increase if the sector grows. The coke intensity in the
primary coke-consuming sector, Metal Products, has declined slowly. Although
the coke intensities in most sectors decreased, they are more volatile than the
coal intensities. This could be primarily due to the surplus in China's domestic
coke market and the deficit in the coal market. As a result, governments may
regulate coal consumption heavily and encourage technological innovation to
reduce coal intensity. Also, coke purchasers may not be mainly concerned about
the coal price, because they can always charge more for the coke; thus, the price
elasticity may be greater than for coal. Thus, coke consumption could be more
volatile than coal consumption.
Fourth, compared to coal intensities, most declines in coke intensities
started in the mid1990s, particularly in 1995 and 1996. This could be primarily
due to environmental concerns. China's central government has enacted a
series of environmental regulations since the mid 1990s, and enforced them
rigorously since the late 1990s, such as closing all the non-machinery coke
ovens. Such policies reduce the production capacity of coke and make the
supply more inelastic than before. As shown in Figure 4.3, the total coke
production and consumption stabilized from 1995. Limited supply might have
encouraged technological innovation and uses of cleaner substitutes, thereby
reducing the coke intensities.
Fifth, most appropriate time-series models are ARIMA(1,1,0). This is
because most coal and coke intensities drift upward or downward, so that the
first-order differentiation is relatively stationary. Those models with higher-order
differentiations or more autoregressive lags generally observe sharp increases or
decreases or very smooth horizontal movements. These sectors could have
experienced some economic shocks in the past two decades.
4.2.3. Summary of the Consumption Studies
The top three coal-consuming sectors are Sectors 6, 10, and 9 (Table
4.2). They account for more than 60% of the total coal consumption. Among the
14 sectors, coal consumption kept increasing over the studied period (1985 2000) in sectors 6 (Production and Supply of Electric Power, Steam and Hot
Water) and 7 (Coking, Gas, and Petroleum Refining), while decreasing in the
other four sectors: sectors 1, 11, 13, and 14 (Table 4.3). Except for sector 12
(Construction), which observed relatively little change in the coal consumption,
coal demand in all the remaining seven sectors increased first and then
54
decreased. Thus, during the past decade, coal consumption in 11 of 14 sectors
declined. As I discussed in Section 4.2.2, coal intensities in Sectors 6 and 7
observed less steep and relatively more fluctuating declines than in the other 12
sectors, so that it is not unexpected that the coal consumption in these two
sectors increases. The results highlight the importance of the research on
energy intensities.
TABLE 4.1
SUMMARY OF COAL- AND COKE-INTENSITY ANALYSES
Trend
Current Trend
Since
Trend
Current Trend Coke
Intensity
Since
1
2
3
4
5
6
7
8
9
10
11
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,2,0)
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,3,0)
DE
DE
DE
DE
DE
DE, ST
DE, FL
DE
DE
DE
DE
1985
1989
1989
1989
1989
1996
1998
1989
1989
1989
1989
ARIMA(1,1,0)
ARIMA(1,1,0)
ARIMA(1,1,0)
<1%
<1%
<1%
<1%
ARIMA(1,1,0)
ARIMA(3,1,0)
ARIMA(1,1,0)
ARIMA(0,1,1)
IN
DE, FL
DE, ST
DE, FL
DE, ST
DE, FL
IN, FL
DE
DE
DE, FL
DE
1991
1996
1997
1998
1995
1996
1996
1996
1998
1997
1989
12
13
14
< 1% *
N/A
N/A
DE
DE
DE
1997
1988
1985
<1%
<1%
N/A
IN
DE, ST
DE
1997
1998
1992
Coal
Sector ID Intensity
Source: the author.
Notes: N/A = Not Available; DE = Decrease; IN = Increase; ST = Stabilize; FL =
Fluctuate; <1% = The consumption in this sector is less than 1%.
The top three coke-consuming sectors, Sectors 10, 8, and 11 (Table 4.4)
account for more than 90% of the total coke consumption. Thus, the
consumption of coke is more concentrated than that of coal. Among the 14
sectors, only in sector 5 (Other Manufacturing) did the coke consumption
55
decrease over the past 16 years. Coke consumption in the other four sectors,
Sectors 2 (Mining and Quarrying), 6 (Production and Supply of Electric Power,
Steam, and Hot Water), 8 (Chemicals), and 11 (Machinery and Equipment)
increased first and then decreased (Table 4.5). Except for sector 4 (Textile,
Sewing, Leather, and Fur Products), which has relatively unchanged coke
consumption over the past 16 years, the coke consumption in all the other eight
sectors increased, again, supporting our findings for coke intensities. Coke
intensity in Sector 10, the largest coke-consuming sector, has been fluctuating,
and has not declined much. Consequently, coke consumption in this sector has
had a similar pattern to that of total coke consumption (Figure 4.3): increasing
until 1995 and then remaining at that level. We suggest additional studies to
determine why the coke intensities are not significantly declining.
56
TABLE 4.2.
RANK OF ECONOMIC SECTORS BY COAL CONSUMPTION, 2000
Rank Sector
6
1
10
2
9
3
14
4
8
5
2
6
7
7
3
8
5
9
4
10
1
11
11
12
13
13
14
12
Percent of
Total Coal
Consumption
Sector Name
ID
45.0
Production and Supply of Electric Power, Steam, and Hot Water
10.1
Metal Products
8.0
Building Materials and Non-metal Mineral Products
7.6
Services
7.5
Chemicals
6.5
Mining and Quarrying
6.2
Coking, Gas, and Petroleum Refining
2.1
Food
1.7
Others Manufacturing
1.4
Textile, Sewing, Leather, and Fur Products
1.3
Agriculture
1.3
Equipment
Machinery and
0.9
Transportation, Post, and Telecommunications
0.4
Construction
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data.
TABLE 4.3.
CHANGE PATTERNS OF COAL CONSUMPTION IN 14 SECTORS
Percent of
Total Coal
Sector ID Consumption
1.3
1
6.5
2
2.1
3
1.4
4
1.7
5
45.0
6
6.2
7
7.5
8
8.0
9
10.1
10
1.3
11
0.4
12
0.9
13
7.6
14
Increased
Mixed
Decreased Unchanged
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Source: Compiled by the author from the analysis in Section 4.2.
TABLE 4.4.
RANK OF ECONOMIC SECTORS BY COKE CONSUMPTION, 2000
Rank Sector ID Sector Name
Metal Products
10
1
Chemicals
2
8
Machinery and Equipment
11
3
Building Materials and Non-metal Mineral Products
4
9
Services
14
5
Mining and Quarrying
2
6
Agriculture
1
7
Coking, Gas and Petroleum Refining
7
8
Production and Supply of Electric Power, Steam and
Hot Water
6
9
Food
3
10
Others Manufacturing
11
5
Construction
12
12
Transportation, Post and Telecommunications
13
13
Textile, Sewing, Leather and Fur Products
4
14
Percent of
Total Coke
Consumption
77.1
10.4
3.0
2.9
1.8
1.5
1.4
0.6
0.4
0.3
0.3
0.2
Source: Calculated by the author from China Statistical Yearbook 1986-2002 data.
TABLE 4.5.
CHANGE PATTERNS OF COKE CONSUMPTION IN 14 SECTORS
Percent of
Total Coke
Sector ID Consumption
Increased
Mixed
Decreased Unchanged
X
1
1.4
2
1.5
3
0.3
4
0.1
5
0.3
6
0.4
7
0.6
8
10.4
9
2.9
X
10
77.1
X
11
3.0
12
0.2
X
13
14
0.1
1.8
X
X
X
X
X
X
X
X
X
X
Source: Compiled by the author.
58
0.1
0.1
4.3. Steel Demand and Supply
In this section, I briefly discuss the demand for and supply of steel. Figure
4.5 shows that steel production in China has an approximately linear upward
trend, increasing from 46.8 million tonnes in 1985 to 151.6 million tonnes in 2001.
A simple regression model to explain the data, with the time T as the
independent variable is:
Steelt = -11711.33 + 5.92 *T + Et
(21.7)
(-21.5)
where Steelt represents the domestic steel production in year t, Et is the error
term, and the numbers in parenthesis are t-test statistics for estimated
coefficients. The coefficient 5.92 indicates that the annual increase of steel
production is expected to be about 6 million tonnes.
From the sector-based steel consumption table (Table 3.1), we can see
that the top four steel consumers are (1)the construction industry (41.5%),
including Sectors 9 (Building Materials) and 12 (Construction) in input-output
accounts, (2)the manufacturing industry (35.0%), (3) the machinery industry
(9.0%), and (4) the transportation industry (6.6%). All these industries are
growing rapidly with the boom of China's economy, and, consequently, they
demand an enormous volume of steel products. The above sector-based
analyses have shown that the Metal Products sector is the primary and dominant
consuming sector of coke, and the coking industry is one of the major coalconsuming sectors. All the above four major steel-consuming industries
consume coal and coke directly and indirectly.
59
As I have presented, input-output accounts incorporate intermediate
demands of physical materials. Thus, given the data of sector-based coal and
coke consumption, this study provides a sound analysis of the demand for and
supply of coal and coke in the coal-coke-steel supply chain. Given limited data
on sector-based steel consumption, however, I have not been able to perform the
same type of sector-based analysis on steel as I have done on coal and coke. I
leave such topics for future research.
4.4. Forecast Demand for and Supply of Coal and Coke
In Section 4.2, I have shown that empirical results support the first
hypothesis of this study that coal and coke intensities in individual economic
sectors decline as China's overall energy efficiency improves. To test the
second hypothesis that the supply of coal and coke will meet the demand in the
near future, i.e., in 2005, I first study the supply of coal and coke in China, and
then I apply the model developed in Chapter 3 to forecast the demand for coal
and coke.
4.4.1. Forecast Coal and Coke Supply
On the supply side, both coal- and coke-output in China have been
stabilized since the mid 1990s (Figures 4.1 and 4.3). This is partly due to the
newly adopted environmental regulations, which have forced many small
coalmines to close. Given the business-as-usual (BAU) assumption, both coal
and coke outputs are expected to remain at their current levels in the future two
60
years. Thus, the supply of coal is expected to be 1,200 million tonnes in 2005
and the supply of coke 130 million tonnes.
4.4.2. Forecast Coal Demand
First, I forecast coal intensities, Ei, in each sector using the models
developed in Chapters 3 and 4. As shown in Appendix 9.1, coal intensities in ten
out of the 14 sectors are expected to decrease in the studied period (2003-2005)
while in the remaining four are expected to have little change. The reasons for
such trends need to be explored more in future research. Second, I forecast the
share of final demand in GDP, S;, for each sector using simple linear regression
models developed in Chapter 3. The results are listed in Appendix 6. Third, I
forecast GDP and final demand in each sector. In this step, I assume China's
GDP increases nine percent in 2003 and eight percent in 2004 and 2005.
Fourth, I calculate total output in each sector, X;, by multiplying the Leontief
inverse matrix with the corresponding final-demand matrix. Finally, given Xj and
Ej, I calculate the coal consumption in each sector, Cj, by multiplying Xj and Ej.
The results are listed in Table 4.6.
TABLE 4.6.
FORECASTED COAL DEMAND IN 14 SECTORS IN CHINA, 2003-2005
(million tonnes)
Sector ID Sector Name
Agriculture
1
Mining and Quarrying
2
Food
3
Textile, Sewing, Leather, and Fur Products
4
Others (including paper-making)
5
Production and Supply of Electric Power, Steam, and
Hot Water
6
Coking, Gas, and Petroleum Refining
7
Chemicals
8
Building Materials and Non-metal Mineral Products
9
Metal Products
10
Machinery and Equipment
11
Construction
12
Transportation, Post, and Telecommunication
13
Services
14
Total
2003
13
88
17
10
11
714
97
78
31
116
1
5
13
58
1,252
2005
8
75
10
4
5
2004
10
82
13
8
11
757
103
72
34
112
1
5
14
48
1,271
802
108
65
37
105
1
5
15
39
1,280
Source: calculated by the author with the models developed in Chapters 3 and 4.
