Analytical Input-Output and Supply-Chain Study of China'sCoke and Steel Sectors

byYu Li

Bachelor of Economics 1998Tsinghua University, China

Bachelor of Architecture, 1998Tsinghua University, China

Master of City Planning, 2001University of Cincinnati, OH

Submitted to the Center for Transportation and Logisticsthe requirements for the degrees of

in partial fulfillment of

Master of Science in Transportation

at theMASSACHUSETTS INSTITUTE OF TECHNOLOGY

June 2004@ 2004 Yu Li. All Rights Reserved

The author hereby grants to MIT permission to reproduce and to distributepublicly paper and electronic copies of this thesis docypment in whole or in part.

AuthorCenter for Transportation and Logistics

June 2004

Certified by

Professor of RegionalProfessor Karen R. Polenske

Political Economy and PlanningThesis Supervisor

Accepted by, Professor Nigel Wilson

Professor of Civil and Environmental EngineeringDirector, Center for Transportation and Logistics

MASSACHUSETTS INSTitUEOF TECHNOLOGY

OCTL2RAR

LIBRARIES

Analytical Input-Output and Supply-Chain Study of China'sCoke and Steel Sectors

byYu Li

Submitted to the Center for Transportation and Logistics in partial fulfillment ofthe requirements for the degrees of Master of Science in Transportation

ABSTRACT

I design an input-output model to investigate the energy supply chain of coal-coke-steel in China. To study the demand, supply, and energy-intensity issuesfor coal and coke from a macroeconomic perspective, I apply the model to testtwo hypotheses: (1) coal and coke intensities in individual economic sectorsdecline as China's overall energy efficiency improves, and (2) the supply of coaland coke will satisfy the demand in China in the future three years given abusiness-as-usual assumption. The results support the first hypothesis but donot 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........................................................................... 81.1. Significance of China's Coal-Coke-Steel Supply Chain.................... 91.2. Research Objective and Hypothesis........................................... 111.3. Data Methodology................................................................... 121.4. Input-Output Analysis as a Powerful Tool in Policy Studies.............. 14

2 LITERATURE REVIEW .................................................................. 172.1. Energy Supply, Demand, and Intensity......................................... 172.2. Supply Chains and Supply-Chain Management.............................. 19

2.2.1. Firm-scale Supply-Chain Studies......................................... 202.2.2. Industry-level Supply-Chain Studies...................................... 21

2.3. Input-Output Techniques.......................................................... 222.3.1. Enterprise Input-Output Models........................................... 232.3.2. Macroeconomic Analyses and Policy Implications.................... 23

2.4. Summary.............................................................................. 24

3 MODEL BUILDING.......................................................................... 253.1. Identify Key Supply-Chain Components....................................... 253.2. Redesign Input-Output Tables................................................... 313.3. Calculate and Estimate the Share of Final Demand in Each Sector... 333.4. Calculate Total Outputs.......................................................... 343.5. Calculate Energy Intensities and Forecasts with Time-Series Models. 353.6. Forecast GDP and Final Demand for Each Sector.......................... 373.7. Forecast Demand and Supply of Energy Products......................... 38

4 ENERGY SUPPLY- CHAIN ANALYSIS............................................. 394.1. Overview of the Supply Chain of Coal-Coke-Steel.......................... 394.2. Sector-Based Analyses of the Coal-Coke-Steel Supply Chain.......... 47

4.2.1. An Example of the Sector-Based Statistical Analysis................ 48

4.2.2. Summary of the Intensity Studies.......................................... 524.2.3. Summary of the Consumption Studies................................... 56

4.3. Steel Demand and Supply.......................................................... 604.4. Forecast Demand for and Supply of Coal and Coke......................... 61

4.4.1. Forecast Coal and Coke Supply............................................ 614.4.2. Forecast Coal Demand........................................................ 624.4.3. Forecast Coke Demand....................................................... 64

5 POLICY IMPLICATIONS AND CONCLUSIONS ................................... 665 .1. E nergy P olicy......................................................................... . 665.2. Environmental Policy................................................................ 675 .3 . T rad e P o licy ............................................................................ 6 85.4. Investment Policy..................................................................... 695 .5 . C o nclusions........................................................................... . 69

APPENDICES..................................................................................... 72Appendix 1. Intermediate Sector Classification..................................... 72Appendix 2. China National Input-Output A Matrix..................... 73Appendix 3. China National Input-Output (l-A)1 Matrix............... 78Appendix 4. Demand for and Supply of Coal and Coke in China.............. 83Appendix 5. Sector-Based Analysis of the Coal-Coke-Steel Supply Chain.. 84

Appendix 5.1. Coal Consumption and Intensities.............................. 84Appendix 5.2. Coke Consumption and Intensities............................. 98Appendix 5.3. Time-Series Models for Coal and Coke Intensities......... 113

Appendix 6. Shares of Final Demand of Each Sector (S;)........................ 114Appendix 7. Coal and Coke Consumption in 14 Sectors in China............. 115Appendix 8. Coal and Coke Intensities in 14 Sectors in China................. 117Appendix 9. Forecasted Coal and Coke Intensities in 14 Sectors in China. 119Appendix 10. Economic Sectors Ranked by Coal or Coke Intensities........ 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 1INTRODUCTION

Traditionally, researchers (Flaherty, 1996, Simchi-Levi et al., 2000;

Copacino and Byrnes, 2001) studied supply chains and supply-chain

management (SCM) at a firm level and focused on a corporation's demand

forecasting, inventory control, and distribution-network optimization. Recently,

some researchers have expanded the scope to emphasize SCM's industrial

impacts. They focus on industrial structures and restructuring within different

SCM attributes: physical, technological, strategic, and organizational (Carbonara

et al., 2000). However, most of them have not studied supply chains from a

macroeconomic industrial-sector perspective, nor have they applied the input-

output techniques to assist policy decision-making for energy supply chains.

In this study, I design an input-output technique-based model to study

supply chains of China's coke and steel sectors. The study is from a

macroeconomic perspective, especially from an inter-sector perspective using

China's national input-output accounts. I apply the model to analyze the demand,

supply, and energy-intensity issues of the two major energy products in the chain:

coal and coke. The structure of the study is as follows. In this chapter, I present

the significance of the research and hypotheses. In Chapter 2, I review pertinent

literature. I present analytical model is Chapter 3, including the data collection

methodology, and perform a sector-based analysis in Chapter 4. In Chapter 5, I

summarize policy implications in four areas and draw conclusions.

1.1. Significance of China's Coal-Coke-Steel Supply Chain

China is the world largest producer of coal, coke, and crude steel (IEA,

1999; IEA Coal Research Center, 2001; Table 1.1, llSI, 2002).

TABLE 1.1COAL, COKE AND CRUDE STEEL PRODUCTION, 2000(MILLION TONNES)

World China USA Japan IndiaCoal 4531 998 974 3 311Coke 333 122 19 39 12Steel 847 127 102 106 27

Source: International Iron and Steel Institute, Steel Statistical Yearbook, 2002

Coal accounted for more than 70% of primary energy consumption of

China (IEA, 1999). Out of nearly 1,400 million tonnes of China's coal production

in1997, 14% is used for coking, a process to produce metallurgical coke. Coke is

a crucial material to make steel, particularly, high-quality steel. Since the late

1990s, China has dominated the world coke market and exported coke to many

countries, including India, Japan, and the United States. Domestic demand for

coal, coke, and steel is surging because of the dramatic economic growth in

China in the last two decades. Construction and manufacturing industries, such

as automobile and electric-appliance industries, all require high-quality steel, and,

in turn, need a vast quantity of coke.

Rapid urbanization and heavy investment in infrastructure have been

intensifying the demand for steel products. Although more than 100 million rural

population have migrated to the urban area in the past two decades, China still

has 70 percent rural population, about 900 million, among which more than 20

percent are expected to be under-employed and probably will migrate to urban

areas in the near future. Urbanization has been accelerating since the economic

opening, particularly since the early 1990s. To boost the domestic demand and

improve the employment situation, as well as to attract foreign investment and to

prepare for the future economic development, China has been investing

tremendously in infrastructure projects, including Transferring Western Natural

Gas to the East, Transferring Southern Water to the North, and the Tibetan

Railroad System. All these gigantic projects require a vast quantity of steel.

Moreover, since the mid 1990s, China has been eager to develop its

automobile industry. The government has encouraged local manufacturers to

form joint ventures with foreign automakers or even permitted foreign makers to

set up their own plants in China. Central and local governments offer

automakers tax subsidies to attract them and have been pouring tremendous

amounts of money into railway and highway systems to accommodate the

surging transportation demand. The household electric-appliances industry is

another surging steel-consuming industry in China. Giant appliance makers,

such as Haier and Changhong, have aggressively expanded their production

capacities as well as market shares, both domestically and globally. Hence, the

supply chain of coal-coke-steel is critical to China's economy.

Additionally, China is facing significant environmental challenges with the

prospect of a further deterioration of its environment unless governments

introduce new technologies and remedial policies rapidly. The environmental

problem can be partly attributed to the pollution from the coal-coke-steel supply

chain, including both production and transportation (Chen, 2002), because the

major industries in the supply chain are heavy polluters. Therefore, the coal-

coke-steel chain is significant not only to the future of China, but also to the entire

world. The study I present here should provide valuable insights for both

domestic and foreign decision-makers.

1.2. Research Objective and Hypothesis

As just discussed, with the rapidly growing economy, China is becoming a

"world factory" and is facing dramatically increasing energy demands. On the

one hand, fast-growing manufacturing sectors, like automobiles and electric-

appliances, as well as traditional steel-consumer sectors, like construction, all

require vast quantities of steel and, in turn, coke and coal. On the other hand,

China is the world's largest producer and a major exporter of coal, coke, and

crude-steel. In particular, it now dominates the global coke market (Polenske,

2003). The central and local governments have been investing heavily in energy

sectors and infrastructure projects to accommodate the surging demand for

production and transportation of these energy-intensive products.

I examine coke and steel industries from a macro-level supply-chain

perspective. I focus on two major energy products in China: coal and coke. By

building an input-output, econometric model and applying it to analyze the supply

chain of the coke and steel industries, I test two hypotheses: (1) both coal and

coke intensities in each economic sector have declined as China's overall energy

efficiency improves, and (2) the supply of coal and coke will satisfy the demand

in the future three years in China given the business-as-usual (BAU) assumption.

My research is partially funded by the Alliances for Global Sustainability (AGS),

the National Science Foundation (NSF), the United Nations Industrial

Development Organization (UNIDO), and the Martin Fellowship Foundation. As

a part of our multiregional planning (MRP) research on China's energy efficiency,

this study is primarily empirical concerning the demand, supply, and energy-

intensity issues for coal and coke. I use data from the past two decades for the

analysis.

1.3. Data Methodology

I collect data from a variety of sources. The major sources are China

National Input-Output Tables 1981, 1987, 1992, 1995, 1997, China Statistical

Yearbooks from 1985 to 2002, and International Energy Agency Reports on coal,

coke, and steel. Other data sources include: China's Energy Statistic Reports,

China's 10th Five-Year National Plan, and pertinent research papers and reports.

As discussed later in Chapter 3 on model building, different input-output

tables have different classifications of economic sectors. Based on the

classifications of available input-output accounts and key components, I

aggregate the data to 14 sectors in my input-output table. The reclassified input-

output tables for 1981, 1987, 1992, 1995, and 1997 are listed in Appendix 2. The

corresponding (I - A) 1 (Leontief's Inverse) matrices are listed in Appendix 3.

To compare energy intensity, I use real GDP to account for inflation. I

calculate real GDP (1985) by the formula:

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 1.2GDP, CPI, AND REAL GDP OF CHINA, 1985-2001

CPI100.0106.0113.7134.8158.8165.2170.8181.7208.4258.6302.8327.9337.1334.4329.7331.0333.3

Real GDP(billion

yuan)896.4962.7

1051.91107.91065.11122.81265.71466.11661.91808.21931.32070.32208.92342.92489.22702.22878.3

GDP Growth(percent)

7.49.35.3

-3.95.4

12.715.813.4

8.86.87.26.76.16.28.66.5

Source: China Statistical Yearbook 1986-2002 andCPI = Consumer Price IndexGDP = Gross Domestic Product

calculated by the author.

I perform statistical tests on the proposed models and make forecasts

using SAS, a statistical-analysis package developed by SAS Inc., a software

company in the United States.

Year19851986198719881989199019911992199319941995199619971998199920002001

GDP(billion

yuan)896.4

1020.21196.31492.81690.91854.82161.82663.83463.44675.95847.86788.57446.37834.58206.88944.29593.3

1.4. Input-Output Analysis as a Powerful Tool in Policy Studies

Input-output analysis is a powerful tool to assist policy decision-making. It

provides information on the flow of goods and services among an economy's

different sectors. Input-output tables consist of intermediate transactions

between producing and purchasing sectors, as well as each sector's final

demand and value added. They show the state and process of an economic

system and are particularly useful when analyzing the impacts of changes in final

demands of certain sectors on the overall economic system.

Table 1.3 shows an input-output table of physical goods flows in the coal-

coke-steel supply chain. Cokemaking is the second largest sector consuming

coal (10,731 million tonnes) in China after the power-generation sector

(electricity), and the two largest coke-consuming sectors are iron- and steel-

making. Table 1.4 shows the direct-input coefficients of each sector in the chain.