As shown in the results, under the BAU assumption, the total coal
consumption would be 1,252 million tonnes in 2003, 1,271 million in 2004, and
1,280 million in 2005, which are all greater than the expected supply of 1200
million and the expected gap is increasing. The major increase in the coal
demand is expected to be associated with Sectors 6 (Power Generation) and 7
(Coking, Gas, and Petroleum Refining). Thus, the increased demand for power,
steel products (indirectly for coking), gas, and petroleum refining are the
underlying factors to boost the demand for coal in China.
Although the coal intensity (E) in each sector, as well as in the whole
economy, has decreased substantially since the late 1980s, the rapidly growing
GDP, which would incur high level of total output (X;) in almost every sector, will
eventually raise the demand for coal.
4.4.3. Forecast Coke Demand
Similar to the forecast procedure in Section 4.5.2, I first forecast the coke
intensities (E) in each sector. As shown in Appendix 9.2, coke intensities in
seven out of the 14 sectors are expected to decrease in the studied period
(2003-2005) while in the remaining seven are expected to have little change.
The reasons for such trends need to be explored more in future research.
Second, I forecast the share of final demand (S;) of each sector in the overall
GDP. Then, I use the forecasted GDP to calculate the final demand (Y) and the
total output (Xj) in each sector by multiplying the Leontief-Inverse matrix with the
final-demand matrix. Finally, given Xj and Ej, I estimate the expected coal
consumption (C) in each sector by multiplying X; and Ej (Table 4.7).
TABLE 4.7.
FORECASTED COKE DEMAND IN 14 SECTORS IN CHINA, 2003-2005
(million tonnes)
Sector Name
Sector ID
Agriculture
1
Mining and Quarrying
2
Food
3
Textile, Sewing, Leather, and Fur Products
4
Others (including paper-making)
5
Production and Supply of Electric Power, Steam, and
6
Hot Water
Coking, Gas and Petroleum Refining
7
Chemicals
8
Building Materials and Non-metal Mineral Products
9
Metal Products
10
Machinery and Equipment
11
Construction
12
Transportation, Post, and Telecommunication
13
Services
14
Total
2003
2.1
2.0
0.4
0.1
0.3
0.4
0.6
8.8
3.4
86.8
2.9
0.2
0.1
1.9
110.1
2005
2.6
2.2
0.5
0.1
0.3
2004
2.3
2.1
0.4
0.1
0.3
0.4
0.6
7.9
3.7
89.6
2.6
0.2
0.1
1.9
112.3
0.4
0.6
6.8
3.8
92.2
2.2
0.2
0.1
1.9
113.9
Source: calculated by the author with the models developed in Chapters 3 and 4.
As shown in Table 4.7, the total coke consumption is expected to be 110.1
million tonnes in 2003, 112.3 million in 2004, and 113.9 million in 2005. Although
they are all less than the expected supply of 130 million tonnes, the domestic
demand is growing, and, under the BAU assumption, China would have less
coke to export in the future. The major increase in the coke demand is expected
to be associated with Sector 10 (Metal Products). In addition, the coke
consumption in Sectors 1 (Agriculture) and 9 (Building Materials) is also
expected to increase by 0.5 and 0.4 million tonnes, respectively, in the future two
years. At the same, time, the coke consumption in Sectors 8 (Chemicals) and 11
(Machinery and Equipment) is expected to decrease by two million and 0.7
million tonnes, respectively. These would offset the increase in other sectors
except Metal Products. Thus, the expected increase in demand for coke is
mainly due to the increase in the steelmaking industry.
Regarding the second hypothesis of this paper, although China's rapidly
growing GDP may not incur a higher demand for coke than the expected supply
(130 million) in the short term, the growing domestic demand is expected to
reduce the supply to the international coke market and increase the price in both
domestic and international markets. If we also consider the possible impacts of
the energy and environmental regulations recently enacted by the Chinese
government, the expected coke supply could be even lower, and thereby, the
second hypothesis that the supply of coal and coke will meet the demand in the
near future would have to be rejected.
CHAPTER 5
POLICY IMPLICATIONS AND CONCLUSIONS
Using the results from the models I developed for this analysis, I study the
policy implications in four areas: (1) energy policy, (2) environmental policy, (3)
trade policy, and (4) investment policy. Finally, I draw conclusions on the two
hypotheses proposed in Chapter 1.
5.1. Energy Policy
Coal and coke are both important industrial energy products. Given the
BAU assumption, China will face more coal shortages and fewer coke surpluses
in the near future than in the past. Therefore, the Chinese government should
consider strategies to improve the energy efficiency of using coal and coke
directly and indirectly.
As shown in the energy model (Chapter 3), total energy consumption, C,
is determined by total economic output, X;, and energy intensity, Ej. If the
government wants to reduce the total energy consumption, it is possible either to
reduce the total outputs in energy-intensive sectors or to lower energy intensities
in high-volume-output sectors, or to do both. As shown in Appendix 10, ranked
by the total output (X;), the five top economic sectors are sectors 14 (Services),
11 (Machinery and Equipment), 1 (Agriculture), 12 (Construction), and 8
(Chemicals). The government could target these sectors for energy-efficiency
coal and coke consumption policies. However, except for the Chemicals sector
(sector 8), coal and coke intensities in the other four sectors are already very low.
Also, none of the four sectors has a large consumption of coal or coke. Thus, it
65
might be difficult to lower the current coal and/or coke intensities in these sectors
effectively.
The Chinese policy makers could also try to reduce Cj by reducing Ej in
general. Sectors, such as Chemicals (sector 8), Metal Products (sector 10),
Building Materials (sector 9), and Mining and Quarrying (sector 2), have high
levels of coal and coke consumption (C) and high-level coal and/or coke
intensities (E;). These sectors may be able to improve their coal and coke energy
efficiencies. A similar situation is associated with sectors 6 (Production and
Supply of Electric Power, Steam and Hot Wate) and 7 (Coking, Gas and
Petroleum Refining) given their high coal intensities. As discussed in Chapter 2,
technological innovation has been a primary way in China to improve energy
efficiency. Setting strict energy and/or environment regulations in these sectors
to encourage innovation to improve energy efficiency may prove effective in
reducing coal and coke consumption.
5.2. Environmental Policy
Setting strict environment regulations in high-output (Xj) sectors, such as
the Services sector (sector 14) and the Machinery and Equipment sector (sector
11), may encourage innovations to improve energy efficiency (E) and effectively
reduce the total demand (C) for coal and/or coke. In particular, strict
environmental regulations may force energy-intensive firms in these sectors to
make full use of their capacity, which eventually would improve their energy
efficiencies.
Given the BAU assumption, keeping current environmental regulations on
coke production, or imposing stricter regulations on its upstream suppliers, may
reduce the coke supply, particularly the supply to the international market,
because the domestic demand for coke in China is expected to increase in the
short run. Similarly, imposing stricter environmental policies on coal producers
may exacerbate the existing shortage of coal. To find a balance between
environmental protection and economic development is not easy, particularly for
such a developing country as China. Basically, there are two fundamental ways
to deal with this trade-off. One is, again, to encourage technological innovations
in production and transportation, and set incentives to improve energy efficiency
and environmental sustainability. The other way is to restructure industries,
improving the energy efficiency and environmental sustainability from a
macroeconomic perspective.
5.3. Trade Policy
If these analyses prove correct, we expect that China would impose a
stricter export quota on coke if current coke-production capacity does not
increase in the short term. Under the regulations of the World Trade
Organization (WTO), however, this may be illegal. A recent (2004) dispute
between the European Union (EU) and China about the coke-export licenses well
illustrates the possible problems of such policies: in May 2005, the EU filed to the
WTO to sue China for its export-quota on coke, arguing that it is against the
WTO's rules. Regarding the import of coal, under the BAU assumption, China
67
may need to import more coal in the next few years, so that the government and
related firms may need to prepare in advance.
5.4. Investment Policy
To improve energy efficiency, the government could invest in the energyefficient technologies in high-output sectors and/or coal- and coke-intensive
sectors. Similarly, private companies and research agencies could be
encouraged to invest in such technologies.
If the forecasts prove correct, the Chinese government may need to
consider the possible coal shortage when investing in coal-consuming industries.
For coke-consuming industries, investment in such industries as steelmaking and
automaking would further increase the domestic demand for coke and increase
the coal and coke prices in both domestic and international markets.
5.5. Conclusions
Input-output accounts provide valuable information to study supply chains
from a macroeconomic perspective. Inthis study, I develop an input-output
econometric model to study China's coke and steel industries. By investigating
the demand, supply, and energy-intensity issues of the coal-coke-steel supply
chain, I test two hypotheses: (1) both coal and coke intensities in individual
economic sectors have declined as China's overall energy efficiency improves,
and (2)the supply of coal and coke will satisfy the demand in the short run in
China given the BAU assumption.
68
Regarding the first hypothesis, both coal and coke intensities in the 14
economic sectors, except the coke intensity in the Agriculture sector, have
declined since 1985 as the overall energy intensity in China has declined.
Regarding the second hypothesis, given the BAU assumption, although China's
rapidly growing GDP may not incur a higher demand for coke than the expected
supply, the increasing domestic demand is expected to reduce the coke supply in
the international market and thereby increase the price in the near future. If we
also take the possible impacts of energy and environmental regulations into
consideration, the expected domestic coal and coke supply would be even lower.
Therefore, the supply of coal and coke may not be able to meet the domestic and
international demand in the near future, and we need to reject the second
hypothesis.
Inthis study, I choose to make short-term forecasts because China's
economy is in transition, and everything changes rapidly. Also, recent (2003)
dramatic price increases of coal, coke, steel, and freight transport rates for these
commodities make short-run forecasts important. If analysts believe these
commodities have their intrinsic values that will be eventually revealed by their
prices, long-run forecasting may be less significant at this time. Compared to the
actual outcome in 2003, I can obtain satisfactory forecasts from the model I
developed. This is one of the contributions of this study.
One problem with this study is the data constraint. If I could break down
the sectors to a more disaggregate level, the analysis would be more useful and
forecasts more robust than at present. If analysts could collect sufficient data on
69
input-output accounts over years, they might be able to trace not only short-term,
but maybe intermediate-term, demand and energy intensities. I leave all these to
the future research.
The main contribution of this study lies not so much with the exact
estimates of the demand for coal and coke as with the procedure put forth to
examine the energy consumption and intensity issues in a country's economic
system. The model I developed may help policy makers and business leaders to
understand the underlying linkages among individual economic sectors in terms
of their demand for and supply of different energy products.
APPENDICES
Appendix 1. Intermediate Sector Classification
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Economic Sectors
Agriculture
Mining and Quarrying
Food
Textile, Sewing, Leather, and Fur Products
Other Manufacturing
Production and Supply of Electric Power, Steam, and Hot Water
Coking, Gas, and Petroleum Refining
Chemicals
Building Materials and Non-metal Mineral Products
Metal Products
Machinery and Equipment
Construction
Transportation, Post, and Telecommunications
Services
Source: compiled by the author from China's national input-output accounts for 1981,1987,1992,1995,1997
Appendix 2.1. China National Input-Output Table A Matrix, 1981
Al
A2
A3
A4
A5
A6
A7
A8
A9
A10
All
A12
A13
A14
A2
0.1580 0.0000 0.6294 0.1600 0.1804 0.0140 0.0187 0.0918 0.0367 0.0219 0.0312 0.1399 0.0088 0.0451
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
A3
A4
A5
A6
A7
A8
A9
A10
All
A12
A13
A14
0.0062
0.0201
0.0070
0.0025
0.0080
0.0080
0.0008
0.0050
0.0142
0.0000
0.0048
0.0085
Al
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0607
0.0021
0.0118
0.0026
0.0038
0.0038
0.0004
0.0007
0.0044
0.0000
0.0146
-0.0075
0.0039
0.4444
0.0031
0.0086
0.0080
0.0080
0.0006
0.0027
0.0035
0.0000
0.0051
0.0260
0.0088
0.0534
0.1716
0.0355
0.0286
0.0286
0.0053
0.0101
0.0202
0.0000
0.0168
0.0194
0.0000
0.0014
0.0011
0.0000
0.1856
0.1856
0.0007
0.0049
0.0241
0.0000
0.0322
0.0252
0.0009
0.0076
0.0233
0.0398
0.1393
0.1393
0.0117
0.0281
0.0513
0.0000
0.0194
0.0425
0.0397
0.0289
0.0282
0.0625
0.0724
0.0724
0.0024
0.0098
0.0044
0.0000
0.0202
0.0327
0.0032
0.0111
0.0464
0.0812
0.1169
0.1169
0.0450
0.0414
0.0575
0.0000
0.0535
0.0008
0.0013
0.0041
0.0188
0.0735
0.0566
0.0566
0.0140
0.3204
0.0546
0.0000
0.0436
0.0201
0.0034
0.0153
0.0496
0.0212
0.0275
0.0275
0.0070
0.1920
0.1608
0.0000
0.0151
0.0212
0.0024
0.0124
0.0666
0.0096
0.0251
0.0251
0.1922
0.0881
0.1095
0.0000
0.0395
0.0172
0.0005
0.0047
0.0081
0.0069
0.1268
0.1268
0.0018
0.0091
0.0384
0.0000
0.0201
0.0356
0.1058
0.0167
0.0591
0.0085
0.0189
0.0189
0.0059
0.0014
0.0385
0.0000
0.0124
0.0258
Source: Calculated by the author from data in China National Input-Output Tables 1981 (China National Statistical
Bureau, 1983).
Appendix 2.2. China National Input-Output Table A Matrix, 1987
Al
A2
A3
A4
A5
A6
A7
A8
A9
A10
All1
A12
A13
A14
Al
0.1472
0.0008
0.0388
0.0026
0.0033
0.0042
0.0047
0.0684
0.0007
0.0021
0.0059
0.0000
0.0110
0.0255
A2
0.0156
0.0256
0.0010
0.0130
0.0118
0.0433
0.0168
0.0376
0.0141
0.0386
0.0863
0.0000
0.0089
0.0400
A3
0.4428
0.0029
0.1209
0.0028
0.0172
0.0038
0.0012
0.0125
0.0090
0.0050
0.0032
0.0000
0.0463
0.0694
A4
0.1504
0.0018
0.0074
0.3798
0.0088
0.0053
0.0013
0.0707
0.0005
0.0033
0.0082
0.0000
0.0132
0.0863
A5
0.0756
0.0231
0.0021
0.0845
0.2096
0.0153
0.0072
0.0783
0.0053
0.0552
0.0218
0.0000
0.0152
0.0764
A6
0.0004
0.2207
0.0008
0.0055
0.0074
0.0182
0.0593
0.0085
0.0054
0.0101
0.0375
0.0000
0.0209
0.0293
A7
0.0002
0.4544
0.0006
0.0026
0.0022
0.0096
0.0185
0.0149
0.0037
0.0072
0.0139
0.0000
0.0363
0.0330
A8
0.0682
0.0464
0.0295
0.0385
0.0179
0.0325
0.0166
0.2898
0.0091
0.0191
0.0223
0.0000
0.0170
0.0691
A9
0.0089
0.0646
0.0014
0.0210
0.0709
0.0613
0.0367
0.0646
0.0733
0.0572
0.0495
0.0000
0.0186
0.0622
Al0
0.0015
0.0807
0.0012
0.0164
0.0190
0.0387
0.0345
0.0315
0.0227
0.2981
0.0571
0.0000
0.0177
0.0573
Al1
0.0020
0.0050
0.0011
0.0111
0.0220
0.0119
0.0093
0.0669
0.0158
0.1395
0.3015
0.0000
0.0135
0.0723
A12
0.0049
0.0493
0.0009
0.0098
0.0351
0.0047
0.0158
0.0277
0.2256
0.1836
0.0878
0.0000
0.0249
0.0437
A13
0.0001
0.0148
0.0011
0.0088
0.0160
0.0088
0.1142
0.0297
0.0035
0.0107
0.0700
0.0000
0.0110
0.0835
A14
0.0292
0.0066
0.0298
0.0171
0.0552
0.0095
0.0110
0.0439
0.0150
0.0135
0.0349
0.0000
0.0289
0.1229
Source: Calculated by the author from data in China National Input-Output Tables 1987 (China National Statistical
Bureau, 1989).
Appendix 2.3. China National Input-Output Table A Matrix, 1992
Al
A2
A3
A4
A5
A6
A7
A8
A9
Al0
All1
A12
A13
A14
Al
0.1393
0.0023
0.0349
0.0025
0.0038
0.0021
0.0058
0.0788
0.0059
0.0065
0.0141
0.0001
0.0112
0.0485
A2
0.0085
0.0577
0.0002
0.0098
0.0151
0.0606
0.0162
0.0418
0.0338
0.0475
0.1123
0.0019
0.0208
0.0882
A3
0.4358
0.0063
0.1007
0.0049
0.028
0.0079
0.0039
0.0287
0.011
0.0128
0.0159
0.0005
0.0141
0.0727
A4
0.1236
0.0048
0.0077
0.3799
0.018
0.0079
0.0025
0.0843
0.0017
0.0062
0.0193
0.0002
0.0098
0.1266
A5
0.072
0.0203
0.001
0.0941
0.1896
0.0162
0.0064
0.0835
0.0109
0.0639
0.0363
0.0002
0.017
0.1297
A6
0.0001
0.1871
0.0002
0.0025
0.0099
0.0223
0.0448
0.0115
0.0166
0.0144
0.0765
0.0015
0.0421
0.0828
A7
0.0001
0.4985
0.0003
0.0017
0.0028
0.0097
0.0267
0.0161
0.0091
0.0058
0.0234
0.0003
0.0368
0.0967
A8
0.0467
0.0418
0.0193
0.032
0.0197
0.0359
0.0124
0.3084
0.0135
0.0216
0.031
0.0002
0.0197
0.1192
A9
0.0098
0.0624
0.0005
0.0199
0.0601
0.0624
0.0283
0.0602
0.0979
0.0539
0.0556
0.0004
0.0265
0.1154
Al0
0.0006
0.0763
0.0002
0.0089
0.0179
0.0366
0.0164
0.0177
0.0229
0.3419
0.0463
0.0004
0.0185
0.1249
All1 A12
0.0006 0.0035
0.0059 0.0374
0.0003 0.0002
0.0054 0.0063
0.0185 0.0306
0.0112 0.0007
0.0057 0.0082
0.0496 0.023
0.0196 0.209
0.1592 0.1682
0.08
0.3074
0.0004 0.0069
0.0141 0.0298
0.1295 0.1002
A13
0.0001
0.0133
0.001
0.0086
0.0155
0.0095
0.1122
0.0342
0.011
0.0147
0.1195
0.0014
0.0142
0.0846
A14
0.0174
0.0094
0.0378
0.0077
0.0539
0.0118
0.0107
0.0307
0.0226
0.0168
0.0521
0.0122
0.0721
0.1459
Source: Calculated by the author from data in China National Input-Output Tables 1992 (China National Statistical
Bureau, 1994).
Appendix 2.4. China National Input-Output Table A Matrix, 1995
Al
A2
A3
A4
A5
A6
A7
A8
A9
A10
All
A12
A13
A14
Al
0.1723
0.0023
0.0504
0.0034
0.0045
0.0028
0.0086
0.0706
0.0065
0.0079
0.0155
0.0002
0.0146
0.0426
A2
0.0133
0.0537
0.0003
0.0148
0.0085
0.0497
0.0151
0.0402
0.0374
0.0439
0.1192
0.0030
0.0275
0.0712
A3
0.4023
0.0036
0.1164
0.0032
0.0133
0.0106
0.0038
0.0389
0.0056
0.0081
0.0085
0.0003
0.0086
0.0343
A4
0.1122
0.0032
0.0097
0.4491
0.0089
0.0072
0.0025
0.0884
0.0014
0.0061
0.0147
0.0002
0.0100
0.0858
A5
0.0770
0.0221
0.0010
0.0992
0.2563
0.0144
0.0072
0.0676
0.0082
0.0537
0.0250
0.0002
0.0181
0.0962
A6
0.0002
0.1927
0.0003
0.0045
0.0073
0.0273
0.0407
0.0096
0.0170
0.0194
0.0781
0.0019
0.0508
0.0657
A7
0.0002
0.3948
0.0005
0.0027
0.0025
0.0245
0.0285
0.0295
0.0144
0.0090
0.0347
0.0007
0.0720
0.1264
A8
0.0446
0.0465
0.0195
0.0440
0.0200
0.0240
0.0174
0.3858
0.0104
0.0266
0.0230
0.0002
0.0180
0.0631
A9
0.0121
0.0638
0.0007
0.0260
0.0529
0.0801
0.0270
0.0535
0.1186
0.0621
0.0544
0.0005
0.0312
0.0877
A10
0.0011
0.0885
0.0003
0.0160
0.0107
0.0319
0.0166
0.0184
0.0257
0.3358
0.0621
0.0007
0.0306
0.1028
All
0.0007
0.0065
0.0004
0.0076
0.0148
0.0107
0.0062
0.0610
0.0212
0.1427
0.3500
0.0005
0.0176
0.0965
A12
0.0043
0.0405
0.0002
0.0089
0.0335
0.0006
0.0125
0.0240
0.2039
0.1783
0.0790
0.0084
0.0344
0.0810
A13
0.0089
0.0200
0.0219
0.0187
0.0230
0.0226
0.1363
0.0626
0.0376
0.0295
0.1360
0.0103
0.0273
0.1176
A14
0.0178
0.0080
0.0370
0.0089
0.0376
0.0090
0.0139
0.0317
0.0184
0.0142
0.0514
0.0124
0.0617
0.1331
Source: Calculated by the author from data in China National Input-Output Tables 1995 (China National Statistical
Bureau, 1997).
75
Appendix 2.5. China National Input-Output Table A Matrix, 1997
Al
A2
A3
A4
A5
A6
A7
A8
A9
Al0
All
A12
A13
A14
Al
0.1606
0.0021
0.0663
0.0029
0.0042
0.0073
0.0085
0.0740
0.0025
0.0031
0.0160
0.0020
0.0119
0.0412
A2
0.0099
0.0760
0.0003
0.0086
0.0154
0.0488
0.0224
0.0487
0.0119
0.0391
0.0875
0.0022
0.0434
0.0633
A3
0.4294
0.0046
0.1281
0.0023
0.0283
0.0079
0.0023
0.0253
0.0068
0.0077
0.0084
0.0005
0.0125
0.0586
A4
0.0896
0.0026
0.0158
0.4037
0.0124
0.0061
0.0016
0.0766
0.0013
0.0031
0.0128
0.0006
0.0131
0.0669
A5
0.0518
0.0165
0.0009
0.0729
0.2190
0.0244
0.0053
0.0773
0.0083
0.0468
0.0247
0.0009
0.0211
0.0780
A6
0.0001
0.2002
0.0000
0.0039
0.0128
0.0348
0.0526
0.0074
0.0078
0.0069
0.1170
0.0028
0.0325
0.0894
A7
0.0000
0.5324
0.0000
0.0028
0.0074
0.0219
0.0496
0.0213
0.0088
0.0063
0.0392
0.0012
0.0287
0.0578
A8
0.0464
0.0472
0.0127
0.0405
0.0226
0.0383
0.0190
0.3653
0.0099
0.0131
0.0251
0.0009
0.0231
0.0672
A9
0.0029
0.1132
0.0008
0.0142
0.0611
0.0438
0.0280
0.0573
0.1418
0.0533
0.0446
0.0008
0.0420
0.0804
A10
0.0003
0.1013
0.0000
0.0045
0.0458
0.0453
0.0259
0.0228
0.0239
0.3569
0.0467
0.0009
0.0382
0.0721
All
0.0003
0.0081
0.0000
0.0078
0.0234
0.0110
0.0071
0.0726
0.0211
0.1524
0.3307
0.0012
0.0194
0.0629
A12
0.0041
0.0262
0.0006
0.0033
0.0261
0.0070
0.0286
0.0209
0.2707
0.1225
0.0806
0.0006
0.0367
0.0846
A13
0.0016
0.0062
0.0058
0.0065
0.0190
0.0164
0.0795
0.0154
0.0042
0.0061
0.1428
0.0194
0.0444
0.0743
A14
0.0209
0.0043
0.0344
0.0140
0.0514
0.0113
0.0129
0.0372
0.0102
0.0074
0.0797
0.0202
0.0427
0.1613
Source: Calculated by the author from data in China National Input-Output Tables 1997 (China National Statistical
Bureau, 1999).