Excluding other inputs and labor, coal is the largest input into the cokemaking

sector (35.5%), coke is the largest input into the iron-making sector (15.3%), and

iron is the largest input into the steel-making (9.8%). As shown in Table 1.5, the

direct-and-indirect coefficient table shows that the largest backward linkage,

defined as the sum of a column of direct-and-indirect coefficients in an input-

output table, is from the Motor Vehicles sector (1.618), which means that

investment in this sector would generate the largest output in the economy. This

partially explains China's current investment policy in the automobile industry.

TABLE 1.3CHINA PARTIAL 1997 INPUT-OUTPUT(MILLION YUAN)

SectorCoalCokeIronSteelMotorVehiclesElectricityWaterLaborOther InputsTotal

Coal6,229

059

989

1,25210,526

29978,052

125,341222,748

Coke10,731

22000

742,047

373,374

13,75930,243

Iron1,8168,916

7201,123

3502,301

757,134

35,67658,111

Source: 1997 Input-Output Table of China,Bureau of Statistics, People's Republic of CNote: 1 yuan ~ US$ 1.0.

FLOW TABLE

Steel1,3851,6936,8913,109

5256,247

2459,997

40,20870,300

MotorVehicles

872156775

8,159

104,5583,007

25428,650

155,694302,125

Electri-city

65,8251000

1,84112,644

1,10140,843

255,100377,363

Water83000

2567,6532,2427,212

20,83338,279

Department of National Accounts, National

TABLE 1.4CHINA PARTIAL 1997 DIRECT-INPUT COEFFICIENT TABLE(DIRECT INPUT PER UNIT OF OUTPUT)

Motor Electri-SectorCoalCokeIronSteelMotor VehiclesElectricityWaterLaborOtherTotal

Inputs

Coal0.0280.0000.0000.0040.0060.0470.0010.3500.5631.000

each entry in a column by the

Coke0.3550.0070.0000.0000.0020.0680.0010.1120.4551.000

Iron0.0310.1530.0120.0190.0060.0400.0010.1230.6141.000

Steel0.0200.0240.0980.0440.0070.0890.0030.1420.5721.000

Vehicles0.0030.0010.0030.0270.3460.0100.0010.0950.5151.000

city0.1740.0000.0000.0000.0050.0340.0030.1080.6761.000

Water0.0020.0000.0000.0000.0070.2000.0590.1880.5441.000

Source: Calculated by author from Table 1.3 by dividingoutput of the respective sector.

TABLE 1.5CHINA PARTIAL 1997 DIRECT-AND-INDIRECT-COEFFICIENT TABLE(DIRECT-AND-INDIRECT INPUT PER UNIT OF FINAL DEMAND)

Motor Electri-Sector Coal Coke Iron Steel Vehicles city WaterCoal 1.038 0.384 0.101 0.059 0.011 0.188 0.042Coke 0.000 1.007 0.157 0.042 0.003 0.000 0.000Iron 0.001 0.000 1.015 0.104 0.008 0.000 0.000Steel 0.005 0.002 0.021 1.049 0.043 0.001 0.001Motor Vehicles 0.009 0.008 0.012 0.014 1.530 0.009 0.013Electricity 0.052 0.090 0.060 0.108 0.021 1.045 0.222Water 0.002 0.002 0.002 0.005 0.002 0.004 1.063Total 1.107 1.494 1.368 1.381 1.618 1.247 1.341

Source: Calculated by author by taking the I-A inverse, where I is a 7x7 identity matrixand 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 2LITERATURE 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 - 1.2Consumption at 1977 Intensity

3000

2500

. 2000 Energy Intensity

0.61500 -- Actual Consumption

E1000 -

500

0 01965 1970 1975 1980 1985 1990 1995

Average annual energy intensity decline since 1977: 4.1 percent

Source: http://www.pnl.qov/china/aboutcen.htmMtce = million tonnes coal equivalent

FIGURE 2.1ENERGY CONSUMPTION AND ENERGY INTENSITY IN CHINA 1955-1997

Since 1977, energy intensity in China has declined more than 50 percent.

As shown in Figure 2.1, if China had maintained the energy-intensity level of

1977, the total energy consumption would be twice the actual consumption in

1997. Researchers (Sinton, Levine, and Wang, 2001; Sinton, 1996; Polenske

and Lin, 1994; Xie, 1994; Huang, 1993) have attributed China's great

achievements in energy intensity to the great improvement in technology and

internal structural changes within industries. This is a remarkable reduction, but

compared to other major countries, China's energy intensity is still higher (Figure

2.2). Policy makers not only in China but in many other developed countries

wonder how they can further lower energy intensity and thereby lower total

energy consumption, because China has been the second largest energy-

consumption country in the world and is expected to be the largest one by the

middle of this century (IEA, 2001).

140- History Projections

120-

0o00- China

80,

S 60-

40-

S 20-

United States0 .... ...... I 7" -- l-T- rTI" 1 .r~ '1[ T T-1 7r.FITW 11 1 "

1970 1975 1980 1965 1990 1995 2000 2005 2010 2015 2020

Source: IEA, "International Energy Outlook 2000"Btu = British thermal units

FIGURE 2.2ENERGY INTENSITY IN SELECTED COUNTRIES 1970-2020

2.2. Supply Chains and Supply-Chain Management

In the logistics and management literature, a supply chain is often defined

as an integrated process wherein manufacturers acquire raw materials from

suppliers, convert these raw materials into final products, and deliver these final

products to distributors and retailers (Ellram, 1991). Supply-chain management

(SCM) is a set of approaches utilized to integrate suppliers, manufacturers,

warehouses, and stores efficiently, so that merchandise is produced and

distributed in an optimized manner, thereby minimizing system-wide costs while

satisfying service-level requirements (Simchi-Levi et al., 2000). Traditionally,

supply-chain analysts have focused on the firm-scale SCM and industrial-level

supply-chain design.

2.2.1. Firm-scale Supply-Chain Studies

The initial objectives of SCM are to reduce firms' transportation and

inventory costs and to improve their service levels. Analysts usually consider

SCM a way to minimize costs by restructuring physical logistics networks. For

instance, to expand the market share in China in the late 1990s, Shell found that

selling its lubricants through Chinese agents, then relying on state-controlled

distribution, was not a satisfactory mix. In a major corrective step, the oil giant

established three manufacturing plants in China and turned to a local logistics

company, Hong Kong-based EAC Logistics, to manage its supply-chain network

in China, and thereby setting up a supply chain for fast, direct delivery to

customers nationwide (Bowman, 1999).

Another good example happened in the steel industry. In the current

business environment, margins of steel products are shrinking as service and

quality demands continue to escalate. To remain competitive, many steel firms

try to reduce production lead-time, slash planning-cycle time, and eliminate

unnecessary work-in-process inventory in their plants. Bethlehem Steel in the

United States hired a consulting firm, Experio Solution, to identify areas where

the company could retain product and service quality while trimming costs. After

the implementation of Experio Solutions' strategy, Bethlehem Steel was able to

eliminate overloads, increase throughput, and identify and refuse orders that it

could not fulfill. Consequently, the steel firm reduced its inventory by 15%, the

production lead-time by one week, and the weekly cash flow by $1.75 million.

(The Internet: http://www.experio.com, October 5, 2001)

2.2.2. Industry-level Supply-Chain Studies

Recently, many researchers and analysts have broadened the scope of

SCM and considered it not just as a cost-reduction mechanism, but also as a

way to integrate the key business processes from suppliers to the end users. A

supply chain provides products, services, and information that add customer

values (Carbonara et al., 2000). When analysts study supply chains in such a

context, they are more concerned about the roles SCM plays in firms' or

industrial restructuring than about those of reducing costs and improving service

levels. Also, as globalization expands, managers must make their business

strategies with reference to international markets, customers, suppliers, and

competitors as a whole.

From an industrial perspective, SCM often plays a crucial role in industrial

restructuring. Ellram (1990, p. 21) cites the Japanese automobile industry in the

1980s as a good example of a successful SCM system. Car manufacturers

acquired automobile parts from a number of trader companies, who shared trade

information with their subcontractors. These subcontractors needed information

from transportation firms who could provide timely delivery of raw materials and

intermediate products. In the supply chain, dealers sold cars and sent the

demand-forecasting information to distributors and manufacturers. In this way,

they were able to maintain an excellent coordination within the supply chain. As

Ellram (1990, p. 19) points out, the SCM mechanism is more suitable for those

firms with differentiated (customized) products than for those with standardized

ones.

So far, however, few scholars have studied supply chains and SCM from a

macroeconomic perspective, especially, from an interindustry view. In this study,

I use input-output techniques to study the energy supply chain of China's coal-

coke-steel-manufacturing. I develop an input-output framework to model the

energy intensities of coal and coke consumption and make forecasts. I apply the

model to test the two hypotheses discussed in Chapter 1.

2.3. Input-Output Techniques

Polenske and Fournier indicate (1993) that an input-output table provides

a detailed statistical account of the flow of goods and services between the

producing and purchasing sectors of an economy. It shows all intermediate

transactions among producers and purchasers within a consistent accounting

framework.

Since Leontief (1972 Nobel Laureate in Economics) completed the first

input-output table in the 1930s, researchers from all over the world have

extensively used input-output techniques to study economic issues and analyze

government policies. Input-output models provide direct, indirect, and induced

effects among different sectors within an economy and among economies.

Analysts can also derive valuable multipliers, including output multipliers, income

22

multipliers, and employment multipliers, to assist policy design and decision-

making. I provide two examples of input-output applications.

2.3.1. Enterprise Input-Output Models

Researchers in the Anshan Iron and Steel Corporation (AISC) designed

an enterprise input-output model to optimize production plans (Zhang et al.,

1991). In addition to using fundamental input-output techniques and a consistent

accounting framework, they also applied mathematical programming to construct

optimization models and thereby maximize profit. The general constraints in their

optimization models include: (1) equipment capacity constraints; (2) technology

and safety constraints; (3) constraints of the quantities of the purchased raw

materials, fuels, and materials from the market; (4) constraints of the national

plan and quantities for sale in the market (given China was still in a semi-planned

economy in 1991).

2.3.2. Macroeconomic Analyses and Policy Implications

Input-output models have been widely used in macroeconomic analyses,

investment planning, and policy decision-making. Voigtlaender (2002) uses a

dynamic input-output model to study U.S. freight transportation. He first projects

final demands based on the historical U.S. GDP data and, then, uses an input-

output framework to project U.S. commodity 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

In this study, I apply input-output techniques. Actually, input-output

accounts can be considered interrelated supply chains. The direct coefficient

matrix shows direct relationships among all the supply-chain components. For

instance, a simple supply chain of agriculture products includes the sectors of

agriculture, transportation, food industry, trade, and final demand of end

customers. Leontief's inverse matrix (direct-and-indirect input-coefficient matrix)

presents detailed direct and indirect transactions among different supply-chain

components. I focus on the supply chains of the coke and steel sectors.

24

CHAPTER 3MODEL 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 3.1KEY COMPONENTS IN A SUPPLY CHAIN

Generally, there are many intermediate suppliers, distributors, and

customers. I study the supply chain from a macroeconomic perspective, in which

individual economic sectors are the components of the supply chain. Because

each of the original five input-output tables I used had a different number of

sectors, I had to consolidate them to 14 sectors.

The major components in the coal-coke-steel supply chain include: (1)

supplier sectors, such as Coal Mining, (2) distributor sectors, such as

Transportation, (3) intermediate customer sectors, such as Coking and Metal

Products, and (4) final customer sectors, such as Construction, Manufacturing,

Machinery and Equipment. Figure 3.2 shows a simplified diagram of the

interactions of different sectors and the flows of physical goods and services in

the coal-coke-steel supply chain. The products of the coal-mining sector are

transported to the cokemaking sector, traded in the international market

(imported/exported), and/or transported to other coal-consumer sectors.

Similarly, coke is transported to the metal-products sector, to other coke-

consumer sectors, and/or exported to the international market. On the right-hand

side, construction, manufacturing, machinery and other steel-customer sectors

receive inputs from the metal-product sector.

----------------------------------------------------------

Transportation

Construction

Manufacturing

Minig PProductsMachinery and

Equipment

Other Coal- Other Coke- Other Steel-Consumers Consumers Customers

mprt/Export

Source: the author

FIGURE 3.2THE 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.

Source: International Energy Agency, 1999

FIGURE 3.3PRIMARY 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

HydroGas 2% Nuclear2% 0.4%

EJCoalHOilEJGas. Hydro

SNuclear

Coal77%

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 3.1CRUDE STEEL PRODUCTION BY PROCESS, 2000

Crude Steel OpenProduction BOF EAF Hearth Other

Country (million tonnes) (%) (%) (%) (%)China 123.7 66 16 2 16Japan 94.2 70 30 0 0USA 97.3 54 46 0 0Worldwide 786.4 60 33 4 3

Source: International Iron and Steel Institute, 2000BOF = Basic Oxygen FurnaceEAF = 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 3.2STEEL CONSUMPTION IN CHINA BY MARKET 1997

Steel ConsumedConsuming Industries (1000 tonnes) (Percent)Construction 45,110 41.5Manufacturing 37,950 34.9Machinery 9,821 9Transportation (Railroads and other) 7,151 6.6Electrical machinery 3,451 3.2Mining, quarrying, lumbering 2,660 2.5Oil and gas 2,445 2.3

Source: Central Iron & Steel Research Institute, Beijing, China. Reference in "China: TheChanging 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 input-

output table by highlighting the key sectors. A problem I encountered in this step

is that China's input-output accounts in different years are different in terms of

the number of sectors and sectors' definitions. The 1981 input-output account

has 24 sectors, while the input-output accounts of 1987, 1992, and 1995 have 33

sectors as well as 100 or more sectors. As of 2003, when I conducted this

research, the latest available (1997) input-output account has 17 sectors (China

National Statistical Bureau, 2002). As discussed above, I redesign the input-

output table by consolidating sectors. Generally, an input-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 3.3INTERMEDIATE SECTOR CLASSIFICATION

ID Economic Sectors1 Agriculture2 Mining and Quarrying3 Food4 Textile, Sewing, Leather, and Fur Products5 Other Manufacturing6 Production and Supply of Electric Power, Steam, and Hot Water7 Coking, Gas, and Petroleum Refining8 Chemicals9 Building Materials and Non-metal Mineral Products10 Metal Products11 Machinery and Equipment12 Construction13 Transportation, Post, and Telecommunications14 Services

Source: compiled by the author from China's national input-output accounts for1981,1987,1992,1995,1997

3.3. Calculate and Estimate the Share of Final Demand in Each Sector

In an input-output table, the basic formula to show the relationships

among different economic sectors is:

AX + Y = X,

where A is the direct-input-coefficient matrix, X is the output matrix, and Y is the

final-demand matrix. The summation of the total final demand (with some

adjustments) is equal to the nation's gross domestic product (GDP). Suppose

we have n sectors in the economy and if we define Sj as the share of the final

demand in the j-th sector in the total final demand (GDP), then we can calculate

the share Si as:

Sj= Yj /GDP.