76
1 Matrix (Leontief's Inverse), 1981
Appendix 3.1. China National Input-Output Table (I-A)~
Al
A2
A3
A4
A5
A6
A7
A8
A9
Al0
All
A12
A13
A14
Al
1.2132
0.0000
0.0110
0.0480
0.0150
0.0083
0.0196
0.0196
0.0020
0.0179
0.0251
0.0000
0.0092
0.0151
A2
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
A3
0.8196
0.0000
1.0721
0.0389
0.0263
0.0107
0.0236
0.0236
0.0021
0.0164
0.0246
0.0000
0.0232
0.0043
A4
0.3731
0.0000
0.0182
1.8203
0.0186
0.0236
0.0351
0.0351
0.0030
0.0193
0.0223
0.0000
0.0161
0.0570
A5
0.3215
0.0000
0.0223
0.1377
1.2231
0.0585
0.0752
0.0752
0.0096
0.0407
0.0488
0.0000
0.0321
0.0412
A6
0.0834
0.0000
0.0185
0.0295
0.0275
1.0336
0.2607
0.2607
0.0063
0.0405
0.0570
0.0000
0.0495
0.0527
A7
0.0983
0.0000
0.0203
0.0434
0.0570
0.0754
1.2182
0.2182
0.0184
0.0839
0.0929
0.0000
0.0401
0.0707
A8
0.1998
0.0000
0.0560
0.0761
0.0517
0.0829
0.1274
1.1274
0.0061
0.0336
0.0279
0.0000
0.0344
0.0525
A9
0.1286
0.0000
0.0191
0.0539
0.0862
0.1227
0.2156
0.2156
1.0537
0.1109
0.1069
0.0000
0.0800
0.0324
A10
0.1075
0.0000
0.0179
0.0390
0.0605
0.1378
0.1690
0.1690
0.0267
1.5222
0.1260
0.0000
0.0852
0.0569
Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.1.
All
0.1176
0.0000
0.0170
0.0592
0.0948
0.0692
0.1015
0.1015
0.0169
0.3590
1.2338
0.0000
0.0457
0.0499
A12
0.2589
0.0000
0.0177
0.0642
0.1219
0.0643
0.1200
0.1200
0.2087
0.2058
0.1811
1.0000
0.0751
0.0460
A13
0.0674
0.0000
0.0162
0.0307
0.0321
0.0334
0.1845
0.1845
0.0065
0.0454
0.0686
0.0000
1.0345
0.0572
A14
0.1840
0.0000
0.1212
0.0517
0.0851
0.0217
0.0450
0.0450
0.0086
0.0257
0.0604
0.0000
0.0226
1.0370
Appendix 3.2. China National Input-Output Table (I-A) 1' Matrix (Leontief's Inverse), 1987
Al
A2
A3
A4
A5
A6
A7
A8
A9
Al0
All
A12
A13
A14
Al
A2
A3
A4
A5
A6
A7
A8
A9
A10
All
A12
A13
A14
1.2224
0.0201
0.0608
0.0196
0.0165
0.0133
0.0138
0.1327
0.0053
0.0178
0.0263
0.0000
0.0229
0.0619
0.0482
1.0736
0.0109
0.0438
0.0361
0.0607
0.0341
0.1000
0.0249
0.1020
0.1589
0.0000
0.0233
0.0936
0.6340
0.0303
1.1747
0.0293
0.0466
0.0182
0.0205
0.1108
0.0179
0.0325
0.0395
0.0000
0.0721
0.1433
0.3435
0.0367
0.0442
1.6443
0.0459
0.0258
0.0197
0.2281
0.0104
0.0382
0.0561
0.0000
0.0419
0.2123
0.1913
0.0815
0.0274
0.2050
1.2971
0.0433
0.0318
0.2177
0.0198
0.1390
0.0909
0.0000
0.0422
0.1864
0.0254
0.2831
0.0078
0.0299
0.0283
1.0402
0.0778
0.0586
0.0162
0.0605
0.1092
0.0000
0.0350
0.0794
0.0332
0.5100
0.0095
0.0324
0.0277
0.0428
1.0434
0.0822
0.0186
0.0697
0.1075
0.0000
0.0529
0.0975
0.1864
0.1182
0.0653
0.1141
0.0590
0.0650
0.0442
1.4800
0.0245
0.0786
0.0925
0.0000
0.0452
0.1716
0.0676
0.1501
0.0172
0.0803
0.1254
0.0930
0.0664
0.1690
1.0933
0.1453
0.1370
0.0000
0.0426
0.1502
0.0493
0.1899
0.0154
0.0715
0.0650
0.0808
0.0741
0.1306
0.0478
1.4886
0.1744
0.0000
0.0462
0.1592
0.0569
0.0814
0.0189
0.0680
0.0750
0.0481
0.0428
0.1995
0.0410
0.3230
1.4984
0.0000
0.0429
0.1907
0.0591
0.1491
0.0161
0.0702
0.1022
0.0540
0.0580
0.1451
0.2636
0.3525
0.2182
1.0000
0.0550
0.1558
0.0303
0.0947
0.0112
0.0370
0.0417
0.0248
0.1302
0.0878
0.0133
0.0594
0.1362
0.0000
1.0281
0.1399
0.0965
0.0429
0.0497
0.0598
0.0961
0.0254
0.0282
0.1186
0.0256
0.0576
0.0865
0.0000
0.0472
1.1915
Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.2.
Appendix 3.3. China National Input-Output Table (I-A)" Matrix (Leontief's Inverse), 1992
Al
A2
A3
A4
A5
A6
A7
A8
A9
A10
All
A12
A13
A14
Al
A2
A3
A4
A5
A6
A7
A8
A9
A10
All
A12
A13
1.2079
0.0315
0.0563
0.0223
0.0264
0.0174
0.0180
0.1635
0.0198
0.0455
0.0634
0.0021
0.0326
0.1329
0.0441
1.1332
0.0160
0.0455
0.0598
0.0942
0.0419
0.1320
0.0660
0.1716
0.2538
0.0057
0.0613
0.2482
0.6069
0.0491
1.1486
0.0379
0.0707
0.0305
0.0243
0.1583
0.0349
0.0789
0.0964
0.0037
0.0512
0.2178
0.2926
0.0622
0.0478
1.6516
0.0822
0.0423
0.0281
0.2863
0.0309
0.0898
0.1342
0.0055
0.0638
0.3762
0.1785
0.0966
0.0313
0.2226
1.2873
0.0571
0.0359
0.2533
0.0466
0.2037
0.1704
0.0054
0.0722
0.3659
0.0283
0.2708
0.0135
0.0301
0.0496
1.0582
0.0714
0.0861
0.0483
0.1167
0.2148
0.0053
0.0805
0.2301
0.0377
0.6014
0.0173
0.0373
0.0527
0.0685
1.0611
0.1179
0.0546
0.1283
0.2016
0.0054
0.0876
0.2884
0.1415
0.1303
0.0540
0.1051
0.0801
0.0825
0.0443
1.5353
0.0500
0.1285
0.1632
0.0052
0.0744
0.3394
0.0641
0.1620
0.0215
0.0786
0.1280
0.1072
0.0616
0.1829
1.1415
0.1881
0.2013
0.0052
0.0783
0.3167
0.0431
0.1962
0.0207
0.0555
0.0816
0.0918
0.0535
0.1199
0.0701
1.6188
0.2115
0.0059
0.0772
0.3633
0.0471
0.0953
0.0232
0.0522
0.0860
0.0581
0.0383
0.1799
0.0659
0.4195
1.5579
0.0060
0.0726
0.3846
0.0507
0.1393
0.0202
0.0580
0.1050
0.0565
0.0457
0.1403
0.2696
0.3791
0.2488
1.0119
0.0858
0.3304
0.0302
0.1150
0.0149
0.0388
0.0535
0.0363
0.1338
0.1119
0.0370
0.1132
0.2465
0.0048
1.0508
0.2265
Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.3.
A14
0.0805
0.0636
0.0608
0.0466
0.1068
0.0372
0.0373
0.1178
0.0516
0.1052
0.1606
0.0165
0.1116
1.2942
Appendix 3.4. China National Input-Output Table (I-A) 1 Matrix (Leontief's Inverse), 1995
A2
0.0561
1.1272
0.0184
0.0704
0.0456
0.0823
0.0445
0.1573
A3
0.5962
0.0429
1.1769
0.0421
0.0432
0.0300
0.0254
0.1816
A4
0.3168
0.0641
0.0605
1.8720
0.0591
0.0403
0.0338
0.3625
A5
0.2117
0.1021
0.0373
0.2938
1.3858
0.0530
0.0421
0.2796
A6
0.0352
0.2746
0.0162
0.0489
0.0415
1.0632
0.0730
0.1074
A7
0.0512
0.4974
0.0243
0.0615
0.0471
0.0766
1.0731
0.1696
A8
0.1644
0.1489
0.0620
0.1746
0.0773
0.0702
0.0581
1.7434
A9
0.0771
0.1724
0.0247
0.1164
0.1184
0.1291
0.0677
0.2102
A10
0.0535
0.2161
0.0238
0.0941
0.0631
0.0863
0.0611
0.1553
All
0.0563
0.1038
0.0258
0.0832
0.0732
0.0573
0.0454
0.2471
A12
0.0621
0.1531
0.0234
0.0904
0.1028
0.0605
0.0567
0.1742
A13
0.0792
0.1505
0.0507
0.0939
0.0764
0.0648
0.1758
0.2268
A14
0.0840
0.0606
0.0635
0.0599
0.0824
0.0330
0.0427
0.1367
Al
A2
A3
A4
A5
A6
A7
A8
Al
1.2697
0.0366
0.0829
0.0359
0.0262
0.0187
0.0256
0.1842
A9
Al0
0.0227 0.0736 0.0262 0.0302 0.0444 0.0546 0.0666 0.0493 1.1720 0.0810 0.0740 0.2753 0.0846 0.0485
0.0522 0.1662 0.0614 0.0883 0.1881 0.1263 0.1337 0.1430 0.2054 1.6153 0.4043 0.3981 0.1715 0.0956
All
0.0746 0.2831 0.0790 0.1317 0.1637 0.2422 0.2391 0.1654 0.2253 0.2720 1.6693 0.2792 0.3377 0.1645
A12
A13
0.0024 0.0072 0.0030 0.0052 0.0055 0.0066 0.0078 0.0051 0.0059 0.0072 0.0064 1.0142 0.0162 0.0172
0.0391 0.0707 0.0413 0.0633 0.0751 0.0953 0.1330 0.0752 0.0901 0.0994 0.0823 0.0987 1.0907 0.1004
A14
0.1160 0.2011 0.1321 0.2868 0.2893 0.1943 0.2983 0.2403 0.2593 0.3086 0.3107 0.2767 0.2991 1.2487
Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.4.
1 Matrix (Leontief's Inverse), 1997
Appendix 3.5. China National Input-Output Table (I-A)~
Al
A2
A3
A4
A5
A6
A7
A8
A9
Al0
All
A12
A13
A14
Al
1.2630
0.0407
0.1039
0.0282
0.0318
0.0265
0.0244
0.1819
0.0144
0.0386
0.0764
0.0060
0.0350
0.1108
A2
0.0429
1.1631
0.0140
0.0459
0.0620
0.0829
0.0521
0.1573
0.0349
0.1423
0.2308
0.0086
0.0834
0.1664
A3
0.6394
0.0503
1.2056
0.0359
0.0745
0.0342
0.0248
0.1667
0.0236
0.0572
0.0904
0.0068
0.0470
0.1667
A4
0.2406
0.0550
0.0623
1.7145
0.0645
0.0375
0.0252
0.2812
0.0186
0.0569
0.1106
0.0077
0.0552
0.2120
A5
0.1378
0.0957
0.0282
0.1920
1.3280
0.0657
0.0351
0.2512
0.0324
0.1526
0.1462
0.0083
0.0683
0.2185
A6
0.0306
0.3106
0.0136
0.0387
0.0633
1.0745
0.0859
0.1112
0.0337
0.1165
0.2951
0.0102
0.0802
0.2077
A7
0.0388
0.6801
0.0150
0.0451
0.0645
0.0821
1.0939
0.1575
0.0393
0.1253
0.2343
0.0097
0.0940
0.2020
A8
0.1456
0.1621
0.0476
0.1391
0.0895
0.0932
0.0598
1.6794
0.0379
0.1022
0.1682
0.0090
0.0788
0.2238
A9
0.0517
0.2454
0.0195
0.0758
0.1437
0.0967
0.0704
0.2105
1.1910
0.1865
0.2117
0.0093
0.1015
0.2298
A10
0.0447
0.2778
0.0180
0.0592
0.1449
0.1160
0.0801
0.1686
0.0688
1.6599
0.2515
0.0106
0.1140
0.2534
Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.5.
All
0.0464
0.1284
0.0185
0.0665
0.1095
0.0669
0.0491
0.2621
0.0651
0.4181
1.6222
0.0096
0.0834
0.2326
A12
0.0472
0.1787
0.0189
0.0562
0.1182
0.0668
0.0757
0.1683
0.3446
0.3140
0.2710
1.0096
0.1041
0.2498
A13
0.0318
0.1054
0.0189
0.0393
0.0646
0.0447
0.1075
0.1074
0.0309
0.1062
0.3001
0.0257
1.0816
0.1738
A14
0.0860
0.0650
0.0610
0.0612
0.1120
0.0394
0.0387
0.1491
0.0377
0.0920
0.2133
0.0286
0.0821
1.2776
Appendix 4. Demand for and Supply of Coal and Coke in China, 1985-2001
(million tonnes)
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Coal
Coal
Coal
Demand supply Surplus
55.97
872
816.03
33.85
894
860.15
928
0.01
927.99
980 -13.54
993.54
19.73
1,034.27 1,054
24.77
1,055.23 1,080
1,104.32 1,087 -17.32
-24.85
1,140.85 1,116
1,213.09 1,150 -63.09
1,285.32 1,240 -45.32
1,376.77 1,361 -15.77
1,447.34 1,397 -50.34
1,392.48 1,373 -19.48
1,294.92 1,250 -44.92
1,263.65 1,045 -218.65
998 -247.37
1,245.37
1,161
Percent of
Coke
Surplus in Coal
Demand Demand
7
46.9
4
52.49
57.21
0
-1
60.27
2
63.68
2
69.15
-2
71.18
-2
78.39
84.67
-5
90.94
-4
-1 107.25
-3 107.98
-1 109.27
-3 110.78
-17 104.57
104.4
-20
Percent of
Coke
Coke Surplus in Coke
Supply
Supply Surplus
2
1.12
48.02
1
0.27
52.76
1
0.74
57.95
1
0.81
61.08
4
2.56
66.24
73.28
4.13
6
2.34
3
73.52
2
1.45
79.84
9
93.2
8.53
21
114.77
23.83
21
27.77
135.02
21
136.43
28.45
20
28.04
137.31
13
17.28
128.06
13
120.74
16.17
14
121.84
17.44
131.31
Source: Assembled by the author using data from China Statistical Yearbook 1986-2002.
Appendix 5. Sector-Based Analysis of the Coal-Coke-Steel Supply Chain
(I present a detailed analysis for sector 7 in Chapter 4)
5.1. Coal Consumption and Intensities
5.1.1. Agriculture (Sector 1)
I ,
I
70
25 --
----
20
--
-
----
--
- -
-
---
I
C: 60 -- - - - - - - - - - - - - - - - - - - ----
EC 15 - ----
-
-
----------------- - - - -
0
C
0
10
E 5 --------
0-
----
----- ----- ----
1 1
LO D eo C 0) 0 o i c
om
omoo
~ooom
co
0
= 40
E
-
---------
co
co
mm
- --
iii
o ( Co o
omomo
om
C
0
0
o)MMMM
0
1
I I
I
I
I
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
Source: Calculated by the author
FIGURE 5.1.1.1
COAL CONSUMPTION: SECTOR 1, 1985-2000
FIGURE 5.1.1.2
COAL INTENSITY: SECTOR 1, 1985-2000
83
- -
5.1.2. Mining and Quarryinq (Sector 2)
1200
120
100
-
c
2
:
----
-
-
-------------
800-------
--
-
0
C
60
-
40
--
600
20
E
200
0
LO CD
Cl
OLoO(O
-400
--
--
-
-
OO)OC
-0
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
Source: Calculated by the author
FIGURE 5.1.2.1
COAL CONSUMPTION: SECTOR 2, 1985-2000
FIGURE 5.1.2.2
COAL INTENSITY: SECTOR 2,1985-2000
84
5.1.3. Food (Sector 3)
250 45
--
40
-
8i
25~
20 -----------------------------15
0
25I
-
c-200
35
Cn
-------------------
10
8
--
c
----
- -
- -
5C -----------------------
-
-
----
----
-
50
--
---
-
--
-----
-
---
--
-
o-
--------
Year
--
-
-
-
- -
-
-
- -
-
-
-
Source: Calculated by the author
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.1.3.2
COAL INTENSITY: SECTOR 3, 1985-2000
FIGURE 5.1.3.1
COAL CONSUMPTION: SECTOR 3, 1985-2000
85
-
-
-
-
-
5.1.4. Textile, Sewinq, Leather, and Fur Products (Sector 4)
140-
35------------------------------
-120
30
-
--
-C
25
c
4
--
20
----
--
---
-0-
10
--
C
C
15
0
E
10
5
----
-
------
0 -
-
-
-
----
--
-
-
-
----
--
-
---
--------
20
60
-
--
20
o-
c7
- ---
T
or-T--T
T--
- ------
--
-
0
M M
-r-
-
o
--------
0 !
10
00
-
i I i
( 1
m
w
i
-
i
00C 0)
w
w
-
-
-
--
-
0
0M
0M
CMj CO)
0M 0M
-
i
i
i
-
"I
0)
-
M0 N
0M 0)
Year
Source: Calculated by the author
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.1.4.2
COAL INTENSITY: SECTOR 4,1985-2000
FIGURE 5.1.4.1
COAL CONSUMPTION: SECTOR 4,1985-2000
1
-
-
-
-
0M
0)
0
0
i
i
10
0)
-
w
0)
5.1.5. Other Manufacturing (Sector 5)
500 60
--
-
--
450
- - -------- - -----------
400
C50
350
S40
0
300
c'
30
0
20
10
250
-
200
150
---
100
0
50
co
00 co 0 0)
0)
) MM
M M
0)0) 0) 0) a) 0) M0))
0) 0) 0
MM0M0))
--- --- ---- --- ---- --
0
I'0-C M0M0
)
COO O )O O
0) ) ) )) ) )
Year
N
)
)
MC T
0
0
0
0
LO)
1(D
0
0
0
0
Source: Calculated by the author
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.1.5.1
COAL CONSUMPTION: SECTOR 5,1985-2000
1-
FIGURE 5.1.5.1
COAL INTENSITY: SECTOR 5,1985-2000
1
87
r-CMO
0
0
0
0
0
M
00
0
0
5.1.6. Production and Supply of Electric Power, Steam, and Hot Water (Sector 6)
I
8000
6 00 -- - - - - - - - - - - - - - - - - - - 5 0 0 -- - - - - - - - - - - - - - - - - - - <n
- ---
400
C
c
C
E4000
-
O.
100
n
3000
5
2000
CL
-
---
i
10-
--
----
1000
----
0 -
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.1.6.1
COAL CONSUMPTION: SECTOR 6,1985-2000
i
1O
00
0)
(0
00
0)
i i
fr00
0)
i
CO 0)
00 00
0)
0)
CD
0)
0)
V0)
0)
i
N
0)
0)
i
CO) 'I
0)
0)
0)
0)
i
10
0)
0M
i
(D
0)
0)
Source: Calculated by the author
FIGURE 5.1.6.2
COAL INTENSITY: SECTOR 6,1985-2000
88
i
[0)
0)
i
CO 0)
CD
0
0)
0)
M) 0
0)
5.1.7. Coking, Gas and Petroleum Refining (Sector 7)
-
-
1400
12 00 - - -
- - - - - - - - - - - - -
--
c
1000
0
CL
E
- - - - - - - --- - - - ---------- --
10 -- -c 320
-
600 -- - - - - - - - - - - - - - - - - - 0
0 -
i
10
oo
0')
i
i
%
00
0')
0)
0Y)
t
T-
0)
M)
0)
0)
-D-
-
0)
M)
0
0)
M
-11-
200 ---
0
M
0cjM
-
---
-
"I
0--
0
0
T-
Year
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
Source: Calculated by the author
FIGURE 5.1.7.2
COAL INTENSITY: SECTOR 7,1985-2000
FIGURE 5.1.7.1
COAL CONSUMPTION: SECTOR 7,1985-2000
89
0
--
-
T
-
5.1.8. Chemicals (Sector 8)
600
000
5oo
0
-
--
- -
--
-
-_
-_-
-_
----- -----
- - - - - -
C
- --
20 0
1 04
- - 100
- - - - - ----0-2~
- -
0-- --
0-- - - 0
- -- - - - -
-
- - - - - - - - - ---
- - -- - - - - -- - -
10o
--- --
- - - - -
C
0
80
0-
Year
c)co
coi
00)0)
cO c)i
0M
co
0)
iC) ia) iC) i0) i
0)
0)
M)
M)
1 M) 1
M)
)
) 1 )
M) a)
Source: Calculated by the author
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
FIGURE 5.1.8.2
COAL INTENSITY: SECTOR 8,1985-2000
FIGURE 5.1.8.1
COAL CONSUMPTION: SECTOR 8,1985-2000
I
90
10) ia)
C)
a)
Q
0
1,
5.1.9. Building Materials and Non-Metal Mineral Products (Sector 9)
I
1600
160
---
140
-------
--------------
1400
n
120
C
100
0
C
o
5
-------
60
40
----
0
1200
0
1000
E
800
- -----
----
-
600
C
---
----
20
---------
----
80
c
i|
--
-
| |
O
LO o 0 P co Q0Cy
---
-
-
8
----
|
Clzl
N
LO
WOI*, 0MM
40o
200
O
Y0ar
0
(0
Year
0M 0
N'
M~ "I
IO
(0
FIGURE 5.1.9.2
COAL INTENSITY: SECTOR 9, 1985-2000
FIGURE 5.1.9.1
SECTOR 9,1985-2000
W
Source: Calculated by the author
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
COAL CONSUMPTION:
r--
0)
1
r~-
M
M) 0
MCJ
5.1.10. Metal Products (Sector 10)
700
1 60
-
_
---
700-------
-
---
-
--
---
-
--
-
-
---
o
80
20
-
-
60
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
_
-- -
- ----
-----
- - - - -
C 00
-
-
_
-
soo - - - - 04-
_
------- --------------
1409~
80
0
1
00
LC)
(D
r-
CO M 0
N' M~ "T
LO (D
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.1.10.1
COAL CONSUMPTION: SECTOR 10, 1985-2000
Source: Calculated by the author
FIGURE 5.1.10.2
COAL INTENSITY: SECTOR 10, 1985-2000
92
r'-
Mc C
- -
5.1.11. Machinery and Equipment (Sector 11)
140
35
-
---
--
30
Q)
100
2 5 -- - - - - - - - - - - - - - - - - - - -
w
2
2 0
C
15
C:
120
--
--
--
-
250
10
--
-
-
-
-
-
-
-
-
-
-
-
-
-
-
--------
80
-
60
-
0 5
-0 -T I
LO) CO I*-
40
i i i I i iI i i i
0
0
N '9
2 OM
i
i i- -
20
00-M 0
CO
wco
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.1.11.1
COAL CONSUMPTION: SECTOR 11, 1985-2000
M
MO
MO
M~ M~ M
0M0
M
M
Source: Calculated by the author
FIGURE 5.1.11.2
COAL INTENSITY: SECTOR 11, 1985-2000
93
0M
0
5.1.12. Construction (Sector 12)
I
______________________I__________
7
----------------
5
c=
4
0
0
-
--------------
- -----
-
--
--
--
-
---
-
---
3
1
-
30
6
c
-
-
--
---
-
20
--
----
-
--
- --
25
--
---
15 ----10+-
---
00)(D
-
0)0)
0
0)
0)1 0)
0 0), 0)
M0 0
Year
Source: Calculated by the author from China Statistical
0O
0M
0)
M
0M 0
0
0N
0M
0)
IT
0)
0)
0)
Source: Calculated by the author
Yearbook 1986-2002 data
FIGURE 5.1.12.1
COAL CONSUMPTION: SECTOR 12,1985-2000
FIGURE 5.1.12.2
COAL INTENSITY: SECTOR 12,1985-2000
94
0
M0
5.1.13. Transportation, Post, and Telecommunications (Sector 13)
400 -r 25
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
350
20
300 --
15 - - - - - - - - - - - - -
250 +---
CD,
0
C
- - ---
200
----
150
E
5
-
100
0
0
- ------
50
0
0))
)
0
M0 0
)0
Year
~10(0N)
0
C~
0) ) )
0) ) )
UM0
0M0
)
0
0M0
I
"r- T1T-
Source: Calculated by the author
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.1.13.1
COAL CONSUMPTION: SECTOR 13, 1985-2000
--
FIGURE 5.1.13.2
COAL INTENSITY: SECTOR 13,1985-2000
1
95
0
0
0
C\i
5.1.14. Services (Sector 14)
I
800
250
--
-
200
---
--
c
150
o
100
0
C
--
0
----
-
600
Q)
-
-
--
-----
--
i i
0 -- i
oo.