GDP in previous years is available in a country's statistical yearbooks.

The final demand of each sector in a certain year, however, is often unknown

unless the nation's input-output accounts are available for that year. Therefore, it

is necessary to estimate the share Sj with empirical data. If we have sufficient

data, i.e., input-output accounts for many years, we can perform econometric

analysis to estimate the shares for each sector in the future. Unfortunately, such

accounts are only available every three or five years for China. My study focuses

on the period from China's economic reform (1979) to the present (2004), and

the available data include only five input-output tables (1981, 1987, 1992, 1995,

and 1997). Thus, in this study, I estimate Si by smoothing the time-series data

and averaging them between successive available data points. For instance, if

we have input-output tables for 1987 and 1992 and we denote S;.x as the

percentage of final demand of the j-th sector in the total final demand in the year

x, then

Sj-1989 = Sj-1987+ 2 * [(Sj-1992 - Sj-1987)/5].

Given the data constraint, this is a simplified approximation method. In

forecasting, I apply simple linear-regression models if they explain the data well.

If they do not, I estimate Si based on careful qualitative analyses.

3.4. Calculate Total Outputs

Given the annual GDP and the yearly shares of final demand in each

sector, I calculate the total output of each sector, Xi, in each year.

Xj = ( - A) -4 Y; = (1-A) - (Sj* GDP)

where the I represents the identity matrix and (I - A) -1 is the direct and indirect-

coefficient matrix, often referred to as the "Leontief Inverse" in input-output

economics. Here I encounter another problem due to the constraint of limited

data: I need the direct-input-coefficient matrix, A, for each year to calculate X;,

but I only have such matrices for five discrete years, so that I have to estimate A

matrices for the years for which I do not have input-output accounts. In his

master's thesis, Voigtlaender (2002) estimates the A matrix using linear

regression models with time as the independent variable. It is a possible way to

estimate the A matrix if sufficient historical data are available and the forecasting

is for a long term. For this study, however, I only have five years' input-output

accounts and I am only interested in a short-term forecast. Because input

coefficients generally do not change over a short period of time, I assume they

remain constant over three years. Admittedly, such an assumption might not be

held strongly now, when technologies are advancing so quickly. But given the

limited data and the time constraint on the research, I make this assumption in

the model.

3.5. Calculate Energy Intensities and Forecasts with Time-Series Models

As discussed in Chapter 2, energy intensity in the j-th sector, Ej, can be

defined as the energy consumption per unit of economic output in the j-th sector

(Sinton, Levine, and Wang, 1998). With the result from the previous step (3.4), I

calculate the total economic output of each sector, X;, in each year. Then, I

derive the total consumption of coal or coke in each sector from the China

Statistical Yearbook (1986-2002). However, I encounter the same problem as

the one in calculating each sector's final demand (Y) from input-output accounts:

the data table, Consumption of Energy and Its Main Varieties By Sector

(available for 1986-2002), has a different classification of economic sectors from

the input-output table I am using. Thus, before using these tables, I reclassify

their economic sectors according to the input-output accounts I redesigned.

Then, I calculate the energy intensity of the j-th sector, E;, with the formula:

Ej = CI / X;,

where C represents the total consumption of a given energy product by the j-th

sector in a given year. Given a time series of Ej, I build time-series models for

empirical analysis and forecasting. Different possible time-series models include

autoregressive (AR), moving average (MA), mixed autoregressive and moving

average (ARMA), and integrated ARMA with differential independent variables

(ARIMA).

In this study, I find the first-order autoregressive model, AR(1), or the first-

order differenced model, ARIMA(1,1,0), is often the most appropriately model for

most sectors. The general functional form of the AR(1) is as follows:

Eit = 6 + 1/(1 - $1* B) * ct

where 6 is a constant term related to the mean of the stochastic process, Et is the

disturbance error term, B is the backward-shift operator with one period time lag,

and $1is the coefficient of Et.1. The general functional form of ARIMA(1 ,1,0) is:

(1-B) * Eit= 8 + 1/(1 - $1* B) * si

where 6, Et, B, and $1 represent the similar variables and parameters in AR(1).

To test whether Ei follows a random walk, I apply the Augmented Dickey-

Fuller (ADF) unit-root test on each Ei series. If the test result does not reject the

unit-root hypothesis, the model would be a random walk:

Eit = 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.

In addition, to crosscheck the correctness of each model, I compare the

result with the output from Holt's procedure, a deterministic smoothing model

widely used in demand forecasting for trended data in supply-chain management.

Generally, I find selected time-series models perform better or at least as well as

the Holt's procedure. In the future studies, we need more detailed data to build

statistically sounder models to make more accurate forecasts.

3.6. Forecast GDP and Final Demand for Each Sector

GDP is often given for each past year and a widely used GDP forecasting

model is in an exponential functional form (IMF, 2001). In this study, however, I

do not use such a theoretical model because the forecast is only for a short term,

i.e., the future three years. Many economists and governmental agencies have

done extensive research work to forecast the growth rates of China's GDP

(World Bank 2002, IMF 2002, Asian Development Bank 2002). These forecasts

are based on very comprehensive analyses of China's economy as well as the

global economic context. Hence, they are more convincing than the results

derived from a pure theoretical model. In this study, I use these forecasted GDP

directly.

To forecast final demand in the j-th sector, X;, it is necessary first to

estimate the share of each sector in GDP, Sj. I build a simple time-trend linear

model to forecast Si in the short run:

Sj-t = Do + P1* t + Et

where Bo is the intercept of the linear model, 1 is the coefficient of the

independent time variable t, and et is the disturbance error term with the attribute

ei~ N(0, cy2). In addition to the standard t-test and F-test, I apply the Durbin-

Watson test to test the serial correlation in the data.

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 4ENERGY 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 energy-

efficient technologies and the implementations of energy-efficiency policies.

Regarding the supply side, Figures 4.1 and 4.3 show China's annual total

production of coal and coke, respectively, from 1985 to 2001. Both outputs have

a relatively similar pattern to their respective consumption. From 1991, the

surplus of coal became a deficit and the deficit kept growing, which means an

annual net import of coal to China in the past decade (Appendix 4). In contrast,

the coke consumption was less than coke production in each year from 1985 to

2000, and the surplus increased substantially in 1993 -1994 to above 20 percent

of the total coke production for the next four years. Although the net export

dropped to around 14 percent in 1998 and has remained at that level since then,

China still dominates the global coke market.

Comparing production, consumption, and energy intensities of coal and

coke, I find the following: (1) the total coal consumption is more than the total

output so that China has been a net-importer of coal since 1991, (2) the total

coke consumption is less than the total coke output, so that China has been a

coke net-exporter since 1985, the starting year of my analysis, and (3) both coal

and coke intensities have declined for the last decade, and the coal intensity

decreased faster than the coke intensity. I will explain the possible underlying

reasons in Section 4.3.

16001400

cn 1200

10000

8000

E600E 400----

200

L) CO r- 00 0' O0 C\1 CO 1; LO C0 P- 00 o oo o 0o 0o 00 00 o o) o M M M M M M o o

-+- Coal Consumption Year

-u- Coal Production

Source: China Statistical Yearbook 1986-2002

FIGURE 4.1COAL CONSUMPTION AND PRODUCTION IN CHINA, 1985-2001

1.2

Cz 1.0

00.8

> 0.60

.: a- 0.4 -ao0.E

'0 0.2

0

0 0.0

Year

Source: Calculated by the author from China Statistical Yearbook 1986-2002 data

FIGURE 4.2COAL INTENSITY IN CHINA, 1985-2001

160

c120

100

0E80

40

20 ~......

0o o 00 00 00 0 > > M > M > o> M > M > o> M > M > M oc

--- Coke Consumption Year

-m- Coke Production

Source: China Statistical Yearbook 1986-2002

FIGURE 4.3COKE CONSUMPTION AND PRODUCTION IN CHINA, 1985-2001

0.07

o 0.06 -

S0.05

c - 0.04o -,~ .

0 0.03

O 0.02 - -----

0.01

o 0.000 LO C '- C M0 \1M t -)CO r- 0

00 ~ ~ ~ ~ C ) 0) w) w0) MMMM 0

Year

Source: Calculated by the author from China Statistical Yearbook 1986-2002 data

FIGURE 4.4COKE 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.

Source: China Statistical Yearbook 1986-2002

FIGURE 4.5STEEL CONSUMPTION IN CHINA, 1985-2001

Source: Calculated by the author from China Statistical Yearbook 1986-2002 data.

FIGURE 4.6STEEL INTENSITY IN CHINA, 1985-2001

44

160

140c.o 1200- oDE c: 100

~' 400. 80

200

LO C0 o C) 0 \ CO It LO C0 0 oo

YearM M0M00

Year

0.07

'O.06

. 0.05 &.=======.4..42.m====c 0

00.0

E=3 0.03

0 c--' 0.02

CD0

0.00L0 Co P o- C CN M It LO CD 1'*Y e

Year

Steel consumption in China increased sharply in recent years. This is

primarily due to the dramatically increased domestic demand for steel products

and the vast volume of investments in steel-related industries from central and

local governments as well as from foreign companies and agencies (Woetzel,

2001). The International Iron and Steel Institute (IISI) recently published a report,

forecasting that the average annual growth rate of steel demand in China would

be 6.7 percent; but it only would be about 1.7 percent in the rest of the world

(Asia Pulse, 2003). As discussed in Chapter 1, China is building and planning to

build many steel-intensive projects, including the West-East Gas Transmission

project, South-North Water Diversion project, the Qinghai-Tibet Railway, urban

subway systems in major metropolitan areas, and urban and rural infrastructure

construction projects in almost every major city. China's market demand for such

large engineering machinery as excavators, loaders, and caterpillar tractors is

expected to increase sharply. Figures 4.7, 4.8, and 4.9 show the outputs of

several fast-growing steel-consuming industries: automobiles, air-conditioners,

and household-refrigerators. All three charts show a pattern of fast growth for

the major steel-intensive industries during the past decade. From a supply-chain

perspective, all these industries require a vast volume of steel and, in turn, coke

and coal, as discussed in Section 4.2.

45

80

70

602E 50

o 40

10

02 0 ---- --

10

0o0 - C It L( CD - O ) 0

Year

Source: China Statistical Yearbook 1990-2002

FIGURE 4.7AUTOMOBILE OUTPUT IN CHINA, 1990-2001

2500

2000C

1500

001000

500

00- C CM It LO CN D- a) 0

1- - 1 1- 1- 1- - 1 1- 1- C~j C\1

Year

Source: China Statistical Yearbook 1990-2002

FIGURE 4.8AIR-CONDITIONER OUTPUT IN CHINA, 1990-2001

160014001200

U,2E 1000

o 8000/*-600

400

200

0

Year

Source: China Statistical Yearbook 1990-2002

FIGURE 4.9HOUSEHOLD-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-and-

quarrying output in the 1981 table, but not in the other four tables; therefore, the

share of final demand is much larger in the 1981 table than in other ones. The

share S7 decreased to less than zero (-0.02%) in 1997, which indicates that

China had became a net exporter in this sector. This is the case given China has

been the dominant supplier in the global coke market since the mid1990s.

2.5%

c 2.0%-EE 1.5%

1.0%

0.5%

CU

$5) 0.0% -- +-~

1981 1987 1992 19

-0.5%

Year

Source: calculated by the author from the China's national input-output accounts 1981,

1987,1992,1995,1997.