M M
--
-----
----
-
---
C
C
200
CO
-
--
---
-
-
--
-------------------
L)
7
------
-------
500
300 ----50
--
---
---
---
-- ------
--
-----
-
---
~---~--~--
-
--
700 -700 -----------
--
0
i
i
-
N
:
i
LO (D r
i
i -
Mo M
0
i
0-
10 00
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
CD
1
i
1
LO
-
------
-----
r'-
00
00
00
0)
10
1
1
0
0) 0)
N~ CO
0)
0)
I
1
It
0)
0)
(D
0)
Source: Calculated by the author
FIGURE 5.1 .14.2
COAL INTENSITY: SECTOR 14,1985-2000
FIGURE 5.1.14.1
COAL CONSUMPTION: SECTOR 14, 1985-2000
96
i
1
1O
----
r0M
00
0)
1
0)
0)
0
0
5.2. Coke Consumption and Intensities
5.2.1. Agriculture (Sector 1)
1.6 ------------------------1.4
1.2
1.0 --- - -- - - -- --- - - ---- - -- 0.8
---- - -- ------ -- -- - - 0.6
0.4
0.2
I
I
I
I I
I
I
I
2.0 -
1.51.0 0.5 -
|
ot -co
LoY (Dr MMo
2.5
0.0
o
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.2.1.1
COKE CONSUMPTION: SECTOR 1, 2985-2000
o
I
oo )
co ao ao ao
M M M M
v-
-r-
--
-
wo
M
-r-
N
M
M
N-
M
M
v
M
M
M
M
T-
qt
UL (o
M
M
M
M
T-
r-
M
M
i
r-
0o
O
M
M
M
M
M 0
M 0
T-
1-
1--
Source: Calculated by the author using the model developed in
Chapter 3
FIGURE 5.2.1.2
COKE INTENSITY: SECTOR 1, 1985-2000
5.2.2. Mining and Quarrying (Sector 2)
2.5
16
c
2.0
1.5
14
----
0
1.0 +
E
=
8
0.5
.
"-
i
I
I
I
I
I
)0)0
0)~~~
M
r
M
)0
M
0
0)0
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
Source: Calculated by the author
FIGURE 5.2.2.2
COKE INTENSITY: 19850-2000, 1985-2000
FIGURE 5.2.2.1
COKE CONSUMPTION: SECTOR 2,1985-2000
98
5.2.3. Food (Sector 3)
0.45
1.6 -
0.40
7
7 - -7 -- -
--
--
-----
-A
--
-
-
-
0.35 t
0.30t
0.25
0.20
0.15
0.10
0.05
1.4
-
1.2
-
1.0
-
0.8
-
-/- ----------------- - -
--
-
-
-
-
-
-
-
--
-
0.6
-
------- ------------ ------------------------
----- --------------- ----------------------- - ------------------------------------- - ------ ---- --------- ---------------------------------------------------
--------------------
-
0.4 -
-
0.2 -
0.00 co
(D coc00
0- 0 0) M
0
) oo
0
OJ
0 M 0)
-
LO
0
0-
M
oo) 0) c)
0
0.0 i
I
i
I
I
i
i
r,-
o)
I
i
I
i
M0
cococo~
0)a~0)0M
I
i
I
i
II
N M c
o0~a)0M
Year
I
I
)
T--T T-T--
Source: Calculated by the author
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.2.3.1
FIGURE 5.2.3.2
COKE INTENSITY: SECTOR 4,1985-2000
COKE CONSUMPTION: THE SECTOR 3,1985-2000
99
I
I
-r-
I
M M
M)0 M0)
M M 0)
r- ---
I
5.2.4. Textile, Sewing, Leather, and Fur Products (Sector 4)
0.3
0.7
--
0.2
0.
0.2
0.4
>
0.1
-
-
0
0 0 .1 - - - - - - - - - - - - - - - - - - - - 0.1
I
|
|
I
I
I
I
L
<D
0)
0)MM0)M0)00)a)
0o
o
10
I
I
;
|
co
o
a
00
|
o
a)
M
0.0-2I
I
|
I
I
I
I
I
|
C\j 'IT LO
M M M
.0D
M
M
00 0)
M M
o
0)0)0)MYea
Year
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
FIGURE 5.2.4.1
COKE CONSUMPTION: SECTOR 4, 1985-2000
100
Source: Calculated by the author
FIGURE 5.2.4.2
COKE INTENSITY: SECTOR 4, 1985-2000
0-
5.2.5. Other Manufacturing (Sector 5)
3.0 ,
-
-
-
-
-
-
----
35.0
,1
3 0 .0
2.5
2.0
1.5
5,2
5 .0
O
20.0
- - - - ----- - - - -
-
--
0.5
I
M
I
M M
I
I
to
o
o
I
M
I
I
M
M
I
M
i
I
M
M
I
M
I
M o
-
--
--
-
- - - - - - -
--
-- -
-
- -
-
----
-- - - - - - - -
-
102
- -
-
---
-
-
-
--
-- - - - - - -
0) ) ) ) 0
FIGURE 5.2.5.1
COKE CONSUMPTION: SECTOR 5,1985-2000
-
-
Year
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
-
-
-
15.0
a>
- 1 0 .0
C: 0. 5 .0
.95.
0.0
1.0
---
-
Source: Calculated by the author
FIGURE 5.2.5.2
COKE INTENSITY: SECTOR 5,1985-2000
-
- -
0
- -
M0
0)
-
5.2.6. Production and Supply of Electric Power, Steam, and Hot Water (Sector 6)
1.4
1.2
1.0
0.8
0.6
0.4
0.2
12.0
>.
10.0
:o
----
8.0
-
---
4
0
0.
2.0
-
---
-
--
---
6.0
aL
-
-
---------
---
- -
- -
-
- -
-
- - -
- -
-
-
-
-
-
-------
---
-
--
--- ---
-
--
--
--
T
0.0
T-,-
T-
Year
T-
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
FIGURE 5.2.6.1
COKE CONSUMPTION: SECTOR 6, 1985-2000
103
-
-
-
T-
-
--
--
r- ~
-r
-
T-
Source: Calculated by the author
FIGURE 5.2.6.2
COKE INTENSITY: SECTOR 6, 1985-2000
--
-
5.2.7. Coking, Gas, and Petroleum Refining (Sector 7)
I
___________________I________
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
20
----- - -
-------20 -- - - - - -------- - -
--
-
-
-
-
-
-
-----
-
--
--
-
-
-
-
-
-
-
-
-----
- --
-
-
----
-
-
-
-
-
-
-
-
-
-
-
-
--------------
------
-
-
-
-
-
-
-
-
-
----
-
- --
-
-
-
-
--
----
--
-
--
--
-- - -
-
-
-
-
-
-
-
-
-
-
-
-
-
-
------
-
-
-
-
-
-
-
9
15-
0
10 --- -
- - -- - -- -
-
o
S --
-- - - -- - --
5
--
-
0
0 -............... I
LO C
coo0 M
C
..........
t
LO C
Year
Source: Calculated by the author
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
FIGURE 5.2.7.2
COKE INTENSITY: SECTOR 7,1985-2000
FIGURE 5.2.7.1
COKE CONSUMPTION: SECTOR 7,1985-2000
104
o
M
0O
5.2.8. Chemicals (Sector 8)
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
60.0
-- - - - - - - -- --- ------ - ------- - - - - --- - - - ---- - - - --- - --- --- - -- - - ------- ----------
------------- -
c 50.0
40.0 0
---
2c>
0
LO (Dr'-
W
M0
N~ i~tLO (0
- M
.
0.0
M
|
LO
CD
0
CO
M
C
C
t
UD
C
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
Source: Calculated by the author
FIGURE 5.2.8.1
COKE CONSUMPTION: SECTOR 8,1985-2000
FIGURE 5.2.8.2
COKE INTENSITY: SECTOR 8, 1985-2000
I
105
oo
M
.
M
5.2.9. Buildinq Materials and Non-metal Mineral Products (Sector 9)
I
25.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
20.0
a,
15.0
----
-
0
LO
(0
-
M
M0
O~~~~D~
0)0)00)0))0)00
N
qtULO
))0
-r-
D
I,-
M
T-
-r-
r
0.0
M
M)
0 0Q
M)0
M0M)0M00
J-
0)~~
0
Year
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
FIGURE 5.2.9.1
COKE CONSUMPTION: SECTOR 9,1985-2000
106
Source: Calculated by the author
FIGURE 5.2.9.2
COKE INTENSITY: SECTOR 9,1985-2000
M0)
0
5.2.10. Metal Products (Sector 10)
I
350
90
7 0 -- - - - - - - - - - - - - - - - - - u) 80
--------- -----
c 300 - - - - - - -
- -
=3 2 5 0 - - - - - - - -
.0 50 - - - - - - - - - - - - - - - - - - - - C
CD
20 0 - - - - - - - - - - - - - - .2
0
20--
- - - - - - - - - - - - -
---
10
0
E
150
0-
100
c
50
------
--
-
--
0
0o
Year
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
o
00o0 co
a0)a)
o
) Cyo C>
0)
)
y
) M> M
MMC)G)0)
Source: Calculated by the author
FIGURE 5.2.10.2
COKE INTENSITY: SECTOR 10, 1985-2000
FIGURE 5.2.10.1
COKE CONSUMPTION: SECTOR 10, 1985-2000
107
>y)
0> 0
M0
5.2.11. Machinery and Equipment (Sector 11)
1 .
4.5
4.0
S
-
E
--
3.5
-
2 .5
-
-
-
-
-
-
-
-
-
-
-
-
16.
-
2.0
0)
00
0)0)
0000 0 00 )Cyo
)
0) 0)
0)
Source: Calculated by the author from China Statistical
Yearbook 1986-2002 data
Source: Calculated by the author
FIGURE 5.2.11.1
COKE CONSUMPTION: SECTOR 11, 1985-2000
COKE INTENSITY: SECTOR 11, 1985-2000
FIGURE 5.2.11.2
108
5.2.12. Construction (Sector 12)
1~
0.20-
0.7
----------------------
0.6
0.15
C
0.10
03
-, 0.2 -
- ------- ------ ----
0.05
.