FIGURE 4.10

SHARE OF FINAL DEMAND IN SECTOR 7

48

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 E jt = 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). In this 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 petroleum-

refining industry to reduce the consumption intensity of coke, or (2) less-

expensive 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 4.1SUMMARY OF COAL- AND COKE-INTENSITY ANALYSES

Coal Current Trend Coke Current TrendSector ID Intensity Trend Since Intensity Trend Since

1 ARIMA(1,1,0) DE 1985 ARIMA(1,1,0) IN 19912 ARIMA(1,1,0) DE 1989 ARIMA(1,1,0) DE, FL 1996

3 ARIMA(1,1,0) DE 1989 ARIMA(1,1,0) DE, ST 19974 ARIMA(1,1,0) DE 1989 <1% DE, FL 1998

5 ARIMA(1,2,0) DE 1989 <1% DE, ST 1995

6 ARIMA(1,1,0) DE, ST 1996 <1% DE, FL 19967 ARIMA(1,1,0) DE, FL 1998 <1% IN, FL 1996

8 ARIMA(1,1,0) DE 1989 ARIMA(1,1,0) DE 19969 ARIMA(1,1,0) DE 1989 ARIMA(3,1,0) DE 1998

10 ARIMA(1,1,0) DE 1989 ARIMA(1,1,0) DE, FL 199711 ARIMA(1,3,0) DE 1989 ARIMA(0,1,1) DE 198912 < 1% * DE 1997 <1% IN 199713 N/A DE 1988 <1% DE, ST 199814 N/A DE 1985 N/A DE 1992

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

Sector NameProduction and Supply of Electric Power, Steam, and Hot WaterMetal ProductsBuilding Materials and Non-metal Mineral ProductsServicesChemicalsMining and QuarryingCoking, Gas, and Petroleum RefiningFoodOthers ManufacturingTextile, Sewing, Leather, and Fur ProductsAgricultureMachinery and EquipmentTransportation, Post, and TelecommunicationsConstruction

Percent ofTotal CoalConsumption

45.010.18.07.67.56.56.22.11.71.41.31.30.90.4

Source: Calculated by the author from China Statistical Yearbook 1986-2002 data.

TABLE 4.3.CHANGE PATTERNS OF COAL CONSUMPTION IN 14 SECTORS

Percent ofTotal Coal

Sector ID Consumption Increased Mixed Decreased Unchanged

1 1.3 X

2 6.5 X

3 2.1 X4 1.4 X

5 1.7 X

6 45.0 X

7 6.2 X

8 7.5 X

9 8.0 X

10 10.1 X

11 1.3 X

12 0.4 X

13 0.9 X14 7.6 X

Source: Compiled by the author from the analysis in Section 4.2.

Rank1234567891011121314

Sector ID6109148273541111312

TABLE 4.4.RANK OF ECONOMIC SECTORS BY COKE CONSUMPTION, 2000

Sector NameMetal ProductsChemicalsMachinery and EquipmentBuilding Materials and Non-metal Mineral ProductsServicesMining and QuarryingAgricultureCoking, Gas and Petroleum RefiningProduction and Supply of Electric Power, Steam andHot WaterFoodOthers ManufacturingConstructionTransportation, Post and TelecommunicationsTextile, Sewing, Leather and Fur Products

Percent ofTotal Coke

Consumption77.110.43.02.91.81.51.40.6

0.40.30.30.20.10.1

Source: Calculated by the author from China Statistical Yearbook 1986-2002 data.

TABLE 4.5.CHANGE PATTERNS OF COKE CONSUMPTION IN 14 SECTORS

Percent ofTotal Coke

Sector ID Consumption Increased Mixed Decreased Unchanged

1 1.4 X

2 1.5 X

3 0.3 X4 0.1 X

5 0.3 X

6 0.4 X

7 0.6 X

8 10.4 X

9 2.9 X

10 77.1 X

11 3.0 X

12 0.2 X

13 0.1 X

14 1.8 X

Source: Compiled by the author.

58

Rank12345678

91011121314

Sector ID10811914217

63512134

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

(-21.5) (21.7)

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 coal-

consuming 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 2003 2004 20051 Agriculture 13 10 82 Mining and Quarrying 88 82 753 Food 17 13 104 Textile, Sewing, Leather, and Fur Products 10 8 45 Others (including paper-making) 11 11 5

Production and Supply of Electric Power, Steam, and6 Hot Water 714 757 8027 Coking, Gas, and Petroleum Refining 97 103 1088 Chemicals 78 72 659 Building Materials and Non-metal Mineral Products 31 34 3710 Metal Products 116 112 10511 Machinery and Equipment 1 1 112 Construction 5 5 513 Transportation, Post, and Telecommunication 13 14 1514 Services 58 48 39

Total 1,252 1,271 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 ID Sector Name 2003 2004 20051 Agriculture 2.1 2.3 2.62 Mining and Quarrying 2.0 2.1 2.23 Food 0.4 0.4 0.54 Textile, Sewing, Leather, and Fur Products 0.1 0.1 0.15 Others (including paper-making) 0.3 0.3 0.36 Production and Supply of Electric Power, Steam, and

Hot Water 0.4 0.4 0.47 Coking, Gas and Petroleum Refining 0.6 0.6 0.68 Chemicals 8.8 7.9 6.89 Building Materials and Non-metal Mineral Products 3.4 3.7 3.8

10 Metal Products 86.8 89.6 92.211 Machinery and Equipment 2.9 2.6 2.212 Construction 0.2 0.2 0.213 Transportation, Post, and Telecommunication 0.1 0.1 0.114 Services 1.9 1.9 1.9

Total 110.1 112.3 113.9

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 5POLICY 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 energy-

efficient 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. In this 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.

In this 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 Economic Sectors1 Agriculture2 Mining and Quarrying3 Food4 Textile, Sewing, Leather, and Fur Products5 Other Manufacturing6 Production and Supply of Electric Power, Steam, and Hot Water7 Coking, Gas, and Petroleum Refining8 Chemicals9 Building Materials and Non-metal Mineral Products10 Metal Products11 Machinery and Equipment12 Construction13 Transportation, Post, and Telecommunications14 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

Al 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

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 0.0062 0.0000 0.0607 0.0039 0.0088 0.0000 0.0009 0.0397 0.0032 0.0013 0.0034 0.0024 0.0005 0.1058A4 0.0201 0.0000 0.0021 0.4444 0.0534 0.0014 0.0076 0.0289 0.0111 0.0041 0.0153 0.0124 0.0047 0.0167A5 0.0070 0.0000 0.0118 0.0031 0.1716 0.0011 0.0233 0.0282 0.0464 0.0188 0.0496 0.0666 0.0081 0.0591A6 0.0025 0.0000 0.0026 0.0086 0.0355 0.0000 0.0398 0.0625 0.0812 0.0735 0.0212 0.0096 0.0069 0.0085

A7 0.0080 0.0000 0.0038 0.0080 0.0286 0.1856 0.1393 0.0724 0.1169 0.0566 0.0275 0.0251 0.1268 0.0189A8 0.0080 0.0000 0.0038 0.0080 0.0286 0.1856 0.1393 0.0724 0.1169 0.0566 0.0275 0.0251 0.1268 0.0189A9 0.0008 0.0000 0.0004 0.0006 0.0053 0.0007 0.0117 0.0024 0.0450 0.0140 0.0070 0.1922 0.0018 0.0059A10 0.0050 0.0000 0.0007 0.0027 0.0101 0.0049 0.0281 0.0098 0.0414 0.3204 0.1920 0.0881 0.0091 0.0014

All 0.0142 0.0000 0.0044 0.0035 0.0202 0.0241 0.0513 0.0044 0.0575 0.0546 0.1608 0.1095 0.0384 0.0385A12 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 0.0000A13 0.0048 0.0000 0.0146 0.0051 0.0168 0.0322 0.0194 0.0202 0.0535 0.0436 0.0151 0.0395 0.0201 0.0124

A14 0.0085 0.0000 -0.0075 0.0260 0.0194 0.0252 0.0425 0.0327 0.0008 0.0201 0.0212 0.0172 0.0356 0.0258

Source: Calculated by the author from data in China National Input-Output Tables 1981 (China National StatisticalBureau, 1983).

Appendix 2.2. China National Input-Output Table A Matrix, 1987

AlA2A3A4A5A6A7A8A9A10All1A12A13A14

Al0.14720.00080.03880.00260.00330.00420.00470.06840.00070.00210.00590.00000.01100.0255

A20.01560.02560.00100.01300.01180.04330.01680.03760.01410.03860.08630.00000.00890.0400

Source: Calculated byBureau, 1989).

A30.44280.00290.12090.00280.01720.00380.00120.01250.00900.00500.00320.00000.04630.0694

A40.15040.00180.00740.37980.00880.00530.00130.07070.00050.00330.00820.00000.01320.0863

A50.07560.02310.00210.08450.20960.01530.00720.07830.00530.05520.02180.00000.01520.0764

A60.00040.22070.00080.00550.00740.01820.05930.00850.00540.01010.03750.00000.02090.0293

A70.00020.45440.00060.00260.00220.00960.01850.01490.00370.00720.01390.00000.03630.0330

A80.06820.04640.02950.03850.01790.03250.01660.28980.00910.01910.02230.00000.01700.0691

A90.00890.06460.00140.02100.07090.06130.03670.06460.07330.05720.04950.00000.01860.0622

Al00.00150.08070.00120.01640.01900.03870.03450.03150.02270.29810.05710.00000.01770.0573

Al10.00200.00500.00110.01110.02200.01190.00930.06690.01580.13950.30150.00000.01350.0723

A120.00490.04930.00090.00980.03510.00470.01580.02770.22560.18360.08780.00000.02490.0437

A130.00010.01480.00110.00880.01600.00880.11420.02970.00350.01070.07000.00000.01100.0835

A140.02920.00660.02980.01710.05520.00950.01100.04390.01500.01350.03490.00000.02890.1229

the author from data in China National Input-Output Tables 1987 (China National Statistical

Appendix 2.3. China National Input-Output Table A Matrix, 1992

AlA2A3A4A5A6A7A8A9Al0All1A12A13A14

Al0.13930.00230.03490.00250.00380.00210.00580.07880.00590.00650.01410.00010.01120.0485

A20.00850.05770.00020.00980.01510.06060.01620.04180.03380.04750.11230.00190.02080.0882

A30.43580.00630.10070.0049

0.0280.00790.00390.0287

0.0110.01280.01590.00050.01410.0727

A40.12360.00480.00770.3799

0.0180.00790.00250.08430.00170.00620.01930.00020.00980.1266

A50.072

0.02030.001

0.09410.18960.01620.00640.08350.01090.06390.03630.0002

0.0170.1297

A60.00010.18710.00020.00250.00990.02230.04480.01150.01660.01440.07650.00150.04210.0828

A70.00010.49850.00030.00170.00280.00970.02670.01610.00910.00580.02340.00030.03680.0967

A80.04670.04180.0193

0.0320.01970.03590.01240.30840.01350.02160.031

0.00020.01970.1192

Source: Calculated by the author from dataBureau, 1994).

in China National Input-Output Tables 1992 (China National Statistical

A90.00980.06240.00050.01990.06010.06240.02830.06020.09790.05390.05560.00040.02650.1154

Al00.00060.07630.00020.00890.01790.03660.01640.01770.02290.34190.04630.00040.01850.1249

All10.00060.00590.00030.00540.01850.01120.00570.04960.01960.15920.30740.00040.01410.1295

A120.00350.03740.00020.00630.03060.00070.0082

0.0230.209

0.16820.08

0.00690.02980.1002

A130.00010.0133

0.0010.00860.01550.00950.11220.0342

0.0110.01470.11950.00140.01420.0846

A140.01740.00940.03780.00770.05390.01180.01070.03070.02260.01680.05210.01220.07210.1459

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 0.0133 0.4023 0.1122 0.0770 0.0002 0.0002 0.0446 0.0121 0.0011 0.0007 0.0043 0.0089 0.0178A2 0.0023 0.0537 0.0036 0.0032 0.0221 0.1927 0.3948 0.0465 0.0638 0.0885 0.0065 0.0405 0.0200 0.0080A3 0.0504 0.0003 0.1164 0.0097 0.0010 0.0003 0.0005 0.0195 0.0007 0.0003 0.0004 0.0002 0.0219 0.0370A4 0.0034 0.0148 0.0032 0.4491 0.0992 0.0045 0.0027 0.0440 0.0260 0.0160 0.0076 0.0089 0.0187 0.0089A5 0.0045 0.0085 0.0133 0.0089 0.2563 0.0073 0.0025 0.0200 0.0529 0.0107 0.0148 0.0335 0.0230 0.0376A6 0.0028 0.0497 0.0106 0.0072 0.0144 0.0273 0.0245 0.0240 0.0801 0.0319 0.0107 0.0006 0.0226 0.0090A7 0.0086 0.0151 0.0038 0.0025 0.0072 0.0407 0.0285 0.0174 0.0270 0.0166 0.0062 0.0125 0.1363 0.0139A8 0.0706 0.0402 0.0389 0.0884 0.0676 0.0096 0.0295 0.3858 0.0535 0.0184 0.0610 0.0240 0.0626 0.0317A9 0.0065 0.0374 0.0056 0.0014 0.0082 0.0170 0.0144 0.0104 0.1186 0.0257 0.0212 0.2039 0.0376 0.0184

A10 0.0079 0.0439 0.0081 0.0061 0.0537 0.0194 0.0090 0.0266 0.0621 0.3358 0.1427 0.1783 0.0295 0.0142

All 0.0155 0.1192 0.0085 0.0147 0.0250 0.0781 0.0347 0.0230 0.0544 0.0621 0.3500 0.0790 0.1360 0.0514

A12 0.0002 0.0030 0.0003 0.0002 0.0002 0.0019 0.0007 0.0002 0.0005 0.0007 0.0005 0.0084 0.0103 0.0124A13 0.0146 0.0275 0.0086 0.0100 0.0181 0.0508 0.0720 0.0180 0.0312 0.0306 0.0176 0.0344 0.0273 0.0617

A14 0.0426 0.0712 0.0343 0.0858 0.0962 0.0657 0.1264 0.0631 0.0877 0.1028 0.0965 0.0810 0.1176 0.1331

Source: Calculated by the author from data in China National Input-Output Tables 1995 (China National StatisticalBureau, 1997).