0.00
I
LO (Dr-
I
I
0 .1
--
-
-
I
0.0
LO (0N- M M 0
N I
MC)
M 0
0)0)0))0)0M000
M0M000
0)0)00)0))0)00)0)
T- "-T- IT- C'J
Year
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
FIGURE 5.2.12.1
COKE CONSUMPTION: SECTOR 12,1985-2000
Source: Calculated by the author
1
109
FIGURE 5.2.12.2
COKE INTENSITY: SECTOR 12, 1985-2000
5.2.13. Transportation, Post, and Telecommunications (Sector 13)
0.12
1.0
0.10
0.08
0.8
0.06
0.6 +
0.04
0.4
0.02
--
-
-
-- -
\-
0.2
0.00
U')M
-
M
M
N
t
OD
rl-M
0.0 -14
MO
I
M w
Year
I
I
II
~
I
I
M M M M M M0
co w 00 M M M
0)0)0)0)0M)M)0M0M)M)0M0M00 -r- J
-r- - r---r- v- -r--
FIGURE 5.2.13.2
COKE INTENSITY: SECTOR 13, 1985-2000
,
110
I1~.1.
I
Source: Calculated by the author
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
FIGURE 5.2.13.1
COKE CONSUMPTION: SECTOR 13,1985-2000
I
5.2.14. Services (Sector 14)
I
2.5
2.5
2.0
-----------------
-
-
__
-
-
-_
-
1.5
--------
1.5
1.0
0
-
-
0.5
0.
0.0-O
-
- - NY-- -
CO M
oo
0 0o
0 o
0
0)o
a)0)
Year
Source: Calculated by the author
Source: Calculated by the author from China Statistical Yearbook
1986-2002 data
FIGURE 5.2.14.1
COKE CONSUMPTION: SECTOR 14,1985-2000
FIGURE 5.2.14.1
COKE INTENSITY: SECTOR 14, 1985-2000
±
111
a)0
0o
Appendix 5.3
Time-Series Models for Coal and Coke Intensities of 14 Economic Sectors
Time-Series
Model for
Sector ID Coal Intensity Equations
1
ARIMA(1,1,0)
(1-B) * E1t = -0.0257 + 1/(1 - 0.26015 * B) * Et
2
ARIMA(1,1,0)
(1-B) * E2 t= -0.3521 + 1/(1 + 0.19513 * B) * Et
3
ARIMA(1,1,0)
(1-B) * Eat
=
-0.06617+ 1/(1 - 0.52796 * B) *
4
ARIMA(1,1,0)
(1-B)
E4 t
=
-0.04998 + 1/(1 - 0.251 * B)
*
2
5
ARIMA(1,2,0)
(1-B)
6
7
ARIMA(1,1,0)
ARIMA(1,1,0)
(1-B) * E6t
8
ARIMA(1,1,0)
(1-B) * E8t = -0.1585 + 1/(1 - 0.10575
9
10
ARIMA(1,1,0)
ARIMA(1,1,0)
(1-B)
11
12
13
14
ARIMA(1,3,0)
< 1% *
N/A
N/A
(1-B) 3
*Et
Et
*Et
= 0.075017 + 1/(1 - 0.95015
*
B) * Et
-0.93239 + 1/(1 + 0.09423
*
B) *
=
E-
N/A
*
Est = -0.7493 + 1/(1 + 0.13995
B) * Et
*
*
B) *
Et
N/A
= -0.15926 + 1/(1 - 0.64632 * B) *
*Ent
Et
N/A
N/A
N/A
Time-Series
Model for
ID
Sector
Coke Intensity Equations
1 ARIMA(1,1,0) (1-B) * E1 = - 0.000707 + 1/(1 + 0.20567 * B) * Et
2
ARIMA(1,1,0)
(1-B) * E2t = -0.00206 + 1/(1 + 0.22612 * B)
3
(1-B) * Est
5
6
7
ARIMA(1,1,0)
<1%
<1%
<1%
<1%
N/A
N/A
N/A
N/A
8
ARIMA(1,1,0)
(1-B)
* Et
9
10
ARIMA(3,1,0)
ARIMA(1,1,0)
(1-B)
*
Est = -0.00317+ 1/(1 + 0.13299 B + 0.161 37 B
11
12
13
14
ARIMA(0,1,1)
<1%
<1%
(1-B)
N/A
*
Elit = -0.00462 + (1 - B) * E-
N/A
N/A
4
=
-0.00007 + 1/(1 - 0.00948 * B)
Et
*
*t
= -0.02093 + 1/(1 - 0.09599 * B) *
Et
-
0.4287 B3)
N/A
Note: Ejt represents the coal intensity in Sector j in year t, Et is the disturbance
error term, and B is the backward-shift operator with one-period time lag.
113
*t
Appendix 6. Shares of Final Demand of Each Sector in the Total Final Demand (S;)
2
Year Sector 1
n.a.
0.26
1981
0.21 0.01
1987
0.18 0.00
1992
0.16 0.01
1995
0.15 0.00
1997
0.15 -0.01
2003
0.15 -0.01
2004
0.15 -0.01
2005
1.73
Po
0.00
0I
3
0.13
0.10
0.10
0.12
0.11
0.11
0.11
0.11
1.05
0.00
4
0.11
0.08
0.09
0.09
0.09
0.08
0.08
0.08
1.46
0.00
5
0.04
0.02
0.03
0.03
0.03
0.03
0.03
0.03
6
0.01
0.00
0.00
0.01
0.01
0.01
0.01
0.01
-0.35
0.00
7
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.37
0.00
8
0.02
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.13
0.00
10
9
0.01 0.00
0.00 0.00
0.01 -0.01
0.01 0.00
0.01 0.00
0.01 0.00
0.01 0.00
0.01 0.00
-0.62
0.00
12
11
0.15 0.17
0.20
0.11
0.11 0.19
0.12 0.22
0.12 0.23
0.10 0.24
0.10 0.24
0.10 0.25
3.42 -6.11
0.00 0.00
14
13
0.08
0.02
0.18
0.03
0.25
0.03
0.21
0.02
0.25
0.02
0.28
0.02
0.29
0.02
0.29
0.02
0.43 -10.72
0.01
0.00
Total
1.00
0.96
0.99
1.02
1.03
1.04
1.05
1.05
Source: Calculated by the author using data from China's national input-output tables for 1981, 1987, 1992, 1995, and
1997.
Notes:
. Sj is derived from the existing input-output tables and forecasted using the simple linear regression models discussed
in Chapter 3
* Estimates for 1for some sectors are not available because the result coefficients are not statistically significant.
114
Appendix 7.1. Consumption and Percentages of Consumption of Coal in 14 Sectors in China, 1985-2000
1985
Sector
Consumption
2209
1
6316
2
2449
3
4
1868
3676
5
6
16619
3259
7
5875
8
8614
9
10
7189
11
2748
12
532
2307
13
17942
14
Percent
2.7
7.7
3.0
2.3
4.5
20.4
4.0
7.2
10.6
8.8
3.4
0.7
2.8
22.0
.
.
1986
1987
1988
1989
1990
1991
1992
1994
1995
1996
1997
1998
1999
2000
2297
6648
2604
1968
3679
18050
3597
6382
9172
7734
2817
498
2295
18274
2287
7254
2907
2096
3975
20220
3993
7369
9883
8064
3014
453
2242
19042
2378
7280
3113
2274
4193
22894
4171
7874
10500
8468
3141
445
2259
20364
2181
8129
3321
2370
4401
24904
4798
8383
10670
8549
3039
453
2284
19945
2095
8822
3325
2359
4686
27059
4802
8237
9963
8905
2933
438
2161
19738
2125
9705
3414
2376
5108
29792
4255
8794
10320
9645
2950
432
2025
19491
1768
9491
3523
2470
4720
33230
5253
9309
10778
10392
3084
466
1876
17724
1783
10994
3788
3113
3418
40310
5478
11958
12219
13476
3017
504
1873
16601
1857
9861
4142
3256
3221
44600
8025
13420
13424
14731
2890
440
1315
16494
1917
11014
3977
2753
2966
50457
7757
13400
13589
14946
3027
446
1176
17309
1927
11280
3652
2530
2703
51589
8488
11580
12792
14444
2611
383
1431
13837
1923
9597
3397
2255
2423
51811
7563
11119
11663
12919
2205
612
1391
10614
1736
8641
3258
1911
2020
53189
7730
9896
11006
13094
2012
522
1294
10056
1648
8147
2651
1699
2077
56059
7710
9346
9940
12538
1564
537
1140
9483
2.7
7.7
3.0
2.3
4.3
21.0
4.2
7.4
10.7
9.0
3.3
0.6
2.7
21.3
2.5
7.8
3.1
2.3
4.3
21.8
4.3
7.9
10.7
8.7
3.3
0.5
2.4
20.5
2.4
7.3
3.1
2.3
4.2
23.0
4.2
7.9
10.6
8.5
3.2
0.5
2.3
20.5
2.1
7.9
3.2
2.3
4.3
24.1
4.6
8.1
10.3
8.3
2.9
0.4
2.2
19.3
2.0
8.4
3.2
2.2
4.4
25.6
4.6
7.8
9.4
8.4
2.8
0.4
2.1
18.7
1.9
8.8
3.1
2.2
4.6
27.0
3.9
8.0
9.4
8.7
2.7
0.4
1.8
17.7
17-7
1.6
8.3
3.1
2.2
4.1
29.1
4.6
8.2
9.5
9.1
2.7
0.4
1.6
15.5
1.4
8.6
3.0
2.4
2.7
31.4
4.3
9.3
9.5
10.5
2.4
0.4
1.5
12.9
1.4
7.2
3.0
2.4
2.3
32.4
5.8
9.8
9.8
10.7
2.1
0.3
1.0
12.0
1.3
7.6
2.8
1.9
2.1
34.9
5.4
9.3
9.4
10.3
2.1
0.3
0.8
12.0
1.4
8.1
2.6
1.8
1.9
37.1
6.1
8.3
9.2
10.4
1.9
0.3
1.0
9.9
1.5
7.4
2.6
1.7
1.9
40.0
5.8
8.6
9.0
10.0
1.7
0.5
1.1
8.2
1.4
6.8
2.6
1.5
1.6
42.1
6.1
7.8
8.7
10.4
1.6
0.4
1.0
8.0
1.3
6.5
2.1
1.4
1.7
45.0
6.2
7.5
8.0
10.1
1.3
0.4
0.9
7.6
Source: Calculated by the author using data from China Statistical Yearbook 1986-2002 (in 10,000 tonnes)
115
Appendix 7.2. Consumption and Percentages of Consumption of Coke in 14 Sectors in China, 1985-2000
Sector
Consumption
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Percent
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1985
20.8
71.7
12.3
7.1
119.2
3.1
18.2
732.2
93.5
3310.3
260.1
7.8
5.7
27.7
0.4
1.5
0.3
0.2
2.5
0.1
0.4
15.6
2.0
70.6
5.6
0.2
0.1
0.6
1990
1991
1992
1994
1995
60.1
53.5
59.1
48.0
49.8
104.1
102.8
83.0
64.4
62.4
22.6
20.9
19.9
22.6
17.3
10.7
11.6
11.2
7.8
9.0
149.3
154.8
269.5
166.0
128.2
1.2
2.6
2.6
2.4
2.5
77.7
51.5
51.2
45.0
17.6
963.9
910.8
829.0
781.6
702.0
182.2
176.8
143.6
129.8
120.5
3762.3 4068.0 4442.3 4456.2 4921.7
375.4
369.1
65.7
324.8
314.8
5.2
9.1
5.8
11.7
11.0
4.1
4.6
4.3
4.9
6.6
36.5
44.1
39.9
43.9
45.0
34.0
114.8
26.2
11.3
159.6
0.9
72.3
968.9
182.2
5131.0
367.6
5.8
4.4
39.0
52.3
126.5
31.5
11.4
160.1
1.1
87.3
1039.5
218.3
5530.8
403.9
14.4
6.1
155.9
111.2
135.3
22.8
9.5
71.9
115.8
5.6
1060.9
212.4
6779.3
388.5
11.8
7.3
161.9
128.6
151.4
32.4
8.8
38.3
16.8
31.6
1328.0
276.7
8128.3
399.9
10.8
10.1
163.8
0.9
1.5
0.3
0.2
2.2
0.0
1.1
13.9
2.6
71.2
5.4
0.1
0.1
0.5
0.5
1.6
0.4
0.2
2.2
0.0
1.0
13.6
2.6
72.1
5.2
0.1
0.1
0.6
0.7
1.6
0.4
0.2
2.0
0.0
1.1
13.3
2.8
70.6
5.2
0.2
0.1
2.0
1.2
1.5
0.3
0.1
0.8
1.3
0.1
11.7
2.3
74.6
4.3
0.1
0.1
1.8
1.2
1.4
0.3
0.1
0.4
0.2