75

Appendix 2.5. China National Input-Output Table A Matrix, 1997

Al A2 A3 A4 A5 A6 A7 A8 A9 A10 All A12 A13 A14

Al 0.1606 0.0099 0.4294 0.0896 0.0518 0.0001 0.0000 0.0464 0.0029 0.0003 0.0003 0.0041 0.0016 0.0209A2 0.0021 0.0760 0.0046 0.0026 0.0165 0.2002 0.5324 0.0472 0.1132 0.1013 0.0081 0.0262 0.0062 0.0043

A3 0.0663 0.0003 0.1281 0.0158 0.0009 0.0000 0.0000 0.0127 0.0008 0.0000 0.0000 0.0006 0.0058 0.0344

A4 0.0029 0.0086 0.0023 0.4037 0.0729 0.0039 0.0028 0.0405 0.0142 0.0045 0.0078 0.0033 0.0065 0.0140

A5 0.0042 0.0154 0.0283 0.0124 0.2190 0.0128 0.0074 0.0226 0.0611 0.0458 0.0234 0.0261 0.0190 0.0514

A6 0.0073 0.0488 0.0079 0.0061 0.0244 0.0348 0.0219 0.0383 0.0438 0.0453 0.0110 0.0070 0.0164 0.0113A7 0.0085 0.0224 0.0023 0.0016 0.0053 0.0526 0.0496 0.0190 0.0280 0.0259 0.0071 0.0286 0.0795 0.0129A8 0.0740 0.0487 0.0253 0.0766 0.0773 0.0074 0.0213 0.3653 0.0573 0.0228 0.0726 0.0209 0.0154 0.0372A9 0.0025 0.0119 0.0068 0.0013 0.0083 0.0078 0.0088 0.0099 0.1418 0.0239 0.0211 0.2707 0.0042 0.0102

Al0 0.0031 0.0391 0.0077 0.0031 0.0468 0.0069 0.0063 0.0131 0.0533 0.3569 0.1524 0.1225 0.0061 0.0074All 0.0160 0.0875 0.0084 0.0128 0.0247 0.1170 0.0392 0.0251 0.0446 0.0467 0.3307 0.0806 0.1428 0.0797A12 0.0020 0.0022 0.0005 0.0006 0.0009 0.0028 0.0012 0.0009 0.0008 0.0009 0.0012 0.0006 0.0194 0.0202

A13 0.0119 0.0434 0.0125 0.0131 0.0211 0.0325 0.0287 0.0231 0.0420 0.0382 0.0194 0.0367 0.0444 0.0427

A14 0.0412 0.0633 0.0586 0.0669 0.0780 0.0894 0.0578 0.0672 0.0804 0.0721 0.0629 0.0846 0.0743 0.1613

Source: Calculated by the author from data in China National Input-Output Tables 1997 (China National StatisticalBureau, 1999).

76

Appendix 3.1. China National Input-Output Table (I-A)~1 Matrix (Leontief's Inverse), 1981

Al A2 A3 A4 A5 A6 A7 A8 A9 A10 All A12 A13 A14

Al 1.2132 0.0000 0.8196 0.3731 0.3215 0.0834 0.0983 0.1998 0.1286 0.1075 0.1176 0.2589 0.0674 0.1840

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 0.0000A3 0.0110 0.0000 1.0721 0.0182 0.0223 0.0185 0.0203 0.0560 0.0191 0.0179 0.0170 0.0177 0.0162 0.1212

A4 0.0480 0.0000 0.0389 1.8203 0.1377 0.0295 0.0434 0.0761 0.0539 0.0390 0.0592 0.0642 0.0307 0.0517A5 0.0150 0.0000 0.0263 0.0186 1.2231 0.0275 0.0570 0.0517 0.0862 0.0605 0.0948 0.1219 0.0321 0.0851A6 0.0083 0.0000 0.0107 0.0236 0.0585 1.0336 0.0754 0.0829 0.1227 0.1378 0.0692 0.0643 0.0334 0.0217A7 0.0196 0.0000 0.0236 0.0351 0.0752 0.2607 1.2182 0.1274 0.2156 0.1690 0.1015 0.1200 0.1845 0.0450

A8 0.0196 0.0000 0.0236 0.0351 0.0752 0.2607 0.2182 1.1274 0.2156 0.1690 0.1015 0.1200 0.1845 0.0450

A9 0.0020 0.0000 0.0021 0.0030 0.0096 0.0063 0.0184 0.0061 1.0537 0.0267 0.0169 0.2087 0.0065 0.0086Al0 0.0179 0.0000 0.0164 0.0193 0.0407 0.0405 0.0839 0.0336 0.1109 1.5222 0.3590 0.2058 0.0454 0.0257All 0.0251 0.0000 0.0246 0.0223 0.0488 0.0570 0.0929 0.0279 0.1069 0.1260 1.2338 0.1811 0.0686 0.0604

A12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000A13 0.0092 0.0000 0.0232 0.0161 0.0321 0.0495 0.0401 0.0344 0.0800 0.0852 0.0457 0.0751 1.0345 0.0226

A14 0.0151 0.0000 0.0043 0.0570 0.0412 0.0527 0.0707 0.0525 0.0324 0.0569 0.0499 0.0460 0.0572 1.0370

Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.1.

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 A10 All A12 A13 A14Al 1.2224 0.0482 0.6340 0.3435 0.1913 0.0254 0.0332 0.1864 0.0676 0.0493 0.0569 0.0591 0.0303 0.0965

A2 0.0201 1.0736 0.0303 0.0367 0.0815 0.2831 0.5100 0.1182 0.1501 0.1899 0.0814 0.1491 0.0947 0.0429

A3 0.0608 0.0109 1.1747 0.0442 0.0274 0.0078 0.0095 0.0653 0.0172 0.0154 0.0189 0.0161 0.0112 0.0497

A4 0.0196 0.0438 0.0293 1.6443 0.2050 0.0299 0.0324 0.1141 0.0803 0.0715 0.0680 0.0702 0.0370 0.0598

A5 0.0165 0.0361 0.0466 0.0459 1.2971 0.0283 0.0277 0.0590 0.1254 0.0650 0.0750 0.1022 0.0417 0.0961

A6 0.0133 0.0607 0.0182 0.0258 0.0433 1.0402 0.0428 0.0650 0.0930 0.0808 0.0481 0.0540 0.0248 0.0254

A7 0.0138 0.0341 0.0205 0.0197 0.0318 0.0778 1.0434 0.0442 0.0664 0.0741 0.0428 0.0580 0.1302 0.0282

A8 0.1327 0.1000 0.1108 0.2281 0.2177 0.0586 0.0822 1.4800 0.1690 0.1306 0.1995 0.1451 0.0878 0.1186

A9 0.0053 0.0249 0.0179 0.0104 0.0198 0.0162 0.0186 0.0245 1.0933 0.0478 0.0410 0.2636 0.0133 0.0256

Al0 0.0178 0.1020 0.0325 0.0382 0.1390 0.0605 0.0697 0.0786 0.1453 1.4886 0.3230 0.3525 0.0594 0.0576

All 0.0263 0.1589 0.0395 0.0561 0.0909 0.1092 0.1075 0.0925 0.1370 0.1744 1.4984 0.2182 0.1362 0.0865A12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000

A13 0.0229 0.0233 0.0721 0.0419 0.0422 0.0350 0.0529 0.0452 0.0426 0.0462 0.0429 0.0550 1.0281 0.0472

A14 0.0619 0.0936 0.1433 0.2123 0.1864 0.0794 0.0975 0.1716 0.1502 0.1592 0.1907 0.1558 0.1399 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 A14Al 1.2079 0.0441 0.6069 0.2926 0.1785 0.0283 0.0377 0.1415 0.0641 0.0431 0.0471 0.0507 0.0302 0.0805

A2 0.0315 1.1332 0.0491 0.0622 0.0966 0.2708 0.6014 0.1303 0.1620 0.1962 0.0953 0.1393 0.1150 0.0636

A3 0.0563 0.0160 1.1486 0.0478 0.0313 0.0135 0.0173 0.0540 0.0215 0.0207 0.0232 0.0202 0.0149 0.0608

A4 0.0223 0.0455 0.0379 1.6516 0.2226 0.0301 0.0373 0.1051 0.0786 0.0555 0.0522 0.0580 0.0388 0.0466

A5 0.0264 0.0598 0.0707 0.0822 1.2873 0.0496 0.0527 0.0801 0.1280 0.0816 0.0860 0.1050 0.0535 0.1068

A6 0.0174 0.0942 0.0305 0.0423 0.0571 1.0582 0.0685 0.0825 0.1072 0.0918 0.0581 0.0565 0.0363 0.0372

A7 0.0180 0.0419 0.0243 0.0281 0.0359 0.0714 1.0611 0.0443 0.0616 0.0535 0.0383 0.0457 0.1338 0.0373

A8 0.1635 0.1320 0.1583 0.2863 0.2533 0.0861 0.1179 1.5353 0.1829 0.1199 0.1799 0.1403 0.1119 0.1178

A9 0.0198 0.0660 0.0349 0.0309 0.0466 0.0483 0.0546 0.0500 1.1415 0.0701 0.0659 0.2696 0.0370 0.0516

A10 0.0455 0.1716 0.0789 0.0898 0.2037 0.1167 0.1283 0.1285 0.1881 1.6188 0.4195 0.3791 0.1132 0.1052

All 0.0634 0.2538 0.0964 0.1342 0.1704 0.2148 0.2016 0.1632 0.2013 0.2115 1.5579 0.2488 0.2465 0.1606

A12 0.0021 0.0057 0.0037 0.0055 0.0054 0.0053 0.0054 0.0052 0.0052 0.0059 0.0060 1.0119 0.0048 0.0165

A13 0.0326 0.0613 0.0512 0.0638 0.0722 0.0805 0.0876 0.0744 0.0783 0.0772 0.0726 0.0858 1.0508 0.1116

A14 0.1329 0.2482 0.2178 0.3762 0.3659 0.2301 0.2884 0.3394 0.3167 0.3633 0.3846 0.3304 0.2265 1.2942

Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.3.

Appendix 3.4. China National Input-Output Table (I-A)1 Matrix (Leontief's Inverse), 1995

Al A2 A3 A4 A5 A6 A7 A8 A9 A10 All A12 A13 A14

Al 1.2697 0.0561 0.5962 0.3168 0.2117 0.0352 0.0512 0.1644 0.0771 0.0535 0.0563 0.0621 0.0792 0.0840

A2 0.0366 1.1272 0.0429 0.0641 0.1021 0.2746 0.4974 0.1489 0.1724 0.2161 0.1038 0.1531 0.1505 0.0606A3 0.0829 0.0184 1.1769 0.0605 0.0373 0.0162 0.0243 0.0620 0.0247 0.0238 0.0258 0.0234 0.0507 0.0635A4 0.0359 0.0704 0.0421 1.8720 0.2938 0.0489 0.0615 0.1746 0.1164 0.0941 0.0832 0.0904 0.0939 0.0599A5 0.0262 0.0456 0.0432 0.0591 1.3858 0.0415 0.0471 0.0773 0.1184 0.0631 0.0732 0.1028 0.0764 0.0824

A6 0.0187 0.0823 0.0300 0.0403 0.0530 1.0632 0.0766 0.0702 0.1291 0.0863 0.0573 0.0605 0.0648 0.0330A7 0.0256 0.0445 0.0254 0.0338 0.0421 0.0730 1.0731 0.0581 0.0677 0.0611 0.0454 0.0567 0.1758 0.0427

A8 0.1842 0.1573 0.1816 0.3625 0.2796 0.1074 0.1696 1.7434 0.2102 0.1553 0.2471 0.1742 0.2268 0.1367A9 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

Al0 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 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

A13 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.

Appendix 3.5. China National Input-Output Table (I-A)~1 Matrix (Leontief's Inverse), 1997

Al A2 A3 A4 A5 A6 A7 A8 A9 A10 All A12 A13 A14

Al 1.2630 0.0429 0.6394 0.2406 0.1378 0.0306 0.0388 0.1456 0.0517 0.0447 0.0464 0.0472 0.0318 0.0860A2 0.0407 1.1631 0.0503 0.0550 0.0957 0.3106 0.6801 0.1621 0.2454 0.2778 0.1284 0.1787 0.1054 0.0650A3 0.1039 0.0140 1.2056 0.0623 0.0282 0.0136 0.0150 0.0476 0.0195 0.0180 0.0185 0.0189 0.0189 0.0610A4 0.0282 0.0459 0.0359 1.7145 0.1920 0.0387 0.0451 0.1391 0.0758 0.0592 0.0665 0.0562 0.0393 0.0612A5 0.0318 0.0620 0.0745 0.0645 1.3280 0.0633 0.0645 0.0895 0.1437 0.1449 0.1095 0.1182 0.0646 0.1120

A6 0.0265 0.0829 0.0342 0.0375 0.0657 1.0745 0.0821 0.0932 0.0967 0.1160 0.0669 0.0668 0.0447 0.0394

A7 0.0244 0.0521 0.0248 0.0252 0.0351 0.0859 1.0939 0.0598 0.0704 0.0801 0.0491 0.0757 0.1075 0.0387

A8 0.1819 0.1573 0.1667 0.2812 0.2512 0.1112 0.1575 1.6794 0.2105 0.1686 0.2621 0.1683 0.1074 0.1491

A9 0.0144 0.0349 0.0236 0.0186 0.0324 0.0337 0.0393 0.0379 1.1910 0.0688 0.0651 0.3446 0.0309 0.0377

Al0 0.0386 0.1423 0.0572 0.0569 0.1526 0.1165 0.1253 0.1022 0.1865 1.6599 0.4181 0.3140 0.1062 0.0920All 0.0764 0.2308 0.0904 0.1106 0.1462 0.2951 0.2343 0.1682 0.2117 0.2515 1.6222 0.2710 0.3001 0.2133A12 0.0060 0.0086 0.0068 0.0077 0.0083 0.0102 0.0097 0.0090 0.0093 0.0106 0.0096 1.0096 0.0257 0.0286A13 0.0350 0.0834 0.0470 0.0552 0.0683 0.0802 0.0940 0.0788 0.1015 0.1140 0.0834 0.1041 1.0816 0.0821

A14 0.1108 0.1664 0.1667 0.2120 0.2185 0.2077 0.2020 0.2238 0.2298 0.2534 0.2326 0.2498 0.1738 1.2776

Source: Calculated by the author by inverting I-A matrix, using data in Appendix 2.5.