0.3
12.4
2.6
75.8
3.7
0.1
0.1
1.5
1986
1.0
1.2
0.3
0.2
2.4
0.1
0.3
13.4
2.3
71.7
6.0
0.2
0.1
0.9
1987
0.8
1.1
0.4
0.1
2.9
0.0
0.8
13.7
2.3
71.1
5.7
0.2
0.1
0.8
1988
1.0
1.4
0.3
0.2
4.5
0.0
0.9
13.8
2.4
73.7
1.1
0.1
0.1
0.7
1989
0.8
1.6
0.3
0.2
2.4
0.0
0.8
14.3
2.8
70.0
5.8
0.1
0.1
0.7
1996
1997
144.7
119.8
183.3
223.2
30.6
41.2
12.7
9.8
44.1
37.5
103.4
104.7
54.4
27.0
1476.2 1363.0
269.0
280.3
7873.6 8174.7
349.1
417.9
12.5
13.9
6.5
6.6
179.1
166.5
1.1
2.1
0.4
0.1
0.4
1.0
0.3
13.7
2.6
72.9
3.9
0.1
0.1
1.5
1.3
1.7
0.3
0.1
0.4
1.0
0.5
12.5
2.5
74.8
3.2
0.1
0.1
1.6
1999
2000
145.8
151.4
131.1
180.6
34.8
31.9
8.3
20.0
31.1
38.3
67.1
51.2
61.0
70.7
1261.7 1036.4
286.9
302.3
8399.0 8106.2
328.4
354.6
17.1
14.6
10.1
10.3
192.6
191.6
144.2
153.3
33.6
8.7
33.1
36.8
63.0
1090.3
298.1
8048.9
314.8
19.0
11.2
185.1
1.4
1.3
0.3
0.1
0.3
0.6
0.6
9.9
2.7
77.5
3.1
0.2
0.1
1.8
1.4
1.5
0.3
0.1
0.3
0.4
0.6
10.4
2.9
77.1
3.0
0.2
0.1
1.8
1998
1.4
1.6
0.3
0.2
0.4
0.5
0.6
11.4
2.7
75.8
3.2
0.1
0.1
1.7
Source: Calculated by the author using data from China Statistical Yearbook 1986-2002 (in 10,000 tonnes)
116
Appendix 8.1. Coal Intensities in 14 Sectors in China, 1985-2000
(tonnes per 10,000 yuan of the sector's total output)
Year Sector 1
0.590
1985
0.592
1986
0.560
1987
0.561
1988
0.544
1989
1990
0.527
0.483
1991
1992
0.353
0.315
1993
1994
0.278
0.273
1995
0.267
1996
0.262
1997
0.250
1998
1999
0.216
0.191
2000
2
8.966
9.102
9.428
9.086
10.673
9.498
9.333
7.934
7.226
6.518
5.267
5.798
5.249
4.473
4.044
3.763
3
1.678
1.710
1.801
1.838
2.048
1.944
1.775
1.585
1.379
1.173
1.151
1.053
0.911
0.818
0.757
0.582
4
1.089
1.111
1.129
1.130
1.192
1.123
0.977
0.855
0.790
0.725
0.711
0.564
0.560
0.473
0.379
0.312
5
4.556
4.471
4.669
4.457
4.649
3.976
3.701
2.847
2.294
1.740
1.499
1.274
0.923
0.772
0.599
0.562
6
53.319
55.106
57.765
61.330
68.546
55.557
53.536
50.872
47.617
44.362
43.589
47.112
41.682
40.344
39.867
39.605
7
8.569
9.547
10.589
10.517
12.601
11.538
9.042
9.609
8.096
6.583
8.923
8.150
8.245
7.061
6.928
6.493
8
4.010
4.184
4.566
4.559
4.970
4.175
3.899
3.515
3.283
3.051
3.223
2.974
2.495
2.237
1.856
1.600
9
14.168
13.951
13.665
13.528
14.039
9.772
8.818
7.810
7.097
6.383
6.282
5.891
4.825
4.118
3.632
3.000
Source: calculated by the author with the models developed in Chapters 3 and 4.
117
10
6.023
6.056
5.800
5.849
6.213
4.597
4.442
4.155
4.019
3.883
3.766
3.556
3.695
3.113
2.966
2.613
11
1.152
1.134
1.146
1.129
1.131
0.846
0.750
0.673
0.576
0.480
0.422
0.417
0.338
0.271
0.235
0.169
12
0.310
0.262
0.212
0.201
0.216
0.193
0.172
0.163
0.145
0.128
0.099
0.093
0.073
0.108
0.086
0.081
13
3.788
3.457
3.046
2.916
3.069
1.978
1.631
1.294
1.188
1.081
0.730
0.585
0.671
0.594
0.503
0.395
14
6.844
6.133
5.544
5.367
5.225
3.529
2.989
2.271
2.134
1.996
1.900
1.769
1.307
0.900
0.766
0.637
Appendix 8.2. Coke Intensities in 14 Sectors in China, 1985-2000
(tonnes per 10,000 yuan of the sector's total output)
Year Sector 1
1985
0.006
1986
0.013
0.012
1987
0.014
1988
1989
0.013
1990
0.015
0.008
1991
1992
0.010
0.014
1993
0.017
1994
0.019
1995
0.017
1996
1997
0.020
0.020
1998
1999
0.018
2000
0.017
2
0.102
0.085
0.084
0.104
0.135
0.112
0.110
0.106
0.093
0.080
0.081
0.118
0.085
0.084
0.061
0.071
3
0.008
0.011
0.014
0.012
0.013
0.013
0.014
0.014
0.011
0.007
0.009
0.011
0.008
0.008
0.008
0.007
4
0.004
0.005
0.004
0.006
0.006
0.005
0.005
0.004
0.003
0.002
0.002
0.002
0.003
0.004
0.002
0.002
5
0.148
0.156
0.195
0.287
0.164
0.127
0.116
0.097
0.067
0.037
0.018
0.016
0.015
0.012
0.009
0.009
6
0.010
0.008
0.007
0.007
0.007
0.003
0.002
0.002
0.065
0.128
0.016
0.098
0.084
0.040
0.050
0.026
7
0.048
0.047
0.119
0.129
0.135
0.187
0.154
0.160
0.083
0.007
0.035
0.028
0.053
0.066
0.055
0.053
8
0.500
0.460
0.484
0.480
0.540
0.489
0.430
0.393
0.332
0.271
0.319
0.328
0.294
0.254
0.194
0.187
9
0.154
0.183
0.180
0.185
0.233
0.179
0.156
0.158
0.135
0.111
0.130
0.122
0.102
0.107
0.095
0.090
Source: calculated by the author with the models developed in Chapters 3 and 4.
118
10
2.774
2.946
2.926
3.068
3.238
2.541
2.363
2.212
2.082
1.953
2.078
1.874
2.091
2.024
1.836
1.678
11
0.109
0.127
0.124
0.024
0.137
0.108
0.094
0.088
0.075
0.062
0.058
0.058
0.045
0.044
0.038
0.034
12
0.005
0.006
0.006
0.003
0.004
0.002
0.002
0.005
0.004
0.003
0.002
0.003
0.002
0.003
0.003
0.003
13
0.009
0.010
0.007
0.006
0.006
0.004
0.004
0.004
0.004
0.004
0.006
0.003
0.003
0.004
0.004
0.004
14
0.011
0.015
0.013
0.011
0.012
0.007
0.006
0.020
0.020
0.020
0.019
0.017
0.017
0.016
0.015
0.012
Appendix 9. Forecasted Coal and Coke Intensities in 14 Sectors in China, 2003-2005
9.1. Coal Intensities
(tonnes per 10,000 yuan of the sector's total output)
Year Sector 1
2003
0.115
0.089
2004
0.063
2005
2
2.695
2.343
1.990
3
0.285
0.211
0.141
4
0.157
0.107
0.057
5
0.234
0.222
0.088
6
36.750
35.818
34.885
7
6.101
5.955
5.811
8
1.112
0.954
0.795
9
0.738
0.738
0.738
10
1.961
1.730
1.502
11
0.008
0.008
0.008
12
0.081
0.081
0.081
13
0.395
0.395
0.395
14
0.337
0.257
0.187
10
1.463
1.389
1.315
11
0.026
0.021
0.017
12
0.003
0.003
0.003
13
0.004
0.004
0.004
14
0.011
0.010
0.009
Source: calculated by the author with the models developed in Chapters 3 and 4.
9.2. Coke Intensities
(tonnes per 10,000 yuan of the sector's total output)
Year Sector 1
0.019
2003
0.020
2004
0.021
2005
2
0.062
0.060
0.058
3
0.007
0.007
0.007
4
0.002
0.002
0.002
5
0.009
0.009
0.009
6
0.026
0.026
0.026
7
0.053
0.053
0.053
8
0.125
0.104
0.083
9
0.081
0.080
0.075
Source: calculated by the author with the models developed in Chapters 3 and 4.
119
Appendix 10.1. Economic Sectors Ranked by Coal Intensity, Ej (Coal), in 2000
(tonnes per 10,000 yuan of the sector's total output)
Consumption Share
Coke
Coal
(Percent)
Coke
Coal
Sector Name
ID
0.4
45.0
0.026
39.605
Production and Supply of Electric Power, Steam, and Hot Water
0.6
6.2
0.053
6.493
Coking, Gas, and Petroleum Refining
1.5
6.5
0.071
3.762
Mining and Quarrying
2.9
8.0
0.090
3.000
Building Materials and Non-metal Mineral Products
77.1
10.1
1.678
2.613
Metal Products
10.4
7.5
0.187
1.600
Chemicals
1.8
7.6
0.012
0.637
Services
0.3
2.1
0.007
0.581
Food
0.3
1.7
0.009
0.562
Others (including paper-making)
0.1
0.9
0.004
0.395
Transportation, Post, and Telecommunications
0.1
1.4
0.002
0.312
Textile, Sewing, Leather and Fur Products
1.4
1.3
0.017
0.191
Agriculture
3.0
1.3
0.034
0.169
Machinery and Equipment
0.2
0.4
0.003
0.081
Construction
Energy Intensity
Rank Sector
6
1
7
2
2
3
9
4
10
5
8
6
14
7
3
8
5
9
13
10
4
11
1
12
11
13
12
14
100.0
Total
Source: calculated by the author with the models developed in Chapters 3 and 4.
120
100.0
Appendix 10.2. Economic Sectors Ranked by Coke Intensity, Ei (Coke), in 2000
(tonnes per 10,000 yuan of the sector's total output)
Energy Intensity
1
2
3
4
5
6
7
8
9
10
11
12
13
14
10
8
9
2
7
11
6
1
14
5
3
13
12
4
Coke
Coal
Sector Name
Rank Sector ID
Metal Products
Chemical Industry
Building Materials and Non-metal Mineral Products
Mining and Quarrying
Coking, Gas and Petroleum Refining
Machinery and Equipment
Production and Supply of Electric Power, Steam and Hot Water
Agriculture
Services
Others (including paper-making)
Foodstuff
Transportation, Post and Telecommunications
Construction
Textile, Sewing, Leather and Furs Products
2.613
1.600
3.000
3.763
6.493
0.169
39.605
0.191
0.637
0.562
0.582
0.395
0.081
0.312
Total
Source: calculated by the author with the models developed in Chapters 3 and 4.
121
1.678
0.187
0.090
0.071
0.053
0.034
0.026
0.017
0.012
0.009
0.007
0.004
0.003
0.002
Consumption Share
Coke
Coal
(Percent)
10.1
7.5
8.0
6.5
6.1
1.3
45.0
1.3
7.6
1.7
2.1
0.9
0.4
1.4
77.1
10.4
2.9
1.5
0.6
3.0
0.4
1.4
1.8
0.3
0.3
0.1
0.2
0.1
100.0
100.0
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