Appendix 4. Demand for and Supply of Coal and Coke in China, 1985-2001(million tonnes)

Percent ofSurplus in Coal

Demand740-122-2-2-5-4-1-3-1-3

-17-20

CokeDemand

46.952.4957.2160.2763.6869.1571.1878.3984.6790.94

107.25107.98109.27110.78104.57

104.4

CokeSupply

48.0252.7657.9561.0866.2473.2873.5279.84

93.2114.77135.02136.43137.31128.06120.74121.84131.31

Percent ofCoke Surplus in Coke

Surplus Supply1.12 20.27 10.74 10.81 12.56 44.13 62.34 31.45 28.53 9

23.83 2127.77 2128.45 2128.04 2017.28 1316.17 1317.44 14

Source: Assembled by the author using data from China Statistical Yearbook 1986-2002.

CoalDemand

816.03860.15927.99993.54

1,034.271,055.231,104.321,140.851,213.091,285.321,376.771,447.341,392.481,294.921,263.651,245.37

Year19851986198719881989199019911992199319941995199619971998199920002001

Coalsupply

872894928980

1,0541,0801,0871,1161,1501,2401,3611,3971,3731,2501,045

9981,161

CoalSurplus

55.9733.85

0.01-13.5419.7324.77-17.32-24.85-63.09-45.32-15.77-50.34-19.48-44.92

-218.65-247.37

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)

25 -- ---- - ---- -- - - ---

20 ---- -- - -

EC 15 - ---- ---------------- - - - - -0

C 10 --------- -0

E 5 -------- ---- ----- ----- ---- - --

0- 1 1 iiiLO D eo i C o 0) c 0 o ( - Co oco 0 om co co mm om o)MMMM~ooom omoo omomo

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.1.1COAL CONSUMPTION: SECTOR 1, 1985-2000

70 - I

C : 6 0 -- - - - - - - - - - - - - - - - - - - - - -

C0= 40E

0

0 1 I I I I I

Source: Calculated by the author

FIGURE 5.1.1.2COAL INTENSITY: SECTOR 1, 1985-2000

83

I , I

5.1.2. Mining and Quarryinq (Sector 2)

1200120 ---- -

100 - - -------------- -- 800-------

cC 0

2 60 - 600

: 40 --

E -- --20 -400

0 200 - -

LO CD OLoO(O Cl OO)OC

-0

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data Source: Calculated by the author

FIGURE 5.1.2.1 FIGURE 5.1.2.2COAL CONSUMPTION: SECTOR 2, 1985-2000 COAL INTENSITY: SECTOR 2,1985-2000

84

5.1.3. Food (Sector 3)

250 -

45 --

40 -

35 c-200 - ----- -- - --- --

8i 25~ -- 8 50 - ---- ----

20 ------------------------------15 ------------------- ----

Cn c

10 - - - - - - -- -

0 25I

5C ----------------------- - --- -------- o- -- -

Year - - - - - - - - - - - - - - - -

Source: Calculated by the author from China Statistical Source: Calculated by the authorYearbook 1986-2002 data

FIGURE 5.1.3.2FIGURE 5.1.3.1 COAL INTENSITY: SECTOR 3, 1985-2000COAL CONSUMPTION: SECTOR 3, 1985-2000

85

5.1.4. Textile, Sewinq, Leather, and Fur Products (Sector 4)

35------------------------------ --

30 - -- --

c 25 -- ---- -- --

4 20 -0- --- -

C 15 ----0

E 10 - ---- - - - - - - - ------ --- - -

5 --- -- ---- -- ---- --- --

0 - M M

-r- - o- - c7 - --- T or-- T--T T-- o

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.4.1

140-

120

C

C

10

206 0 - ----- - -- - -- -

0

20 -------- - - - - - - - - - - - - - - - -

0 ! I i i i i i i i i i10 ( 1 - 00C 0) 0 - CMj CO) "I 10 M0 N w 0M 000 m w w w 0M 0M 0M 0M 0) 0) 0M 0) 0) 0) 0

Source: Calculated by the author

FIGURE 5.1.4.2COAL INTENSITY: SECTOR 4,1985-2000

COAL CONSUMPTION: SECTOR 4,1985-2000 1

5.1.5. Other Manufacturing (Sector 5)

60 -- - --

C50

S400c' 30

0 20

10 ---

0co c 00 o 0 0) 0)0) 0) 0) a) 0) M0))M M 0) ) MM MM0M0)) 0) 0) 0

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.5.1COAL CONSUMPTION: SECTOR 5,1985-2000 1

500 -

450

400

350

300

250 -

200

150

100

50

0

1- --- --- ---- --- ---- --

I'0 -C M0M0 N MC T LO) 1(D r-CMO 00CO O O O O ) ) ) 0 0 0 0 0 0 0 00) ) ) ) ) ) ) ) 0 0 0 0 0 0 M 0

Source: Calculated by the author

FIGURE 5.1.5.1COAL INTENSITY: SECTOR 5,1985-2000

87

- - -------- - -----------

5.1.6. Production and Supply of Electric Power, Steam, and Hot Water (Sector 6)

6 0 0 -- - - - - - - - - - - - - - - - - - - -

5 0 0 -- - - - - - - - - - - - - - - - - - - -<n

400 - ---

C

c

100 - --- i

10-

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.6.1COAL CONSUMPTION: SECTOR 6,1985-2000

8000

E4000 -

O.n 3000 ---- --

C

CL

5 2000

1000 ----

0 - i i i i i i i i i i1O (0 fr- CO 0) CD V- N CO) 'I 10 (D [- CO 0) CD00 00 00 00 00 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 00) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0M 0) 0) 0) M) 0

Source: Calculated by the author

FIGURE 5.1.6.2COAL INTENSITY: SECTOR 6,1985-2000

88

I

5.1.7. Coking, Gas and Petroleum Refining (Sector 7)

0

201 0 -c 3 0 ---- --- ------- - - - - - - - - --- - -- ---

0 - i i i10 % 0) T- t -D - - 0oo 00 0) 0) 0) 0) 00') 0') 0Y) 0) M) M) M 0

Year

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.1.7.1COAL CONSUMPTION: SECTOR 7,1985-2000

Source: Calculated by the author

FIGURE 5.1.7.2COAL INTENSITY: SECTOR 7,1985-2000

89

1400 - -

1 2 0 0 - - - -- - - - - - - - - - - - - -

c

1000

E6 0 0 -- - - - - - - - - - - - - - - - - - - -

CL

0

200 --- - --- - -- - --

0 -11-

M 0cjM "I 0 - 0

T- T

5.1.8. Chemicals (Sector 8)

0

1 4 0 --- -- -- -- - --0-- - - 0-- - -- 0- -- -100 02~ 80

Year

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.1.8.1COAL CONSUMPTION: SECTOR 8,1985-2000

600 - -_ -_- -_ - --

0005oo -- - - --- -- ----- - - - - - - --- --

C2 0 0 - -- - -- - - - - - - - - - - - - - - -

10o - - -- - - - - -- - - --- - - - - -C0

0 - i i i i i i i 1 1 1 1 i 1,c)co co cO c) co C) a) C ) 0) ) ) M ) 0) a) Q0 0)0) 0M 0) 0) 0) M) M) M) ) M) a) C) a) 0

Source: Calculated by the author

FIGURE 5.1.8.2COAL INTENSITY: SECTOR 8,1985-2000

90

I

5.1.9. Building Materials and Non-Metal Mineral Products (Sector 9)

160 ---

140 -------

n 120 -------

C 100 ---- ---------0 80Co 605 40 ---- ---- - -----

20 ---- --- - -- --- - - ----

0 | | | | iLO o 0 P co Q0Cy O N Clzl LO WO I*, 0M M O

Y0ar

Year

1600

1400

c 1200 -

0 1000 -

E 800

600C

8 40o

200

0

0)(0 r-- W 0M 0 N' M~ "I IO (0 r~- M M) 0

MCJ

Source: Calculated by the author from China Statistical Source: Calculated by the authorYearbook 1986-2002 data

FIGURE 5.1.9.2FIGURE 5.1.9.1 COAL INTENSITY: SECTOR 9, 1985-2000COAL CONSUMPTION: SECTOR 9,1985-2000 1

I

--------------

5.1.10. Metal Products (Sector 10)

1 6 0 ---- -- --- - -- - --- - -- - --- - -

04- 8 0 - - - - - - - - - - - - - - - -

o 60 -

1 40-

20 - -

9~ 80

0-

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.10.1COAL CONSUMPTION: SECTOR 10, 1985-2000

700 - _ _ _ _

700-- --- ------- --------------

soo - - - - - -- - - ---- ----- - - - - - - -

C 00

0 1

0

LC) (D r- CO M 0 N' M~ "T LO (D r'- Mc C

Source: Calculated by the author

FIGURE 5.1.10.2COAL INTENSITY: SECTOR 10, 1985-2000

92

5.1.11. Machinery and Equipment (Sector 11)

35 - -- ---

30 - -- -- --

Q ) 2 5 -- - - - - - - - - - - - - - - - - - - -C:w 2502 2 0 -- - - - - - - - - - - - - - ----- ----

C 15 - - -

10 -

5 --0

0 -T I I I i i i i i i i i i i i- -LO) CO I*- 0 0 N '9 2 O M 00-M 0

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.11.1COAL CONSUMPTION: SECTOR 11, 1985-2000

140

120

100

80

60

40

20

wco M CO MO MO M~ M~ M M M M 0M0 0 0

Source: Calculated by the author

FIGURE 5.1.11.2COAL INTENSITY: SECTOR 11, 1985-2000

93

5.1.12. Construction (Sector 12)

______________________I__________ I

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.12.1COAL CONSUMPTION: SECTOR 12,1985-2000

30

2520

15 -----

10+-

7 ---------------- - -

6 - -------------- -

c 5 --- - --- - -- --- --

c= 4 --- -- -- - -- -- -0

3 - ---- --0

1 ---

0-

0)(D - 0)0) 0 0) 1 0) 0) 0 0), 0) M0 0

Year

94

0O 0M 0) M 0M 0 0 N 0 M 0 IT 0) 0) 0) 0) 0 M 0

Source: Calculated by the author

FIGURE 5.1.12.2COAL INTENSITY: SECTOR 12,1985-2000

- ----- - --

5.1.13. Transportation, Post, and Telecommunications (Sector 13)

25 - - -

20CD,

C 1 5 - - - - - - - - - - - - -0

E 5 -

0 0

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.13.1COAL CONSUMPTION: SECTOR 13, 1985-2000 1

250 +---

200

150

100

50

0

0) ) ) 0 M0 0 )0

Source: Calculated by the author

FIGURE 5.1.13.2COAL INTENSITY: SECTOR 13,1985-2000

95

C~ ~10(0N- UM0 00) ) ) ) 0 0M0 00) ) ) ) 0 0M0 0

"r- I T1- - T- C\i

400 -r - - - - - - - - - - - - -

350 -

300 --

- - --- ----

- ------ --

5.1.14. Services (Sector 14)

250 - -- --- -- --

200 --- - ----- --

Q)c 150 --0

o 100 ---- - --

50 -------------------

0 -- i i i i i i i i i -L) CO oo. M M 0 - N : LO (D r Mo M 0

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.1.14.1COAL CONSUMPTION: SECTOR 14, 1985-2000

800 -- - ~---~--~-- --- --- --- --

700 -- 7

700 ----------- -- ------ ------- ------ - -

600 - --- -- - -- - -- -----

C 5000

300 ----- --- - ---- ----- --

CC

200 ----- ------ - ----

0 - 1 I i 1 1 1 1 1 i 1LO CD r'- 00 0) 0 N~ CO It 1O (D r- 00 0) 010 00 00 00 10 0) 0) 0) 0) 0) 0) 0) 0M 0) 0) 0

Source: Calculated by the author

FIGURE 5.1 .14.2COAL INTENSITY: SECTOR 14,1985-2000

96

I

5.2. Coke Consumption and Intensities

5.2.1. Agriculture (Sector 1)

-------------------------

--- --- - - -- --- - - ---- --- -

-- -- -- -- - - - --- ---- - - - - -

I I I I I I I I |

LoY (Dr MMo ot -co

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.2.1.1COKE CONSUMPTION: SECTOR 1, 2985-2000

Source: Calculated by the author using the model developed inChapter 3

FIGURE 5.2.1.2COKE INTENSITY: SECTOR 1, 1985-2000

1.61.41.21.00.80.60.40.2

2.5

2.0 -

1.5-

1.0 -

0.5 -

0.0o o I oo ) N qt UL (o r- o 0 O

co ao ao ao wo M M M M M M M M M M 0M M M M M M M M M M M M M M M 0v- -r- -- - -r- N- v T- T- r- i T- 1- 1--

5.2.2. Mining and Quarrying (Sector 2)

2.5

2.0

1.5

1.0 +

0.5

"- i I I I I I M r M M 0

0)~~~ 0)0 )0)0 )0

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.2.2.1COKE CONSUMPTION: SECTOR 2,1985-2000

98

1614 ----c

0

= 8E

.

Source: Calculated by the author

FIGURE 5.2.2.2COKE INTENSITY: 19850-2000, 1985-2000

5.2.3. Food (Sector 3)

0.450.40

0.25 -

0.20 -

0.15 -

0.10 -

0.05 -

0.00

-- -/ ---- --------------- - -- - - - - - -- - -

co (D coc 00 0 0 J O - LO 0 0- M 0

0- 0 0) M ) oo 0 M 0) oo) 0) c) o)

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

1.6 -

1.4 -

1.2 -

1.0 -

0.8

0.6 -

0.4 -

0.2 -

0.0 I II I I I I I I

r,- M0 N M c ) - M Mcococo~ o0~a)0M M)0 M0)0)a~0)0M M M 0)

T--- T- T T-- -r- r- ---

Source: Calculated by the author

FIGURE 5.2.3.2COKE INTENSITY: SECTOR 4,1985-2000

FIGURE 5.2.3.1COKE CONSUMPTION: THE SECTOR 3,1985-2000

99

7 7 - -7 -- - -- -- ----- -A -- - - -

i i i i i i i i

------- ------------ ----------------------------- --- ------------ --------------------- -------------------- ------ - -------------------------------------- - ------ ---- ---------- ---------------------------------

-----------------

0.35 t

0.30t

I I I I I I

5.2.4. Textile, Sewing, Leather, and Fur Products (Sector 4)

0.3 --

0.2

0.2

0.1

0.1

| | | I | | |

L <D 1- co a o C\j 'IT LO .0D 00 0)0o o 0 o 00 a) M M M M M M M M o

0) 0)MM0)M0)00)a) 0)0)0)MYea

Year

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.2.4.1COKE CONSUMPTION: SECTOR 4, 1985-2000

0.7

0.

> 0.4 - -

0

0.0-2I

0 0 .1 - - - - - - - - - - - - - - - - - - - - -

Source: Calculated by the author

FIGURE 5.2.4.2COKE INTENSITY: SECTOR 4, 1985-2000

100

II I I I ; I I I I I I I

0-

5.2.5. Other Manufacturing (Sector 5)

2.5

2.0

1.5

1.0

0.5

I I I I I I I I I i I I

M M M to o o M M M M M M M M o

Year

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.2.5.1COKE CONSUMPTION: SECTOR 5,1985-2000

3.0 , - - - - - - ---- ,1

102

35.0

3 0 .0 --- - - - - -- - - - - - - - - - - - - -

5,2 5 .0 - - -- --- - -- -- - - - - - - -

O 20.0 -

15.0 -- -a>- 1 0 .0 -- - - - - - ---- - - - - - - - - - - -

C : 5 .0 -- - - - - - - - --- - - -- - - - - - -0.

.95.0.0

0) ) ) ) 0 0 M0 0)

Source: Calculated by the author

FIGURE 5.2.5.2COKE INTENSITY: SECTOR 5,1985-2000

5.2.6. Production and Supply of Electric Power, Steam, and Hot Water (Sector 6)

1.41.2

1.00.80.60.4

0.2

T-, - T-

Year

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.2.6.1COKE CONSUMPTION: SECTOR 6, 1985-2000

12.0 --------- - -

>. 10.0 ---- - --- - - --

:o 8.0 --- --- - -

6.0 --- -

aL 4 .0 - - - - - - - - - - --- --- -- -- -

0 2.0 - - - - - - - --- --- ----- - -- -

0.0

T- - - - T- ~ - -- -- r- -r - T- -- -- T

Source: Calculated by the author

FIGURE 5.2.6.2COKE INTENSITY: SECTOR 6, 1985-2000

103

5.2.7. Coking, Gas, and Petroleum Refining (Sector 7)

___________________I________ I

-- - - -- - - - - - - - - - - - - - - - - - - - --- - - -

-- - - - - - - - - - - - - - - - --- --- - - - - - - - - - -- - - - -------- ------ -------------- - - - - -- ----

-- -

-- - -- - - - - - - - - - - - - - - - --- -- - - - - - - - -

S -- -

Year

Source: Calculated by the author from China Statistical

Yearbook 1986-2002 data

FIGURE 5.2.7.1COKE CONSUMPTION: SECTOR 7,1985-2000

20

20 -- - - - - ------- -------- ---- - - -

9 15-

0 1 0 --- - -- - --- ---- - - -- - -- -

o 5 -- -

00 -............... I ..........

LO C M coo0 C t LO C o M 0O

Source: Calculated by the author

FIGURE 5.2.7.2COKE INTENSITY: SECTOR 7,1985-2000

104

1.00.90.80.70.60.50.40.30.20.1

5.2.8. Chemicals (Sector 8)

16.014.012.010.08.06.04.02.0

LO (Dr'- W M0 N~ i~tLO (0 - M M

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.2.8.1COKE CONSUMPTION: SECTOR 8,1985-2000

105

60.0

c 50.0

40.0 -0

2-c>0 .

0.0 |

LO CD CO 0 M .C C t UD C oo M M

Source: Calculated by the author

FIGURE 5.2.8.2COKE INTENSITY: SECTOR 8, 1985-2000

I

- - - - - - - - ---- - -- - - - ----- - - --- - - - - - - - - --- ---- --- - - - - - - --- - - ------ - ---- - - --- --- - ------- ---- ---------- - -- ---

5.2.9. Buildinq Materials and Non-metal Mineral Products (Sector 9)

3.53.02.52.0

1.51.00.5

LO (0 - M M0 N qtULO D I,- M MO~~~~D~ M) M)0 ))0 0 0Q0)0)00)0))0)00 M0M)0M00

-r- T- -r- r J-

Year

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.2.9.1COKE CONSUMPTION: SECTOR 9,1985-2000

25.0

20.0

a,15.0 ---- -

0

0.0

0)~~ 0 M0) 0

Source: Calculated by the author

FIGURE 5.2.9.2COKE INTENSITY: SECTOR 9,1985-2000

106

I

5.2.10. Metal Products (Sector 10)

90

80u ) 7 0 -- - - - - - - - - - - - - - - - - - - - -

.0 5 0 - - - - - - - - - - - - - - - - - - - - -C 0

20-- ---

100

Year

Source: Calculated by the author from China StatisticalYearbook 1986-2002 data

FIGURE 5.2.10.1COKE CONSUMPTION: SECTOR 10, 1985-2000

350

c 3 0 0 -- - - - - - - - - - - - - - - - - - - -

=3 2 5 0 - - - - - - - - - - - - - - - - - - - - -

.2 2 0 0 - - - - - - - - - - - - - - - - - - - - -CD

E 150 --

0- 100 - - --

c 50

0

0o o 00o0 co ) Cyo C> o ) ) M> M >y) 0> 0a 0)a) 0) y MMC)G)0) M0

Source: Calculated by the author

FIGURE 5.2.10.2COKE INTENSITY: SECTOR 10, 1985-2000

107

I

5.2.11. Machinery and Equipment (Sector 11)

108

4.5 1 .4.0 --

S 3.5 -

- 2 .5 - - - - - - - - - - - - -

E 2.0

16.

00 0000 0 00 ) 0) ) 0)0) 0) 0) 0) Cyo

Source: Calculated by the author from China Statistical Source: Calculated by the authorYearbook 1986-2002 data FIGURE 5.2.11.2

FIGURE 5.2.11.1 COKE INTENSITY: SECTOR 11, 1985-2000COKE CONSUMPTION: SECTOR 11, 1985-2000

5.2.12. Construction (Sector 12)

0.20-

0.15

0.10

0.05

0.00LO (Dr- MC) M 0 N I LO (0N- M M 0

0)0)0))0)0M0000)0)00)0))0)00)0) M0M000

T- "- T- IT- C'J

Year

1~

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

COKE CONSUMPTION: SECTOR 12,1985-2000 1

0.7

0.6

C03

-, 0.2 - - - - - - - - - - - - - - - - - - -

. 0 .1 -- - -

0.0

Source: Calculated by the author

FIGURE 5.2.12.2COKE INTENSITY: SECTOR 12, 1985-2000

109

----------------------

FIGURE 5.2.12.1

I I I I

5.2.13. Transportation, Post, and Telecommunications (Sector 13)

U')M - M M N t O D rl-M MO

Year

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.2.13.1COKE CONSUMPTION: SECTOR 13,1985-2000

1.0 -- - -

0.8 \- -- -

0.6 +

0.4

0.20.0 -1 I I1~.1. I I I I

I I I I ~

M w co w 00 M M M M M M M M M00)0)0)0)0M)M)0M0M)M)0M0M00-r- r-- - -r- v- -r-- -r- J

Source: Calculated by the author

FIGURE 5.2.13.2COKE INTENSITY: SECTOR 13, 1985-2000

110

0.12

0.10

0.08

0.06

0.04

0.02

0.004

,

5.2.14. Services (Sector 14)

2.5

2.0

1.5

1.0

0.5

----------------- --------

- -

-O - - - - -NY- CO MYear

Source: Calculated by the author from China Statistical Yearbook1986-2002 data

FIGURE 5.2.14.1COKE CONSUMPTION: SECTOR 14,1985-2000

±

I

111

2.5 - - __ - - -_ -

1.5

0

0.

0.0-

oo o 0 0 0 o 0 0)o a) 0) a)0 0o

Source: Calculated by the author

FIGURE 5.2.14.1COKE INTENSITY: SECTOR 14, 1985-2000

Appendix 5.3Time-Series Models for Coal and Coke Intensities of 14 Economic Sectors

Time-SeriesModel forCoal Intensity

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)< 1% *N/AN/A

Equations

(1-B) * E1t = -0.0257 + 1/(1 - 0.26015 * B) * Et

(1-B) * E2t= -0.3521 + 1/(1 + 0.19513 * B) * Et

(1-B) * Eat = -0.06617+ 1/(1 - 0.52796 * B) * Et

(1-B) * E4 t = -0.04998 + 1/(1 - 0.251 * B) *Et

(1-B) 2 *Et = 0.075017 + 1/(1 - 0.95015 * B) * Et

(1-B) * E6t = -0.93239 + 1/(1 + 0.09423 * B) * E-

N/A

(1-B) * E8t = -0.1585 + 1/(1 - 0.10575 * B) * Et

(1-B) * Est = -0.7493 + 1/(1 + 0.13995 * B) * Et

N/A

(1-B) 3 *Ent = -0.15926 + 1/(1 - 0.64632 * B) * Et

N/AN/A

N/A

Time-SeriesModel forCoke Intensity

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)<1%<1%N/A

Equations

(1-B) * E1 = - 0.000707 + 1/(1 + 0.20567 * B) * Et

(1-B) * E2t = -0.00206 + 1/(1 + 0.22612 * B) * Et

(1-B) * Est = -0.00007 + 1/(1 - 0.00948 * B) *t

N/AN/AN/AN/A

(1-B) * Et = -0.02093 + 1/(1 - 0.09599 * B) * Et

(1-B) * Est = -0.00317+ 1/(1 + 0.13299 B + 0.161

(1-B)N/AN/AN/A

37 B - 0.4287 B3) *t

* Elit = -0.00462 + (1 - B) * E-

Note: Ejt represents theerror term, and B is the

coal intensity in Sector j in year t, Et is the disturbancebackward-shift operator with one-period time lag.

113

Sector ID

1

2

3

4

5

67

8

910

11121314

Sector ID

1

2

34567

8

910

11121314

Appendix 6. Shares of Final Demand of Each Sector in the Total Final Demand (S;)

Year Sector 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Total

1981 0.26 n.a. 0.13 0.11 0.04 0.01 0.02 0.02 0.01 0.00 0.15 0.17 0.02 0.08 1.001987 0.21 0.01 0.10 0.08 0.02 0.00 0.00 0.01 0.00 0.00 0.11 0.20 0.03 0.18 0.961992 0.18 0.00 0.10 0.09 0.03 0.00 0.00 0.02 0.01 -0.01 0.11 0.19 0.03 0.25 0.991995 0.16 0.01 0.12 0.09 0.03 0.01 0.00 0.01 0.01 0.00 0.12 0.22 0.02 0.21 1.02

1997 0.15 0.00 0.11 0.09 0.03 0.01 0.00 0.01 0.01 0.00 0.12 0.23 0.02 0.25 1.032003 0.15 -0.01 0.11 0.08 0.03 0.01 0.00 0.01 0.01 0.00 0.10 0.24 0.02 0.28 1.04

2004 0.15 -0.01 0.11 0.08 0.03 0.01 0.00 0.01 0.01 0.00 0.10 0.24 0.02 0.29 1.052005 0.15 -0.01 0.11 0.08 0.03 0.01 0.00 0.01 0.01 0.00 0.10 0.25 0.02 0.29 1.05

Po 1.73 1.05 1.46 -0.35 0.37 0.13 -0.62 3.42 -6.11 0.43 -10.72

0I 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

Source:1997.

Calculated by the author using data from China's national input-output tables for 1981, 1987, 1992, 1995, and

Notes:. Sj is derived from the existing input-output tables and forecasted using the simple linear regression models discussed

in Chapter 3* Estimates for 1 for 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, 1985-2000Sector 1985 1986 1987 1988 1989 1990 1991 1992 1994 1995 1996 1997 1998 1999 2000Consumption

123456789

1011121314

Percent

22096316244918683676

1661932595875861471892748

5322307

17942

2.77.73.02.34.5

20.44.07.2

10.68.83.40.72.8

22.0

22976648260419683679

1805035976382917277342817

4982295

18274

2.77.73.02.34.3

21.04.27.4

10.79.03.30.62.7

21.3

22877254290720963975

2022039937369988380643014453

224219042

2.57.83.12.34.3

21.84.37.9

10.78.73.30.52.4

20.5

23787280311322744193

2289441717874

1050084683141

4452259

20364

2.47.33.12.34.2

23.04.27.9

10.68.53.20.52.3

20.5

21818129332123704401

2490447988383

1067085493039

4532284

19945

2.17.93.22.34.3

24.14.68.1

10.38.32.90.42.2

19.3

20958822332523594686

2705948028237996389052933

4382161

19738

2.08.43.22.24.4

25.64.67.89.48.42.80.42.1

18.7

21259705341423765108

2979242558794

1032096452950

4322025

19491

1.98.83.12.24.6

27.03.98.09.48.72.70.41.8

17.7

17689491352324704720

3323052539309

10778103923084

4661876

17724

1.68.33.12.24.1

29.14.68.29.59.12.70.41.6

15.5

178310994378831133418

403105478

119581221913476

3017504

187316601

1.48.63.02.42.7

31.44.39.39.5

10.52.40.41.5

12.9

18579861414232563221

446008025

1342013424147312890

4401315

16494

1.47.23.02.42.3

32.45.89.89.8

10.72.10.31.0

12.0

191711014

397727532966

504577757

134001358914946

3027446

117617309

1.37.62.81.92.1

34.95.49.39.4

10.32.10.30.8

12.0

192711280

365225302703

515898488

1158012792144442611

3831431

13837

1.48.12.61.81.9

37.16.18.39.2

10.41.90.31.09.9

19239597339722552423

518117563

111191166312919

2205612

139110614

1.57.42.61.71.9

40.05.88.69.0

10.01.70.51.18.2

17368641325819112020

5318977309896

1100613094

2012522

129410056

1.46.82.61.51.6

42.16.17.88.7

10.41.60.41.08.0

16488147265116992077

56059771093469940

125381564537

11409483

1.36.52.11.41.7

45.06.27.58.0

10.11.30.40.97.6

115

Source: Calculated by the author using data from China Statistical Yearbook 1986-2002 (in 10,000 tonnes)

17-7. .

Appendix 7.2. Consumption and Percentages of Consumption of Coke in 14 Sectors in China, 1985-2000Sector 1985 1986 1987 1988 1989 1990 1991 1992 1994 1995 1996 1997 1998 1999 2000Consumption

1 20.8 49.8 48.0 59.1 53.5 60.1 34.0 52.3 111.2 128.6 119.8 144.7 151.4 145.8 144.2

2 71.7 62.4 64.4 83.0 102.8 104.1 114.8 126.5 135.3 151.4 223.2 183.3 180.6 131.1 153.3

3 12.3 17.3 22.6 19.9 20.9 22.6 26.2 31.5 22.8 32.4 41.2 30.6 31.9 34.8 33.6

4 7.1 9.0 7.8 11.2 11.6 10.7 11.3 11.4 9.5 8.8 9.8 12.7 20.0 8.3 8.7

5 119.2 128.2 166.0 269.5 154.8 149.3 159.6 160.1 71.9 38.3 37.5 44.1 38.3 31.1 33.1

6 3.1 2.5 2.4 2.6 2.6 1.2 0.9 1.1 115.8 16.8 104.7 103.4 51.2 67.1 36.8

7 18.2 17.6 45.0 51.2 51.5 77.7 72.3 87.3 5.6 31.6 27.0 54.4 70.7 61.0 63.0

8 732.2 702.0 781.6 829.0 910.8 963.9 968.9 1039.5 1060.9 1328.0 1476.2 1363.0 1261.7 1036.4 1090.3

9 93.5 120.5 129.8 143.6 176.8 182.2 182.2 218.3 212.4 276.7 280.3 269.0 302.3 286.9 298.1

10 3310.3 3762.3 4068.0 4442.3 4456.2 4921.7 5131.0 5530.8 6779.3 8128.3 7873.6 8174.7 8399.0 8106.2 8048.9

11 260.1 314.8 324.8 65.7 369.1 375.4 367.6 403.9 388.5 399.9 417.9 349.1 354.6 328.4 314.8

12 7.8 11.0 11.7 5.8 9.1 5.2 5.8 14.4 11.8 10.8 13.9 12.5 14.6 17.1 19.0

13 5.7 6.6 4.9 4.3 4.6 4.1 4.4 6.1 7.3 10.1 6.6 6.5 10.3 10.1 11.2

14 27.7 45.0 43.9 39.9 44.1 36.5 39.0 155.9 161.9 163.8 166.5 179.1 191.6 192.6 185.1Percent

1 0.4 1.0 0.8 1.0 0.8 0.9 0.5 0.7 1.2 1.2 1.1 1.3 1.4 1.4 1.4

2 1.5 1.2 1.1 1.4 1.6 1.5 1.6 1.6 1.5 1.4 2.1 1.7 1.6 1.3 1.5

3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3

4 0.2 0.2 0.1 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1

5 2.5 2.4 2.9 4.5 2.4 2.2 2.2 2.0 0.8 0.4 0.4 0.4 0.4 0.3 0.3

6 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.2 1.0 1.0 0.5 0.6 0.4

7 0.4 0.3 0.8 0.9 0.8 1.1 1.0 1.1 0.1 0.3 0.3 0.5 0.6 0.6 0.6

8 15.6 13.4 13.7 13.8 14.3 13.9 13.6 13.3 11.7 12.4 13.7 12.5 11.4 9.9 10.4

9 2.0 2.3 2.3 2.4 2.8 2.6 2.6 2.8 2.3 2.6 2.6 2.5 2.7 2.7 2.9

10 70.6 71.7 71.1 73.7 70.0 71.2 72.1 70.6 74.6 75.8 72.9 74.8 75.8 77.5 77.1

11 5.6 6.0 5.7 1.1 5.8 5.4 5.2 5.2 4.3 3.7 3.9 3.2 3.2 3.1 3.0

12 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.2 0.2

13 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

14 0.6 0.9 0.8 0.7 0.7 0.5 0.6 2.0 1.8 1.5 1.5 1.6 1.7 1.8 1.8

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)

Year1985198619871988198919901991199219931994199519961997199819992000

Sector 10.5900.5920.5600.5610.5440.5270.4830.3530.3150.2780.2730.2670.2620.2500.2160.191

28.9669.1029.4289.086

10.6739.4989.3337.9347.2266.5185.2675.7985.2494.4734.0443.763

31.6781.7101.8011.8382.0481.9441.7751.5851.3791.1731.1511.0530.9110.8180.7570.582

41.0891.1111.1291.1301.1921.1230.9770.8550.7900.7250.7110.5640.5600.4730.3790.312

54.5564.4714.6694.4574.6493.9763.7012.8472.2941.7401.4991.2740.9230.7720.5990.562

653.31955.10657.76561.33068.54655.55753.53650.87247.61744.36243.58947.11241.68240.34439.86739.605

78.5699.547

10.58910.51712.60111.5389.0429.6098.0966.5838.9238.1508.2457.0616.9286.493

84.0104.1844.5664.5594.9704.1753.8993.5153.2833.0513.2232.9742.4952.2371.8561.600

914.16813.95113.66513.52814.0399.7728.8187.8107.0976.3836.2825.8914.8254.1183.6323.000

Source: calculated by the author with the models developed in Chapters 3 and 4.

117

106.0236.0565.8005.8496.2134.5974.4424.1554.0193.8833.7663.5563.6953.1132.9662.613

111.1521.1341.1461.1291.1310.8460.7500.6730.5760.4800.4220.4170.3380.2710.2350.169

120.3100.2620.2120.2010.2160.1930.1720.1630.1450.1280.0990.0930.0730.1080.0860.081

133.7883.4573.0462.9163.0691.9781.6311.2941.1881.0810.7300.5850.6710.5940.5030.395

146.8446.1335.5445.3675.2253.5292.9892.2712.1341.9961.9001.7691.3070.9000.7660.637

Appendix 8.2. Coke Intensities in 14 Sectors in China, 1985-2000(tonnes per 10,000 yuan of the sector's total output)

Year1985198619871988198919901991199219931994199519961997199819992000

Sector 10.0060.0130.0120.0140.0130.0150.0080.0100.0140.0170.0190.0170.0200.0200.0180.017

20.1020.0850.0840.1040.1350.1120.1100.1060.0930.0800.0810.1180.0850.0840.0610.071

30.0080.0110.0140.0120.0130.0130.0140.0140.0110.0070.0090.0110.0080.0080.0080.007

40.0040.0050.0040.0060.0060.0050.0050.0040.0030.0020.0020.0020.0030.0040.0020.002

50.1480.1560.1950.2870.1640.1270.1160.0970.0670.0370.0180.0160.0150.0120.0090.009

60.0100.0080.0070.0070.0070.0030.0020.0020.0650.1280.0160.0980.0840.0400.0500.026

70.0480.0470.1190.1290.1350.1870.1540.1600.0830.0070.0350.0280.0530.0660.0550.053

80.5000.4600.4840.4800.5400.4890.4300.3930.3320.2710.3190.3280.2940.2540.1940.187

90.1540.1830.1800.1850.2330.1790.1560.1580.1350.1110.1300.1220.1020.1070.0950.090

Source: calculated by the author with the models developed in Chapters 3 and 4.

118

102.7742.9462.9263.0683.2382.5412.3632.2122.0821.9532.0781.8742.0912.0241.8361.678

110.1090.1270.1240.0240.1370.1080.0940.0880.0750.0620.0580.0580.0450.0440.0380.034

120.0050.0060.0060.0030.0040.0020.0020.0050.0040.0030.0020.0030.0020.0030.0030.003

130.0090.0100.0070.0060.0060.0040.0040.0040.0040.0040.0060.0030.0030.0040.0040.004

140.0110.0150.0130.0110.0120.0070.0060.0200.0200.0200.0190.0170.0170.0160.0150.012

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 2 3 4 5 6 7 8 9 10 11 12 13 142003 0.115 2.695 0.285 0.157 0.234 36.750 6.101 1.112 0.738 1.961 0.008 0.081 0.395 0.3372004 0.089 2.343 0.211 0.107 0.222 35.818 5.955 0.954 0.738 1.730 0.008 0.081 0.395 0.2572005 0.063 1.990 0.141 0.057 0.088 34.885 5.811 0.795 0.738 1.502 0.008 0.081 0.395 0.187

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 2 3 4 5 6 7 8 9 10 11 12 13 142003 0.019 0.062 0.007 0.002 0.009 0.026 0.053 0.125 0.081 1.463 0.026 0.003 0.004 0.0112004 0.020 0.060 0.007 0.002 0.009 0.026 0.053 0.104 0.080 1.389 0.021 0.003 0.004 0.0102005 0.021 0.058 0.007 0.002 0.009 0.026 0.053 0.083 0.075 1.315 0.017 0.003 0.004 0.009

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)

Energy Intensity Consumption ShareCoal Coke

Rank Sector ID Sector Name Coal Coke (Percent)1 6 Production and Supply of Electric Power, Steam, and Hot Water 39.605 0.026 45.0 0.42 7 Coking, Gas, and Petroleum Refining 6.493 0.053 6.2 0.63 2 Mining and Quarrying 3.762 0.071 6.5 1.54 9 Building Materials and Non-metal Mineral Products 3.000 0.090 8.0 2.95 10 Metal Products 2.613 1.678 10.1 77.16 8 Chemicals 1.600 0.187 7.5 10.47 14 Services 0.637 0.012 7.6 1.88 3 Food 0.581 0.007 2.1 0.39 5 Others (including paper-making) 0.562 0.009 1.7 0.3

10 13 Transportation, Post, and Telecommunications 0.395 0.004 0.9 0.111 4 Textile, Sewing, Leather and Fur Products 0.312 0.002 1.4 0.112 1 Agriculture 0.191 0.017 1.3 1.413 11 Machinery and Equipment 0.169 0.034 1.3 3.014 12 Construction 0.081 0.003 0.4 0.2

Total 100.0 100.0

Source: calculated by the author with the models developed in Chapters 3 and 4.

120

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 Consumption ShareCoal Coke

Rank Sector ID Sector Name Coal Coke (Percent)1 10 Metal Products 2.613 1.678 10.1 77.1

2 8 Chemical Industry 1.600 0.187 7.5 10.4

3 9 Building Materials and Non-metal Mineral Products 3.000 0.090 8.0 2.94 2 Mining and Quarrying 3.763 0.071 6.5 1.5

5 7 Coking, Gas and Petroleum Refining 6.493 0.053 6.1 0.6

6 11 Machinery and Equipment 0.169 0.034 1.3 3.07 6 Production and Supply of Electric Power, Steam and Hot Water 39.605 0.026 45.0 0.4

8 1 Agriculture 0.191 0.017 1.3 1.4

9 14 Services 0.637 0.012 7.6 1.8

10 5 Others (including paper-making) 0.562 0.009 1.7 0.3

11 3 Foodstuff 0.582 0.007 2.1 0.3

12 13 Transportation, Post and Telecommunications 0.395 0.004 0.9 0.1

13 12 Construction 0.081 0.003 0.4 0.2

14 4 Textile, Sewing, Leather and Furs Products 0.312 0.002 1.4 0.1

Total 100.0 100.0

Source: calculated by the author with the models developed in Chapters 3 and 4.

121

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